Patent Publication Number: US-2023147068-A1

Title: Management of distributed computing framework components

Description:
RELATED APPLICATIONS 
     Any and all applications for which a foreign or domestic priority claim is identified in the Application Data Sheet as filed with the present application are incorporated by reference under 37 CFR 1.57 and made a part of this specification. 
     In addition, each of the following U.S. applications is hereby incorporated by reference in its entirety herein and made a part of this specification: 
     
       
         
           
               
               
               
             
               
                   
               
               
                 Application No. 
                 Title 
                 Filing Date 
               
               
                   
               
             
            
               
                 15/276,717 
                 DATA FABRIC SERVICE SYSTEM 
                 Sep. 26, 2016 
               
               
                   
                 ARCHITECTURE 
               
               
                 15/665,159 
                 MULTI-LAYER PARTITION ALLOCATION FOR 
                 Jul. 31, 2017 
               
               
                   
                 QUERY EXECUTION 
               
               
                 15/665,148 
                 QUERY PROCESSING USING QUERY-RESOURCE 
                 Jul. 31, 2017 
               
               
                   
                 USAGE AND NODE UTILIZATION DATA 
               
               
                 15/665,187 
                 RESOURCE ALLOCATION FOR MULTIPLE 
                 Jul. 31, 2017 
               
               
                   
                 DATASETS 
               
               
                 15/665,248 
                 EXTERNAL DATASET CAPABILITY 
                 Jul. 31, 2017 
               
               
                   
                 COMPENSATION 
               
               
                 15/665,197 
                 DATA CONDITIONING FOR DATASET 
                 Jul. 31, 2017 
               
               
                   
                 DESTINATION 
               
               
                 15/665,279 
                 QUERY ACCELERATION DATA STORE 
                 Jul. 31, 2017 
               
               
                 15/665,302 
                 DYNAMIC RESOURCE ALLOCATION FOR COMMON 
                 Jul. 31, 2017 
               
               
                   
                 STORAGE QUERY 
               
               
                 15/665,339 
                 DYNAMIC RESOURCE ALLOCATION FOR REAL- 
                 Jul. 31, 2017 
               
               
                   
                 TIME SEARCH 
               
               
                 16/051,197 
                 GENERATING A SUBQUERY FOR A DISTINCT 
                 Jul. 31, 2018 
               
               
                   
                 DATA INTAKE AND QUERY SYSTEM 
               
               
                 16/051,215 
                 SUBQUERY GENERATION BASED ON A DATA 
                 Jul. 31, 2018 
               
               
                   
                 INGEST ESTIMATE OF AN EXTERNAL DATA 
               
               
                   
                 SYSTEM 
               
               
                 16/051,203 
                 SUBQUERY GENERATION BASED ON SEARCH 
                 Jul. 31, 2018 
               
               
                   
                 CONFIGURATION DATA FROM AN EXTERNAL 
               
               
                   
                 DATA SYSTEM 
               
               
                 16/051,223 
                 DISTRIBUTING PARTIAL RESULTS TO WORKER 
                 Jul. 31, 2018 
               
               
                   
                 NODES FROM AN EXTERNAL DATA SYSTEM 
               
               
                 16/051,304 
                 DISTRIBUTING PARTIAL RESULTS FROM AN 
                 Jul. 31, 2018 
               
               
                   
                 EXTERNAL DATA SYSTEM BETWEEN WORKER 
               
               
                   
                 NODES 
               
               
                 16/051,300 
                 TASK DISTRIBUTION IN AN EXECUTION NODE OF 
                 Jul. 31, 2018 
               
               
                   
                 A DISTRIBUTED EXECUTION ENVIRONMENT 
               
               
                 16/051,310 
                 EXECUTION OF A QUERY RECEIVED FROM A 
                 Jul. 31, 2018 
               
               
                   
                 DATA INTAKE AND QUERY SYSTEM 
               
               
                 16/147,165 
                 GENERATING A SUBQUERY FOR AN EXTERNAL 
                 Sep. 28, 2018 
               
               
                   
                 DATA SYSTEM USING A CONFIGURATION FILE 
               
               
                 16/146,990 
                 CONVERTING AND MODIFYING A SUBQUERY 
                 Sep. 28, 2018 
               
               
                   
                 FOR AN EXTERNAL DATA SYSTEM 
               
               
                 16/398,038 
                 BUCKET DATA DISTRIBUTION FOR EXPORTING 
                 Apr. 29, 2019 
               
               
                   
                 DATA TO WORKER NODES 
               
               
                 16/397,970 
                 PARTITIONING AND REDUCING RECORDS AT 
                 Apr. 29, 2019 
               
               
                   
                 INGEST OF A WORKER NODE 
               
               
                 16/398,044 
                 DETERMINING RECORDS GENERATED BY A 
                 Apr. 29, 2019 
               
               
                   
                 PROCESSING TASK OF A QUERY 
               
               
                 16/397,930 
                 DETERMINING A RECORD GENERATION 
                 Apr. 29, 2019 
               
               
                   
                 ESTIMATE OF A PROCESSING TASK 
               
               
                 16/398,031 
                 QUERY SCHEDULING BASED ON A QUERY- 
                 Apr. 29, 2019 
               
               
                   
                 RESOURCE ALLOCATION AND RESOURCE 
               
               
                   
                 AVAILABILITY 
               
               
                 16/397,968 
                 RECORD EXPANSION AND REDUCTION BASED 
                 Apr. 29, 2019 
               
               
                   
                 ON A PROCESSING TASK IN A DATA INTAKE 
               
               
                   
                 AND QUERY SYSTEM 
               
               
                 16/397,922 
                 ASSIGNING PROCESSING TASKS IN A DATA 
                 Apr. 29, 2019 
               
               
                   
                 INTAKE AND QUERY SYSTEM 
               
               
                 PCT/CN2019/085042 
                 SEARCH TIME ESTIMATE IN A DATA INTAKE 
                 Apr. 29, 2019 
               
               
                   
                 AND QUERY SYSTEM 
               
               
                 16/657,916 
                 SUPPORTING ADDITIONAL QUERY LANGUAGES 
                 Oct. 18, 2019 
               
               
                   
                 THROUGH DISTRIBUTED EXECUTION OF 
               
               
                   
                 QUERY ENGINES 
               
               
                 16/657,872 
                 QUERY EXECUTION AT A REMOTE 
                 Oct. 18, 2019 
               
               
                   
                 HETEROGENEOUS DATA STORE OF A DATA 
               
               
                   
                 FABRIC SERVICE 
               
               
                 16/657,894 
                 REASSIGNING PROCESSING TASKS TO AN 
                 Oct. 18, 2019 
               
               
                   
                 EXTERNAL STORAGE SYSTEM 
               
               
                 16/657,867 
                 ADDRESSING MEMORY LIMITS FOR PARTITION 
                 Oct. 18, 2019 
               
               
                   
                 TRACKING AMONG WORKER NODES 
               
               
                 16/657,899 
                 MANAGEMENT OF DISTRIBUTED COMPUTING 
                 Oct. 18, 2019 
               
               
                   
                 FRAMEWORK COMPONENTS IN A DATA 
               
               
                   
                 FABRIC SERVICE SYSTEM 
               
               
                   
               
            
           
         
       
     
     FIELD 
     At least one embodiment of the present disclosure pertains to one or more tools for facilitating searching and analyzing large sets of data to locate data of interest. 
     BACKGROUND 
     Information technology (IT) environments can include diverse types of data systems that store large amounts of diverse data types generated by numerous devices. For example, a big data ecosystem may include databases such as MySQL and Oracle databases, cloud computing services such as Amazon web services (AWS), and other data systems that store passively or actively generated data, including machine-generated data (“machine data”). The machine data can include performance data, diagnostic data, or any other data that can be analyzed to diagnose equipment performance problems, monitor user interactions, and to derive other insights. 
     The large amount and diversity of data systems containing large amounts of structured, semi-structured, and unstructured data relevant to any search query can be massive, and continues to grow rapidly. This technological evolution can give rise to various challenges in relation to managing, understanding and effectively utilizing the data. To reduce the potentially vast amount of data that may be generated, some data systems pre-process data based on anticipated data analysis needs. In particular, specified data items may be extracted from the generated data and stored in a data system to facilitate efficient retrieval and analysis of those data items at a later time. At least some of the remainder of the generated data is typically discarded during pre-processing. 
     However, storing massive quantities of minimally processed or unprocessed data (collectively and individually referred to as “raw data”) for later retrieval and analysis is becoming increasingly more feasible as storage capacity becomes more inexpensive and plentiful. In general, storing raw data and performing analysis on that data later can provide greater flexibility because it enables an analyst to analyze all of the generated data instead of only a fraction of it. 
     Although the availability of vastly greater amounts of diverse data on diverse data systems provides opportunities to derive new insights, it also gives rise to technical challenges to search and analyze the data. Tools exist that allow an analyst to search data systems separately and collect results over a network for the analyst to derive insights in a piecemeal manner. However, UI tools that allow analysts to quickly search and analyze large set of raw machine data to visually identify data subsets of interest, particularly via straightforward and easy-to-understand sets of tools and search functionality do not exist. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present disclosure is illustrated by way of example, and not limitation, in the figures of the accompanying drawings, in which like reference numerals indicate similar elements and in which: 
         FIG.  1 A  is a block diagram of an example environment in which an embodiment may be implemented; 
         FIG.  1 B  is a block diagram of an example networked computer environment, in accordance with example embodiments; 
         FIG.  2    is a block diagram of an example data intake and query system, in accordance with example embodiments; 
         FIG.  3    is a block diagram of an example cloud-based data intake and query system, in accordance with example embodiments; 
         FIG.  4    is a block diagram of an example data intake and query system that performs searches across external data systems, in accordance with example embodiments; 
         FIG.  5 A  is a flowchart of an example method that illustrates how indexers process, index, and store data received from forwarders, in accordance with example embodiments; 
         FIG.  5 B  is a block diagram of a data structure in which time-stamped event data can be stored in a data store, in accordance with example embodiments; 
         FIG.  5 C  provides a visual representation of the manner in which a pipelined search language or query operates, in accordance with example embodiments; 
         FIG.  6 A  is a flow diagram of an example method that illustrates how a search head and indexers perform a search query, in accordance with example embodiments; 
         FIG.  6 B  provides a visual representation of an example manner in which a pipelined command language or query operates, in accordance with example embodiments; 
         FIG.  7 A  is a diagram of an example scenario where a common customer identifier is found among log data received from three disparate data sources, in accordance with example embodiments; 
         FIG.  7 B  illustrates an example of processing keyword searches and field searches, in accordance with disclosed embodiments; 
         FIG.  7 C  illustrates an example of creating and using an inverted index, in accordance with example embodiments; 
         FIG.  7 D  depicts a flowchart of example use of an inverted index in a pipelined search query, in accordance with example embodiments; 
         FIG.  8 A  is an interface diagram of an example user interface for a search screen, in accordance with example embodiments; 
         FIG.  8 B  is an interface diagram of an example user interface for a data summary dialog that enables a user to select various data sources, in accordance with example embodiments; 
         FIGS.  9 ,  10 ,  11 A,  11 B,  11 C,  11 D,  12 ,  13 ,  14 , and  15    are interface diagrams of example report generation user interfaces, in accordance with example embodiments; 
         FIG.  16    is an example search query received from a client and executed by search peers, in accordance with example embodiments; 
         FIG.  17 A  is an interface diagram of an example user interface of a key indicators view, in accordance with example embodiments; 
         FIG.  17 B  is an interface diagram of an example user interface of an incident review dashboard, in accordance with example embodiments; 
         FIG.  17 C  is a tree diagram of an example a proactive monitoring tree, in accordance with example embodiments; 
         FIG.  17 D  is an interface diagram of an example a user interface displaying both log data and performance data, in accordance with example embodiments; 
         FIG.  18    is a system diagram illustrating a data fabric service system architecture (“DFS system”) in which an embodiment may be implemented; 
         FIG.  19    is an operation flow diagram illustrating an example of an operation flow of a DFS system according to some embodiments of the present disclosure; 
         FIG.  20    is an operation flow diagram illustrating an example of a parallel export operation performed in a DFS system according to some embodiments of the present disclosure; 
         FIG.  21    is a flow diagram illustrating a method performed by the DFS system to obtain time-ordered search results according to some embodiments of the present disclosure; 
         FIG.  22    is a flow diagram illustrating a method performed by a data intake and query system of a DFS system to obtain time-ordered search results according to some embodiments of the present disclosure; 
         FIG.  23    is a flow diagram illustrating a method performed by nodes of a DFS system to obtain batch or reporting search results according to some embodiments of the present disclosure; 
         FIG.  24    is a flow diagram illustrating a method performed by a data intake and query system of a DFS system in response to a reporting search query according to some embodiments of the present disclosure; 
         FIG.  25    is a system diagram illustrating a co-located deployment of a DFS system in which an embodiment may be implemented; 
         FIG.  26 A  is an operation flow diagram illustrating an example of an operation flow of a co-located deployment of a DFS system according to some embodiments of the present disclosure; 
         FIG.  26 B  is an operation flow diagram illustrating an example of an operation flow of a co-located deployment of a DFS system according to some embodiments of the present disclosure; 
         FIG.  26 C  is a flow diagram illustrative of an embodiment of a routine implemented by a co-located deployment of a DFS system to activate worker nodes of a distributed computing framework using a search command according to some embodiments of the present disclosure; 
         FIG.  27    is a cloud based system diagram illustrating a cloud deployment of a DFS system in which an embodiment may be implemented; 
         FIG.  28    is a flow diagram illustrating an example of a method performed in a cloud-based DFS system according to some embodiments of the present disclosure; 
         FIG.  29    is a flow diagram illustrating a timeline mechanism that supports rendering search results in a time-ordered visualization according to some embodiments of the present disclosure; 
         FIG.  30    illustrates a timeline visualization rendered on a GUI in which an embodiment may be implemented; 
         FIG.  31    illustrates a selected bin of a timeline visualization and the contents of the selected bin according to some embodiments of the present disclosure. 
         FIG.  32    is a flow diagram illustrating services of a DFS system according to some embodiments of the present disclosure; 
         FIG.  33    is a system diagram illustrating an environment for ingesting and indexing data, and performing queries on one or more datasets from one or more dataset sources; 
         FIG.  34    is a block diagram illustrating an embodiment of multiple machines, each having multiple nodes; 
         FIG.  35    is a diagram illustrating an embodiment of a DAG; 
         FIG.  36    is a block diagram illustrating an embodiment of multiple partitions being used to implement various search phases of a DAG; 
         FIG.  37    is a data flow diagram illustrating an embodiment of communications between various components within the environment to process and execute a query; 
         FIG.  38    is a flow diagram illustrative of an embodiment of a routine to provide query results; 
         FIG.  39    is a flow diagram illustrative of an embodiment of a routine to process a query; 
         FIG.  40    is a flow diagram illustrative of an embodiment of a routine to generate a query processing scheme; 
         FIG.  41    is a flow diagram illustrative of an embodiment of a routine to execute a query on data from multiple dataset sources; 
         FIG.  42    is a flow diagram illustrative of an embodiment of a routine to execute a query on data from an external data source; 
         FIG.  43    is a flow diagram illustrative of an embodiment of a routine to execute a query based on a dataset destination; 
         FIG.  44    is a flow diagram illustrative of an embodiment of a routine to serialize data for communication; 
         FIG.  45    is a flow diagram illustrative of an embodiment of a routine to execute a query using a query acceleration data store; 
         FIG.  46    is a system diagram illustrating an environment for ingesting and indexing data, and performing queries on one or more datasets from one or more dataset sources including common storage; 
         FIG.  47    is a flow diagram illustrative of an embodiment of a routine to execute a query using common storage; 
         FIG.  48    is a system diagram illustrating an environment for ingesting and indexing data, and performing queries on one or more datasets from one or more dataset sources including an ingested data buffer; 
         FIG.  49    is a flow diagram illustrative of an embodiment of a routine to execute a query using an ingested data buffer; 
         FIG.  50 A  is a block diagram of an embodiment of an environment in which a primary data intake and query system communicates with secondary data intake and query systems to execute a query; 
         FIG.  50 B  is a block diagram of an embodiment of an environment in which a primary data intake and query system communicates with third-party data storage and processing systems to execute a query; 
         FIG.  51    is a data flow diagram illustrating an embodiment of communications between various components described herein to process and execute a federated query; 
         FIG.  52    is a flow diagram illustrative of an embodiment of a routine implemented by a query coordinator to execute a query involving data from a secondary data intake and query system; 
         FIGS.  53 ,  54 ,  55 , and  56    are flow diagrams illustrative of embodiments of routines implemented by the query coordinator to execute a query on data from an external data system; 
         FIG.  57    is a flow diagram illustrative of an embodiment of a routine implemented by a search head to execute a query received from an external data system; 
         FIG.  58    is a block diagram illustrating an embodiment of a data path of data from different data sources in a worker node; 
         FIG.  59    is a flow diagram illustrative of an embodiment of a routine implemented by a worker node to process a partition or task; 
         FIG.  60    is a flow diagram illustrative of an embodiment of a routine implemented by a query coordinator to optimize and execute a query involving data from an external data system; 
         FIG.  61    illustrates an example of an external query configuration file in accordance with disclosed embodiments; 
         FIGS.  62 A and  62 B  are block diagrams illustrating an embodiment of an assignment of bucket data to execution resources based on a bucket distribution policy; 
         FIG.  63    is a flow diagram illustrative of an embodiment of a routine implemented by an indexer to assign bucket data to execution resources; 
         FIG.  64    is a block diagram illustrating an embodiment of a worker node ingesting four chunks of data and reducing the records; 
         FIG.  65    is a flow diagram illustrative of an embodiment of a routine implemented by a worker node to assign records of chunks of data to one or more partitions and combine records of the one or more partitions; 
         FIG.  66    is a flow diagram illustrative of an embodiment of a routine implemented by a search head to allocate resources and/or estimate execution time based on records generated during a processing task; 
         FIG.  67    is a flow diagram illustrative of an embodiment of a routine implemented by a search head to determine a record generation estimate; 
         FIG.  68    is a flow diagram illustrative of an embodiment of a routine implemented by a search head to schedule a query; 
         FIG.  69    is a flow diagram illustrative of an embodiment of a routine implemented by a search head to determine a query execution time for a query; 
         FIG.  70    is a block diagram illustrating an example of an embodiment in which records from multiple chunks of data are used to generate multiple records; 
         FIG.  71    is a flow diagram illustrative of an embodiment of a routine implemented by a worker node to expand and reduce records from one or more chunks of data; 
         FIG.  72    is a block diagram illustrating an example of an embodiment of the system assigning a processing task to one or more worker nodes from a search head and/or a query coordinator; 
         FIG.  73    is a flow diagram illustrative of an embodiment of a routine implemented by the system to assign a processing task from one component to one or more different components; and 
         FIG.  74    is a block diagram illustrating a high-level example of a hardware architecture of a computing system in which an embodiment may be implemented. 
     
    
    
     DETAILED DESCRIPTION 
     Embodiments are described herein according to the following outline: 
     1.0. GENERAL OVERVIEW 
     2.0. OVERVIEW OF DATA INTAKE AND QUERY SYSTEMS 
     3.0. GENERAL OVERVIEW 
     3.1 HOST DEVICES 
     3.2 CLIENT DEVICES 
     3.3. CLIENT DEVICE APPLICATIONS 
     3.4. DATA SERVER SYSTEM 
     3.5. CLOUD-BASED SYSTEM OVERVIEW 
     3.6. SEARCHING EXTERNALLY-ARCHIVED DATA 
     3.7. DATA INGESTION 
     3.7.1. INPUT 
     3.7.2. PARSING 
     3.7.3. INDEXING 
     3.8. QUERY PROCESSING 
     3.9. PIPELINED SEARCH LANGUAGE 
     3.10. FIELD EXTRACTION 
     3.11. EXAMPLE SEARCH SCREEN 
     3.12. DATA MODELS 
     3.13. ACCELERATION TECHNIQUE 
     3.13.1. AGGREGATION TECHNIQUE 
     3.13.2. KEYWORD INDEX 
     3.13.3. HIGH PERFORMANCE ANALYTICS STORE 
     3.13.4. EXTRACTING EVENT DATA USING POSTING 
     3.13.5. ACCELERATING REPORT GENERATION 
     3.14. SECURITY FEATURES 
     3.15. DATA CENTER MONITORING 
     3.16. IT SERVICE MONITORING 
     4.0. DATA FABRIC SERVICE (DFS) 
     4.1. DFS SYSTEM ARCHITECTURE 
     4.2. DFS SYSTEM OPERATIONS 
     5.0. PARALLEL EXPORT TECHNIQUES 
     6.0. DFS QUERY PROCESSING 
     6.1. ORDERED SEARCH RESULTS 
     6.2. TRANSFORMED SEARCH RESULTS 
     7.0. CO-LOCATED DEPLOYMENT ARCHITECTURE 
     7.1. CO-LOCATED DEPLOYMENT OPERATIONS 
     8.0. CLOUD DEPLOYMENT ARCHITECTURE 
     8.1. CLOUD DEPLOYMENT OPERATIONS 
     9.0. TIMELINE VISUALIZATION 
     10.0. MONITORING AND METERING SERVICES 
     11.0. DATA INTAKE AND FABRIC SYSTEM ARCHITECTURE 
     11.1. WORKER NODES 
     11.1.1. SERIALIZATION/DESERIALIZATION 
     11.2. SEARCH PROCESS MASTER 
     11.2.1 WORKLOAD CATALOG 
     11.2.2 NODE MONITOR 
     11.2.3 DATASET COMPENSATION 
     11.3. QUERY COORDINATOR 
     11.3.1. QUERY PROCESSING 
     11.3.2. QUERY EXECUTION AND NODE CONTROL 
     11.3.3. RESULT PROCESSING 
     11.4 QUERY ACCELERATION DATA STORE 
     12.0. QUERY DATA FLOW 
     13.0. QUERY COORDINATOR FLOW 
     14.0. QUERY PROCESSING FLOW 
     15.0. WORKLOAD MONITORING AND ADVISING FLOW 
     16.0. MULTIPLE DATASET SOURCES FLOW 
     17.0. EXTERNAL DATA SOURCE FLOW 
     18.0. DATASET DESTINATION FLOW 
     19.0. SERIALIZATION AND DESERIALIZATION FLOW 
     20.0. ACCELERATED QUERY RESULTS FLOW 
     21.0. COMMON STORAGE ARCHITECTURE 
     22.0. COMMON STORAGE FLOW 
     23.0. INGESTED DATA BUFFER ARCHITECTURE 
     24.0. INGESTED DATA BUFFER FLOW 
     25.0. FEDERATED SEARCH 
     25.1. FEDERATED SEARCH DATA FLOW 
     26.0. SEARCH OF SECONDARY DATA INTAKE AND QUERY SYSTEM FLOW 
     27.0. SEARCH WITH DATA INGEST ESTIMATE FLOW 
     28.0. SEARCH USING SEARCH CONFIGURATION DATA FLOW 
     29.0. DISTRIBUTING PARTIAL RESULTS TO WORKER NODES FLOW 
     30.0. DISTRIBUTION OF PARTIAL RESULTS BETWEEN WORKER NODES FLOW 
     31.0. EXECUTING A QUERY RECEIVED FROM ANOTHER SYSTEM FLOW 
     32.0. TASK DISTRIBUTION WITHIN AN EXECUTION NODE 
     32.1. WORKER NODE TASK DISTRIBUTION FLOW 
     33.0 FEDERATED SEARCH OPTIMIZATION 
     34.0 CONFIGURATION FILE 
     35.0. BUCKET DATA DISTRIBUTION FOR PROCESSING/EXPORT 
     36.0. PARTITIONING AND REDUCING RECORDS DURING INGEST AT A WORKER NODE 
     37.0. ESTIMATING GENERATED RECORDS 
     38.0. QUERY-RESOURCE ALLOCATION AND CONCURRENCY 
     39.0. SEARCH TIME ESTIMATE 
     40.0. PROCESSING HIGH CARDINALITY RECORDS WITH RELATED FIELDS 
     41.0. PUSHING PROCESSING TASKS 
     42.0. HARDWARE EMBODIMENT 
     43.0. EXAMPLE EMBODIMENTS 
     44.0. TERMINOLOGY 
     In this description, references to “an embodiment,” “one embodiment,” or the like, mean that the particular feature, function, structure or characteristic being described is included in at least one embodiment of the technique introduced herein. Occurrences of such phrases in this specification do not necessarily all refer to the same embodiment. On the other hand, the embodiments referred to are also not necessarily mutually exclusive. 
     A data intake and query system can index and store data in data stores of indexers, and can receive search queries causing a search of the indexers to obtain search results. The data intake and query system typically has search, extraction, execution, and analytics capabilities that may be limited in scope to the data stores of the indexers (“internal data stores”). Hence, a seamless and comprehensive search and analysis that includes diverse data types from external data sources, common storage (may also be referred to as global data storage or global data stores), ingested data buffers, query acceleration data stores, etc. may be difficult. Thus, the capabilities of some data intake and query systems remain isolated from a variety of data sources that could improve search results to provide new insights. Furthermore, the processing flow of some data intake and query systems are unidirectional in that data is obtained from a data source, processed, and then communicated to a search head or client without the ability to route data to different destinations. 
     The disclosed embodiments overcome these drawbacks by extending the search and analytics capabilities of a data intake and query system to include diverse data types stored in diverse data systems internal to or external from the data intake and query system. As a result, an analyst can use the data intake and query system to search and analyze data from a wide variety of dataset sources, including enterprise systems and open source technologies of a big data ecosystem. The term “big data” refers to large data sets that may be analyzed computationally to reveal patterns, trends, and associations, in some cases, relating to human behavior and interactions. 
     In particular, introduced herein is a data intake and query system that that has the ability to execute big data analytics seamlessly and can scale across diverse data sources to enable processing large volumes of diverse data from diverse data systems. A “data source” can include a “data system,” which may refer to a system that can process and/or store data. A “data storage system” may refer to a storage system that can store data such as unstructured, semi-structured, or structured data. Accordingly, a data source can include a data system that includes a data storage system. 
     The system can improve search and analytics capabilities of previous systems by employing a search process master and query coordinators combined with a scalable network of distributed nodes communicatively coupled to diverse data systems. The network of distributed nodes can act as agents of the data intake and query system to collect and process data of distributed data systems, and the search process master and coordinators can provide the processed data to the search head as search results. 
     For example, the data intake and query system can respond to a query by executing search operations on various internal and external data sources to obtain partial search results that are harmonized and presented as search results of the query. As such, the data intake and query system can offload search and analytics operations to the distributed nodes. Hence, the system enables search and analytics capabilities that can extend beyond the data stored on indexers to include external data systems, common storage, query acceleration data stores, ingested data buffers, etc. 
     The system can provide big data open stack integration to act as a big data pipeline that extends the search and analytics capabilities of a system over numerous and diverse data sources. For example, the system can extend the data execution scope of the data intake and query system to include data residing in external data systems such as MySQL, PostgreSQL, and Oracle databases; NoSQL data stores like Cassandra, Mongo DB; cloud storage like Amazon S3 and Hadoop distributed file system (HDFS); common storage; ingested data buffers; etc. Thus, the system can execute search and analytics operations for all possible combinations of data types stored in various data sources. 
     The distributed processing of the system enables scalability to include any number of distributed data systems. As such, queries received by the data intake and query system can be propagated to the network of distributed nodes to extend the search and analytics capabilities of the data intake and query system over different data sources. In this context, the network of distributed nodes can act as an extension of the local data intake in query system&#39;s data processing pipeline to facilitate scalable analytics across the diverse data systems. Accordingly, the system can extend and transform the data intake and query system to include data resources into a data fabric platform that can leverage computing assets from anywhere and access and execute on data regardless of type or origin. 
     The disclosed embodiments include services such as new search capabilities, visualization tools, and other services that are seamlessly integrated into the DFS system. For example, the disclosed techniques include new search services performed on internal data stores, external data stores, or a combination of both. The search operations can provide ordered or unordered search results, or search results derived from data of diverse data systems, which can be visualized to provide new and useful insights about the data contained in a big data ecosystem. 
     Various other features of the DFS system introduced here will become apparent from the description that follows. First, however, it is useful to consider an example of an environment and system in which the techniques can be employed, as will now be described. 
     1.0. General Overview 
     The embodiments disclosed herein generally refer to an environment that includes data intake and query system including a data fabric service system architecture (“DFS system”), services, a network of distributed nodes, and distributed data systems, all interconnected over one or more networks. However, embodiments of the disclosed environment can include many computing components including software, servers, routers, client devices, and host devices that are not specifically described herein. As used herein, a “node” can refer to one or more devices and/or software running on devices that enable the devices to provide execute a task of the system. For example, a node can include devices running software that enable the device to execute a portion of a query. 
       FIG.  1 A  is a high-level system diagram of an environment  10  in which an embodiment may be implemented. The environment  10  includes distributed external data systems  12 - 1  and  12 - 2  (also referred to collectively and individually as external data system(s)  12 ). The external data systems  12  are communicatively coupled (e.g., via a LAN, WAN, etc.) to a data intake and query system  16 , various examples of which are described herein at least with reference to  FIGS.  1 A,  2 ,  3 ,  4 ,  18 ,  25 ,  27 ,  33 ,  46 , and  48   . In some embodiments, the external data systems  12  are communicatively coupled to worker nodes  14 - 1  and  14 - 2  (also referred to collectively and individually as worker node(s)  14 ) of the data intake and query system  16 , various examples of which are described herein at least with reference to  FIGS.  18 ,  25 ,  27 ,  33 ,  46 ,  48 , and  58   . The environment  10  can also include a client device  22  and applications running on the client device  22 . An example includes a personal computer, laptop, tablet, phone, or other computing device running a network browser application that enables a user of the client device  22  to access any of the data systems. 
     The data intake and query system  16  and the external data systems  12  can each store data obtained from various data sources. For example, the data intake and query system  16  can store data in internal data stores  20  (also referred to as an internal storage system), and the external data systems  12  can store data in respective external data stores  24  (also referred to as external storage systems). However, the data intake and query system  16  and external data systems  12  may process and store data differently. For example, as explained in greater detail below, the data intake and query system  16  may store minimally processed or unprocessed data (“raw data”) in the internal data stores  20 , which can be implemented as local data stores  20 - 1 , common storage  20 - 2 , or query acceleration data stores  20 - 3 . In contrast, the external data systems  12  may store pre-processed data rather than raw data. Hence, the data intake and query system  16  and the external data systems  12  can operate independent of each other in a big data ecosystem. 
     The worker nodes  14  can act as agents of the data intake and query system  16  to process data collected from the internal data stores  20  and the external data stores  24 . The worker nodes  14  may reside on one or more computing devices such as servers communicatively coupled to the external data systems  12 . Other components of the data intake and query system  16  can finalize the results before returning the results to the client device  22 . As such, the worker nodes  14  can extend the search and analytics capabilities of the data intake and query system  16  to act on diverse data systems. 
     The external data systems  12  may include one or more computing devices that can store structured, semi-structured, or unstructured data. Each external data system  12  can generate and/or collect generated data, and store the generated data in their respective external data stores  24 . For example, the external data system  12 - 1  may include a server running a MySQL database that stores structured data objects such as time-stamped events, and the external data system  12 - 2  may be a server of cloud computing services such as Amazon web services (AWS) that can provide different data types ranging from unstructured (e.g., s3) to structured (e.g., redshift). As yet another non-limiting example, the external data system  12 - 1  and/or  12 - 2  may be a data intake and query system that is separate and distinct from the data intake and query system  16 , but that includes the same or similar architecture as the data intake and query system  16  and/or stores data in a similar format and/or hierarchy. For example, separate divisions of the same company may set up distinct data intake and query systems  16  that are independent from each other. 
     The internal data stores  20  are said to be internal because the data stored thereon has been processed or passed through the data intake and query system  16  in some form. Conversely, the external data systems  12  are said to be external to the data intake and query system  16  because the data stored at the external data stores  24  has not necessarily been processed or passed through the data intake and query system  16 . In other words, the data intake and query system  16  may have no control or influence over how data is processed, controlled, or managed by the external data systems  12 , including other instances of a data intake and query system with the same architecture of the data intake and query system  16 . 
     The external data systems  12  can process data, perform requests received from other computing systems, and perform numerous other computational tasks independent of each other and independent of the data intake and query system  16 . For example, the external data system  12 - 1  may be a server that can process data locally that reflects correlations among the stored data. The external data systems  12  may generate and/or store ever increasing volumes of data without any interaction with the data intake and query system  16 . As such, each of the external data system  12  may act independently to control, manage, and process the data they contain. 
     Data stored in the internal data stores  20  and external data stores  24  may be related. For example, an online transaction could generate various forms of data stored in disparate locations and in various formats. The generated data may include payment information, customer information, and information about suppliers, retailers, and the like. Other examples of data generated in a big data ecosystem include application program data, system logs, network packet data, error logs, stack traces, and performance data. The data can also include diagnostic information and many other types of data that can be analyzed to perform local actions, diagnose performance problems, monitor interactions, and derive other insights. 
     The volume of generated data can grow at very high rates as the number of transactions and diverse data systems grows. A portion of this large volume of data could be processed and stored by the data intake and query system  16  while other portions could be stored in any of the external data systems  12 . In an effort to reduce the vast amounts of raw data generated in a big data ecosystem, some of the external data systems  12  may pre-process the raw data based on anticipated data analysis needs, store the pre-processed data, discard some or all of the remaining raw data, or store it in a different location that data intake and query system  16  does not have access to. However, discarding or not making the massive amounts of raw data available can result in the loss of valuable insights that could have been obtained by searching all of the raw data. 
     In contrast, the data intake and query system  16  or external data systems similar to the data intake and query system  16  can address some of these challenges by collecting and storing raw data as structured “events,” as will be described in greater detail below. In some embodiments, an event includes a portion of raw data and is associated with a specific point in time. For example, events may be derived from “time series data,” where the time series data comprises a sequence of data points (e.g., performance measurements from a computer system) that are associated with successive points in time. 
     In some embodiments, the external data systems  12  can store raw data as events that are indexed by timestamps but are also associated with predetermined data items. This structure is essentially a modification of conventional database systems that require predetermining data items for subsequent searches. These systems can be modified to retain the remaining raw data for subsequent re-processing for other predetermined data items. 
     Specifically, the raw data can be divided into segments and indexed by timestamps. The predetermined data items can be associated with the events indexed by timestamps. The events can be searched only for the predetermined data items during search time; the events can be re-processed later in time to re-index the raw data, and generate events with new predetermined data items. As such, the data systems of the system  10  can store related data in a variety of pre-processed data and raw data in a variety of structures. 
     A number of tools are available to search and analyze data contained in these diverse data systems. As such, an analyst can use a tool to search a database of the external data system  12 - 1 . A different tool could be used to search a cloud services application of the external data system  12 - 2 . Yet another different tool could be used to search the internal data stores  20 . Moreover, different tools can perform analytics of data stored in proprietary or open source data stores. However, existing tools cannot obtain valuable insights from data contained in a combination of the data intake and query system  16  and/or any of the external data systems  12 . Examples of these valuable insights may include correlations between the structured data of the external data stores  24  and raw data of the internal data stores  20  (or external data stores  24  that store data in a similar format or hierarchy as the internal data stores  20 ). 
     The disclosed techniques can extend the search, extraction, execution, and analytics capabilities of data intake and query systems to seamlessly search and analyze multiple diverse data of diverse data systems in a big data ecosystem. The disclosed techniques can transform a big data ecosystem into a big data pipeline between external data systems and a data intake and query system, to enable seamless search and analytics operations on a variety of data sources, which can lead to new insights that were not previously available. Hence, the disclosed techniques include a data intake and query system  16  extended to search external data systems into a data fabric platform that can leverage computing assets from anywhere and access and execute on data regardless of type and origin. In addition, the data intake and query system  16  facilitates implementation of both iterative searches, to read datasets multiple times in a loop, and interactive or exploratory data analysis (e.g., for repeated database-style querying of data). 
     2.0. Overview of Data Intake and Query Systems 
     As indicated above, modern data centers and other computing environments can comprise anywhere from a few host computer systems to thousands of systems configured to process data, service requests from remote clients, and perform numerous other computational tasks. During operation, various components within these computing environments often generate significant volumes of machine data. Machine data is any data produced by a machine or component in an information technology (IT) environment and that reflects activity in the IT environment. For example, machine data can be raw machine data that is generated by various components in IT environments, such as servers, sensors, routers, mobile devices, Internet of Things (IoT) devices, etc. Machine data can include system logs, network packet data, sensor data, application program data, error logs, stack traces, system performance data, etc. In general, machine data can also include performance data, diagnostic information, and many other types of data that can be analyzed to diagnose performance problems, monitor user interactions, and to derive other insights. 
     A number of tools are available to analyze machine data. In order to reduce the size of the potentially vast amount of machine data that may be generated, many of these tools typically pre-process the data based on anticipated data-analysis needs. For example, pre-specified data items may be extracted from the machine data and stored in a database to facilitate efficient retrieval and analysis of those data items at search time. However, the rest of the machine data typically is not saved and is discarded during pre-processing. As storage capacity becomes progressively cheaper and more plentiful, there are fewer incentives to discard these portions of machine data and many reasons to retain more of the data. 
     This plentiful storage capacity is presently making it feasible to store massive quantities of minimally processed machine data for later retrieval and analysis. In general, storing minimally processed machine data and performing analysis operations at search time can provide greater flexibility because it enables an analyst to search all of the machine data, instead of searching only a pre-specified set of data items. This may enable an analyst to investigate different aspects of the machine data that previously were unavailable for analysis. 
     However, analyzing and searching massive quantities of machine data presents a number of challenges. For example, a data center, servers, or network appliances may generate many different types and formats of machine data (e.g., system logs, network packet data (e.g., wire data, etc.), sensor data, application program data, error logs, stack traces, system performance data, operating system data, virtualization data, etc.) from thousands of different components, which can collectively be very time-consuming to analyze. In another example, mobile devices may generate large amounts of information relating to data accesses, application performance, operating system performance, network performance, etc. There can be millions of mobile devices that report these types of information. 
     These challenges can be addressed by using an event-based data intake and query system, such as the SPLUNK® ENTERPRISE system developed by Splunk Inc. of San Francisco, Calif. The SPLUNK® ENTERPRISE system is the leading platform for providing real-time operational intelligence that enables organizations to collect, index, and search machine data from various websites, applications, servers, networks, and mobile devices that power their businesses. The data intake and query system is particularly useful for analyzing data which is commonly found in system log files, network data, and other data input sources. Although many of the techniques described herein are explained with reference to a data intake and query system similar to the SPLUNK® ENTERPRISE system, these techniques are also applicable to other types of data systems. 
     In the data intake and query system, machine data are collected and stored as “events”. An event comprises a portion of machine data and is associated with a specific point in time. The portion of machine data may reflect activity in an IT environment and may be produced by a component of that IT environment, where the events may be searched to provide insight into the IT environment, thereby improving the performance of components in the IT environment. Events may be derived from “time series data,” where the time series data comprises a sequence of data points (e.g., performance measurements from a computer system, etc.) that are associated with successive points in time. In general, each event has a portion of machine data that is associated with a timestamp that is derived from the portion of machine data in the event. A timestamp of an event may be determined through interpolation between temporally proximate events having known timestamps or may be determined based on other configurable rules for associating timestamps with events. 
     In some instances, machine data can have a predefined format, where data items with specific data formats are stored at predefined locations in the data. For example, the machine data may include data associated with fields in a database table. In other instances, machine data may not have a predefined format (e.g., may not be at fixed, predefined locations), but may have repeatable (e.g., non-random) patterns. This means that some machine data can comprise various data items of different data types that may be stored at different locations within the data. For example, when the data source is an operating system log, an event can include one or more lines from the operating system log containing machine data that includes different types of performance and diagnostic information associated with a specific point in time (e.g., a timestamp). 
     Examples of components which may generate machine data from which events can be derived include, but are not limited to, web servers, application servers, databases, firewalls, routers, operating systems, and software applications that execute on computer systems, mobile devices, sensors, Internet of Things (IoT) devices, etc. The machine data generated by such data sources can include, for example and without limitation, server log files, activity log files, configuration files, messages, network packet data, performance measurements, sensor measurements, etc. 
     The data intake and query system uses a flexible schema to specify how to extract information from events. A flexible schema may be developed and redefined as needed. Note that a flexible schema may be applied to events “on the fly,” when it is needed (e.g., at search time, index time, ingestion time, etc.). When the schema is not applied to events until search time, the schema may be referred to as a “late-binding schema.” 
     During operation, the data intake and query system receives machine data from any type and number of sources (e.g., one or more system logs, streams of network packet data, sensor data, application program data, error logs, stack traces, system performance data, etc.). The system parses the machine data to produce events each having a portion of machine data associated with a timestamp. The system stores the events in a data store. The system enables users to run queries against the stored events to, for example, retrieve events that meet criteria specified in a query, such as criteria indicating certain keywords or having specific values in defined fields. As used herein, the term “field” refers to a location in the machine data of an event containing one or more values for a specific data item. A field may be referenced by a field name associated with the field. As will be described in more detail herein, a field is defined by an extraction rule (e.g., a regular expression) that derives one or more values or a sub-portion of text from the portion of machine data in each event to produce a value for the field for that event. The set of values produced are semantically-related (such as IP address), even though the machine data in each event may be in different formats (e.g., semantically-related values may be in different positions in the events derived from different sources). 
     As described above, the system stores the events in a data store. The events stored in the data store are field-searchable, where field-searchable herein refers to the ability to search the machine data (e.g., the raw machine data) of an event based on a field specified in search criteria. For example, a search having criteria that specifies a field name “UserID” may cause the system to field-search the machine data of events to identify events that have the field name “UserID.” In another example, a search having criteria that specifies a field name “UserID” with a corresponding field value “12345” may cause the system to field-search the machine data of events to identify events having that field-value pair (e.g., field name “UserID” with a corresponding field value of “12345”). Events are field-searchable using one or more configuration files associated with the events. Each configuration file includes one or more field names, where each field name is associated with a corresponding extraction rule and a set of events to which that extraction rule applies. The set of events to which an extraction rule applies may be identified by metadata associated with the set of events. For example, an extraction rule may apply to a set of events that are each associated with a particular host, source, or source type. When events are to be searched based on a particular field name specified in a search, the system uses one or more configuration files to determine whether there is an extraction rule for that particular field name that applies to each event that falls within the criteria of the search. If so, the event is considered as part of the search results (and additional processing may be performed on that event based on criteria specified in the search). If not, the next event is similarly analyzed, and so on. 
     As noted above, the data intake and query system utilizes a late-binding schema while performing queries on events. One aspect of a late-binding schema is applying extraction rules to events to extract values for specific fields during search time. More specifically, the extraction rule for a field can include one or more instructions that specify how to extract a value for the field from an event. An extraction rule can generally include any type of instruction for extracting values from events. In some cases, an extraction rule comprises a regular expression, where a sequence of characters form a search pattern. An extraction rule comprising a regular expression is referred to herein as a regex rule. The system applies a regex rule to an event to extract values for a field associated with the regex rule, where the values are extracted by searching the event for the sequence of characters defined in the regex rule. 
     In the data intake and query system, a field extractor may be configured to automatically generate extraction rules for certain fields in the events when the events are being created, indexed, or stored, or possibly at a later time. Alternatively, a user may manually define extraction rules for fields using a variety of techniques. In contrast to a conventional schema for a database system, a late-binding schema is not defined at data ingestion time. Instead, the late-binding schema can be developed on an ongoing basis until the time a query is actually executed. This means that extraction rules for the fields specified in a query may be provided in the query itself, or may be located during execution of the query. Hence, as a user learns more about the data in the events, the user can continue to refine the late-binding schema by adding new fields, deleting fields, or modifying the field extraction rules for use the next time the schema is used by the system. Because the data intake and query system maintains the underlying machine data and uses a late-binding schema for searching the machine data, it enables a user to continue investigating and learn valuable insights about the machine data. 
     In some embodiments, a common field name may be used to reference two or more fields containing equivalent and/or similar data items, even though the fields may be associated with different types of events that possibly have different data formats and different extraction rules. By enabling a common field name to be used to identify equivalent and/or similar fields from different types of events generated by disparate data sources, the system facilitates use of a “common information model” (CIM) across the disparate data sources (further discussed with respect to  FIG.  7 A ). 
     3.0. General Overview 
       FIG.  1 B  is a block diagram of an example networked computer environment  100 , in accordance with example embodiments. Those skilled in the art would understand that  FIG.  1 B  represents one example of a networked computer system and other embodiments, such as the embodiment illustrated in  FIG.  1 A  may use different arrangements. 
     The networked computer environment  100  includes one or more computing devices. These one or more computing devices comprise any combination of hardware and software configured to implement the various logical components described herein. For example, the one or more computing devices may include one or more memories that store instructions for implementing the various components described herein, one or more hardware processors configured to execute the instructions stored in the one or more memories, and various data repositories in the one or more memories for storing data structures utilized and manipulated by the various components. 
     In some embodiments, one or more client devices  102  are coupled to one or more host devices  106  and a data intake and query system  108  via one or more networks  104 . Networks  104  broadly represent one or more LANs, WANs, cellular networks (e.g., LTE, HSPA, 3G, and other cellular technologies), and/or networks using any of wired, wireless, terrestrial microwave, or satellite links, and may include the public Internet. 
     3.1 Host Devices 
     In the illustrated embodiment, an environment  100  includes one or more host devices  106 . Host devices  106  may broadly include any number of computers, virtual machine instances, and/or data centers that are configured to host or execute one or more instances of host applications  114 . In general, a host device  106  may be involved, directly or indirectly, in processing requests received from client devices  102 . Each host device  106  may comprise, for example, one or more of a network device, a web server, an application server, a database server, etc. A collection of host devices  106  may be configured to implement a network-based service. For example, a provider of a network-based service may configure one or more host devices  106  and host applications  114  (e.g., one or more web servers, application servers, database servers, etc.) to collectively implement the network-based application. 
     In general, client devices  102  communicate with one or more host applications  114  to exchange information. The communication between a client device  102  and a host application  114  may, for example, be based on the Hypertext Transfer Protocol (HTTP) or any other network protocol. Content delivered from the host application  114  to a client device  102  may include, for example, HTML documents, media content, etc. The communication between a client device  102  and host application  114  may include sending various requests and receiving data packets. For example, in general, a client device  102  or application running on a client device may initiate communication with a host application  114  by making a request for a specific resource (e.g., based on an HTTP request), and the application server may respond with the requested content stored in one or more response packets. 
     In the illustrated embodiment, one or more of host applications  114  may generate various types of performance data during operation, including event logs, network data, sensor data, and other types of machine data. For example, a host application  114  comprising a web server may generate one or more web server logs in which details of interactions between the web server and any number of client devices  102  is recorded. As another example, a host device  106  comprising a router may generate one or more router logs that record information related to network traffic managed by the router. As yet another example, a host application  114  comprising a database server may generate one or more logs that record information related to requests sent from other host applications  114  (e.g., web servers or application servers) for data managed by the database server. 
     3.2 Client Devices 
     Client devices  102  represent any computing device capable of interacting with one or more host devices  106  via a network  104 . Examples of client devices  102  may include, without limitation, smart phones, tablet computers, handheld computers, wearable devices, laptop computers, desktop computers, servers, portable media players, gaming devices, and so forth. In general, a client device  102  can provide access to different content, for instance, content provided by one or more host devices  106 , etc. Each client device  102  may comprise one or more client applications  110 , described in more detail in a separate section hereinafter. 
     3.3. Client Device Applications 
     In some embodiments, each client device  102  may host or execute one or more client applications  110  that are capable of interacting with one or more host devices  106  via one or more networks  104 . For instance, a client application  110  may be or comprise a web browser that a user may use to navigate to one or more websites or other resources provided by one or more host devices  106 . As another example, a client application  110  may comprise a mobile application or “app.” For example, an operator of a network-based service hosted by one or more host devices  106  may make available one or more mobile apps that enable users of client devices  102  to access various resources of the network-based service. As yet another example, client applications  110  may include background processes that perform various operations without direct interaction from a user. A client application  110  may include a “plug-in” or “extension” to another application, such as a web browser plug-in or extension. 
     In some embodiments, a client application  110  may include a monitoring component  112 . At a high level, the monitoring component  112  comprises a software component or other logic that facilitates generating performance data related to a client device&#39;s operating state, including monitoring network traffic sent and received from the client device and collecting other device and/or application-specific information. Monitoring component  112  may be an integrated component of a client application  110 , a plug-in, an extension, or any other type of add-on component. Monitoring component  112  may also be a stand-alone process. 
     In some embodiments, a monitoring component  112  may be created when a client application  110  is developed, for example, by an application developer using a software development kit (SDK). The SDK may include custom monitoring code that can be incorporated into the code implementing a client application  110 . When the code is converted to an executable application, the custom code implementing the monitoring functionality can become part of the application itself. 
     In some embodiments, an SDK or other code for implementing the monitoring functionality may be offered by a provider of a data intake and query system, such as a system  108 . In such cases, the provider of the system  108  can implement the custom code so that performance data generated by the monitoring functionality is sent to the system  108  to facilitate analysis of the performance data by a developer of the client application or other users. 
     In some embodiments, the custom monitoring code may be incorporated into the code of a client application  110  in a number of different ways, such as the insertion of one or more lines in the client application code that call or otherwise invoke the monitoring component  112 . As such, a developer of a client application  110  can add one or more lines of code into the client application  110  to trigger the monitoring component  112  at desired points during execution of the application. Code that triggers the monitoring component may be referred to as a monitor trigger. For instance, a monitor trigger may be included at or near the beginning of the executable code of the client application  110  such that the monitoring component  112  is initiated or triggered as the application is launched, or included at other points in the code that correspond to various actions of the client application, such as sending a network request or displaying a particular interface. 
     In some embodiments, the monitoring component  112  may monitor one or more aspects of network traffic sent and/or received by a client application  110 . For example, the monitoring component  112  may be configured to monitor data packets transmitted to and/or from one or more host applications  114 . Incoming and/or outgoing data packets can be read or examined to identify network data contained within the packets, for example, and other aspects of data packets can be analyzed to determine a number of network performance statistics. Monitoring network traffic may enable information to be gathered particular to the network performance associated with a client application  110  or set of applications. 
     In some embodiments, network performance data refers to any type of data that indicates information about the network and/or network performance. Network performance data may include, for instance, a URL requested, a connection type (e.g., HTTP, HTTPS, etc.), a connection start time, a connection end time, an HTTP status code, request length, response length, request headers, response headers, connection status (e.g., completion, response time(s), failure, etc.), and the like. Upon obtaining network performance data indicating performance of the network, the network performance data can be transmitted to a data intake and query system  108  for analysis. 
     Upon developing a client application  110  that incorporates a monitoring component  112 , the client application  110  can be distributed to client devices  102 . Applications generally can be distributed to client devices  102  in any manner, or they can be pre-loaded. In some cases, the application may be distributed to a client device  102  via an application marketplace or other application distribution system. For instance, an application marketplace or other application distribution system might distribute the application to a client device based on a request from the client device to download the application. 
     Examples of functionality that enables monitoring performance of a client device are described in U.S. patent application Ser. No. 14/524,748, entitled “UTILIZING PACKET HEADERS TO MONITOR NETWORK TRAFFIC IN ASSOCIATION WITH A CLIENT DEVICE”, filed on 27 Oct. 2014, and which is hereby incorporated by reference in its entirety for all purposes. 
     In some embodiments, the monitoring component  112  may also monitor and collect performance data related to one or more aspects of the operational state of a client application  110  and/or client device  102 . For example, a monitoring component  112  may be configured to collect device performance information by monitoring one or more client device operations, or by making calls to an operating system and/or one or more other applications executing on a client device  102  for performance information. Device performance information may include, for instance, a current wireless signal strength of the device, a current connection type and network carrier, current memory performance information, a geographic location of the device, a device orientation, and any other information related to the operational state of the client device. 
     In some embodiments, the monitoring component  112  may also monitor and collect other device profile information including, for example, a type of client device, a manufacturer, and model of the device, versions of various software applications installed on the device, and so forth. 
     In general, a monitoring component  112  may be configured to generate performance data in response to a monitor trigger in the code of a client application  110  or other triggering application event, as described above, and to store the performance data in one or more data records. Each data record, for example, may include a collection of field-value pairs, each field-value pair storing a particular item of performance data in association with a field for the item. For example, a data record generated by a monitoring component  112  may include a “networkLatency” field (not shown in the Figure) in which a value is stored. This field indicates a network latency measurement associated with one or more network requests. The data record may include a “state” field to store a value indicating a state of a network connection, and so forth for any number of aspects of collected performance data. 
     3.4. Data Server System 
       FIG.  2    is a block diagram of an example data intake and query system  108 , in accordance with example embodiments. In certain embodiments, the data intake and query system  108  may be or may include a data intake and query system  16 . System  108  includes one or more forwarders  204  that receive data from a variety of input data sources  203 , and one or more indexers  206  that process and store the data in one or more data stores  208 . These forwarders  204  and indexers  206  can comprise separate computer systems, or may alternatively comprise separate processes executing on one or more computer systems. 
     Each data source  203  broadly represents a distinct source of data that can be consumed by system  108 . Examples of a data sources  203  include, without limitation, data files, directories of files, data sent over a network, event logs, registries, etc. 
     During operation, the forwarders  204  identify which indexers  206  receive data collected from a data source  203  and forward the data to the appropriate indexers. Forwarders  204  can also perform operations on the data before forwarding, including removing extraneous data, detecting timestamps in the data, parsing data, indexing data, routing data based on criteria relating to the data being routed, and/or performing other data transformations. 
     In some embodiments, a forwarder  204  may comprise a service accessible to client devices  102  and host devices  106  via a network  104 . For example, one type of forwarder  204  may be capable of consuming vast amounts of real-time data from a potentially large number of client devices  102  and/or host devices  106 . The forwarder  204  may, for example, comprise a computing device which implements multiple data pipelines or “queues” to handle forwarding of network data to indexers  206 . A forwarder  204  may also perform many of the functions that are performed by an indexer. For example, a forwarder  204  may perform keyword extractions on raw data or parse raw data to create events. A forwarder  204  may generate time stamps for events. Additionally or alternatively, a forwarder  204  may perform routing of events to indexers  206 . Data store  208  may contain events derived from machine data from a variety of sources all pertaining to the same component in an IT environment, and this data may be produced by the machine in question or by other components in the IT environment. 
     3.5. Cloud-Based System Overview 
     The example data intake and query system  108  described in reference to  FIG.  2    comprises several system components, including one or more forwarders, indexers, and search heads. In some environments, a user of a data intake and query system  108  may install and configure, on computing devices owned and operated by the user, one or more software applications that implement some or all of these system components. For example, a user may install a software application on server computers owned by the user and configure each server to operate as one or more of a forwarder, an indexer, a search head, etc. This arrangement generally may be referred to as an “on-premises” solution. That is, the system  108  is installed and operates on computing devices directly controlled by the user of the system. Some users may prefer an on-premises solution because it may provide a greater level of control over the configuration of certain aspects of the system (e.g., security, privacy, standards, controls, etc.). However, other users may instead prefer an arrangement in which the user is not directly responsible for providing and managing the computing devices upon which various components of system  108  operate. 
     In one embodiment, to provide an alternative to an entirely on-premises environment for system  108 , one or more of the components of a data intake and query system instead may be provided as a cloud-based service. In this context, a cloud-based service refers to a service hosted by one more computing resources that are accessible to end users over a network, for example, by using a web browser or other application on a client device to interface with the remote computing resources. For example, a service provider may provide a cloud-based data intake and query system by managing computing resources configured to implement various aspects of the system (e.g., forwarders, indexers, search heads, etc.) and by providing access to the system to end users via a network. Typically, a user may pay a subscription or other fee to use such a service. Each subscribing user of the cloud-based service may be provided with an account that enables the user to configure a customized cloud-based system based on the user&#39;s preferences. 
       FIG.  3    illustrates a block diagram of an example cloud-based data intake and query system  306 . Similar to the system of  FIG.  2   , the networked computer environment  300  includes input data sources  203  and forwarders  204 . These input data sources and forwarders may be in a subscriber&#39;s private computing environment. Alternatively, they might be directly managed by the service provider as part of the cloud service. In the example environment  300 , one or more forwarders  204  and client devices  302  are coupled to a cloud-based data intake and query system  306  via one or more networks  304 . Network  304  broadly represents one or more LANs, WANs, cellular networks, intranetworks, internetworks, etc., using any of wired, wireless, terrestrial microwave, satellite links, etc., and may include the public Internet, and is used by client devices  302  and forwarders  204  to access the system  306 . Similar to the system of  38 , each of the forwarders  204  may be configured to receive data from an input source and to forward the data to other components of the system  306  for further processing. 
     In some embodiments, a cloud-based data intake and query system  306  may comprise a plurality of system instances  308 . In general, each system instance  308  may include one or more computing resources managed by a provider of the cloud-based system  306  made available to a particular subscriber. The computing resources comprising a system instance  308  may, for example, include one or more servers or other devices configured to implement one or more forwarders, indexers, search heads, and other components of a data intake and query system, similar to system  108 . As indicated above, a subscriber may use a web browser or other application of a client device  302  to access a web portal or other interface that enables the subscriber to configure an instance  308 . 
     Providing a data intake and query system as described in reference to system  108  as a cloud-based service presents a number of challenges. Each of the components of a system  108  (e.g., forwarders, indexers, and search heads) may at times refer to various configuration files stored locally at each component. These configuration files typically may involve some level of user configuration to accommodate particular types of data a user desires to analyze and to account for other user preferences. However, in a cloud-based service context, users typically may not have direct access to the underlying computing resources implementing the various system components (e.g., the computing resources comprising each system instance  308 ) and may desire to make such configurations indirectly, for example, using one or more web-based interfaces. Thus, the techniques and systems described herein for providing user interfaces that enable a user to configure source type definitions are applicable to both on-premises and cloud-based service contexts, or some combination thereof (e.g., a hybrid system where both an on-premises environment, such as SPLUNK® ENTERPRISE, and a cloud-based environment, such as SPLUNK CLOUD™, are centrally visible). 
     3.6. Searching Externally-Archived Data 
       FIG.  4    shows a block diagram of an example of a data intake and query system  108  that provides transparent search facilities for data systems that are external to the data intake and query system. Such facilities are available in the Splunk® Analytics for Hadoop® system provided by Splunk Inc. of San Francisco, Calif. Splunk® Analytics for Hadoop® represents an analytics platform that enables business and IT teams to rapidly explore, analyze, and visualize data in Hadoop® and NoSQL data stores. 
     The search head  210  of the data intake and query system receives search requests from one or more client devices  404  over network connections  420 . As discussed above, the data intake and query system  108  may reside in an enterprise location, in the cloud, etc.  FIG.  4    illustrates that multiple client devices  404   a ,  404   b  . . .  404   n  may communicate with the data intake and query system  108 . The client devices  404  may communicate with the data intake and query system using a variety of connections. For example, one client device in  FIG.  4    is illustrated as communicating over an Internet (Web) protocol, another client device is illustrated as communicating via a command line interface, and another client device is illustrated as communicating via a software developer kit (SDK). 
     The search head  210  analyzes the received search request to identify request parameters. If a search request received from one of the client devices  404  references an index maintained by the data intake and query system, then the search head  210  connects to one or more indexers  206  of the data intake and query system for the index referenced in the request parameters. That is, if the request parameters of the search request reference an index, then the search head accesses the data in the index via the indexer. The data intake and query system  108  may include one or more indexers  206 , depending on system access resources and requirements. As described further below, the indexers  206  retrieve data from their respective local data stores  208  as specified in the search request. The indexers and their respective data stores can comprise one or more storage devices and typically reside on the same system, though they may be connected via a local network connection. 
     If the request parameters of the received search request reference an external data collection, which is not accessible to the indexers  206  or under the management of the data intake and query system, then the search head  210  can access the external data collection through an External Result Provider (ERP) process  410 . An external data collection may be referred to as a “virtual index” (plural, “virtual indices”). An ERP process provides an interface through which the search head  210  may access virtual indices. 
     Thus, a search reference to an index of the system relates to a locally stored and managed data collection. In contrast, a search reference to a virtual index relates to an externally stored and managed data collection, which the search head may access through one or more ERP processes  410 ,  412 .  FIG.  4    shows two ERP processes  410 ,  412  that connect to respective remote (external) virtual indices, which are indicated as a Hadoop or another system  414  (e.g., Amazon S3, Amazon EMR, other Hadoop® Compatible File Systems (HCFS), etc.) and arelational database management system (RDBMS)  416 . Other virtual indices may include other file organizations and protocols, such as Structured Query Language (SQL) and the like. The ellipses between the ERP processes  410 ,  412  indicate optional additional ERP processes of the data intake and query system  108 . An ERP process may be a computer process that is initiated or spawned by the search head  210  and is executed by the search data intake and query system  108 . Alternatively or additionally, an ERP process may be a process spawned by the search head  210  on the same or different host system as the search head  210  resides. 
     The search head  210  may spawn a single ERP process in response to multiple virtual indices referenced in a search request, or the search head may spawn different ERP processes for different virtual indices. Generally, virtual indices that share common data configurations or protocols may share ERP processes. For example, all search query references to a Hadoop file system may be processed by the same ERP process, if the ERP process is suitably configured. Likewise, all search query references to a SQL database may be processed by the same ERP process. In addition, the search head may provide a common ERP process for common external data source types (e.g., a common vendor may utilize a common ERP process, even if the vendor includes different data storage system types, such as Hadoop and SQL). Common indexing schemes also may be handled by common ERP processes, such as flat text files or Weblog files. 
     The search head  210  determines the number of ERP processes to be initiated via the use of configuration parameters that are included in a search request message. Generally, there is a one-to-many relationship between an external results provider “family” and ERP processes. There is also a one-to-many relationship between an ERP process and corresponding virtual indices that are referred to in a search request. For example, using RDBMS, assume two independent instances of such a system by one vendor, such as one RDBMS for production and another RDBMS used for development. In such a situation, it is likely preferable (but optional) to use two ERP processes to maintain the independent operation as between production and development data. Both of the ERPs, however, will belong to the same family, because the two RDBMS system types are from the same vendor. 
     The ERP processes  410 ,  412  receive a search request from the search head  210 . The search head may optimize the received search request for execution at the respective external virtual index. Alternatively, the ERP process may receive a search request as a result of analysis performed by the search head or by a different system process. The ERP processes  410 ,  412  can communicate with the search head  210  via conventional input/output routines (e.g., standard in/standard out, etc.). In this way, the ERP process receives the search request from a client device such that the search request may be efficiently executed at the corresponding external virtual index. 
     The ERP processes  410 ,  412  may be implemented as a process of the data intake and query system. Each ERP process may be provided by the data intake and query system, or may be provided by process or application providers who are independent of the data intake and query system. Each respective ERP process may include an interface application installed at a computer of the external result provider that ensures proper communication between the search support system and the external result provider. The ERP processes  410 ,  412  generate appropriate search requests in the protocol and syntax of the respective virtual indices  414 ,  416 , each of which corresponds to the search request received by the search head  210 . Upon receiving search results from their corresponding virtual indices, the respective ERP process passes the result to the search head  210 , which may return or display the results or a processed set of results based on the returned results to the respective client device. 
     Client devices  404  may communicate with the data intake and query system  108  through a network interface  420 , e.g., one or more LANs, WANs, cellular networks, intranetworks, and/or internetworks using any of wired, wireless, terrestrial microwave, satellite links, etc., and may include the public Internet. 
     The analytics platform utilizing the External Result Provider process described in more detail in U.S. Pat. No. 8,738,629, entitled “EXTERNAL RESULT PROVIDED PROCESS FOR RETRIEVING DATA STORED USING A DIFFERENT CONFIGURATION OR PROTOCOL”, issued on 27 May 2014, U.S. Pat. No. 8,738,587, entitled “PROCESSING A SYSTEM SEARCH REQUEST BY RETRIEVING RESULTS FROM BOTH A NATIVE INDEX AND A VIRTUAL INDEX”, issued on 25 Jul. 2013, U.S. patent application Ser. No. 14/266,832, entitled “PROCESSING A SYSTEM SEARCH REQUEST ACROSS DISPARATE DATA COLLECTION SYSTEMS”, filed on 1 May 2014, and U.S. Pat. No. 9,514,189, entitled “PROCESSING A SYSTEM SEARCH REQUEST INCLUDING EXTERNAL DATA SOURCES”, issued on 6 Dec. 2016, each of which is hereby incorporated by reference in its entirety for all purposes. 
     3.6.1. ERP Process Features 
     The ERP processes described above may include two operation modes: a streaming mode and a reporting mode. The ERP processes can operate in streaming mode only, in reporting mode only, or in both modes simultaneously. Operating in both modes simultaneously is referred to as mixed mode operation. In a mixed mode operation, the ERP at some point can stop providing the search head with streaming results and only provide reporting results thereafter, or the search head at some point may start ignoring streaming results it has been using and only use reporting results thereafter. 
     The streaming mode returns search results in real time, with minimal processing, in response to the search request. The reporting mode provides results of a search request with processing of the search results prior to providing them to the requesting search head, which in turn provides results to the requesting client device. ERP operation with such multiple modes provides greater performance flexibility with regard to report time, search latency, and resource utilization. 
     In a mixed mode operation, both streaming mode and reporting mode are operating simultaneously. The streaming mode results (e.g., the machine data obtained from the external data source) are provided to the search head, which can then process the results data (e.g., break the machine data into events, timestamp it, filter it, etc.) and integrate the results data with the results data from other external data sources, and/or from data stores of the search head. The search head performs such processing and can immediately start returning interim (streaming mode) results to the user at the requesting client device; simultaneously, the search head is waiting for the ERP process to process the data it is retrieving from the external data source as a result of the concurrently executing reporting mode. 
     In some instances, the ERP process initially operates in a mixed mode, such that the streaming mode operates to enable the ERP quickly to return interim results (e.g., some of the machined data or unprocessed data necessary to respond to a search request) to the search head, enabling the search head to process the interim results and begin providing to the client or search requester interim results that are responsive to the query. Meanwhile, in this mixed mode, the ERP also operates concurrently in reporting mode, processing portions of machine data in a manner responsive to the search query. Upon determining that it has results from the reporting mode available to return to the search head, the ERP may halt processing in the mixed mode at that time (or some later time) by stopping the return of data in streaming mode to the search head and switching to reporting mode only. The ERP at this point starts sending interim results in reporting mode to the search head, which in turn may then present this processed data responsive to the search request to the client or search requester. Typically the search head switches from using results from the ERP&#39;s streaming mode of operation to results from the ERP&#39;s reporting mode of operation when the higher bandwidth results from the reporting mode outstrip the amount of data processed by the search head in the streaming mode of ERP operation. 
     A reporting mode may have a higher bandwidth because the ERP does not have to spend time transferring data to the search head for processing all the machine data. In addition, the ERP may optionally direct another processor to do the processing. 
     The streaming mode of operation does not need to be stopped to gain the higher bandwidth benefits of a reporting mode; the search head could simply stop using the streaming mode results—and start using the reporting mode results—when the bandwidth of the reporting mode has caught up with or exceeded the amount of bandwidth provided by the streaming mode. Thus, it will be understood that a variety of triggers and ways to accomplish a search head&#39;s switch from using streaming mode results to using reporting mode results may be used. 
     The reporting mode can involve the ERP process (or an external system) performing event breaking, time stamping, filtering of events to match the search query request, and calculating statistics on the results. The user can request particular types of data, such as if the search query itself involves types of events, or the search request may ask for statistics on data, such as on events that meet the search request. In either case, the search head understands the query language used in the received query request, which may be a proprietary language. One exemplary query language is Splunk Processing Language (SPL) developed by the assignee of the application, Splunk Inc. The search head typically understands how to use that language to obtain data from the indexers, which store data in a format used by the SPLUNK® Enterprise system. 
     The ERP processes support the search head, as the search head is not ordinarily configured to understand the format in which data is stored in external data sources such as Hadoop or SQL data systems. Rather, the ERP process performs that translation from the query submitted in the search support system&#39;s native format (e.g., SPL if SPLUNK® ENTERPRISE is used as the search support system) to a search query request format that will be accepted by the corresponding external data system. The external data system typically stores data in a different format from that of the search support system&#39;s native index format, and it utilizes a different query language (e.g., SQL or MapReduce, rather than SPL or the like). 
     As noted, the ERP process can operate in the streaming mode alone. After the ERP process has performed the translation of the query request and received raw results from the streaming mode, the search head can integrate the returned data with any data obtained from local data sources (e.g., native to the search support system), other external data sources, and other ERP processes (if such operations were required to satisfy the terms of the search query). An advantage of mixed mode operation is that, in addition to streaming mode, the ERP process is also executing concurrently in reporting mode. Thus, the ERP process (rather than the search head) is processing query results (e.g., performing event breaking, timestamping, filtering, possibly calculating statistics if required to be responsive to the search query request, etc.). It should be apparent to those skilled in the art that additional time is needed for the ERP process to perform the processing in such a configuration. Therefore, the streaming mode will allow the search head to start returning interim results to the user at the client device before the ERP process can complete sufficient processing to start returning any search results. The switchover between streaming and reporting mode happens when the ERP process determines that the switchover is appropriate, such as when the ERP process determines it can begin returning meaningful results from its reporting mode. 
     The operation described above illustrates the source of operational latency: streaming mode has low latency (immediate results) and usually has relatively low bandwidth (fewer results can be returned per unit of time). In contrast, the concurrently running reporting mode has relatively high latency (it has to perform a lot more processing before returning any results) and usually has relatively high bandwidth (more results can be processed per unit of time). For example, when the ERP process does begin returning report results, it returns more processed results than in the streaming mode, because, e.g., statistics only need to be calculated to be responsive to the search request. That is, the ERP process doesn&#39;t have to take time to first return machine data to the search head. As noted, the ERP process could be configured to operate in streaming mode alone and return just the machine data for the search head to process in a way that is responsive to the search request. Alternatively, the ERP process can be configured to operate in the reporting mode only. Also, the ERP process can be configured to operate in streaming mode and reporting mode concurrently, as described, with the ERP process stopping the transmission of streaming results to the search head when the concurrently running reporting mode has caught up and started providing results. The reporting mode does not require the processing of all machine data that is responsive to the search query request before the ERP process starts returning results; rather, the reporting mode usually performs processing of chunks of events and returns the processing results to the search head for each chunk. 
     For example, an ERP process can be configured to merely return the contents of a search result file verbatim, with little or no processing of results. That way, the search head performs all processing (such as parsing byte streams into events, filtering, etc.). The ERP process can be configured to perform additional intelligence, such as analyzing the search request and handling all the computation that a native search indexer process would otherwise perform. In this way, the configured ERP process provides greater flexibility in features while operating according to desired preferences, such as response latency and resource requirements. 
     3.7. Data Ingestion 
       FIG.  5 A  is a flow chart of an example method that illustrates how indexers process, index, and store data received from forwarders, in accordance with example embodiments. The data flow illustrated in  FIG.  5 A  is provided for illustrative purposes only; those skilled in the art would understand that one or more of the steps of the processes illustrated in  FIG.  5 A  may be removed or that the ordering of the steps may be changed. Furthermore, for the purposes of illustrating a clear example, one or more particular system components are described in the context of performing various operations during each of the data flow stages. For example, a forwarder is described as receiving and processing machine data during an input phase; an indexer is described as parsing and indexing machine data during parsing and indexing phases; and a search head is described as performing a search query during a search phase. However, other system arrangements and distributions of the processing steps across system components may be used. 
     3.7.1. Input 
     At block  502 , a forwarder receives data from an input source, such as a data source  203  shown in  FIG.  2   . A forwarder initially may receive the data as a raw data stream generated by the input source. For example, a forwarder may receive a data stream from a log file generated by an application server, from a stream of network data from a network device, or from any other source of data. In some embodiments, a forwarder receives the raw data and may segment the data stream into “blocks”, possibly of a uniform data size, to facilitate subsequent processing steps. 
     At block  504 , a forwarder or other system component annotates each block generated from the raw data with one or more metadata fields. These metadata fields may, for example, provide information related to the data block as a whole and may apply to each event that is subsequently derived from the data in the data block. For example, the metadata fields may include separate fields specifying each of a host, a source, and a source type related to the data block. A host field may contain a value identifying a host name or IP address of a device that generated the data. A source field may contain a value identifying a source of the data, such as a pathname of a file or a protocol and port related to received network data. A source type field may contain a value specifying a particular source type label for the data. Additional metadata fields may also be included during the input phase, such as a character encoding of the data, if known, and possibly other values that provide information relevant to later processing steps. In some embodiments, a forwarder forwards the annotated data blocks to another system component (typically an indexer) for further processing. 
     The data intake and query system allows forwarding of data from one data intake and query instance to another, or even to a third-party system. The data intake and query system can employ different types of forwarders in a configuration. 
     In some embodiments, a forwarder may contain the essential components needed to forward data. A forwarder can gather data from a variety of inputs and forward the data to an indexer for indexing and searching. A forwarder can also tag metadata (e.g., source, source type, host, etc.). 
     In some embodiments, a forwarder has the capabilities of the aforementioned forwarder as well as additional capabilities. The forwarder can parse data before forwarding the data (e.g., can associate a time stamp with a portion of data and create an event, etc.) and can route data based on criteria such as source or type of event. The forwarder can also index data locally while forwarding the data to another indexer. 
     3.7.2. Parsing 
     At block  506 , an indexer receives data blocks from a forwarder and parses the data to organize the data into events. In some embodiments, to organize the data into events, an indexer may determine a source type associated with each data block (e.g., by extracting a source type label from the metadata fields associated with the data block, etc.) and refer to a source type configuration corresponding to the identified source type. The source type definition may include one or more properties that indicate to the indexer to automatically determine the boundaries within the received data that indicate the portions of machine data for events. In general, these properties may include regular expression-based rules or delimiter rules where, for example, event boundaries may be indicated by predefined characters or character strings. These predefined characters may include punctuation marks or other special characters including, for example, carriage returns, tabs, spaces, line breaks, etc. If a source type for the data is unknown to the indexer, an indexer may infer a source type for the data by examining the structure of the data. Then, the indexer can apply an inferred source type definition to the data to create the events. 
     At block  508 , the indexer determines a timestamp for each event. Similar to the process for parsing machine data, an indexer may again refer to a source type definition associated with the data to locate one or more properties that indicate instructions for determining a timestamp for each event. The properties may, for example, instruct an indexer to extract a time value from a portion of data for the event, to interpolate time values based on timestamps associated with temporally proximate events, to create a timestamp based on a time the portion of machine data was received or generated, to use the timestamp of a previous event, or use any other rules for determining timestamps. 
     At block  510 , the indexer associates with each event one or more metadata fields including a field containing the timestamp determined for the event. In some embodiments, a timestamp may be included in the metadata fields. These metadata fields may include any number of “default fields” that are associated with all events, and may also include one more custom fields as defined by a user. Similar to the metadata fields associated with the data blocks at block  504 , the default metadata fields associated with each event may include a host, source, and source type field including or in addition to a field storing the timestamp. 
     At block  512 , an indexer may optionally apply one or more transformations to data included in the events created at block  506 . For example, such transformations can include removing a portion of an event (e.g., a portion used to define event boundaries, extraneous characters from the event, other extraneous text, etc.), masking a portion of an event (e.g., masking a credit card number), removing redundant portions of an event, etc. The transformations applied to events may, for example, be specified in one or more configuration files and referenced by one or more source type definitions. 
       FIG.  5 C  illustrates an illustrative example of machine data can be stored in a data store in accordance with various disclosed embodiments. In other embodiments, machine data can be stored in a flat file in a corresponding bucket with an associated index file, such as a time series index or “TSIDX.” As such, the depiction of machine data and associated metadata as rows and columns in the table of  FIG.  5 C  is merely illustrative and is not intended to limit the data format in which the machine data and metadata is stored in various embodiments described herein. In one particular embodiment, machine data can be stored in a compressed or encrypted formatted. In such embodiments, the machine data can be stored with or be associated with data that describes the compression or encryption scheme with which the machine data is stored. The information about the compression or encryption scheme can be used to decompress or decrypt the machine data, and any metadata with which it is stored, at search time. 
     As mentioned above, certain metadata, e.g., host  536 , source  537 , source type  538 , and timestamps  535  can be generated for each event, and associated with a corresponding portion of machine data  539  when storing the event data in a data store, e.g., data store  208 . Any of the metadata can be extracted from the corresponding machine data, or supplied or defined by an entity, such as a user or computer system. The metadata fields can become part of or stored with the event. Note that while the time-stamp metadata field can be extracted from the raw data of each event, the values for the other metadata fields may be determined by the indexer based on information it receives pertaining to the source of the data separate from the machine data. 
     While certain default or user-defined metadata fields can be extracted from the machine data for indexing purposes, all the machine data within an event can be maintained in its original condition. As such, in embodiments in which the portion of machine data included in an event is unprocessed or otherwise unaltered, it is referred to herein as a portion of raw machine data. In other embodiments, the port of machine data in an event can be processed or otherwise altered. As such, unless certain information needs to be removed for some reasons (e.g. extraneous information, confidential information), all the raw machine data contained in an event can be preserved and saved in its original form. Accordingly, the data store in which the event records are stored is sometimes referred to as a “raw record data store.” The raw record data store contains a record of the raw event data tagged with the various default fields. 
     In  FIG.  5 C , the first three rows of the table represent events  531 ,  532 , and  533  and are related to a server access log that records requests from multiple clients processed by a server, as indicated by entry of “access.log” in the source column  537 . 
     In the example shown in  FIG.  5 C , each of the events  531 - 534  is associated with a discrete request made from a client device. The raw machine data generated by the server and extracted from a server access log can include the IP address of the client  540 , the user id of the person requesting the document  541 , the time the server finished processing the request  542 , the request line from the client  543 , the status code returned by the server to the client  545 , the size of the object returned to the client (in this case, the gif file requested by the client)  546  and the time spent to serve the request in microseconds  544 . As seen in  FIG.  5 C , all the raw machine data retrieved from the server access log is retained and stored as part of the corresponding events,  1221 ,  1222 , and  1223  in the data store. 
     Event  534  is associated with an entry in a server error log, as indicated by “error.log” in the source column  537  that records errors that the server encountered when processing a client request. Similar to the events related to the server access log, all the raw machine data in the error log file pertaining to event  534  can be preserved and stored as part of the event  534 . 
     Saving minimally processed or unprocessed machine data in a data store associated with metadata fields in the manner similar to that shown in  FIG.  5 C  is advantageous because it allows search of all the machine data at search time instead of searching only previously specified and identified fields or field-value pairs. As mentioned above, because data structures used by various embodiments of the present disclosure maintain the underlying raw machine data and use a late-binding schema for searching the raw machines data, it enables a user to continue investigating and learn valuable insights about the raw data. In other words, the user is not compelled to know about all the fields of information that will be needed at data ingestion time. As a user learns more about the data in the events, the user can continue to refine the late-binding schema by defining new extraction rules, or modifying or deleting existing extraction rules used by the system. 
     3.7.3. Indexing 
     At blocks  514  and  516 , an indexer can optionally generate a keyword index to facilitate fast keyword searching for events. To build a keyword index, at block  514 , the indexer identifies a set of keywords in each event. At block  516 , the indexer includes the identified keywords in an index, which associates each stored keyword with reference pointers to events containing that keyword (or to locations within events where that keyword is located, other location identifiers, etc.). When an indexer subsequently receives a keyword-based query, the indexer can access the keyword index to quickly identify events containing the keyword. 
     In some embodiments, the keyword index may include entries for field name-value pairs found in events, where a field name-value pair can include a pair of keywords connected by a symbol, such as an equals sign or colon. This way, events containing these field name-value pairs can be quickly located. In some embodiments, fields can automatically be generated for some or all of the field names of the field name-value pairs at the time of indexing. For example, if the string “dest=10.0.1.2” is found in an event, a field named “dest” may be created for the event, and assigned a value of “10.0.1.2”. 
     At block  518 , the indexer stores the events with an associated timestamp in a data store  208 . Timestamps enable a user to search for events based on a time range. In some embodiments, the stored events are organized into “buckets,” where each bucket stores events associated with a specific time range based on the timestamps associated with each event. This improves time-based searching, as well as allows for events with recent timestamps, which may have a higher likelihood of being accessed, to be stored in a faster memory to facilitate faster retrieval. For example, buckets containing the most recent events can be stored in flash memory rather than on a hard disk. In some embodiments, each bucket may be associated with an identifier, a time range, and a size constraint. In certain embodiments, a bucket can correspond to a file system directory and the machine data, or events, of a bucket can be stored in one or more files of the file system directory. The file system directory can include additional files, such as one or more inverted indexes, high performance indexes, permissions files, configuration files, etc. 
     Each indexer  206  may be responsible for storing and searching a subset of the events contained in a corresponding data store  208 . By distributing events among the indexers and data stores, the indexers can analyze events for a query in parallel. For example, using map-reduce techniques, each indexer returns partial responses for a subset of events to a search head that combines the results to produce an answer for the query. By storing events in buckets for specific time ranges, an indexer may further optimize the data retrieval process by searching buckets corresponding to time ranges that are relevant to a query. 
     In some embodiments, each indexer has a home directory and a cold directory. The home directory of an indexer stores hot buckets and warm buckets, and the cold directory of an indexer stores cold buckets. A hot bucket is a bucket that is capable of receiving and storing events. A warm bucket is a bucket that can no longer receive events for storage but has not yet been moved to the cold directory. A cold bucket is a bucket that can no longer receive events and may be a bucket that was previously stored in the home directory. The home directory may be stored in faster memory, such as flash memory, as events may be actively written to the home directory, and the home directory may typically store events that are more frequently searched and thus are accessed more frequently. The cold directory may be stored in slower and/or larger memory, such as a hard disk, as events are no longer being written to the cold directory, and the cold directory may typically store events that are not as frequently searched and thus are accessed less frequently. In some embodiments, an indexer may also have a quarantine bucket that contains events having potentially inaccurate information, such as an incorrect time stamp associated with the event or a time stamp that appears to be an unreasonable time stamp for the corresponding event. The quarantine bucket may have events from any time range; as such, the quarantine bucket may always be searched at search time. Additionally, an indexer may store old, archived data in a frozen bucket that is not capable of being searched at search time. In some embodiments, a frozen bucket may be stored in slower and/or larger memory, such as a hard disk, and may be stored in offline and/or remote storage. 
     Moreover, events and buckets can also be replicated across different indexers and data stores to facilitate high availability and disaster recovery as described in U.S. Pat. No. 9,130,971, entitled “SITE-BASED SEARCH AFFINITY”, issued on 8 Sep. 2015, and in U.S. patent Ser. No. 14/266,817, entitled “MULTI-SITE CLUSTERING”, issued on 1 Sep. 2015, each of which is hereby incorporated by reference in its entirety for all purposes. 
     As will be described in greater detail below with reference to, inter alia,  FIGS.  18 - 49   , some functionality of the indexer can be handled by different components of the system. For example, in some cases, the indexer indexes semi-processed, or cooked data (e.g., data that has been parsed and/or had some fields determined for it), and stores the results in common storage. 
       FIG.  5 B  is a block diagram of an example data store  501  that includes a directory for each index (or partition) that contains a portion of data managed by an indexer.  FIG.  5 B  further illustrates details of an embodiment of an inverted index  507 B and an event reference array  515  associated with inverted index  507 B. 
     The data store  501  can correspond to a data store  208  that stores events managed by an indexer  206  or can correspond to a different data store associated with an indexer  206 . In the illustrated embodiment, the data store  501  includes a _main directory  503  associated with a _main index and a _test directory  505  associated with a _test index. However, the data store  501  can include fewer or more directories. In some embodiments, multiple indexes can share a single directory or all indexes can share a common directory. Additionally, although illustrated as a single data store  501 , it will be understood that the data store  501  can be implemented as multiple data stores storing different portions of the information shown in  FIG.  5 B . For example, a single index or partition can span multiple directories or multiple data stores, and can be indexed or searched by multiple corresponding indexers. 
     In the illustrated embodiment of  FIG.  5 B , the index-specific directories  503  and  505  include inverted indexes  507 A,  507 B and  509 A,  509 B, respectively. The inverted indexes  507 A . . .  507 B, and  509 A . . .  509 B can be keyword indexes or field-value pair indexes described herein and can include less or more information that depicted in  FIG.  5 B . 
     In some embodiments, each inverted index  507 A . . .  507 B, and  509 A . . .  509 B can correspond to a distinct time-series bucket that is managed by the indexer  206  and that contains events corresponding to the relevant index (e.g., _main index, _test index). As such, each inverted index can correspond to a particular range of time for an index. Additional files, such as high performance indexes for each time-series bucket of an index, can also be stored in the same directory as the inverted indexes  507 A . . .  507 B, and  509 A . . .  509 B. In some embodiments inverted index  507 A . . .  507 B, and  509 A . . .  509 B can correspond to multiple time-series buckets or inverted indexes  507 A . . .  507 B, and  509 A . . .  509 B can correspond to a single time-series bucket. 
     Each inverted index  507 A . . .  507 B, and  509 A . . .  509 B can include one or more entries, such as keyword (or token) entries or field-value pair entries. Furthermore, in certain embodiments, the inverted indexes  507 A . . .  507 B, and  509 A . . .  509 B can include additional information, such as a time range  523  associated with the inverted index or an index identifier  525  identifying the index associated with the inverted index  507 A . . .  507 B, and  509 A . . .  509 B. However, each inverted index  507 A . . .  507 B, and  509 A . . .  509 B can include less or more information than depicted. 
     Token entries, such as token entries  511  illustrated in inverted index  507 B, can include a token  511 A (e.g., “error,” “itemID,” etc.) and event references  511 B indicative of events that include the token. For example, for the token “error,” the corresponding token entry includes the token “error” and an event reference, or unique identifier, for each event stored in the corresponding time-series bucket that includes the token “error.” In the illustrated embodiment of  FIG.  5 B , the error token entry includes the identifiers 3, 5, 6, 8, 11, and 12 corresponding to events managed by the indexer  206  and associated with the index _main  503  that are located in the time-series bucket associated with the inverted index  507 B. 
     In some cases, some token entries can be default entries, automatically determined entries, or user specified entries. In some embodiments, the indexer  206  can identify each word or string in an event as a distinct token and generate a token entry for it. In some cases, the indexer  206  can identify the beginning and ending of tokens based on punctuation, spaces, as described in greater detail herein. In certain cases, the indexer  206  can rely on user input or a configuration file to identify tokens for token entries  511 , etc. It will be understood that any combination of token entries can be included as a default, automatically determined, a or included based on user-specified criteria. 
     Similarly, field-value pair entries, such as field-value pair entries  513  shown in inverted index  507 B, can include a field-value pair  513 A and event references  513 B indicative of events that include a field value that corresponds to the field-value pair. For example, for a field-value pair sourcetype::sendmail, a field-value pair entry would include the field-value pair sourcetype::sendmail and a unique identifier, or event reference, for each event stored in the corresponding time-series bucket that includes a sendmail sourcetype. 
     In some cases, the field-value pair entries  513  can be default entries, automatically determined entries, or user specified entries. As a non-limiting example, the field-value pair entries for the fields host, source, sourcetype can be included in the inverted indexes  507 A . . .  507 B, and  509 A . . .  509 B as a default. As such, all of the inverted indexes  507 A . . .  507 B, and  509 A . . .  509 B can include field-value pair entries for the fields host, source, sourcetype. As yet another non-limiting example, the field-value pair entries for the IP_address field can be user specified and may only appear in the inverted index  507 B based on user-specified criteria. As another non-limiting example, as the indexer indexes the events, it can automatically identify field-value pairs and create field-value pair entries. For example, based on the indexers review of events, it can identify IP_address as a field in each event and add the IP_address field-value pair entries to the inverted index  507 B. It will be understood that any combination of field-value pair entries can be included as a default, automatically determined, or included based on user-specified criteria. 
     Each unique identifier  517 , or event reference, can correspond to a unique event located in the time series bucket. However, the same event reference can be located in multiple entries. For example if an event has a sourcetype splunkd, host www1 and token “warning,” then the unique identifier for the event will appear in the field-value pair entries sourcetype::splunkd and host::www1, as well as the token entry “warning.” With reference to the illustrated embodiment of  FIG.  5 B  and the event that corresponds to the event reference 3, the event reference 3 is found in the field-value pair entries  513  host::hostA, source::sourceB, sourcetype::sourcetypeA, and IP_address::91.205.189.15 indicating that the event corresponding to the event reference 3 is from hostA, sourceB, of sourcetypeA, and includes 91.205.189.15 in the event data. 
     For some fields, the unique identifier is located in only one field-value pair entry for a particular field. For example, the inverted index may include four sourcetype field-value pair entries corresponding to four different sourcetypes of the events stored in a bucket (e.g., sourcetypes: sendmail, splunkd, web_access, and web_service). Within those four sourcetype field-value pair entries, an identifier for a particular event may appear in only one of the field-value pair entries. With continued reference to the example illustrated embodiment of  FIG.  5 B , since the event reference 7 appears in the field-value pair entry sourcetype::sourcetypeA, then it does not appear in the other field-value pair entries for the sourcetype field, including sourcetype::sourcetypeB, sourcetype::sourcetypeC, and sourcetype::sourcetypeD. 
     The event references  517  can be used to locate the events in the corresponding bucket. For example, the inverted index can include, or be associated with, an event reference array  515 . The event reference array  515  can include an array entry  517  for each event reference in the inverted index  507 B. Each array entry  517  can include location information  519  of the event corresponding to the unique identifier (non-limiting example: seek address of the event), a timestamp  521  associated with the event, or additional information regarding the event associated with the event reference, etc. 
     For each token entry  511  or field-value pair entry  513 , the event reference  501 B or unique identifiers can be listed in chronological order or the value of the event reference can be assigned based on chronological data, such as a timestamp associated with the event referenced by the event reference. For example, the event reference 1 in the illustrated embodiment of  FIG.  5 B  can correspond to the first-in-time event for the bucket, and the event reference  12  can correspond to the last-in-time event for the bucket. However, the event references can be listed in any order, such as reverse chronological order, ascending order, descending order, or some other order, etc. Further, the entries can be sorted. For example, the entries can be sorted alphabetically (collectively or within a particular group), by entry origin (e.g., default, automatically generated, user-specified, etc.), by entry type (e.g., field-value pair entry, token entry, etc.), or chronologically by when added to the inverted index, etc. In the illustrated embodiment of  FIG.  5 B , the entries are sorted first by entry type and then alphabetically. 
     As a non-limiting example of how the inverted indexes  507 A . . .  507 B, and  509 A . . .  509 B can be used during a data categorization request command, the indexers can receive filter criteria indicating data that is to be categorized and categorization criteria indicating how the data is to be categorized. Example filter criteria can include, but is not limited to, indexes (or partitions), hosts, sources, sourcetypes, time ranges, field identifier, keywords, etc. 
     Using the filter criteria, the indexer identifies relevant inverted indexes to be searched. For example, if the filter criteria includes a set of partitions, the indexer can identify the inverted indexes stored in the directory corresponding to the particular partition as relevant inverted indexes. Other means can be used to identify inverted indexes associated with a partition of interest. For example, in some embodiments, the indexer can review an entry in the inverted indexes, such as an index-value pair entry  513  to determine if a particular inverted index is relevant. If the filter criteria does not identify any partition, then the indexer can identify all inverted indexes managed by the indexer as relevant inverted indexes. 
     Similarly, if the filter criteria includes a time range, the indexer can identify inverted indexes corresponding to buckets that satisfy at least a portion of the time range as relevant inverted indexes. For example, if the time range is last hour then the indexer can identify all inverted indexes that correspond to buckets storing events associated with timestamps within the last hour as relevant inverted indexes. 
     When used in combination, an index filter criterion specifying one or more partitions and a time range filter criterion specifying a particular time range can be used to identify a subset of inverted indexes within a particular directory (or otherwise associated with a particular partition) as relevant inverted indexes. As such, the indexer can focus the processing to only a subset of the total number of inverted indexes that the indexer manages. 
     Once the relevant inverted indexes are identified, the indexer can review them using any additional filter criteria to identify events that satisfy the filter criteria. In some cases, using the known location of the directory in which the relevant inverted indexes are located, the indexer can determine that any events identified using the relevant inverted indexes satisfy an index filter criterion. For example, if the filter criteria includes a partition main, then the indexer can determine that any events identified using inverted indexes within the partition main directory (or otherwise associated with the partition main) satisfy the index filter criterion. 
     Furthermore, based on the time range associated with each inverted index, the indexer can determine that that any events identified using a particular inverted index satisfies a time range filter criterion. For example, if a time range filter criterion is for the last hour and a particular inverted index corresponds to events within a time range of 50 minutes ago to 35 minutes ago, the indexer can determine that any events identified using the particular inverted index satisfy the time range filter criterion. Conversely, if the particular inverted index corresponds to events within a time range of 59 minutes ago to 62 minutes ago, the indexer can determine that some events identified using the particular inverted index may not satisfy the time range filter criterion. 
     Using the inverted indexes, the indexer can identify event references (and therefore events) that satisfy the filter criteria. For example, if the token “error” is a filter criterion, the indexer can track all event references within the token entry “error.” Similarly, the indexer can identify other event references located in other token entries or field-value pair entries that match the filter criteria. The system can identify event references located in all of the entries identified by the filter criteria. For example, if the filter criteria include the token “error” and field-value pair sourcetype::web_ui, the indexer can track the event references found in both the token entry “error” and the field-value pair entry sourcetype::web_ui. As mentioned previously, in some cases, such as when multiple values are identified for a particular filter criterion (e.g., multiple sources for a source filter criterion), the system can identify event references located in at least one of the entries corresponding to the multiple values and in all other entries identified by the filter criteria. The indexer can determine that the events associated with the identified event references satisfy the filter criteria. 
     In some cases, the indexer can further consult a timestamp associated with the event reference to determine whether an event satisfies the filter criteria. For example, if an inverted index corresponds to a time range that is partially outside of a time range filter criterion, then the indexer can consult a timestamp associated with the event reference to determine whether the corresponding event satisfies the time range criterion. In some embodiments, to identify events that satisfy a time range, the indexer can review an array, such as the event reference array  1614  that identifies the time associated with the events. Furthermore, as mentioned above using the known location of the directory in which the relevant inverted indexes are located (or other index identifier), the indexer can determine that any events identified using the relevant inverted indexes satisfy the index filter criterion. 
     In some cases, based on the filter criteria, the indexer reviews an extraction rule. In certain embodiments, if the filter criteria includes a field name that does not correspond to a field-value pair entry in an inverted index, the indexer can review an extraction rule, which may be located in a configuration file, to identify a field that corresponds to a field-value pair entry in the inverted index. 
     For example, the filter criteria includes a field name “sessionID” and the indexer determines that at least one relevant inverted index does not include a field-value pair entry corresponding to the field name sessionID, the indexer can review an extraction rule that identifies how the sessionID field is to be extracted from a particular host, source, or sourcetype (implicitly identifying the particular host, source, or sourcetype that includes a sessionID field). The indexer can replace the field name “sessionID” in the filter criteria with the identified host, source, or sourcetype. In some cases, the field name “sessionID” may be associated with multiples hosts, sources, or sourcetypes, in which case, all identified hosts, sources, and sourcetypes can be added as filter criteria. In some cases, the identified host, source, or sourcetype can replace or be appended to a filter criterion, or be excluded. For example, if the filter criteria includes a criterion for source S1 and the “sessionID” field is found in source S2, the source S2 can replace S1 in the filter criteria, be appended such that the filter criteria includes source S1 and source S2, or be excluded based on the presence of the filter criterion source S1. If the identified host, source, or sourcetype is included in the filter criteria, the indexer can then identify a field-value pair entry in the inverted index that includes a field value corresponding to the identity of the particular host, source, or sourcetype identified using the extraction rule. 
     Once the events that satisfy the filter criteria are identified, the system, such as the indexer  206  can categorize the results based on the categorization criteria. The categorization criteria can include categories for grouping the results, such as any combination of partition, source, sourcetype, or host, or other categories or fields as desired. 
     The indexer can use the categorization criteria to identify categorization criteria-value pairs or categorization criteria values by which to categorize or group the results. The categorization criteria-value pairs can correspond to one or more field-value pair entries stored in a relevant inverted index, one or more index-value pairs based on a directory in which the inverted index is located or an entry in the inverted index (or other means by which an inverted index can be associated with a partition), or other criteria-value pair that identifies a general category and a particular value for that category. The categorization criteria values can correspond to the value portion of the categorization criteria-value pair. 
     As mentioned, in some cases, the categorization criteria-value pairs can correspond to one or more field-value pair entries stored in the relevant inverted indexes. For example, the categorization criteria-value pairs can correspond to field-value pair entries of host, source, and sourcetype (or other field-value pair entry as desired). For instance, if there are ten different hosts, four different sources, and five different sourcetypes for an inverted index, then the inverted index can include ten host field-value pair entries, four source field-value pair entries, and five sourcetype field-value pair entries. The indexer can use the nineteen distinct field-value pair entries as categorization criteria-value pairs to group the results. 
     Specifically, the indexer can identify the location of the event references associated with the events that satisfy the filter criteria within the field-value pairs, and group the event references based on their location. As such, the indexer can identify the particular field value associated with the event corresponding to the event reference. For example, if the categorization criteria include host and sourcetype, the host field-value pair entries and sourcetype field-value pair entries can be used as categorization criteria-value pairs to identify the specific host and sourcetype associated with the events that satisfy the filter criteria. 
     In addition, as mentioned, categorization criteria-value pairs can correspond to data other than the field-value pair entries in the relevant inverted indexes. For example, if partition or index is used as a categorization criterion, the inverted indexes may not include partition field-value pair entries. Rather, the indexer can identify the categorization criteria-value pair associated with the partition based on the directory in which an inverted index is located, information in the inverted index, or other information that associates the inverted index with the partition, etc. As such a variety of methods can be used to identify the categorization criteria-value pairs from the categorization criteria. 
     Accordingly based on the categorization criteria (and categorization criteria-value pairs), the indexer can generate groupings based on the events that satisfy the filter criteria. As a non-limiting example, if the categorization criteria includes a partition and sourcetype, then the groupings can correspond to events that are associated with each unique combination of partition and sourcetype. For instance, if there are three different partitions and two different sourcetypes associated with the identified events, then the six different groups can be formed, each with a unique partition value-sourcetype value combination. Similarly, if the categorization criteria includes partition, sourcetype, and host and there are two different partitions, three sourcetypes, and five hosts associated with the identified events, then the indexer can generate up to thirty groups for the results that satisfy the filter criteria. Each group can be associated with a unique combination of categorization criteria-value pairs (e.g., unique combinations of partition value sourcetype value, and host value). 
     In addition, the indexer can count the number of events associated with each group based on the number of events that meet the unique combination of categorization criteria for a particular group (or match the categorization criteria-value pairs for the particular group). With continued reference to the example above, the indexer can count the number of events that meet the unique combination of partition, sourcetype, and host for a particular group. 
     Each indexer communicates the groupings to the search head. The search head can aggregate the groupings from the indexers and provide the groupings for display. In some cases, the groups are displayed based on at least one of the host, source, sourcetype, or partition associated with the groupings. In some embodiments, the search head can further display the groups based on display criteria, such as a display order or a sort order as described in greater detail above. 
     As a non-limiting example and with reference to  FIG.  5 B , consider a request received by an indexer  206  that includes the following filter criteria: keyword=error, partition=_main, time range=3/1/17 16:22.00.000-16:28.00.000, sourcetype=sourcetypeC, host=hostB, and the following categorization criteria: source. 
     Based on the above criteria, the indexer  206  identifies _main directory  503  and can ignore _test directory  505  and any other partition-specific directories. The indexer determines that inverted partition  507 B is a relevant partition based on its location within the _main directory  503  and the time range associated with it. For sake of simplicity in this example, the indexer  206  determines that no other inverted indexes in the _main directory  503 , such as inverted index  507 A satisfy the time range criterion. 
     Having identified the relevant inverted index  507 B, the indexer reviews the token entries  511  and the field-value pair entries  513  to identify event references, or events, that satisfy all of the filter criteria. 
     With respect to the token entries  511 , the indexer can review the error token entry and identify event references 3, 5, 6, 8, 11, 12, indicating that the term “error” is found in the corresponding events. Similarly, the indexer can identify event references 4, 5, 6, 8, 9, 10, 11 in the field-value pair entry sourcetype::sourcetypeC and event references 2, 5, 6, 8, 10, 11 in the field-value pair entry host::hostB. As the filter criteria did not include a source or an IP_address field-value pair, the indexer can ignore those field-value pair entries. 
     In addition to identifying event references found in at least one token entry or field-value pair entry (e.g., event references 3, 4, 5, 6, 8, 9, 10, 11, 12), the indexer can identify events (and corresponding event references) that satisfy the time range criterion using the event reference array  1614  (e.g., event references 2, 3, 4, 5, 6, 7, 8, 9, 10). Using the information obtained from the inverted index  507 B (including the event reference array  515 ), the indexer  206  can identify the event references that satisfy all of the filter criteria (e.g., event references 5, 6, 8). 
     Having identified the events (and event references) that satisfy all of the filter criteria, the indexer  206  can group the event references using the received categorization criteria (source). In doing so, the indexer can determine that event references 5 and 6 are located in the field-value pair entry source::sourceD (or have matching categorization criteria-value pairs) and event reference 8 is located in the field-value pair entry source::sourceC. Accordingly, the indexer can generate a sourceC group having a count of one corresponding to reference 8 and a sourceD group having a count of two corresponding to references 5 and 6. This information can be communicated to the search head. In turn the search head can aggregate the results from the various indexers and display the groupings. As mentioned above, in some embodiments, the groupings can be displayed based at least in part on the categorization criteria, including at least one of host, source, sourcetype, or partition. 
     It will be understood that a change to any of the filter criteria or categorization criteria can result in different groupings. As a one non-limiting example, a request received by an indexer  206  that includes the following filter criteria: partition=_main, time range=3/1/17 3/1/17 16:21:20.000-16:28:17.000, and the following categorization criteria: host, source, sourcetype would result in the indexer identifying event references 1-12 as satisfying the filter criteria. The indexer would then generate up to 24 groupings corresponding to the 24 different combinations of the categorization criteria-value pairs, including host (hostA, hostB), source (sourceA, sourceB, sourceC, sourceD), and sourcetype (sourcetypeA, sourcetypeB, sourcetypeC). However, as there are only twelve events identifiers in the illustrated embodiment and some fall into the same grouping, the indexer generates eight groups and counts as follows: 
     Group 1 (hostA, sourceA, sourcetypeA): 1 (event reference 7)
 
Group 2 (hostA, sourceA, sourcetypeB): 2 (event references 1, 12)
 
Group 3 (hostA, sourceA, sourcetypeC): 1 (event reference 4)
 
Group 4 (hostA, sourceB, sourcetypeA): 1 (event reference 3)
 
Group 5 (hostA, sourceB, sourcetypeC): 1 (event reference 9)
 
Group 6 (hostB, sourceC, sourcetypeA): 1 (event reference 2)
 
Group 7 (hostB, sourceC, sourcetypeC): 2 (event references 8, 11)
 
Group 8 (hostB, sourceD, sourcetypeC): 3 (event references 5, 6, 10)
 
     As noted, each group has a unique combination of categorization criteria-value pairs or categorization criteria values. The indexer communicates the groups to the search head for aggregation with results received from other indexers. In communicating the groups to the search head, the indexer can include the categorization criteria-value pairs for each group and the count. In some embodiments, the indexer can include more or less information. For example, the indexer can include the event references associated with each group and other identifying information, such as the indexer or inverted index used to identify the groups. 
     As another non-limiting examples, a request received by an indexer  206  that includes the following filter criteria: partition=_main, time range=3/1/17 3/1/17 16:21:20.000-16:28:17.000, source=sourceA, sourceD, and keyword=itemID and the following categorization criteria: host, source, sourcetype would result in the indexer identifying event references 4, 7, and 10 as satisfying the filter criteria, and generate the following groups: 
     Group 1 (hostA, sourceA, sourcetypeC): 1 (event reference 4)
 
Group 2 (hostA, sourceA, sourcetypeA): 1 (event reference 7)
 
Group 3 (hostB, sourceD, sourcetypeC): 1 (event references 10)
 
     The indexer communicates the groups to the search head for aggregation with results received from other indexers. As will be understand there are myriad ways for filtering and categorizing the events and event references. For example, the indexer can review multiple inverted indexes associated with an partition or review the inverted indexes of multiple partitions, and categorize the data using any one or any combination of partition, host, source, sourcetype, or other category, as desired. 
     Further, if a user interacts with a particular group, the indexer can provide additional information regarding the group. For example, the indexer can perform a targeted search or sampling of the events that satisfy the filter criteria and the categorization criteria for the selected group, also referred to as the filter criteria corresponding to the group or filter criteria associated with the group. 
     In some cases, to provide the additional information, the indexer relies on the inverted index. For example, the indexer can identify the event references associated with the events that satisfy the filter criteria and the categorization criteria for the selected group and then use the event reference array  515  to access some or all of the identified events. In some cases, the categorization criteria values or categorization criteria-value pairs associated with the group become part of the filter criteria for the review. 
     With reference to  FIG.  5 B  for instance, suppose a group is displayed with a count of six corresponding to event references 4, 5, 6, 8, 10, 11 (e.g., event references 4, 5, 6, 8, 10, 11 satisfy the filter criteria and are associated with matching categorization criteria values or categorization criteria-value pairs) and a user interacts with the group (e.g., selecting the group, clicking on the group, etc.). In response, the search head communicates with the indexer to provide additional information regarding the group. 
     In some embodiments, the indexer identifies the event references associated with the group using the filter criteria and the categorization criteria for the group (e.g., categorization criteria values or categorization criteria-value pairs unique to the group). Together, the filter criteria and the categorization criteria for the group can be referred to as the filter criteria associated with the group. Using the filter criteria associated with the group, the indexer identifies event references 4, 5, 6, 8, 10, 11. 
     Based on a sampling criteria, discussed in greater detail above, the indexer can determine that it will analyze a sample of the events associated with the event references 4, 5, 6, 8, 10, 11. For example, the sample can include analyzing event data associated with the event references 5, 8, 10. In some embodiments, the indexer can use the event reference array  1616  to access the event data associated with the event references 5, 8, 10. Once accessed, the indexer can compile the relevant information and provide it to the search head for aggregation with results from other indexers. By identifying events and sampling event data using the inverted indexes, the indexer can reduce the amount of actual data this is analyzed and the number of events that are accessed in order to generate the summary of the group and provide a response in less time. 
     3.8. Query Processing 
       FIG.  6 A  is a flow diagram of an example method that illustrates how a search head and indexers perform a search query, in accordance with example embodiments. At block  602 , a search head receives a search query from a client. At block  604 , the search head analyzes the search query to determine what portion(s) of the query can be delegated to indexers and what portions of the query can be executed locally by the search head. At block  606 , the search head distributes the determined portions of the query to the appropriate indexers. In some embodiments, a search head cluster may take the place of an independent search head where each search head in the search head cluster coordinates with peer search heads in the search head cluster to schedule jobs, replicate search results, update configurations, fulfill search requests, etc. In some embodiments, the search head (or each search head) communicates with a master node (also known as a cluster master, not shown in  FIG.  2   ) that provides the search head with a list of indexers to which the search head can distribute the determined portions of the query. The master node maintains a list of active indexers and can also designate which indexers may have responsibility for responding to queries over certain sets of events. A search head may communicate with the master node before the search head distributes queries to indexers to discover the addresses of active indexers. 
     At block  608 , the indexers to which the query was distributed, search data stores associated with them for events that are responsive to the query. To determine which events are responsive to the query, the indexer searches for events that match the criteria specified in the query. These criteria can include matching keywords or specific values for certain fields. The searching operations at block  608  may use the late-binding schema to extract values for specified fields from events at the time the query is processed. In some embodiments, one or more rules for extracting field values may be specified as part of a source type definition in a configuration file. The indexers may then either send the relevant events back to the search head, or use the events to determine a partial result, and send the partial result back to the search head. 
     At block  610 , the search head combines the partial results and/or events received from the indexers to produce a final result for the query. In some examples, the results of the query are indicative of performance or security of the IT environment and may help improve the performance of components in the IT environment. This final result may comprise different types of data depending on what the query requested. For example, the results can include a listing of matching events returned by the query, or some type of visualization of the data from the returned events. In another example, the final result can include one or more calculated values derived from the matching events. 
     The results generated by the system  108  can be returned to a client using different techniques. For example, one technique streams results or relevant events back to a client in real-time as they are identified. Another technique waits to report the results to the client until a complete set of results (which may include a set of relevant events or a result based on relevant events) is ready to return to the client. Yet another technique streams interim results or relevant events back to the client in real-time until a complete set of results is ready, and then returns the complete set of results to the client. In another technique, certain results are stored as “search jobs” and the client may retrieve the results by referring the search jobs. 
     The search head can also perform various operations to make the search more efficient. For example, before the search head begins execution of a query, the search head can determine a time range for the query and a set of common keywords that all matching events include. The search head may then use these parameters to query the indexers to obtain a superset of the eventual results. Then, during a filtering stage, the search head can perform field-extraction operations on the superset to produce a reduced set of search results. This speeds up queries, which may be particularly helpful for queries that are performed on a periodic basis. 
     As will be described in greater detail below with reference to, inter alia,  FIGS.  18 - 49   , some functionality of the search head or indexers can be handled by different components of the system or removed altogether. For example, in some cases, a query coordinator analyzes the query, identifies dataset sources to be accessed, generates subqueries for execution by dataset sources, such as indexers, collects partial results to produce a final result and returns the final results to the search head for delivery to a client device or delivers the final results to the client device without the search head. In some cases, results from dataset sources, such as the indexers, are communicated to nodes, which further process the data, and communicate the results of the processing to the query coordinator, etc. In some embodiments, the search head spawns a search process, which communicates the query to a search process master. The search process master can communicate the query to the query coordinator for processing and execution. 
     In addition, in some embodiments, the indexers are not involved in search operations or only search some data, such as data in hot buckets, etc. For example, nodes can perform the search functionality described herein with respect to indexers. For example, nodes can use late-binding schema to extract values for specified fields from events at the time the query is processed and/or use one or more rules specified as part of a source type definition in a configuration file for extracting field values, etc. Furthermore, in some embodiments, nodes can perform search operations on data in common storage or found in other dataset sources, such as external data stores, query acceleration data stores, ingested data buffers, etc. 
     3.9. Pipelined Search Language 
     Various embodiments of the present disclosure can be implemented using, or in conjunction with, a pipelined command language. A pipelined command language is a language in which a set of inputs or data is operated on by a first command in a sequence of commands, and then subsequent commands in the order they are arranged in the sequence. Such commands can include any type of functionality for operating on data, such as retrieving, searching, filtering, aggregating, processing, transmitting, and the like. As described herein, a query can thus be formulated in a pipelined command language and include any number of ordered or unordered commands for operating on data. 
     Splunk Processing Language (SPL) is an example of a pipelined command language in which a set of inputs or data is operated on by any number of commands in a particular sequence. A sequence of commands, or command sequence, can be formulated such that the order in which the commands are arranged defines the order in which the commands are applied to a set of data or the results of an earlier executed command. For example, a first command in a command sequence can operate to search or filter for specific data in particular set of data. The results of the first command can then be passed to another command listed later in the command sequence for further processing. 
     In various embodiments, a query can be formulated as a command sequence defined in a command line of a search UI. In some embodiments, a query can be formulated as a sequence of SPL commands. Some or all of the SPL commands in the sequence of SPL commands can be separated from one another by a pipe symbol “|”. In such embodiments, a set of data, such as a set of events, can be operated on by a first SPL command in the sequence, and then a subsequent SPL command following a pipe symbol “|” after the first SPL command operates on the results produced by the first SPL command or other set of data, and so on for any additional SPL commands in the sequence. As such, a query formulated using SPL comprises a series of consecutive commands that are delimited by pipe “|” characters. The pipe character indicates to the system that the output or result of one command (to the left of the pipe) should be used as the input for one of the subsequent commands (to the right of the pipe). This enables formulation of queries defined by a pipeline of sequenced commands that refines or enhances the data at each step along the pipeline until the desired results are attained. Accordingly, various embodiments described herein can be implemented with Splunk Processing Language (SPL) used in conjunction with the SPLUNK® ENTERPRISE system. 
     While a query can be formulated in many ways, a query can start with a search command and one or more corresponding search terms at the beginning of the pipeline. Such search terms can include any combination of keywords, phrases, times, dates, Boolean expressions, fieldname-field value pairs, etc. that specify which results should be obtained from an index. The results can then be passed as inputs into subsequent commands in a sequence of commands by using, for example, a pipe character. The subsequent commands in a sequence can include directives for additional processing of the results once it has been obtained from one or more indexes. For example, commands may be used to filter unwanted information out of the results, extract more information, evaluate field values, calculate statistics, reorder the results, create an alert, create summary of the results, or perform some type of aggregation function. In some embodiments, the summary can include a graph, chart, metric, or other visualization of the data. An aggregation function can include analysis or calculations to return an aggregate value, such as an average value, a sum, a maximum value, a root mean square, statistical values, and the like. 
     Due to its flexible nature, use of a pipelined command language in various embodiments is advantageous because it can perform “filtering” as well as “processing” functions. In other words, a single query can include a search command and search term expressions, as well as data-analysis expressions. For example, a command at the beginning of a query can perform a “filtering” step by retrieving a set of data based on a condition (e.g., records associated with server response times of less than 1 microsecond). The results of the filtering step can then be passed to a subsequent command in the pipeline that performs a “processing” step (e.g. calculation of an aggregate value related to the filtered events such as the average response time of servers with response times of less than 1 microsecond). Furthermore, the search command can allow events to be filtered by keyword as well as field value criteria. For example, a search command can filter out all events containing the word “warning” or filter out all events where a field value associated with a field “clientip” is “10.0.1.2.” 
     The results obtained or generated in response to a command in a query can be considered a set of results data. The set of results data can be passed from one command to another in any data format. In one embodiment, the set of result data can be in the form of a dynamically created table. Each command in a particular query can redefine the shape of the table. In some implementations, an event retrieved from an index in response to a query can be considered a row with a column for each field value. Columns contain basic information about the data and also may contain data that has been dynamically extracted at search time. 
       FIG.  6 B  provides a visual representation of the manner in which a pipelined command language or query operates in accordance with the disclosed embodiments. The command or query  630  can be inputted by the user into a search field. The query comprises a search, the results of which are piped to two commands (namely, command 1 and command 2) that follow the search step. 
     Disk  622  represents the event data in the raw record data store. 
     When a user query is processed, a search step will precede other queries in the pipeline in order to generate a set of events at block  640 . For example, the query can comprise search terms “sourcetype=syslog ERROR” at the front of the pipeline as shown in  FIG.  6 B . Intermediate results table  624  shows fewer rows because it represents the subset of events retrieved from the index that matched the search terms “sourcetype=syslog ERROR” from search command  630 . By way of further example, instead of a search step, the set of events at the head of the pipeline may be generating by a call to a pre-existing inverted index (as will be explained later). 
     At block  642 , the set of events generated in the first part of the query may be piped to a query that searches the set of events for field-value pairs or for keywords. For example, the second intermediate results table  626  shows fewer columns, representing the result of the top command, “top user” which summarizes the events into a list of the top 10 users and displays the user, count, and percentage. 
     Finally, at block  644 , the results of the prior stage can be pipelined to another stage where further filtering or processing of the data can be performed, e.g., preparing the data for display purposes, filtering the data based on a condition, performing a mathematical calculation with the data, etc. As shown in  FIG.  6 B , the “fields—percent” part of command  630  removes the column that shows the percentage, thereby, leaving a final results table  628  without a percentage column. In different embodiments, other query languages, such as the Structured Query Language (“SQL”), can be used to create a query. In some embodiments, each stage can correspond to a search phase or layer in a DAG. The processing performed in each stage can be handled by one or more partitions allocated to each stage. 
     3.10. Field Extraction 
     The search head  210  allows users to search and visualize events generated from machine data received from homogenous data sources. The search head  210  also allows users to search and visualize events generated from machine data received from heterogeneous data sources. The search head  210  includes various mechanisms, which may additionally reside in an indexer  206 , for processing a query. A query language may be used to create a query, such as any suitable pipelined query language. For example, Splunk Processing Language (SPL) can be utilized to make a query. SPL is a pipelined search language in which a set of inputs is operated on by a first command in a command line, and then a subsequent command following the pipe symbol “|” operates on the results produced by the first command, and so on for additional commands. Other query languages, such as the Structured Query Language (“SQL”), can be used to create a query. 
     In response to receiving the search query, search head  210  uses extraction rules to extract values for fields in the events being searched. The search head  210  obtains extraction rules that specify how to extract a value for fields from an event. Extraction rules can comprise regex rules that specify how to extract values for the fields corresponding to the extraction rules. In addition to specifying how to extract field values, the extraction rules may also include instructions for deriving a field value by performing a function on a character string or value retrieved by the extraction rule. For example, an extraction rule may truncate a character string or convert the character string into a different data format. In some cases, the query itself can specify one or more extraction rules. 
     The search head  210  can apply the extraction rules to events that it receives from indexers  206 . Indexers  206  may apply the extraction rules to events in an associated data store  208 . Extraction rules can be applied to all the events in a data store or to a subset of the events that have been filtered based on some criteria (e.g., event time stamp values, etc.). Extraction rules can be used to extract one or more values for a field from events by parsing the portions of machine data in the events and examining the data for one or more patterns of characters, numbers, delimiters, etc., that indicate where the field begins and, optionally, ends. 
     As mentioned above, and as will be described in greater detail below with reference to, inter alia,  FIGS.  18 - 49   , some functionality of the search head or indexers can be handled by different components of the system or removed altogether. For example, in some cases, a query coordinator or nodes use extraction rules to extract values for fields in the events being searched. The query coordinator or nodes obtain extraction rules that specify how to extract a value for fields from an event, etc., and apply the extraction rules to events that it receives from indexers, common storage, ingested data buffers, query acceleration data stores, or other dataset sources. 
       FIG.  7 A  is a diagram of an example scenario where a common customer identifier is found among log data received from three disparate data sources, in accordance with example embodiments. In this example, a user submits an order for merchandise using a vendor&#39;s shopping application program  701  running on the user&#39;s system. In this example, the order was not delivered to the vendor&#39;s server due to a resource exception at the destination server that is detected by the middleware code  702 . The user then sends a message to the customer support server  703  to complain about the order failing to complete. The three systems  701 ,  702 , and  703  are disparate systems that do not have a common logging format. The order application  701  sends log data  704  to the data intake and query system in one format, the middleware code  702  sends error log data  705  in a second format, and the support server  703  sends log data  706  in a third format. 
     Using the log data received at one or more indexers  206  from the three systems, the vendor can uniquely obtain an insight into user activity, user experience, and system behavior. The search head  210  allows the vendor&#39;s administrator to search the log data from the three systems that one or more indexers  206  are responsible for searching, thereby obtaining correlated information, such as the order number and corresponding customer ID number of the person placing the order. The system also allows the administrator to see a visualization of related events via a user interface. The administrator can query the search head  210  for customer ID field value matches across the log data from the three systems that are stored at the one or more indexers  206 . The customer ID field value exists in the data gathered from the three systems, but the customer ID field value may be located in different areas of the data given differences in the architecture of the systems. There is a semantic relationship between the customer ID field values generated by the three systems. The search head  210  requests events from the one or more indexers  206  to gather relevant events from the three systems. The search head  210  then applies extraction rules to the events in order to extract field values that it can correlate. The search head may apply a different extraction rule to each set of events from each system when the event format differs among systems. In this example, the user interface can display to the administrator the events corresponding to the common customer ID field values  707 ,  708 , and  709 , thereby providing the administrator with insight into a customer&#39;s experience. 
     Note that query results can be returned to a client, a search head, or any other system component for further processing. In general, query results may include a set of one or more events, a set of one or more values obtained from the events, a subset of the values, statistics calculated based on the values, a report containing the values, a visualization (e.g., a graph or chart) generated from the values, and the like. 
     The search system enables users to run queries against the stored data to retrieve events that meet criteria specified in a query, such as containing certain keywords or having specific values in defined fields.  FIG.  7 B  illustrates the manner in which keyword searches and field searches are processed in accordance with disclosed embodiments. 
     If a user inputs a search query into search bar  710  that includes only keywords (also known as “tokens”), e.g., the keyword “error” or “warning”, the query search engine of the data intake and query system searches for those keywords directly in the event data  711  of the events  713 ,  714 ,  715 ,  719  stored in the raw record data store. Note that while  FIG.  7 B  only illustrates four events, the raw record data store (which may to data store  208  in  FIG.  2   ) may contain records for millions of events. 
     As disclosed above, an indexer can optionally generate a keyword index to facilitate fast keyword searching for event data. The indexer includes the identified keywords in an index, which associates each stored keyword with reference pointers to events containing that keyword (or to locations within events where that keyword is located, other location identifiers, etc.). When an indexer subsequently receives a keyword-based query, the indexer can access the keyword index to quickly identify events containing the keyword. For example, if the keyword “HTTP” was indexed by the indexer at index time, and the user searches for the keyword “HTTP”, events  713  to  715  will be identified based on the results returned from the keyword index. As noted above, the index contains reference pointers to the events containing the keyword, which allows for efficient retrieval of the relevant events from the raw record data store. 
     If a user searches for a keyword that has not been indexed by the indexer, the data intake and query system would nevertheless be able to retrieve the events by searching the event data for the keyword in the raw record data store directly as shown in  FIG.  7 B . For example, if a user searches for the keyword “frank”, and the name “frank” has not been indexed at index time, the DATA INTAKE AND QUERY system will search the event data directly and return the first event  713 . Note that whether the keyword has been indexed at index time or not, in both cases the raw data with the events  713 ,  714 ,  715 ,  719  is accessed from the raw data record store to service the keyword search. In the case where the keyword has been indexed, the index will contain a reference pointer that will allow for a more efficient retrieval of the event data from the data store. If the keyword has not been indexed, the search engine will need to search through all the records in the data store to service the search. 
     In most cases, however, in addition to keywords, a user&#39;s search will also include fields. The term “field” refers to a location in the event data containing one or more values for a specific data item. Often, a field is a value with a fixed, delimited position on a line, or a name and value pair, where there is a single value to each field name. A field can also be multivalued, that is, it can appear more than once in an event and have a different value for each appearance, e.g., email address fields. Fields are searchable by the field name or field name-value pairs. Some examples of fields are “clientip” for IP addresses accessing a web server, or the “From” and “To” fields in email addresses. 
     By way of further example, consider the search, “status=404”. This search query finds events with “status” fields that have a value of “404.” When the search is run, the search engine does not look for events with any other “status” value. It also does not look for events containing other fields that share “404” as a value. As a result, the search returns a set of results that are more focused than if “404” had been used in the search string as part of a keyword search. Note also that fields can appear in events as “key=value” pairs such as “user_name=Bob.” But in most cases, field values appear in fixed, delimited positions without identifying keys. For example, the data store may contain events where the “user_name” value always appears by itself after the timestamp as illustrated by the following string: “Nov 15 09:33:22 johnmedlock.” 
     The data intake and query system advantageously allows for search time field extraction. In other words, fields can be extracted from the event data at search time using late-binding schema as opposed to at data ingestion time, which was a major limitation of the prior art systems. 
     In response to receiving the search query, search head  210  uses extraction rules to extract values for the fields associated with a field or fields in the event data being searched. The search head  210  obtains extraction rules that specify how to extract a value for certain fields from an event. Extraction rules can comprise regex rules that specify how to extract values for the relevant fields. In addition to specifying how to extract field values, the extraction rules may also include instructions for deriving a field value by performing a function on a character string or value retrieved by the extraction rule. For example, a transformation rule may truncate a character string, or convert the character string into a different data format. In some cases, the query itself can specify one or more extraction rules. 
       FIG.  7 B  illustrates the manner in which configuration files may be used to configure custom fields at search time in accordance with the disclosed embodiments. In response to receiving a search query, the data intake and query system determines if the query references a “field.” For example, a query may request a list of events where the “clientip” field equals “127.0.0.1.” If the query itself does not specify an extraction rule and if the field is not a metadata field, e.g., time, host, source, source type, etc., then in order to determine an extraction rule, the search engine may, in one or more embodiments, need to locate configuration file  712  during the execution of the search as shown in  FIG.  7 B . 
     Configuration file  712  may contain extraction rules for all the various fields that are not metadata fields, e.g., the “clientip” field. The extraction rules may be inserted into the configuration file in a variety of ways. In some embodiments, the extraction rules can comprise regular expression rules that are manually entered in by the user. Regular expressions match patterns of characters in text and are used for extracting custom fields in text. 
     In one or more embodiments, as noted above, a field extractor may be configured to automatically generate extraction rules for certain field values in the events when the events are being created, indexed, or stored, or possibly at a later time. In one embodiment, a user may be able to dynamically create custom fields by highlighting portions of a sample event that should be extracted as fields using a graphical user interface. The system would then generate a regular expression that extracts those fields from similar events and store the regular expression as an extraction rule for the associated field in the configuration file  712 . 
     In some embodiments, the indexers may automatically discover certain custom fields at index time and the regular expressions for those fields will be automatically generated at index time and stored as part of extraction rules in configuration file  712 . For example, fields that appear in the event data as “key=value” pairs may be automatically extracted as part of an automatic field discovery process. Note that there may be several other ways of adding field definitions to configuration files in addition to the methods discussed herein. 
     The search head  210  can apply the extraction rules derived from configuration file  1402  to event data that it receives from indexers  206 . Indexers  206  may apply the extraction rules from the configuration file to events in an associated data store  208 . Extraction rules can be applied to all the events in a data store, or to a subset of the events that have been filtered based on some criteria (e.g., event time stamp values, etc.). Extraction rules can be used to extract one or more values for a field from events by parsing the event data and examining the event data for one or more patterns of characters, numbers, delimiters, etc., that indicate where the field begins and, optionally, ends. 
     In one more embodiments, the extraction rule in configuration file  712  will also need to define the type or set of events that the rule applies to. Because the raw record data store will contain events from multiple heterogeneous sources, multiple events may contain the same fields in different locations because of discrepancies in the format of the data generated by the various sources. Furthermore, certain events may not contain a particular field at all. For example, event  719  also contains “clientip” field, however, the “clientip” field is in a different format from events  713 - 715 . To address the discrepancies in the format and content of the different types of events, the configuration file will also need to specify the set of events that an extraction rule applies to, e.g., extraction rule  716  specifies a rule for filtering by the type of event and contains a regular expression for parsing out the field value. Accordingly, each extraction rule will pertain to only a particular type of event. If a particular field, e.g., “clientip” occurs in multiple events, each of those types of events would need its own corresponding extraction rule in the configuration file  712  and each of the extraction rules would comprise a different regular expression to parse out the associated field value. The most common way to categorize events is by source type because events generated by a particular source can have the same format. 
     The field extraction rules stored in configuration file  712  perform search-time field extractions. For example, for a query that requests a list of events with source type “access_combined” where the “clientip” field equals “127.0.0.1,” the query search engine would first locate the configuration file  712  to retrieve extraction rule  716  that would allow it to extract values associated with the “clientip” field from the event data  720  “where the source type is “access_combined. After the “clientip” field has been extracted from all the events comprising the “clientip” field where the source type is “access_combined,” the query search engine can then execute the field criteria by performing the compare operation to filter out the events where the “clientip” field equals “127.0.0.1.” In the example shown in  FIG.  7 B , events  713 - 715  would be returned in response to the user query. In this manner, the search engine can service queries containing field criteria in addition to queries containing keyword criteria (as explained above). 
     The configuration file can be created during indexing. It may either be manually created by the user or automatically generated with certain predetermined field extraction rules. As discussed above, the events may be distributed across several indexers, wherein each indexer may be responsible for storing and searching a subset of the events contained in a corresponding data store. In a distributed indexer system, each indexer would need to maintain a local copy of the configuration file that is synchronized periodically across the various indexers. 
     The ability to add schema to the configuration file at search time results in increased efficiency. A user can create new fields at search time and simply add field definitions to the configuration file. As a user learns more about the data in the events, the user can continue to refine the late-binding schema by adding new fields, deleting fields, or modifying the field extraction rules in the configuration file for use the next time the schema is used by the system. Because the data intake and query system maintains the underlying raw data and uses late-binding schema for searching the raw data, it enables a user to continue investigating and learn valuable insights about the raw data long after data ingestion time. 
     The ability to add multiple field definitions to the configuration file at search time also results in increased flexibility. For example, multiple field definitions can be added to the configuration file to capture the same field across events generated by different source types. This allows the data intake and query system to search and correlate data across heterogeneous sources flexibly and efficiently. 
     Further, by providing the field definitions for the queried fields at search time, the configuration file  712  allows the record data store to be field searchable. In other words, the raw record data store can be searched using keywords as well as fields, wherein the fields are searchable name/value pairings that distinguish one event from another and can be defined in configuration file  1402  using extraction rules. In comparison to a search containing field names, a keyword search does not need the configuration file and can search the event data directly as shown in  FIG.  7 B . 
     It should also be noted that any events filtered out by performing a search-time field extraction using a configuration file can be further processed by directing the results of the filtering step to a processing step using a pipelined search language. Using the prior example, a user could pipeline the results of the compare step to an aggregate function by asking the query search engine to count the number of events where the “clientip” field equals “127.0.0.1.” 
     As mentioned above, and as will be described in greater detail below with reference to, inter alia,  FIGS.  18 - 49   , some functionality of the search head or indexers can be handled by different components of the system or removed altogether. For example, in some cases, the data is stored in a dataset source, which may be an indexer (or data store controlled by an indexer) or may be a different type of dataset source, such as a common storage or external data source. In addition, a query coordinator or node can request events from the indexers or other dataset source, apply extraction rules and correlate, automatically discover certain custom fields, etc., as described above. 
     3.11. Example Search Screen 
       FIG.  8 A  is an interface diagram of an example user interface for a search screen  800 , in accordance with example embodiments. Search screen  800  includes a search bar  802  that accepts user input in the form of a search string. It also includes a time range picker  812  that enables the user to specify a time range for the search. For historical searches (e.g., searches based on a particular historical time range), the user can select a specific time range, or alternatively a relative time range, such as “today,” “yesterday” or “last week.” For real-time searches (e.g., searches whose results are based on data received in real-time), the user can select the size of a time window to search for real-time events. Search screen  800  also initially displays a “data summary” dialog as is illustrated in  FIG.  8 B  that enables the user to select different sources for the events, such as by selecting specific hosts and log files. 
     After the search is executed, the search screen  800  in  FIG.  8 A  can display the results through search results tabs  804 , wherein search results tabs  804  includes: an “events tab” that displays various information about events returned by the search; a “statistics tab” that displays statistics about the search results; and a “visualization tab” that displays various visualizations of the search results. The events tab illustrated in  FIG.  8 A  displays a timeline graph  805  that graphically illustrates the number of events that occurred in one-hour intervals over the selected time range. The events tab also displays an events list  808  that enables a user to view the machine data in each of the returned events. 
     The events tab additionally displays a sidebar that is an interactive field picker  806 . The field picker  806  may be displayed to a user in response to the search being executed and allows the user to further analyze the search results based on the fields in the events of the search results. The field picker  806  includes field names that reference fields present in the events in the search results. The field picker may display any Selected Fields  820  that a user has pre-selected for display (e.g., host, source, sourcetype) and may also display any Interesting Fields  822  that the system determines may be interesting to the user based on pre-specified criteria (e.g., action, bytes, categoryid, clientip, date_hour, date_mday, date_minute, etc.). The field picker also provides an option to display field names for all the fields present in the events of the search results using the All Fields control  824 . 
     Each field name in the field picker  806  has a value type identifier to the left of the field name, such as value type identifier  826 . A value type identifier identifies the type of value for the respective field, such as an “a” for fields that include literal values or a “#” for fields that include numerical values. 
     Each field name in the field picker also has a unique value count to the right of the field name, such as unique value count  828 . The unique value count indicates the number of unique values for the respective field in the events of the search results. 
     Each field name is selectable to view the events in the search results that have the field referenced by that field name. For example, a user can select the “host” field name, and the events shown in the events list  808  will be updated with events in the search results that have the field that is reference by the field name “host.” 
     3.12. Data Models 
     A data model is a hierarchically structured search-time mapping of semantic knowledge about one or more datasets. It encodes the domain knowledge used to build a variety of specialized searches of those datasets. Those searches, in turn, can be used to generate reports. 
     A data model is composed of one or more “objects” (or “data model objects”) that define or otherwise correspond to a specific set of data. An object is defined by constraints and attributes. An object&#39;s constraints are search criteria that define the set of events to be operated on by running a search having that search criteria at the time the data model is selected. An object&#39;s attributes are the set of fields to be exposed for operating on that set of events generated by the search criteria. 
     Objects in data models can be arranged hierarchically in parent/child relationships. Each child object represents a subset of the dataset covered by its parent object. The top-level objects in data models are collectively referred to as “root objects.” 
     Child objects have inheritance. Child objects inherit constraints and attributes from their parent objects and may have additional constraints and attributes of their own. Child objects provide a way of filtering events from parent objects. Because a child object may provide an additional constraint in addition to the constraints it has inherited from its parent object, the dataset it represents may be a subset of the dataset that its parent represents. For example, a first data model object may define a broad set of data pertaining to e-mail activity generally, and another data model object may define specific datasets within the broad dataset, such as a subset of the e-mail data pertaining specifically to e-mails sent. For example, a user can simply select an “e-mail activity” data model object to access a dataset relating to e-mails generally (e.g., sent or received), or select an “e-mails sent” data model object (or data sub-model object) to access a dataset relating to e-mails sent. 
     Because a data model object is defined by its constraints (e.g., a set of search criteria) and attributes (e.g., a set of fields), a data model object can be used to quickly search data to identify a set of events and to identify a set of fields to be associated with the set of events. For example, an “e-mails sent” data model object may specify a search for events relating to e-mails that have been sent, and specify a set of fields that are associated with the events. Thus, a user can retrieve and use the “e-mails sent” data model object to quickly search source data for events relating to sent e-mails, and may be provided with a listing of the set of fields relevant to the events in a user interface screen. 
     Examples of data models can include electronic mail, authentication, databases, intrusion detection, malware, application state, alerts, compute inventory, network sessions, network traffic, performance, audits, updates, vulnerabilities, etc. Data models and their objects can be designed by knowledge managers in an organization, and they can enable downstream users to quickly focus on a specific set of data. A user iteratively applies a model development tool (not shown in  FIG.  8 A ) to prepare a query that defines a subset of events and assigns an object name to that subset. A child subset is created by further limiting a query that generated a parent subset. 
     Data definitions in associated schemas can be taken from the common information model (CIM) or can be devised for a particular schema and optionally added to the CIM. Child objects inherit fields from parents and can include fields not present in parents. A model developer can select fewer extraction rules than are available for the sources returned by the query that defines events belonging to a model. Selecting a limited set of extraction rules can be a tool for simplifying and focusing the data model, while allowing a user flexibility to explore the data subset. Development of a data model is further explained in U.S. Pat. Nos. 8,788,525 and 8,788,526, both entitled “DATA MODEL FOR MACHINE DATA FOR SEMANTIC SEARCH”, both issued on 22 Jul. 2014, U.S. Pat. No. 8,983,994, entitled “GENERATION OF A DATA MODEL FOR SEARCHING MACHINE DATA”, issued on 17 Mar. 2015, U.S. Pat. No. 9,128,980, entitled “GENERATION OF A DATA MODEL APPLIED TO QUERIES”, issued on 8 Sep. 2015, and U.S. Pat. No. 9,589,012, entitled “GENERATION OF A DATA MODEL APPLIED TO OBJECT QUERIES”, issued on 7 Mar. 2017, each of which is hereby incorporated by reference in its entirety for all purposes. 
     A data model can also include reports. One or more report formats can be associated with a particular data model and be made available to run against the data model. A user can use child objects to design reports with object datasets that already have extraneous data pre-filtered out. In some embodiments, the data intake and query system  108  provides the user with the ability to produce reports (e.g., a table, chart, visualization, etc.) without having to enter SPL, SQL, or other query language terms into a search screen. Data models are used as the basis for the search feature. 
     Data models may be selected in a report generation interface. The report generator supports drag-and-drop organization of fields to be summarized in a report. When a model is selected, the fields with available extraction rules are made available for use in the report. The user may refine and/or filter search results to produce more precise reports. The user may select some fields for organizing the report and select other fields for providing detail according to the report organization. For example, “region” and “salesperson” are fields used for organizing the report and sales data can be summarized (subtotaled and totaled) within this organization. The report generator allows the user to specify one or more fields within events and apply statistical analysis on values extracted from the specified one or more fields. The report generator may aggregate search results across sets of events and generate statistics based on aggregated search results. Building reports using the report generation interface is further explained in U.S. patent application Ser. No. 14/503,335, entitled “GENERATING REPORTS FROM UNSTRUCTURED DATA”, filed on 30 Sep. 2014, and which is hereby incorporated by reference in its entirety for all purposes. Data visualizations also can be generated in a variety of formats, by reference to the data model. Reports, data visualizations, and data model objects can be saved and associated with the data model for future use. The data model object may be used to perform searches of other data. 
       FIGS.  9 - 15    are interface diagrams of example report generation user interfaces, in accordance with example embodiments. The report generation process may be driven by a predefined data model object, such as a data model object defined and/or saved via a reporting application or a data model object obtained from another source. A user can load a saved data model object using a report editor. For example, the initial search query and fields used to drive the report editor may be obtained from a data model object. The data model object that is used to drive a report generation process may define a search and a set of fields. Upon loading of the data model object, the report generation process may enable a user to use the fields (e.g., the fields defined by the data model object) to define criteria for a report (e.g., filters, split rows/columns, aggregates, etc.) and the search may be used to identify events (e.g., to identify events responsive to the search) used to generate the report. That is, for example, if a data model object is selected to drive a report editor, the graphical user interface of the report editor may enable a user to define reporting criteria for the report using the fields associated with the selected data model object, and the events used to generate the report may be constrained to the events that match, or otherwise satisfy, the search constraints of the selected data model object. 
     The selection of a data model object for use in driving a report generation may be facilitated by a data model object selection interface.  FIG.  9    illustrates an example interactive data model selection graphical user interface  900  of a report editor that displays a listing of available data models  901 . The user may select one of the data models  902 . 
       FIG.  10    illustrates an example data model object selection graphical user interface  1000  that displays available data objects  1001  for the selected data object model  902 . The user may select one of the displayed data model objects  1002  for use in driving the report generation process. 
     Once a data model object is selected by the user, a user interface screen  1100  shown in  FIG.  11 A  may display an interactive listing of automatic field identification options  1101  based on the selected data model object. For example, a user may select one of the three illustrated options (e.g., the “All Fields” option  1102 , the “Selected Fields” option  1103 , or the “Coverage” option (e.g., fields with at least a specified % of coverage)  1104 ). If the user selects the “All Fields” option  1102 , all of the fields identified from the events that were returned in response to an initial search query may be selected. That is, for example, all of the fields of the identified data model object fields may be selected. If the user selects the “Selected Fields” option  1103 , only the fields from the fields of the identified data model object fields that are selected by the user may be used. If the user selects the “Coverage” option  1104 , only the fields of the identified data model object fields meeting a specified coverage criteria may be selected. A percent coverage may refer to the percentage of events returned by the initial search query that a given field appears in. Thus, for example, if an object dataset includes 10,000 events returned in response to an initial search query, and the “avg_age” field appears in  854  of those 10,000 events, then the “avg_age” field would have a coverage of 8.54% for that object dataset. If, for example, the user selects the “Coverage” option and specifies a coverage value of 2%, only fields having a coverage value equal to or greater than 2% may be selected. The number of fields corresponding to each selectable option may be displayed in association with each option. For example, “97” displayed next to the “All Fields” option  1102  indicates that 97 fields will be selected if the “All Fields” option is selected. The “3” displayed next to the “Selected Fields” option  1103  indicates that 3 of the 97 fields will be selected if the “Selected Fields” option is selected. The “49” displayed next to the “Coverage” option  1104  indicates that 49 of the 97 fields (e.g., the 49 fields having a coverage of 2% or greater) will be selected if the “Coverage” option is selected. The number of fields corresponding to the “Coverage” option may be dynamically updated based on the specified percent of coverage. 
       FIG.  11 B  illustrates an example graphical user interface screen  1105  displaying the reporting application&#39;s “Report Editor” page. The screen may display interactive elements for defining various elements of a report. For example, the page includes a “Filters” element  1106 , a “Split Rows” element  1107 , a “Split Columns” element  1108 , and a “Column Values” element  1109 . The page may include a list of search results  1111 . In this example, the Split Rows element  1107  is expanded, revealing a listing of fields  1110  that can be used to define additional criteria (e.g., reporting criteria). The listing of fields  1110  may correspond to the selected fields. That is, the listing of fields  1110  may list only the fields previously selected, either automatically and/or manually by a user.  FIG.  11 C  illustrates a formatting dialogue  1112  that may be displayed upon selecting a field from the listing of fields  1110 . The dialogue can be used to format the display of the results of the selection (e.g., label the column for the selected field to be displayed as “component”). 
       FIG.  11 D  illustrates an example graphical user interface screen  1105  including a table of results  1113  based on the selected criteria including splitting the rows by the “component” field. A column  1114  having an associated count for each component listed in the table may be displayed that indicates an aggregate count of the number of times that the particular field-value pair (e.g., the value in a row for a particular field, such as the value “BucketMover” for the field “component”) occurs in the set of events responsive to the initial search query. 
       FIG.  12    illustrates an example graphical user interface screen  1200  that allows the user to filter search results and to perform statistical analysis on values extracted from specific fields in the set of events. In this example, the top ten product names ranked by price are selected as a filter  1201  that causes the display of the ten most popular products sorted by price. Each row is displayed by product name and price  1202 . This results in each product displayed in a column labeled “product name” along with an associated price in a column labeled “price”  1206 . Statistical analysis of other fields in the events associated with the ten most popular products have been specified as column values  1203 . A count of the number of successful purchases for each product is displayed in column  1204 . These statistics may be produced by filtering the search results by the product name, finding all occurrences of a successful purchase in a field within the events and generating a total of the number of occurrences. A sum of the total sales is displayed in column  1205 , which is a result of the multiplication of the price and the number of successful purchases for each product. 
     The reporting application allows the user to create graphical visualizations of the statistics generated for a report. For example,  FIG.  13    illustrates an example graphical user interface  1300  that displays a set of components and associated statistics  1301 . The reporting application allows the user to select a visualization of the statistics in a graph (e.g., bar chart, scatter plot, area chart, line chart, pie chart, radial gauge, marker gauge, filler gauge, etc.), where the format of the graph may be selected using the user interface controls  1302  along the left panel of the user interface  1300 .  FIG.  14    illustrates an example of a bar chart visualization  1400  of an aspect of the statistical data  1301 .  FIG.  15    illustrates a scatter plot visualization  1500  of an aspect of the statistical data  1301 . 
     3.13. Acceleration Technique 
     The above-described system provides significant flexibility by enabling a user to analyze massive quantities of minimally-processed data “on the fly” at search time using a late-binding schema, instead of storing pre-specified portions of the data in a database at ingestion time. This flexibility enables a user to see valuable insights, correlate data, and perform subsequent queries to examine interesting aspects of the data that may not have been apparent at ingestion time. 
     However, performing extraction and analysis operations at search time can involve a large amount of data and require a large number of computational operations, which can cause delays in processing the queries. Advantageously, the data intake and query system also employs a number of unique acceleration techniques that have been developed to speed up analysis operations performed at search time. These techniques include: (1) performing search operations in parallel across multiple indexers; (2) using a keyword index; (3) using a high performance analytics store; and (4) accelerating the process of generating reports. These novel techniques are described in more detail below. Although described as being performed by an indexer, it will be understood that various components can be used to perform similar functionality. For example, nodes can perform any one or any combination of the search functions described herein. In some cases, the nodes perform the search functions based on instructions received from a query coordinator. 
     3.13.1. Aggregation Technique 
     To facilitate faster query processing, a query can be structured such that multiple indexers perform the query in parallel, while aggregation of search results from the multiple indexers is performed locally at the search head. For example,  FIG.  16    is an example search query received from a client and executed by search peers, in accordance with example embodiments.  FIG.  16    illustrates how a search query  1602  received from a client at a search head  210  can split into two phases, including: (1) subtasks  1604  (e.g., data retrieval or simple filtering) that may be performed in parallel by indexers  206  for execution, and (2) a search results aggregation operation  1606  to be executed by the search head when the results are ultimately collected from the indexers. 
     During operation, upon receiving search query  1602 , a search head  210  determines that a portion of the operations involved with the search query may be performed locally by the search head. The search head modifies search query  1602  by substituting “stats” (create aggregate statistics over results sets received from the indexers at the search head) with “prestats” (create statistics by the indexer from local results set) to produce search query  1604 , and then distributes search query  1604  to distributed indexers, which are also referred to as “search peers” or “peer indexers.” Note that search queries may generally specify search criteria or operations to be performed on events that meet the search criteria. Search queries may also specify field names, as well as search criteria for the values in the fields or operations to be performed on the values in the fields. 
     Moreover, the search head may distribute the full search query to the search peers as illustrated in  FIG.  6 A , or may alternatively distribute a modified version (e.g., a more restricted version) of the search query to the search peers. In this example, the indexers are responsible for producing the results and sending them to the search head. After the indexers return the results to the search head, the search head aggregates the received results  1606  to form a single search result set. By executing the query in this manner, the system effectively distributes the computational operations across the indexers while minimizing data transfers. 
     As mentioned above, and as will be described in greater detail below with reference to, inter alia, 18-49, some functionality of the search head or indexers can be handled by different components of the system or removed altogether. For example, in some cases, the data is stored in one or more dataset sources, such as, but not limited to an indexer (or data store controlled by an indexer), common storage, external data source, ingested data buffer, query acceleration data store, etc. In addition, in some cases a query coordinator can aggregate results from multiple indexers or nodes, perform an aggregation operation  1606 , determine what, if any, portion of the operations of the search query are to be performed locally by the query coordinator, modify or translate a search query for an indexer or other dataset source, distribute the query to indexers, peers, or nodes, etc. 
     3.13.2. Keyword Index 
     As described above with reference to the flow charts in  FIG.  5 A ,  FIG.  5 B , and  FIG.  6 A , data intake and query system  108  can construct and maintain one or more keyword indices to quickly identify events containing specific keywords. This technique can greatly speed up the processing of queries involving specific keywords. As mentioned above, to build a keyword index, an indexer first identifies a set of keywords. Then, the indexer includes the identified keywords in an index, which associates each stored keyword with references to events containing that keyword, or to locations within events where that keyword is located. When an indexer subsequently receives a keyword-based query, the indexer can access the keyword index to quickly identify events containing the keyword. In some embodiments, a node or other components of the system that performs search operations can use the keyword index to identify events, etc. 
     3.13.3. High Performance Analytics Store 
     To speed up certain types of queries, some embodiments of system  108  create a high performance analytics store, which is referred to as a “summarization table,” that contains entries for specific field-value pairs. Each of these entries keeps track of instances of a specific value in a specific field in the events and includes references to events containing the specific value in the specific field. For example, an example entry in a summarization table can keep track of occurrences of the value “94107” in a “ZIP code” field of a set of events and the entry includes references to all of the events that contain the value “94107” in the ZIP code field. This optimization technique enables the system to quickly process queries that seek to determine how many events have a particular value for a particular field. To this end, the system can examine the entry in the summarization table to count instances of the specific value in the field without having to go through the individual events or perform data extractions at search time. Also, if the system needs to process all events that have a specific field-value combination, the system can use the references in the summarization table entry to directly access the events to extract further information without having to search all of the events to find the specific field-value combination at search time. 
     In some embodiments, the system maintains a separate summarization table for each of the above-described time-specific buckets that stores events for a specific time range. A bucket-specific summarization table includes entries for specific field-value combinations that occur in events in the specific bucket. Alternatively, the system can maintain a separate summarization table for each indexer. The indexer-specific summarization table includes entries for the events in a data store that are managed by the specific indexer. Indexer-specific summarization tables may also be bucket-specific. 
     The summarization table can be populated by running a periodic query that scans a set of events to find instances of a specific field-value combination, or alternatively instances of all field-value combinations for a specific field. A periodic query can be initiated by a user, or can be scheduled to occur automatically at specific time intervals. A periodic query can also be automatically launched in response to a query that asks for a specific field-value combination. 
     In some cases, when the summarization tables may not cover all of the events that are relevant to a query, the system can use the summarization tables to obtain partial results for the events that are covered by summarization tables, but may also have to search through other events that are not covered by the summarization tables to produce additional results. These additional results can then be combined with the partial results to produce a final set of results for the query. The summarization table and associated techniques are described in more detail in U.S. Pat. No. 8,682,925, entitled “DISTRIBUTED HIGH PERFORMANCE ANALYTICS STORE”, issued on 25 Mar. 2014, U.S. Pat. No. 9,128,985, entitled “SUPPLEMENTING A HIGH PERFORMANCE ANALYTICS STORE WITH EVALUATION OF INDIVIDUAL EVENTS TO RESPOND TO AN EVENT QUERY”, issued on 8 Sep. 2015, and U.S. patent application Ser. No. 14/815,973, entitled “GENERATING AND STORING SUMMARIZATION TABLES FOR SETS OF SEARCHABLE EVENTS”, filed on 1 Aug. 2015, each of which is hereby incorporated by reference in its entirety for all purposes. 
     To speed up certain types of queries, e.g., frequently encountered queries or computationally intensive queries, some embodiments of system  108  create a high performance analytics store, which is referred to as a “summarization table,” (also referred to as a “lexicon” or “inverted index”) that contains entries for specific field-value pairs. Each of these entries keeps track of instances of a specific value in a specific field in the event data and includes references to events containing the specific value in the specific field. For example, an example entry in an inverted index can keep track of occurrences of the value “94107” in a “ZIP code” field of a set of events and the entry includes references to all of the events that contain the value “94107” in the ZIP code field. Creating the inverted index data structure avoids needing to incur the computational overhead each time a statistical query needs to be run on a frequently encountered field-value pair. In order to expedite queries, in most embodiments, the search engine will employ the inverted index separate from the raw record data store to generate responses to the received queries. 
     Note that the term “summarization table” or “inverted index” as used herein is a data structure that may be generated by an indexer that includes at least field names and field values that have been extracted and/or indexed from event records. An inverted index may also include reference values that point to the location(s) in the field searchable data store where the event records that include the field may be found. Also, an inverted index may be stored using well-known compression techniques to reduce its storage size. 
     Further, note that the term “reference value” (also referred to as a “posting value”) as used herein is a value that references the location of a source record in the field searchable data store. In some embodiments, the reference value may include additional information about each record, such as timestamps, record size, meta-data, or the like. Each reference value may be a unique identifier which may be used to access the event data directly in the field searchable data store. In some embodiments, the reference values may be ordered based on each event record&#39;s timestamp. For example, if numbers are used as identifiers, they may be sorted so event records having a later timestamp always have a lower valued identifier than event records with an earlier timestamp, or vice-versa. Reference values are often included in inverted indexes for retrieving and/or identifying event records. 
     In one or more embodiments, an inverted index is generated in response to a user-initiated collection query. The term “collection query” as used herein refers to queries that include commands that generate summarization information and inverted indexes (or summarization tables) from event records stored in the field searchable data store. 
     Note that a collection query is a special type of query that can be user-generated and is used to create an inverted index. A collection query is not the same as a query that is used to call up or invoke a pre-existing inverted index. In one or more embodiment, a query can comprise an initial step that calls up apre-generated inverted index on which further filtering and processing can be performed. For example, referring back to  FIG.  13   , a set of events generated at block  1320  by either using a “collection” query to create a new inverted index or by calling up a pre-generated inverted index. A query with several pipelined steps will start with a pre-generated index to accelerate the query. 
       FIG.  7 C  illustrates the manner in which an inverted index is created and used in accordance with the disclosed embodiments. As shown in  FIG.  7 C , an inverted index  722  can be created in response to a user-initiated collection query using the event data  723  stored in the raw record data store. For example, a non-limiting example of a collection query may include “collect clientip=127.0.0.1” which may result in an inverted index  722  being generated from the event data  723  as shown in  FIG.  7 C . Each entry in the inverted index  722  includes an event reference value that references the location of a source record in the field searchable data store. The reference value may be used to access the original event record directly from the field searchable data store. 
     In one or more embodiments, if one or more of the queries is a collection query, the responsive indexers may generate summarization information based on the fields of the event records located in the field searchable data store. In at least one of the various embodiments, one or more of the fields used in the summarization information may be listed in the collection query and/or they may be determined based on terms included in the collection query. For example, a collection query may include an explicit list of fields to summarize. Or, in at least one of the various embodiments, a collection query may include terms or expressions that explicitly define the fields, e.g., using regex rules. In  FIG.  7 C , prior to running the collection query that generates the inverted index  722 , the field name “clientip” may need to be defined in a configuration file by specifying the “access_combined” source type and a regular expression rule to parse out the client IP address. Alternatively, the collection query may contain an explicit definition for the field name “clientip” which may obviate the need to reference the configuration file at search time. 
     In one or more embodiments, collection queries may be saved and scheduled to run periodically. These scheduled collection queries may periodically update the summarization information corresponding to the query. For example, if the collection query that generates inverted index  722  is scheduled to run periodically, one or more indexers would periodically search through the relevant buckets to update inverted index  722  with event data for any new events with the “clientip” value of “127.0.0.1.” 
     In some embodiments, the inverted indexes that include fields, values, and reference value (e.g., inverted index  722 ) for event records may be included in the summarization information provided to the user. In other embodiments, a user may not be interested in specific fields and values contained in the inverted index, but may need to perform a statistical query on the data in the inverted index. For example, referencing the example of  FIG.  7 C  rather than viewing the fields within inverted index  722 , a user may want to generate a count of all client requests from IP address “127.0.0.1.” In this case, the search engine would simply return a result of “4” rather than including details about the inverted index  722  in the information provided to the user. 
     The pipelined search language, e.g., SPL of the SPLUNK® ENTERPRISE system can be used to pipe the contents of an inverted index to a statistical query using the “stats” command for example. A “stats” query refers to queries that generate result sets that may produce aggregate and statistical results from event records, e.g., average, mean, max, min, rms, etc. Where sufficient information is available in an inverted index, a “stats” query may generate their result sets rapidly from the summarization information available in the inverted index rather than directly scanning event records. For example, the contents of inverted index  722  can be pipelined to a stats query, e.g., a “count” function that counts the number of entries in the inverted index and returns a value of “4.” In this way, inverted indexes may enable various stats queries to be performed absent scanning or search the event records. Accordingly, this optimization technique enables the system to quickly process queries that seek to determine how many events have a particular value for a particular field. To this end, the system can examine the entry in the inverted index  722  to count instances of the specific value in the field without having to go through the individual events or perform data extractions at search time. 
     In some embodiments, the system maintains a separate inverted index for each of the above-described time-specific buckets that stores events for a specific time range. A bucket-specific inverted index includes entries for specific field-value combinations that occur in events in the specific bucket. Alternatively, the system can maintain a separate inverted index for each indexer. The indexer-specific inverted index includes entries for the events in a data store that are managed by the specific indexer. Indexer-specific inverted indexes may also be bucket-specific. In at least one or more embodiments, if one or more of the queries is a stats query, each indexer may generate a partial result set from previously generated summarization information. The partial result sets may be returned to the search head that received the query and combined into a single result set for the query 
     As mentioned above, the inverted index can be populated by running a periodic query that scans a set of events to find instances of a specific field-value combination, or alternatively instances of all field-value combinations for a specific field. A periodic query can be initiated by a user, or can be scheduled to occur automatically at specific time intervals. A periodic query can also be automatically launched in response to a query that asks for a specific field-value combination. In some embodiments, if summarization information is absent from an indexer that includes responsive event records, further actions may be taken, such as, the summarization information may generated on the fly, warnings may be provided the user, the collection query operation may be halted, the absence of summarization information may be ignored, or the like, or combination thereof. 
     In one or more embodiments, an inverted index may be set up to update continually. For example, the query may ask for the inverted index to update its result periodically, e.g., every hour. In such instances, the inverted index may be a dynamic data structure that is regularly updated to include information regarding incoming events. 
     In some cases, e.g., where a query is executed before an inverted index updates, when the inverted index may not cover all of the events that are relevant to a query, the system can use the inverted index to obtain partial results for the events that are covered by inverted index, but may also have to search through other events that are not covered by the inverted index to produce additional results on the fly. In other words, an indexer would need to search through event data on the data store to supplement the partial results. These additional results can then be combined with the partial results to produce a final set of results for the query. Note that in typical instances where an inverted index is not completely up to date, the number of events that an indexer would need to search through to supplement the results from the inverted index would be relatively small. In other words, the search to get the most recent results can be quick and efficient because only a small number of event records will be searched through to supplement the information from the inverted index. The inverted index and associated techniques are described in more detail in U.S. Pat. No. 8,682,925, entitled “DISTRIBUTED HIGH PERFORMANCE ANALYTICS STORE”, issued on 25 Mar. 2014, U.S. Pat. No. 9,128,985, entitled “SUPPLEMENTING A HIGH PERFORMANCE ANALYTICS STORE WITH EVALUATION OF INDIVIDUAL EVENTS TO RESPOND TO AN EVENT QUERY”, filed on 31 Jan. 2014, and U.S. patent application Ser. No. 14/815,973, entitled “STORAGE MEDIUM AND CONTROL DEVICE”, filed on 21 Feb. 2014, each of which is hereby incorporated by reference in its entirety. In some cases, the inverted indexes can be made available, as part of a common storage, to nodes or other components of the system that perform search operations. 
     3.13.4. Extracting Event Data Using Posting 
     In one or more embodiments, if the system needs to process all events that have a specific field-value combination, the system can use the references in the inverted index entry to directly access the events to extract further information without having to search all of the events to find the specific field-value combination at search time. In other words, the system can use the reference values to locate the associated event data in the field searchable data store and extract further information from those events, e.g., extract further field values from the events for purposes of filtering or processing or both. 
     The information extracted from the event data using the reference values can be directed for further filtering or processing in a query using the pipeline search language. The pipelined search language will, in one embodiment, include syntax that can direct the initial filtering step in a query to an inverted index. In one embodiment, a user would include syntax in the query that explicitly directs the initial searching or filtering step to the inverted index. 
     Referencing the example in  FIG.  7 C , if the user determines that she needs the user id fields associated with the client requests from IP address “127.0.0.1,” instead of incurring the computational overhead of performing a brand new search or re-generating the inverted index with an additional field, the user can generate a query that explicitly directs or pipes the contents of the already generated inverted index  722  to another filtering step requesting the user ids for the entries in inverted index  722  where the server response time is greater than “0.0900” microseconds. The search engine would use the reference values stored in inverted index  722  to retrieve the event data from the field searchable data store, filter the results based on the “response time” field values and, further, extract the user id field from the resulting event data to return to the user. In the present instance, the user ids “frank” and “matt” would be returned to the user from the generated results table  725 . 
     In one embodiment, the same methodology can be used to pipe the contents of the inverted index to a processing step. In other words, the user is able to use the inverted index  722  to efficiently and quickly perform aggregate functions on field values that were not part of the initially generated inverted index. For example, a user may want to determine an average object size (size of the requested gif) requested by clients from IP address “127.0.0.1.” In this case, the search engine would again use the reference values stored in inverted index  722  to retrieve the event data from the field searchable data store and, further, extract the object size field values from the associated events  731 ,  732 ,  733  and  734 . Once, the corresponding object sizes have been extracted (e.g., 2326, 2900, 2920, and 5000), the average can be computed and returned to the user. 
     In one embodiment, instead of explicitly invoking the inverted index in a user-generated query, e.g., by the use of special commands or syntax, the SPLUNK® ENTERPRISE system can be configured to automatically determine if any prior-generated inverted index can be used to expedite a user query. For example, the user&#39;s query may request the average object size (size of the requested gif) requested by clients from IP address “127.0.0.1.” without any reference to or use of inverted index  722 . The search engine, in this case, would automatically determine that an inverted index  722  already exists in the system that could expedite this query. In one embodiment, prior to running any search comprising a field-value pair, for example, a search engine may search though all the existing inverted indexes to determine if a pre-generated inverted index could be used to expedite the search comprising the field-value pair. Accordingly, the search engine would automatically use the pre-generated inverted index, e.g., inverted index  722  to generate the results  725  without any user-involvement that directs the use of the inverted index. 
     Using the reference values in an inverted index to be able to directly access the event data in the field searchable data store and extract further information from the associated event data for further filtering and processing is highly advantageous because it avoids incurring the computation overhead of regenerating the inverted index with additional fields or performing a new search. 
     The data intake and query system includes one or more forwarders that receive raw machine data from a variety of input data sources, and one or more indexers that process and store the data in one or more data stores. By distributing events among the indexers and data stores, the indexers can analyze events for a query in parallel. In one or more embodiments, a multiple indexer implementation of the search system would maintain a separate and respective inverted index for each of the above-described time-specific buckets that stores events for a specific time range. A bucket-specific inverted index includes entries for specific field-value combinations that occur in events in the specific bucket. As explained above, a search head would be able to correlate and synthesize data from across the various buckets and indexers. 
     This feature advantageously expedites searches because instead of performing a computationally intensive search in a centrally located inverted index that catalogues all the relevant events, an indexer is able to directly search an inverted index stored in a bucket associated with the time-range specified in the query. This allows the search to be performed in parallel across the various indexers. Further, if the query requests further filtering or processing to be conducted on the event data referenced by the locally stored bucket-specific inverted index, the indexer is able to simply access the event records stored in the associated bucket for further filtering and processing instead of needing to access a central repository of event records, which would dramatically add to the computational overhead. 
     In one embodiment, there may be multiple buckets associated with the time-range specified in a query. If the query is directed to an inverted index, or if the search engine automatically determines that using an inverted index would expedite the processing of the query, the indexers will search through each of the inverted indexes associated with the buckets for the specified time-range. This feature allows the High Performance Analytics Store to be scaled easily. 
     In certain instances, where a query is executed before a bucket-specific inverted index updates, when the bucket-specific inverted index may not cover all of the events that are relevant to a query, the system can use the bucket-specific inverted index to obtain partial results for the events that are covered by bucket-specific inverted index, but may also have to search through the event data in the bucket associated with the bucket-specific inverted index to produce additional results on the fly. In other words, an indexer would need to search through event data stored in the bucket (that was not yet processed by the indexer for the corresponding inverted index) to supplement the partial results from the bucket-specific inverted index. 
       FIG.  7 D  presents a flowchart illustrating how an inverted index in a pipelined search query can be used to determine a set of event data that can be further limited by filtering or processing in accordance with the disclosed embodiments. 
     At block  742 , a query is received by a data intake and query system. In some embodiments, the query can be receive as a user generated query entered into search bar of a graphical user search interface. The search interface also includes a time range control element that enables specification of a time range for the query. 
     At block  744 , an inverted index is retrieved. Note, that the inverted index can be retrieved in response to an explicit user search command inputted as part of the user generated query. Alternatively, the search engine can be configured to automatically use an inverted index if it determines that using the inverted index would expedite the servicing of the user generated query. Each of the entries in an inverted index keeps track of instances of a specific value in a specific field in the event data and includes references to events containing the specific value in the specific field. In order to expedite queries, in most embodiments, the search engine will employ the inverted index separate from the raw record data store to generate responses to the received queries. 
     At block  746 , the query engine determines if the query contains further filtering and processing steps. If the query contains no further commands, then, in one embodiment, summarization information can be provided to the user at block  754 . 
     If, however, the query does contain further filtering and processing commands, then at block  750 , the query engine determines if the commands relate to further filtering or processing of the data extracted as part of the inverted index or whether the commands are directed to using the inverted index as an initial filtering step to further filter and process event data referenced by the entries in the inverted index. If the query can be completed using data already in the generated inverted index, then the further filtering or processing steps, e.g., a “count” number of records function, “average” number of records per hour etc. are performed and the results are provided to the user at block  752 . 
     If, however, the query references fields that are not extracted in the inverted index, then the indexers will access event data pointed to by the reference values in the inverted index to retrieve any further information required at block  756 . Subsequently, any further filtering or processing steps are performed on the fields extracted directly from the event data and the results are provided to the user at step  758 . 
     As described throughout, it will be understood that although described as being performed by an indexer, these functions can be performed by another component of the system, such as a query coordinator or node. For example, nodes can use inverted indexes to identify relevant data, etc. The inverted indexes can be stored with buckets in a common storage, etc. 
     3.13.5. Accelerating Report Generation 
     In some embodiments, a data server system such as the data intake and query system can accelerate the process of periodically generating updated reports based on query results. To accelerate this process, a summarization engine automatically examines the query to determine whether generation of updated reports can be accelerated by creating intermediate summaries. If reports can be accelerated, the summarization engine periodically generates a summary covering data obtained during a latest non-overlapping time period. For example, where the query seeks events meeting a specified criteria, a summary for the time period includes only events within the time period that meet the specified criteria. Similarly, if the query seeks statistics calculated from the events, such as the number of events that match the specified criteria, then the summary for the time period includes the number of events in the period that match the specified criteria. 
     In addition to the creation of the summaries, the summarization engine schedules the periodic updating of the report associated with the query. During each scheduled report update, the query engine determines whether intermediate summaries have been generated covering portions of the time period covered by the report update. If so, then the report is generated based on the information contained in the summaries. Also, if additional event data has been received and has not yet been summarized, and is required to generate the complete report, the query can be run on these additional events. Then, the results returned by this query on the additional events, along with the partial results obtained from the intermediate summaries, can be combined to generate the updated report. This process is repeated each time the report is updated. Alternatively, if the system stores events in buckets covering specific time ranges, then the summaries can be generated on a bucket-by-bucket basis. Note that producing intermediate summaries can save the work involved in re-running the query for previous time periods, so advantageously only the newer events needs to be processed while generating an updated report. These report acceleration techniques are described in more detail in U.S. Pat. No. 8,589,403, entitled “COMPRESSED JOURNALING IN EVENT TRACKING FILES FOR METADATA RECOVERY AND REPLICATION”, issued on 19 Nov. 2013, U.S. Pat. No. 8,412,696, entitled “REAL TIME SEARCHING AND REPORTING”, issued on 2 Apr. 2011, and U.S. Pat. Nos. 8,589,375 and 8,589,432, both also entitled “REAL TIME SEARCHING AND REPORTING”, both issued on 19 Nov. 2013, each of which is hereby incorporated by reference in its entirety for all purposes. 
     3.14. Security Features 
     The data intake and query system provides various schemas, dashboards, and visualizations that simplify developers&#39; tasks to create applications with additional capabilities. One such application is the an enterprise security application, such as SPLUNK® ENTERPRISE SECURITY, which performs monitoring and alerting operations and includes analytics to facilitate identifying both known and unknown security threats based on large volumes of data stored by the data intake and query system. The enterprise security application provides the security practitioner with visibility into security-relevant threats found in the enterprise infrastructure by capturing, monitoring, and reporting on data from enterprise security devices, systems, and applications. Through the use of the data intake and query system searching and reporting capabilities, the enterprise security application provides a top-down and bottom-up view of an organization&#39;s security posture. 
     The enterprise security application leverages the data intake and query system search-time normalization techniques, saved searches, and correlation searches to provide visibility into security-relevant threats and activity and generate notable events for tracking. The enterprise security application enables the security practitioner to investigate and explore the data to find new or unknown threats that do not follow signature-based patterns. 
     Conventional Security Information and Event Management (SIEM) systems lack the infrastructure to effectively store and analyze large volumes of security-related data. Traditional SIEM systems typically use fixed schemas to extract data from pre-defined security-related fields at data ingestion time and store the extracted data in a relational database. This traditional data extraction process (and associated reduction in data size) that occurs at data ingestion time inevitably hampers future incident investigations that may need original data to determine the root cause of a security issue, or to detect the onset of an impending security threat. 
     In contrast, the enterprise security application system stores large volumes of minimally-processed security-related data at ingestion time for later retrieval and analysis at search time when a live security threat is being investigated. To facilitate this data retrieval process, the enterprise security application provides pre-specified schemas for extracting relevant values from the different types of security-related events and enables a user to define such schemas. 
     The enterprise security application can process many types of security-related information. In general, this security-related information can include any information that can be used to identify security threats. For example, the security-related information can include network-related information, such as IP addresses, domain names, asset identifiers, network traffic volume, uniform resource locator strings, and source addresses. The process of detecting security threats for network-related information is further described in U.S. Pat. No. 8,826,434, entitled “SECURITY THREAT DETECTION BASED ON INDICATIONS IN BIG DATA OF ACCESS TO NEWLY REGISTERED DOMAINS”, issued on 2 Sep. 2014, U.S. Pat. No. 9,215,240, entitled “INVESTIGATIVE AND DYNAMIC DETECTION OF POTENTIAL SECURITY-THREAT INDICATORS FROM EVENTS IN BIG DATA”, issued on 15 Dec. 2015, U.S. Pat. No. 9,173,801, entitled “GRAPHIC DISPLAY OF SECURITY THREATS BASED ON INDICATIONS OF ACCESS TO NEWLY REGISTERED DOMAINS”, issued on 3 Nov. 2015, U.S. Pat. No. 9,248,068, entitled “SECURITY THREAT DETECTION OF NEWLY REGISTERED DOMAINS”, issued on 2 Feb. 2016, U.S. Pat. No. 9,426,172, entitled “SECURITY THREAT DETECTION USING DOMAIN NAME ACCESSES”, issued on 23 Aug. 2016, and U.S. Pat. No. 9,432,396, entitled “SECURITY THREAT DETECTION USING DOMAIN NAME REGISTRATIONS”, issued on 30 Aug. 2016, each of which is hereby incorporated by reference in its entirety for all purposes. Security-related information can also include malware infection data and system configuration information, as well as access control information, such as login/logout information and access failure notifications. The security-related information can originate from various sources within a data center, such as hosts, virtual machines, storage devices and sensors. The security-related information can also originate from various sources in a network, such as routers, switches, email servers, proxy servers, gateways, firewalls and intrusion-detection systems. 
     During operation, the enterprise security application facilitates detecting “notable events” that are likely to indicate a security threat. A notable event represents one or more anomalous incidents, the occurrence of which can be identified based on one or more events (e.g., time stamped portions of raw machine data) fulfilling pre-specified and/or dynamically-determined (e.g., based on machine-learning) criteria defined for that notable event. Examples of notable events include the repeated occurrence of an abnormal spike in network usage over a period of time, a single occurrence of unauthorized access to system, a host communicating with a server on a known threat list, and the like. These notable events can be detected in a number of ways, such as: (1) a user can notice a correlation in events and can manually identify that a corresponding group of one or more events amounts to a notable event; or (2) a user can define a “correlation search” specifying criteria for a notable event, and every time one or more events satisfy the criteria, the application can indicate that the one or more events correspond to a notable event; and the like. A user can alternatively select a pre-defined correlation search provided by the application. Note that correlation searches can be run continuously or at regular intervals (e.g., every hour) to search for notable events. Upon detection, notable events can be stored in a dedicated “notable events index,” which can be subsequently accessed to generate various visualizations containing security-related information. Also, alerts can be generated to notify system operators when important notable events are discovered. 
     The enterprise security application provides various visualizations to aid in discovering security threats, such as a “key indicators view” that enables a user to view security metrics, such as counts of different types of notable events. For example,  FIG.  17 A  illustrates an example key indicators view  1700  that comprises a dashboard, which can display a value  1701 , for various security-related metrics, such as malware infections  1702 . It can also display a change in a metric value  1703 , which indicates that the number of malware infections increased by 63 during the preceding interval. Key indicators view  1700  additionally displays a histogram panel  1704  that displays a histogram of notable events organized by urgency values, and a histogram of notable events organized by time intervals. This key indicators view is described in further detail in pending U.S. patent application Ser. No. 13/956,338, entitled “KEY INDICATORS VIEW”, filed on 31 Jul. 2013, and which is hereby incorporated by reference in its entirety for all purposes. 
     These visualizations can also include an “incident review dashboard” that enables a user to view and act on “notable events.” These notable events can include: (1) a single event of high importance, such as any activity from a known web attacker; or (2) multiple events that collectively warrant review, such as a large number of authentication failures on a host followed by a successful authentication. For example,  FIG.  17 B  illustrates an example incident review dashboard  1710  that includes a set of incident attribute fields  1711  that, for example, enables a user to specify a time range field  1712  for the displayed events. It also includes a timeline  1713  that graphically illustrates the number of incidents that occurred in time intervals over the selected time range. It additionally displays an events list  1714  that enables a user to view a list of all of the notable events that match the criteria in the incident attributes fields  1711 . To facilitate identifying patterns among the notable events, each notable event can be associated with an urgency value (e.g., low, medium, high, critical), which is indicated in the incident review dashboard. The urgency value for a detected event can be determined based on the severity of the event and the priority of the system component associated with the event. 
     3.15. Data Center Monitoring 
     As mentioned above, the data intake and query platform provides various features that simplify the developers&#39; task to create various applications. One such application is a virtual machine monitoring application, such as SPLUNK® APP FOR VMWARE® that provides operational visibility into granular performance metrics, logs, tasks and events, and topology from hosts, virtual machines and virtual centers. It empowers administrators with an accurate real-time picture of the health of the environment, proactively identifying performance and capacity bottlenecks. 
     Conventional data-center-monitoring systems lack the infrastructure to effectively store and analyze large volumes of machine-generated data, such as performance information and log data obtained from the data center. In conventional data-center-monitoring systems, machine-generated data is typically pre-processed prior to being stored, for example, by extracting pre-specified data items and storing them in a database to facilitate subsequent retrieval and analysis at search time. However, the rest of the data is not saved and discarded during pre-processing. 
     In contrast, the virtual machine monitoring application stores large volumes of minimally processed machine data, such as performance information and log data, at ingestion time for later retrieval and analysis at search time when a live performance issue is being investigated. In addition to data obtained from various log files, this performance-related information can include values for performance metrics obtained through an application programming interface (API) provided as part of the vSphere Hypervisor™ system distributed by VMware, Inc. of Palo Alto, Calif. For example, these performance metrics can include: (1) CPU-related performance metrics; (2) disk-related performance metrics; (3) memory-related performance metrics; (4) network-related performance metrics; (5) energy-usage statistics; (6) data-traffic-related performance metrics; (7) overall system availability performance metrics; (8) cluster-related performance metrics; and (9) virtual machine performance statistics. Such performance metrics are described in U.S. patent application Ser. No. 14/167,316, entitled “CORRELATION FOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCE METRICS OF COMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOG DATA FROM THAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan. 2014, and which is hereby incorporated by reference in its entirety for all purposes. 
     To facilitate retrieving information of interest from performance data and log files, the virtual machine monitoring application provides pre-specified schemas for extracting relevant values from different types of performance-related events, and also enables a user to define such schemas. 
     The virtual machine monitoring application additionally provides various visualizations to facilitate detecting and diagnosing the root cause of performance problems. For example, one such visualization is a “proactive monitoring tree” that enables a user to easily view and understand relationships among various factors that affect the performance of a hierarchically structured computing system. This proactive monitoring tree enables a user to easily navigate the hierarchy by selectively expanding nodes representing various entities (e.g., virtual centers or computing clusters) to view performance information for lower-level nodes associated with lower-level entities (e.g., virtual machines or host systems). Example node-expansion operations are illustrated in  FIG.  17 C , wherein nodes  1733  and  1734  are selectively expanded. Note that nodes  1731 - 1739  can be displayed using different patterns or colors to represent different performance states, such as a critical state, a warning state, a normal state or an unknown/offline state. The ease of navigation provided by selective expansion in combination with the associated performance-state information enables a user to quickly diagnose the root cause of a performance problem. The proactive monitoring tree is described in further detail in U.S. Pat. No. 9,185,007, entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATE SORTING”, issued on 10 Nov. 2015, and U.S. Pat. No. 9,426,045, also entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATE SORTING”, issued on 23 Aug. 2016, each of which is hereby incorporated by reference in its entirety for all purposes. 
     The virtual machine monitoring application also provides a user interface that enables a user to select a specific time range and then view heterogeneous data comprising events, log data, and associated performance metrics for the selected time range. For example, the screen illustrated in  FIG.  17 D  displays a listing of recent “tasks and events” and a listing of recent “log entries” for a selected time range above a performance-metric graph for “average CPU core utilization” for the selected time range. Note that a user is able to operate pull-down menus  1742  to selectively display different performance metric graphs for the selected time range. This enables the user to correlate trends in the performance-metric graph with corresponding event and log data to quickly determine the root cause of a performance problem. This user interface is described in more detail in U.S. patent application Ser. No. 14/167,316, entitled “CORRELATION FOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCE METRICS OF COMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOG DATA FROM THAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan. 2014, and which is hereby incorporated by reference in its entirety for all purposes. 
     3.16. IT Service Monitoring 
     As previously mentioned, the data intake and query platform provides various schemas, dashboards and visualizations that make it easy for developers to create applications to provide additional capabilities. One such application is an IT monitoring application, such as SPLUNK® IT SERVICE INTELLIGENCE™, which performs monitoring and alerting operations. The IT monitoring application also includes analytics to help an analyst diagnose the root cause of performance problems based on large volumes of data stored by the data intake and query system as correlated to the various services an IT organization provides (a service-centric view). This differs significantly from conventional IT monitoring systems that lack the infrastructure to effectively store and analyze large volumes of service-related events. Traditional service monitoring systems typically use fixed schemas to extract data from pre-defined fields at data ingestion time, wherein the extracted data is typically stored in a relational database. This data extraction process and associated reduction in data content that occurs at data ingestion time inevitably hampers future investigations, when all of the original data may be needed to determine the root cause of or contributing factors to a service issue. 
     In contrast, an IT monitoring application system stores large volumes of minimally-processed service-related data at ingestion time for later retrieval and analysis at search time, to perform regular monitoring, or to investigate a service issue. To facilitate this data retrieval process, the IT monitoring application enables a user to define an IT operations infrastructure from the perspective of the services it provides. In this service-centric approach, a service such as corporate e-mail may be defined in terms of the entities employed to provide the service, such as host machines and network devices. Each entity is defined to include information for identifying all of the events that pertains to the entity, whether produced by the entity itself or by another machine, and considering the many various ways the entity may be identified in machine data (such as by a URL, an IP address, or machine name). The service and entity definitions can organize events around a service so that all of the events pertaining to that service can be easily identified. This capability provides a foundation for the implementation of Key Performance Indicators. 
     One or more Key Performance Indicators (KPI&#39;s) are defined for a service within the IT monitoring application. Each KPI measures an aspect of service performance at a point in time or over a period of time (aspect KPI&#39;s). Each KPI is defined by a search query that derives a KPI value from the machine data of events associated with the entities that provide the service. Information in the entity definitions may be used to identify the appropriate events at the time a KPI is defined or whenever a KPI value is being determined. The KPI values derived over time may be stored to build a valuable repository of current and historical performance information for the service, and the repository, itself, may be subject to search query processing. Aggregate KPIs may be defined to provide a measure of service performance calculated from a set of service aspect KPI values; this aggregate may even be taken across defined timeframes and/or across multiple services. A particular service may have an aggregate KPI derived from substantially all of the aspect KPI&#39;s of the service to indicate an overall health score for the service. 
     The IT monitoring application facilitates the production of meaningful aggregate KPI&#39;s through a system of KPI thresholds and state values. Different KPI definitions may produce values in different ranges, and so the same value may mean something very different from one KPI definition to another. To address this, the IT monitoring application implements a translation of individual KPI values to a common domain of “state” values. For example, a KPI range of values may be 1-100, or 50-275, while values in the state domain may be ‘critical,’ ‘warning,’ ‘normal,’ and ‘informational’. Thresholds associated with a particular KPI definition determine ranges of values for that KPI that correspond to the various state values. In one case, KPI values 95-100 may be set to correspond to ‘critical’ in the state domain. KPI values from disparate KPI&#39;s can be processed uniformly once they are translated into the common state values using the thresholds. For example, “normal 80% of the time” can be applied across various KPI&#39;s. To provide meaningful aggregate KPI&#39;s, a weighting value can be assigned to each KPI so that its influence on the calculated aggregate KPI value is increased or decreased relative to the other KPI&#39;s. 
     One service in an IT environment often impacts, or is impacted by, another service. The IT monitoring application can reflect these dependencies. For example, a dependency relationship between a corporate e-mail service and a centralized authentication service can be reflected by recording an association between their respective service definitions. The recorded associations establish a service dependency topology that informs the data or selection options presented in a GUI, for example. (The service dependency topology is like a “map” showing how services are connected based on their dependencies.) The service topology may itself be depicted in a GUI and may be interactive to allow navigation among related services. 
     Entity definitions in the IT monitoring application can include informational fields that can serve as metadata, implied data fields, or attributed data fields for the events identified by other aspects of the entity definition. Entity definitions in the IT monitoring application can also be created and updated by an import of tabular data (as represented in a CSV, another delimited file, or a search query result set). The import may be GUI-mediated or processed using import parameters from a GUI-based import definition process. Entity definitions in the IT monitoring application can also be associated with a service by means of a service definition rule. Processing the rule results in the matching entity definitions being associated with the service definition. The rule can be processed at creation time, and thereafter on a scheduled or on-demand basis. This allows dynamic, rule-based updates to the service definition. 
     During operation, the IT monitoring application can recognize notable events that may indicate a service performance problem or other situation of interest. These notable events can be recognized by a “correlation search” specifying trigger criteria for a notable event: every time KPI values satisfy the criteria, the application indicates a notable event. A severity level for the notable event may also be specified. Furthermore, when trigger criteria are satisfied, the correlation search may additionally or alternatively cause a service ticket to be created in an IT service management (ITSM) system, such as a systems available from ServiceNow, Inc., of Santa Clara, Calif. 
     SPLUNK® IT SERVICE INTELLIGENCE™ provides various visualizations built on its service-centric organization of events and the KPI values generated and collected. Visualizations can be particularly useful for monitoring or investigating service performance. The IT monitoring application provides a service monitoring interface suitable as the home page for ongoing IT service monitoring. The interface is appropriate for settings such as desktop use or for a wall-mounted display in a network operations center (NOC). The interface may prominently display a services health section with tiles for the aggregate KPI&#39;s indicating overall health for defined services and a general KPI section with tiles for KPI&#39;s related to individual service aspects. These tiles may display KPI information in a variety of ways, such as by being colored and ordered according to factors like the KPI state value. They also can be interactive and navigate to visualizations of more detailed KPI information. 
     The IT monitoring application provides a service-monitoring dashboard visualization based on a user-defined template. The template can include user-selectable widgets of varying types and styles to display KPI information. The content and the appearance of widgets can respond dynamically to changing KPI information. The KPI widgets can appear in conjunction with a background image, user drawing objects, or other visual elements, that depict the IT operations environment, for example. The KPI widgets or other GUI elements can be interactive so as to provide navigation to visualizations of more detailed KPI information. 
     The IT monitoring application provides a visualization showing detailed time-series information for multiple KPI&#39;s in parallel graph lanes. The length of each lane can correspond to a uniform time range, while the width of each lane may be automatically adjusted to fit the displayed KPI data. Data within each lane may be displayed in a user selectable style, such as a line, area, or bar chart. During operation a user may select a position in the time range of the graph lanes to activate lane inspection at that point in time. Lane inspection may display an indicator for the selected time across the graph lanes and display the KPI value associated with that point in time for each of the graph lanes. The visualization may also provide navigation to an interface for defining a correlation search, using information from the visualization to pre-populate the definition. 
     The IT monitoring application provides a visualization for incident review showing detailed information for notable events. The incident review visualization may also show summary information for the notable events over a time frame, such as an indication of the number of notable events at each of a number of severity levels. The severity level display may be presented as a rainbow chart with the warmest color associated with the highest severity classification. The incident review visualization may also show summary information for the notable events over a time frame, such as the number of notable events occurring within segments of the time frame. The incident review visualization may display a list of notable events within the time frame ordered by any number of factors, such as time or severity. The selection of a particular notable event from the list may display detailed information about that notable event, including an identification of the correlation search that generated the notable event. 
     The IT monitoring application provides pre-specified schemas for extracting relevant values from the different types of service-related events. It also enables a user to define such schemas. 
     4.0. Data Fabric Service (DFS) 
     The capabilities of a data intake and query system are typically limited to resources contained within that system. For example, the data intake and query system has search and analytics capabilities that are limited in scope to the indexers responsible for storing and searching a subset of events contained in their corresponding internal data stores. 
     Even if a data intake and query system has access to external data stores that may include data relevant to a query, the data intake and query system typically has limited capabilities to process the combination of partial search results from the indexers and external data sources to produce comprehensive search results. In particular, the search head of a data intake and query system may retrieve partial search results from external data systems over a network. The search head may also retrieve partial results from its indexers, and combine those partial search results with the partial results of the external data sources to produce final results for a query. 
     For example, the search head can implement map-reduce techniques, where each data source returns partial search results and the search head can combine the partial search results to produce the final results of a query. However, obtaining results in this manner from distributed data systems including internal data stores and external data stores has limited value because the search head can act as a bottleneck for processing complex search queries on distributed data systems. The bottleneck effect at the search head worsens as the number of distributed data systems increases. Furthermore, even without processing queries on distributed data systems, the search head  210  and the indexers  206  can act as bottlenecks due to the number of queries received by the data intake and query system  108  and the amount of processing done by the indexers during data ingestion, indexing, and search. 
     Embodiments of the disclosed data fabric service (DFS) system overcome the aforementioned drawbacks by expanding on the capabilities of a data intake and query system to enable application of a query across distributed data systems, which may also be referred to as dataset sources, including internal data stores coupled to indexers (illustrated in  FIG.  33   ), external data stores coupled to the data intake and query system over a network (illustrated in  FIGS.  33 ,  46 ,  48   ), common storage (illustrated in  FIGS.  46 ,  48   ), query acceleration data stores (e.g., query acceleration data store  3308  illustrated in  FIGS.  33 ,  46 ,  48   ), ingested data buffers (illustrated in  FIG.  48   ) that include ingested streaming data. Moreover, the disclosed embodiments are scalable to accommodate application of a query on a growing number of diverse data systems. 
     In certain embodiments, the disclosed DFS system extends the capabilities of the data intake and query system and mitigates the bottleneck effect at the search head by including one or more query coordinators communicatively coupled to worker nodes distributed in a big data ecosystem. In some embodiments, the worker nodes can be communicatively coupled to the various dataset sources (e.g., indexers, common storage, external data systems that contain external data stores, ingested data buffers, query acceleration data stores, etc.) 
     The data intake and query system can receive a query input by a user at a client device via a search head. The search head can coordinate with a search process master and/or one or more query coordinators (the search process master and query coordinators can collectively referred to as a search process service) to execute a search scheme applied to one or more dataset sources (e.g., indexers, common storage, ingested data buffer, query acceleration data store, external data stores, etc.). The worker nodes can collect, process, and aggregate the partial results from the dataset sources, and transfer the aggregate results to a query coordinator. In some embodiments, the query coordinator can operate on the aggregate results, and send finalized results to the search head, which can render the results of the query on a display device. 
     Hence, the search head in conjunction with the search process master and query coordinator(s) can apply a query to any one or more of the distributed dataset sources. The worker nodes can act in accordance with the instructions received by a query coordinator to obtain relevant datasets from the different dataset sources, process the datasets, aggregate the partial results of processing the different datasets, and communicate the aggregated results to the query coordinator, or elsewhere. In other words, the search head of the data intake and query system can offload at least some query processing to the query coordinator and worker nodes, to both obtain the datasets from the dataset sources and aggregate the results of processing the different datasets. This system is scalable to accommodate any number of worker nodes communicatively coupled to any number and types of data sources. 
     Thus, embodiments of the DFS system can extend the capabilities of a data intake and query system by leveraging computing assets from anywhere in a big data ecosystem to collectively execute queries on diverse data systems regardless of whether data stores are internal of the data intake and query system and/or external data stores that are communicatively coupled to the data intake and query system over a network. 
     4.1. DFS System Architecture 
       FIG.  18    is a system diagram illustrating a DFS system architecture in which an embodiment may be implemented. The DFS system  200  includes a data intake and query system  202  communicatively coupled to a network of distributed components that collectively form a big data ecosystem. The data intake and query system  202  may include the components of data intake and query systems discussed above including any combination of forwarders, indexers, data stores, and a search head. However, the data intake and query system  202  is illustrated with fewer components to aid in understanding how the disclosed embodiments extend the capabilities of data intake and query systems to apply search queries and analytics operations on distributed data systems including internal data systems (e.g., indexers with associated data stores) and/or external data systems in a big data ecosystem. 
     The data intake and query system  202  includes a search head  210  communicatively coupled to multiple peer indexers  206  (also referred to individually as indexer  206 ). Each indexer  206  is responsible for storing and searching a subset of events contained in a corresponding data store (not shown). The peer indexers  206  can analyze events for a search query in parallel. For example, each indexer  206  can return partial results in response to a search query as applied by the search head  210 . 
     The disclosed technique expands the capabilities of the data intake and query system  202  to obtain and harmonize search results from external data sources  209 , alone or in combination with the partial search results of the indexers  206 . More specifically, the data intake and query system  202  runs various processes to apply a search query to the indexers  206  as well as external data sources  209 . For example, a daemon  211  of the data intake and query system  202  can operate as a background process that coordinates the application of a search query on the indexers and/or the external data stores. As shown, the daemon  211  includes software components for the search head  210  and indexers  206  to interface with a DFS master  212  and a distributed network of worker nodes  214 . In some embodiments, the worker nodes  214  may be considered external to the data intake and query system  202 . In certain embodiments, the worker nodes  214  may be considered part of the data intake and query system  202 . 
     The DFS master  212  is communicatively coupled to the search head  210  via the daemon  211 - 3 . In some embodiments, the DFS master  212  can include software components running on a device of any system, including the data intake and query system  202 . As such, the DFS master  212  can include software and underlying logic for establishing a logical connection to the search head  210  when external data systems need to be searched. The DFS master  212  is part of the DFS search service (“search service”) that includes a search service provider  216  (also referred to as a query coordinator), which interfaces with the worker nodes  214 . 
     Although shown as separate components, the DFS master  212  and the search service provider  216  are components of the search service that may reside on the same machine, or may be distributed across multiple machines. In some embodiments, running the DFS master  212  and the search service provider  216  on the same machine can increase performance of the DFS system by reducing communications over networks. As such, the search head  210  can interact with the search service residing on the same machine or on different machines. For example, the search head  210  can dispatch requests for search queries to the DFS master  212 , which can spawn search service providers  216  of the search service for each search query. 
     Other functions of the search service provider  216  can include providing data isolation across different searches based on role/access control, as well as fault tolerance (e.g., localized to a search head). For example, if a search operation fails, then its spawned search service provider may fail but other search service providers for other searches can continue to operate. 
     The search head  210  can analyze a query and determine that the DFS system  200  can execute the query. Accordingly, the search head  210  can send the query to the query master  212 , which can send it to, or spawn, a search service provider  216 . The search service provider can define a search scheme in response to a received search query that requires searching both the indexers  206  and the external data sources  209 . A portion of the search scheme can be applied  210  to the indexers  206  and another portion of the search scheme can be communicated to the worker nodes  214  for application to the external data sources  209 . The search service provider  216  can collect an aggregate of partial search results of the indexers  206  and of the external data sources  209  from the worker nodes  214 , and communicate the aggregate partial search results to the search head  210 . In some embodiments, the DFS master  212 , search head  210 , or the worker nodes  214  can produce the final search results, which the search head  210  can cause to be presented on a user interface of a display device. 
     More specifically, the worker nodes  214  can act as agents of the DFS master  212  via the search service provider  216 , which can act on behalf of the search head  210  to apply a search query to distributed data systems. For example, the DFS master  212  can manage different search operations and balance workloads in the DFS system  200  by keeping track of resource utilization while the search service provider  216  is responsible for executing search operations and obtaining the search results. 
     For example, the search service provider  216  can cause the worker nodes  214  to apply a search query to the external data sources  209 . The search service provider  216  can also cause the worker nodes  214  to collect the partial search results from the indexers  206  and/or the external data sources  209  over the computer network. Moreover, the search service provider  216  can cause the worker nodes  214  to aggregate the partial search results collected from the indexers  206  and/or the external data sources  209 . 
     Hence, the search head  210  can offload at least some processing to the worker nodes  214  because the distributed worker nodes  214  can extract partial search results from the external data sources  209 , and collect the partial search results of the indexers  206  and the external data sources  209 . Moreover, the worker nodes  214  can aggregate the partial search results collected from the diverse data systems and transfer them to the search service, which can finalize the search results and send them to the search head  210 . Aggregating the partial search results of the diverse data systems can include combining partial search results, arranging the partial search results in an ordered manner, and/or performing operations derive other search results from the collected partial search results (e.g., transform the partial search results). 
     Once a logical connection is established between the search head  210 , the DFS master  212 , the search service provider  216 , and the worker nodes  214 , control and data flows can traverse the components of the DFS system  200 . For example, the control flow can include instructions from the DFS master  212  to the worker nodes  214  to carry out the operations detailed further below. Moreover, the data flow can include aggregate partial search results transferred to the search service provider  216  from the worker nodes  214 . Further, the partial search results of the indexers  206  can be transferred by peer indexers to the worker nodes  214  in accordance with a parallel export technique. A more detailed description of the control flow, data flow, and parallel export techniques are provided further below. 
     In some embodiments, the DFS system  200  can use a redistribute operator of a data intake and query system. The redistribute operator can distribute data in a sharded manner to the different worker nodes  214 . Use of the redistribute operator may be more efficient than the parallel exporting because it is closely coupled to the existing data intake and query system. However, the parallel exporting techniques have capabilities to interoperate with open source systems other than the worker nodes  214 . Hence, use of the redistribute operator can provide greater efficiency but less interoperability and flexibility compared to using parallel export techniques. 
     The worker nodes  214  can be communicatively coupled to each other, and to the external data sources  209 . Each worker node  214  can include one or more software components or modules  218  (“modules”) operable to carry out the functions of the DFS system  200  by communicating with the search service provider  216 , the indexers  206 , and the external data sources  209 . The modules  218  can run on a programming interface of the worker nodes  214 . An example of such an interface is APACHE SPARK, which is an open source computing framework that can be used to execute the worker nodes  214  with implicit parallelism and fault-tolerance. 
     In particular, SPARK includes an application programming interface (API) centered on a data structure called a resilient distributed dataset (RDD), which is a read-only multiset of data items distributed over a cluster of machines (e.g., the devices running the worker nodes  214 ). The RDDs function as a working set for distributed programs that offer a form of distributed shared memory. 
     Thus, the search service provider  216  can act as a manager of the worker nodes  214 , including their distributed data storage systems, to extract, collect, and store partial search results via their modules  218  running on a computing framework such as SPARK. However, the embodiments disclosed herein are not limited to an implementation that uses SPARK. Instead, any open source or proprietary computing framework running on a computing device that facilitates iterative, interactive, and/or exploratory data analysis coordinated with other computing devices can be employed to run the modules  218  for the DFS master  212  to apply search queries to the distributed data systems. 
     Accordingly, the worker nodes  214  can harmonize the partial search results of a distributed network of data storage systems, and provide those aggregated partial search results to the search service provider  216 . In some embodiments, the search service provider  216  or DFS master  212  can further operate on the aggregated partial search results to obtain final results that are communicated to the search head  210 , which can output the search results as reports or visualizations on a display device. 
     The DFS system  200  is scalable to accommodate any number of worker nodes  214 . As such, the DFS system can scale to accommodate any number of distributed data systems upon which a search query can be applied and the search results can be returned to the search head and presented in a concise or comprehensive way for an analyst to obtain insights into big data that is greater in scope and provides deeper insights compared to existing systems. 
     4.2. DFS System Operations 
       FIG.  19    is an operation flow diagram illustrating an example of an operation flow of the DFS system  200 . The operation flow  2100  includes control flows and data flows of the data intake and query system  202 , the DFS master  212  and/or the search service provider  216  (the DFS master  212  and search service provider  216  collectively the “search service  220 ”), one or more worker nodes  214 , and/or one or more external data sources  209 . A combination of the search service  220  and the worker nodes  214  collectively enable the data fabric services that can be implemented on the distributed data systems including, for example, the data intake and query system  202  and the external data sources  209 . 
     In step  2102 , the search head  210  of the data intake and query system  202  receives a search query. For example, an analyst may submit a search query to the search head  210  over a network from an application (e.g., web browser) running on a client device, through a network portal (e.g., website) administered by the data intake and query system  202 . In another example, the search head  210  may receive the search query in accordance with a schedule of search queries. The search query can be expressed in a variety of languages such as a pipeline search language, a structured query language, etc. 
     In step  2104 , the search head  210  processes the search query to determine whether the DFS system  200  is to handle the search query. In some embodiments, if the search query only requires searching the indexers  206 , the search head  210  can conduct the search on the indexers  206  by using, for example, map-reduce techniques without invoking or engaging the DFS system. In some embodiments, however, the search head  210  can invoke or engage the DFS system to utilize the worker nodes  214  to search the indexers  206  alone, search the external data sources  209  alone, or search both and harmonize the partial search results of the indexers  206  alone, and return the search results to the search head  210  via the search service  220 . 
     If, search head  210  determines that the DFS system  200  is to handle the search query, then the search head  210  can invoke and engage the DFS system  200 . Accordingly, in some embodiments, the search head  210  can engage the search service  220  when a search query is to be applied to at least one external data system, such as a combination of the indexers  206  and at least one of the external data sources  209 , or is otherwise to be handled by the DFS system  200 .  210  The search head  210  can pass search query to the DFS master  212 , which can create (e.g., spawn) a search service provider (e.g., search service provider  216 ) to conduct the search. 
     In some embodiments, the DFS system  200  can be launched by using a modular input, which refers to a platform add-on of the data intake and query system  202  that can be accessed in a variety of ways such as, for example, over the Internet on a network portal. For example, the search head  210  can use a modular input to launch the search service  220  and worker nodes  214  of the DFS system  200 . In some embodiments, a modular input can be used to launch a monitor function used to monitor nodes of the DFS system. In the event that a launched service or node fails, the monitor allows the search head to detect the failed service or node, and re-launch the failed service or node or launch or reuse another launched service or node to provide the functions of the failed service or node. In some embodiments, the monitor function for monitoring nodes can be launched and controlled by the search service provider  216 . 
     In step  2104 , the search head  210  executes a search phase generation process to define a search scheme based on the scope of the search query. The search phase generation process involves an evaluation of the scope of the search query to define one or more phases to be executed by the data intake and query system  202  and/or the DFS system, to obtain search results that would satisfy the search query. The search phases, or layers, may include a combination of phases for initiating search operations, searching the indexers  206 , searching the external data sources  209 , and/or finalizing search results for return back to the search head  210 . 
     In some embodiments, the combination of search phases can include phases for operating on the partial search results retrieved from the indexers  206  and/or the external data sources  209 . For example, a search phase may require correlating or combining partial search results of the indexers  206  and/or the external data sources  209 . In some embodiments, a combination of phases may be ordered as a sequence that requires an earlier phase to be completed before a subsequent phase can begin. However, the disclosure is not limited to any combination or order of search phases. Instead, a search scheme can include any number of search phases arranged in any order that could be different from another search scheme applied to the same or another arrangement or subset of data systems. 
     For example, a first search phase may be executed by the search head  210  to extract partial search results from the indexers  206 . A second search phase may be executed by the worker nodes  214  to extract and collect partial search results from the external data sources  209 . A third search phase may be executed by the indexers  206  and worker nodes  214  to export partial search results in parallel to the worker nodes  214  from the (peer) indexers  206 . As such, the third phase involves collecting the partial search results from the indexers  206  by the worker nodes  214 . A fourth search phase may be executed by the worker nodes  214  to aggregate (e.g., combine and/or operate on) the partial search results of the indexers  206  and/or the worker nodes  214 . A sixth and seventh phase may involve transmitting the aggregate partial search results to the search service  220 , and operating on the aggregate partial search results to produce final search results, respectively. The search results can then be transmitted to the search head  210 . In some cases, an eighth search phase may involve further operating on the search results by the search head  210  to obtain final search results that can be, for example, rendered on a user interface of a display device. 
     In step  2106 , the search head  210  initiates a communications search protocol that establishes a logical connection with the worker nodes  214  via the search service  220 . Specifically, the search head  210  may communicate information to the search service  220  including a portion of the search scheme to be performed by the worker nodes  214 . For example, a portion of the search scheme transmitted to the DFS master  212  may include search phase(s) to be performed by the DFS master  212  and the worker nodes  214 . The information may also include specific control information enabling the worker nodes  214  to access the indexers  206  as well as the external data sources  209  subject to the search query. 
     In step  2108 , the search service  220  can define an executable search process performed by the DFS system. For example, the DFS master  212  or the search service provider  216  can define a search process as a logical directed acyclic graph (DAG) based on the search phases included in the portion of the search scheme received from the search head  210 . 
     The DAG includes a finite number of vertices and edges, with each edge directed from one vertex to another, such that there is no way to start at any vertex and follow a consistently-directed sequence of edges that eventually loops back to the same vertex. Here, the DAG can be a directed graph that defines a topological ordering of the search phases performed by the DFS system. As such, a sequence of the vertices represents a sequence of search phases such that every edge is directed from earlier to later in the sequence of search phases. For example, the DAG may be defined based on a search string for each phase or metadata associated with a search string. The metadata may be indicative of an ordering of the search phases such as, for example, whether results of any search string depend on results of another search string such that the later search string must follow the former search string sequentially in the DAG. 
     In step  2110 , the search head  210  starts executing local search phases that operate on the indexers  206  if the search query requires doing so. If the scope of the search query requires searching at least one external data system, then, in step  2112 , the search head  210  sends information to the DFS master  212  triggering execution of the executable search process defined in step  2108 . 
     In step  2114 , the search service  220  starts executing the search phases that cause the worker nodes  214  to extract partial search results from the external data stores  209  and collect the extracted partial search results at the worker nodes  214 , respectively. For example, the search service  220  can start executing the search phases of the DAG that cause the worker nodes  214  to search the external data sources  209 . Then, in step  2116 , the worker nodes  214  collect the partial search results extracted from the external data sources  209 . 
     The search phases executed by the DFS system can also cause the worker nodes  214  to communicate with the indexers  206 . For example, in step  2118 , the search head  210  can commence a search phase that triggers a remote pipeline executed on the indexers  206  to export their partial search results to the worker nodes  214 . As such, the worker nodes  214  can collect the partial search results of the indexers  206 . However, if the search query does not require searching the indexers  206 , then the search head  210  may bypass triggering the pipeline of partial search results from the indexers  206 . 
     In step  2122 , the worker nodes  214  can aggregate the partial search results and send them to the search service  220 . For example, the search service provider  216  can begin collecting the aggregated search results from the worker nodes  214 . The aggregation of the partial search results may include combining the partial search results of indexers  206 , the external data stores  209 , or both. In some embodiments, the aggregated partial search results can be time-ordered or unordered depending on the requirements of the type of search query. 
     In some embodiments, aggregation of the partial search results may involve performing one or more operations on a combination of partial search results. For example, the worker nodes  214  may operate on a combination of partial search results with an operator to output a value derived from the combination of partial search results. This transformation may be required by the search query. For example, the search query may be an average or count of data events that include specific keywords. In another example, the transformation may involve determining a correlation among data from different data sources that have a common keyword. As such, transforming the search results may involve creating new data derived from the partial search results obtained from the indexers  206  and/or external data sources  209 . 
     In step  2124 , a data pipeline is formed to the search head  210  through the search service  220  once the worker nodes  214  have received the partial search results from the indexers  206  and the external data stores  209 , and aggregated the partial search results (e.g., and transformed the partial search results). 
     In step  2126 , the aggregate search received by the search service  220  may optionally be operated on to produce final search results. For example, the aggregate search results may include different statistical values of partial search results collected from different worker nodes  214 . The search service  220  may operate on those statistical values to produce search results that reflect statistical values of the statistical values obtained from the all the worker nodes  214 . 
     As such, the produced search results can be transferred in a big data pipeline to the search head  210 . The big data pipeline is essentially a pipeline of the data intake and query system  202  extended into the big data ecosystem. Hence, the search results are transmitting to the search head  210  where the search query was received by a user. Lastly, in step  2128 , the search head  210  can render the search results or data indicative of the search results on a display device. For example, the search head  210  can make the search results available for visualizing on a user interface rendered via a computer portal. 
     It will be understood that fewer or more steps can be included in the operation flow  2100 . Further, some operations can be performed by different components of the system. In some embodiments, for example, some of the tasks described as being performed by the search head  210  can be performed by the search service  220 , such as the search service provider  216 . As a non-limiting example, step  2104  can be omitted and steps  2110 ,  2112 , and  2118  can be performed by the search service provider  216 . For example, upon receiving the search query at step  2102 , the search head  210  can determine that the DFS system  200  will handle the query. Accordingly, at  2106 , the search head can communicate the search query to the search service  220  to initiate the search. In turn, the search service provider  216  can define the search scheme  2104  and search process  2108 . As part of defining the search scheme and process  2108 , the search service provider  216  can determine whether any indexers  206  or external data sources  209  will be accessed. Once the scheme and process are defined, the search service provider  216  can trigger a search of the indexers ( 2110 ) and an external search of the external data sources ( 2112 ). The partial search results from both can be communicated to the worker nodes  214  for processing ( 2116 ,  2118 ), which can aggregate them together ( 2122 ). The results can then be provided to the search service  220  ( 2124 ), further processed ( 2126 ), and then communicated to the search head  210  for rendering for the client device ( 2128 ). In some cases, the further processing  2126  performed by the search service  220  can include additional transforms on the results received from the worker nodes  214  based on the query. Accordingly, in such an embodiment, the system can delegate some of the search head  210  processing to the search service  220 , thereby freeing up the search head  210  to handle additional queries. 
     5.0. Parallel Export Techniques 
     The disclosed embodiments include techniques for exporting partial search results in parallel from peer indexers of a data intake and query system to the worker nodes. In particular, partial search results (e.g., time-indexed events) obtained from peer indexers can be exported in parallel from the peer indexers to worker nodes. Exporting the partial search results from the peer indexers in parallel can improve the rate at which the partial search results are transferred to the worker nodes for subsequent combination with partial search results of the external data systems. As such, the rate at which the search results of a search query can be obtained from the distributed data system can be improved by implementing parallel export techniques. 
       FIG.  20    is an operation flow diagram illustrating an example of a parallel export operation performed in a DFS system according to some embodiments of the present disclosure. The operation  2200  for parallel exporting of partial search results from peer indexers  206  begins by processing a search query that requires transferring of partial search results from the peer indexers  206  to the worker nodes  214 . 
     In step  2202 , the search head  210  receives a search query as, for example, input by a user of a client device. In step  2204 , the search head  210  processes the search query to determine whether internal data stores  222  of peer indexers  206  must be searched for partial search results. If so, in step  2206 , the search head  210  executes a process to search the peer indexers  206  and retrieve the partial search results. In step  2209 , each peer indexer  206  can return its partial search results retrieved from respective internal data stores  222 . 
     In step  2210 , the partial search results (e.g., time-indexed events) obtained by the peer indexers  206  can be sharded into chunks of events (“event chunks”). Sharding involves partitioning large data sets into smaller, faster, more easily managed parts called data shards. The sharded partitions can be determined from policies, which can be based on hash values by default. Accordingly, the retrieved events can be grouped into chunks (e.g., micro-batches) based on a value associated with a search query and/or the corresponding retrieved events. For example, the retrieved events can be sharded in chunks based on the field names passed as part of a search query process of the data intake and query system. The event chunks can then be exported from the peer indexers  206  in parallel over the network to the worker nodes  214 . 
     If time-ordering is required, the parallel exporting technique can include a mechanism to reconstruct the ordering of event chunks at the worker nodes  214 . In particular, the order from which the event chunks flowed from peer indexers  206  can be tracked to enable collating the chunks in time order at the worker nodes  214 . For example, metadata of event chunks can be preserved when parallel exporting such that the chunks can be collated by the worker nodes  214  that receive the event chunks. Examples of the metadata include SearchResultsInfo (SRI) (a data structure of SPLUNK® which carries control and meta information for the search operations) or timestamps indicative of, for example, the times when respective events or event chunks started flowing out from the peer indexers  206 . If time ordering is not required, preserving the time ordering of chunks by using timestamps may be unnecessary. 
     The parallel exporting technique can be modified in a variety of ways to improve performance of the DFS system. For example, in step  2214 , the event chunks can be load balanced across the peer indexers  206  and/or receiving worker nodes  214  to improve network efficiency and utilization of network resources. In particular, a dynamic list of receivers (e.g., worker nodes  214 ) can be maintained by software running on hardware implementing the DFS system. The list may indicate a current availability of worker nodes to receive chunks from export processors of the peer indexers  206 . The list can be updated dynamically to reflect the availability of the worker nodes  214 . Further, parameters on the list indicative of the availability of the worker nodes  214  can be passed to the export processers periodically or upon the occurrence of an event (e.g., a worker node  214  becomes available). The export processers can then perform a load balancing operation on the event chunks over the receiving worker nodes  214 . 
     The worker nodes  214  may include driver programs that consume the events and event chunks. In some embodiments, the worker nodes  214  can include a software development kit (SDK) that allows third party developers to control the consumption of events from the peer indexers  206  by the worker nodes  214 . As such, third party developers can control the drivers causing the consumption of events and event chunks from the peer indexers  206  by the worker nodes  214 . Lastly, in step  2216 , the event chunks are exported from the peer indexers  206  in parallel to the worker nodes  214 . 
     In some embodiments, the rate of exporting events or event chunks in parallel by the peer indexers  206  can be based on an amount of shared memory available to the worker nodes  214 . Accordingly, techniques can be employed to reduce the amount of memory required to store transferred events. For example, when the worker nodes  214  are not local (e.g., remote from the peer indexers  206 ), compressed payloads of the event chunks can be transferred to improve performance. 
     Thus, the disclosed DFS system can provide abig data pipeline and native processor as a mechanism to execute infrastructure, analytics, and domain-based processors based on data from one or more external data sources over different compute engines. In addition, the mechanism can execute parallelized queries to extract results from external systems. 
     It will be understood that fewer or more steps can be included in the operation flow  2100 . Further, some operations can be performed by different components of the system. In some embodiments, for example, some of the tasks described as being performed by the search head  210  can be performed by the search service  220 , such as the search service provider  216 . 
     As a non-limiting example, the search head  210  can process the search query to determine whether the search query is to be handled by the DFS system  202 . For example, in some embodiments, the search head  210  can handle queries for the indexers  206  and in other embodiments, the search service  220  can handle queries for the indexers  206 . Based on a determination that the search process is to handle the search query, the search head  210  can forward the query to the search service  220 . The search service provider  216  can further process the query ( 2210 ) and determine that the search includes searching the indexer  206 . As such, the search service provider can execute a process to search the peer indexers  206  and provide the partial search results to the worker nodes  214 , or instruct the worker nodes  214  to instruct the indexers  206  to execute the search. Steps  2210 ,  2212 ,  2214 ,  2216 , and  2218  can then perform as illustrated such that the partial search results are exported to the worker nodes  214  for further processing. 
     6.0. DFS Query Processing 
     The disclosed embodiments include techniques to process search queries in different ways by the DFS system depending on the type of search results sought in response to a search query. In other words, a data intake and query system can receive search queries that cause the DFS system to process the search queries differently based on the search results sought in accordance with the search queries. For example, some search queries may require ordered search results, and an order of the search results may be unimportant for other search queries. 
     To obtain ordered search results, a search query executed on internal data sources (e.g., indexers) and/or external data sources may require sorting and organizing timestamped partial search results across the multiple diverse data sources. However, the multiple internal or external data sources may not store timestamped data. That is, some data sources may store time-ordered data while other data sources may not store time-ordered data, which prevents returning time-ordered search results for a search query. The disclosed embodiments provide techniques for harmonizing time-ordered and unordered data from across multiple internal or external data sources to provide time-ordered search results. 
     In other instances, a search query may require search results that involve performing a transformation of data collected from multiple internal and/or external data sources. The transformed data can be provided as the search results in response to the search query. In some cases, the search query may be agnostic to the ordering of the search results. For example, the search results of a search query may require counts of different types of events generated over the same period of time. Hence, search results that satisfy the search query could be ordered or unordered counts. As such, there is no requirement to maintain the time order of the partial search results obtained from data systems subject to the count search query. Thus, the techniques described below provide mechanisms to obtain search result from the big data ecosystem that are transformed, time-ordered, unordered, or any combinations of these types of search results. 
     6.1. Ordered Search Results 
     The disclosed embodiments include techniques to obtain ordered search results based on partial search results from across multiple diverse internal and/or external data sources. The ordering of the search results may be with respect to a parameter associated with the partial search results. An example of a parameter includes time. As such, the disclosed technique can provide a time-ordered search result based on partial search results obtained from across multiple internal and/or external data sources. Moreover, the disclosed technique can provide time-ordered search results regardless of whether the partial search results obtained from the diverse data sources are timestamped. 
     An ordered search (e.g., ordered data execution) can be referred to as “cursored” mode of data access. According to this mode of data access, the DFS system can execute time-ordered searches or retrieve events from multiple data sources and presents the events in a time ordered manner. For searches involving only local data sources, the DFS system can implement a micro-batching mechanism based on the event time across worker nodes. The DFS system can ensure that per peer ordering is enforced across the worker nodes and final collation is performed at a local search head or search service provider. In case of event retrieval from multiple data sources, the DFS system can maintain per source ordering prior to ordered collation in the local search head or search service provider. 
       FIG.  21    is a flowchart illustrating a method  2300  performed the DFS system to obtain time-ordered search results in response to a cursored search query according to some embodiments of the present disclosure. As described below, the method  2300  for processing cursored search queries can involve a micro-batching process executed by worker nodes to ensure time orderliness of partial search results obtained from data sources. 
     In step  2302 , one or more worker nodes collect partial search results from the internal and/or external data sources. For example, the worker node may collect partial search results corresponding to data having a data structure as specified by the search query. In another example, the worker nodes may query an external data source for partial search results based on specific keywords specified by a cursored search query, and collect the partial search results. The worker nodes may also collect partial search results from indexers, which were returned in response to application of the search query by the search head (or search service provider) to the indexers. In some embodiments, the partial search results may be communicated from each data source to the worker node in chunks (e.g., micro-batches). 
     In step  2304 , the worker nodes perform deserialization of the partial search results obtained from the data sources. Specifically, partial search results transmitted by the data sources could been serialized such that data objects were converted into a stream of bytes in order to transmit the object, or store the object in memory. The serialization process allows for saving the state of an object in order to reconstruct it at the worker node by using reverse process of deserialization. 
     In step  2306 , the worker nodes receive the partial search results collected from the data sources and transform them into a specified format. As such, partial search results in diverse formats can be transformed into a common specified format. The specified format may be specified to facilitate processing by the worker nodes. Hence, diverse data types obtained from diverse data sources can be transformed into a common format to facilitate subsequent aggregation across all the partial search results obtained in response to the search query. As a result, the partial search results obtained by the worker nodes can be transformed into, for example, data events having structures that are compatible to the data intake and query system. 
     In step  2308 , the worker nodes may determine whether the partial search results are associated with respective time values. For example, the worker nodes may determine that events or event chunks from an internal data source are timestamped as shown in  FIG.  2   , but events or event chunks from an external data source may not be timestamped. The timestamped events may also be marked with an “OriginType” (e.g., mysql-origin, cloud-aws-s), “SourceType” (e.g., cvs, json, sql), and “Host&lt; &gt;” (e.g., IP address where the event originated), or other data useful for ordering the partial search. If all the partial search results from across the diverse data systems are adequately marked, then harmonizing the partial search results may not require different types of processing. However, typically at least some partial search results from across the diverse distributed data systems are not adequately marked to facilitate harmonization. 
     Accordingly, the worker nodes can implement bifurcate processing of the partial search results depending on whether or not the partial search results are adequately marked. Specifically, the partial search results that are timestamped can be processed one way, and the partial search results that are not timestamped can be processed a different way. The worker nodes can execute the different types of processing interchangeably, or execute one type of processing after the other type of processing has completed. 
     In step  2310 , for time-ordered partial search results, respective worker nodes can be assigned (e.g., fixed) to receive time-ordered partial search results (e.g., events or event chunks) from respective data sources in an effort to maintain the time orderliness of the data. Assigning a worker node to obtain time-ordered partial search results of the same data source avoids the need for additional processing among multiple nodes otherwise required if they each received different time-ordered chunks from the same data source. In other words, setting a worker node to collect all the time-ordered partial search results from its source avoids the added need to distribute the time-ordered partial search results between worker nodes to reconstruct the overall time orderliness of the partial search results. 
     For example, a worker node can respond to timestamped partial search results it receives by setting itself (or another worker node) to receive all of the partial search results from the source of the time-stamped partial search results. For example, the worker node can be set by broadcasting the assignment to other worker nodes, which collectively maintain a list of assigned worker nodes and data sources. In some embodiments, a worker node that receives timestamped partial search results can communicate an indication about the timestamped partial search results to the DFS master or search service provider. Then the DFS master or search service provider can set a specific set of worker nodes to receive all the timestamped data from the specific source. 
     In step  2312 , the worker nodes read the collected partial search results (e.g., events or event chunks) and arrange the partial search results in time order. For example, each collected event or event chunk may be associated with any combination of a start time, an end time, a creation time, or some other time value. The worker node can use the time values (e.g., timestamps) associated with the events or event chunks to arrange the events and/or the event chunks in a time-order. Lastly, in step  2314 , the worker nodes may stream the time-ordered partial search results in parallel as time-ordered chunks via the search service (e.g., to the DFS master or search service provider of the DFS system). 
     Referring back to step  2308 , the worker nodes can respond differently to partials search results that are not associated with timestamps (e.g., lack an associated time value that facilitates time ordering). In step  2316 , the worker nodes can associate events or chunks with a time value indicative of the time of ingestion of the events or event chunks by the respective worker nodes (e.g., an ingestion timestamp). The worker nodes can associate the partial search results with any time value that can be measured relative to a reference time value (e.g., not limited to an ingestion timestamp). In some embodiments, the partial search results timestamped by the worker nodes can also be marked with a flag to distinguish those partial search results from the partial search results that were timestamped before being collected by the worker nodes. 
     In step  2318 , the worker nodes sort the newly timestamped partial search results and create chunks (e.g., micro-batches) upon completion of collecting all of the partial search results from the data sources. In some embodiments, the chunks may be created to contain a default minimum or maximum number of partial search results (e.g., a default chunk size). As such, the worker nodes can create time-ordered partial search results obtained from data sources that did not provide time-ordered partial search results. 
     In step  2320 , the worker nodes can apply spillover techniques to disk as needed. In some embodiments, the worker nodes can provide an extensive HB/status update mechanism to notify the DFS master of its current blocked state. In some embodiments, the worker nodes can ensure a keep-alive to override timeout and provide notifications. Lastly, in step  2322 , the worker nodes may stream the time-ordered partial search results in parallel as time-ordered chunks via the search service (e.g., to the DFS master or search service provider of the DFS system). 
     Accordingly, time-ordered partial search results can be created from a combination of time-ordered and non-time-ordered partial search collected from diverse data sources. The time-ordered partial search results can be streamed in parallel from multiple worker nodes to the service provider, which can stream each search stream to the search head of the data intake and query system. As such, time-ordered search results can be produced from diverse data types of diverse data systems when the scope of a search query requires doing so. 
       FIG.  22    is a flowchart illustrating a method  2400  performed by a data intake and query system of a DFS system in response to a cursored search query according to some embodiments of the present disclosure. Specifically, the method  2400  can be performed by the data intake and query system to collate the time-ordered partial search results obtained by querying internal and/or external data sources. 
     In step  2402 , the search head, search service provider, or one or more worker nodes receive one or more streams of time-ordered partial search results (e.g., event chunks) from a data source. In step  2404 , the search head or search service provider creates multiple search collectors to collect the time ordered event chunks. 
     For example, the search head or search service provider can add a class of collectors to collate search results from the worker nodes. In some embodiments, the search head or search service provider can create multiple collectors; such as a collector for each indexer, as well as a single collector for each external data source or other data source. In some embodiments, the search head or search service provider may create a collector for each stream, which could include time-ordered chunks from a single worker node or a single data source. Hence, each collector receives time-ordered chunks. 
     In step  2406 , the collectors perform a deserialization process on the received chunks and their contents, which had been serialized for transmission from the search service. In step  2408 , each collector adds the de-serialized partial search or their chunks to a collector queue. The search head or search service provider may include any number of collector queues. For example, the search head or search service provider may include a collector queue for each collector or for each data source that provided partial search results. 
     In step  2410 , the search head, search service provider, or designated worker node(s) can collate the time-ordered partial search results obtained from the data sources as time-ordered search results of the presented search query. For example, the search head, search service provider, or designated worker node(s) may apply a collation operation based on the time-order of events contained in the chunks from the queues of different collectors to provide time-ordered search results. 
     Lastly, in step  2412 , the time-ordered search results could be provided to an analyst on a variety of mediums and in a variety of formats. For example, the time-ordered search results may be rendered as a timeline visualization on a user interface on a display device. In some embodiments, the raw search results (e.g., entire raw events) are provided for the timeline visualization. 
     The visualization can allow the analyst to investigate the search results. In another example, the time-ordered results may be provided to an analyst automatically on printed reports, or transmitted in a message sent over a network to a device to alert the analyst of a condition based on the search results. 
     Although the methods illustrated in  FIGS.  21  and  22    include a combination of steps to obtain time-ordered search results from across diverse data sources that may or may not provide timestamped data, the disclosed embodiments are not so limited. Instead, any portion of the combination of steps illustrated in  FIGS.  21  and  22    could be performed depending on the scope of the search query. For example, only a subset of steps may be performed when the search results for a search query are obtained exclusively from a single external data source that stores timestamped data. 
     6.2. Transformed Search Results 
     The disclosed embodiments include a technique to obtain search results from the application of transformation operations on partial search results obtained from across internal and/or external data sources. Examples of transformation operations include arithmetic operations such as an average, mean, count, or the like. Examples of reporting transformations include join operations, statistics, sort, top head. Hence, the search results of a search query can be derived from partial search results rather than include the actual partial search results. In this case, the ordering of the search results may be nonessential. An example of a search query that requires a transformation operation is a “batch” or “reporting” search query. The related disclosed techniques involve obtaining data stored in the big data ecosystem, and returning that data or data derived from that data. 
     According to a reporting or batch mode of data access, the DFS system executes blocking transforming searches, for example, to join across one or multiple available data sources. Since ordering is not needed, the DFS system can implement sharding of the data from the various data sources and execute aggregation (e.g., reduction of map-reduction) in parallel. The DFS architecture can also execute multiple DFS operations in parallel to receive sharded data from the different sources. 
       FIG.  23    is a flowchart illustrating a method  2500  performed by nodes of a DFS system to obtain search results in response to a batch or reporting search query according to some embodiments of the present disclosure. The method  2500  for processing batch or reporting search queries can involve steps performed by the DFS master, the service provider, and/or worker nodes to transform partial search results into search results into batch or reporting search results. The disclosed techniques also support both streaming and non-streaming for multiple data sources. 
     The transformation operations generally occur at the worker nodes. For example, an operation may include a statistical count of events having a particular IP address. The DFS can shard the data in certain partitions, and then each worker node can apply the transformation to that particular partition. In case it is the last reporting/transforming processor, then the transformed results are collated at the search service provider, and then transmitted to the search head. However, if there is a reporting search beyond the statistical count, then another reshuffle of the partial search results can be executed among the worker nodes to put the different partitions on the same worker node, and then transforms can be applied. If this is the last reporting search, then results are sent back to the service provide node and then to the search head. This process continues as dictated by the DAG generated from the phase desired by the search head. 
     In step  2502 , the worker nodes collect partial search results from the internal and/or external data sources. For example, a worker node may collect partial search results including data having data structures specified by the search query. In another example, the worker node may query an external data source for partial search results based on specific keywords included in a reporting search query, and collect the partial search results. The worker node may also collect partial search results from indexers, which were returned in response to application of the reporting search query by the search head (or search service provider or nodes) to the indexers. The partial search results may be communicated from each data source to the worker nodes individually or in chunks (e.g., micro-batches). The worker nodes thus ingest partial search results obtained from the data sources in response to a search query. 
     In step  2504 , the worker nodes can perform deserialization of the partial search results obtained from the data sources. Specifically, the partial search results transmitted by the data sources can be serialized by converting objects into a stream of bytes, which allows for saving the state of an object for subsequent recreation of the object at the worker nodes by using the reverse process of deserialization. 
     In step  2506 , the worker nodes transform the de-serialized partial search results into a specified format. As such, partial search results collected in diverse formats can be transformed into a common specified format. The specified format may be specified to facilitate processing by a worker node. As such, diverse data types obtained from diverse data sources can be transformed into a common format to facilitate subsequent aggregation across all the partial search results obtained in response to the search query. As a result, the partial search results obtained by worker nodes can be transformed into, for example, data events having structures that are compatible to the data intake and query system. 
     Unlike cursored search queries, the time-order of partial search results is not necessarily considered when processing reporting queries. However, in step  2508 , if a data source returns partial search results that are not associated with time values (e.g., no timestamp), the worker nodes can associate events or event chunks with a time value indicative of the time of ingestion of the events or chunks by the worker nodes (e.g., ingestion timestamp). In some embodiments, the worker nodes can associate the partial search results with any time value that can be measured relative to a reference time value. Associating time values with partial search results may facilitate tracking partial search results when processing reporting searches, or may be necessary when performing reporting searches that require time-ordered results (e.g., a hybrid of cursored and reporting searches). 
     In step  2510 , the worker nodes determine whether the ingested partial search results were obtained by an internal data source or an external data source to bifurcate processing respectively. In other words, the worker nodes process the ingested partial search results differently depending on whether they were obtained from an internal data source (e.g., indexers) or an external data source, if needed. That is, this can be the case only when reporting searches are run in the indexers; however, if all the processors in the indexers are streaming, then no processing unique to the indexer data is needed. However, data from external data sources can be sanitized in terms of coding, timestamped, and throttles based on the timestamp. 
     In step  2512 , for internal data sources, the worker nodes read the partial search results obtained from indexers of a data intake and query system in a sharded way. In particular, the worker nodes may use a list identifying indexers from which to pull the sharded partial search results. As discussed above, sharding involves partitioning datasets into smaller, faster, and more manageable parts called data shards. The sharded partitions can be determined from policies, which can be based on hash values by default. In the context of map-reduce techniques, the map step can be determined by the sharding and a predicate passed, which maps records matching the predicate to whatever is needed as the search result. The reduce step involves the aggregation of the shards. The results of a query are those items for which the predicate returns true. 
     In step  2514 , the partial search results of the indexers are aggregated (e.g., combined and/or transformed) by the worker nodes. In particular, the partial search results can be in a pre-streaming format (semi-reduced), and need to be aggregated (e.g., reduced or combined) prior to aggregation with partial search results of external data sources. In step  2516 , the aggregated partial search results of the indexers are aggregated (e.g., combined and/or transformed) with the partial search results obtained from external data sources. Lastly, in step  2518 , the aggregated partial search results of internal and external data stores can be transmitted from the worker nodes in parallel to the search service (e.g., to the DFS master or search service provider of the DFS system). 
     In step  2520 , for external data sources, the worker nodes push predicates for the reporting search query to the external data sources. A predicate is a function that takes an argument, and returns a Boolean value indicating of true or false. The predicate can be passed as a query expression including candidate items, which can be evaluated to return a true or false value for each candidate item. 
     In step  2522 , the network nodes can determine whether the external data sources may or may not be able to execute a sharded query. In step  2526 , for an external data source that can execute a sharded query, the worker node reads the results in different shards. In some embodiments, the DFS master randomly chooses which worker nodes will execute the shards. In step  2524 , for an external data sources that cannot execute a sharded query, a worker node has the ability to spillover to disk, and redistribute to other worker nodes. 
     In step  2528 , the worker nodes can apply an aggregation (e.g., (e.g., combine and/or transform) or stream processing to have the partial search results ready for further processing against results from partial search results from the internal sources. Thus, referring back to step  2516 , the worker nodes aggregate the partial search results from all data sources in response in response to the search query. For example, the worker nodes can apply a process similar to a reduction step of a map-reduce operation across all the partial search results obtained from diverse data sources. Then, in step  2518 , the aggregate partial search results can be transmitted from the worker nodes in parallel to the search service provider  216 . In particular, the search service provider, can collect all the finalized searches results from the worker nodes, and return the results to the search head. 
       FIG.  24    is a flowchart illustrating a method performed by a data intake and query system of a DFS system in response to a batch or reporting search query according to some embodiments of the present disclosure. In particular, the method  2600  is performed by the data intake and query system to provide the batch or reporting search results obtained by querying internal and/or external data sources. 
     In step  2602 , a search head, search service provider, or designated worker node(s) of receives the aggregate partial search results via a hybrid collector. The number and function of the hybrid collectors is defined depending on the type of search executed. For example, for the transforming search, the search head or search service provider can create only one collector to receive the final results from the worker nodes and after serialization directly pushes into the search result queue. In step  2604 , the search head or search service provider uses an existing job pool to de-serialize search results, and can push the search results out. In such an operation, collation is not needed. 
     Lastly, in step  2606 , the transformed search results could be provided to an analyst on a variety of mediums and in a variety of formats. For example, the time-ordered search results may be rendered as a timeline visualization on a user interface on a display device. The visualization can allow the analyst to investigate the search results. In another example, the time-ordered results may be provided to an analyst automatically on printed reports, or transmitted in a message sent over a network to a device to alert the analyst of a condition based on the search results. 
     Although the methods illustrated in  FIGS.  23  through  26    include a combination of steps to obtain time ordered, unordered, or transformed search results from across multiple data sources that may or may not store timestamped data, the disclosed embodiments are not so limited. Instead, a portion of a combination of steps illustrated in any of these figures could be performed depending on the scope of the search query. For example, only a subset of steps may be performed when the partial search results for a search query is obtained exclusively from an external data source. 
     7.0. Co-Located Deployment Architecture 
     The capabilities of a data intake and query system can be improved by implementing the DFS system described above in a co-located deployment with the data intake and query system. For example,  FIG.  25    is a system diagram illustrating a co-located deployment of a DFS system with the data intake and query system in which an embodiment may be implemented. 
     In the illustrated embodiment, the system  224  shows only some components of a data intake and query system but can include other components (e.g., forwarders, internal data stores) that have been omitted for brevity. In particular, the system  224  includes search heads  226 - 1  and  226 - 2  (referred to collectively as search heads  226 ). The search heads  226  collectively form a search head cluster  228 . Although shown with only two search heads, the cluster  228  can include any number of search heads. Alternatively, an embodiment of the co-located deployment can include a single search head rather than the cluster  228 . 
     The search heads  226  can operate alone or collectively to carry out search operations in the context of the co-located deployment. For example, a search head of the cluster  228  can operate as a leader that orchestrates search. As shown, the search head  226 - 1  is a leader of the cluster  228 . Any of the search heads  226  can receive search queries that are processed collectively by the cluster  228 . In some embodiments, a particular search head can be designated to receive a search query and coordinate the operations of some or all of the search heads of a cluster  228 . In some embodiments, a search head of the cluster  228  can support failover operations in the event that another search head of the cluster  228  fails. 
     In various implementations, one of the search heads of the cluster  228  may operate as a “captain” among the search heads of the cluster  228 . The search head captain (which may also be referred to as a search head cluster captain) may be the same as the leader described in the preceding paragraph, or it may perform a different role. The search head captain may be a coordinating component that serves to orchestrate search and coordinate communication between members of the search environment, such as communication between the search heads of the cluster  228 . In some implementations, any of the search heads of the cluster  228  may be able to interchangeably act as the search head captain as the need arises, such that there is no single search head that is permanently designated as the captain. Thus, if a search head captain fails or goes down, another search head in the cluster  228  may be selected to become the next search head captain as part of a recovery protocol. 
     Any suitable method of selecting a search head to serve as the search head captain may be used. Some approaches for selecting the search head captain are described in U.S. Pub. No. 2016/0034490, titled “INTELLIGENT CAPTAIN SELECTION FOR DISASTER RECOVERY OF SEARCH HEAD CLUSTER”, which is hereby incorporated by reference for all purposes as if fully set forth herein. As one example, a search head in the cluster  228  may be elected to be the search head captain through an election protocol such as a Raft consensus algorithm (e.g., based on voting performed by the search heads in the cluster  228 ), with the remainder of the search heads in the cluster  228  being followers. In some implementations, the follower search heads in the cluster  228  may engage in intra-cluster communications exclusively with the search head captain (i.e., there is no follower-to-follower search head communication). 
     In various implementations, the search head captain may be configured to update, generate/replicate, and distribute configuration files across the search heads  226  of the cluster  228  to ensure that all the search heads are synchronously operating with the same configuration. In some cases, the search head captain may be capable of leveraging the existing infrastructure or protocols of the data intake and query system in order to distribute configuration files to other search heads. 
     Any suitable method of replicating a configuration across the search head cluster  228  may be used. Some approaches for configuration replication are described in U.S. Pub. No. 2018/0314601, titled “CONFIGURATION REPLICATION IN A SEARCH HEAD CLUSTER”, which is hereby incorporated by reference for all purposes as if fully set forth herein. As one example, the search head captain may be responsible for synchronizing knowledge object customizations (e.g., any action relating to a knowledge object, such as, for example, the deletion, creation, modification, change, or update of a knowledge object) across search heads in the cluster  228 , including configurations associated with search and/or visualization of the data intake and query system (exemplary knowledge objects may include but are not limited to, a saved search, an event type, a transaction, a tag, a field extraction, a field transform, a lookup, a workflow action, a search command, a view, and late-binding schema). 
     The cluster  228  is coupled to N peer indexers  230 . In particular, the search head  226 - 1  can be a leader or search head captain of the cluster  228  that is coupled to each of the N peer indexers  230 . The system  224  can run one or more daemons  232  that can carry out the DFS operations of the co-located deployment. In particular, the daemon  232 - 1  of the search head  226 - 1  is communicatively coupled to a DFS master  234 , which coordinates control of DFS operations. Moreover, each of the N peer indexers  230  run daemons  232  communicatively coupled to respective worker nodes  236 . The worker nodes  236  are coupled to one or more data sources from which data can be collected as the partial search results of a search query. For example, the worker nodes  236  can collect partial search results of the indexers from internal data sources (not shown) and one or more of external data sources  240 . Lastly, the worker nodes  236  are communicatively coupled to the DFS master  234  or a search service provider to form the DFS architecture of the illustrated co-located embodiment. 
     More specifically, the worker nodes  236  may be communicatively coupled to the DFS master  234  as part of a distributed computing framework (e.g., SPARK) used to execute the worker nodes  236  with implicit parallelism and fault-tolerance. The DFS master  234  may be a software component or instance of the distributed computing framework that is launched on a search head, the worker nodes  236  may be software components or instances of the distributed computing framework that are launched on the indexers  230 , and the worker nodes  236  may act as agents of the DFS master  234  (e.g., the DFS master  234  may provide instructions to the worker nodes  236  to carry out a search operation using the resources of the indexers  230 ). 
     In various implementations, the software for the DFS master  234  (e.g., the application or program instructions executed to run the DFS master  234 ) may be installed on each of the search heads  226  of the cluster  228 . Furthermore, each of the search heads  226  may also have an additional software or process that is installed and running on it (e.g., daemon  232 ), which can launch and activate the DFS master  234  (e.g., by triggering execution of the installed DFS master software). Thus, the DFS master  234  may be launched on any of the search heads  226  of the cluster  228 . 
     However, in practice, only one of the search heads  226  should launch the DFS master  234  to ensure that there is only one instance of the DFS master  234  running at a time. In some implementations, this single instance of the DFS master  234  may also start on the search head captain (e.g., search head  226 - 1 , as depicted in  FIG.  25   ). Once launched by the search head captain, the DFS master  234  may run alongside the search head captain in order to address the issue of high availability. In some cases, this may be implemented by taking advantage of existing protocols in the data intake and query system for identifying a search head captain among the search heads of the cluster  228  in any circumstance, such that the role is reserved. Accordingly, the DFS master  234  may be configured to run specifically on the search head captain by piggybacking on the search head captain selection mechanism (e.g., the DFS master  234  may be launched by a search head upon its election as the search head captain). If a search head captain fails or goes down, another search head in the cluster  228  may be selected to become the next search head captain (e.g., in accordance with a recovery protocol) and it may also launch the DFS master  234  using the software (e.g., DFS master software) installed on it. In this manner, the DFS master  234  may always be run on the current search head captain. 
     In various implementations, the software for the worker nodes  236  (e.g., the application or program instructions executed to run the worker nodes  236 ) may be installed on each of the peer indexers  230 . Furthermore, each of the peer indexers  230  may also have an additional software or process that is installed and running on it (e.g., daemon  232 ), which can launch and activate the worker node (e.g., by triggering execution of the installed worker node software). Thus, each of the peer indexers  230  may be able to launch an instance of a worker node  236  using the software (e.g., worker node software) installed on it. 
     In various implementations, installation of both the software for the DFS master  234  (on the search heads  226  of the cluster  228 ) and the software for the worker nodes  236  (on the indexers  230 ) may occur at an initialization step or any time that a new search head or indexer is created and added to the system. A deployer for the search heads  226  may deploy the software for the DFS master  234  by installing it on each of the search heads  226 , and a deployer for the indexers  230  may deploy the software for the worker nodes  236  by installing it on each of the indexers  230 . The role of these deployers may be to make installation of the software on each of the search heads  226  and indexers  230  easier, since the deployers can simply push the software to those components and restart them after installation has been completed. Thus, the software for the DFS master  234  and the worker nodes  236  can be deployed to, and installed on, the search heads  226  and indexers  230 , respectively. Afterwards, those search heads  226  and indexers  230  may be restarted. 
     In various implementations, the search heads  226  and indexers  230  may have an additional software or process or agent that is installed and running on them (e.g., daemon  232 ), which may run continuously in the background of the search heads  226  and indexers  230  after installation of the corresponding software for the DFS master  234  or the worker nodes  236 . For instance, a daemon  232  may run in the background of each of the search heads  226  and indexers  230  to perform monitoring and various other functions. 
     In various implementations, the daemon  232  running on each of the search heads  226  may periodically check the respective search head that it is installed on to determine if that search head is now the search head captain (e.g., in the event that a search head is elected to be search head captain, such as if a previous search head captain failed). If the search head is not the search head captain, then the daemon  232  may do nothing but continue to periodically check if the search head is the search head captain. However, for one of the search heads, the daemon  232  may determine that the search head is elected to role of search head captain (e.g., if there were no prior search head captains or a previous search head captain failed). The daemon  232  may then launch and activate the DFS master  234  by running the software for the DFS master  234  that was installed on the search head (now the search head captain). By having the daemon  232  perform this monitoring, it ensures that the DFS master  234  will be run on the current search head captain. 
     It should be noted that after the DFS master  234  has been launched on the search head captain (e.g., search head  226 - 1 ), the search head captain, the DFS master  234  running on the search head captain, and/or the daemon  232  running on the search head captain may interchangeably fulfill various responsibilities and perform tasks described herein. However, for the purpose of facilitating ease of understanding, these tasks will be described as being performed by the search head captain, but it should be understood that any suitable component, software, or process of the search head captain may be initiating the performance of these tasks. 
     With respect to these tasks, the search head captain may generate a new shared secret key that will be used by the entire system  224  (e.g., for processing a search using the distributed computing framework). In particular, an indexer  230  may only be permitted to launch and run a worker node  236  as part of the distributed computing framework if the indexer  230  is in possession of the shared secret key. 
     The search head captain may also update a DFS configuration file residing on the search head captain. In particular, the search head captain may update the DFS configuration file with information such as an instance identifier of the DFS master  234  (e.g., a DFS master instance identifier), the shared secret key selected by the DFS master  234  (e.g., a DFS master shared secret key), the IP address and port associated with the DFS master  234  (e.g., IP address and port of the search head captain), and a list of active indexers. The list of active indexers may consist of indexers (or identifiers of the indexers) that have been selected for launching and running worker nodes (e.g., a selected subset of all the indexers  230  capable of launching and running worker nodes because a particular user may not wish to use all the available indexers  230 , but instead may wish to use certain indexers based on criteria like the load on the indexers or the configuration of those indexers). The search head captain may be configured to retrieve a list of active indexers. 
     In some implementations, this list of active indexers may initially be an empty set. However, in some implementations, a user may be able to add to this list of active indexers in various ways. For instance, there may be a web-based user interface (e.g., accessible through a web URL) associated with the DFS master  234  running on the search head captain. The web-based UI may provide a user with a list of all the indexers  230  in the system  224  that are capable of launching and running worker nodes  236  (e.g., that have the software for the worker nodes  236  installed). Through this web-based UI, a user may be able to select any of the indexers  230  and add them to the list of active indexers (e.g., the indexers qualified to launch and run worker nodes). Upon doing so, the web-based UI may trigger the update of the list of active indexers in the DFS configuration file (e.g., the backend API of the web-based UI may trigger a call to the daemon  232 , which may add the updated list of active indexers to the configuration file). So for example, if the user selected indexers {1, 2, and 3} to be added to the list of active indexers, there would then be identifiers for the indexers {1, 2, and 3} in the updated list of active indexers. In some implementations, the list of active indexers may contain identifiers associated with the indexers, such as IP address, hostname, or some other global unique ID (GUID) for the indexers. In some cases, using GUIDs in the list of active indexers may be beneficial, especially when the IP address or hostname associated with the indexers may be affected by the presence of a firewall or network address translation (NAT). 
     In some implementations, there may be a workload management feature that may be implemented by notifying a user of indexer capacity or restricting the indexers that the user is able to pick and choose from to add to the list of active indexers. For instance, the web-based UI may warn or notify a user about the capacity of an indexer if the user is attempting to add an indexer that is above or below a certain capacity threshold (e.g., warn if the index has less than 20% available capacity) to the list of active indexers. Or the indexers may not be available to add to the list of active indexers unless they have capacity or processing availability over or under a certain configurable threshold (e.g., only display indexers that have &gt;50% capacity to the user via the web-based UI). In some implementations, this workload management feature may be implemented elsewhere, such as by having an indexer  230  perform an additional check on its own capacity once it determines that it is in the list of active indexers and launching a worker node only if that capacity is over the threshold. Or alternatively, worker nodes can be instantiated by all the active indexers, but only the worker nodes that correspond to indexers with sufficient capacity may be used at the time of processing a DFS search. 
     In various implementations, the search head captain may be configured to propagate to the other search heads  226  in the cluster  228  any updates made to this DFS configuration file. After updating the DFS configuration file, the search head captain may initiate communications with each of the other search heads  226  in the cluster  228 , for which the search head captain may replicate its updated DFS configuration file and distribute a copy of it to each of the search heads  226  in the cluster  228 . Thus, these communications may inform the other search heads  226  that the search head captain is the current captain running the DFS master  234 , which can be connected to using the IP address and port provided in the DFS configuration file. In some implementations, the search head captain may implement replication and distribution of the DFS configuration file to all the search heads  226  using an existing mechanism in the system, such as a horizontal replication mechanism. When the search heads  226  receive the DFS configuration file from the search head captain, each of the search heads  226  may save the DFS configuration file locally to be applied to a future DFS search. Thus, any updates to the DFS configuration file at the search head captain are synchronized and propagated to all the other search heads  226 , so even if the search head captain dies and another search head is elected to take over and become the new search head captain, the new search head captain would start off with the latest configuration file information. It should also be noted that once indexers are selected and added to the list of active indexers in the DFS configuration file of the search head captain, those indexers will quickly be configured to launch worker nodes because the search head captain will replicate the DFS configuration file for all the search heads  226 , such that all the search heads  226  will have a copy of the updated list of active indexers. 
     Once the search head captain has distributed the updated DFS configuration file to the other search heads  226  in the cluster  228 , the search head captain may also distribute (e.g., indirectly) a copy of the updated DFS configuration file to each of the indexers  230 . In some implementations, the search head captain may be able replicate and distribute the DFS configuration file to all the indexers  230  using an existing mechanism in the system, such as a distributed bundled replication mechanism. The distributed bundle replication mechanism may involve a communication technique used by a search head for processing a search query through a set of indexers, in which the search head may provide a bundle of configuration information specific to the search head to the set of indexers, such that the indexers are aware of the search head and the configuration settings to be applied during the search. This bundle of configuration information may include the DFS configuration file (e.g., containing information for the DFS master  234 , the list of active indexers, relevant security information, etc.) as part of the bundle, as well as other information including knowledge of objects, labels (e.g., how a field is renamed in the search), server configuration information, license check information, and so forth. In order for the search head captain to leverage this distributed bundle replication mechanism, the search head captain may trigger an ad hoc (e.g., a “dummy” or fake) DFS search, for which the all the indexers  230  in the system  224  will be contacted by the search head that handles the search (e.g., a comprehensive search). The search head captain will have that search head push a bundle of configuration information (including the DFS configuration file available to the search head) to every indexer  230  for performing the search. Since all of the search heads  226  will have been provided the updated DFS configuration file at this point (including the search head handling this fake DFS search), it is ensured that the updated DFS configuration will be in the bundle of configuration information provided to every indexer  230  for the fake DFS search. Thus, using the distributed bundle replication mechanism, the search head captain is able to indirectly distribute the updated DFS configuration file to all the indexers  230  in the system  224 . 
     It should be noted that after an indexer  230  has received a DFS configuration file from a search head, the indexer  230  and/or the daemon  232  running on the indexer  230  may interchangeably fulfill various responsibilities and perform tasks described herein. However, for the purpose of facilitating ease of understanding, these tasks will be described as being performed by the indexer  230 , but it should be understood that any suitable component, software, or process of the indexer  230  may be initiating the performance of these tasks. 
     With respect to these tasks, the indexer  230  may specifically monitor receipt of any DFS configuration files. For instance, the daemon  232  running on the indexer  230  may be continually monitoring to see if a new bundle of configuration information has been received. If a bundle of configuration information has been received, the contents of the bundle may be checked to determine if it includes a DFS configuration file. From the contents of an updated DFS configuration file, the indexer  230  will know about the DFS master  234 , the shared secret key, the current list of active indexers (e.g., the selected indexers qualified to operate worker nodes), and so forth. In particular, the list of active indexers will be used by the indexers  230  to determine which of the indexers among them (e.g., from the IP address, hostname, GUID, etc., listed for those indexers) will have worker nodes instantiated for the search. The indexer  230  may continually monitor for receipt of a new bundle of configuration information, because the search head captain may continue to make updates to the DFS configuration file as changes are made (e.g., an indexer is added or removed from the list of active indexers) or in the event that a new search head captain arises. 
     Once an indexer  230  has been provided with an updated DFS configuration file, the indexer  230  may check to see if it is on the list of active indexers (e.g., the selected indexers qualified to operate worker nodes) provided in the DFS configuration file. If the indexer  230  is not on the list of active indexers, the indexer  230  may do nothing. If the indexer  230  is on the list of active indexers, it may launch and activate a worker node (e.g., by running the worker node software) and configure the worker node to connect to the DFS master  234  using the available information for the DFS master  234  (including connection information for the DFS master  234 , such as IP address) and the shared secret key, both of which were included in the updated DFS configuration file. The worker node can use this information to connect to the DFS master  234  and the connection will be successful because the worker node and the DFS master  234  will share the same secret key. Thus, the shared secret key may serve as a security mechanism for ensuring that worker nodes (e.g., rogue worker nodes) that are not part of the system  224  cannot connect to the DFS master  234  for search processing. In some cases, it is the daemon  232  of the indexer  230  that makes this determination of whether to launch and activate a worker node on the indexer  230 , triggers execution of the installed worker node software, and configures the worker node to communicate with the DFS master  234 . In some implementations, a user may be able to configure and assign a resource quota for this activated worker node operating on the indexer  230 . For instance, the user may be able to configure how resources, including CPU and memory, may be allocated for this activated worker node operating on the indexer  230 . Resources may be allocated as an absolute resource amount, or as a percentage of total resources associated with the indexer  230 . The resource usage and allocation of the worker node operating on the indexer  230  may be continually monitored, and resources may be assigned to the worker node in accordance with a workload management policy, which may be set per indexer (e.g., or per worker node). For instance, a user may specify that the indexer  230  may allocate X % of an indexer resource for indexing, Y % of the resource for searching, and Z % of the resource for operating the worker node. 
     In various implementations, there may be additional security mechanisms in place. In some implementations, the DFS master  234  may maintain an awareness of the worker nodes that successfully connected to it with the shared secret key, the corresponding indexers on which those worker nodes are instantiated, and/or the total number of indexers  230  in the system  224 . When performing a DFS search, a check can be made to see if the number of worker nodes connected to the DFS master  234  exceeds the number of indexers available. If the number of worker nodes is greater than the number of indexers, then the DFS search is not performed (e.g., because there may be a rogue worker node). Another check may also be made to determine that the worker nodes connected to the DFS master  234  correspond to actual indexers  230  in the system  224 . For instance, there may be a mapping table that maps the relationship between worker nodes and their corresponding indexers (e.g., worker node identifiers and corresponding indexer identifiers). If each connected worker node does not correspond to the correct indexer, then the DFS search is not performed. 
     In various implementations, for which a multi-site deployment is used, there may be a location preference that is assessed during the configuration and management of the components of the distributed computing framework. In a multi-site deployment, the physical locations of individual components (e.g., the search heads  226  and indexers  230 ) may be different. For example, there may be a first set of search heads and indexers that are located on the east coast, with a second set of search heads and indexers that are located on the west coast. In this example, the search head captain may be one of the search heads on the east coast. If the underlying data for a DFS search is stored in the east coast, then the location preference may dictate that the only east coast worker nodes (e.g., worker nodes instantiated on indexers located on the east coast) should be used as part of the DFS search. In this case, even if the list of active indexers contains both east coast indexers and west coast indexers, only the worker nodes running on the east coast indexers may be used. Thus, there may be a location preference for using worker nodes that are in the same geographical location as the underlying data being processed for a DFS search. In another example scenario, if the data for the DFS search was split between the east coast and the west coast, then worker nodes in both locations can be used to process the DFS search. However, each site may perform as much of the search processing as it can in order to minimize the amount of information that would have to be passed between the sites over the wide area network (WAN). For instance, if the search processing involved the determination of a metric (e.g., that is associative, commutative, and can be aggregated) such as a count, then the worker nodes on the east coast indexers may process the east coast data for a count, the worker nodes on the west coast indexers may process the west coast data for a count, and the count between the two sites can be added. 
     7.1. Co-Located Deployment Operations 
       FIG.  26 A  is an operation flow diagram illustrating an example of an operation flow of a co-located deployment of a DFS system with a data intake and query system according to some embodiments of the present disclosure. The operational flow  2800  shows the processes for establishing the co-located DFS system and search operations carried out in the context of the co-located deployment. 
     In step  2802 , a search head of the cluster  228  can launch the DFS master  234  and/or launch a connection to the DFS master  234 . For example, a search head can use a modular input to launch an open source DFS master  234 . Moreover, the search head can use the modular input to launch a monitor of the DFS master  234 . The modular input can be a platform add-on of the data intake and query system that can be accessed in a variety of ways such as, for example, over the Internet on a network portal. 
     In step  2804 , the peer indexers  230  can launch worker nodes  236 . For example, each peer indexer  230  can use a modular input to launch an open source worker node. In some embodiments, only some of the peer indexers  230  launch worker nodes, which results in a topology where not all of the peer indexers  230  have an associated worker node. Moreover, the peer indexers  206  can use the modular input to launch a monitor of the worker nodes  236 . 
     In step  2806 , the cluster  228  can launch one or more instances of a DFS service. For example, any or each of the search heads of the cluster  228  can launch or communicate with an instance of the DFS service. Hence, the co-located deployment can launch and use multiple instances of a DFS service but need only launch and use a single DFS master  234 . In the event that a launched DFS master fails, the lead search head using the monitoring modular input can restart the failed DFS master. However, if the DFS master fails along with the lead search head, another search head can be designated as the cluster  228 &#39;s leader and can re-launch the DFS master. 
     In step  2808 , a search head of the cluster  228  can receive a search query. For example, a search query may be input by a user on a user interface of a display device. In another example, the search query can be input to the search head in accordance with a scheduled search. 
     In step  2810 , a search head of the cluster  228  can initiate a DFS search session with the local DFS service. For example, any of the member search heads of the cluster  228  can receive a search query and, in response to the search query, a search head can initiate a DFS search session using an instance of the DFS service. 
     In step  2812 , a search head of the cluster  228  (or a search service provider) triggers a distributed search on the peer indexers  230  if the search query requires doing so. In other words, the search query is applied on the peer indexers  230  to collect partial search results from internal data stores (not shown). 
     In step  2814 , the distributed search operations continue with the peer indexers  230  retrieving partial search results from internal data stores, and transporting those partial search results to the worker nodes  236 . In some embodiments, the internal partial search results are partially reduced (e.g., combined), and transported by the peer indexers  230  to their respective worker nodes  236  in accordance with parallel exporting techniques. In some embodiments, if each peer indexer does not have an associated worker node, the peer indexer can transfer its partial search results to the nearest worker node in the topology of worker nodes. In step  2816 , the worker nodes  236  collect the partial search results extracted from the external data sources  240 . 
     In step  2818 , the worker nodes  236  can aggregate (e.g., merge and reduce) the partial search results from the internal data sources and the external data sources  240 . For example, the aggregation of the partial search results may include combining the partial search results of indexers  230  and/or the external data stores  240 . Hence, the worker nodes  236  can aggregate the collective partial search results at scale based on DFS native processors residing at the worker nodes  236 . 
     In some embodiments, the aggregated partial search results can be stored in memory at worker nodes before being transferred between other worker nodes to execute a multi-staged parallel aggregation operation. Once aggregation of the partial search results has been completed (e.g., completely reduced) at the worker node  236 , the aggregated partial search results can be read by the DFS service running locally to the cluster  228 . For example, the DFS service can commence reading the aggregated search results as event chunks. 
     In step  2820 , the aggregate partial search results read by the DFS service are transferred to the DFS master  234  or search service provider. Then, in step  2822 , the DFS master  234  can transfer the final search results to the cluster  228 . For example, the aggregated partial search results can be transferred by the worker nodes  236  as event chunks at scale to the DFS master  234 , which can transfer search results (e.g., those received or derived therefrom) to the lead search head orchestrating the DFS session. 
     Lastly, in step  2822 , a search head can cause the search results or data indicative of the search results to be rendered on user interface of a display device. For example, the search head member can make the search results available for visualizing on a user interface rendered on the display device. 
     It will be understood that fewer or more, or different steps can be included in the operation flow  2800 . Further, some operations can be performed by different components of the system. In some embodiments, for example, some of the tasks described as being performed by the search head  210  can be performed by the search service  220 , such as the search service provider  216 . In some cases, step  2806  can be omitted. In some cases, upon determining that a search query is to be handled by the search service, the cluster  228  can communicate the query to the search service. In turn, the search service can trigger the distributed search, etc. 
       FIG.  26 B  is an operation flow diagram illustrating an example of an operation flow of a co-located deployment of a DFS system according to some embodiments of the present disclosure. Within the context provided by  FIG.  26 A , the operational flow  2830  of  FIG.  26 B  shows the processes for establishing and managing the components used in a distributed computing framework (e.g., steps  2802 ,  2804 , and  2806  of the operational flow  2800  of  FIG.  26 A ). Prior to the steps shown in the operational flow  2830 , software for the DFS manager  234  may be installed on the search heads  226 , software for the worker nodes  236  may be installed on the indexers  230 , and there may be a daemon  232  that runs on each of the search heads  226  and indexers  230 . 
     In step  2832 , the search heads  226  of the cluster  228  may identify, among themselves, a search head captain (e.g., search head captain  226 - 1 ). Any suitable method of selecting a search head to serve as the search head captain may be used, including the example described herein for the election of one of the search heads to be the search head captain based on the votes of all the search heads  226  (e.g., using a Raft consensus algorithm). 
     In step  2834 , the DFS manager  234  may be launched on the search head captain  226 - 1 . For example, the daemon  232  running on the search head selected as search head captain from step  2832  may be aware of the change in search head captain status and trigger the execution of the software for the DFS manager  234  installed on the search head. Once launched, this instance of the DFS master  234  will run on the search head captain  226 - 1  until the status of the search head captain  226 - 1  changes (e.g., it fails or is no longer the captain), but there should always be an instance of DFS master  234  in the system that is running on the search head captain for that particular point in time. 
     In step  2836 , the DFS manager  234  or the search head captain  226 - 1  may determine a list of active indexers, which is a list of indexers (e.g., the identifiers associated with those indexers) that are qualified to launch and run worker nodes. The list of active indexers may, in some instances, be referred to as a list of active workers, since all the indexers in this list may be registered as workers (either currently configured, or in the process of being configured). In some implementations, a user may select the list of active indexers and provide the list to the DFS manager  234 . For instance, there may be a web-based UI that enables a user to interface with the DFS manager  234  and configure the list of active indexers. In some cases, the user may be able to select indexers from among all the available indexers to add to the list of active indexers, based on criteria that may be of relevance to the user (e.g., indexer capacity, and so forth). 
     In step  2838 , the DFS manager  234  or the search head captain  226 - 1  may generate a shared secret key that will be provided to the worker nodes  236  for security purposes. 
     In step  2840 , the DFS manager  234  or the search head captain  226 - 1  may update the DFS configuration file on the search head captain  226 - 1 . This updated DFS configuration file will include information associated with the instance of the DFS master  234  running on the search head captain  226 - 1  (e.g., an instance identifier, an IP address or port associated with the DFS master  234 , etc.), the shared secret key, and the list of active indexers. 
     In step  2842 , the DFS manager  234  or the search head captain  226 - 1  may replicate and distribute the updated DFS configuration file to all of the other search heads  226 . The updated DFS configuration file will then be integrated by those other search heads  226  (e.g., saved on each of the search heads  226 ). In some cases, the daemon  232  operating on each of those search heads  226  may be configured to continually monitor for this updated DFS configuration file and integrate it when received. 
     In step  2844 , the DFS manager  234  or the search head captain  226 - 1  may initiate an ad hoc search (e.g., a fake search) with the search heads  226  that would involve all the indexers  230  in the system, in order to indirectly distribute the updated DFS configuration file to all the indexers  230 . 
     In step  2846 , the search heads  226  may distribute the updated DFS configuration file to all the indexers  230  as part of the ad hoc search (e.g., a copy of the updated DFS configuration file that each of the search heads  226  have after step  2842 ). The updated DFS configuration file may be distributed with other information, such as a bundle of configuration information typically used to configure the indexers  230  for a search query. 
     In step  2848 , each of the indexers  230  or the daemon  232  running on those indexers  230  may receive the updated DFS configuration file and check to determine if the indexer is part of the list of active indexers in the received DFS configuration file. This can be done as part of a continual and ongoing process since the indexers  230  may expect to receive an updated DFS configuration file at any time (e.g., in the event the current search head captain crashes, the list of active indexers changes, and so forth) 
     In step  2850 , the daemon  232  for the indexers  230  that are in the list of active indexers may launch and activate instances of worker nodes  236  on those active indexers. The worker nodes  236  may be configured with information needed for them to connect to the DFS master  234 , such as the connection information for the instance of the DFS master  234  and the shared secret key generated by that instance of the DFS master  234 , which was provided in the DFS configuration file. 
     In step  2852 , the worker nodes  236  may use the information and attempt to connect to the DFS master  234  to become part of the distributed computing framework. The connection to the DFS master  234  should be successful if the shared secret key provided to the worker nodes  236  are the same as the shared secret key generated by the instance of the DFS master  234  at step  2838 . 
       FIG.  26 C  is a flow diagram illustrative of an embodiment of a routine implemented by a co-located deployment of a DFS system to activate worker nodes of a distributed computing framework using a search command according to some embodiments of the present disclosure. More specifically, the routine  2860  may be associated with the establishment and management of components of a distributed computing framework, which may require the synchronization of configuration information among those components. The activation of worker nodes through a search command may be one way in which components of the distributed computing framework can be synchronized using existing techniques and protocols available to a data intake and query system. Although described as being implemented by an indexer, it will be understood that the elements outlined for routine  2860  can be implemented by one or more computing devices/components that are associated with the data intake and query system  108 , such as, but not limited to, the indexing manager  402 , the ingest manager  406 , the partition manager  408 , the indexer  410 , and so forth. Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  2862 , a computing device (e.g., an indexer) of a data intake and query system may receive a search command from a search head of the data intake and query system. In some cases, the search command may be associated with an ad hoc search initiated by a search captain. 
     At block  2864 , a configuration file associated with the received search command may also be received. This configuration file may be received by the computing device (e.g., the indexer). The configuration file may include information regarding a distributed computing framework (e.g., SPARK) associated with the data intake and query system. For instance, the configuration file may be a DFS configuration file that includes information associated with a DFS master, a shared secret key, and a list of active indexers that are qualified to operate worker nodes. In some cases, this configuration file may also be received from the search head, and it may be received along with the search command. In some cases, the search head sending the configuration file may have previously received the configuration file (or at least the relevant information contained within the configuration file) from another search head, such as a search head captain. 
     At block  2866 , a determination is made regarding whether the configuration file specifically identifies the computing device (e.g., the indexer). The computing device (e.g., the indexer) may make this determination itself. For instance, the configuration file may include a list of active indexers that are qualified to operate a worker node in the distributed computing framework, and the computing device may check that it is in the list of active indexers. Thus, a determination is made regarding whether the configuration file identifies the computing device to include a worker node of the distributed computing framework. 
     At block  2868 , a particular worker node of the distributed computing framework is activated on the computing device (e.g., the indexer) if it is determined that the configuration file does identify the computing device to include a worker node of the distributed computing framework. The computing device may launch and active this particular worker node itself. In some cases, the particular worker node may be configured using information in the received configuration file, and the particular worker node may use that information to connect to the DFS master and become part of the distributed computing framework. 
     It should be noted that, in some cases, additional indexers may be added to the system, on which additional worker nodes can be instantiated on. Some of such indexers may be configured to dedicate/allocate a majority or all of their capacity or resources for operating worker nodes used as part of the distributed computing framework. 
     8.0. Cloud Deployment Architecture 
     The performance and flexibility of a data intake and query system having capabilities extended by a DFS system can be improved with deployment on a cloud computing platform. For example,  FIG.  27    is a cloud-based system diagram illustrating a cloud deployment of a DFS system in which an embodiment may be implemented. 
     In particular, a cloud computing platform can share processing resources and data in a multi-tenant network. As such, the platform&#39;s computing services can be used on demand in a cloud deployment of a DFS system. The platform&#39;s ubiquitous, on-demand access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services), which can be rapidly provisioned and released with minimal effort, can be used to improve the performance and flexibility of a data intake and query system extended by a DFS system. 
     In the illustrated embodiment, a cloud-based system  242  includes components of a data intake and query system extended by the DFS system implemented on a cloud computing platform. However, the cloud-based system  242  is shown with only some components of a data intake and query system in a cloud deployment but can include other components (e.g., forwarders) that have been omitted for brevity. As such, the components of the cloud-based system  242  can be understood by analogy to other embodiments described elsewhere in this disclosure. 
     An example of a suitable cloud computing platform include Amazon web services (AWS), which includes elastic MapReduce (EMR) web services. However, the disclosed embodiments are not so limited. Instead, the cloud-based system  242  could include any cloud computing platform that uses EMR-like clusters (“EMR clusters”). 
     In particular, the cloud-based system  242  includes a search head  244  as a tenant of a cloud computing platform. Although shown with only the search head  244 , the cloud-based system  242  can include any number of search heads that act independently or collectively in a cluster. The search head  244  and other components of the cloud-based system  242  can be configured on the cloud computing platform. 
     The cloud-based system  242  also includes any number of worker nodes  246  as cloud instances (“cloud worker nodes  246 ”). The cloud worker nodes  246  can include software modules  248  running on hardware devices of a cloud computing platform. The software modules  248  of the cloud worker nodes  246  are communicatively coupled to a search service (e.g., including a DFS master  250  or search service provider), which is communicatively coupled to a daemon  252  of the search head  244  to collectively carry out operations of the cloud-based system  242 . 
     The cloud-based system  242  includes index cache components  254 . The index cache components  254  are communicatively coupled to cloud storage  256 , which can form a global index  258 . The index cache components  254  are analogous to indexers, and the cloud storage  256  is analogous to internal data stores described elsewhere in this disclosure. The index cache components  254  are communicatively coupled to the cloud worker nodes  246 , which can collect partial search results from the cloud storage  256  by applying a search query to the index cache components  254 . 
     Lastly, the cloud worker nodes  246  can be communicatively coupled to one or more external data sources  260 . In some embodiments, only some of the cloud worker nodes  246  are coupled to the external data sources  260  while others are only coupled to the index cache components  254 . For example, the cloud worker nodes  246 - 1  and  246 - 3  are coupled to both the external data sources  260  and the index cache component  254 , while the cloud worker node  246 - 2  is coupled to the index cache component  254 - 1  but not the external data sources  260 . 
     The scale of the cloud-based system  242  can be changed dynamically as needed based on any number of metrics. For example, the scale can change based on pricing constraints. In another example, the scale of the EMR cluster of nodes can be configured to improve the performance of search operations. For example, the cloud-based system  242  can scale the EMR cluster depending on the scope of a search query to improve the efficiency and performance of search processing. 
     In some embodiments, the EMR clusters can have access to flexible data stores such as a Hadoop distributed file system (HDFS), Amazon simple storage services (S3), NoSQL, SQL, and custom SQL. Moreover, in some embodiments, the cloud-based system  242  can allow for a sharded query of data within these flexible data stores in a manner which makes scaling and aggregating partial search results (e.g., merging) most efficient while in place (e.g., reduces shuffling of partial search results between cloud worker nodes). 
     8.1. Cloud Deployment Operations 
       FIG.  28    is a flow diagram illustrating an example of a method  3000  performed in a cloud-based DFS system (“cloud-based system”) according to some embodiments of the present disclosure. The operations of the cloud-based system are analogous to those described elsewhere in this disclosure with reference to other embodiments and, as such, a person skilled in the art would understand those operations in the context of a cloud deployment. Accordingly, a description of the flow diagram highlights some distinctions of the cloud deployment over other embodiments described herein. 
     In step  3002 , the search head of the cloud-based system receives a search query. In step  3004 , the cloud-based system determines the type of EMR cluster to use based on the scope of the received search query. For example, the cloud-based system can support two different types of EMR clusters. In a first type scenario, a single large EMR cluster could be used for all search operations. In a second type scenario, subsets of smaller EMR clusters can be used for each type of search load. That is, a smaller subset of an EMR cluster can be used for a less complex aggregation processing of partial search results from different data sources. In some embodiments, the scale of an EMR cluster for the first or second type can be set for each search load by a user or based on a role quota. In other words, the scale of the EMR cluster can depend on the user submitting the search query and/or the user&#39;s designated role in the cloud-based system. 
     In step  3006 , the cloud-based system is dynamically scaled based on the needs determined from the received search query. For example, the search heads or cloud worker nodes can be scaled under the control of a search service to grow or shrink as needed based on the scale of the EMR cluster used to process search operations. 
     In step  3008 , the cloud worker nodes can collect the partial search results from various data sources. Then, in step  3010 , the cloud worker nodes can aggregate the partial search results collected from the various data sources. Since the cloud worker nodes can scale dynamically, this allows for aggregating (e.g., merging) partial search results in an EMR cluster of any scale. 
     In step  3012 , the resulting aggregated search results can be computed and reported at scale to the search head or search service provider. Thus, the cloud-based system can ensure that data (e.g., partial search results) from diverse data sources (e.g., including time-indexed events with raw data or other type of data) are reduced (e.g., combined) at scale on each EMR node of the EMR cluster before sending the aggregated search results to the search head or search service provider. 
     The cloud-based system may include various other features that improve on the data intake and query system extended by the DFS system. For example, in some embodiments, the cloud-based system can collect metrics which can allow for a heuristic determination of spikes in DFS search requirements. The determination can also be accelerated through auto-scaling of the EMR clusters. 
     In some embodiments, the cloud-based system can allow DFS apps of the data intake and query system to be bundled and replicated over an EMR cluster to ensure that they are executed at scale. Lastly, the cloud-based system can include mechanisms that allow user- or role-quota-honoring based on a live synchronization between the data intake and query system user management features and a cloud access control features. 
     9.0. Timeline Visualization 
     The disclosed embodiments include techniques for organizing and presenting search results obtained from within a big data ecosystem via a data intake and query system. In particular, a data intake and query system may cause output of the search results or data indicative of the search results on a display device. An example of a display device is the client device  22  shown in  FIG.  1 A  connected to the data intake and query system  16  over the network  33 . 
     For example, the data intake and query system  16  can receive a search query input by a user at the client device  22 . The data intake and query system  16  can run the query on distributed data systems to obtain search results. The search results are then communicated to the client device  22  over the network  33 . The search results can be rendered in a visual way on the display of the client device  22  using items such as windows, icons, menus, and other graphics or controls. 
     For example, a client device can run a web browser that renders a website, which can grant a user access to the data intake and query system  16 . In another example, the client device can run a dedicated application that grants a user access to the data intake and query system  16 . In either case, the client device can render a graphical user interface (GUI), which includes components that facilitate submitting search queries, and facilitate interacting with and interpreting search results obtained by applying the submitted search queries on distributed data systems of a big data ecosystem. 
     The disclosed embodiments include a timeline tool for visualizing the search results obtained by applying a search query to a combination of internal data systems and/or external data systems. The timeline tool includes a mechanism that supports visualizing the search results by organizing the search results in a time-ordered manner. For example, the search results can be organized into graphical time bins. The timeline tool can present the time bins and the search results contained in one or more time bins. Hence, the timeline tool can be used by an analyst to visually investigate structured or raw data events which can be of interest to the analyst. 
     The timeline mechanism supports combining timestamped and non-timestamped search results obtained from diverse data systems to present a visualization of the combined search results. For example, a search query may be applied to the external data systems that each use different compute resources and run different execution engines. The timeline mechanism can harmonize the search results from these data systems, and a GUI rendered on a display device can present the harmonized results in a time-ordered visualization. 
       FIG.  29    is a flowchart of a method  3100  for illustrating a timeline mechanism that supports rendering search results in a time-ordered visualization according to some embodiments of the present disclosure. For example, the search head can dictate to the DFS master whether a cursored or reporting search should be executed, or a search service provider can make this determination. The search service provider can define a search scheme and/or search process and create a DAG. The DAG can orchestrate the search operations performed by the worker nodes for the cursored or reporting search. 
     In step  3102 , the search service receives an indication that a request for a timeline visualization was received by the data intake and query system. For example, a user may input a request for a timeline visualization before, after, or when a search query is input at a client device. In another example, the data intake and query system automatically processes time-ordered requests to visualize in a timeline 
     In step  3104 , the search service determines whether the requested visualization is for the search results of a cursored search or a time-ordered reporting search. For example, a cursored search may query indexers of the data intake and query system as well as external data stores for a combination of time ordered partial search results. In another example, a time-ordered reporting search may require querying the indexers and external data stores for a time-ordered statistic based on the combination of time ordered partial search results. 
     The search results for the timeline tool can be obtained in accordance with a “Fast,” “Smart,” or “Verbose” search mode depending on whether a cursored search or a reporting search was received. In particular, a cursored search supports all three modes whereas a reporting search may only support the Verbose mode. The Fast mode prioritizes performance of the search and does not return nonessential search results. This means that the search returns what is essential and required. The Verbose mode returns all of the field and event data it possibly can, even if the search takes longer to complete, and even if the search includes reporting commands. Lastly, the default Smart mode switches between the Fast and Verbose modes depending on the type of search being run (e.g., cursored or reporting). 
     In step  3106 , if the search is a cursored search, the search service creates buckets for the search results obtained from distributed data systems. The buckets are created based on a timespan value. The timespan value may be a default value or a value selected by a user. For example, a timespan value may be 24 hours. The buckets may each represent a distinct portion of the timespan. For example, each bucket may represent a distinct hour over a time-span of 24 hours. 
     The number of buckets that are created may be a default value depending on the timespan, or depending on the number of data systems from which search results were collected. For example, a default number of buckets (e.g., 1,000 buckets) may be created to span a default or selected timespan. In another example, distinct and unique buckets are created for portions of the timespan. In another example, a unique bucket is created per data system. In yet another example, buckets are created for the same portion of the timespan but for different data systems. 
     In step  3108 , search results obtained by application of the search query to the different data systems are collected into the search buckets. For example, each bucket can collect the partial search results from different data systems that are timestamped with values within the range of the bucket. As such, the buckets support the timeline visualization by organizing the search results. 
     In step  3110 , the search service transfers a number of search results contained in the buckets to the search head. However, the search service may need to collect all the search results from across the data systems into the buckets before transferring the search results to the search head to ensure that the timeline visualization is rendered accurately. Moreover, the search results of the bucket may be transferred from the buckets in chronological order. For example, the contents of the buckets representing beginning of the timespan are transferred first, and the contents of the next buckets in time are transferred next, and so on. 
     In some embodiments, the number of search results transferred to the search head from the buckets may be a default or maximum value. For example, the first 1,000 search results from the buckets at the beginning of the timespan may be first transferred to the search head first. In some embodiments, the search service transfer a maximum number of search results per bin to the search head. In other words, the number of search results transferred to the search head corresponds to the maximum number that can be contained in one or more bin of the timeline visualization. Lastly, in step  3112 , the search results of the reporting search received by the search head from the buckets are rendered in a timeline visualization. 
     In step  3114 , if the search is a time-ordered reporting search, the search service creates buckets for the search results obtained from distributed data systems. The buckets can be created based on the number of shards or partitions from which the search results are collected. 
     In step  3116 , the search results are collected from across the partitions. For external data sources, partial search results (e.g., treated as raw events) are collected from across the shards/partitions in time-order and transferred to the timeline mechanism. In case of external data systems which have the capability to support sharded partitions, multiple worker nodes can request for each specific shard or partition. If needed, each partition can be sorted based on user specified constraints such as, for example, a time ordering constraint. For sorting purposes, sometimes instead of unique shards, the DFS system can provide overlapping shards. For overlapping buckets across multiple data sources, the search service may need to collect partial search results across the different data sources before sending search results to the search head. 
     In step  3118 , the search service transfers a number of search results contained in the buckets to the search head. However, the search service may need to collect all the search results from across the data systems into the buckets before transferring the search results to the search head to ensure that the timeline visualization is rendered accurately. Moreover, the search results of the bucket may be transferred from the buckets in chronological order. For example, the contents of the buckets representing beginning of the timespan are transferred first, and the contents of the next buckets in time are next, and so on. 
     In some embodiments, the number of search results transferred to the search head from the buckets may be a default or maximum value. For example, the first 1,000 search results from the buckets at the beginning of the timespan may be first transferred to the search head first. In some embodiments, the search service transfers a maximum number of search results per bin to the search head. In other words, the number of search results transferred to the search head corresponds to the maximum number that can be contained in one or more bin of the timeline visualization. Lastly, in step  3120 , the search results of the reporting search received by the search head from the buckets are rendered in a timeline visualization. 
       FIG.  30    illustrates a timeline visualization rendered on a user interface  62  in which an embodiment may be implemented. The timeline visualization presents event data obtained in accordance with a search query submitted to a data intake and query system. In the illustrated embodiment, the search query is input to search field  64  using SPL, in which a set of inputs is operated on by a first command line, and then a subsequent command following the pipe symbol “|” operates on the results produced by the first command, and so on for additional commands. As shown, a command on the left of the pipe symbol can set the scope of the search, which could include external data systems. Other commands on the right of the pipe symbol (and subsequent pipe symbols) can specify a field name and/or statistical operation to perform on the data sources. 
     In some embodiments, the search head or search service provider can implement specific mechanism to parse the SPL. The search head or search service provider can determine that some portion of the search query is to be executed on the worker nodes base on the scope of the search query. In some embodiments, the search query can include a specific search command that triggers the search head to realize which portion of the search query should be executed by the DFS system. As a result, the phase generator can define the search phases, and where each of those phases will be executed. In addition, once the phase generator decides an operation needs to be executed by the DFS system, the search head or search service provider can optimize to push as much of the search operation as possible, for example, first to the external data source and then to the DFS system. In some embodiments, only the commands not included in the DFS command set will be executed back on the search head or search service provider once the results are retrieved to the search head or search service provider. 
     The timeline visualization presents multiple dimensions of data in a compact view, which reduced the cognitive burden on analysts viewing a complex collection of data from internal and/or external data systems. That is, the timeline visualization provides a single unified view to facilitate analysis of events stored across the big data ecosystem. Moreover, the timeline visualization includes selectable components to manipulate the view in a manner suitable for the needs of an analyst. 
     The timeline visualization includes a graphic  66  that depicts a summary of the search results in a timeline lane (e.g., in the form of raw events), as well as a list of the specific search results  68 . As shown, the timeline summary of the search results are presented as rectangular bins that are chronologically ordered and span a period of time (e.g., Sep. 5, 2016 5:00 PM through Sep. 6, 2016 3:00 PM). The height of a bin represents the magnitude of the quantity of events in that group relative to another group arranged along the timeline. As such, the height of each bin indicates a count of events for a subset of the period of events relative to other counts for other bins within the period of time. The events in a group represented by a bin may have a timestamp value included in the range of time values of the corresponding bin. Below the timeline summary is a listing of events of the search results presented in chronological order. 
       FIG.  31    illustrates a selected bin  70  of the timeline visualization and the contents of the selected bin  70  according to some embodiments of the present disclosure. Specifically, the timeline visualization may include graphic components that enable an analyst to investigate additional dimensions of the search results summarized in the timeline. As shown, each bin representing a group of events may be selectable by an analyst. Selecting a bin may cause the GUI to display the specific group of events associated with the bin in the list below the timeline summary. Specifically, selecting a bin may cause the GUI to display the events of the search results that are timestamped within a range of the corresponding group. 
     The timeline visualization is customizable and adaptable to present search results in various convenient manners. For example, a user can change the ordering of groups of events to obtain a different visualization of the same groups. In another example, a user can change the range of the timeline to obtain a filtered visualization of the search results. In yet another example, a user can hide some events to obtain a sorted visualization of a subset of the search results. 
     In some embodiments, the activity for each data system may appear in a separate timeline lane. If an activity start-time and duration are available for a particular data system, the respective timeline may show a duration interval as a horizontal bar in the lane. If a start time is available, the timeline visualization may render an icon of that time on the visualization. As such, the timeline visualization can be customized and provide interactive features to visualize search results, and communicate the results in dashboards and reports. 
     Thus, the timeline visualization can support a timeline visualization of external data systems, where each external data system may operate using different compute resources and engines. For example, the timeline visualization can depict search results obtained from one or more external data systems, collated and presented in a single and seamless visualization. As such, the timeline visualization is a tool of underlying logic that facilitates investigating events obtained from any of the external data systems, internal data systems (e.g., indexers), or a combination of both. 
     The underlying logic can manage and control the timeline visualization rendered on the GUI in response to data input and search results obtained from within the big data ecosystem. In some embodiments, the underlying logic is under the control and management of the data intake and query system. As such, an analyst can interface with the data intake and query system to use the timeline visualization. For example, the timeline logic can cause the timeline visualization to render activity time intervals and discrete data events obtained from various data system resources in internal and/or external data systems. 
     The underlying logic includes the search service. Since the bins may include events data from multiple data systems, each bin can represent an overlapping bin across multiple data systems. Accordingly, the search service can collect the data events across the different data systems before sending them to the search head. To finalize a search operation, the search service may transmit the maximum number of events per bin or the maximum size per bin to the search head. 
     In some embodiments, the underlying logic uses the search head of the data intake and query system to collect data events from the various data systems that are presented on the timeline visualization. In some embodiments, the events are collected in accordance with any of the methods detailed above, and the timeline visualization is a portal for viewing the search results obtained by implementing those methods. As such, the collected events can have timestamps indicative of, for example, times when the event was generated. 
     The timestamps can be used by the underlying logic to sort the events into the bins associated with any parameter such as a time range. For example, the underlying logic may include numerous bins delineated by respective chronological time ranges over a total period of time that includes all the bins. In some embodiments, a maximum amount of events transferred into the time bins could be set. 
     In some embodiments, the underlying logic of the timeline visualization can automatically create bins for a default timespan in response to cursored searches of ordered data. For example, an analyst may submit a cursored search, and the underlying logic may cause the timeline visualization to render a display for events within a default timespan. The amount and rate at which the events are transferred to the search head for subsequent display on the timeline visualization could vary under the control of the underlying logic. For example, a maximum number of events could be transferred on a per bin basis by the worker nodes to the search head. As such, the DFS system could balance the load on the network. 
     In some embodiments, the underlying logic of the timeline visualization can utilize the sharding mechanism detailed above for reporting searches of ordered data from external data systems. Specifically, the data could be sharded across partitions in response to a reporting search, where executors have overlapping partitions. Further, the underlying logic may control the search head or search service provider to collect the events data across the shards/partitions in time order for rendering on the timeline visualization. Under either the cursored search or reporting search, the underlying logic may impose the maximum size of total events transferred into bins. 
     10.0. Monitoring and Metering Services 
     The disclosed embodiments also include monitoring and metering services of the DFS system. Specifically, these services can include techniques for monitoring and metering metrics of the DFS system. The metrics are standards for measuring use or misuse of the DFS system. Examples of the metrics include data or components of the DFS system. For example, a metric can include data stored or communicated by the DFS system or components of the DFS system that are used or reserved for exclusive use by customers. The metrics can be measured with respect to time or computing resources (e.g., CPU utilization, memory usage) of the DFS system. For example, a DFS service can include metering the usage of particular worker nodes by a customer over a threshold period of time. 
     In some embodiments, a DFS service can meter the amount hours that a worker node spends running one or more tasks (e.g., a search requests) for a customer. In another example, a DFS service can meter the amount of resources used to run one or more tasks rather than, or in combination with, the amount of time taken to complete the task(s). In some embodiments, the licensing approaches include the total DFS hours used per month billed on a per hour basis; the maximum capacity that can be run at any one time, e.g. the total number of workers with a cap on the amount of size of each worker defined by CPU and RAM available to that worker; and finally a data volume based approach where the customer is charged by the amount of data brought into the DFS for processing. 
       FIG.  32    is a flow diagram illustrating monitoring and metering services of the DFS system according to some embodiments of the present disclosure. In the illustrated embodiment, in step  3202 , the DFS services can monitor one or more metrics of a DFS system. The DFS services can monitor the DFS system for a variety of reasons. For example, in step  3204 , a DFS service can track metrics and/or display monitored metrics or data indicative of the monitored metrics. Hence, the metrics can be preselected by, for example, a system operator or administrator seeking to analyze system stabilities, instabilities, or vulnerabilities. 
     In some embodiments, the DFS services can meter use of the DFS system as a mechanism for billing customers. For example, in step  3206 , the DFS services can monitor specific metrics for specific customers that use the DFS system. The metering services can differ depending on whether the customer has a subscription to use the DFS system or is using the DFS system on an on-demand basis. As such, a DFS service can run a value-based licensing agreement that allows customers to have a fair exchange of value for their use of the DFS service. 
     In step  3208 , a determination is made about whether a customer has a subscription to use the DFS system. The subscription can define the scope of a license granted to a customer to access or use the DFS system. The scope can define an amount of functionality available to the customer. The functionality can include, for example, the number or types of searches that can be performed on the DFS system. In some embodiments, the scope granted to a user can vary in proportion to cost. For example, customers can purchase subscriptions of different scope for different prices, depending on the needs of the customers. As such, a DFS service can run a value-based licensing agreement that allows customers to have a fair exchange of value for their use of the DFS service. 
     In step  3210 , if the customer is subscribed, the DFS service can meter metrics based on a subscription purchased by the customer. For example, a subscription to a DFS service may limit the amount of searches that a customer can submit to the DFS system. As such, the DFS service will meter the number of searches that are submitted by the customer. In another example, a subscription to the DFS service may limit the time a user can actively access a DFS service. As such, the DFS service will meter the amount of time that a user spends actively using the DFS service. 
     In step  3212 , a DFS service determines whether the customer&#39;s use of the DFS system exceeded a threshold amount granted by the subscription. For example, a customer may exceed the scope of a paid subscription by using functionality not included in the paid subscription or using more functionality than that granted by the subscription. In some embodiments, the excess use can be measured with respect to a metric such as time or use of computing resources. 
     In step  3212 , a DFS service determines whether a customer exceeded the scope of the customer&#39;s subscription. In step  3214 , if the customer did not exceed the subscription, no action is taken (e.g., the customer is not charged additional fees). Referring back to step  3212 , a variety of actions can be taken if the customer has exceed the subscription. In step  3216 , the DFS service can charge the customer for the excess amount of the metered metric. For example, the DFS service may begin metering the amount of time a customer spends using the DFS system after a threshold amount of time has been exceeded. In step  3218 , the DFS service can alternatively or additionally prevent the customer from accessing the DFS system if the customer exceeds the subscription or has not paid the additional charges of step  3216 . 
     Referring back to step  3208 , if the customer is not subscribed to a DFS subscription service, then customer may still access the DFS system through a variety of other techniques. For example, a DFS service may provide limited or temporary access to the DFS system to a non-subscribed customer. In another example, a DFS service may provide access to the DFS service on-demand. 
     Either way, in step  3220 , a DFS service meters metrics on a non-subscription basis. For example, in step  3222 , the customer can pay for each instance the customer uses the DFS system. In another example, in step  3224 , a DFS service can start charging a non-subscribed customer for using the DFS system once the metrics of the service exceed a threshold amount. For example, a DFS service may provide free limited access or temporary full access to the DFS system. When the measuring metrics exceed the free limited access, the customer may be charged for access that exceeds the free amount. In either case, in step  3218 , the DFS service can prevent the customer from accessing the DFS system if the measuring metrics exceed the threshold amount or the customer has not paid the charges of step  3222  or  3224 . In some embodiments, a DFS server can allow the customer to complete an active search that exceeded a measuring metric but deny the customer from using the DFS system any further until additional payment authorized. 
     11.0. Data Intake and Fabric System Architecture 
       FIG.  33    is a system diagram illustrating an environment  3300  for ingesting and indexing data, and performing queries on one or more datasets from one or more dataset sources. In the illustrated embodiment, the environment  3300  includes data sources  201 , client devices  404 , described in greater detail above with reference to  FIG.  4   , and external data sources  3318  communicatively coupled to a data intake and query system  3301 . The external data sources  3318  can be similar to the external data systems  12 - 1 ,  12 - 2  described above with reference to  FIG.  1 A  or the external data sources described above with reference to  FIG.  4     
     In the illustrated embodiment, the data intake and query system  3301  includes any combination of forwarders  204 , indexers  206 , data stores  208 , and a search head  210 , as discussed in greater detail above with reference to  FIGS.  2 - 4   . For example, the forwarders  204  can forward data from the data sources  203  to the indexers  206 , the indexers  206  can ingest, parse, index, and store the data in the data stores  208 , and the search head  210  can receive queries from, and provide the results of the queries to, client devices  404  on behalf of the system  3301 . 
     In addition to forwarders  204 , indexers  206 , data stores  208 , and the search head  210 , the system  3301  further includes a search process master  3302  (in some embodiments also referred to as DFS master), one or more query coordinators  3304  (in some embodiments also referred to as search service providers), worker nodes  3306 , and a query acceleration data store  3308 . In some embodiments, a workload advisor  3310 , workload catalog  3312 , node monitor  3314 , and dataset compensation module  3316  can be included in the search process master  3302 . However, it will be understood that any one or any combination of the workload advisor  3310 , workload catalog  3312 , node monitor  3314 , and dataset compensation module  3316  can be included elsewhere in the system  3301 , such as in as a separate device or as part of a query coordinator  3304 . 
     As will be described in greater detail below, the functionality of the search head  210  and the indexers  206  in the illustrated embodiment of  FIG.  33    can differ in some respects from the functionality described previously with respect to other embodiments. For example, in the illustrated embodiment of  FIG.  33   , the search head  210  can perform some processing on the query and then communicate the query to the search process master  3302  and coordinator(s)  3304  for further processing and execution. For example, the search head  210  can authenticate the client device or user that sent the query, check the syntax and/or semantics of the query, or otherwise determine that the search request is valid. In some cases, a daemon running on the search head  210  can receive a query. In response, the search head  210  can spawn a search process to further handle the query, including communicating the query to the search process master  3302  or query coordinator  3304 . Upon completion of the query, the search head  210  can receive the results of the query from the search process master  3302  or query coordinator  3304  and serve the results to the client device  404 . In such embodiments, the search head  210  may not perform any additional processing on the results received from the search process master  3302  or query coordinator  3304 . In some cases, upon receiving and communicating the results, the search head  210  can terminate the search process. 
     In addition, the indexers  206  in the illustrated embodiment of  FIG.  33    can receive the relevant subqueries from the query coordinator  3304  rather than the search head  210 , search the corresponding data stores  208  for relevant events, and provide their individual results of the search to the worker nodes  3306  instead of the search head  210  for further processing. As described previously, the indexers  206  can analyze events for a query in parallel. For example, each indexer  206  can search its corresponding data stores  208  in parallel and communicate its partial results to the worker nodes  3306 . 
     The search head  210 , search process master  3302 , and query coordinator  3304  can be implemented using separate computer systems, processors, isolated execution environments (e.g., container, virtual machines, etc.), or may alternatively comprise separate processes executing on one or more computer systems, processors, or isolated execution environments. In some embodiments, running the search head  210 , search process master  3302 , and/or query coordinator  3304  on the same machine can increase performance of the system  3301  by reducing communications over networks. In either case, the search process master  3302  and query coordinator  3304  can be communicatively coupled to the search head  210 . 
     The search process master  3302  and query coordinator  3304  can be used to reduce the processing demands on the search head  210 . Specifically, the search process master  3302  and coordinator  3304  can perform some of the preliminary query processing to reduce the amount of processing done by the search head  210  upon receipt of a query. In addition, the search process master  3302  and coordinator  3304  can perform some of the processing on the results of the query to reduce the amount of processing done by the search head  210  prior to communicating the results to a client device. For example, upon receipt of a query, the search head  210  can determine that the query can be processed by the search process master  3302 . In turn, the search process master  3302  can identify a query coordinator  3304  that can process the query. In some cases, if there is not a query coordinator  3304  that can handle the incoming query, the search process master  3302  can spawn an additional query coordinator  3304  to handle the query. 
     The query coordinator(s)  3304  can coordinate the various tasks to execute queries assigned to them and return the results to the search head  210 . For example, as will be described in greater detail below, the query coordinator  3304  can determine the amount of resources available for a query, allocate resources for the query, determine how the query is to be broken up between dataset sources, generate commands for the dataset sources to execute, determine what tasks are to be handled by the worker nodes  3306 , spawn the worker nodes  3306  for the different tasks, instruct different worker nodes  3306  to perform the different tasks and where to route the results of each task, monitor the worker nodes  3306  during the query, control the flow of data between the worker nodes  3306 , process the aggregate results from the worker nodes  3306 , and send the finalized results to the search head  210  or to another dataset destination. In addition, the query coordinators  3304  can provide data isolation across different searches based on role/access control, as well as fault tolerance (e.g., localized to a search head). For example, if a search operation fails, then its spawned query coordinator  3304  may fail but other query coordinators  3304  for other queries can continue to operate. In addition, queries that are to be isolated from one another can use different query coordinators  3304 . 
     The worker nodes  3306  can perform the various tasks assigned to them by a query coordinator  3304 . For example, the worker nodes  3306  can intake data from the various dataset sources, process the data according to the query, collect results from the processing, combine results from various operations, route the results to various destinations, etc. In certain cases, the worker nodes  3306  and indexers  206  can be implemented using separate computer systems, processors, or isolated execution environments (e.g., containers, virtual machines, etc.), or may alternatively comprise separate processes executing on one or more computer systems, processors, or virtual machines. Moreover, the worker nodes  3306  can be similar to or perform functions similar to worker nodes  214  described herein. 
     The query acceleration data store  3308  can be used to store datasets for accelerated access. In some cases, the worker nodes  3306  can obtain data from the indexers  206 , external data sources  3318 , or other location (e.g., common storage, ingested data buffer, etc.) and store the data in the query acceleration data store  3308 . In such embodiments, when a query is received that relates to the data stored in the query acceleration data store  3308 , the worker nodes  3306  can access the data in the query acceleration data store  3308  and process the data according to the query. Furthermore, if the query also includes a request for datasets that are not in the query acceleration data store  3308 , the worker nodes  3306  can begin working on the dataset obtained from the query acceleration data store  3308 , while also obtaining the other dataset(s) from the other dataset source(s). In this way, a client device  414   a - 404   n  can rapidly receive a response to a provided query, while the worker nodes  3306  obtain datasets from the other dataset sources. 
     The query acceleration data store  3308  can be, for example, a distributed in-memory database system, storage subsystem, and so on, which can maintain (e.g., store) datasets in both low-latency memory (e.g., random access memory, such as volatile or non-volatile memory) and longer-latency memory (e.g., solid state storage, disk drives, and so on). To increase efficiency and response times, the accelerated data store  3308  can maintain particular datasets in the low-latency memory, and other datasets in the longer-latency memory. For example, the datasets can be stored in-memory (non-limiting examples: RAM or volatile memory) with disk spillover (non-limiting examples: hard disks, disk drive, non-volatile memory, etc.). In this way, the query acceleration data store  3308  can be used to serve interactive or iterative searches. In some cases, datasets which are determined to be frequently accessed by a user can be stored in the lower-latency memory. Similarly, datasets of less than a threshold size can be stored in the lower-latency memory. 
     As will be described below, a user can indicate in a query that particular datasets are to be stored in the query acceleration data store  3308 . The query can then indicate operations to be performed on the particular datasets. For subsequent queries directed to the particular datasets (e.g., queries that indicate other operations), the worker nodes  3306  can obtain information directly from the query acceleration data store  3308 . Additionally, since the query acceleration data store  3308  can be utilized to service requests from different clients  404   a - 404   n , the query acceleration data store  3308  can implement access controls (e.g., an access control list) with respect to the stored datasets. In this way, the stored datasets can optionally be accessible only to users associated with requests for the datasets. Optionally, a user who provides a query can indicate that one or more other users are authorized to access particular requested datasets. In this way, the other users can utilize the stored datasets, thus reducing latency associated with their queries. 
     In certain embodiments, the worker nodes  3306  can store data from any dataset source, including data from a dataset source that has not been transformed by the nodes  3306 , processed data (e.g., data that has been transformed by the nodes  3306 ), partial results, or aggregated results from a query in the query acceleration data store  3308 . In such embodiments, the results stored in the query acceleration data store  3308  can be served at a later time to the search head  210 , combined with additional results obtained from a later query, transformed or further processed by the worker nodes  3306 , etc. 
     It will be understood that the system  3301  can include fewer or more components as desired. For example, in some embodiments, the system  3301  does not include a search head  210 . In such embodiments, the search process master  3302  can receive query requests from clients  404  and return results of the query to the client devices  404 . Further, it will be understood that in some embodiments, the functionality described herein for one component can be performed by another component. For example, although the workload advisor  3310  and dataset compensation module  3316  are described as being implemented in the search process master  3302 , it will be understood that these components and their functionality can be implemented in the query coordinator  3304 . Similarly, as will be described in greater detail below, in some embodiments, the nodes  3306  can be used to index data and store it in one or more data stores, such as the common storage or ingested data buffer, described in greater detail below. 
     11.1. Worker Nodes 
       FIG.  34    is a block diagram illustrating an embodiment of multiple machines  3402 , each having multiple nodes  3306 - 1 ,  3306 - n  (individually and collectively referred to as node  3306  or nodes  3306 ) residing thereon. The worker nodes  3306  across the various machines  3402  can be communicatively coupled to each other, to the various components of the system  3301 , such as the indexers  206 , query coordinator  3304 , search head  210 , common storage, ingested data buffer, etc., and to the external data sources  3318 . 
     The machines  3402  can be implemented using multi-core servers or computing systems and can include an operating system layer  3404  with which the nodes  3306  interact. For example, in some embodiments, each machine  3402  can include 32, 48, 64, or more processor cores, multiple terabytes of memory, etc. 
     In the illustrated embodiment, each node  3306  includes four processors  3406 , memory  3408 , a monitoring module  3410 , and a serialization/deserialization module  3412 . It will be understood that each node  3306  can include fewer or more components as desired. Furthermore, it will be understood that the nodes  3306  can include different components and resources from each other. For example, node  3306 - 1  can include fewer or more processors  3406  or memory  3408  than the node  3306 - n.    
     The processors  3406  and memory  3408  can be used by the nodes  3306  to perform the tasks assigned to it by the query coordinator  3304  and can correspond to a subset of the memory and processors of the machine  3402 . Thus, reference to a worker node  3306  can also be understood to be a reference to one or more processors  3406  of a worker node  3306  and vice versa (e.g., allocating, assigning, or selecting a worker node  3306  can refer to allocating, assigning, or selecting one or more processors  3406  of a worker node  3306 ). The serialization/deserialization module  3412  can be used to serialize/deserialize data for communication between components of the system  3301 , as will be described in greater detail below. 
     The monitoring module  3410  can be used to monitor the state and utilization rate of the node  3306  or processors  3406  and report the information to the search process master  3302  or query coordinator  3304 . For example, the monitoring module  3410  can indicate the number of processors in use by the node  3306 , the utilization rate of each processor, whether a processor is unavailable or not functioning, the amount of memory used by the processors  3406  or node  3306 , etc. 
     In addition, each worker node  3306  can include one or more software components or modules (“modules”) operable to carry out the functions of the system  3301  by communicating with the query coordinator  3304 , the indexers  206 , and the dataset sources. The modules can run on a programming interface of the worker nodes  3306 . An example of such an interface is APACHE SPARK, which is an open source computing framework that can be used to execute the worker nodes  3306  with implicit parallelism and fault-tolerance. 
     In particular, SPARK includes an application programming interface (API) centered on a data structure called a resilient distributed dataset (RDD), which is a read-only multiset of data items distributed over a cluster of machines (e.g., the devices running the worker nodes  3306 ). The RDDs function as a working set for distributed programs that offer a form of distributed shared memory. 
     Based on instructions received from the query coordinator  3304 , the worker nodes  3306  can collect and process data or partial search results of a distributed network of data storage systems, and provide aggregated partial search results or finalized search results to the query coordinator  3304  or other destination. Accordingly, the query coordinator  3304  can act as a manager of the worker nodes  3306 , including their distributed data storage systems, to extract, collect, and store partial search results via their modules running on a computing framework such as SPARK. However, the embodiments disclosed herein are not limited to an implementation that uses SPARK. Instead, any open source or proprietary computing framework running on a computing device that facilitates iterative, interactive, and/or exploratory data analysis coordinated with other computing devices can be employed to run the modules  218  for the query coordinator  3304  to apply search queries to the distributed data systems. 
     As a non-limiting example, as part of processing a query, a node  3306  can receive instructions from a query coordinator  3304  to perform one or more tasks. For example, the node  3306  can be instructed to intake data from a particular dataset source, parse received data from a dataset source to identify relevant data in the dataset, collect partial results from the parsing, join results from multiple datasets, or communicate partial or completed results to a destination, etc. In some cases, the instructions to perform a task can come in the form of a DAG. In response, the node  3306  can determine what task it is to perform in the DAG, and execute it. 
     As part of performing the assigned task, the node  3306  can determine how many processors  3406  to allocate to the different tasks. In some embodiments the node can determine that all processors  3406  are to be used for a particular task or only a subset of the processors  3406 . In certain embodiments, each processor  3406  of the node  3306  can be used in association with one or more a partitions to intake, process, or collect data according to a task. Upon completion of the task, the node  3306  can inform the query coordinator  3304  that the task has been completed. 
     Depending on its context, partition can refer to different things. For example, in some cases, a partition can refer to a set of data in one or more data stores, such as an index, or a stream of data. In certain cases, a partition can refer to smaller sets of data, such as when data is partitioned (or split up) into smaller parts. In yet other cases, one or more partitions can be assigned to a processor  3406  or a worker node  3306 , and reference to a partition performing an action can refer to a processor  3406  performing the action on one or more groups of data or data entries assigned thereto. Similarly, in some cases, reference to assigning a job or action to a partition can refer to the assignment of a processor  3406  or worker node  3306  to perform that job or action. For example, the assignment of a partition to receive data from an external data source can refer to a processor  3406  receiving data from the external data source and grouping the data into one or more groups or partitions of data. Thus, as used herein and based on the context provided, a partition can refer to an index, a task, a set or group of data, data entries, events, or records, or can refer to a processor  3406  that performs a particular action on one or more groups or sets of data, data entries, or records. Further, in some instances, a partition can refer to a group of data, data entries, events, or records and computer-executable instructions that indicate how the group of data is to be processed by a processor  3406  or worker node  3306 . 
     When instructed to intake data, the processors  3406  of the node  3306  can be used to communicate with a dataset source (non-limiting examples: external data sources  3318 , indexers  206 , common storage, query acceleration data store  3308 , ingested data buffer, etc.). Once the node  3306  is in communication with the dataset source it can intake the data from the dataset source. As described in greater detail below, in some embodiments, multiple processors of a node (or different nodes) can be assigned to intake data from a particular source as one or more partitions. 
     When instructed to parse or otherwise process data, the processors  3406  of the node  3306  can be used to review the data and identify portions of the data that are relevant to the query. For example, if a query includes a request for events with certain errors or error types, the processors  3406  of the node  3306  can parse the incoming data to identify different events, parse the different events to identify error fields or error keywords in the events, and determine the error type of the error. In some cases, this processing can be similar to the processing described in greater detail above with reference to the indexers  206  processing data to identify relevant results in the data stores  208 . 
     When instructed to collect data, the processors  3406  of the node  3306  can be used to receive data from dataset sources or processing nodes. With continued reference to the error example, a collector partition or processor  3406  can collect all of the errors of a certain type from one or more parsing partitions or processors  3406 . For example, if there are seven possible types of errors coming from a particular dataset source, a collector partition could collect all type 1 errors (or events with a type 1 error), while another collector partition could collect all type 2 errors (or events with a type 2 error), etc. 
     When instructed to join results from multiple datasets, the processors  3406  of the node  3306  can be used to receive data corresponding to two different datasets and combine or further process them. For example, if data is being retrieved from an external data source and a data store  208  of the indexers  206 , join partitions could be used to compare and collate data from the different data stores in order to aggregate the results. 
     When instructed to communicate results to a particular destination, the processors  3406  of the node  3306  can be used to prepare the data for communication to the destination and then communicate the data to the destination. For example, in communicating the data to a particular destination, the node  3306  can communicate with the particular destination to ensure the data will be received. Once communication with the destination has been established, the partition, or processor associated with the partition, can begin sending the data to the destination. As described in greater detail below, in some embodiments, multiple partitions of a node (or different nodes) can be assigned to communicate data to a particular destination. Furthermore, the nodes  3306  can be instructed to transform the data so that the destination can properly understand and store the data. Furthermore, the nodes can communicate the data to multiple destinations. For example, one copy of the data may be communicated to the query coordinator  3304  and another copy can be communicated to the query acceleration data store  3308 . 
     The system  3301  is scalable to accommodate any number of worker nodes  3306 . As such, the system  3301  can scale to accommodate any number of distributed data systems upon which a search query can be applied and the search results can be returned to the search head and presented in a concise or comprehensive way for an analyst to obtain insights into big data that is greater in scope and provides deeper insights compared to existing systems. 
     11.1.1. Serialization/Deserialization 
     In some cases, the serialization/deserialization module  3412  can generate and transmit serialized event groups. An event group can include the following information: number of events in the group, header information, event information, and changes to the cache or cache deltas. The serialization/deserialization module  3412  can identify the differences between the pieces of information using a type code or token. In certain cases, the type code can be in the form of a type byte. For example, prior to identifying header information, the serialization/deserialization module  3412  can include a header type code indicating that header information is to follow. Similarly, type codes can be used to identify event data or cache deltas. 
     The header information can indicate the number and order of fields in the events, as well as the name of each field. Similarly, the event information for each event can include the number of fields in the event, as well as the value for that field. The cache deltas can identify changes to make to the cache relied upon to serialize/deserialize the data. 
     As part of generating the group and serializing the data, the serialization/deserialization module  3412  can determine the number of events to group, determine the order and field names for the fields in the events of the group, parse the events, determine the number of fields for each event, identify and serialize serializable field values in the event fields, and identify cache deltas. In some cases, the serialization/deserialization module  3412  performs the various tasks in a single pass of the data, meaning that it performs the identification, parsing, and serializing during a single review of the data. In this manner, the serialization/deserialization module  3412  can operate on streaming data and avoid adding delay to the serialization/deserialization process. 
     
       
         
           
               
             
               
                   
               
               
                 4 (number of events) 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 Header_Code 
                 5 
                 Data_Code “source” 
               
               
                   
                 (number of fields) 
                 Data_Code “sourcetype” 
               
               
                   
                   
                 Data_Code “sale_type” 
               
               
                   
                   
                 Data_Code “company name” 
               
               
                   
                   
                 Data_Code “price” 
               
               
                 Cache_Delta_Code 
                 5 
                 “source” x15 
               
               
                   
                 (entries to add) 
                 “sourcetype” x16 
               
               
                   
                   
                 “sale_type” x17 
               
               
                   
                   
                 “company name” x18 
               
               
                   
                   
                 “price” x19 
               
               
                   
                 0 
               
               
                   
                 (entries to drop) 
               
               
                 Event_Code 
                 5 
                 Data_Code “ronnie.sv.splunk.com” 
               
               
                   
                 (number of fields in event) 
                 Data_Code “access_combined” 
               
               
                   
                   
                 Data_Code “SALE” 
               
               
                   
                   
                 Data_Code “World of Cheese” 
               
               
                   
                   
                 Data_Code “14.95” 
               
               
                 Cache_Delta_Code 
                 5 
                 “ronnie.sv.splunk.com” x21 
               
               
                   
                 (number of new entries) 
                 “access_combined” x22 
               
               
                   
                   
                 “SALE” x23 
               
               
                   
                   
                 “World of Cheese” x24 
               
               
                   
                   
                 “14.95” x25 
               
               
                   
                 0 
               
               
                   
                 (entries to drop) 
               
               
                 Event_Code 
                 5 
                 Cache_Code x21 
               
               
                   
                 (number of fields in event) 
                 Cache_Code x22 
               
               
                   
                   
                 Data_Code “NO SALE” 
               
               
                   
                   
                 Cache_Code x24 
               
               
                   
                   
                 Data_Code “16.75” 
               
               
                 Cache_Delta_Code 
                 2 
                 “NO SALE” x26 
               
               
                   
                 (entries to add) 
                 “16.75” x27 
               
               
                   
                 0 
               
               
                   
                 (entries to drop) 
               
               
                 Event_Code 
                 4 
                 Cache_Code x21 
               
               
                   
                 (number of fields in event) 
                 Cache_Code x22 
               
               
                   
                   
                 Cache_Code x23 
               
               
                   
                   
                 Cache_Code x24 
               
               
                 Event_Code 
                 5 
                 Cache_Code x21 
               
               
                   
                 (number of fields in event) 
                 Cache_Code x22 
               
               
                   
                   
                 Cache_Code x23 
               
               
                   
                   
                 Data_Code “World of Cheese” 
               
               
                   
                   
                 Data_Code “20.95” 
               
               
                 Cache_Delta_Code 
                 2 
                 “World of Cheese” 
               
               
                   
                 (number of new entries) 
                 “20.95” 
               
               
                   
                 1 
                 x25 
               
               
                   
                 (entry to drop) 
               
               
                   
               
            
           
         
       
     
     In some embodiments, an event group includes an identifier indicating the number of events in the group followed by a header type code and a number of fields indicating the number of fields in the events. For each field designated by the header, the event group can include a type code indicating whether the field name is already stored in cache or a type code indicating that the field name is included. Depending on the type code, the event group can include an identifier or the field name. For example, if the type code indicates the field name is stored in cache (e.g., a cache code), an identifier can be included to enable a receiving component to lookup the field name using the cache. If the type code indicates the field name is not stored in cache (e.g., a data code), the name of the field name can be included. 
     Similar to the header information, for each event in the event group, the event group can include number of fields in the event. For each field of the event, the event group can include a type code indicating whether the field name is already stored in cache or a type code indicating that the field name is included. 
     As mentioned above, the event group can also include cache delta information. The cache delta information can include a cache delta type code indicating that the cache is to be changed, a number of new entries, and a number of dropped entries. For each new entry the cache delta information can include the data or string being cached, and an identifier for the data. For each entry being dropped, the cache delta information can include the identifier of the cache entry to be dropped. 
     As a non-limiting example, consider the following portions of events: 
     ronnie.sv.splunk.com, access_combined, SALE, World of Cheese, 14.95 
     ronnie.sv.splunk.com, access_combined, NO SALE, World of Cheese, 16.75 ronnie.sv.splunk.com, access_combined, SALE, World of Cheese 
     ronnie.sv.splunk.com, access_combined, SALE, Fondue Warrior, 20.95 
     In serializing the above-referenced events, the serialization/deserialization module  3412  can determine that the field names for the events are source, sourcetype, sale_type, company name, and price and that this information is not in cache. The serialization/deserialization module  3412  can then generate the following event group: 
     By generating the group, the serialization/deserialization module  3412  can reduce the amount of data communicated for each group. For example, instead of transmitting the string “ronnie.sv.splunk.com” each time, the serialization/deserialization module  3412  serializes it and then communicates the cache ID thereafter. 
     Entries can be added or dropped using a variety of techniques. In some cases, every new field value is cached. In certain cases, a field value is cached after it has been identified a threshold number of times. Similarly, an entry can be dropped after a threshold number of events or event groups have been processed without the particular value being identified. As a non-limiting example, the serialization/deserialization module  3412  can track X values at a time in a cache C and track up to Y values at a time that are not cached and how many time those values have been identified in a candidate set D. When a value is received, if it is in the cache C, then the identifier can be returned. If the value is not in the cache C, then it can be added to D. If Y has been reached in D, then the least recently used value can be dropped. If the count of the value in D satisfies a threshold T, then it can be moved to the cache C and receive an identifier. If the size of C is more than X, then the least recently used value in C can be dropped. 
     In some embodiments, the cache is built as the data is processed, and changes are transmitted as they occur. For example, the receiver can start with an empty cache, and apply each delta as it comes along. As mentioned above, each delta can have two sections: new entries, and dropped entries. In certain embodiments, the receiver (or deserializer) does not drop cache entries until told to do so, otherwise, it may not be able interpret identifiers received from the serializer. In such embodiments, the serializer performs cache maintenance by informing the deserializer when to drop entries. Upon receipt of such a command, the deserializer can remove the identified entries. 
     11.2. Search Process Master 
     As mentioned above, the search process master  3302  can perform various functions to reduce the workload of the search head  210 . For example, the search process master  3302  can parse an incoming query and allocate the query to a particular query coordinator  3304  for execution or spawn an additional query coordinator  3304  to execute the query. In addition, the search process master  3302  can track and store information regarding the system  3301 , queries, external data stores, etc., to aid the query coordinator  3304  in processing and executing a particular query. In some embodiments, the search process master  3302 . 
     In some cases, the search process master  3302  can determine whether a query coordinator  3304  should be spawned based on user information. For example, for data protection or isolation, the search process master  3302  can spawn query coordinators  3304  for different users. In addition, the search process master  3302  can spawn query coordinators  3304  if it determines that a query coordinator  3304  is over utilized. 
     In some cases, to accomplish these various tasks the search process master  3302  can include a workload advisor  3310 , workload catalog  3312 , node monitor  3314 , and dataset compensation module  3316 . Although illustrated as being a part of the search process master  3302 , it will be understood that any one or any combination of these components can be implemented separately or included in one or more query coordinators  3304 . Furthermore, although illustrated as individual components, it will be understood that any one or any combination of the workload advisor  3310 , workload catalog  3312 , node monitor  3314 , and dataset compensation module  3316  can be implemented by the same machine, processor, or computing device. 
     As a brief introduction, the workload advisor  3310  can be used to provide resource allocation recommendations to a query coordinator  3304  for processing queries, the workload catalog  3312  can store data related to previous queries, the node monitor  3314  can receive information from the worker nodes  3306  regarding a current status and/or utilization rate of the nodes  3306 , and the dataset compensation module  3316  can be used by the query coordinator  3304  to enhance interactions with external data sources. 
     11.2.1 Workload Catalog 
     The workload catalog  3312  can store relevant information to aid the workload advisor  3310  in providing a resource allocation recommendation to a query coordinator  3304 . As queries are received and processed by the system  3301 , the workload catalog  3312  can store relevant information about the queries to improve the workload advisor&#39;s  3310  ability to recommend the appropriate amount of resources for each query. For example, the system  3301  can track any one or any combination of the following data points about a query: which dataset sources were accessed, what was accessed in each dataset source (particular tables, buckets, etc.), the amount of data retrieved from the dataset sources (individually and collectively), the time taken to obtain the data from the dataset sources, the number of nodes  3306  used to obtain the data from each dataset source, the utilization rate of the nodes  3306  while obtaining the data from the dataset source, the number of transformations or phases (processing, collecting, reducing, joining, branching, etc.) performed on the data obtained from the dataset sources, the time to complete each transformation, the number of nodes  3306  assigned to each phase, the utilization rate of each node  3306  assigned to the particular phase, the processing performed by the query coordinator  3304  on results (individual or aggregatee), time to store or deliver results to a particular destination, resources used to store/deliver results, total time to complete query, time of day of query request, etc. Furthermore, the workload catalog can include identifying information corresponding to the datasets with which the system interacts (e.g., indexers, common storage, ingested data buffer, external data sources, query acceleration data store, etc.). This information can include, but is not limited to, relationships between datasets, size of dataset, rate of growth of dataset, type of data, selectivity of dataset, provider of dataset, indicator for private information (e.g., personal health information, etc.), trustworthiness of a dataset, dataset preferences, etc. 
     The workload catalog  3312  can collect the data from the various components of the system  3301 , such as the query coordinator  3304 , worker nodes  3306 , indexers  206 , etc. For example, for each task performed by each node  3306 , the node  3306  can report relevant timing and resource utilization information to the query coordinator  3304  or directly to the workload catalog  3312 . Similarly, the query coordinator  3304  can report relevant timing, usage, and data information for each phase of a search, each transformation of data, or for a total query. 
     Using the information collected in the workload catalog  3312 , the workload advisor  3310  can estimate the compute cost to perform a particular data transformation or query, or to access a particular dataset. Further, the workload advisor can determine the amount of resources (nodes, memory, processors, partitions, etc.) to recommend for a query in order to provide the results within a particular amount of time. 
     11.2.2 Node Monitor 
     The node monitor  3314  can also store relevant information to aid the workload advisor  3310  in providing a resource allocation recommendation. For example, the node monitor  3314  can track and store information regarding any one or any combination of: total number of processors or nodes in the system  3301 , number of processors or nodes that are not available or not functioning, number of available processors or nodes, utilization rate of the processors or nodes, number of worker nodes, current tasks being completed by the worker nodes  3306  or processors, estimated time to complete a task by the nodes  3306  or processors, amount of available memory, total memory in the system  3301 , tasks awaiting execution by the nodes  3306  or processors, etc. 
     The node monitor  3314  can collect the relevant information by communicating with the monitoring module  3410  of each node  3306  of the system  3301 . As described above, the monitoring modules  3410  of each node  3306  can report relevant information about the node state and utilization rate. Using the information from the node monitor  3314 , the workload advisor  3310  can ascertain the general state of any particular processor, node, or the system  3301 , and determine the number of nodes  3306  or processors  3406  available for a particular task or query. 
     11.2.3 Dataset Compensation 
     As discussed above, the external data sources  3318  with which the system  3301  can interact vary significantly. For example, some external data source may have processing capabilities that can be used to perform some processing on the data that resides there prior to communicating the data to the nodes  3306 . In addition, the external data sources  3318  may support parallel reads from multiple partitions. Conversely, other external data sources  3318  may not be able to perform much, if any, processing on the data contained therein and/or may only be able to provide serial reads from a single partition. Additionally, each external data source  3318  may have particular requirements for interacting with it, such as a particular API, throttling requirements, etc. Further, the type and amount of data stored in each external data source  3318  can vary significantly. As such, the system&#39;s  3301  interaction with the different external data sources  3318  can vary significantly. 
     To aid the system  3301  in interacting with the different external data sources  3318 , the dataset compensation model  3316  can include relevant information related to each external data source  3318  with which the system  3301  can interact. For example, the dataset compensation model  3316  can include any one or any combination of: the amount of data stored in an external data source  3318 , the type of data stored in an external data source, query commands supported by an external data source (e.g., aggregation, filtering ordering), query translator to translate a query into tasks supported by an external data source, the file system type and hierarchy of the external data source  3318 , number of partitions supported by an external data source  3318 , endpoint locations (e.g., location of processing nodes or processors), throttling requirements (e.g., number and rate at which requests can be sent to the external data source), etc. 
     The information about each external data source  3318  can be collected in a variety of ways. In some cases, some of the information about the external data source  3318  can be received when a customer sets up the external data source  3318  for use with the system  3301 . For example, a customer can indicate the type of external data source  3318  e.g., MySQL, PostgreSQL, and Oracle databases; NoSQL data stores like Cassandra, Mongo DB, cloud storage like Amazon S3 HDFS, etc. Based on this information, the system  3301  can determine certain characteristics about the external data store  3318 , such as whether it supports multiple partitions. 
     In addition, as discussed herein, different dataset sources have different capabilities. For example, not only can different datasets sources support a different number of partitions, but the dataset sources can support different functions. For example, some dataset sources may be capable of data aggregation, filtering, or ordering, etc., while others may not be. The dataset compensation module  3316  can store the capabilities of the different dataset sources to aid in providing a seamless experience to users. 
     In certain cases, the system  3301  can collect relevant information about an external data source by communicating with it. For example, the query coordinator  3304  or a worker node  3306  can interact with the external data source  3318  to determine the number of partitions available for accessing data. In some cases, the number of available partitions may change as computing resources on the external data source  3318  become available or unavailable, etc. In addition, when the system  3301  accesses the external data source  3318  as part of a query it can track relevant information, such as the tables or amount of data accessed, tasks that the external data source was able to perform, etc. Similarly, the system  3301  can interact with an external data source  3318  to identify the endpoint that will handle any subqueries and its location. The endpoint and endpoint location may change depending on the subquery that is to be run on the external data source. Accordingly, in some embodiments, the system  3301  can request endpoint information with each query that is to access the particular external data source. 
     Using the information about the external data sources  3318 , a query coordinator  3304  can determine how to interact with it and how to process data obtained from the external data source  3318 . For example, if an external data source  3318  supports parallel reads, the query coordinator  3304  can allocate multiple worker nodes  3306  to read the data from the external data source  3318  in parallel. In some embodiments, the query coordinator  3304  can allocate sufficient worker nodes  3306  or processors  3406  to establish a 1:1 relationship with the available partitions at the external data source  3318 . Similarly, if the external data source  3318  can perform some processing of the data, the query coordinator  3304  can use the information from the dataset compensation module  3316  to translate the query into commands understood by the external data source  3318  and push some processing to the external data source  3318 , thereby reducing the amount of system  3301  resources (e.g., nodes  3306 ) used to process the query. 
     Furthermore, in some cases, using the dataset compensation module  3316 , the query coordinator can determine the amount of data in the different external data sources that will be accessed by a particular query. Using that information, the query coordinator  3304  can intelligently interact with the external data sources  3318 . For example, if the query coordinator  3304  determines that data with similar characteristics in two external data sources are to be accessed and the data from each will eventually be combined, the query coordinator  3304  can first interact with or query the external data source  3318  that includes less data and then using information gleaned from that data prepare a more narrowly tailored query for the external data source  3318  with more data. 
     As a specific example, suppose a user wants to identify the source of a particular error using information from an HDFS data source and an Oracle data source, but does not know what the error is or what generated it. To do so, the user enters a query that includes a request to identify errors generated within a particular timeframe and stored in an HDFS data source and an Oracle data source and then correlate the errors based on the error source. Based on the query, the query coordinator  3304  determines that a union operation is to be performed on the data from the HDFS data source and the Oracle data source based on the source of the errors. 
     Additionally, suppose that the dataset compensation module  3316  has identified the HDFS data source as being relatively small and identified the Oracle data source as being significantly larger than the HDFS data source. Accordingly, based on the information in the dataset compensation module  3316 , the query coordinator  3304  can instruct the nodes  3306  to first intake and process the data from the HDFS data source. Suppose that by doing so, the nodes  3306  determine that the HDFS data source only includes fifty types of errors in the specified timeframe from ten sources. Accordingly, using that information, the query coordinator  3304  can instruct the nodes  3306  to limit the intake of data from the Oracle data store based on the error type and/or the source based on the error types and sources identified by first analyzing the HDFS data source. 
     As such, the query coordinator  3304  can reduce the amount of data requested by the Oracle data store and the amount of processing needed to obtain the relevant result. For example, if the Oracle data store included two hundred error types from one hundred sources, the query coordinator  3304  avoided having to intake and process the data from all one hundred sources. Instead only the data from sources that matched the ten sources from the HDFS data source were requested and processed by the nodes  3306 . 
     11.3. Query Coordinator 
     The query coordinator(s)  3304  can act as the primary coordinator or controller for queries that are assigned to it by the search head  210  or search process master  3302 . As such, the query coordinator can process a query, identify the resources to be used to execute the query, control and monitor the nodes to execute the query, process aggregate results of the query, and provide finalized results to the search head  210  or search process master  3302  for delivery to a client device  404 . 
     11.3.1. Query Processing 
     Upon receipt of a query, the query coordinator  3304  can analyze the query. In some cases analyzing the query can include verifying that the query is semantically correct or performing other checks on the query to determine whether it is executable by the system. In addition, the query coordinator  3304  can analyze the query to identify the dataset sources that are to be accessed and to define an executable search process. For example, the query coordinator  3304  can determine whether data from the indexers  206 , external data sources  3318 , query acceleration data store  3308 , or other dataset sources (e.g., common storage, ingested data buffers, etc.) are to be accessed to obtain the relevant datasets. 
     As part of defining the executable search process, the query coordinator  3304  can identify the different entities that can perform some processing on the datasets. For example, the query coordinator  3304  can determine what portion(s) of the query can be delegated to the indexers  206 , nodes  3306 , and external data sources  3318 , and what portions of the query can be executed by the query coordinator  3304 , search process master  3302 , or search head  210 . For tasks that can be completed by the indexers  206 , the query coordinator  3304  can generate task instructions for the indexers  206  to complete, as well as instructions to route all results from the indexers  206  to the nodes  3306 . For tasks that can be completed by the external data sources  3318 , the query coordinator  3304  can use the dataset compensation module  3316  to generate task instructions for the external data sources  3318  and to determine how to set up the nodes  3306  to receive data from the external data sources  3318 . 
     In addition, as part of defining the executable search process, the query coordinator  3304  can generate a logical directed acyclic graph (DAG) based on the query.  FIG.  35    is a diagram illustrating an embodiment of a DAG  2000  generated as part of a search process. In the illustrated embodiment, the DAG  2000  includes seven vertices and six edges, with each edge directed from one vertex to another, such that by starting at any particular vertex and following a consistently-directed sequence of edges the DAG  2000  will not return to the same vertex. 
     Here, the DAG  2000  can correspond to a topological ordering of search phases, or layers, performed by the nodes  3306 . As such, a sequence of the vertices can represent a sequence of search phases such that each edge is directed from earlier to later in the sequence of search phases. For example, the DAG  2000  may be defined based on a search string for each phase or metadata associated with a search string. The metadata may be indicative of an ordering of the search phases such as, for example, whether results of any search string depend on results of another search string such that the later search string must follow the former search string sequentially in the DAG  2000 . 
     In the illustrated embodiment of  FIG.  35   , the DAG  2000  can correspond to a query that identifies data from two dataset sources that are to be combined and then communicated to different locations. Accordingly, the DAG  2000  includes intake vertices  3502 ,  3508 , a process vertex  3504 , collect vertices  3506 ,  3510 , a join vertex  3512 , and a branch vertex  3514 . 
     Each vertex  3502 ,  3504 ,  3506 ,  3508 ,  3510 ,  3512 ,  3514  can correspond to a search phase performed by one or more processors  3406  of one or more nodes  3306  on a particular set of data or partitions. For example, the intake, process, and collect vertices  3502 ,  3504 ,  3506  can correspond to data search phases, or transformations, on data received from a first dataset source. More specifically, the intake phase or vertex  3502  can correspond to the processing of one or more partitions associated with data received from the first dataset source, the process phase  3504  can correspond to the processing of one or more partitions that resulted from the intake phase  3502 , and the collect phase  3506  can correspond to one or more partitions that collect the results of the processing of the partitions in the process phase  3504 . 
     Similarly, the intake and collect vertices  3508 ,  3510  can correspond to data search phases performed using one or more partitions or by one or more processors  3406  on data received from a second dataset source. For example, the intake phase  3508  can correspond to one or more partitions that receive data from the second dataset source and the collect phase  3510  can correspond to one or more partitions that collect the results from the partitions in the intake phase  3508 . 
     The join and branch phases  3512 ,  3514  can correspond to data search phases performed by one or more processors  3406  on partitions corresponding to data received from the different branches of the DAG  2000 . For example, the join phase  3512  can correspond to one or more partitions used to combine the data received from the partitions in the collect phases  3506 ,  3510 . The branch phase  3514  can correspond to one or more partitions used to communicate results of the join phase  3512  to one or more destinations. For example, the partitions in the branch phase  3514 , or processors assigned to the partitions in the branch phase  3514 , can communicate results of the query to the query coordinator  3304 , an external data source  3318 , accelerated data source  3308 , ingested data buffer, etc. 
     It will be understood that the number, order, and types of search phases in the DAG  2000  can be determined based on the query. As a non-limiting example, consider a query that indicates data is to be obtained from common storage and an Oracle database, collated, and the results sent to the query coordinator  3304  and an HDFS data store. In this example, in response to determining that the common storage do not provide processing capabilities, the query coordinator  3304  can generate vertices  3502 ,  3504 ,  3506  indicating that an intake phase  3502 , process phase  3504 , and collect phase  3506  will be used to process the data from the common storage sufficiently to be combined with data from the Oracle database. Similarly, based on a determination that the Oracle database can perform some processing capabilities, the query coordinator can generate vertices  3508 ,  3510  indicating that an intake phase  3508  and collect phase  3510  will be used to sufficiently process the data from the Oracle database for combination with the data from the common storage. 
     The query coordinator  3304  can further generate the join phase  3512  based on the query indicating that the data from the Oracle database and common storage is to be collated or otherwise combined (e.g., joined, unioned, etc.). In addition, based on the query indicating that the results of the combination are to be communicated to the query coordinator  3304  and the HDFS data store, the query coordinator  3304  can generate the branch phase  3514 . As mentioned above, in each phase, the query coordinator  3304  can allocate one or more nodes  3306  or processors  3406  to perform the particular search phase on the partitions of the particular phase. 
     It will be understood that the DAG  2000  is a non-limiting example of the search phases that can be included as part of a search process. In some cases, depending on the query, the DAG  2000  can include fewer or more phases of any type. For example, the DAG  2000  can include fewer or more intake phases depending on the number of dataset sources. Additionally, depending on the particular processing requirements of a query, the DAG  2000  can include multiple processing, collect, join, union, stats, or branch phases, in any order. 
     In addition to determining the number and types of search phases for a search process, the query coordinator  3304  can calculate the relative cost of each phase of the search process, determine the amount of resources to allocate for each phase of the search process, generate tasks and instructions for particular nodes to be assigned to a particular search process, generate instructions for dataset sources, generate tasks for itself and/or the search head  210 , etc. 
     To calculate the relative cost of each phase of the search process and determine the amount of resources to allocate for each phase of the search process, the query coordinator  3304  can communicate with the workload advisor  3310 , workload catalog  3312 , and/or the node monitor  3314 . As described previously, the workload advisor  3310  can use the data collected in the workload catalog  3312  to determine the cost of a query or an individual transformation or search phase of a search process and to provide a resource allocation recommendation. Furthermore, the workload advisor  3310  can use the data from the node monitor module  3314  to determine the available resources in the system  3301 . Using this information, the query coordinator  3304  can determine the cost for each search phase, the amount of resources available for allocation, and the amount of resources to allocate for each search phase. 
     In determining the amount of resources to allocate for each search phase, the query coordinator  3304  can also generate the tasks and instructions for each node  3306 . The instructions can include computer executable instructions that when executed by the node  3306  cause the node  3306  to perform the task assigned to it by the query coordinator  3304 . For example, for nodes  3306  that are to be assigned to an intake phase  3502 ,  3508 , the query coordinator  3304  can generate instructions on how to access a particular dataset source, what instructions are to be sent to the dataset source, what to do with the data received from the dataset source, where do send the received data, how to perform any load balancing or other tasks assigned to it, etc. For nodes  3306  that are to process data in the process phase  3504 , the query coordinator  3304  can generate instructions indicating how to parse the received data, relevant fields or keywords that are to be identified in the data, what to do with the identified field and keywords, where to send the results of the processing, etc. Similarly, for nodes  3306  in the collect phases  3506 ,  3510 , join phase  3512 , or branch phase  3514 , the query coordinator  3304  can generate task instructions so that the nodes  3306  (which can also refer to one or more processors  3406  within a worker node  3306 , execution environments within a worker node  3306  or processor  3406  of a worker node  3306 , such as a virtualized computing device or software-based container, etc.) are able to perform the task assigned to that particular phase or partition. The task instructions can tell the nodes  3306  what data or partitions they are to process, how they are to process the data, where they are to route the results of the processing of that phase, either between each other or to another destination. In some cases, the query coordinator  3304  can generate the tasks and instructions for all nodes  3306  and send the instructions to all of the allocated nodes  3306 . Between them, the nodes  3306  can determine or assign which nodes  3306  will execute the different instructions and tasks. The instructions sent to the nodes  3306  or processors  3406  can include additional parameters, such as a preference to use nodes  3306  or processors  3406  on the same machine  3402  for subsequent tasks. Such instructions can help reduce the amount of data communicated over the network, etc. Each node  3306  can assign specific processors  3406  and/or memory  3408  to execute particular tasks or partitions. 
     In some embodiments, to generate instructions for the dataset sources, the query coordinator  3304  can use the dataset compensation module  3316 . As described previously, the dataset compensation module  3316  can include relevant data about external data sources including, inter alia, processing abilities of the external dataset sources, number of partitions of the external dataset sources, instruction translators, etc. Using this information, the query coordinator  3304  can determine what processing to assign to the external data sources, and generate instructions that will be understood by the external data sources. In addition, the query coordinator  3304  can have access to similar information about other dataset sources and/or communicate with the dataset sources to determine their processing capabilities and how to interact with them (non-limiting examples: number of partitions to use, processing that can be pushed to the dataset source, etc.). Similarly, the query coordinator  3304  can determine how to interact with the dataset destinations so that the datasets can be properly sent to the correct location in a manner that the destination can store them correctly. 
     In some cases, the query coordinator  3304  can interact with one partition of the external dataset source using multiple nodes  3306  or processors  3406 . For example, the query coordinator  3304  can allocate multiple nodes  3306  or processors  3406  to interact with a single partition of the external dataset source. The query coordinator  3304  can break up a query or a subquery into multiple parts. Each part can be assigned to a different node  3306  or processor  3406 , which can communicate the subqueries to the external dataset source. Thus, unbeknownst to the external dataset source, it can concurrently process data from a single query. 
     Furthermore, the query coordinator  3304  can determine the order for conducting the search process. As mentioned above, in some embodiments, the query coordinator  3304  can determine that processing datafrom one dataset source could speed up the search process as a whole (non-limiting example: using data from one dataset source to generate a more targeted search of another dataset source). Accordingly, the query coordinator  3304  can determine that one or more search phases are to be completed first and then based on information obtained from the search phase, additional search phases are to be initiated. Similarly, other optimizations can be determined by the query coordinator  3304 . Such optimizations can include, but are not limited to, pushing processing to the edges (e.g., to external data sources, etc.), identifying fields in a query that are key to the query and reducing processing based on the identified field (e.g., if a relevant field is identified in a final processing step, use the field to narrow the set of data that is searched for earlier in the search process), allocating the query to nodes that are physically close to each other or on the same machine, etc. 
     11.3.2. Query Execution and Node Control 
     Once the query is processed and the search scheme determined, the query coordinator  3304  can initiate the query execution. In some cases, in initiating the query, the query coordinator  3304  can communicate the generated task instructions to the various locations that will process the data. For example, the query coordinator  3304  can communicate task instructions to the indexers  206 , based on a determination that the indexers  206  are to perform some amount of processing on the dataset. Similarly, the query coordinator  3304  can communicate task instructions to the nodes  3306 , external data sources  3318 , query acceleration data store  3308 , common storage, and/or ingested data buffer, etc. 
     In some embodiments, rather than communicating with the various dataset sources, the query coordinator  3304  can generate task instructions for the nodes  3306  to interact with the dataset sources such that the dataset sources receive any task instructions from the nodes  3306  as opposed to the query coordinator  3304 . For example, rather than communicating the task instructions directly to a dataset source, the query coordinator  3304  can assign one or more nodes  3306  to communicate task instructions to the external data sources  3318 , indexers  206 , or query acceleration data store  3308 . In certain embodiments, the query coordinator  3304  can communicate the same search scheme or task instructions to the nodes  3306  or processors  3406  of the nodes  3306  that have been allocated for the query. The allocated nodes  3306  or processors  3406  of the nodes  3306  can then assign different nodes  3306  to perform different portions of the search scheme. 
     Upon receipt of the task instructions, the dataset sources and nodes  3306  can begin operating in parallel. For example, if task instructions are sent to the indexers  206  and to the nodes  3306 , both can begin executing the instructions in parallel. In executing the task instructions, the nodes  3306  can organize their processors  3406  according to task instructions. For example, some of the nodes  3306  can allocate one or more processors  3406  as part of an intake phase, another processor  3406  as part of a processing phase, etc. In some cases, all processors  3406  of a node  3306  can be allocated to the same task or to different tasks. For example, during an intake phase, some or all processors  3406  of a node  3306  can be allocated to tasks of the intake phase, and during a processing phase, all processors  3406  of a node  3306  can be allocated to tasks of the processing phase, etc. In certain embodiments, it can be beneficial to allocate processors  3406  from the same node  3306  to different tasks or subsequent phases to reduce network traffic between nodes  3306  or machines  3402 . 
       FIG.  36    is a block diagram illustrating an embodiment of layers of partitions used to implement various search phases of a query. In some cases, the layers can correspond to search phases in a DAG, such as the DAG  2000  described in greater detail above. In the illustrated embodiment of  FIG.  36   , based on task instructions received from the query coordinator  3304 , various partitions are used to perform different search phases on data coming from a dataset source  3602 . As described previously, the dataset source  3602  can correspond to indexers  206 , external data sources  3318 , the query acceleration data store  3308 , common storage, an ingested data buffer, or other source of data from which the nodes  3306  can receive data. 
     The processors  3406  or worker nodes  3306  assigned to each layer can interact with the data or partitions based on task instructions received by the query coordinator  3304 . In the illustrated embodiment of  FIG.  36   , the partitions in the intake layer  3604  can correspond to data received from the dataset source  3602 , which can be communicated or transformed to partitions in the processing layer  3606  by worker nodes  3306  in a load-balanced fashion. The worker nodes  3306  can process the data of the partitions in the processing layer  3606  based on the task instructions, which are generated based on the query, and provide or transform the results to or into the partitions in the collector layer  3608 . Similarly, upon completing their assigned task, the processors  3406  of the worker nodes  3306  associated with the partitions in the collector layer  3608  can communicate the results of their processing to the branch layer  3610 . In the illustrated embodiment of  FIG.  36   , the branch layer  3610  communicates the results received from the partitions in the collector layer  3608  to a first dataset destination  3614  and to partitions in a storage layer  3612  for storage in a second dataset destination  3616 . It will be understood that fewer or more layers can be included as desired, and can be based on the content of the particular query being executed. Furthermore, it will be understood that the layers can correspond to different map-reduce procedures or commands. For example, as described herein, in the illustrated embodiments, the processing layer  3606  can correspond to a map procedure and the collector layer  3608  can correspond to a reduce procedure. However, as described herein, it will be understood that various layers can correspond to map or reduce procedures. 
     In the illustrated embodiment, four partitions are included in the intake layer  3604 , eight partitions are included in the processing layer  3606 , five partitions are included in the collector layer  3608 , one partition is included in the branch layer  3610 , and three partitions are included in the storage layer  3612 . In some embodiments, the number of partitions can correspond to the number of tasks or amount of data being processed in the layer. Thus, there is a larger amount of data to be processed in the processing layer  3606  than in the intake layer  3604  or collector layer  3608 . Further, it will be understood that fewer or more partitions can be used in any layer as desired and fewer or additional layers can be included. For example, based on a query that indicates multiple dataset sources are to be accessed, the query coordinator  3304  can allocate separate intake, processing, and collector layers  3604 ,  3606 ,  3608  for each dataset source  3602 . Furthermore, based on the query commands, the query coordinator can allocate additional layers, such as a join layer to combine data received from multiple dataset sources, etc. 
     In determining the number of partitions and/or processor  3406  for each search phase or layer, the query coordinator  3304  can use the workload advisor  3310  and/or dataset compensation module  3316 . For example, the workload advisor  3310  can use historical data about executing individual search phases in queries to recommend an allocation scheme that provides sufficient resources to process the query in a reasonable amount of time. Furthermore, in some embodiments, the query coordinator  3304  can determine the number of partitions based on the amount of processors  3406  assigned to the query, the amount of memory available, the amount of data (or number of events) to be processed, and information about the events or query, such as the number of fields used in the query or part of the events. 
     In some cases, the query coordinator  3304  can allocate partitions or processors  3406  for the intake layer  3604  and storage layer  3612  based on information about the number of partitions available for reading from the dataset source  3602  and writing data to the dataset destination  3616 , respectively. The query coordinator  3304  can obtain the information about the dataset source  3602  or dataset destination  3616  from a number of locations, including, but not limited to, the workload catalog  3312 , the dataset compensation module  3316 , or from the dataset source  3602  or dataset destination  3616  itself. The information can inform the query coordinator  3304  as to the number of partitions available for reading from the dataset source  3602  and writing to the dataset destination  3616 . 
     In some cases, the query coordinator  3304  can allocate worker nodes  3306  or processors  3406  in the intake layer  3604  or the storage layer  3612  to have a one-to-one, one-to-many, or many-to-one correspondence with partitions supported by the dataset source  3602  or dataset destination  3616 , respectively. The correspondence between the worker nodes  3306  or processors  3406  in the intake or storage layer  3604 ,  3612  and the partitions supported by the dataset source or destination  3602 ,  3616 , respectively, can be based on a threshold number of partitions, the type of the dataset source/destination, etc. 
     In certain embodiments, if the query coordinator  3304  determines that the dataset source  3602  (or dataset destination  3616 ) has or supports a number of partitions that satisfies a threshold number of partitions or determines that the number of partitions supported by the dataset source  3602  (or dataset destination  3616 ) can be matched without overextending the nodes  3306 , the query coordinator  3304  can allocate nodes  3306  or processors  3406  in the intake layer  3604  (or storage layer  3612 ) to have a one-to-one correspondence to partitions supported by the dataset source  3602  (or dataset destination  3616 ). 
     The number of partitions that satisfy the threshold number of partitions can be determined based on the number of nodes  3306  or processors  3406  in the system  3301 , the number of available nodes  3306  in the system  3301 , scheduled usage of nodes  3306 , amount of memory available, etc. Accordingly, the threshold number of partitions can be dynamic depending on the status of the processors  3406 , nodes  3306 , or the system  3301 . For example, if a large number of nodes  3306  are available, the threshold number of nodes can be larger, whereas, if only arelatively small number of nodes  3306  are available, the threshold number can be smaller. Similarly, if the workload advisor  33010  expects a large number of queries in the near term it can allocate fewer worker nodes  3306  or processors  3406  to an individual query. Alternatively, if the workload advisor  33010  does not expect many queries in the near term it can allocate a greater number of worker nodes  3306  or processors  3406  to an individual query. 
     In some cases, the query coordinator  3304  can determine whether to match the number of partitions supported by the dataset source  3602  or dataset destination  3616  with corresponding worker nodes  3306  or processors  3406  in the intake layer  3604  or storage layer  3612 , respectively, based on the type of the dataset source  3602  or dataset destination  3616 . For example, the query coordinator  3304  can determine there should be a one-to-one correspondence of intake layer  3604  worker nodes  3306  or processors  3406  to dataset source  3602  supported partitions (or storage layer  3612  worker nodes  3306  or processors  3406  to dataset destination  3616  supported partitions) when the dataset source  3602  (or dataset destination  3616 ) is an external data source or ingested data buffer and that there should be a one-to-multiple correspondence when the dataset source  3602  (or dataset destination  3616 ) is indexers  206 , common storage, query acceleration data store  3308 , etc. 
     As a non-limiting example, if the dataset source  3602  is an external data source or ingested data buffer that supports four partitions and the query coordinator  3304  determines that it can support a one-to-one correspondence, the query coordinator  3304  can allocate four worker nodes  3306  or processors  3406  to the intake layer  3604 . The allocated worker nodes  3306  or processors can intake the data as four or more partitions, as illustrated in  FIG.  36   . Similarly, if the dataset destination  3616  is an external data source or ingested data buffer that supports three partitions and the query coordinator  3304  determines that it can support a one-to-one correspondence, the query coordinator  3304  can allocate three worker nodes  3306  or processors  3406  to the storage layer  3612 , which can result in three or more partitions being worked on concurrently, as illustrated in  FIG.  36   . 
     As another non-limiting example, if the dataset source  3602  (or dataset destination  3616 ) is indexers  206 , common storage, or query acceleration data stores  3308  that supports hundreds of potential partitions, and/or the query coordinator  3304  determines that it cannot support a one-to-one correspondence, it can allocate four worker nodes  3306  or processors  3406  to the intake layer  3604  resulting in at least four partitions being worked on concurrently (or three worker nodes  3306  or processors  3406  to the storage layer  3612  resulting in at least three partitions being worked on concurrently), as illustrated in  FIG.  36   . However, it will be understood that in some embodiments, the query coordinator  3304  can allocate all worker nodes  3306  or all worker nodes  3306  assigned to its query to the intake layer  3604  for reading data from dataset source  3602  or sending data to dataset destination  3616 . 
     In addition, during intake of the data from the dataset source  3602 , the query coordinator  3304  can dynamically adjust the number of worker nodes  3306  or processors  3406  in the intake layer  3604 . For example, if an additional partition of the dataset source  3602  becomes available or one of the partitions becomes unavailable, the query coordinator  3304  can dynamically increase or decrease the number of worker nodes  3306  or processors  3406  in the intake layer  3604 . Similarly, if the query coordinator  3304  determines that the intake layer  3604  is taking too much time and additional resources are available, it can dynamically increase the number of worker nodes  3306  or processors  3406  in the intake layer  3604 . In addition, if the query coordinator  3304  determines that additional resources are available or become unavailable, it can dynamically increase or decrease the number of worker nodes  3306  or processors  3406  in the intake layer  3604 . Similarly, the query coordinator can dynamically adjust the number of worker nodes  3306  or processors  3406  in the storage layer  3612 . 
     Similar to the intake layer  3604  and storage layer  3612 , the query coordinator  3304  can estimate or determine a number of partitions for the different search layers  3606 ,  3608 ,  3610  based on information about the query and information in the workload catalog  3312  and allocate worker nodes  3306  or processors  3406  accordingly. For example, the query may include requests to process the data in a way that is resource intensive, resulting in a larger number of partitions. As such, the query coordinator  3304  can estimate that a larger number of partitions will be used in the processing layer and allocate additional worker nodes  3306  or processors  3406  to the processing layer  3606  or use multiple processing layers  3606  to process the data. In some cases, more partitions, worker nodes  3306 , and/or processors  3406  can be allocated to the search layers for queries of larger datasets. 
     In addition, during execution of the query, the query coordinator  3304  can monitor the partitions or processors  3406  in the search layers  3606 ,  3608 ,  3610  and dynamically adjust the number of partitions or processors  3406  in each depending on the status of the individual partitions, the status of the nodes  3306 , the status of the query, etc. For example, if a partition becomes larger than a threshold size due to high cardinality or other reasons, a worker node  3306  can generate additional partitions and redistribute the data of the partition between the different partitions. 
     In some cases, if a worker node  3306  is assigned a large number partitions compared to other worker nodes  3306  or otherwise falls behind in processing the tasks or partitions, the worker nodes  3306  can redistribute partitions or tasks assigned to the worker node  3306  amongst themselves. For example, the query coordinator  3304  can determine that a significant number of results or partitions are being sent or assigned to a particular worker node  3306  in the collector layer  3608 . As such, the query coordinator  3304  can allocate an additional worker node  3306  to the collector layer and/or instruct that the results from the partitions in the processing layer  3606  be distributed in a different manner to reduce the load on the particular worker node  3306  in the collector layer. 
     In certain embodiments, if a search layer is taking more time than expected, the query coordinator  3304  can allocate additional worker nodes  3306  or processors  3406  to the layer to increase parallelism and decrease the execution time. For example, the query coordinator  3304  can determine that a worker node  3306  assigned to the processing layer  3606  is not functioning or that there is significantly more data coming from the dataset source  3602  than was anticipated. Accordingly, the query coordinator  3304  can allocate additional worker nodes  3306  or processors  3406  to the intake layer  3604  or processing layer  3606 . Conversely, if the query coordinator  3304  determines that some of the worker nodes  3306  or processors  3406  are underutilized, then it can deallocate it from a particular layer and make it available for other queries, or assign it to a different layer, etc. Accordingly, the query coordinator  3304  can dynamically allocate and deallocate resources to intake and process the data from the dataset source  3602  in a time-efficient and performant manner. 
     As a non-limiting example, consider a query that includes a request to count the number of different types of errors in data stored in an external data source within a timeframe and to return the results to the user and store the results in the query acceleration data store  3308 . Based on the query, the query coordinator  3304  can generate a DAG that includes the intake layer  3604 , processing layer  3606 , collector layer  3608 , branch layer  3610 , and storage layer  3612 . Additionally, based on a determination that the external data source supports four partitions, the query coordinator  3304  allocates four worker nodes  3306  or processors  3406  to the intake layer  3604  to process the data from incoming partitions. In addition, based on the expected amount of data to be processed, the query coordinator  3304  allocates eight partitions to the processing layer  3606 , and five partitions to the collector layer  3608 . Further, based on resource availability and the determination that the dataset destination is the query acceleration data store  3308 , which can support more than a threshold number of partitions, the query coordinator  3304  allocates three worker nodes  3306  or processors to the storage layer  3612  to process partitions at that layer. The task instructions for each search layer can be sent to the nodes  3306 , which assign processors  3406  to the various tasks and partitions. 
     During execution, the partitions in the intake layer  3604  (or processors assigned to the partition) communicate with the dataset source  3602  to receive the relevant data from the partitions of the dataset source  3602 . The data is then communicated to the partitions in the processing layer  3606 . In the illustrated embodiment, each worker node  3306  of the intake layer  3604  communicates data in a load-balanced fashion to partitions in the processing layer  3606 . The worker nodes  3306  or processors  3406  in the processing layer  3606  can parse the incoming data or partitions to identify events that include an error and identify the type of error. 
     The worker nodes  3306  or processors  3406  in the processing layer  3606  can communicate the results to partitions in the collector layer  3608 . For example, one or more processors  3406  can apply a modulo five to the error type to each partition in the processing layer  3606  in order to attempt to equally separate the results between the partitions in the collector layer  3608 . As such, for each error type, a partition (or multiple related partitions) in the collector layer  3608  can include the total count of errors for that type. Depending on the query, in some cases, the partitions in the collector layer  3608  can also include the event that included the particular error type. 
     The worker nodes  3306  or processors  3406  can send the results of processing the partitions in the collector  3608  to a partition in the branch layer  3610 . The worker nodes  3306  or processors  3406  can communicate the results in the partition of the branch layer  3610  to the query coordinator  3304 , which can communicate the results to the search head or client device. In addition, the branch layer  3610  can communicate the results to the partitions in the storage layer  3612 , which communicate the results in parallel to the query acceleration data store  3308 . 
     Throughout the execution of the query, the query coordinator  3304  can monitor the worker nodes  3306  or processors  3406  processing partitions in the intake layer  3604 , processing layer  3606 , collector layer  3608 , branch layer  3610 , and storage layer  3612 . If a worker node  3306  or processor  3406  becomes unavailable or becomes overloaded, the query coordinator  3304  can allocate additional resources or redistribute tasks or partitions. Similarly, if a worker node  3306  or processor  3406  is not being utilized, the query coordinator  3304  can deallocate it from a layer or redistribute the tasks or partitions. For example, if a partition on the external data source becomes unavailable, a corresponding worker node  3306  or processor  3406  in the intake layer  3604  may no longer receive any data. As such, the query coordinator  3304  can deallocate that worker node  3306  or processor  3406  from the intake layer  3604 . In some embodiments, any change in state of a worker node  3306  or processor  3406  can be reported to the node monitor module  3314 , which can be used by the query coordinator to allocate resources. 
     11.3.3. Result Processing 
     Once the nodes  3306  have completed processing the query or particular results of the query, they can communicate the results to the query coordinator  3304 . The query coordinator  3304  can perform any final processing. For example, in some cases, the query coordinator  3304  can collate the data from the nodes  3306 . The query coordinator  3304  can also send the results to the search head  210  or to a dataset destination. For example, based on a command (non-limiting example “into”), the query coordinator  210  can store results in the query acceleration data store  3308 , an external data source  3318 , an ingested data buffer, etc. In addition, the query coordinator  3304  can communicate to the search process master  3302  that the query has been completed. In the event all queries assigned to the query coordinator  3304  have been completed, the query coordinator can shut down or enter a hibernation state and await additional queries assigned to it by the search process master  3302 . 
     11.4. Query Acceleration Data Store 
     As described herein, a query can indicate that information is to be stored (e.g., stored in non-volatile or volatile memory) in the query acceleration data store  3308 . 
     As described above, the query acceleration data store  3308  can store information (e.g., datasets) sourced from other dataset sources, such as, external data sources  3318 , indexers  206 , ingested data buffers, indexers, and so on. For example, when providing a query, a user can indicate that particular information is to be stored in the query acceleration data store  3308  (e.g., cached). The information can include the results of the query, partial results of the query, data (processed or unprocessed) received from another dataset source via the nodes  3306 , etc. Subsequently, the data intake and query system  3301  can cause queries directed to the particular information to utilize the query acceleration data store  3308 . In this way, the stored information can be rapidly accessed and utilized. 
     As an example, the query can indicate that information is to be obtained from the external data sources  3318 . Since the external data sources  3318  may have potentially high latency, response times to particular queries, the query can be constrained according to characteristics of the external data sources  3318 . For example, particular external data sources  3318  may be limited in their processing speed, network bandwidth, and so on, such that the worker nodes  3306  are required to wait longer for information. As described herein, the query can therefore specify that particular information from the external data sources  3318  (or other dataset sources) be stored in the query acceleration data store  3308 . Subsequent queries that utilize this particular information can then be executed more quickly. For example, in subsequent queries the worker nodes  3306  can obtain the particular information from the query acceleration data store  3308  rather than from the external data source  3318 . 
     An example query can be of a particular form, such as: 
       Query=&lt;from [dataset source]&gt;|&lt;[logic]&gt;|[accelerated directive] 
     In the above example, the query indicates that information is to be obtained from a dataset source, such as an external data source  3318 . Optionally, the query can indicate particular tables, documents, records, structured or unstructured information, and so on. As described above, the data intake and query system  3301  can process the query and determine that the external data source is being referenced. The next element of the query (e.g., a request parameter) includes logic to be applied to the data from the external data source, for example the logic can be implemented as structured query language (SQL), search processing language (SPL), and so on. As described above, the worker nodes  3306  can obtain the requested data, and apply the logic to obtain information to be provided in response to the query. 
     In the above example query, an accelerated directive is included. For example, the accelerated directive can be a particular term (e.g., “into query acceleration data store”), symbol, and so on, included in the query. The accelerated directive can optionally be manually included in the query (e.g., a user can type the directive), or automatically. As an example of automatically including the directive, a user can indicate in a user interface associated with entering queries that information is to be stored in the query acceleration data store  3308 . As another example, the user&#39;s client device or query coordinator  3304  can determine that information is to be stored in the data store  3308 . For example, the query can be analyzed by the client device or query coordinator  3304 , and based on a quantity of information being requested, the client device or query coordinator  3304  can automatically include the accelerated directive (e.g., if greater than a threshold quantity is being requested, the directive can be included). Optionally, the data intake and query system  3301  can automatically store the requested information in the query acceleration data store  3308  without an accelerated directive in a received query. For example, the query system  3301  can automatically store data in the query acceleration data store  3308  based on a user ID (e.g., always store results for a particular user or based on recent use by the user), time of day (e.g., store results for queries made at the beginning or end of a work day, etc.), dataset source identity (e.g., store data from dataset source identified has having a slower response time, etc.), network topology (e.g., store data from sources on a particular network given the network bandwidth, etc.) etc. Although the above example shows the accelerated directive at the end of the query, it will be understood that it can be placed at any part of it. In some cases, the result of the command preceding the accelerated directive corresponds to the data stored in the query acceleration data store  3308 . 
     Upon receipt of the query, the data intake and query system  3301  (e.g., the query coordinator  3304 ) can cause the requested information from the dataset source to be stored in the query acceleration data store  3308 . Optionally, the query acceleration data store  3308  can receive the processed result associated with the query (e.g., from the worker nodes  3306 ). The query acceleration data store  3308  can then provide the processed result to the query coordinator  3304  to be relayed to the requesting client. However, to increase response times, the worker nodes  3306  can provide processed information to the query acceleration data store  3308 , and also to the query coordinator  3304 . In this way, the query acceleration data store  3308  can store (e.g., in low latency memory, or longer latency memory such as solid state storage or disk storage) the received processed information, while the query coordinator  3304  can relay the received processed information to the requesting client. 
     The processed result may be stored by the query acceleration data store  3308  in association with an identifier, such that the information can be easily referenced. For example, the query acceleration data store  3308  can generate a unique identifier upon receipt of information for storage by the worker nodes  3306 . For subsequent queries, the query coordinator  3304  can receive the identifier, such that the query coordinator  3304  can replace the initial portion with the unique identifier. 
     In some embodiments, the query coordinator  3304  can generate the unique identifier. For example, the query coordinator can receive information from the query acceleration data store  3308  indicating that it stored information. The query coordinator  3304  can maintain a mapping between generated unique identifiers and datasets, partitions, and so on, that are associated with information stored by the query acceleration data store  3308 . The query coordinator  3304  may optionally provide a unique identifier to the requesting client, such that a user of the requesting client can re-use the unique identifier. For example, the user&#39;s client can present a list of all such identifiers along with respective queries that are associated with the identifier. The user can select an identifier, and generate a new query that is based on an associated query. 
     In addition to storing the data or the results or partial results of the query, the query acceleration data store can store additional information regarding the results. For example, the query acceleration data store can store information about the size of the dataset, the query that resulted in the dataset, the dataset source of the dataset, the time of the query that resulted in the dataset, the time range of data that was processed to produce the dataset, etc. This information can be used by the system  3301  to prompt a user as to what data is stored and can be used in the query acceleration data store, determine whether portions of an incoming query correspond to datasets in the accelerate data store, etc. This information can also be stored in the workload catalog  3312 , or otherwise made available to the query coordinator  3304 . 
     Subsequently, for received queries that reference the processed information, the query coordinator  3304  can cause the worker nodes  3306  to obtain the information from the query acceleration data store  3308 . 
     For example, a subsequent query can be 
       Query=&lt;from [dataset source]&gt;|&lt;[logic]&gt;|&lt;[subsequent_logic]&gt; 
     In the above query, the query coordinator  3304  can determine that some portion of the data referenced in the query corresponds to data that is stored in the query acceleration data store  3308  (previously stored data) or was previously processed according to a prior query (e.g., the query represented above) and the results of the processing stored in the query acceleration data store  3308 . For example, the query coordinator  3304  can compare the query to prior queries, and any portion of data that was referenced in a prior query. The query coordinator  3304  can then instruct the worker nodes  3306  to obtain the previously stored data or the results of processing the data from the query acceleration data store  3308 . In some cases, the subsequent query can include an explicit command to obtain the data or results from the query acceleration data store  3308 . 
     Obtaining the previously stored data or results of processing the data provides multiple technical advantages. For example, the worker nodes  3306  can avoid having to reprocess the data, and instead can utilize the prior processed result. Additionally, the worker nodes  3306  can more rapidly obtain information from the query acceleration data store  3308  than, for example, the external data sources  3318 . As an example, the worker nodes  3306  may be in communication with the query acceleration data store  3308  via a direct connection (e.g., virtual networks, local area networks, wide area networks). In contrast, the worker nodes  3306  may be in communication with the external data sources  3318  via a global network (e.g., the internet). 
     As a non-limiting example, in some cases, a first query can indicate that data from a dataset source is to be stored in the query acceleration data store  3308  with minimal processing by the nodes  3306  or without transforming the data from the dataset source. A subsequent query can indicate that the data stored in the query acceleration data store  3308  is to be processed or transformed, or combined with other data or results to obtain a result. In certain cases, the first query can indicate that data from the dataset source is to be transformed and the results stored in the query acceleration data store  3308 . The subsequent query can indicate that the results stored in the query acceleration data store  3308  are to be further processed, combined with data or results from another dataset source, or provided to a client device. 
     Furthermore, in certain embodiments, the worker nodes  3306  can perform any additional processing on the results obtained from the query acceleration data store  3308 , while concurrently obtaining data from another dataset source and processing it to obtain additional results. In some cases, the results stored in the query acceleration data store  3308  can be communicated to a client device while the nodes concurrently obtain data from another dataset source and process it to obtain additional results. By obtaining, processing, and displaying the results of the previously processed data while concurrently obtaining additional data to be processed, processing the additional data, and communicating the results of processing the additional data, the system  3301  can provide a more effective responsiveness to a user and decrease the response time of a query. 
     For the subsequent query identified above, the ‘subsequent_logic’ can be applied by the worker nodes  3306  based on the processed result stored by the query acceleration data store  3308 . The result of the subsequent query can then be provided to the query coordinator  3304  to be relayed to the requesting client. 
     The query acceleration data store  3308 , as described herein, can maintain information in low-latency memory (e.g., random access memory) or longer-latency memory. That is, the query acceleration data store  3308  can cause particular information to spill to disk when needed, ensuring that the data store  3308  can service large amounts of queries. Since, in some implementations, the low-latency memory can be less than the longer-latency memory, the query acceleration data store  3308  can determine which datasets are to be stored in the low-latency memory. In some embodiments, to provide this functionality, the query acceleration data store  3308  can be implemented as a distributed in-memory data store with spillover to disk capabilities. For example, the data in the query acceleration data store  3308  can be stored in low-latency volatile memory, and in the event, the capacity of the low-latency volatile memory is reached, the data can be stored to disk. 
     In some embodiments, the query acceleration data store  3308  can utilize one or more storage policies to swap datasets between low-latency memory and longer-latency memory. Additionally, the query acceleration data store  3308  can flush particular datasets after determining that the datasets are no longer needed (e.g., the user can indicate that the datasets can be flushed, or a threshold amount of time can pass). 
     As an example of a storage policy, the query acceleration data store  3308  can store a portion of a dataset in low-latency memory while storing a remaining portion in longer-latency memory. In this way, the query acceleration data store  3308  can have faster access to at least a portion each user&#39;s dataset. If a subsequent query is received by the data intake and query system  3301  that references a stored dataset, the query acceleration data store  3308  can access the portion of the stored dataset that is in low-latency memory. Since this access is, in general, with low-latency, the query acceleration data store  3308  can quickly provide this information to the worker nodes  3306  for processing. At a same, or similar, time, the query acceleration data store  3308  can access the longer-latency memory and obtain a remaining portion of the stored dataset. The worker nodes  3306  can then receive this remaining portion for processing. Therefore, the worker nodes  3306  can quickly respond to a request, based on the initially received portion from the low-latency memory. In this way, the user can receive search results in a manner that appears to be in ‘real-time’, that is, the search results can be provided in a less than a threshold amount of time (e.g., 1 second, 5 seconds, 10 seconds). Subsequent search results can then be provided upon the worker nodes  3306  processing the portion from the longer-latency memory. 
     The above-described storage policy may be based on a size of the dataset(s). For example, an example dataset may be less than a threshold, and the query acceleration data store  3308  may store the entirety of the dataset in low-latency memory. For an example dataset greater than the threshold, the data store  3308  may store a portion in low-latency memory. As the size of the dataset increases, the query acceleration data store  3308  can store an increasingly lesser sized portion in low-latency memory. In this way, the data store  3308  can ensure that large data sets do not consume the low-latency memory. 
     While the queries described above indicate, a first query that includes an accelerated directive, and a second query that includes the first query (e.g., as an initial portion), optionally the data intake and query system  3301  can receive a first query that is a combination of the first query and second query described above. For example, an example initial query can be 
       Query=&lt;from [dataset source]&gt;|&lt;[logic]&gt;|[accelerated directive]|&lt;[subsequent_logic]&gt; 
     The above example query indicates that the data intake and query system  3301  is to obtain information from an example dataset source (e.g., external data source  3318 ), process the information, and cause the query acceleration data store  3308  to store the processed information. In addition, subsequent logic is to be applied to the processed information, and the result provided to the requesting client  404   a - 404   n.    
       FIG.  36    illustrates a branch layer  3610 , which for the example query described above, can be utilized to provide information both to the query acceleration data store  3308  and the data destination  3614  (e.g., the requesting client). For example, subsequent to the worker nodes  3306  obtaining processed information (e.g., based on the dataset source and logic), the worker nodes  3306  can provide the processed information for storage in the query acceleration data store  3308  while continuing to process the query (e.g., apply the subsequent logic). That is, the worker nodes  3306  can bifurcate the data (e.g., at branch layer  3610 ), such that the query acceleration data store  3308  can store partial results while the worker nodes  3306  service the query and provide the completed results to the query coordinator  3304 . Optionally, another query may be received that references the partial results in the data store  3308 , and one or more worker nodes  3306  may access the data store  3308  to service the other query. For example, the other query may be processed at a same time as the above-described example initial query. 
     Received queries can further indicate multiple datasets stored by the query acceleration data store  3308 . For example, a first query can indicate that first information is to be obtained (e.g., from external data source  3318 , indexers  206 , common storage, and so on) and stored in the query acceleration data store  3308  as a first dataset. Additionally, a second query can indicate that second information is to obtained and stored in the data store  3308  as a second dataset. Subsequent queries can then reference the stored first dataset and second dataset, such that logic can be applied to both the first and second dataset via rapid access to the query acceleration data store  3308 . 
     Furthermore, queries can reference datasets stored by the query acceleration data store  3308 , and also datasets to be obtained from another dataset source (e.g., from external data source  3318 , indexers  206 , ingested data buffer, and so on). For particular queries, the data intake and query system  3301  may be able to provide results (e.g., search results) from the query acceleration data store  3308  while datasets is being obtained from another dataset source. Similarly, the system  3301  may be able to provide results from the data store  3308  while data obtained from another dataset source is being processed. 
     As an example, a first query can cause a dataset to be stored in the query acceleration data store  3308 , with the dataset being from an external data source  3318  and representing records from a prior time period (e.g., one hour). Subsequently, a second query can reference the stored dataset and further cause newer records to be obtained from the external data source (e.g., a subsequent hour). For this second query, particular logic indicated in the second query can enable the data intake and query system  3301  to provide results to a requesting client based on the stored dataset in the query acceleration data store  3308 . As an example, the second query can indicate that the system  3301  is to search for a particular name. The worker nodes  3306  can obtain stored information from the query acceleration data store  3308 , and identify instances of the particular name. 
     This access to the query acceleration data store  3308 , as described above, can be low-latency. For example, the query acceleration data store  3308  may have a portion of the stored information in low-latency memory, such as RAM or volatile memory, and the worker nodes  3306  can quickly obtain the information and identify instances of the particular name. These identified instances can then be relayed to the requesting client. Similarly, the query acceleration data store  3308  may have a different portion of the stored information in longer-latency memory, and can similarly identify instances of the particular name to be provided to the requesting client. 
     The above-described worker node  3306  interactions with the query acceleration data store  3308  can occur while information is being obtained, or processed, from the external data source  3318  referenced by the second query. In this way, the requesting client can view search results, for example search results based on the dataset stored by the query acceleration data store  3308 , while subsequent search results are being determined (e.g., search results based on information from a different dataset source). Furthermore, and as described above, the dataset being obtained from the other dataset source can be provided to the query acceleration data store  3308  for storage, for example, provided while the worker nodes  3306  apply logic to determine results from the obtained dataset. 
     To increase security of the datasets stored by the query acceleration data store, access controls can be implemented. For example, each dataset can be associated with an access control list, and the query coordinator  3304  can provide an identification of a requesting user to the worker nodes  3306  and/or query acceleration data store  3308 . For example, the identification can be an authorization or authentication token associated with the user. The query acceleration data store  3308  can then ensure that only authorized users are allowed access to stored datasets. For example, a user who causes a dataset to be stored in the query acceleration data store  3308  (e.g., based on a provided query) can be indicated as being authorized (e.g., in an access control list associated with the dataset). Optionally, the user can indicate one or more other users as having access. Optionally, the data intake and query system  3301  can utilize role-based access controls to allow any user associated with a particular role to access particular datasets. In this way, the stored information can be secure while enabling the query acceleration data store  3308  to service multitudes of users. 
     12.0. Query Data Flow 
       FIG.  37    is a data flow diagram illustrating an embodiment of communications between various components within the environment  3300  to process and execute a query. At ( 1 ), the search head  210  receives and processes a query. At ( 2 ), the search head  210  communicates the query to the search process service  3702 , which can refer to the search process master  3302  and/or query coordinator  3304 . 
     At ( 3 ) the search process service processes the query. As described in greater detail above, as part of processing the query, the query coordinator  3304  can identify the dataset sources (e.g., external data sources  3318 , indexers  206 , query acceleration data store  3308 , common storage, ingested data buffer, etc.) to be accessed, generate instructions for the dataset sources based on their processing capabilities or communication protocols, determine the size of the query, determine the amount of resources to allocate for the query, generate instructions for the nodes  3306  to execute the query, and generate tasks for itself to process results from the nodes  3306 . 
     At ( 4 ), the query coordinator  3304  communicates the task instructions for the query to the worker nodes  3306  and/or the dataset sources  3704 . As described above, in some embodiments, the query coordinator  3304  can communicate task instructions to the dataset sources  3704 . In certain embodiments, the nodes  3306  communicate task instructions to the dataset sources  3704 . 
     At ( 5 ), the nodes  3306  and/or dataset sources  3704  process the received instructions. As described in greater detail above, the instructions for the dataset sources  3704  can include instructions for performing certain transformations on the data prior to communicating the data to the nodes  3306 , etc. As described in greater detail above, the instructions for the nodes  3306  can include instructions on how to access the relevant data, the number of search phases or layers to be generated, the number of partitions, worker nodes  3306 , or processors  3406  to be allocated for each search phase or layer, the tasks for the partitions or processors  3406  in the different layer, data routing information to route data between the nodes  3306  and to the search process service  3702 , etc. As such, based on the received instructions, the nodes  3306  can assign processors  3406  to different layers and partitions and begin executing the task instructions. 
     At ( 6 ), the nodes  3306  receive the data from the dataset source(s). As described in greater detail above, the nodes  3306  can receive the data from one or more dataset sources  3704  in parallel. In addition, the nodes  3306  can receive the data from a dataset source using one or more partitions or processors  3406 . The data received from the dataset sources  3704  can be semi-processed data based on the processing capabilities of the dataset source  3704  or it can be unprocessed data from the dataset source  3704 . 
     At ( 7 ), the nodes  3306  process the data based on the task instructions received from the query coordinator  3304 . As described in greater detail above, the nodes  3306  can process the data using one or more layers, each having one or more partitions or processors  3406  assigned thereto. Although not illustrated in  FIG.  37   , it will be understood that the search process service  3702  can monitor the nodes  3306  and dynamically allocate resources based on the monitoring. 
     At ( 8 ), the nodes  3306  communicate the results of the processing to the query coordinator  3304  and/or to a dataset destination  3704 . In some cases the dataset destination  3704  can be the same as the dataset source. For example, the nodes  3306  can obtain data from the ingested data buffer and then return the results of the processing to a different section of the ingested data buffer, or obtain data from the query acceleration data store  3308  or an external data source  3318  and then return the results of the processing to the query acceleration data store  3308  or external data source  3318 , respectively. However, in certain embodiments, the dataset destination  3704  can be different from the dataset source  3704 . For example, the nodes  3306  can obtain data from the ingested data buffer and then return the results of the processing to the query acceleration data store  3308  or an external data source  3318 . 
     At ( 9 ), the search process service  3702  can perform additional processing, and at ( 10 ) the results can be communicated to the search head  210  for communication to the client device. In some cases, prior to communicating the results to the client device, the search head  210  can perform additional processing on the results. 
     It will be understood that the query data flow can include fewer or more steps. For example, in some cases, the search process service  3702  does not perform any further processing on the results and can simply forward the results to the search head  210 . In certain embodiments, nodes  3306  receive data from multiple dataset sources  3704 , etc. 
     13.0. Query Coordinator Flow 
       FIG.  38    is a flow diagram illustrative of an embodiment of a routine  3800  implemented by the query coordinator  3304  to provide query results. Although described as being implemented by the query coordinator  3304 , it will be understood that one or more elements outlined for routine  3800  can be implemented by one or more computing devices/components that are associated with the system  3301 , such as the search head  210 , search process master  3302 , indexer  206 , and/or worker nodes  3306 . Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  3802 , the query coordinator  3304  receives a query. As described in greater detail above, the query coordinator  3304  can receive the query from the search head  210 , search process master  3302 , etc. In some cases, the query coordinator  3304  can receive the query from a client  404 . The query can be in a query language as described in greater detail above. In some cases, the query received by the query coordinator  3304  can correspond to a query received and reviewed by the search head  210 . For example, the search head  210  can determine whether the query was submitted by an authenticated user and/or review the query to determine that it is in a proper format for the data intake and query system  3301 , has correct semantics and syntax, etc. In some cases, the search head  210  can run a daemon to receive search queries, and in some cases, spawn a search process, to communicate the received query to and receive the results from the query coordinator  3304  or search process master  3302   
     At block  3804 , the query coordinator  3304  processes the query. As described in greater detail above and as will be described in greater detail in  FIG.  39   , processing the query can include any one or any combination of: identifying relevant dataset sources and destinations for the query, obtaining information about the dataset sources and destinations, determining processing tasks to execute the query, determining available resources for the query, and/or generating a query processing scheme to execute the query based on the information. In some embodiments, as part of generating a query processing scheme, the query coordinator  3304  allocates multiple layers or search phases of partitions or processors  3406  to execute the query. Each level or phase can be given a different task in order to execute the query. For example, as described in greater detail above with reference to  FIGS.  20  and  21   , one level can be given the task of interacting with the dataset source and receiving data from the dataset source, another level can be tasked with processing the data received from the dataset source, a third level can be tasked with collecting results of processing the data, and additional levels can be tasked with communicating results to different destinations, storing the results in one or more dataset destinations, etc. The query coordinator  3304  can allocate as many or as few levels of partitions or processors  3406  to execute the query. 
     At block  3806 , the query coordinator  3304  distributes the query for execution. Distributing the query for execution can include any one or any combination of: communicating the query processing scheme to the nodes  3306 , monitoring the nodes  3306  during the processing of the query, or allocating/deallocating resources based on the status of the nodes and the query, and so forth, as described herein. 
     At block  3808 , the query coordinator  3304  receives the results. In some embodiments, the query coordinator  3304  receives the results from the nodes  3306 . For example, upon completing the query processing scheme, or as a part of it, the nodes  3306  can communicate the results of the query to the query coordinator  3304 . In certain cases, the query coordinator  3304  receives the results from the query acceleration data store, or indexers  206 , etc. In some cases, the query coordinator  3304  receives the results from one or more components of the data intake and query system  3301  depending on the dataset sources used in the query. 
     At block  3810 , the query coordinator  3304  processes the results. As described in greater detail above, in some cases, the results of a query cannot be finalized by the nodes  3306 . For example, in some cases, all of the data must be gathered before the results can be determined. As a non-limiting example, for some cursored searches, a result cannot be determined until all relevant data has been collected by the worker nodes. In such cases, the query coordinator  3304  can receive the results from the worker nodes  3306 , and then collate the results. 
     At block  3812 , the query coordinator  3304  communicates the results. In some embodiments, the query coordinator  3304  communicates the results to the search head  210 , such as a search process generated by the search to handle the query. In certain cases, the query coordinator  3304  communicates the results to the search process master  3302  or client device  404 , etc. 
     It will be understood that fewer, more, or different blocks can be used as part of the routine  3800 . In some cases, one or more blocks can be omitted. For example, in certain embodiments, the results received from nodes  3306  can be in a form that does not require any additional processing by the query coordinator  3304 . In such embodiments, the query coordinator  3304  can communicate the results without additional processing. As another example, the routine  3800  can include monitoring nodes during execution of the query or query processing scheme, allocating or deallocating resources during the execution of the query, etc. Similarly, routine  3800  can include reporting completion of the query to a component, such as the search process master  3302 , etc. 
     Furthermore, it will be understood that the various blocks described herein with reference to  FIG.  38    can be implemented in a variety of orders. In some cases, the query coordinator  3304  can implement some blocks concurrently or change the order as desired. For example, the query coordinator  3304  can receive ( 3808 ), process ( 3810 ), and/or communicate results ( 3812 ) concurrently or in any order, as desired. 
     14.0. Query Processing Flow 
       FIG.  39    is a flow diagram illustrative of an embodiment of a routine  3900  implemented by the query coordinator  3304  to process a query. Although described as being implemented by the query coordinator  3304 , it will be understood that one or more elements outlined for routine  3900  can be implemented by one or more computing devices/components that are associated with the system  3301 , such as the search head  210 , search process master  3302 , indexer  206 , and/or worker nodes  3306 . Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  3902 , the query coordinator  3304  identifies dataset sources and/or destinations for the query. In some cases, the query explicitly identifies the dataset sources and destinations that are to be used in the query. For example, the query can include a command indicating that data is to be retrieved from the query acceleration data store  3308 , ingested data buffer, common storage, indexers, or an external data source (inclusive of external data systems  12 ). In certain cases, the query coordinator  3304  parses the query to identify the dataset sources and destinations that are to be used in the query. For example, the query may identify the name (or other identifier) or the location (e.g., my_index) of the relevant data and the query coordinator  3304  can use the name or identifier to determine whether that particular location is associated with the query acceleration data store  3308 , ingested data buffer, common storage, indexers  206 , or an external data source  3318 . 
     In certain embodiments, the query can include a reference or identifier that can be used to look up or otherwise identify the dataset source. For example, the query can include a reference to an external query configuration file that includes information about dataset sources, etc. In some cases, the external query configuration file can include details about the dataset source, such as, but not limited to, an identifier for the dataset source, search type to be performed on the dataset source (e.g., streaming, batch, reporting, etc.), maximum or estimate number (or size) of results expected from the dataset source, number IP address, port number, access credentials (e.g., account name/type, password, etc. to access the dataset source), etc. 
     In some cases, the query coordinator identifies the dataset source based on timing requirements of the search. For example, in some cases, queries for data that satisfy a timing threshold or are within a time period are handled by indexers or correspond to data in an ingested data buffer, as described herein. In some embodiments, data that does not satisfy the timing threshold or is outside of the time period are stored in common storage, query acceleration data stores, external data sources, or by indexers. For example, as described in greater detail herein, in some cases, the indexers fill hot buckets with incoming data. Once a hot bucket is filled, it is stored. In some embodiments hot buckets are searchable and in other embodiments hot buckets are not. Accordingly, in embodiments where hot buckets are searchable, a query that reflects a time period that includes hot buckets can indicate that the dataset source is the indexers, or hot buckets being processed by the indexers. Similarly, in embodiments where warm buckets are stored by the indexers, a query that reflects a time period that includes warm buckets can indicate that the dataset source is the indexers. 
     In certain embodiments, a query for data that satisfies the timing threshold or is within the time period can indicate that the ingested data buffer is the dataset source. Further, in embodiments, where warm buckets are stored in a common storage, a query for data that does not satisfy the timing threshold or is outside of the time period can indicate that the common storage is the dataset source. In some embodiments, the time period can be reflective of the time it takes for data to be processed by the data intake and query system  3301  and stored in a warm bucket. Thus, a query for data within the time period can indicate that the data has not yet been indexed and stored by the indexers  206  or that the data resides in hot buckets that are still being processed by the indexers  206 . 
     In some embodiments, the query coordinator  3304  identifies the dataset source based on the architecture of the system  3301 . As described herein, in some architectures, real-time searches or searches for data that satisfy the timing threshold are handled by indexers. In other architectures, these same types of searches are handled by the nodes  3306  in combination with the ingested data buffer. Similarly, in certain architectures, historical searches, or searches for data that do not satisfy the timing threshold are handled by the indexers. In other architectures, these same types of searches are handled by the nodes  3306  in combination with the common storage. 
     At block  3904  the query coordinator  3304  obtains relevant information about the dataset sources/destinations. The query coordinator  3304  can obtain the relevant information from a variety of sources, such as the workload advisor  3310 , workload catalog  3312 , dataset compensation module  3316 , the dataset sources/destinations themselves, etc. For example, if the dataset source/destination is an external data source, the query coordinator  3304  can obtain relevant information about the external dataset source  3318  from the dataset compensation module or by communicating with the external data source  3318 . Similarly, if the dataset source/destination is an indexer  206 , common storage, query acceleration data store  3308 , ingested data buffer, etc., the query coordinator can obtain relevant information by communicating with the dataset source/destination and/or the workload advisor  3310  or workload catalog  3312 . 
     The relevant information can include, but is not limited to, information to enable the query coordinator  3304  to generate a search scheme with sufficient information to interact with and obtain data from a dataset source or send data to a dataset destination. For example, the relevant information can include information related to the number of partitions supported by the dataset source/destination, location of compute nodes at the dataset source/destination, computing functionality of the dataset source/destination, commands supported by the dataset source/destination, physical location of the dataset source/destination, network speed and reliability in communicating with the dataset source/destination, amount of information stored by the dataset source/destination, computer language or protocols for communicating with the dataset source/destination, summaries or indexes of data stored by the dataset source/destination, data format of data stored by the dataset source/destination, etc. 
     At block  3906 , the query coordinator  3304  determines processing requirement for the query. In some cases, to determine the processing requirements, the query coordinator  3304  parses the query. As described previously, the workload catalog  3312  can store information regarding the various transformations or commands that can be executed on data and the amount of processing to perform the transformation or command. In some cases, this information can be based on historical information from previous queries executed by the system  3301 . For example, the query coordinator  3304  can determine that a “join” command will have significant computational requirements, whereas a “count by” command may not. Using the information about the transformations included in the query, the query coordinator can determine the processing requirements of individual transformations on the data, as well as the processing requirements of the query. 
     At block  3908 , the query coordinator  3304  determines available resources. As described in greater detail above, the nodes  3306  can include monitoring modules that monitor the performance and utilization of its processors. In some cases, a monitoring module can be assigned for each processor on a node. The information about the utilization rate and other scheduling information can be used by the query coordinator  3304  to determine the amount of resources available for the query. 
     At block  3910 , the query coordinator  3304  generates a query processing scheme. In some cases, the query coordinator  3304  can use the information regarding the dataset sources/destinations, the processing requirements of the query and/or the available resources to generate the query processing scheme. As part of generating the query processing scheme, the query coordinator  3304  can generate instructions to be executed by the dataset sources/destinations, allocate partitions/processors for the query, generate instructions for the processors/nodes, generate instructions for itself, generate a DAG, etc. 
     As described in greater detail above, in some embodiments, to generate instructions for the dataset sources/destinations, the query coordinator  3304  can use the information from the dataset compensation module  3316 . This information can be used by the query coordinator  3304  to determine what processing can be done by an external data source, how to translate the commands or subqueries for execution to the external dataset source, the number of partitions, worker nodes  3306 , or processors  3406  that can be used to read data from the external dataset source, etc. Similarly, the query coordinator  3304  can generate instructions for other dataset sources, such as the indexers, query acceleration data store, common storage, etc. For example, the query coordinator  3304  can generate instructions for the ingested data buffer to retain data until it receives an acknowledgment from the query coordinator that the data from the ingested data buffer has been received and processed. 
     In addition, as described in greater detail above, to generate instructions for the processors/nodes, the query coordinator  3304  can determine how to break up the processing requirements of the query into discrete or individual tasks, determine the number of partitions/processors to execute the task, etc. In some cases, to determine how to break up the processing requirements of the query into discrete or individual tasks, the query coordinator  3304  can parse the query into its different portions of the query and then determine the tasks to use to execute the different portions. 
     The query coordinator  3304  can then use this information to generate specific instructions for the nodes that enable the nodes to execute the individual tasks, route the results of each task to the next location, and route the results of the query to the proper destination. The instructions for the nodes can further include instructions for interacting with the dataset sources/destinations. In some cases, instructions for the dataset sources can be embedded in the instructions for the nodes so that the nodes can communicate the instructions to the dataset sources/destinations. Accordingly, the instructions generated by the query coordinator  3304  for the nodes can include all of the information in order to enable the nodes to handle the various tasks of the query and provide the query coordinator with the appropriate data so that the query coordinator  3304  can finalize the results and communicate them to the search head  210 . 
     In some cases, the query coordinator  3304  can use network topology information of the machines that will be executing the query to generate the instructions for the nodes. For example, the query coordinator  3304  can use the physical location of the processors that will execute the query to generate the instructions. As one example, the query coordinator  3304  can indicate that it is preferred that the processors assigned to execute the query be located on the same machine or close to each other. 
     In some embodiments, the instructions for the nodes can be generated in the form of a DAG, as described in greater detail above. The DAG can include the instructions for the nodes to carry out the processing tasks included in the DAG. In some cases, the DAG can include additional information, such as instructions on how to select processors  3406  for the different tasks or distribute partitions. For example, the DAG can indicate that it is preferable that a partition that will be receiving data from another partition be on the same machine, or nearby machine, in order to reduce network traffic. 
     In addition to generating instructions for the dataset sources/destinations and the nodes, the query coordinator  3304  can generate instructions for itself. In some cases, the instructions generated for itself can depend on the query that is being processed, the capabilities of the nodes  3306 , and the results expected from the nodes. For example, in some cases, the type of query requested may require the query coordinator  3304  to perform more or less processing. For example, a cursored search may require more processing by the query coordinator  3304  than a batch search. Accordingly, the query coordinator  3304  can generate tasks or instructions for itself based on the query requested. 
     In addition, if the nodes  3306  are unable to perform certain tasks on the data, then the query coordinator  3304  can assign those tasks to itself and generate instructions for itself based on those tasks. Similarly, based on the form of the data that the query coordinator  3304  is expected to receive, it can generate instructions for itself in order to finalize the results for reporting. 
     It will be understood that fewer, more, or different blocks can be used as part of the routine  3900 . In some cases, one or more blocks can be omitted. Furthermore, it will be understood that the various blocks described herein with reference to  FIG.  39    can be implemented in a variety of orders. In some cases, the query coordinator  3304  can implement some blocks concurrently or change the order as desired. For example, the query coordinator  3304  can obtain information about the dataset sources/destinations ( 3904 ), determine processing requirements ( 3906 ), and determine available resources ( 3908 ) concurrently or in any order, as desired. 
     15.0. Workload Monitoring and Advising Flow 
       FIG.  40    is a flow diagram illustrative of an embodiment of a routine  4000  implemented by the system  3301  to generate a query processing scheme. It will be understood that one or more elements outlined for routine  4000  can be implemented by one or more computing devices/components that are associated with the system  3301 , such as the search head  210 , search process master  3302 , query coordinator  3304 , indexer  206 , and/or worker nodes  3306 . Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  4002 , the system  3301  tracks query-resource usage data. As described in greater detail above, the system  3301  can track detailed information related to queries that are executed by the system  3301 , which in some embodiments can be stored in the workload catalog  3312 , or otherwise stored to be accessible to the system  3301 . For example, the system can track data indicating the resources used to execute the queries or timing information indicating the amount of time a query took to execute. Furthermore, the system can track information on a per transformation level, indicating the resources used to perform a particular task or transformation on a set of data, the amount of data involved, the time it took to perform the transformation, etc. In some embodiments, this information and other information related to previous queries, datasets, and system components can be stored in the workload catalog  3312 . 
     At block  4004 , the system  3301  tracks resource utilization data. As described in greater detail above, the system  3301  can track detailed information related to utilization rates of system resources, which in some cases can be stored in the node monitoring module  3314 . In some embodiments, the nodes  3306  can include monitoring modules  3410 , which can monitor the utilization rates of processors, I/O, memory, and other components of the nodes  3306 . The information from the nodes  3306  of the system  3301  can be communicated to the node monitoring module  3314  for storage. In some cases, each node  3306  can include at least one monitoring module  3410 . In certain embodiments, each node  3306  can include at least one monitoring module for each processor  3406  of the node  3306 . 
     At block  4006 , the system  3301  receives a query, as described in greater detail above. At block  4008 , the system  3301  defines a query processing scheme, as described in greater detail above. In some cases the system  3301  can use the query-resource usage data and/or the resource utilization data to define the query processing scheme. 
     In some embodiments, the system  3301  can use the query-resource usage data to determine the amount of time the query will take to complete compared to the amount of resources assigned to process the query. The system can use this information to determine an amount of resources to allocate based on query. For example, the system can compare the datasets used for the received query with datasets used for previous queries, the types of transformations required by the received query compared to previous queries. Based on the comparison, the system  3301  can determine the effect of the amount of resources assigned to the query compared to the time to execute the query. 
     In certain embodiments, the system  3301  can further use the resource utilization data to define the query processing scheme. For example, the system  3301  can determine the amount of resources that are currently available for use to execute the query. Based on the amount of currently available resources, the system  3301  can determine how many resources should be allocated to the query. As an example, assume that based on the query-resource usage data, the system  3301  determines that thirty processors are preferred to process a query and that fewer than twenty processors would result in an undue delay. Based on the system  3301  determining that thirty processors are available, the system  3301  can allocate all thirty processors or at least twenty for the query. 
     In some cases, the system  3301  can track usage over time to predict surges in queries or determine whether additional queries are expected in the near term. For example, the system  3301  may determine that there is a surge in queries around 9:00 AM when most users begin work. With continued reference to the example above, if the query is received at 8:55 AM and the thirty processors are available, the system  3301  may determine to allocate twenty processors rather than the preferred thirty because a large number of queries are expected at 9:00 AM. 
     At block  4010 , the system executes the query. In some cases, as described in greater detail above, to execute the query, the system communicates a query processing scheme to the nodes  3306 . In turn, the nodes obtain relevant data from the datasets, process the data, and return results to the query coordinator. The query coordinator performs any additional processing based on the query processing scheme and communicates the results to the search head  210  for display on the client device  404 . 
     It will be understood that fewer, more, or different blocks can be used as part of the routine  4000 . For example, in some embodiments, the routine  4000  can further include, monitoring nodes during query execution, allocating/deallocating resources based on the query, Furthermore, it will be understood that the various blocks described herein with reference to  FIG.  40    can be implemented in a variety of orders. In some cases, the system  3301  can implement some blocks concurrently or change the order as desired. For example, the system  3301  can track query-resource usage data  4002 , track resource utilization of nodes  4004 , and receive a query  4006  concurrently or in any order, as desired. Similarly, the system  3301  can track resource utilization of nodes  4004  while executing the query  4010 , etc. 
     16.0. Multiple Dataset Sources Flow 
       FIG.  41    is a flow diagram illustrative of an embodiment of a routine  4100  implemented by the query coordinator  3304  to execute a query on data from multiple dataset sources. Although described as being implemented by the query coordinator  3304 , it will be understood that one or more elements outlined for routine  4100  can be implemented by one or more computing devices/components that are associated with the system  3301 , such as the search head  210 , search process master  3302 , indexer  206 , and/or worker nodes  3306 . Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  4102 , the query coordinator  3304  receives a query, as described in greater detail above with reference to block  3802  of  FIG.  38   . At block  4104 , the query coordinator identifies the dataset sources, including the indexers  206  as one dataset source, as described in greater detail above with reference to block  3902  of  FIG.  39   . The query coordinator  3304  can also identify a second dataset source, such as an external data source, a common storage, an ingested data buffer, query acceleration data store, etc. 
     At block  4106 , the query coordinator  3304  generates a subquery for the indexers. As described herein, the subquery can be generated based on the processing capabilities of the indexers. The subquery can indicate to the indexers that data to be processed by the indexers and the manner of processing the data by the indexers. Further, the subquery can instruct the indexers to provide the results (or partial results) of the subquery to the nodes  3306  for further processing. Accordingly, using the subquery, the indexers can identify the data to process, process the data, and communicate the results to the nodes  3306 . The subquery can be in any query language, as described herein. 
     At block  4108 , the query coordinator  3304  allocates resources, such as partitions, worker nodes  3306 , or processors  3406 , for a second dataset. The resource allocation can be based on the information about the dataset and/or the query requirements, as described in greater detail in blocks  3906 ,  3908 , and  3910  of  FIG.  39   . At block  4110 , the query coordinator  3304  determines or allocates resources to combine the results (or partial results) from the two datasets. Similar to block  4108 , the query coordinator  3304  can determine or allocate partitions, worker nodes  3306 , or processors  3406  to combine the partial results from the different datasets based on the query requirements. For example, the query can include a command indicating that the results from different dataset sources are to be combined in some way. 
     At block  4112 , the query coordinator  3304  executes the query as described in greater detail above with reference to block  4010  of  FIG.  40   . In executing the query, the query coordinator  3304  can communicate the subquery to the indexers  206  or embed the subquery into the instructions to the nodes  3306  such that the nodes  3306  communicate the subquery to the indexers  206 . 
     It will be understood that fewer, more, or different blocks can be used as part of the routine  4100 . For example, in some embodiments, the routine  4100  can further include, monitoring nodes during query execution, allocating/deallocating resources based on the query, etc. As another example, in certain embodiments, the identification of the dataset sources, generation of a subquery and resource allocation can form part of a processing query block, similar to the process query block  3804  of  FIG.  38   . In some cases, the routine  4100  can include allocating resources to receive and process the partial results from the indexers  206  prior to combining the partial results from the different datasets. In certain embodiments, the system  3301  can dynamically allocate resources based on the number of indexers  206  from which the nodes  3306  will receive data. Furthermore, although described as interacting with indexers  206 , it will be understood that the system  3301  can process and execute the query on any two or more dataset sources, and that the system  3301  can generate subqueries or instructions for the dataset sources or allocate resources for the dataset sources based on information about the dataset sources, as described in greater detail herein. 
     Furthermore, it will be understood that the various blocks described herein with reference to  FIG.  41    can be implemented in a variety of orders. In some cases, the system  3301  can implement some blocks concurrently or change the order as desired. For example, the system  3301  can generate a subquery for the indexers  4106 , allocate resources for the second dataset  4108 , and allocate resources to combine partial results from the indexers and second dataset  4110  concurrently, or in any order, as desired. 
     17.0. External Data Source Flow 
       FIG.  42    is a flow diagram illustrative of an embodiment of a routine  4200  implemented by the query coordinator  3304  to execute a query on data from an external data source. Although described as being implemented by the query coordinator  3304 , it will be understood that one or more elements outlined for routine  4200  can be implemented by one or more computing devices/components that are associated with the system  3301 , such as the search head  210 , search process master  3302 , indexer  206 , and/or worker nodes  3306 . Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  4202 , the query coordinator  3304  receives a query, as described in greater detail above with reference to block  3802  of  FIG.  38   . At block  4204 , the query coordinator identifies the external data sources, as described in greater detail above with reference to block  3902  of  FIG.  39   . 
     At block  4206 , the query coordinator  3304  dynamically generates a subquery for the external data source. As described herein, the query coordinator  3304  can generate the subquery for the external data source based on information obtained about the external data source as described herein with reference to, inter alia, blocks  3904  and  3910  of  FIG.  39   . In certain embodiments, the query coordinator  3304  obtains information about the external data source using an external query configuration file, as described herein at least with reference to  FIGS.  50 A,  50 B,  51 ,  52 ,  54 , and  61   . The information can indicate the type of external data source, APIs and languages to use to interface with the external data source, the type and amount of data stored in the external data source. In addition, the information can indicate whether the external data source supports multiple partitions, and if so, how many. Further, the information can indicate the location of the processors of the external data source with which the nodes  3306  will interact. The information can also indicate the processing capabilities of the external data source, such as what commands or transformations the external data source can perform on the data stored therein. 
     Using the information about the external data source, the query coordinator  3304  can generate a subquery. In certain embodiments, the query coordinator  3304  generates a subquery that tasks the external data source with merely returning the data, performing some processing of the data, or processing the data as much as it can based on its capabilities. By pushing some processing of the data to the external data source, the query coordinator  3304  can reduce the processing load on the system  3301 . 
     At block  4208 , the query coordinator  3304  allocates resources, such as, but not limited to, partitions, worker nodes  3306 , or processors  3406  to receive and process results from the external data source. As described herein, the query coordinator  3304  can allocate resources based on the query requirements and the data received from the external data source. For example, if the external data source can perform some processing on the data, then the query coordinator  3304  can allocate resources to receive the results of the processing. If the subquery indicated that the external data source was to return results without processing them, then the query coordinator  3304  can allocate resources to receive the unprocessed results from the external data source, and process them according to the query. 
     In addition, the query coordinator  3304  can allocate resources based on the number of partitions supported by the external data source. For example, if the external data source supports four partitions for reading data, then the query coordinator  3304  can allocate four worker nodes  3306  or processors  3406  to read from each of the partitions supported by the external data source. However, it will be understood that the query coordinator  3304  can allocate fewer or more worker nodes  3306  or processors  3406  as desired. Further, the number of worker nodes  3306  or processors  3406  allocated can be based on the resources available on the system  3301 . 
     In some cases, the query coordinator  3304  can allocate more worker nodes  3306  or processors  3406  than is supported by the external data source and/or submit multiple subqueries to the external data source. For example, if the external data source only supports a single partition, the query coordinator  3304  can allocate multiple worker nodes  3306  or processors  3406  to send different subqueries to the external data source and receive the results back. In this way, the query coordinator  3304  can increase the number of parallel reads from the external data source. As a non-limiting example, suppose an external data source only supports one partition and the query indicates that a data based on an age range of 20-49 is to be obtained from the external data source. The query coordinator can break up the age range into three sets (20-29, 30-39, 40-49) and send (or have nodes send) a subquery for each set to the external data source. The external data source can process the requests concurrently and return results, and may not know that the requests are coming from the same system  3301 . In this way, the system  3301  can receive results in parallel from an external data source that supports a single partition. The query coordinator  3304  can similarly send multiple subqueries to one partition of a multi-partition-supporting external data source to increase the parallel reads from the external data source. 
     At block  4210 , the query coordinator  3304  executes the query as described in greater detail above with reference to block  4010  of  FIG.  40   . It will be understood that fewer, more, or different blocks can be used as part of the routine  4200 . For example, in some embodiments, the routine  4200  can further include, monitoring nodes during query execution, allocating/deallocating resources based on the query, etc. As another example, in certain embodiments, the identification of the external data source, generation of a subquery and resource allocation can form part of a processing query block, similar to the process query block  3804  of  FIG.  38   . 
     Furthermore, it will be understood that the various blocks described herein with reference to  FIG.  42    can be implemented in a variety of orders. In some cases, the system  3301  can implement some blocks concurrently or change the order as desired. For example, the system  3301  can generate a subquery for the external data source  4206  and allocate resources concurrently  4208  or in any order, as desired. 
     18.0. Dataset Destination Flow 
       FIG.  43    is a flow diagram illustrative of an embodiment of a routine  4300  implemented by the query coordinator  3304  to execute a query based on a dataset destination. Although described as being implemented by the query coordinator  3304 , it will be understood that one or more elements outlined for routine  4300  can be implemented by one or more computing devices/components that are associated with the system  3301 , such as the search head  210 , search process master  3302 , indexer  206 , and/or worker nodes  3306 . Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  4302 , the query coordinator  3304  receives a query, as described in greater detail above with reference to block  3802  of  FIG.  38   . At block  4304 , the query coordinator identifies the dataset destination, as described in greater detail above with reference to block  3902  of  FIG.  39   . In some embodiments, the dataset destination can refer to the location where query results or partial query results are to be stored by the system  3301 . For example, the nodes  3306  can process data from any dataset source and then store the data in a dataset destination, as well as provide the results to a client device  404 . In some cases, the dataset destination can be the same as the dataset source. For example, data can be read from the ingested data buffer, processed, and then stored back in the ingested data buffer. However, in certain cases, the dataset destination and dataset source are different. For example, in some embodiments, data is read from the common storage, processed by the nodes, and the results stored in the query acceleration data store  3308 , an external data source  3318 , an ingested data buffer, etc. 
     At block  4306 , the query coordinator  3304  determines the functionality of the dataset destination. As described herein with reference to inter alia block  3904  of  FIG.  39   , each dataset destination, like dataset sources, can have different functionality and capabilities. This functionality can correspond to how to communicate with the dataset destination (e.g., the number of partitions supported by the dataset destination, the APIs, language, or communication protocols of the dataset destination), processing supported by the dataset destination (e.g., commands supported by the dataset destination), etc. 
     At block  4308 , the query coordinator  3304  allocates or assigns resources, such as, but not limited to, worker nodes  3306  or processors  3406  to process and communicate results to the dataset destination. Similar to allocating resources to receive data from a dataset source, the query coordinator  3304  can allocate resources to process and communicate data to a dataset destination. For example, the query coordinator  3304  can allocate worker nodes  3306  or processors  3406  based on the partitions supported by the dataset destination, the processing capabilities of the dataset destination, etc. As part of allocating worker nodes  3306  or processors  3406 , the query coordinator  3304  can instruct the worker nodes  3306  or processors  3406  on how to communicate the data to the dataset destination, include translated commands for the dataset destination, etc. 
     At block  4310 , the query coordinator  3304  executes the query as described in greater detail above with reference to block  4010  of  FIG.  40   . It will be understood that fewer, more, or different blocks can be used as part of the routine  4300 . For example, in some embodiments, the routine  4300  can further include, monitoring nodes during query execution, allocating/deallocating resources based on the query, etc. As another example, in certain embodiments, the identification of the dataset destination, determination of dataset destination functionality, and resource allocation can form part of a processing query block, similar to the process query block  3804  of  FIG.  38   . 
     Furthermore, it will be understood that the various blocks described herein with reference to  FIG.  43    can be implemented in a variety of orders. In some cases, the system  3301  can implement some blocks concurrently or change the order as desired. For example, the system  3301  can determine dataset destination functionality  4306  and allocate resources  4308  concurrently or in any order, as desired. 
     19.0. Serialization and Deserialization Flow 
       FIG.  44    is a flow diagram illustrative of an embodiment of a routine  4400  implemented by a serialization module, of a component of the data intake and query system  3301  to serialize data for communication to a destination, similar to the serialization/deserialization module  3412  of  FIG.  34   . The destination can be another component of the data intake and query system  3301  or external to the data intake and query system  3301 . Although described as being implemented by serialization module, it will be understood that one or more elements outlined for routine  4300  can be implemented by one or more computing devices/components that are associated with the system  3301 , such as the search head  210 , search process master  3302 , indexer  206 , and/or worker nodes  3306 . Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  4402 , the serialization module identifies events for serialization. In some cases, as part of identifying the events for serialization, the serialization module groups the events. In some embodiments, the serialization module identifies the events for serialization based on a common source or sourcetype of the events, or other shared attribute, or based on a destination for the events. In certain embodiments, the serialization module identifies events for serialization based on timing information. For example, the serialization module can serialize events received within a certain time period, such as one second, ten second, one minute, etc. 
     At block  4404 , the serialization module determines header information for the events. The header information can include the number of events in a group, the field names for the events in the group, etc. In some cases, the field names in the header can include all field names across all events. For example, if some events have different field names, both can be included in the header information. In some cases, the header information can also include mapping information for mapping field names to field positions (e.g., where a particular field name is located within an event, etc.). In some embodiments, as part of determining the header information for the events, the serialization module can serialize the header information. For example, if some field names are repetitive or have been identified before in previous groups, they can be replaced with an identifier indicating a cache entry that has that field name. The identifier can be used by the receiving component to deserialize the data. Furthermore, the serialization module can update the cache based on the header information. For example, if some of the header information had not been seen before, the serialization module can update the cache so that an identifier can be used in place of the header information in the future. 
     At block  4406 , the serialization module serializes the events. As part of serializing the events, the serialization module can identify field values in the events and determine whether the field values in each event are stored in cache. The field values that are stored in cache can be replaced with cache identifiers. In addition, the serialization module can identify data other data for removal. For example, in some embodiments, certain delimiters, such as ‘,’ or ‘\n’ can be removed from the events. 
     Further, as part of serializing the events, the serialization module can update the cache or generate update cache commands for the receiver. Updating the cache can include adding entries for data encountered in the events or removing entries that have not been used recently. The cache can be updated with each event or each group and can be performed prior to, after, or concurrently with an event. For example, upon receiving a group of events, the receiver can update the cache and then process the events, update the cache while processing the events, or update the cache after the events are processed. In some cases, the receiver updates the cache following each event. In some cases, new entries are added to the cache prior to processing the events and entries are removed from the cache after processing the events in a group. 
     At  4408 , the serialization module communicates the serialized events to the destination. In some cases, the serialization module communicates the events in a streaming fashion. In such embodiments, the serialization module communicates the events once the serialization process for that event is completed. In certain embodiments, the serialization module communicates the events as a group. In such embodiments, the serialization module waits until the group of events is serialized before transmitting the events as a group. 
     As part of generating the group and serializing the data, the serialization/deserialization module  3412  can determine the number of events to group, determine the order and field names for the fields in the events of the group, parse the events, determine the number of fields for each event, identify and serialize serializable field values in the event fields, and identify cache deltas. In some cases, the serialization/deserialization module  3412  performs the various tasks in a single pass of the data, meaning that it performs the identification, parsing, and serializing during a single review of the data. In this manner, the serialization/deserialization module  3412  can operate on streaming data and avoid adding delay to the serialization/deserialization process. 
     It will be understood that fewer, more, or different blocks can be used as part of the routine  4400 . For example, in some embodiments, the routine  4400  can further include, building and updating the cache at the receiver, etc. Furthermore, it will be understood that the various blocks described herein with reference to  FIG.  44    can be implemented in a variety of orders. In some cases, the serialization module can implement some blocks concurrently or change the order as desired. For example, the serialization module can determine header information  4404  and serialize the events  4406  concurrently or in any order, as desired. Furthermore, although not explicitly described herein, it will be understood that the data can be deserialized in a similar manner. That is, the receiver can determine the number of events in the group and the fields based on the header information and deserialize each event using the cache and data in the serialized group. 
     20.0. Accelerated Query Results Flow 
       FIG.  45    is a flow diagram illustrative of an embodiment of a routine  4500  implemented by the query coordinator  3304  to execute a query utilizing a data store (e.g., query acceleration data store  3308 ). Although described as being implemented by the query coordinator  3304 , it will be understood that one or more elements outlined for routine  4500  can be implemented by one or more computing devices/components that are associated with the system  3301 , such as the search head  210 , search process master  3302 , indexer  206 , and/or worker nodes  3306 . Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  4502 , the query coordinator  3304  receives a query, as described in greater detail above with reference to block  3802  of  FIG.  38   . In the example of  FIG.  45   , the query can reference a particular dataset stored by the query acceleration data store  3308 , and reference information which is to be obtained from another dataset source (e.g., external data source  3318 , ingested data buffer, common storage, indexers  206 , etc.). 
     At block  4504 , first partial results are identified. As described above, a query can indicate datasets, including a particular dataset that is stored in the query acceleration data store  3308 . The query acceleration data store  3308  can store datasets that are indicated (e.g., by users, for example based on the users including a particular command) as benefiting from storage in the query acceleration data store  3308  (e.g., benefitting from caching). In addition, the datasets stored in the query acceleration data store  3308  can correspond to results or partial results of queries previously processed by the system  3301 . The query coordinator  3304  can determine that the received query references one or more datasets stored by the query acceleration data store. For example, the query may specify a dataset is stored in the query acceleration data store  3308  and/or provide a unique identifier associated with a stored dataset, and the system  3301  (e.g., the query coordinator  3304 ) may relay this unique identifier to the worker nodes  3306  to obtain the referenced dataset(s). In certain cases, the system  3301  can prompt the user with identifiers of datasets stored in the query acceleration data store  3308 . 
     In some cases, the query coordinator  3304  can intelligently determine that a portion of the data identified for processing in the query corresponds to data that was previously processed. For example, the query coordinator  3304  can compare the query with previous queries. The comparison can be made against all queries received by the system or queries received by the system from a particular user or group of users. As yet another example, suppose a query indicates that the last sixty minutes of data from a particular dataset source is to be processed. The query coordinator  3304  can compare the query with previous queries from the user and determine that a similar query was received thirty minutes previously indicating that the prior thirty minutes of data from the dataset source was to be processed and the results of the query stored in the query acceleration data store  3308 . Based on that information, the query coordinator  3304  can determine that the first thirty minutes of the sixty minutes&#39; worth of data has already been processed and the results are accessible in the query acceleration data store  3308 . 
     As described above, worker nodes  3306  can utilize the particular dataset obtained from the data store to determine results. Since the query acceleration data store  3308  stores the particular dataset, first partial results can be rapidly identified by the worker nodes  3306 , and the query coordinator  3304  can provide the first partial results to a requesting client. For example, the first partial results may be minimally processed data that was previously obtained from another dataset source (e.g., an external data source  3318 , indexers  206 , ingested data buffer) and stored in the query acceleration data store  3308  with little or no processing by the worker nodes  3306 . For example, the worker nodes  3306  may have imported the data from an external data source  3318  and stored the received data as-is in the query acceleration data store  3308 . The imported results can correspond to raw machine data or processed data. 
     Additionally, the first partial results can correspond to results or partial results of a previous query that were obtained after data received by a dataset source was processed the worker nodes  3306 . For example, the worker nodes  3306  may have imported the data from an external data source  3318 , ingested data buffer, indexers  206 , or even data stored in the query acceleration data store  3308 , performed one or more transformations on the data, (e.g., extracted relevant portions, combined the data with results from other dataset sources, etc.), and then stored the results of the processing in the query acceleration data store  3308 . 
     At block  4506 , the query coordinator  3304  dynamically allocates resources, such as, but not limited to, partitions, worker nodes  3306 , or processors  3406 . The resources can be allocated to receive and process data from a dataset source referenced in the received query (second portion of the set of data), combine results of processing the data from the dataset source (second partial results) with the first partial results, process the combined results, and communicate the results to a destination, such as the query coordinator  3304 , search head  210 , client device  404 , or a dataset destination. As described in block  4504 , the query can indicate a particular dataset stored in the query acceleration data store  3308 . Additionally, the query can further indicate that data is to be obtained from another dataset source, processed, and the second partial results combined with the first partial results. The query coordinator  3304  can allocate resources based on the query requirements and the data received from the dataset source as described herein. In some cases, the query does not indicate that the first partial results are stored in the query acceleration data store  3308 . In such embodiments, the query can identify a dataset source for obtaining data and the query coordinator  3304  can analyze the query to determine that a first portion of the data requested corresponds to the first partial results stored in the query acceleration data store  3308 . 
     In some embodiments, the dynamic resource allocation can include allocating resources to receive and process the first partial results from the query acceleration data store  3308 . In addition, in some cases, the query coordinator  3304  can allocate resources to store the second partial results or combined results in the accelerated data store  3308  for later use, similar to the first partial results. 
     At block  4508 , the query coordinator  3304  executes the query as described in greater detail above with reference to block  4010  of  FIG.  40   . It will be understood that fewer, more, or different blocks can be used as part of the routine  4500 . For example, in some embodiments, the routine  4500  can further include, monitoring nodes during query execution, allocating/deallocating resources based on the query, etc. As another example, in certain embodiments, identification of the first partial results and resource allocation can form part of a processing query block, similar to the process query block  3804  of  FIG.  38   . Further, the first partial results can be communicated to the client as-is or further processed by the worker nodes  3306  (e.g., logic can be applied to the first partial results), and then provided to the requesting client. 
     Furthermore, it will be understood that the various blocks described herein with reference to  FIG.  45    can be implemented in a variety of orders. In some cases, the system  3301  can implement some blocks concurrently or change the order as desired. For example, the query coordinator  3304  can identify the first partial results  4504  and allocate resources  4506  concurrently or in any order, as desired. During execution, the nodes can concurrently obtain the first partial results from the query acceleration data store  3308  and obtain and process other data from another dataset source, or concurrently provide the first partial results to the query coordinator  3304  or client device  404  and obtain and process other data from another dataset source, etc. 
     21.0. Common Storage Architecture 
     As discussed above, indexers  206  may in some embodiments operate both to ingest information into a data intake and query system  3301 , and to search that information in response to queries from client devices  404 . The use of an indexer  206  to both ingest and search information may be beneficial, for example, because indexers  206  may have ready access to information that they have ingested, and thus be enabled to quickly access that information for searching purposes. However, use of an indexer  206  to both ingest and search information may not be desirable in all instances. As an illustrative example, consider an instance in which information within the system  3301  is organized into buckets, and each indexer  206  is responsible for maintaining buckets within a data store  208  corresponding to the indexer  206 . Illustratively, a set of 10 indexers  206  may maintain 100 buckets, distributed evenly across ten data stores  208  (each of which is managed by a corresponding indexer  206 ). Information may be distributed throughout the buckets according to a load-balancing mechanism used to distribute information to the indexers  206  during data ingestion. In an idealized scenario, information responsive to a query would be spread across the 100 buckets, such that each indexer  206  may search their corresponding 10 buckets in parallel, and provide search results to a search head  360 . However, it is expected that this idealized scenario may not always occur, and that there will be at least some instances in which information responsive to a query is unevenly distributed across data stores  208 . As an extreme example, consider a query in which responsive information exists within 10 buckets, all of which are included in a single data store  208  associated with a single indexer  206 . In such an instance, a bottleneck may be created at the single indexer  206 , and the effects of parallelized searching across the indexers  206  may be minimal. To increase the speed of operation of search queries in such cases, it may therefore be desirable to configure the data intake and query system  3301  such that parallelized searching of buckets may occur independently of the operation of indexers  206 . 
     Another potential disadvantage in utilizing an indexer  206  to both ingest and search data is that computing resources of the indexers  206  may be split among those two tasks. Thus, ingestion speed may decrease as resources are used to search data, or vice versa. It may further be desirable to separate ingestion and search functionality, such that computing resources available to either task may be scaled or distributed independently. 
     One example of a configuration of the data intake and query system  3301  that enables parallelized searching of buckets independently of the operation of indexers  206  is shown in  FIG.  46   . The embodiment of system  3301  that is shown in  FIG.  46    substantially corresponds to the embodiment of the system  3301  as shown in  FIG.  33   , and thus, corresponding elements of the system  3301  will not be re-described. However, unlike the embodiment as shown in  FIG.  33   , where individual indexers  206  are assigned to maintain individual data stores  208 , the embodiment of  FIG.  46    includes a common storage  4602 . Common storage  4602  may correspond to any data storage system accessible to each of the indexers  206 . For example, common storage  4602  may correspond to a storage area network (SAN), network attached storage (NAS), other network-accessible storage system (e.g., a ho33sted storage system, which may also be referred to as “cloud” storage), or combination thereof. The common storage  4602  may include, for example, hard disk drives (HDDs), solid state storage devices (SSDs), or other substantially persistent or non-transitory media. Data stores  208  within common storage  4602  may correspond to physical data storage devices (e.g., an individual HDD) or a logical storage device, such as a grouping of physical data storage devices or a virtualized storage device hosted by an underlying physical storage device. In one embodiment, common storage  4602  may be multi-tiered, with each tier providing more rapid access to information stored in that tier. For example, a first tier of the common storage  4602  may be physically co-located with indexers  206  and provide rapid access to information of the first tier, while a second tier may be located in a different physical location (e.g., in a hosted or “cloud” computing environment) and provide less rapid access to information of the second tier. Distribution of data between tiers may be controlled by any number of algorithms or mechanisms. In one embodiment, a first tier may include data generated or including timestamps within a threshold period of time (e.g., the past seven days), while a second tier or subsequent tiers includes data older than that time period. In another embodiment, a first tier may include a threshold amount (e.g., n terabytes) or recently accessed data, while a second tier stores the remaining less recently accessed data. In one embodiment, data within the data stores  208  is grouped into buckets, each of which is commonly accessible to the indexers  206 . The size of each bucket may be selected according to the computational resources of the common storage  4602  or the data intake and query system  3301  overall. For example, the size of each bucket may be selected to enable an individual bucket to be relatively quickly transmitted via a network, without introducing excessive additional data storage requirements due to metadata or other overhead associated with an individual bucket. In one embodiment, each bucket is 750 megabytes in size. 
     The indexers  206  may operate to communicate with common storage  4602  and to generate buckets during ingestion of data. Data ingestion may be similar to operations described above. For example, information may be provided to the indexers  206  by forwarders  204 , after which the information is processed and stored into buckets. However, unlike some embodiments described above, the buckets may be stored in common storage  4602 , rather than in a data store  208  maintained by an individual indexer  206 . Thus, the common storage  4602  can render information of the data intake and query system  3301  commonly accessible to elements of that system  3301 . As will be described below, such common storage  4602  can beneficial enable parallelized searching of buckets to occur independently of the operation of indexers  206 . 
     As noted above, it may be beneficial in some instances to separate within the data intake and query system  3301  functionalities of ingesting data and searching for data. As such, in the illustrative configuration of  FIG.  46   , worker nodes  3306  may be enabled to search for data stored within common storage  4602 . The nodes  3306  may therefore be communicatively attached (e.g., via a communication network) with the common storage  4602 , and be enabled to access buckets within the common storage  4602 . The nodes  3306  may search for data within buckets in a manner similar to how searching may occur at the indexers  206 , as discussed in more detail above. However, because nodes  3306  in some instances are not statically assigned to individual data stores  208  (and thus to buckets within such a data store  208 ), the buckets searched by an individual node  3306  may be selected dynamically, to increase the parallelization with which the buckets can be searched. For example, using the example provided above, consider again an instance where information is stored within 100 buckets, and a query is received at the data intake and query system  3301  for information within 10 such buckets. Unlike the example above (in which only indexers  206  already associated with those 10 buckets could be used to conduct a search), the 10 buckets holding relevant information may be dynamically distributed across worker nodes  3306 . Thus, if 10 worker nodes  3306  are available to process a query, each worker node  3306  may be assigned to retrieve and search within 1 bucket, greatly increasing parallelization when compared to the low-parallelization scenario discussed above (e.g., where a single indexer  206  is required to search all 10 buckets). Moreover, because searching occurs at the worker nodes  3306  rather than at indexers  206 , computing resources can be allocated independently to searching operations. For example, worker nodes  3306  may be executed by a separate processor or computing device than indexers  206 , enabling computing resources available to worker nodes  3306  to scale independently of resources available to indexers  206 . 
     Operation of the data intake and query system  3301  to utilize worker nodes  3306  to search for information within common storage  4602  will now be described. As discussed above, a query can be received at the search head  210 , processed at the search process master  3302 , and passed to a query coordinator  3304  for execution. The query coordinator  3304  may generate a DAG corresponding to the query, in order to determine sequences of search phases within the query. The query coordinator  3304  may further determine based on the query whether each branch of the DAG requires searching of data within the common storage  4602  (e.g., as opposed to data within external storage, such as remote systems  414  and  416 ). 
     It will be assumed for the purposes of described that at least one branch of the DAG requires searching of data within the common storage  4602 , and as such, description will be provided for execution of such a branch. While interactions are described for executing a single branch of a DAG, these interactions may be repeated (potentially concurrently or in parallel) for each branch of a DAG that requires searching of data within the common storage  4602 . As discussed above with reference to  FIG.  36   , executing a search representing a branch of a DAG can include a number of phases, such as an intake phase  3604 , processing phase  3606 , and collector phase  3608 . It is therefore illustrative to discuss execution of a branch of a DAG that requires searching of the common storage  4602  with reference to such phases. As also discussed above, each phase may be carried out using a number of partitions operated on by one or more worker nodes  3306 , which can also refer to one or more processors  3406  within a worker node  3306 , execution environments within a worker node  3306  or processor  3406  of a worker node  3306 , such as a virtualized computing device or software-based container, etc. 
     When a branch requires searching within common storage  4602 , the query coordinator  3304  can select a worker node  3306  at random or according to a load-balancing algorithm to gather metadata regarding the information within the common storage  4602 , for use in dynamically assigning partitions or worker nodes  3306  to implement an intake phase  3604 . Metadata is discussed in more detail above, but may include, for example, data identifying a host, a source, and a source type related to a bucket of data. Metadata may further indicate a range of timestamps of information within a bucket. The metadata can then be compared against a query to determine a subset of buckets within the common storage  4602  that may contain information relevant to a query. For example, where a query specifies a desired time range, host, source, source type, or combination thereof, only buckets in the common storage  4602  that satisfy those specified parameters may be considered relevant to the query. In one embodiment, the subset of buckets is determined by the assigned worker node  3306  and returned to the query coordinator  3304 . In another embodiment, the metadata retrieved by a worker node  3306  is returned to the query coordinator  3304  and used by the query coordinator  3304  to determine the subset of buckets. 
     Thereafter, the query coordinator  3304  can dynamically assign worker nodes  3306  to intake individual buckets within the determined subset of buckets. During execution, the buckets can be assigned to one or more partitions and processed by the worker nodes  3306  or processors  3406 . For example, the contents of a bucket can be assigned to a worker node  3306 . Based on the size of the contents of the bucket, the worker node  3306  can generate one or more partitions from the bucket&#39;s contents. The worker node  3306  can then assign the one or more partitions to a processor  3406  for processing. 
     In one embodiment, the query coordinator  3304  attempts to maximize parallelization of the intake phase  3604 , by attempting to intake the subset of buckets with a number of worker nodes  3306  or processors  3406  equal to the number of buckets in the subset (e.g., resulting in a one-to-one mapping of buckets in the subset to worker nodes  3306  or processors  3406 ). However, such parallelization may not be feasible or desirable, for example, where the total number of worker nodes  3306  or processors  3406  is less than the number of buckets within the determined subset, where some worker nodes  3306  or processors  3406  are processing other queries, or where some worker nodes  3306  or processors  3406  should be left in reserve to process other queries. Accordingly, the query coordinator  3304  may interact with the workload advisor  3310  to determine a number of partitions, worker nodes  3306 , or processors  3406  that are to be utilized to conduct the intake phase  3604  of the query. Illustratively, the query coordinator  3304  may initially request a one-to-one correspondence between buckets and worker nodes  3306  or processors  3406 , and the workload advisor  3310  may reduce the number of worker nodes  3306  or processors  3406  used for the intake phase  3604  of the query, resulting in a 2-to-1, 3-to-1, or n-to-1 correspondence between buckets and worker nodes  3306  or processors  3406 . Operation of the workload advisor  3310  is described in more detail above. 
     The query coordinator  3304  can then assign the worker nodes  3306  or processors  3406  (e.g., those worker nodes  3306  or processors  3406  identified by interaction with the workload advisor  3310 ) to intake the buckets previously identified as potentially containing relevant information (e.g., based on metadata of the buckets). In one embodiment, the query coordinator  3304  may assign all buckets as a single operation. For example, where 10 buckets are to be searched by 5 worker nodes  3306  or processors  3406 , the query coordinator  3304  may assign 2 buckets to a first worker node  3306  or processor  3406 , two buckets to a second worker node  3306  or processor  3406 , etc. In another embodiment, the query coordinator  3304  may assign buckets iteratively. For example, where 10 buckets are to be searched by 5 worker nodes  3306  or processors  3406 , the query coordinator  3304  may initially assign five buckets (e.g., one bucket to each worker node  3306  or processor  3406 ), and assign additional buckets to each worker node  3306  or processor  3406  as the respective worker node  3306  or processor  3406  completes intake of previously assigned buckets. 
     In some instances, buckets may be assigned to worker nodes  3306  randomly, or in a simple sequence (e.g., a first worker node  3306  is assigned a first bucket, a second worker nodes  3306  is assigned a second bucket, etc.). In other instances, the query coordinator  3304  may assign buckets to worker nodes  3306  based on buckets previously assigned to worker nodes  3306 , in a prior or current search. Illustratively, in some embodiments each worker node  3306  may be associated with a local cache of information (e.g., in memory, such as random access memory (“RAM”) or disk-based cache). Each worker node  3306  may store copies of one or more buckets from the common storage  4602  within the local cache, such that the buckets may be more rapidly searched by the worker node  3306 . The query coordinator  3304  may maintain or retrieve from worker nodes  3306  information identifying, for each relevant node  3306 , what buckets are copied within local cache of the respective nodes  3306 . Where a search node  3306  assigned to execute a search has within its local cache a copy of a bucket determined to be potentially relevant to the search, that worker node  3306  may be preferentially assigned to search that locally-cached bucket. In some instances, local cache information can further be used to determine the worker nodes  3306  to be used to conduct a search. For example, worker nodes  3306  that have locally-cached copies of buckets potentially relevant to a search may be preferentially selected by the query coordinator  3304  or workload advisor  3310  to execute the intake phase  3604  of a search. In some instances, the query coordinator  3304  or other component of the system  3301  (e.g., the search process master  3302 ) may instruct worker nodes  3306  to retrieve and locally cache copies of various buckets from the common storage  4602 , independently of processing queries. In one embodiment, the system  3301  is configured such that each bucket from the common storage  4602  is locally cached on at least one worker node  3306 . In another embodiment, the system  3301  is configured such that at least one bucket from the common storage  4602  is locally cached on at least two worker nodes  3306 . Caching a bucket on at least two worker nodes  3306  may be beneficial, for example, in instances where different queries both require searching the bucket (e.g., because the at least two worker nodes  3306  may process their respective local copies in parallel). In still other embodiments, the system  3301  is configured such that all buckets from the common storage  4602  are locally cached on at least a given number n of worker nodes  3306 , wherein n is defined by a replication factor on the system  3301 . For example, a replication factor of 5 may be established to ensure that 5 searches of buckets can be executed concurrently by 5 different worker nodes  3306 , each of which has locally cached a copy of a given bucket potentially relevant to the searches. 
     In some embodiments, buckets may further be assigned to partitions to assist with time ordering of search results. For example, where a search requests time ordering of results, the query coordinator  3304  may attempt to assign buckets with overlapping time ranges to the same partition, such that information within the buckets can be sorted in the partition. Where the buckets assigned to different partitions are non-overlapping in time, the query coordinator  3304  may sort information from different partitions according to an absolute ordering of the buckets processed by the different worker nodes  3306 . That is, if all timestamps in all buckets processed by a first worker node  3306  occur prior to all timestamps in all buckets processed by a second worker node  3306 , query coordinator  3304  can quickly determine (e.g., without referencing timestamps of information) that all information identified by the first worker node  3306  in response to a search occurs in time prior to information identified by the second worker node  3306  in response to the search. Thus, assigning buckets with overlapping time ranges to the same partition can reduce computing resources needed to time-order results. 
     In still more embodiments, partitions may be assigned to worker nodes  3306  based on overlaps of computing resources. For example, where a worker node  3306  is required to retrieve a bucket from common storage  4602  (e.g., where a local cached copy of the bucket does not exist on the worker node  3306  assigned to the partition), such retrieval may use a relatively high amount of network bandwidth or disk read/write bandwidth on the worker node  3306 . Thus, assigning a second bucket that requires retrieval to the same worker node  3306  might strain or exceed the network or disk read/write bandwidth of the worker node  3306 . For this reason, it may be preferential to assign buckets to partitions such that multiple processors  3406  of a common worker node  3306  are not both required to retrieve buckets from the common storage  4602 . Illustratively, it may be preferential to evenly assign all buckets containing potentially relevant information among the different worker nodes  3306  used to implement the intake phase  3604 . For similar reasons, where a given worker node  3306  has within its local cache two buckets that potentially include relevant information, it may be preferential to assign both such buckets to same worker node  3306 , such that both buckets can be searched in parallel on the worker node  3306  by the respective processor  3406 . In some instances, commonality of computing resources between partitions can further be used to determine the worker nodes  3306  to be used to conduct an intake phase  3604 . For example, the query coordinator  3304  may preferentially assign different worker nodes  3306  to implement an intake phase  3604  (e.g., in order to maximize network or disk read/write bandwidth). However, where a worker node  3306  has locally cached multiple buckets with information potentially relevant to the search, the query coordinator  3304  may preferentially assign those buckets to that worker node  3306 . 
     The above mechanisms for assigning buckets to worker nodes may be combined based on priorities of each potential outcome. For example, the query coordinator  3304  may give an initial priority to distributing buckets across a maximum number of different worker nodes  3306 , but a higher priority to assigning the same worker node  3306  to process buckets with overlapping timestamps. The query coordinator  3304  may give yet a higher priority to assigning worker nodes  3306  to process buckets that have been locally cached. The query coordinator  3304  may still further give higher priority to ensuring that each worker node  3306  is searching at least one bucket for information responsive to a query at any given time. Thus, the query coordinator  3304  can dynamically alter the assignment of buckets to worker nodes  3306  to increase the parallelization of a search, and to increase the speed and efficiency with which the search is executed. 
     When searching for information within the common storage  4602 , the intake phase  3604  may be carried out according to bucket-to-worker node  3306  mapping discussed above, as determined by the query coordinator  3304 . Specifically, after assigning at least one bucket to each worker node  3306  during the intake phase  3604 , each worker node  3306  may begin to retrieve its assigned bucket. Retrieval may include, for example, downloading a corresponding bucket from the common storage  4602 , or locating a copy of the bucket in a local cache of the worker node  3306 . Thereafter, each worker node  3306  may conduct an initial search of the bucket for information responsive to a query. The initial search may include processing that is expected to be disk or network intensive, rather than processing (e.g., CPU) intensive. For example, the initial search may include accessing the bucket, which may include decompressing the bucket from a compressed format, and accessing an index file stored within the bucket. The initial search may further include referencing the index or other information (e.g., metadata within the bucket) to locate one or more portions (e.g., records or individual files) of the bucket that potentially contain information relevant to the search. Moreover, in some embodiments, processing the bucket can include generating one or more partitions from the bucket and assigning the one or more partitions to one or more processors  3406  for processing. 
     Thereafter, the search proceeds to the processing phase  3606 , where the portions of buckets identified during the intake phase  3604  are searched to locate information responsive to the search. Illustratively, the searching that occurs during the processing phase  3606  may be predicted to be more processor (e.g., CPU) intensive than that which occurred during the intake phase  3604 . As such, the number of partitions used to conduct the processing phase  3606  may vary from that of the intake phase  3604 . For example, during or after the conclusion of the intake phase  3604 , each worker node  3306  implementing that phase  3604  may communicate to the query coordinator  3304  information regarding the portions identified as potentially containing information relevant to the query (e.g., the number, size, or formatting of portions, etc.). The query coordinator  3304  may thereafter determine from that information (e.g., based on interactions with the workload advisor  3310 ) the partitions to be used to conduct the processing phase  3606 . In other embodiments, the query coordinator  3304  may select worker nodes  3306  to be used to conduct the processing phase  3606  prior to implementation of the intake phase  3604  (e.g., contemporaneously with selecting worker nodes  3306  to conduct the intake phase  3604 ). The worker node  3306  selected for conducting the processing phase  3606  may include one or more worker node  3306  that previously conducted the intake phase  3604 . However, because the processing phase  3606  may be expected to be more resource intensive than the intake phase  3604  (e.g., with respect to use of processing cycles), the number of partitions used in the processing phase  3606  may exceed the number of partitions used in the intake phase  3604 . To reduce network communications, the additional partitions used in the processing phase  3606  may be preferentially selected to be collocated on a worker node  3306  with a partition that was previously used during intake phase  3604 . 
     At the processing phase  3606 , the worker nodes  3306  may parse the portions of buckets located during the intake phase  3604  in order to identify information relative to a search. For example, the worker node  3306  may parse the portions of buckets (e.g., individual files or records) to identify specific lines or segments that contain values specified within the search, such as one or more error types desired to be located during the search. Where the search is conducted according to map-reduce techniques, the processing phase  3606  can correspond to implementing a map function. Where the search requires that results be time-ordered, the processing phase  3606  may further include sorting results at each partition into a time-ordering. 
     The remainder of the search may be executed in phases according to the DAG determined by the query coordinator  3304 . For example, where the branch of the DAG currently being processed includes a collection node, the search may proceed to a collector phase  3608 . The collector phase  3608  may be executed using one or more worker nodes  3306  selected by the query coordinator  3304  (e.g., based on the information identified during the processing phase  3606 ), and operate to aggregate information identified during the processing phase  3606  (e.g., according to a reduce function). Where the processing phase  3606  represents a top-node of a branch of the DAG being executed, the information located during the processing phase  3606  may be transmitted to the query coordinator  3304 , where any additional nodes of the DAG are completed, and search results are transmitted to a data destination  3616 . These additional phases may be implemented in a similar manner as described above, and they are therefore not discussed in detail with respect to searches against a common storage  4602 . 
     As will be appreciated in view of the above description, the use of a common storage  4602  can provide many advantages within the data intake and query system  3301 . Specifically, use of a common storage  4602  can enable the system  3301  to decouple functionality of data ingestion, as implemented by indexers  206 , with functionality of searching, as implemented by worker nodes  3306 . Moreover, because buckets containing data are accessible by each worker node  3306 , a query coordinator  3304  can dynamically allocate buckets to worker nodes  3306  at the time of a search in order to maximize parallelization. Thus, use of a common storage  4602  can substantially improve the speed and efficiency of operation of the system  3301 . 
     22.0. Common Storage Flow 
       FIG.  47    is a flow diagram illustrative of an embodiment of a routine  4700  implemented by the query coordinator  3304  to execute a query on data within common storage  4602 . Although described as being implemented by the query coordinator  3304 , it will be understood that one or more elements outlined for routine  4700  can be implemented by one or more computing devices/components that are associated with the system  3301 , such as the search head  360 , search process master  3302 , indexer  206 , and/or worker nodes  3306 . Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  4702 , the query coordinator  3304  receives a query, as described in greater detail above with reference to block  3802  of  FIG.  38   . At block  4704 , the query coordinator identifies the common storage  4602  as a data source for the query (e.g., based on parameters of the query, based on timing requirements as described in greater detail above with reference to block  3902  of  FIG.  39   , etc.). 
     At block  4706 , the query coordinator  3304  determines one or more buckets within the common storage  4602  that may contain potentially relevant information for the query. As noted above, the one or more buckets may be identified based on metadata of the buckets within common storage  4602 , including time ranges, sources, source types, or hosts related to information stored within each bucket. In one embodiment, the query coordinator  3304  may utilize a worker node  3306  to retrieve current metadata of buckets within the common storage  4602 , and the query coordinator  3304  may utilize this information to determine potentially relevant buckets. In another embodiment, the query coordinator  3304  may direct a worker node  3306  to retrieve current metadata of buckets within the common storage  4602  and to utilize this information to determine potentially relevant buckets, after which the worker node  3306  may notify the query coordinator  3304  of the potentially relevant buckets. 
     At block  4708 , the query coordinator  3304  allocates resources, such as, but not limited to partitions, worker nodes  3306 , or processors  3406 , to intake the potentially relevant buckets during an intake phase  3604 . As described above, the query coordinator  3304  can allocate resources based on a number of factors, including a number of potentially relevant buckets, amount of memory available, a number of worker nodes  3306  or processors  3406  available to intake the buckets, a number of potentially relevant buckets that exist as cached copies within local storage of a worker node  3306 , or a distribution of partitions across different worker nodes  3306  (e.g., to maximize an availability of network or disk read/write bandwidth). In some embodiments, the query coordinator  3304  may interact with the workload advisor  3310  to intake potentially relevant buckets. In general, worker nodes  3306  may be allocated to intake potentially relevant buckets in a manner that maximizes either or both of use of locally-cached copies of buckets on worker nodes  3306  and parallelization of retrieval of buckets from common storage  4602 . 
     At block  4710 , the query coordinator  3304  executes the query as described in greater detail above with reference to  FIGS.  36  and  46   . It will be understood that fewer, more, or different blocks can be used as part of the routine  4700 . For example, in some embodiments, the routine  4700  can further include allocating resources to conduct subsequent phases of a query, such as a processing phase  3606  or collection phase  3608 . As another example, in certain embodiments, the identification of the common storage  4602 , determination of potentially relevant buckets, and allocation of worker nodes  3306  to perform an intake phase  3604  can form part of a processing query block, similar to the process query block  3804  of  FIG.  38   . 
     Furthermore, it will be understood that the various blocks described herein with reference to  FIG.  47    can be implemented in a variety of orders. In some cases, the system  3301  can implement some blocks concurrently or change the order as desired. For example, the system  3301  can in some instances allocate or instruct worker nodes  3306  to intake potentially relevant buckets iteratively, during execution of a query (e.g., by allocating worker nodes  3306  to a first portion of potentially relevant buckets, and allocating worker nodes  3306  to additional buckets from the potentially relevant buckets as the worker nodes  3306  complete intake of buckets from the first portion). 
     The above interactions generally discuss information that is stored within common storage  4602 . However, because the information in common storage  4602  is generated by indexers  206 , searching of common storage  4602  may be undesirable in instances in which search results are desired immediately. Specifically, where information from a data source  203  is required to pass through a forwarder  204 , be processed at an indexer  206 , and stored in common storage  4602  before searching of that information can be conducted by a worker node  3306 , a significant delay (e.g., 2-4 minutes) may occur between generation of the information at the data source  203  and searching of the information by a worker node  3306 . Thus, in the architecture of  FIG.  46   , the indexers  206  may be configured to enable searching of information received at an indexer  206  (prior to processing of that information and storage in the common storage  4602 ), in a manner similar to that described above with reference to  FIG.  39   . However, utilization of the indexers  206  to conduct searching of not-yet-indexed information may incur some of the disadvantages described above, such as the comingling of computing resources used to ingest information with resources used to search information. It may therefore be desirable to provide an architecture that enables worker nodes  3306 , rather than indexers  206 , to search not-yet-indexed information, without inhibiting operation of the indexers  206 . 
     23.0. Ingested Data Buffer Architecture 
     One embodiment of the system  3301  that enables worker nodes  3306  to search not-yet-indexed information is shown in  FIG.  48   . Searching of not-yet-indexed information (e.g., prior to processing of the information by an indexer  206 ) may be beneficial, for example, where information is desired on a continuous or streaming basis. For example, a client device  404   a  may desire to establish a long-running (e.g., until manually halted) search of data received at the data intake and query system  3301 , such that the client is quickly notified on occurrence of specific types of information within the data, such as errors within machine records. Thus, it may be desirable to conduct the search against the data as it enters intake and query system  3301 , rather than waiting for the data to be processed by the indexers  206  and saved into a data store  208 . 
     The embodiment of  FIG.  48    is similar to that of  FIG.  46   , and corresponding elements will not be re-described. However, unlike the embodiment of  FIG.  46   , the embodiment of  FIG.  48    includes an ingested data buffer  4802 . The ingested data buffer  4802  of  FIG.  48    operates to receive information obtained by the forwarders  204  from the data sources  203 , and make such information available for searching to both indexers  206  and worker nodes  3306 . As such, the ingested data buffer  4802  may represent a computing device or computing system in communication with both the indexers  206  and the worker nodes  3306  via a communication network. 
     In one embodiment, the ingested data buffer  4802  operates according to a publish-subscribe (“pub-sub”) messaging model. For example, each data source  203  may be represented as one or more “topics” within a pub-sub model, and new information at the data source may be represented as a “message” within the pub-sub model. Elements of the system  3301 , including indexers  206  and worker nodes  3306  (or processors  3406  of worker nodes  3306 ) may subscribe to a topic representing desired information (e.g., information of a particular data source  203 ) to receive messages within the topic. Thus, an element subscribed to a relevant topic will be notified of new data categorized under the topic within the ingested data buffer  4802 . A variety of implementations of the pub-sub messaging model are known in the art, and may be usable within the ingested data buffer  4802 . As will be appreciated based on the description below, use of a pub-sub messaging model can provide many benefits to the system  3301 , including the ability to search data quickly after the data is received at the ingested data buffer  4802  (relative to waiting of the data to be processed by an indexer  206 ) while maintaining or increasing data resiliency. 
     In embodiments that utilize an ingested data buffer  4802 , operation of the indexer  206  may be modified to receive information from the buffer  4802 . Specifically, each indexer  206  may be configured to subscribe to one or more topics on the ingested data buffer  4802  and to thereafter process the information in a manner similarly to as described above with respect to other embodiments of the system. After data representing a message has been processed by an indexer  206 , the indexer  206  can send an acknowledgement of the message to the ingested data buffer  4802 . In accordance with the pub-sub messaging model, the ingested data buffer  4802  can delete a message once acknowledgements have been received from all subscribers (which may include, for example, a single indexer  206  configured to process the message). Thereafter, operation of the system  3301  to store the information processed by the indexer  206  and enable searching of such information is similar to embodiments described above (e.g., with reference to  FIGS.  33  and  46   , etc.). 
     As discussed above, the ingested data buffer  4802  is also in communication with the worker nodes  3306 . As such, the data intake and query system  3301  can be configured to utilize the worker nodes  3306  to search data from the ingested data buffer  4802  directly, rather than waiting for the data to be processed by the indexers  206 . As discussed above, a query can be received at the search head  360 , processed at the search process master  3302 , and passed to a query coordinator  3304  for execution. The query coordinator  3304  may generate a DAG corresponding to the query, in order to determine sequences of search phases within the query. The query coordinator  3304  may further determine based on the query whether any branch of the DAG requires searching of data within the ingested data buffer  4802 . For example, the query coordinator  3304  may determine that at least one branch of the query requires searching of data within the ingested data buffer  4802  by identifying, within the query, a topic of the ingested data buffer  4802  for searching. It will be assumed for the purposes of described that at least one branch of the DAG requires searching of data within the ingested data buffer  4802 , and as such, description will be provided for execution of such a branch. While interactions are described for executing a single branch of a DAG, these interactions may be repeated (potentially concurrently or in parallel) for each branch of a DAG that requires searching of data within the ingested data buffer  4802 . As discussed above with reference to  FIG.  36   , executing a search representing a branch of a DAG can include a number of phases, such as an intake phase  3604 , processing phase  3606 , and collector phase  3608 . It is therefore illustrative to discuss execution of a branch of a DAG that requires searching of the common storage  4602  with reference to such phases. As also discussed above, each phase may be carried out using a number of partitions, each of which may be assigned to a worker node  3306  (e.g., a specific worker node  3306 , processor within the worker node  3306 , execution environment within a worker node  3306 , etc.). Particularly in the case of streaming or continuous searching, different instances of the phases may be carried out at least partly concurrently. For example, the processing phase  3606  may occur with respect to a first set of information while the intake phase  3604  occurs with respect to a second set of information, etc. Thus, while the phases will be discussed in sequence below, it should be appreciated that this sequence can occur multiple times with respect to a single query (e.g., as new data enters the system  3301 ), and each sequence may occur at least partially concurrently with one or more other sequences. Moreover, because the ingested data buffer  4802  can be configured to make messages available to any number of subscribers, the sequence discussed below may occur with respect to multiple different searches, potentially concurrently. Thus, the architecture of  FIG.  48    provides a highly scalable, highly resilient, high availability architecture for searching information received at the system  3301 . 
     When a branch requires searching within ingested data buffer  4802 , the query coordinator  3304  can select a worker node  3306  at random or according to a load-balancing algorithm to gather metadata regarding the topic specified within the query from the ingested data buffer  4802 . Metadata regarding a topic may include, for example, a number of message queues within the ingested data buffer  4802  corresponding to the topic. Each message queue can represent a collection of messages published to the topic, which may be time-ordered (e.g., according to a time that the message was received at the ingested data buffer  4802 ). In some instances, the ingested data buffer  4802  may implement a single message queue for a topic. In other instances, the ingested data buffer  4802  may implement multiple message queues (e.g., across multiple computing devices) to aid in load-balancing operation of the ingested data buffer  4802  with respect to the topic. The selected worker node  3306  can determine the number of message queues maintained at the ingested data buffer  4802  for a topic, and return this information to the query coordinator. 
     Thereafter, the query coordinator  3304  can dynamically assign worker nodes  3306  to an intake phase  3604  by retrieving individual message queues of the topic within the ingested data buffer  4802 . In one embodiment, the query coordinator  3304  attempts to maximize parallelization of the intake phase  3604 , by attempting to retrieve messages from the message queues with a number of worker nodes  3306  or processors  3406  equal to or greater than the number of message queues for the topic maintained at the ingested data buffer  4802  (e.g., resulting in a one-to-one mapping of message queues in the topic to worker nodes  3306  or processors  3406 ). However, such parallelization may not be feasible or desirable, for example, where the total number of worker nodes  3306  or processors  3406  is less than the number of message queues, where some worker nodes  3306  or processors  3406  are processing other queries, or where some worker nodes  3306  or processors  3406  should be left in reserve to process other queries. Accordingly, the query coordinator  3304  may interact with the workload advisor  3310  to determine a number of worker nodes  3306  or processors  3406  that are to be utilized to intake messages from the message queues during the intake phase  3604 . Illustratively, the query coordinator  3304  may initially request a one-to-one correspondence between message queues and worker nodes  3306  or processors  3406 , and the workload advisor  3310  may reduce the number of worker nodes  3306  or processors  3406  used to read the message queues, resulting in a 2-to-1, 3-to-1, or n-to-1 correspondence between message queues and worker nodes  3306  or processors  3406 . Operation of the workload advisor  3310  is described in more detail above. When a greater than 1-to-1 correspondence exists between queues and worker nodes  3306  or processors  3406  (e.g., 2-to-1, 3-to-1, etc.), the message queues may be evenly assigned among different worker nodes  3306  used to implement the intake phase  3604 , to maximize network or read/write bandwidth available to partitions conducting the intake phase  3604 . 
     During the intake phase  3604 , each worker node  3306  or processor  3406  used during the intake phase  3604  can subscribe to those message queues assigned to the worker node  3306  or processor  3406 . Illustratively, where worker node  3306  or processor  3406  are assigned in a 1-to-1 correspondence with message queues for a topic in the ingested data buffer  4802 , each worker node  3306  or processor  3406  may subscribe to one corresponding message queue. Thereafter, in accordance with the pub-sub messaging model, the worker node  3306  or processor  3406  can receive from the ingested data buffer  4802  messages publishes within those respective message queues. However, to ensure message resiliency, a worker node  3306  or processor  3406  may decline to acknowledge the messages until such messages have been fully searched, and results of the search have been provided to a data destination (as will be described in more detail below). 
     In some embodiments, a worker node  3306  or processor  3406  may, during the intake phase  3604  act as an aggregator of messages published to a respective message queue of the ingested data buffer  4802 , to define a collection of data as a partition to be processed during an instance of the processing phase  3606 . For example, the worker node  3306  or processor  3406  may collect messages corresponding to a given time-window (such as a 30 second time window, 1 minute time window, etc.), and bundle the messages together as a partition for further processing during a processing phase  3606  of the search. In one instance, the time window may be set to a duration lower than a typical delay needed for an indexer  206  to process information from the ingested data buffer  4802  and place the processed information into a data store  208  (as, if a time-window greater than this delay were used, a search could instead be conducted against the data stores  208 ). The time window may further be set based on an expected variance between timestamps in received information and the time at which the information is received at the ingested data buffer  4802 . For example, it is possible the information arrives at the ingested data buffer  4802  in an out-of-order manner (e.g., such that information with a later timestamp is received prior to information with an earlier timestamp). If the actual delay in receiving out-of-order information (e.g., the delay between when information is actually received and when it should have been received to maintain proper time-ordering) exceeds the time window, it is possible that the delayed information will be processed during a later instance of the processing phase  3606  (e.g., with a subsequent bundle of messages), and as such, results derived from the delayed information may be delivered out-of-order to a data destination. Thus, a longer time-window can assist in maintaining order of search results. In some instances, the ingested data buffer  4802  may guarantee time ordering of results within each message queue (though potentially not across message queues), and thus, modification of a time window in order to maintain ordering of results may not be required. In still more embodiments, the time-window may further be set based on computing resources available at the worker nodes  3306 . For example, a longer time window may reduce computing resources used by a worker node  3306  or processor  3406  by enabling a larger collection of messages to be processed as a single partition in the processing phase  3606 . However, the longer time window may increase the size of the partition and/or delay how quickly an initial set of results are delivered to a data destination. Thus, the specific time-window may vary across embodiments of the present disclosure. 
     While embodiments are described herein with reference to a collection of messages or a partition defined according to a time-window, other embodiments of the present disclosure may utilize additional or alternative collection techniques. For example, a worker node  3306  or processor  3406  may be configured to include no more than a threshold number of messages or a threshold amount of data in a partition or collection, regardless of a time-window for collection. As another example, a worker node  3306  or processor  3406  may be configured during the intake phase  3604  not to aggregate messages, but rather to pass each message to a processing phase  3606  immediately or substantially immediately. Thus, embodiments related to time-windowing of messages are illustrative in nature. 
     In some embodiments, the worker nodes or processors  3406 , during the intake phase  3604  may further conduct coarse filtering on the messages received during a given time-window, in order to identify any messages not relevant to a given query. Illustratively, the coarse filtering may include comparison of metadata regarding the message (e.g., a source, source type, or host related to the message), in order to determine whether the metadata indicates that the message is irrelevant to the query. If so, such a message may be removed from the collection or partition prior to the search process proceeding to the processing phase  3606 . In one embodiment, the coarse filtering does not include searching for or processing the actual content of a message, as such processing may be predicted to be relatively computing resource intensive. 
     After generating a collection of messages or partition from a respective message queue, the search can proceed to the processing phase  3606 , where one or more worker nodes or processors  3406  are utilized to search the messages for information relevant to the search query. Illustratively, the searching that occurs during the processing phase  3606  may be predicted to be more processor (e.g., CPU) intensive than that which occurred during the intake phase  3604 . As such, the number of partitions used to conduct the processing phase  3606  may vary from that of the intake phase  3604 . For example, during or after the conclusion of the intake phase  3604 , each partition worker node  3306  implementing that phase  3604  may communicate to the query coordinator  3304  information regarding the collections of messages received during a given time-window (e.g., the number, size, or formatting of messages, etc.). The query coordinator  3304  may thereafter determine from that information (e.g., based on interactions with the workload advisor  3310 ) the partitions to be used to conduct the processing phase  3606 . In other embodiments, the query coordinator  3304  may select worker nodes  3306  to be used to conduct the processing phase  3606  prior to implementation of the intake phase  3604  (e.g., contemporaneously with selecting worker nodes  3306  to conduct the intake phase  3604 ). The worker nodes  3306  selected for conducting the processing phase  3606  may include one or more worker nodes  3306  that were part of the intake phase  3604 . However, because the processing phase  3606  may be expected to be more resource intensive than the intake phase  3604  (e.g., with respect to use of processing cycles), the number of partitions used in the processing phase  3606  may exceed the number of partitions used in the intake phase  3604 . To reduce network communications, the additional partitions used in the processing phase  3606  may be preferentially selected to be collocated on a worker node  3306  with a partition that was used in the intake phase  3604 . 
     At the processing phase  3606 , the worker nodes  3306  may parse the portions of buckets located during the intake phase  3604  in order to identify information relative to a search. For example, the worker node  3306  may parse the portions of buckets (e.g., individual files or records) to identify specific lines or segments that contain values specified within the search, such as one or more error types desired to be located during the search. Where the search is conducted according to map-reduce techniques, the processing phase  3606  can correspond to implementing a map function. Where the search requires that results be time-ordered, the processing phase  3606  may further include sorting results at each partition into a time-ordering. 
     The remainder of the search may be executed in phases according to the DAG determined by the query coordinator  3304 . For example, where the branch of the DAG currently being processed includes a collection node, the search may proceed to a collector phase  3608 . The collector phase  3608  may be executed using one or more worker nodes  3306  selected by the query coordinator  3304  (e.g., based on the information identified during the processing phase  3606 ), and operate to aggregate information identified during the processing phase  3606  (e.g., according to a reduce function). Where the processing phase  3606  represents a top-node of a branch of the DAG being executed, the information located during the processing phase  3606  may be transmitted to the query coordinator  3304 , where any additional nodes of the DAG are completed, and search results are transmitted to a data destination  3616 . These additional phases may be implemented in a similar manner as described above, and they are therefore not discussed in detail with respect to searches against a common storage  4602 . 
     Subsequent to these phases, a set of search results corresponding to each collection of messages or partition (e.g., as received during a time-window) may be transmitted to a data destination. On transmission of such information (and potentially verification of arrival of such information at the data destination), the search head  210  may cause an acknowledgement of each message within the collection to be transmitted to the ingested data buffer  4802 . For example, the search head  210  may notify the query coordinator  3304  that search results for a particular set of information (e.g., information corresponding to a range of timestamps representing a given time window) have been transmitted to a data destination. The query coordinator  3304  can thereafter notify the worker nodes  3306  used to ingest messages making up the set of information that the search results have been transmitted. The worker nodes  3306  can then acknowledge to the ingested data buffer  4802  receipt of the messages. In accordance with the pub-sub messaging model, the ingested data buffer  4802  may then delete the messages after acknowledgement by subscribing parties. By delaying acknowledgement of messages until after search results based on such messages are transmitted to (or acknowledged by) a data destination, resiliency of such search results can be improved or potentially guaranteed. For example, in the instance that an error occurs between receiving a message from the ingested data buffer  4802  and search results based on that message being passed to a data destination (e.g., a worker node  3306  fails, causing a copy of the message maintained at the worker node  3306  to be lost), the query coordinator  3304  can detect the failure (e.g., based on heartbeat information from a worker node  3306 ), and cause the worker node  3306  to be restarted, or a new worker node  3306  to replace the failed worker node  3306 . Because the message has not yet been acknowledged to the ingested data buffer  4802 , the message is expected to still exist within a message queue of the ingested data buffer  4802 , and thus, the restarted or new worker node  3306  can retrieve and process the message as described below. Thus, by delaying acknowledgement of a message, failures of worker nodes  3306  during the process described above can be expected not to result in data loss within the data intake and query system  3301 . 
     In some embodiments, the ingested data buffer  4802  and search functionalities described above may be used to make “enhanced” or annotated data available for searching in a streaming or continuous manner. For example, search results may in some instances be represented by codes or other machine-readable information, rather than in an easy-to-comprehend format (e.g., as error codes, rather than textual descriptions of what such a code represents). Thus, the embodiment of  FIG.  48    may enable a client to define a long-running search that locates codes within messages of the ingested data buffer  4802  (e.g., via regular expression or other pattern matching criteria), correlates the codes to a corresponding textual description (e.g., via a mapping stored in common storage  4602 ), annotates or modifies the messages to include relevant textual descriptions for any code appearing within the message, and re-publishes the messages to the ingested data buffer  4802 . In this manner, the information maintained at the ingested data buffer  4802  may be readily annotated or transformed by searches executed at the system  3301 . Any number of types of processing or transformation may be applied to information of the ingested data buffer  4802  to produce search results, and any of such search results may be republished to the ingested data buffer  4802 , such that the search results are themselves made available for searching. 
     As will be appreciated in view of the above description, the use of an ingested data buffer  4802  can provide many advantages within the data intake and query system  3301 . Specifically, use of a ingested data buffer  4802  can enable the system  3301  to utilize worker nodes  3306  to search not-yet-indexed information, thus decoupling searching of such information from the functionality of data ingestion, as implemented by indexers  206 . Moreover, because the ingested data buffer  4802  can make messages available to both indexers  206  and worker nodes  3306 , searching of not-yet-indexed information by worker nodes  3306  can be expected not to detrimentally effect the operation of the indexers  206 . Still further, because the ingested data buffer  4802  can operate according to a pub-sub messaging model, the system  3301  may utilize selective acknowledgement of messages (e.g., after indexing by an indexer  206  and after delivery of search results based on a message to a data destination) to increase resiliency of the data on the data intake and query system  3301 . Thus, use of an ingested data buffer  4802  can substantially improve the speed, efficiency, and reliability of operation of the system  3301 . 
     24.0. Ingested Data Buffer Flow 
       FIG.  49    is a flow diagram illustrative of an embodiment of a routine  4900  implemented by the query coordinator  3304  to execute a query on data from an ingested data buffer  4802 . Although described as being implemented by the query coordinator  3304 , it will be understood that one or more elements outlined for routine  4900  can be implemented by one or more computing devices/components that are associated with the system  3301 , such as the search head  360 , search process master  3302 , indexer  206 , and/or worker nodes  3306 . Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  4902 , the query coordinator  3304  receives a query, as described in greater detail above with reference to block  3802  of  FIG.  38   . At block  4904 , the query coordinator identifies the ingested data buffer  4802  as a data source for the query (e.g., based on parameters of the query, based on timing requirements as described in greater detail above with reference to block  3902  of  FIG.  39   , etc.). 
     At block  4906 , the query coordinator  3304  determines a set of message queues on the ingested data buffer  4802  to which messages potentially relevant to the query are published. The message queues may be determined, for example, by querying the ingested data buffer  4802  based on a topic specified within the query. In one embodiment, the query coordinator  3304  may utilize a processor  3406  of a worker node  3306  to retrieve identifying information for the message queues from the ingested data buffer  4802 . In another embodiment, the query coordinator  3304  may directly query the ingested data buffer  4802  for the identifying information of the message queues. 
     At block  4908 , the query coordinator  3304  allocates worker nodes  3306  to conduct windowed-intake of messages from message queues assigned to the worker nodes  3306 . As described above, the query coordinator  3304  can allocate worker nodes  3306  based on a number of factors, including a number of message queues to which potentially relevant messages are posted, a number of worker nodes  3306  (or processors  3406 ) available to intake the buckets, or a distribution across different worker nodes  3306  (e.g., to maximize an availability of network or disk read/write bandwidth). In some embodiments, the query coordinator  3304  may interact with the workload advisor  3310  to allocate worker nodes  3306  to intake messages from message queues. In general, the worker nodes  3306  may be allocated to intake potentially relevant buckets in a manner that maximizes parallelization of retrieval of messages from message queues on the ingested data buffer  4802 . As noted above, each worker nodes  3306  may function to collect messages from its respective message queue during a given time-window (such as a 30 second time window, 1 minute time window, etc.) using one or more of its processors  3406 , and bundle the messages together as one or more partitions for further processing during a processing phase  3606  of the search. The time-window may be selected based on a number of factors, as described in more detail above. 
     At block  4910 , the query coordinator  3304  executes the query as described in greater detail above with reference to  FIGS.  36  and  48   . It will be understood that fewer, more, or different blocks can be used as part of the routine  4700 . For example, in some embodiments, the routine  4700  can further include allocating worker nodes  3306  to conduct subsequent phases of a query, such as a processing phase  3606  or collection phase  3608 . As another example, in certain embodiments, the identification of the ingested data buffer  4802 , determination of message queues containing potentially relevant messages, and allocation of worker nodes  3306  to perform an intake phase  3604  can form part of a processing query block, similar to the process query block  3804  of  FIG.  38   . 
     Furthermore, it will be understood that the various blocks described herein with reference to  FIG.  47    can be implemented in a variety of orders. In some cases, the system  3301  can implement some blocks concurrently or change the order as desired. For example, the system  3301  can in some instances allocate worker nodes  3306  to intake potentially relevant messages from message queues dynamically. For example, the system  3301  may periodically or in response to information received from the ingested data buffer  4802  determine that the number of message queues containing potentially relevant messages has changed, and alter the allocation of worker nodes  3306  to those message queues accordingly. 
     25.0. Federated Search 
     As mentioned above and with reference to  FIG.  1 A , in some instances it can be beneficial to perform queries across multiple data systems, such as the data intake and query system  16  and the external data systems  12 . Such queries may result in the correlation of additional data and/or may provide additional insights. 
     In some cases, the external data systems  12  may be distinct deployments of a data intake and query system  16  or  108 . Specifically, the external data systems  12  can include a similar or the same architecture as the data intake and query system  16  or  108 , which may include one or more of the previously described systems, such as, for example: forwarders  204 , indexers  206 , data stores  208 , search head  210 , search process master  3302 , query coordinator  3304 , worker nodes  3306 , accelerated data store  3308 , common storage  4602 , and/or ingested data buffer  4802 , etc. For example, different divisions of the same company may each use a separate and independent data intake and query system  16  to ingest, store, and search their respective datasets. As such, the different and independent data intake and query systems  16  may have no control over each other or over the data managed by another data intake and query system  16 . Furthermore, each deployment of the independent data intake and query system  16  can include system-specific search configuration data that may not be understood by other data intake and query systems  16 . Moreover, in some cases, different divisions or subsidiaries of a company may use different versions of a data intake and query system  16 , which may each have different capabilities of features. For example, a company implementing one version of the data intake and query system  16  may purchase or acquire another company that uses another version of the data intake and query system  16 . In some such cases, the purchased company may remain separately operated or may not have its systems integrated with the systems of the purchasing company. 
     Despite the independent and separate nature of the different data intake and query systems  16 , it can be beneficial for one data intake and query system  16  to communicate with and receive and process data from another data intake and query system  16 . For example, a user of one data intake and query system  16  may want to analyze data managed by a different data intake and query system  16  or correlate data across multiple data intake and query systems  16 . For instance, continuing the example of the previous paragraph, it may be desirable for an employee of the purchasing company to request a query that analyzes data from both the data intake and query system  16  of the purchasing company and the data intake and query system  16  of the purchased company. As such, one data intake and query system  16  may receive a query that involves data that is managed by another data intake and query system  16 . 
       FIG.  50 A  is a block diagram of an embodiment of the environment  100  in which the external data systems  12 - 1  and  12 - 2  described with respect to  FIG.  1 A  correspond to data intake and query systems  16 B and  16 C. For simplicity, the data intake and query system that receives a query that involves data managed by another data intake and query system (also referred to herein as a federated query or multi-system query) may be referred to as the primary data intake and query system  16 A, and the data intake and query systems that perform a query or subquery at the request of another data intake and query system may be referred to as secondary data intake and query systems  16 B,  16 C. However, it will be understood that any data intake and query system  16 A,  16 B,  16 C (generically referred to as data intake and query system  16 ) could be a primary or secondary data intake and query system  16  depending on which data intake and query system  16 A,  16 B,  16 C receives the federated query and which data intake and query system  16 A,  16 B,  16 C executes a query or subquery at the request of another data intake and query system  16 A,  16 B,  16 C. Furthermore, it will be understood that any data intake and query system  16 A,  16 B,  16 C can include any one or any combination of components described herein, such as those described with respect to the data intake and query system  108 . Accordingly, the data intake and query systems  16 A,  16 B,  16 C may each have the same or a different architecture and components. 
     As will be described herein, upon receipt of a query, a primary data intake and query system  16 A can parse the query and determine that the query involves one or more secondary data intake and query systems  16 B,  16 C, or is a federated query. The primary data intake and query system  16 A can communicate with the secondary data intake and query systems  16 B,  16 C to determine the capabilities of each secondary data intake and query system  16 B,  16 C and/or estimate the amount of data to be ingested from the secondary data intake and query systems  16 B,  16 C. In some cases, the primary data intake and query system  16 A can obtain information regarding search configuration data of the secondary data intake and query systems  16 B,  16 C. 
     Based on the received information, the primary data intake and query system  16 A can determine the size and number of tasks to be executed in relation to the query, generate one or more subqueries for each secondary data intake and query system  16 B,  16 C, and/or distribute the subqueries to the secondary data intake and query systems  16 B,  16 C for execution. In certain embodiments, based on the information received from the secondary data intake and query systems  16 B,  16 C, the primary data intake and query system  16 A can generate the subquery for different components of the secondary data intake and query systems  16 B,  16 C. For example, the primary data intake and query system  16 A can generate the subquery for a search head  210  of a secondary data intake and query system  16 B,  16 C and/or for indexers  206  or worker nodes  3306  of the secondary data intake and query system  16 B,  16 C. 
     In certain embodiments, the primary data intake and query system  16 A uses the search configuration data received from the secondary data intake and query system  16 B,  16 C to generate a native subquery for the secondary data intake and query system  16 B,  16 C. In some embodiments, if the primary data intake and query system  16 A is unable to obtain the system-specific search configuration data from the secondary data intake and query system  16 B,  16 C, it can generate or use a non-native subquery for the secondary data intake and query system  16 . In such embodiments, the secondary data intake and query system  16  can process the subquery to determine the native subquery. However, it will be understood that the primary data intake and query system  16 A can generate native or non-native subqueries for different components of the secondary data intake and query system  16 B,  16 C as desired. 
     In some cases, the components of the secondary data intake and query systems  16 B,  16 C treat the subqueries similar to other queries that they receive. For example, if the subquery is received by a search head  210 , the search head  210  can process and execute the query as described in greater detail herein with reference to at least  FIGS.  6 ,  30 , and  38   . Similarly, if a subquery is received by one or more indexers  206  or worker nodes  3306  of the secondary data intake and query system  16 B,  16 C, they can process and execute the queries as described herein. 
     Further, the secondary data intake and query systems  16 B,  16 C can communicate results of the subqueries (also referred to herein as partial results or partial results of the federated or multi-system search) to the primary data intake and query system  16 A for further processing. The results of the subqueries can include pre-processed or processed data. For example, depending on the capabilities or processing power of the secondary data intake and query systems  16 B,  16 C, the primary data intake and query system  16 A can generate subqueries that push more or less processing to the secondary data intake and query systems  16 B,  16 C. 
     In embodiments where the primary data intake and query system  16 A includes worker nodes  3306 , the primary data intake and query system  16 A can interact with and receive partial results from the secondary data intake and query systems  16 B,  16 C using the worker nodes  3306 . The worker nodes  3306  can concurrently receive and process data received from one or more secondary data intake and query systems  16 B,  16 C, and provide the results to one or more components of the primary data intake and query system  16 A, such as a search head  210 , search process master  3302 , or query coordinator  3304 . 
     In some cases, the subqueries sent to the secondary data intake and query systems  16 B,  16 C can indicate that the partial results are to be distributed among multiple worker nodes  3306 . In certain embodiments, the subqueries sent to the secondary data intake and query systems  16 B,  16 C can indicate that the partial results are to be sent to a single worker node  3306 , which can distribute the partial results between multiple worker nodes  3306 . 
     In certain embodiments, the worker nodes  3306  combine the data received from the secondary data intake and query systems into tasks or partitions for execution by processors of the worker nodes  3306 . Moreover, the worker nodes can distribute the tasks or partitions between worker nodes  3306  in a load-balanced fashion in order to process the tasks or partitions in a distributed manner. 
     In some embodiments, the primary and one or more secondary data intake and query systems  16 A,  16 B,  16 C can include worker nodes  3306 . In such embodiments, each data intake and query system  16 A,  16 B,  16 C can independently use the worker nodes  3306  to execute their corresponding query or subquery in a distributed manner. 
     Further, in some embodiments, one or more worker nodes  3306  may be shared between the primary and one or more secondary data intake and query systems  16 B,  16 C. For example, the physical machines on which the worker nodes  3306  are implemented can be communicatively coupled to and receive instructions from the primary and secondary data intake and query systems  16 B,  16 C. Accordingly, in some cases, a secondary data intake and query system  16 B,  16 C may use one or more worker nodes  3306  to execute a subquery and then provide results of the subquery to the one or more worker nodes  3306  for further execution based on the federated query. As such, as part of the same query, one or more of the worker nodes  3306  may process data at the direction of a secondary data intake and query system  16 B,  16 C and process data at the request of a primary data intake and query system  16 A. Further, the data processed at the request of the primary data intake and query system  16 A can correspond to the data processed at the request of the secondary data intake and query system  16 B,  16 C. For example, a worker node  3306  may perform one or more transformations on a first dataset at the request of the secondary data intake and query system  16 B,  16 C and then, at the request of the primary data intake and query system  16 A, perform one or more transformations on the dataset that resulted from the transformations on the first dataset. 
       FIG.  50 B  is a block diagram of an embodiment of the environment  100  in which a primary data intake and query system  16 A communicates with third-party data storage and processing systems  5000 A and/or  5000 B to execute a query. The system of  FIG.  50 B  is similar to the system of  50 A. For example, the primary data intake and query system  16 A is capable of communicating with external data systems  12 - 1  and  12 - 2 , which may include other data intake and query systems (e.g., the secondary data intake and query systems  16 B and  16 C, respectively). However, the primary data intake and query system  16 A of  FIG.  50 B  may also be capable of communicating with external data systems  12 - 3  and  12 - 4 , which may include third-party data storage and processing systems  5000 A and  5000 B, respectively (collectively and individually referred to as third-party data storage and processing system(s)  5000 ). 
     The third-party data storage and processing systems  5000  may include any data storage and processing system that may be designed, created, implemented, published, or otherwise made available from an entity that differs from an entity that designed and/or created the data intake and query system  16  or  108 . Further, the third-party data storage and processing systems  5000  may use a different query or command language, or a different interface language than the data intake and query system  16 . For example, while the data intake and query system may be a SPLUNK® system that is configured to use the Splunk Processing Language (SPL), the third-party data storage and processing systems  5000  may be alternative systems that use alternative languages. For instance, the third-party data storage and processing systems  5000  may be or may include a system that implements the Elastic Stack® (sometimes referred to as Elasticsearch, Logstash, and Kibana, or the “ELK stack”) and that uses a query syntax based on the Lucene® query syntax and/or a JSON-based Elasticsearch Query DSL, or a system that implements an Oracle® system and that uses a search syntax based on Structured Query Language (SQL). In some embodiments, the third-party data storage and processing systems  5000  may differ from each other. For example, the third-party data storage and processing system  5000 A may be an Elastic Stack® system and the third-party data storage and processing system  5000 B may be an Oracle® system. 
     It should be understood that the number and type of external data systems  12  are not limited by the example system  100  illustrated in  FIG.  50 B . The system  100  can have any number of external data systems  12  that can comprise any number of data intake and query systems  16  in communication with the primary data intake and query system  16 A. In some cases, at least some of the data intake and query system  16  may be different versions of a data intake and query system. For instance, some entities may be using an older or a newer version of the data intake and query system, or, in some cases, a more or less feature-rich version (e.g., a lite version or a full version) of the data intake and query system. Further, the system  100  can have any number of external data systems  12  that can comprise any type of third-party data storage and processing system  5000  that differs in type or version, and/or that differs in the query, command, or programming language used to interact with the third-party data storage and processing system  5000 . Moreover, in some embodiments, at least some of the external data systems  12  may communicate with other external data systems in addition to, or instead of, the primary data intake and query system  16 A. 
     25.1. Federated Search Data Flow 
       FIG.  51    is a data flow diagram illustrating an embodiment of communications between various components described herein to process and execute a federated or multi-system query. At ( 1 ), the search head  210  receives and processes a query. At ( 2 ), the search head  210  communicates the query to the search process service  3702 , which can refer to the search process master  3302  and/or query coordinator  3304 . Upon receipt of the query, the search process service  3702  can initiate a query planning or query processing phase  5102  followed by a query execution phase  5104 . 
     The query processing phase  5102  can include various steps or communications between one or more components of a data intake and query system  16 A (e.g., search head  210 , search process service  3702 , query coordinator  3304 , worker nodes  3306 , etc.) and external data system(s)  12 , such as, but not limited to, a secondary data intake and query system  16 B,  16 C or third-party data storage and processing system  5000 A  5000 B, in order to generate query instructions or a query processing scheme. 
     The query execution phase  5104  can include various steps or communications between the primary data intake and query system, worker nodes  3306 , and external data system(s)  12  as part of executing the query to provide results to the search head  210 . Although illustrated in a particular order, it will be understood that in some cases one or more portions of the query processing phase  5102  can be performed before, after, or concurrently with one or more portions of the query execution phase  5104  or each other. 
     As part of the query processing phase  5102  the search process service  3702  can ( 3 ) parse the query. As described herein, as part of parsing the query, the query coordinator  3304  can determine that the query to be executed is a multi-system query, or involves data managed by an external data system  12 , such as another data intake and query system  16  or a third-party data storage and processing system  5000 . In some cases, the query coordinator  3304  can determine that the query to be executed is a multi-system query based on a command, function call, or term in the query. However, it will be understood that a variety of methods can be used to indicate that a search is a multi-system query. 
     In some cases, the query can include details of the subquery for the external data systems  12 . For example, the query can include a search string for the subquery, access information to access the external data systems  12 , and/or other relevant information to enable the primary data intake and query system to generate a subquery for the external data system  12 . 
     As a non-limiting example, in the search below, the term “federated” can indicate that data relevant to the search is located in an external data system  12 : 
     
       
         
           
               
             
               
                   
               
             
            
               
                  |dfsjob[|union[|search   index=“airline2008”|   stats   count   by   FlightNum][|from 
               
               
                 federated:my_dep_3_search_5]| join usetime=f left=L right=R where L.FlightNum=R.FlightNum [|union[|search 
               
               
                 index=“airline2008”| stats count by FlightNum][|from federated:my_dep_2_search_6]|stats count by FlightNum ] | 
               
               
                 sort -L.FlightNum| head 100] 
               
               
                   
               
            
           
         
       
     
     Thus, according to the above-example, the query includes two non-local datasets or two subqueries: “my_dep_3_search_5” and “my_dep_2_search_6.” 
     In certain embodiments, the query can include a reference that can be used to look up or determine the details of the subquery or external data system  12 . In the above-example, the query includes the references “my_dep_3_search_5” and “my_dep_2_search_6” that can be used to lookup the details of the subquery using an external query configuration file, directory, or other tool. The external query configuration file can include details for the subquery including, but not limited to, syntax or a string for the subquery that is to be executed on the external data systems  12 , an identifier for the external data systems  12 , search type (e.g., streaming, batch, reporting, etc.), maximum or estimate number (or size) of results expected, number of fields used by the subquery or found in the relevant results, IP address, port number, access credentials (e.g., account name/type, password, etc. to access the external data system), type of deployment (e.g., secondary data intake and query system  16 , third-party data storage and processing system  5000 , or other external data system  12 ), version information, processing capabilities, etc. For example, for “my_dep_3_search_5,” an external query configuration file can include the following entries referring to one of the secondary data intake and query systems  16 : 
     
       
         
           
               
               
             
               
                   
                   
               
             
            
               
                   
                 [federated:my_dep_3_search_5] 
               
               
                   
                 search = “search index=airlinedata | stats count by FlightNum” 
               
               
                   
                 deployment_name = remote_deployment_3 
               
               
                   
                 hint = reporting 
               
               
                   
                 maxResultCount=1000000 
               
               
                   
                 numFields = 2 
               
               
                   
                 [remote_deployment_3] 
               
               
                   
                 IP = 10.183.45.30 
               
               
                   
                 Port = 8089 
               
               
                   
                 serviceAccount = eva_emerson 
               
               
                   
                 password = changed 
               
               
                   
                 Type = Splunk 
               
               
                   
                 version = 10.1.4.6 
               
               
                   
                   
               
            
           
         
       
     
     As another example, for “my_dep_2_search_6,” an external query configuration file can include the following entries referring to one of the third-party data storage and processing systems  5000 : 
     
       
         
           
               
             
               
                   
               
             
            
               
                 [federated:my_dep_2_search_6] 
               
               
                 search = “SELECT COUNT (DISTINCT FlightNum) FROM airlinesdata” 
               
               
                 deployment_name = remote_deployment_2 
               
               
                 hint = reporting 
               
               
                 maxResultCount=500000 
               
               
                 numFields = 1 
               
               
                 [remote_deployment_2] 
               
               
                 IP = 10.125.13.72 
               
               
                 Port = 8089 
               
               
                 serviceAccount = eliza_emmeline 
               
               
                 password = changed 
               
               
                 Type = SQL 
               
               
                 version = 6.4.0 
               
               
                   
               
            
           
         
       
     
     Using the information in the external query configuration file, the search process service  3702  can determine that the search “search index=airlinedata|stats count by FlightNum” is to be executed on “remote_deployment_3,” which is a “Splunk” system, version 10.1.4.6, that is accessible via port 8089 at the IP address 10.183.45.30 using the eva_emerson service account. Moreover, the search process service  3702  can determine that executing this search will return a maximum number of 1,000,000 records or events and that the search may use no more than two fields to process the received records. 
     Similarly, using the information in the external query configuration file, the search process service  3702  can determine that the search “SELECT COUNT (DISTINCT FlightNum) FROM airlinesdata” is to be executed on “remote_deployment_2,” which is an “SQL” system, version 6.4.0, that is accessible via port 8089 at the IP address 10.125.13.72 using the eliza_emmeline service account. Moreover, the search process service  3702  can determine that executing this search will return a maximum number of 500,000 records or events and that the search may use no more than one field to process the received records. 
     Moreover, using the information in the external query configuration file, the search process service  3702  can generate at least a portion of a subquery for the external data systems  12 , and/or generate one or more query instructions for the worker nodes  3306  or external data systems  12 . In addition, in certain cases, the search process service  3702  can assign a primary search identifier to each subquery to enable the primary data intake and query system to identify and distinguish partial results from different external data systems  12 . With reference to the example above, the search process service  3702  can assign one primary identifier to the federated:my_dep_3_search_5 search and a different primary identifier to the federated:my_dep_2_search_6 search. 
     In addition, as part of parsing the query or query processing phase  5102 , the search process service  3702  can receive a resource allocation for the query. The resource allocation can indicate an amount of memory, processors, and/or worker nodes  3306  that will be made available for the query. The search process service  3702  can use the resource allocation to further generate instructions for the worker nodes  3306  and/or subqueries for the external data systems  12 . 
     As described herein, the resource allocation can be based on the number of processors and amount of memory in the data intake and query system, the number of worker nodes  3306  in the data intake and query system, the amount of data being ingested and number of searches being executed by the data intake and query system, the number of searches that the data intake and query system is configured to execute, etc. For example, if each machine  3402  includes 48 processors and 12 TB of memory and is configured to handle 12 concurrent searches, then each machine  3402  can provisionally allocate 4 processors and 1 TB of memory for each search. In some cases, the allocated processors and memory from a particular machine can be referred to as a worker node  3306 . Thus, with continued reference to the previous example, if there are ten machines, then ten worker nodes, each with 4 processors and 1 TB of memory can be provisionally allocated for each search. 
     Further, the search process service  3702  can receive an identification of one or more worker nodes  3306  that can be used to communicate with the external data systems  12 , and map the worker nodes  3306  to one or more external data systems  12  for communication purposes. Each worker node  3306  can be mapped to one or more external data systems  12 . 
     At ( 4 ), the search process service  3702  communicates a request for a data ingest estimate to the worker nodes  3306 . The data ingest estimate can refer to the amount of data that is expected to be received from the different external data systems  12 . In some cases, the request for a data ingest estimate can include a request for a record or event count or the number of events or records that are expected to be received from an external data system  12  based on the subquery to be sent to the external data system  12 . In certain embodiments, the data ingest estimate can include a request to provide an estimated size of the data (non-limiting example: amount of memory required to store the data) to be ingested from the external data system  12 . 
     At ( 5 A), the worker nodes  3306  determine the data ingest estimate in conjunction with the external data system  12 . As mentioned, in certain cases, the worker nodes  3306  are mapped to external data systems  12  for communication purposes, such as for control layer communications. Accordingly, a worker node  3306  can establish communication with an external data system  12  to determine the data ingest estimate for the external data system  12 . 
     In some cases, as part of determining the data ingest estimate, the worker node  3306  determines the functionality or version number of the external data system  12  and determines the estimate based on the determined functionality or version. For example, in some cases, the external data system  12  may be able to dynamically determine and return a data ingest estimate based on search parameters that it parses from the subquery. In other cases, the external data system  12  may not be able to parse the subquery, but may be able to dynamically determine and return a data ingest estimate based on search parameters received from the worker node  3306  after the worker node  3306  (or search process service  3702 ) has parsed the subquery. In yet other instances, neither the worker node  3306  nor the external data system  12  may be able to parse the subquery or dynamically determine the data ingest estimate based on the subquery. For example, in some cases, a third-party data storage and processing system  5000  may be incapable of providing query result estimates or may be incapable of determining a result estimate separately from performing the query. In some such cases, the search process service  3702  may use a pre-determined or static data ingest estimate. 
     As mentioned, in some cases, the worker node  3306  can send the subquery to the external data system  12  and request the external data system  12  to return an estimate. In such cases, the external data system  12  can parse the subquery to identify relevant search parameters, such as, but not limited to, partitions, tables, directories, inverted indexes, or indexes to be searched, time ranges of potentially relevant results, etc. 
     The external data system  12  can use the identified search parameters to identify potentially relevant results. For example, the external data system  12  can parse the subquery to identify an index (also referred to herein as a partition) to be searched as part of the query and a time range of potentially relevant results. Using the index and time range, the external data system  12  can identify records that overlap with the time range. In certain embodiments, the external data system  12  can, using the index and time range, identify buckets in the index that include events or records that overlap with at least a portion of the time range and return the number of events in the identified buckets as the data ingest estimate. As a non-limiting example, the external data system  12  may support a DBInspect command that uses an identified index, start time, and end time to identify a count of potentially relevant events in buckets that at least partially fall within the start time and last time and that are located in the identified index. In some cases, the count may be previously determined and stored, such as in an external query configuration file or inverted index. In certain cases, the external data system  12  can perform a count on the identified buckets. 
     It will be understood that a variety of methods can be used by the external data system  12  to determine and return the data ingest estimate. For example, the external data system  12  can use an inverted index or summary table to identify potentially relevant results, etc. In certain cases, the external data system  12  can estimate an amount of memory used to store potentially relevant records and return the amount of memory as the data ingest estimate, etc. 
     As also mentioned, in some cases, the worker node  3306  (or search process service  3702 ) can parse the query to identify relevant search parameters, and communicate the search parameters to the external data system  12  with a request for the data ingest estimate. For example, in some cases, the external data system  12  may be unable to parse the subquery received from the worker node  3306  and identify relevant search parameters, but may be able to use search parameters received from a worker node  3306  to identify and return a data ingest estimate. With continued reference to the DBInspect example above, the worker node  3306  (or search process service  3702 ) can identify a relevant index or partition, start time, and end time and communicate those parameters to the external data system  12  along with a DBInspect command. Using the parameters, the external data system  12  can determine and return a data ingest estimate. However, it will be understood that other commands or search parameters can be used to determine the data ingest estimate. 
     Furthermore, in some instances, neither the worker node  3306  nor the external data system  12  may be able to parse the subquery to identify query parameters. For example, the subquery may include references to system-specific objects, metadata, or definitions of the external data system  12  that cannot be interpreted or understood by the worker node  3306 , and the external data system  12  may be unable to accept and parse the subquery from the worker node  3306  for data ingest estimate purposes. In such cases, the worker node  3306  (or search process service  3702 ) can determine a data ingest estimate based on a predetermined estimate, such as an estimate located in an external query configuration file. With reference to the query example provided above, in the event neither the assigned worker node  3306  nor external data system  12  can parse the subquery and dynamically determine the data ingest estimate, the worker node  3306  can indicate to the search process service  3702  that no data ingest estimate could be determined or that a search parameter is to be used as the data ingest estimate, such as the maxResultCount of 1,000,000. In some cases, such as when a search parameter is used as the data ingest estimate, the search process service  3702  can determine the search parameter by parsing an external query configuration file that includes the search parameter. In certain cases, the search parameter used as the data ingest estimate can be included in the query or subquery itself. 
     At ( 5 B), the worker nodes  3306  can obtain system-specific search configuration data from the external data system  12 . The search configuration data, which may also be referred to as search parameter configuration data, subquery structure or syntax data, or knowledge objects, can include information specific to the external data systems  12 , such as definitions, metadata, query processing instructions, macros, or conversion tables for expanding a query string for execution. Further, in some embodiments, the search parameter configuration data can include instructions for parsing one or more search parameters of the query or subquery. For example, search configuration data in one external data system  12  could provide the definition of the string “search1” in a query or subquery to be “search index=myIndex|sort-c FlightNum|head 1000” and the search configuration data in a different external data system  12  could provide the definition of the string “search1” to be “search index=airlinesdata7m|stats count by ArrDelay.” As another example, where the external data system  12  is a third-party data storage and processing system  5000 , the external data system  12  could provide the definition of the string “search1” to be “SELECT COUNT (DISTINCT FlightNum) FROM airlinesdata.” In any of the cases, the worker node  3306  or primary data intake and query system may be unable to parse or determine the meaning of the search parameter “search1” without the aid of the search configuration data or search parameter configuration data for particular external data systems  12 . 
     In some cases, depending on the version, capabilities, and/or functionality of the external data system  12  and the permissions or authorizations granted to the primary data intake and query system (or user thereof) to access the external data systems  12 , an assigned worker node  3306  can obtain search configuration data related to the external data system  12 . For example, the sharing of search configuration data may be prohibited or not be supported by a particular external data system  12 . Similarly, only some search configuration data may be made available by the external data system  12  for use by worker nodes  3306  or a primary data intake and query system based on the authorizations associated with the primary data intake and query system or a user thereof. 
     To determine which search configuration data to retrieve, the worker node  3306  can provide the external data system  12  with the subquery that is to be run on it. The external data system  12  can parse the subquery, identify portions of the query that have corresponding search configuration data, and retrieve and return the search configuration data to the worker node  3306 . The retrieved search configuration data may correspond to an ingest phase of the query or subquery or processing phase (e.g., join, reduction operation, etc.). In some cases, the search process service  3702  or worker node  3306  can parse the subquery to identify search parameters that it cannot understand or interpret and communicate the identified search parameters to the external data system  12 . In certain cases, the worker node  3306  can request the external data system  12  to return any portion or all search configuration data or any portion or all search configuration data that is accessible based on the account or user credentials used to access the external data system  12 . In certain embodiments, the external data system  12  verifies the credentials or authorizations of the primary data intake and query system or a user thereof prior to making the search configuration data available. 
     In some embodiments, the external data system  12  can return a transformed subquery to the worker node  3306  with the data ingest estimate and/or the search configuration data. For example, in some cases, to determine the data ingest estimate, the external data system  12  can transform the subquery received from the worker node  3306 . For example, as described herein, the subquery received from the worker node  3306  may include references to search configuration data that is specific to the external data system. The external system  12  can transform the subquery using the search configuration data. In certain embodiments, the transformed subquery can be sent to the worker node  3306  along with the data ingest estimate and/or the search configuration data. Further, in some embodiments, the subquery returned by the external data system can refer to additional search configuration data. This additional search configuration data can be returned to enable the worker node  3306  and/or query coordinator  3304  to process the subquery and generate a subquery for execution by the external data system  12 . For example, the subquery may, as part of a later search phase, refer to system-specific search parameters, that would not be understandable by a worker node  3306  during query execution. Accordingly, the external data system  12  can communicate relevant search configuration data to the worker node  3306  to enable the worker node  3306  to process the various phases of the query or subquery. 
     In some embodiments, the external data system  12  may not be capable of understanding syntax generated by the data intake and query system  16 A. For example, a third-party data storage and processing system  5000  may not understand a query, subquery, or other command generated by the data intake and query system  16 A that uses SPL. In some such embodiments, the data intake and query system  16 A, or elements thereof (e.g., the search process service  3702  or worker node  3306 ) may use configuration data or an external query configuration file associated with the external data system  12  to convert the query, sub-query, or command to a format understood by the external data system  12 , such as Lucene or SQL. 
     At ( 6 ), the worker nodes  3306  return the data ingest estimate and/or search configuration data from one or more external data systems  12  to the search process service  3702 . It will be understood that the data ingest estimate and method for obtaining it may be different across different external data systems  12 . For example, one external data system  12  may be able to determine the data ingest estimate by parsing a subquery received from a worker node  3306 , another external data system  12  may determine the data ingest estimate based on search parameters received from a worker node  3306 , while a third external data system  12  may be unable to determine the data ingest estimate. In any case, the worker nodes  3306  can provide the data ingest estimates, or lack thereof, to the search process service  3702 . Furthermore, the worker nodes  3306  can provide the search process service  3702  with the search configuration data, if any, received from the external data systems  12 . 
     At ( 7 ), the search process service  3702  continues the query processing phase  5102  by determining a size and quantity of tasks or partitions to be performed as part of ingesting data from the secondary did take query systems. During query execution, if too much data is being operated on by a particular processor, the processor may run out of memory and may store some data or results to disk, which can significantly increase the execution time of the query. As such, in certain embodiments, the search process service  3702  can select a particular partition size in order to reduce the likelihood of spilling data to disk. 
     In some cases, the search process service  3702  determines the size of the partitions based on resources that have been allocated to execute the query and search parameters parsed from the query itself. For example, the size of the partitions can be determined based on the number of processors  3406  allocated for the query, an amount of memory allocated for the query, and/or the number of fields of the records to be analyzed as part of the query or subquery. As mentioned previously, the processor and memory allocation can be based on the amount of processors and memory available to the system  16  as a whole, and configuration for the number of concurrent searches that are to be supported by the system  16 . The number of fields can be determined by parsing the query or subquery. For example, if a subquery identifies two fields that will be used to process events, the search process service  3702  can determine that two fields will be used as part of the query. It will be understood that a variety of mechanisms can be used to identify the number of fields for the query or subquery and/or to determine the size of the partitions. For example, in some cases, the search process service  3702  can use an estimated size or average size of the records or data that is to be processed, or the number of field can be included in an external query configuration file. Moreover, the search process service  3702  can use other search parameters to determine the size and quantity of tasks. For example, the search process service  3702  can use an average size or estimate size of each record to be received, etc. 
     Furthermore, the relationship between the size of the partition and the data used to determine the size can vary. For example, in some cases, as the amount of memory allocated for the search increases relative to the number of processors, the size of partitions can increase. In certain embodiments, as the number of processors increases relative to the amount of memory, the size of the partitions can decrease. In some embodiments, as the number of fields increases, the size of the partitions can decrease. 
     Based on the determined size of the partitions or tasks and the data ingest estimates corresponding to the various external data systems  12 , the search process service  3702  can determine the number of estimated partitions or tasks to be executed as part of the ingestion of data from the external data systems  12 . In certain embodiments, the number of tasks can be determined by dividing the data ingest estimate by the size of the partitions. 
     Further, the number and size of partitions can be used to estimate the size of and duration for executing the query. In some cases, if the search process service  3702  determines that the size of the query satisfies a size threshold or the duration for executing the query satisfies a duration threshold, it can abandon the query or notify a user that the query will take longer than a threshold amount of time. In certain embodiments, if the search process service  3702  determines that the size of the query satisfies the size threshold or the duration for executing the query satisfies a duration threshold, the search process service  3702  can request that additional resources be allocated, such as additional memory and/or processors. In this way, the search process service  3702  can dynamically respond to queries of different sizes in order to return results in a performance manner. Moreover, if additional resources are allocated, the search process service can determine the size and number of tasks to be executed based on the additional resources. 
     In embodiments where the search process service  3702  is unable to determine an estimated size or number of entries because, for example, the external data system  12  does not provide a data ingest estimate, the search process service  3702  may allocate a default number of resources or partitions. Further, in some embodiments, the search process service  702  may dynamically adjust the allocated resources or partitions during performance of the query based, for example, on results being obtained as the query, or sub-query, is executed. 
     At ( 8 ), the search process service  3702  generates query instructions for the worker nodes  3306 . In some embodiments, generating query instructions can include generating subqueries for the external data systems  12 , processing and/or optimizing the subqueries for the different external data system  12  and/or worker nodes  3306 , etc. Similar to determining the data ingest estimate, generating subqueries for the external data systems  12  can be based on the versions, functionality, and capabilities of the external data systems  12 . For example, in an embodiment where a worker node  3306  is able to obtain search configuration data for a particular external data system  12 , the search process service  3702  can use the obtained search configuration data to generate the subquery. Thus, the subquery can be transformed into a native state for execution by the external data system  12 . Such a transformation can reduce the workload of the external data system  12 . For example, in some embodiments, the transformation may reduce the amount of processing performed by a search head  210  or controller of the external data system. Further, in embodiments where the external data system  12  includes a third-party data storage and processing system  5000 , associated configuration data or an associated configuration file can provide conversion information that enables the data intake and query system  16 A to convert a query or subquery into a language understood by the third-party data storage and processing system  5000 . The configuration file may include one or more entries that translate the sub-query from SPL to a query language understood by the third-party data storage and processing system  5000 , such as Lucene, JSON, or SQL. Alternatively, or in addition, the configuration file may identify a type of the third-party data storage and processing system  5000  and/or a language (e.g., query language) understood or supported by the third-party data storage and processing system  5000 . Based on the identity of the third-party data storage and processing system  5000  and/or a language understood or supported by the third-party data storage and processing system  5000 , the data intake and query system  16 A can translate or transform a query, sub-query, or command to be provided to the third-party data storage and processing system  5000  to the language supported or understood by the third-party data storage and processing system  5000 . For example, the data intake and query system  16 A may translate or transform an SPL query or sub-query to an SQL query. 
     In some embodiments, the search process service  3702  can generate the subquery or perform the transformation of the subquery for the external data system  12 . In certain embodiments, the search process service  3702  includes instructions for a worker node  3306  in communication with the external data system  12  to generate the subquery or perform the transformation. By enabling or assigning a worker node  3306  to perform the transformation, the processing by the search process service  3702  can be reduced. 
     In certain cases, such as when search configuration data cannot be retrieved from an external data system  12 , the search process service  3702  can generate, determine, or use subqueries that can be further transformed by the respective external data system  12 . For example, the search process service  3702  can determine that a subquery identified from the query or an external query configuration file is to be used for a particular external data system  12 , and communicate the identified subquery to the external data system  12 . In such cases, the external data system  12  can transform the subquery for execution, including using relevant search configuration data to expand the search parameters or generate a native query. 
     In addition, in certain embodiments, a query or subquery may not include reference to search parameters specific to a particular external data system  12  or the query or subquery can be processed without reference to search configuration data from the particular external data system  12 . In such embodiments, the search process service  3702  can determine or generate a subquery for the external data system  12  and may not request search configuration data from the worker node  3306 . 
     Further, depending on the capability of the external data systems  12 , the search process service  3702  can include instructions for the external data system  12  to send partial results to a single worker node  3306  or distribute results of the subquery to multiple worker nodes  3306 . For example, an external data system  12  may not have the functionality or ability to partition results amongst multiple destinations. In such embodiments, the search process service  3702  can include instructions for the external data system  12  to communicate all results to a particular worker node  3306 . In turn, the assigned worker node  3306  can distribute the results to multiple worker nodes  3306  (in some cases, including itself). In such embodiments, the search process service  3702  can include instructions for a daemon operating on the external data system  12  to send the results to the particular work node  3306 . In such cases, the external data system  12  (non-limiting example: search process service  3702  of a secondary data intake and query system) can, after executing the query, store the results to disk. The daemon can pull the results from the disk and send them to the assigned worker node  3306 . 
     In embodiments where the external data system  12  can partition, or distribute, results amongst multiple destinations, the search process service  3702  can include an instruction for the external data system  12  to do so. In some embodiments, the instruction can be an instruction for a search process service  3702  of a secondary data intake and query system to send results from the indexers (or worker nodes  3306 ) of the secondary data intake and query system to the worker nodes  3306  of the primary data intake and query system  16  without storage of the results to disk. Furthermore, the search process service  3702  can assign worker nodes  3306  to receive results from the various external data systems  12 . In some embodiments, the primary data intake and query system  16 A may instruct one external data system  12  to provide query results to another external data system  12 , which can then use the query results to perform further operations or queries. For example, the data intake and query system  16 A may instruct the external data system  12 - 1  to provide query results to the external data system  12 - 2 . In some such embodiments, the primary data intake and query system  16 A may instruct the external data system to convert the query results, or a sub-query that includes the query results, from one format to another format prior to providing the query results to another external data system. For example, the primary data intake and query system  16 A may instruct the external data system  12 - 1  to convert a sub-query and/or query results from an SPL format to an SQL format prior to providing the sub-query and/or query results to the external data system  12 - 3 . 
     The instructions to distribute results amongst multiple worker nodes  3306  can include instructions as to how the results are to be distributed. As described herein, a variety of mechanisms can be used to distribute results between the worker nodes  3306 . For example, the search process service  3702  can include instructions to distribute the results in a round robin, random, or particular order. In some cases, the search process service  3702  can instruct the external data system  12  to perform a hash on the results and based on the hash send the results to a particular worker node  3306 . As a non-limiting example, the search process service  3702  can include instructions for the external data system  12  to use a modulo operand on the data to be distributed to determine to which worker node  3306  that data is to be assigned. However, it will be understood that a variety of mechanisms can be used to distribute partial results among worker nodes  3306 . For example, in some cases, the external data system  12  can determine the manner in which results are to be distributed between worker nodes  3306 . 
     As mentioned, in some cases, the worker nodes  3306  can be shared between the primary data intake and query system and the external data system  12 . In such embodiments, the search process service  3702  can include instructions for the external data system  12  to send results from the worker nodes  3306  of the external data system  12  to the worker nodes  3306  of the primary data intake and query system  16 A. During execution, in embodiments where the worker nodes  3306  are shared between the primary data intake and query system and external data systems  12 , worker nodes  3306  can be assigned to reduce the communication of data over a network or between machines. Accordingly, in certain embodiments, an instruction from the external data system  12  to transmit results from one worker node  3306  to another worker node  3306  can result in the same worker node  3306  retaining the data. 
     As part of generating the query instructions, the search process service  3702  can designate worker nodes  3306  to receive results from the external data systems  12 . Further, the search process service  3702  can reach out to the worker nodes  3306  and obtain communication information or network access information, such as, but not limited to, a device, network or IP address, or port number, etc., so that the external data systems  12  can send the data directly to the worker nodes  3306 . Moreover, the search process service  3702  can instruct the worker nodes  3306  to set up buffers or other receivers to receive the partial results from the external data systems  12 . Moreover, the search process service  3702  can further process and/or optimize the query or subqueries for execution by the worker nodes  3306 . For example, the search process service  3702  can request that the worker nodes  3306  be located on the same machine to reduce network traffic, etc. 
     As part of the query execution phase  5104 , the search process service  3702  can ( 9 ) communicate the query instructions to the worker nodes  3306 . As described herein, the query instructions can include sufficient information to enable the worker nodes  3306  to execute the query, including instructions to communicate any subqueries to the external data systems  12 . In some embodiments, the search process service  3702  can include a mapping of worker nodes  3306  to particular external data systems  12 . The worker node  3306 -external data system  12  mapping can be the same as or different from the mapping used to obtain data ingest estimates from the external data system  12 . For example, the mapping used to obtain data ingest estimates may use any available worker node  3306 , while the mapping for the query execution phase  5104  may be a mapping to one of the worker nodes  3306  allocated for the query. In certain embodiments, the search process service  3702  can include instructions for the worker nodes  3306  to determine the mapping between the worker nodes  3306  and the external data systems  12 . 
     In accordance with the received instructions, the worker nodes  3306  can execute the query, which can include ( 10 ) distributing the subqueries to the external data systems  12 . Distributing the subqueries to the external data systems  12  can include translating the query from one language to another language based, for example, on a mapping between query terms or a system-type identifier included in an external query configuration file. As described herein, as part of executing the query, the worker nodes  3306  can gather and process data from other datasets, such as data from indexers  206  of the primary data intake and query system. 
     At ( 11 ), the external data systems  12  execute the subquery. The external data systems  12  can process and execute the query in a manner similar to the processing and execution of the federated query by the primary data intake and query system. For example, in some embodiments, the external data systems  12  can parse the subquery to identify relevant data to be searched, generate subqueries for components of the external data systems  12 , such as, but not limited to, indexers  206  (or other query executors), and obtain the relevant data and process it according to the subquery received from the worker nodes  3306 . Furthermore, in embodiments where an external data system  12  includes worker nodes  3306 , the external data system  12  can generate query instructions for the worker nodes  3306 . 
     In addition, as part of processing the subquery, the external data system  12  can assign a local search identifier to the search. For example, the external data system  12  can assign search identifiers to all searches that it receives in order to identify and distinguish between the different processes and results of each search. Moreover, when the external data system  12  communicates partial results to the worker node  3306 , it can include the local search identifier that it assigned in each data chunk that it communicates to the worker node  3306 . In some cases, based on the local search identifier, the worker node  3306  can distinguish between partial results received from different external data systems  12 . 
     As described herein, in certain embodiments, such as when the worker nodes  3306  are able to obtain search configuration data of a particular external data systems  12 , the worker nodes  3306  can perform some of the tasks that would otherwise be performed by the search head  210  or controller of an external data system  12 . For example, a worker node  3306  can parse the subquery and generate instructions for indexers  206  (or query executors) of the external data system  12 . In this manner, a worker node  3306  can reduce the processing performed by the external data system  12 . 
     At ( 12 ), the worker nodes  3306  receive the subquery results or partial results from the external data systems  12 . As described herein, in some cases, one worker node  3306  can receive the partial results from a particular external data system  12  and distribute the results to multiple worker nodes  3306 . As further described herein, the partial results from a particular data intake and query system can be distributed to various worker nodes  3306  in a variety of ways. In certain embodiments, multiple worker nodes  3306  can receive partial results from a particular external data system  12  and/or one worker node  3306  can concurrently receive partial results from multiple external data systems  12 . As mentioned, data chunks corresponding to the partial results from each external data system  12  can include a local search identifier that uniquely identifies the search to which the data chunk belongs within the external data system  12 . In certain embodiments, the external data system  12  and/or the worker nodes  3306  may translate or transform query results from a format or language supported by the external data system  12  to a format or language supported by the data intake and query system  16 A. The external data system  12  and/or the worker nodes  3306  may determine the supported format to convert the query results based on an entry in an external query configuration file of the external data system  12  and/or of the data intake and query system  16 A. 
     At ( 13 ), the worker nodes  3306  process the results of the subqueries. As described herein, the worker nodes  3306  can concurrently process partial results received from different external data systems  12 . Furthermore, the worker nodes  3306  can perform additional processing on partial results from one external data system  12  alone or in combination with partial results received from the other external data system  12 . The processing of partial results by the worker nodes  3306  can be done in accordance with the query instructions received from the search process service  3702 . Further, the additional processing may include converting or transforming the partial results or query results from one format supported by the external data system  12  to another format supported by the data intake and query system  16 A. 
     Although not illustrated in  FIG.  51   , it will be understood that the search process service  3702  can monitor the nodes  3306  and dynamically allocate resources based on the monitoring. For example, if more partial results are received from the external data systems  12  than were expected, the search process service  3702  can request additional processors and/or worker nodes  3306  to ingest and process the partial results. Similarly, if fewer partial results are received than was expected, the search process service  3702  can de-allocate processors and/or worker nodes  3306 . 
     In addition, during execution, the worker nodes  3306  can communicate with each other to process the partial results in a distributed manner. If, for example, one worker node  3306  receives a larger portion of the partial results than other worker nodes  3306  and/or begins to lag in processing its partial results, the worker nodes  3306  can dynamically re-assign data or tasks between the worker nodes  3306  for execution. 
     In some cases, the worker nodes  3306  use a mapping of the primary search identifier (assigned to subqueries by the primary data intake and query system  16 ) to the local search identifiers (assigned by an external data system  12  to the subquery that it executed) to identify and process the partial results. As described herein, the primary data intake and query system  16 A can assign primary search identifiers to logically identify the different subqueries that will be operated on by the different worker nodes  3306 . Similarly, the external data systems  12  can assign local search identifiers to the subquery to uniquely identify the subquery (and its result) from other queries that the external data system is executing. Accordingly, the same subquery may be referred to by the primary data intake and query system  16 A using a primary search identifier that does not match the local search identifier that is used by the external data system  12  to identify the subquery. 
     To address the mismatch, as the external data systems  12  assign local search identifiers to the subquery, they can communicate the assigned local search identifier to the primary data intake and query system  16 A (e.g., via the worker node  3306 ). In turn, the primary data intake and query system  16 A can map the local search identifier assigned to a subquery by the external data system to the primary search identifier assigned to the same subquery by the primary data intake and query system  16 A. Thus, as a worker node  3306  receives and processes partial results from different external data systems  12  it can use the mapping to determine what transformations (based on instructions from the search process service  3702  that refer to the subquery using the primary search identifier) are to be performed on the partial results from different external data systems  12  (which refers to the partial results using the local search identifier). 
     At ( 14 ), the worker nodes  3306  communicate the results of the processing to the search process service  3702  or to another dataset destination as described herein. At ( 15 ), the search process service  3702  can perform additional processing, and at ( 16 ) the results can be communicated to the search head  210  for communication to the client device. In some cases, prior to communicating the results to the client device, the search head  210  can perform additional processing on the results. 
     It will be understood that the query data flow can include fewer or more steps. For example, in some cases, the search process service  3702  does not perform any further processing on the results and can simply forward the results to the search head  210 . In certain embodiments, nodes  3306  receive data from multiple dataset sources  3704 , etc. 
     Although not shown in  FIG.  51   , it will be understood that primary data intake and query system can concurrently execute a local search, the results of which, can be combined with the partial results of the external data system  12 . In some embodiments, partial results from a local search can be combined with partial results from the external data systems  12  by the worker nodes  3306 , the search process service  3702 , or the search head  210 . 
     Moreover, it will be understood that the various functions described can be performed concurrently or in any order. For example, search process service  3702  can generate query instructions before, after, or concurrently with determining a size and quantity of partitions or tasks and/or requesting or obtaining data ingest estimates, etc. 
     26.0. Search of Secondary Data Intake and Query System Flow 
       FIG.  52    is a flow diagram illustrative of an embodiment of a routine  5200  implemented by a query coordinator  3304  to execute a query involving data from a secondary data intake and query system. Although described as being implemented by the query coordinator  3304 , it will be understood that one or more elements outlined for routine  5200  can be implemented by one or more computing devices/components that are associated with a data intake and query system  16 , such as the search head  210 , search process master  3302 , indexer  206 , and/or worker nodes  3306 . Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  5202 , the query coordinator  3304  receives a query, as described herein at least with reference to block  3802  of  FIG.  38   . At block  5204 , the query coordinator  3304  identifies one or more external data systems. In some embodiments, the external data systems can be one or more secondary data intake and query system  16  or  108 . As described herein, in some embodiments, the query can include an indicator that it is a federated query. Based on the indication that the query is a federated query, the query coordinator  3304  can identify one or more secondary data intake and query systems  16 B and/or  16 C and/or one or more third-party data storage and processing systems  5000  that are to be part of the search. For example, the query can include a command indicating that an external data system is to be used or searched and/or a subquery is to be executed by an external data system. Based on identification of the command, the query coordinator  3304  can look up or otherwise identify the external data system that is to be searched, used or is to execute the subquery. For example, the data intake and query system can include an external query configuration file that provides additional information, such as the name and location of external data systems associated with the primary data intake and query system  16 A, access information for the external data system, query languages supported by the external data system, etc. 
     In certain embodiments, the query can explicitly identify a secondary data intake and query system or a third-party data storage and processing system  5000  that is to execute a subquery. In certain cases, the query coordinator  3304  parses the query to identify the external data system. For example, the query may include the name (or other identifier) or the location (e.g., IP address, port, access protocol) of the external data system. 
     At block  5206 , the query coordinator  3304  generates a subquery for the secondary data intake and query system. 
     Similar to the identification of the external data system, the query coordinator  3304  can identify a subquery for the external data system by parsing the query. In some embodiments, the query can include the subquery that is to be executed by the external data system. In certain embodiments, the query can include a reference and the query coordinator  3304  can refer to an external query configuration file or other location to identify the subquery that is to be executed by the external data system. The query coordinator  3304  may identify the subquery in the external query configuration file based on the reference included in the query. 
     Based on the identification of the subquery, the query coordinator  3304  can generate a subquery for the external data system. As part of generating the subquery for the external data system, the query coordinator  3304  can request search configuration data from the external data system. As described herein, the search configuration data can include definitions and/or additional search parameters that are specific to the external data system. For example, the subquery identified by the query coordinator  3304  may reference an instruction set, macro, or naming convention that is not understood or known by the query coordinator  3304 , but is understood by the external data system. Accordingly, the query coordinator  3304  can request the instruction set, macro information, or naming convention from the external data system, and use this information to generate the subquery that is to be executed by the external data system. 
     In addition, the query coordinator  3304  can request a version number or other indications of the capabilities of the external data system. For example, the query coordinator  3304  can request the external data system to provide information as to the number or amount of processing resources it has available. Based on this information, the query coordinator  3304  can generate the subquery to increase or decrease the amount of processing performed by the external data system. For example, if the query coordinator  3304  determines that the external data system will take too long to process data or has insufficient resources to process the data within a particular time frame, the query coordinator  3304  can generate a subquery for the external data system to reduce the amount of processing performed thereon. For example, rather than instructing the external data system to perform multiple transformations on its data, the query coordinator  3304  can instruct the external data system to send the data to the worker nodes  3306  without performing any transformations or performing a limited number of transformations. 
     Similarly, the query coordinator  3304  can, based on the version or capabilities of the external data system, generate the subquery to instruct the external data system to distribute its results across multiple worker nodes  3306  or communicate its results to a single worker. In some embodiments, such as when the external data system is to send the results to a single worker node  3306 , the query coordinator  3304  can instruct the worker node  3306  to distribute the results across multiple worker nodes  3306 . 
     In some embodiments, as part of generating or determining a subquery for the external data system, the query coordinator  3304  can request a data ingest estimate from the external data system. Based on the estimate, the query coordinator  3304  can determine or estimate a number of tasks or partitions to use to ingest the data and determine whether additional processing should be performed by the external data system prior to communicating the results. Further, the query coordinator  3304  can use this information to determine whether additional worker nodes  3306  should be allocated to process the results received from the external data system, estimate an ingest or search time, etc. 
     Accordingly, using information from the query and/or the external data system, the query coordinator  3304  can generate a subquery. However, it will be understood that in some cases, the query coordinator  3304  can instruct a worker node  3306  to generate a subquery for the secondary data intake and query system. For example, the worker node  3306  may have the search configuration data associated with the secondary data intake and query system and be able to generate a subquery in a native format for the external data system. In some cases, by having a worker generate the subquery, the system  16  can distribute processing tasks between multiple processors and reduce the likelihood of creating a bottleneck at the query coordinator  3304 . 
     As described herein, in certain embodiments, the query coordinator  3304  generates a subquery that tasks the external data system with returning the data, performing some processing of the data, or processing the data as much as it can based on its capabilities. 
     At block  5208 , the query coordinator  3304  generates instructions for the worker nodes  3306 . In some cases, as part of generating instructions for the worker nodes  3306 , the query coordinator  3304  can instruct the worker nodes  3306  to set up or provide a location for the external data system to send results, such as a network address, MAC address, device identifier, IP address, port number, or other network access information, etc. In addition, the query coordinator  3304  can include instructions for the worker nodes  3306  to communicate the subqueries to the external data system. In some cases, the query coordinator  3304  can instruct the worker nodes  3306  to generate at least a portion of the subquery for the external data system. For example, the query coordinator  3304  can instruct the worker nodes  3306  to use the search configuration data to generate the subquery for the external data system. In this way, the query coordinator  3304  can distribute some processing to the worker nodes  3306 . 
     Moreover, the query coordinator  3304  can include instructions for the worker nodes  3306  to perform additional processing on the partial results received from the external data system, combine partial results from multiple external data systems, and perform additional processing on the combined partial results. The query coordinator  3304  can also provide the worker nodes  3306  with the data ingest estimate. The worker nodes  3306  can use this information to configure themselves to process the incoming data in a distributed manner. 
     As described herein, in certain embodiments, as part of generating instructions for the worker nodes  3306  (or generating the subqueries), the query coordinator  3304  can assign a primary search identifier for each subquery and include the primary search identifier in the instructions sent to each worker node  3306  to be mapped to local search identifiers received from the external data systems. As described herein, the worker nodes  3306  can use the mapping to determine how to process data from particular external data systems. 
     At block  5210 , the query coordinator  3304  executes the query. In some cases, as described herein, to execute the query, the query coordinator  3304  communicates a query processing scheme or the generated instructions to the worker nodes  3306 . In turn, the worker nodes  3306  execute the instructions, which can include, communicating subqueries to the external data systems, receiving partial results therefrom, processing the partial results, and returning results to the query coordinator  3304 . The query coordinator  3304  can perform processing based on the query processing scheme and communicate the results to the search head  210  for display on the client device  404 . 
     As described herein, in some embodiments, the external data system processes and executes the subquery similar to the manner in which the primary data intake and query system processes and executes the query. Further, the external data system can process and execute the subquery similar to the manner in which it executes other queries received from a user or client device, except that results are communicated to one or more worker nodes  3306  instead of (or in addition) to a user or client device. In some embodiments, as part of executing the subquery, the external data system can assign the subquery a local search identifier and communicate the local search identifier to the worker node  3306 . The worker node  3306  can map the local search identifier with the primary search identifier received from the primary data intake and query system to determine how the partial results from the external data system are to be processed according to the instructions received from the primary data intake and query system. 
     It will be understood that fewer, more, or different blocks can be used as part of the routine  5200 . For example, in some embodiments, the routine  5200  can further include, monitoring nodes  3306  during query execution, allocating/deallocating resources based on the query, etc. As another example, in certain embodiments, identifying the secondary data intake and query system, generating a subquery, and generating instructions for the worker nodes  3306  can form part of a processing query block, similar to the process query block  3804  of  FIG.  38   . Moreover, it will be understood that one or more blocks described herein with reference to routine  5200  can be combined with one or more blocks of other routines described herein, such as the routines described herein at least with reference to  FIGS.  5 ,  6 ,  23 - 26 ,  31 ,  34 ,  38 - 45 ,  47 ,  49 ,  52 - 57 , and  59   . 
     Furthermore, it will be understood that the various blocks described herein with reference to  FIG.  52    can be implemented in a variety of orders. In some cases, the system  16  can implement some blocks concurrently or change the order as desired. For example, the system  16  can concurrently generate a subquery for the secondary data intake and query system (e.g., block  5206 ) and generate instructions for the worker nodes  3306  (e.g., block  5208 ), or in any order, as desired. As yet another example, the query coordinator  3304  can concurrently coordinate a search of data within the primary data intake and query system. In some cases, the results from the query of data within the primary data intake and query system can become linked with the partial results received from the secondary data intake and query systems. 
     27.0. Search with Data Ingest Estimate Flow 
       FIG.  53    is a flow diagram illustrative of an embodiment of a routine  5300  implemented by the query coordinator  3304  to execute a query on data from an external data system  12 . Although described as being implemented by the query coordinator  3304 , it will be understood that one or more elements outlined for routine  5300  can be implemented by one or more computing devices/components that are associated with a data intake and query system  16 , such as the search head  210 , search process master  3302 , indexer  206 , and/or worker nodes  3306 . Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  5302 , the query coordinator  3304  receives a query, as described herein at least with reference to block  3802  of  FIG.  38   . At block  5304 , the query coordinator  3304  identifies an external data system  12 , as described in greater detail herein at least with reference to block  3902  of  FIG.  39    and block  5204  of  FIG.  52   . At block  5306 , the query coordinator  3304  dynamically generates a subquery for the external data system  12 , as described in greater detail herein at least with reference to block  4206  of  FIG.  42    and block  5206  of  FIG.  52   . 
     At block  5308 , the query coordinator  3304  determines a data ingest estimate for the external data system  12 , such as a secondary data intake and query system. As described herein, the query coordinator  3304  can determine the data ingest estimate for the external data system  12  in a variety of ways. In some embodiments as part of determining a data ingest estimate, the query coordinator  3304  maps one or more worker nodes  3306  to different external data systems  12  for communication purposes. The query coordinator  3304  requests the worker nodes  3306  to determine a data ingest estimate for each of their assigned external data systems  12 . 
     To obtain a data ingest estimate for a particular external data system  12 , the worker node  3306  can request the external data system  12  to return its version number or use other information to determine the functionality of the external data system  12 . Based on the determined functionality of the external data system  12 , the worker node  3306  can obtain a data ingest estimate. For example, in some cases, the worker node  3306  can send the external data system  12  the subquery and the external data system  12  can return the data ingest estimate based on its analysis of the subquery. In certain cases the worker node  3306  can parse the query to identify one or more search parameters and communicate the search parameters to the external data system  12 . Based on the search parameters, the external data system  12  can determine and return a data ingest estimate. In some embodiments, neither the worker node  3306  nor the external data system  12  can parse the subquery to identify relevant search parameters. For example, the subquery may include commands or references that are not understood by the worker node  3306  or that are specific to the external data system and the external data system  12  may not support receiving and parsing a subquery from the worker node  3306  to determine a data ingest estimate. In such cases, the worker node  3306  can use a predetermined estimate as the data ingest estimate for the external data system  12 . However, as described herein, the worker node  3306  and/or external data system  12  can use a variety of techniques to determine the data ingest estimate. 
     The worker nodes  3306  can return the data ingest estimate to the query coordinator  3304  for each external data system  12  assigned thereto. Based on the data ingest estimate from the various worker nodes  3306 , the query coordinator  3304  can determine a data ingest estimate for the query as a whole. This information can be used to estimate the size of ingest for the query and/or the time to ingest the data. In some cases, based on the data ingest estimate and the amount of resources allocated for the search, the query coordinator  3304  can determine that the query will take longer than a threshold period of time. As such, the query coordinator  3304  can request additional resources for the search and/or reject the search. 
     At block  5310 , the query coordinator  3304  determines the size and quantity of partitions/tasks for an ingest stage. As described herein, the query coordinator  3304  can determine the size of each partition or task based on resources allocated to it for the search and/or one or more search parameters of the query. For example, the size of each partition can be based on the number of processors and amount of memory allocated for the query and the number of fields used during the query. In addition, as described herein, the query coordinator  3304  can determine the number of partitions based on the data ingest estimate and the partition size. However, as described herein, it will be understood that the query coordinator  3304  can use a variety of techniques to determine the size and quantity of the partitions for the ingest stage. 
     At block  5312 , the query coordinator  3304  generates instructions for the worker nodes  3306 . As described herein, at least with reference to block  5208  of  FIG.  52   , the query coordinator  3304  can generate instructions for the worker nodes  3306  based on a variety of parameters and can include instructions to: distribute subqueries to external data systems  12 , receive local search identifiers used by the external data systems  12  to identify their respective subqueries (and partial results), map the local search identifiers for subqueries to corresponding primary search identifiers, concurrently receive and process partial results from multiple external data systems  12  (in some cases based on the local search identifier-primary search identifier mapping), distribute partial results from one multiple external data system  12  to multiple worker nodes  3306 , combine, and further process results, and communicate search results to the query coordinator  3304 , etc. 
     In some embodiments, the instructions are generated based on the determined partition size and quantity. For example, the instructions can inform the worker nodes  3306  as to the quantity and size of partitions. In this way, the worker nodes  3306  can be dynamically configured to process the results in a distributed manner. In some embodiments, based on the partition size and quantity, the worker nodes  3306  (or query coordinator  3304 ) can allocate worker nodes  3306  with greater processing resources to ingest data from secondary data intake and query systems that are expected to output a larger amount of partial results. In this way, the partial results can be received and processed in a performant manner. In addition, the query coordinator  3304  can use the partition size and quantity to determine a time duration to execute the query, request additional resources, or deallocate resources, etc. For example, if the estimated time to execute the query exceeds a threshold amount of time or the estimated number of partitions exceeds a threshold number, the query coordinator  3304  can request additional resources, notify a user, and/or cancel the query. Similarly, if the estimated number of partitions is less than a threshold amount, the query coordinator  3304  can deallocate resources for use with other queries. 
     At block  5314 , the query coordinator  3304  executes the query as described in greater detail herein at least with reference to block  4010  of  FIG.  40    and block  5210  of  FIG.  52   . It will be understood that fewer, more, or different blocks can be used as part of the routine  5300 . For example, in some embodiments, the routine  5300  can further include, monitoring nodes  3306  during query execution, allocating/deallocating resources based on the query, etc. As another example, in certain embodiments, the determination of the data ingest estimate and the partition size and quantity can form part of a processing query block, similar to the process query block  3804  of  FIG.  38   . Moreover, it will be understood that one or more blocks described herein with reference to routine  5300  can be combined with one or more blocks of other routines described herein, such as the routines described herein at least with reference to  FIGS.  5 ,  6 ,  23 - 26 ,  31 ,  34 ,  38 - 45 ,  47 ,  49 ,  52 , and  54 - 59   . 
     Furthermore, it will be understood that the various blocks described herein with reference to  FIG.  53    can be implemented in a variety of orders. In some cases, the system  16  can implement some blocks concurrently or change the order as desired. For example, the system  16  can concurrently generate a subquery for the external data system  12  ( 5306 ) and instructions for the worker nodes  3306  or in any order, as desired. 
     28.0. Search Using Search Configuration Data Flow 
       FIG.  54    is a flow diagram illustrative of an embodiment of a routine  5400  implemented by the query coordinator  3304  to execute a query on data from an external data system  12 . Although described as being implemented by the query coordinator  3304 , it will be understood that one or more elements outlined for routine  5400  can be implemented by one or more computing devices/components that are associated with a data intake and query system  16 , such as the search head  210 , search process master  3302 , indexer  206 , and/or worker nodes  3306 . Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  5402 , the query coordinator  3304  receives a query, as described herein at least with reference to block  3802  of  FIG.  38   . At block  5404 , the query coordinator  3304  identifies an external data system  12 , as described in greater detail herein at least with reference to block  3902  of  FIG.  39    and block  5204  of  FIG.  52   . 
     At block  5406 , the query coordinator  3304  obtains search configuration data for the external data system  12 . As described herein, the query coordinator  3304  can obtain the search configuration data in a variety of ways. For example, the query coordinator  3304  can obtain the search configuration data using an external query configuration file and/or by communicating with the external data system  12 . 
     In some embodiments, to obtain the search configuration data, the query coordinator  3304  maps one or more worker nodes  3306  to different external data systems  12  for communication purposes. The query coordinator  3304  can instruct the worker nodes  3306  to request search configuration data from each of their assigned external data systems  12 . 
     The worker node  3306  can request the search configuration data in a variety of ways. For example, the worker node  3306  can request search configuration data by sending the subquery to the external data system, sending unrecognized search parameters to the subquery, requesting all search configuration data associated with a particular user, etc. 
     In some embodiments, a worker node  3306  requests search configuration data by sending the external data system  12  the subquery that it is to execute. The external data system  12  can parse the subquery and return search configuration data to the worker node  3306  so that the worker node can understand or interpret external data system-specific search parameters in the subquery, such as macros, commands, or references specific to the external data system  12 . 
     In certain embodiments, the worker node  3306  requests search configuration data by sending search parameters to the external data system  12 , such as macros, commands, or references, in the subquery that it (or the query coordinator  3304 ) is unable to parse, interpret, or understand. The external data system  12  can return the corresponding search configuration data to enable the worker node  3306  to interpret search parameters specific to the external data system  12 . 
     In some cases, the worker node  3306  can request search configuration data that is associated with a particular user or account. For example, each user or account may have different authorizations or permissions on the external data system  12 . Accordingly, the worker node  3306  can use the authorizations or permissions of a specific account or user to request the search configuration data that the particular user or account is allowed to access. In response, the external data system  12  can return the search configuration data associated with the requested account or user. Moreover, it will be understood that the worker node  3306  can use any one any combination of methods to obtain search configuration data from the external data system  12 . 
     In some cases, prior to requesting the search configuration data, the worker nodes  3306  can request the external data systems  12  to return its version or some other indication of the functionality of the external data system  12 . Based on the determined functionality of the external data system  12 , the worker node  3306  can determine whether it will be able to obtain search configuration data from the external data system  12 . In certain embodiments, search configuration data received by a worker node  3306  from an external data system  12  is returned to the query coordinator  3304 . In some embodiments, the worker nodes  3306  retains the search configuration data as described herein. 
     At block  5408 , the query coordinator  3304  dynamically generates a subquery for the external data systems  12 , as described in greater detail herein at least with reference to block  4206  of  FIG.  42    and block  5206  of  FIG.  52   . As described, in some embodiments, the query coordinator  3304  can generate the subquery for the external data system  12  based on the search configuration data. For example, the search configuration data can include definitions, instruction sets, or naming conventions specific to the external data system  12 . This information can be used to further generate a subquery for execution by the external data system  12 . For example, using the search configuration data the query coordinator  3304  can transform an initial subquery (e.g., subquery as found in a query or in an external query configuration file) into a native format for execution by the external data system  12 . In this way, the system  16  can reduce the amount of processing to be performed by the external data system  12 . 
     In some embodiments, the subquery can include instructions for the external data system  12  to communicate the partial results to one or more worker nodes  3306 . As described herein, the partial results can be distributed amongst multiple worker nodes  3306  in a variety of ways. Furthermore, in some cases, such as when the external data system  12  is a secondary data intake and query system, the subquery can include instructions for indexers  206  or worker nodes  3306  of the external data system  12  to communicate the partial results to the worker nodes  3306 . By instructing the indexers  206  or worker nodes  3306  of the external data system  12  to communicate the partial results to the worker nodes  3306 , the system  16  can avoid a bottleneck at the search head  210  or controller of the external data system  12 . However it will be understood that the subquery can include instructions for partial results from indexers  206  or worker nodes  3306  of the external data system  12  to be communicated to the search head  210  of the external data system  12 , which can communicate the partial results to the worker nodes  3306 . In some embodiments, the subquery may not include explicit instructions for the indexers or worker nodes  3306 , but may include instructions for the search head  210  to generate instructions for the indexers  206  or nodes  3306  to communicate the results to the worker nodes  3306 . 
     In certain embodiments, the worker nodes  3306  generate the subquery using the search configuration data. For example, a worker node  3306  can use the subquery received from the query coordinator  3304  and the search configuration data received from the external data system  12  to generate a subquery for execution by the external data system  12 . In some embodiments, the worker node  3306  can generate the subquery before, after, or concurrently with the query coordinator  3304  generating instructions for the worker node  3306  as will be described herein with reference to block  5410 . It will be understood that the worker nodes  3306  can generate the subqueries as part of the query processing phase  5102  and/or as part of the query execution phase  5104 . By generating the subquery on the worker node  3306 , the system  16  can distribute processing tasks across various nodes  3306  and reduce the amount of processing performed by the query coordinator  3304 . In this way, the system  16  can reduce the likelihood of creating a bottleneck at the query coordinator  3304 . 
     At block  5410 , the query coordinator  3304  generates instructions for the worker nodes  3306 . As described herein, at least with reference to block  5208  of  FIG.  52   , the query coordinator  3304  can generate instructions for the worker nodes  3306  based on a variety of parameters and can include instructions to: distribute subqueries to external data systems  12 , receive local search identifiers used by the external data systems  12  to identify their respective subqueries (and partial results), map the local search identifiers for subqueries to corresponding primary search identifiers, concurrently receive and process partial results from multiple external data systems  12  (in some cases based on the local search identifier-primary search identifier mapping), distribute partial results from one multiple external data system  12  to multiple worker nodes  3306 , combine, and further process results, and communicate search results to the query coordinator  3304 , etc. 
     At block  5412 , the query coordinator  3304  executes the query as described in greater detail herein at least with reference to block  4010  of  FIG.  40    and block  5210  of  FIG.  52   . It will be understood that fewer, more, or different blocks can be used as part of the routine  5400 . For example, in some embodiments, the routine  5400  can further include, monitoring nodes  3306  during query execution, allocating/deallocating resources based on the query, etc. As another example, in certain embodiments, the determination of the data ingest estimate and the partition size and quantity can form part of a processing query block, similar to the process query block  3804  of  FIG.  38   . As yet another example, in some embodiments, block  5406  can be omitted. Instead, the query coordinator  3304  can generate instructions for the worker node  3306  to generate the subquery for the external data system  12  as described herein. Moreover, it will be understood that one or more blocks described herein with reference to routine  5400  can be combined with one or more blocks of other routines described herein, such as the routines described herein at least with reference to  FIGS.  5 ,  6 ,  23 - 26 ,  31 ,  34 ,  38 - 45 ,  47 ,  49 ,  52 ,  53 ,  55 - 57 , and  59   . 
     Furthermore, it will be understood that the various blocks described herein with reference to  FIG.  54    can be implemented in a variety of orders. In some cases, the system  16  can implement some blocks concurrently or change the order as desired. For example, the system  16  can concurrently generate a subquery for the external data system  12  ( 5408 ) and instructions for the worker nodes  3306  ( 5410 ), or in any order, as desired. Moreover, in some embodiments, the query coordinator  3304  can receive a transformed subquery from the external data system  12  and include the transformed subquery in the instructions for the worker node  3306  to execute the query or subquery. In certain cases, the query coordinator  3304  can further process the transformed subquery based on search configuration data received from the external data system  12 . 
     29.0. Distributing Partial Results to Worker Nodes Flow 
       FIG.  55    is a flow diagram illustrative of an embodiment of a routine  5500  implemented by the query coordinator  3304  to execute a query on data from an external data system  12 . Although described as being implemented by the query coordinator  3304 , it will be understood that one or more elements outlined for routine  5500  can be implemented by one or more computing devices/components that are associated with a data intake and query system  16 , such as the search head  210 , search process master  3302 , indexer  206 , and/or worker nodes  3306 . Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  5502 , the query coordinator  3304  receives a query, as described herein at least with reference to block  3802  of  FIG.  38   . At block  5504 , the query coordinator  3304  identifies an external data system  12 , as described in greater detail herein at least with reference to block  3902  of  FIG.  39    and block  5204  of  FIG.  52   . 
     At block  5506 , the query coordinator  3304  dynamically generates a subquery for the external data system  12 . As described herein, the query coordinator  3304  can generate a subquery for the external data system  12  based on the determined functionality of the external data system, and can determine the version or functionality of the external data systems  12  in a variety of ways. In some cases, the query coordinator  3304  can obtain location and/or communication information from an external query configuration file that enables the query coordinator  3304  to communicate with the external data system  12 . Using the obtained information, the query coordinator  3304  can communicate with the external data system  12  to determine its functionality. 
     In certain embodiments, the query coordinator  3304  can map one or more worker nodes  3306  to different external data systems  12  for communication purposes. The query coordinator  3304  can instruct the worker nodes  3306  to obtain information regarding the functionality of the external data system  12 . Based on the determined functionality of the external data system  12 , the worker node  3306  can dynamically generate the subquery for execution by the external data system  12 . For example, the query coordinator  3304  can determine that the external data system  12  is capable of communicating its partial results to multiple worker nodes  3306 . As such, the query coordinator  3304  can generate a subquery that instructs the external data system  12  to communicate its partial results to multiple worker nodes  3306  in a distributed manner. 
     As described herein, in some cases, the query coordinator  3304  can generate instructions for indexers  206  and/or worker nodes  3306  of an external data system  12  to communicate results to the worker nodes  3306 . In certain cases, the query coordinator  3304  can generate instructions for the search head  210  of an external data system  12  to communicate partial results to the worker nodes  3306  or to generate instructions for the indexers  206  and/or worker nodes  3306  to communicate partial results to the worker nodes  3306 . In certain embodiments, the instructions can cause the indexers  206 , worker nodes  3306  (of the external data system  12 ), and/or search head  210  to communicate the partial results to the worker nodes  3306  without storing the results to disk. For example, the instructions can cause the search head  210  to stream results received from the indexers  206  or worker nodes  3306  (of the external data system  12 ) to the worker nodes  3306  prior to, concurrently with, or instead of storing the results to disk. However, it will be understood that in some cases, the partial results from the external data system  12  can be stored to disk prior to being communicated to the worker nodes  3306 . 
     Additional details regarding the process of generating a subquery for the external data systems  12  is described in greater detail herein at least with reference to block  4206  of  FIG.  42    and block  5206  of  FIG.  52   . For example, as described herein, in some embodiments, the worker nodes  3306  can generate a portion or all of a subquery for an external data system  12 . By generating the subquery on the worker node  3306 , the system  16  can distribute processing tasks across various nodes  3306  and reduce the amount of processing performed by the query coordinator  3304 . 
     At block  5508 , the query coordinator  3304  generates instructions for the worker nodes  3306 . As described herein, at least with reference to block  5208  of  FIG.  52   , the query coordinator  3304  can generate instructions for the worker nodes  3306  based on a variety of parameters and can include instructions to: distribute subqueries to external data systems  12 , receive local search identifiers used by the external data systems  12  to identify their respective subqueries (and partial results), map the local search identifiers for subqueries to corresponding primary search identifiers, concurrently receive and process partial results from multiple external data systems  12  (in some cases based on the local search identifier-primary search identifier mapping), distribute partial results from one multiple external data system  12  to multiple worker nodes  3306 , combine, and further process results, and communicate search results to the query coordinator  3304 , etc. In some embodiments, the query coordinator  3304  generates instructions for the worker nodes  3306  based on the functionality and capabilities of the external data system  12 , the amount of resources allocated for the search, the amount of processing to be performed by the query coordinator  3304 , worker nodes  3306  and external data system  12 , etc. 
     At block  5510 , the query coordinator  3304  executes the query as described in greater detail herein at least with reference to block  4010  of  FIG.  40    and block  5210  of  FIG.  52   . It will be understood that fewer, more, or different blocks can be used as part of the routine  5500 . For example, in some embodiments, the routine  5500  can further include, monitoring nodes  3306  during query execution, allocating/deallocating resources based on the query, etc. As another example, in some certain embodiments, the generation of the subquery for the external data system  12  can form part of a processing query block, similar to the process query block  3804  of  FIG.  38   . As yet another example, in some embodiments, block  5506  can be omitted. Instead, the query coordinator  3304  can generate instructions for the worker node  3306  to generate the subquery for the external data system  12  as described herein. Moreover, it will be understood that one or more blocks described herein with reference to routine  5500  can be combined with one or more blocks of other routines described herein, such as the routines described herein at least with reference to  FIGS.  5 ,  6 ,  23 - 26 ,  31 ,  34 ,  38 - 45 ,  47 ,  49 ,  52 - 54  and  56 - 58 , and  59   . 
     Furthermore, it will be understood that the various blocks described herein with reference to  FIG.  55    can be implemented in a variety of orders. In some cases, the system  16  can implement some blocks concurrently or change the order as desired. For example, the system  16  can concurrently generate a subquery for the external data system  12  ( 5506 ) and instructions for the worker nodes  3306  ( 5508 ) or in any order, as desired. 
     30.0. Distribution of Partial Results Between Worker Nodes Flow 
       FIG.  56    is a flow diagram illustrative of an embodiment of a routine  5600  implemented by the query coordinator  3304  to execute a query on data from an external data system  12 . Although described as being implemented by the query coordinator  3304 , it will be understood that one or more elements outlined for routine  5600  can be implemented by one or more computing devices/components that are associated with a data intake and query system  16 , such as the search head  210 , search process master  3302 , indexer  206 , and/or worker nodes  3306 . Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  5602 , the query coordinator  3304  receives a query, as described herein at least with reference to block  3802  of  FIG.  38   . At block  5604 , the query coordinator  3304  identifies an external data system  12 , as described in greater detail herein at least with reference to block  3902  of  FIG.  39    and block  5204  of  FIG.  52   . 
     At block  5606 , the query coordinator  3304  dynamically generates a subquery for the external data system  12 . As described herein at least with reference to block  5506  of  FIG.  55   , in some embodiments, the query coordinator  3304  can determine the functionality or version of the external data system  12 . Based on the determined functionality of the external data system  12 , the query coordinator  3304  can dynamically generate a subquery for execution by the external data system  12 . For example, the query coordinator  3304  can determine that the external data system  12  is not capable of communicating its partial results to multiple worker nodes  3306 . As such, the query coordinator  3304  can generate a subquery that instructs the external data system  12  to communicate its partial results to a single worker node  3306 . In some cases, the external data system  12  stores the results to disk and then communicates the results from disk to the worker nodes  3306 . However, it will be understood that in some embodiments, the external data system  12  can stream the results to the worker nodes  3306  prior to, concurrently with, or instead of storing the results to disk. 
     Additional details regarding the process of generating a subquery for the external data systems  12  is described in greater detail herein at least with reference to block  4206  of  FIG.  42    and block  5206  of  FIG.  52   . For example, as described herein, in some embodiments, the worker nodes  3306  can generate a portion or all of the subquery for an external data system  12 . By generating the subquery on the worker node  3306 , the system  16  can distribute processing tasks across various nodes  3306  and reduce the amount of processing performed by the query coordinator  3304 . 
     At block  5608 , the query coordinator  3304  generates instructions for the worker nodes  3306 . As described herein, at least with reference to block  5208  of  FIG.  52   , the query coordinator  3304  can generate instructions for the worker nodes  3306  based on a variety of parameters and can include instructions to: distribute subqueries to external data systems  12 , receive local search identifiers used by the external data systems  12  to identify their respective subqueries (and partial results), map the local search identifiers for subqueries to corresponding primary search identifiers, concurrently receive and process partial results from multiple external data systems  12  (in some cases based on the local search identifier-primary search identifier mapping), distribute partial results from one multiple external data system  12  to multiple worker nodes  3306 , combine and further process results, and communicate search results to the query coordinator  3304 , etc. 
     In some embodiments, the query coordinator  3304  generates instructions for the worker nodes  3306  based on the functionality and capabilities of the external data system  12 , the amount of resources allocated for the search, the amount of processing to be performed by the query coordinator  3304 , worker nodes  3306  and external data system  12 , etc. In some embodiments, the instructions for the worker nodes  3306  can include instructions for a worker node  3306  assigned to receive partial results from the external data system  12  to distribute the partial results amongst multiple worker nodes  3306 . In this way, the results from the external data system  12  can be processed in a distributed manner. 
     At block  5610 , the query coordinator  3304  executes the query as described in greater detail herein at least with reference to block  4010  of  FIG.  40    and block  5210  of  FIG.  52   . It will be understood that fewer, more, or different blocks can be used as part of the routine  5600 . For example, in some embodiments, the routine  5600  can further include, monitoring nodes  3306  during query execution, allocating/deallocating resources based on the query, etc. As another example, in some certain embodiments, the generation of the subquery for the external data system  12  can form part of a processing query block, similar to the process query block  3804  of  FIG.  38   . As yet another example, in some embodiments, block  5606  can be omitted. Instead, the query coordinator  3304  can generate instructions for the worker node  3306  to generate the subquery for the external data system  12  as described herein. Moreover, it will be understood that one or more blocks described herein with reference to routine  5600  can be combined with one or more blocks of other routines described herein, such as the routines described herein at least with reference to  FIGS.  5 ,  6 ,  23 - 26 ,  31 ,  34 ,  38 - 45 ,  47 ,  49 ,  52 - 55 ,  57 , and  59   . 
     Furthermore, it will be understood that the various blocks described herein with reference to  FIG.  56    can be implemented in a variety of orders. In some cases, the system  16  can implement some blocks concurrently or change the order as desired. For example, the system  16  can concurrently generate a subquery for the external data system  12  ( 5606 ) and instructions for the worker nodes  3306  ( 5608 ) or in any order, as desired. 
     31.0. Executing a Query Received from Another System Flow 
     As described herein, in some cases, a data intake and query system can receive a query from an external data system  12 . For example, a secondary data intake and query system can receive a subquery from a primary data intake and query system (non-limiting examples: from a search head, query coordinator  3304 , and/or a worker node  3306  of the primary data intake and query system). 
     Moreover, in some embodiments, the secondary data intake and query system can route partial results of the query that it receives (e.g., a subquery received from a primary data take and system) to worker nodes  3306  (or other component) of a primary data intake and query system. The partial results can be routed from one or more components of the secondary data intake and query system to one or more components of the primary data intake and query system. For example, the partial results can be routed from a search head  210 , query coordinator  3304 , indexers  206 , or worker nodes  3306  of the secondary data intake and query system to a search head  210 , query coordinator  3304 , or worker nodes  3306  of the primary data intake and query system. In some cases, the results can be communicated to the primary data intake and query system without passing through the search head  210  or query coordinator  3304  of the secondary data intake and query system. In this way, results can be communicated in a distributed manner without passing through a single point and reducing the likelihood of a bottleneck at the search head  210  or query coordinator  3304 . 
     Further, in some cases, the secondary data intake and query system can use worker nodes  3306  to execute the query that it receives. Accordingly, in some embodiments, worker nodes  3306  of the secondary data intake and query system are used to execute a subquery of a primary data intake and query system, and worker nodes  3306  of the primary data intake and query system are used to execute the query of the primary data intake and query system (including processing the results of the subquery). 
     Moreover, in some cases the secondary data intake and query system and primary data intake and query system can use the same or similar group of worker nodes  3306  to execute the query and subquery. Accordingly, in certain embodiments, a worker node  3306  can execute portions of a subquery at the behest of a secondary data intake and query system and execute portions of the query that corresponds to the subquery at the behest of the primary data intake and query system. 
     As anon-limiting example, one worker node  3306  can receive instructions from a query coordinator  3304  of the primary data intake and query system to communicate a subquery to a secondary data intake and query system and to receive partial results of the subquery from the secondary data intake and query system. In turn, the same worker node  3306  can receive instructions from a query coordinator  3304  of the secondary data intake and query system to execute portions of the subquery on data managed by the secondary data intake and query system. Further, the same worker node  3306  can receive instructions from the query coordinator  3304  of the secondary data intake and query system to communicate partial results of the subquery to a worker node  3306  of the primary data intake and query system, which in this example can be itself. Moreover, the same worker node  3306  can receive instructions from the query coordinator  3304  of the primary data intake and query system to process the partial results that it receives from the secondary data intake and query system (the partial results that the worker node  3306  determined in accordance with instructions received from query coordinator  3304  of the secondary data intake and query system). As such, in some cases, the same worker node  3306  can process or perform multiple transformations on the same set of data based on instructions received from distinct and independent data intake and query systems. Further, the same worker node  3306  can perform the operations and transformations without either data intake and query system being aware that it is the same worker node  3306  performing the operations and transformations on the set of data identified by both data intake and query systems. 
       FIG.  57    is a flow diagram illustrative of an embodiment of a routine  5700  implemented by a search head  210  to execute a query received from an external data system  12 . Although described as being implemented by the search head  210 , it will be understood that one or more elements outlined for routine  5700  can be implemented by one or more computing devices/components that are associated with a data intake and query system  16 , such as the query coordinator  3304 , search process master  3302 , indexer  206 , and/or worker nodes  3306 . For example, depending on the architecture of the data intake and query system  16 , portions or all of the routine  5700  can be implemented by a component of the data intake and query system other than the search head  210 . Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  5702 , the search head  210  receives a query, as described in greater detail at least with reference to block  602  of  FIG.  6   , block  3002  of  FIG.  30   , and block  3802  of  FIG.  38   . At block  5704 , the search head  210  processes the query as described in greater detail herein at least with reference to block  604  of  FIG.  6   , blocks  3004  and  3006  of  FIG.  30   , and block  3804  of  FIG.  38   . As will be understood, the manner in which the search head  210  (or query coordinator  3304 ) processes the query can be based on the architecture of the data intake and query system (e.g., whether the architecture includes worker nodes  3306 , whether the architecture is cloud based or on premises, etc.). For example, as described herein, the search head  210  can generate instructions for indexers  206  to execute portions of the query and/or generate instructions for worker nodes  3306  that have been allocated for the search to execute portions of the query. 
     At block  5706 , the search head  210  initiates execution of the query. In some embodiments, initiating execution can include distributing at least a portion of the query for execution as described herein at least with reference to block  606  of  FIG.  6    and block  3806  of  FIG.  38   . For example, the search head  210  can distribute portions of the query, such as instructions or subqueries, to indexers  206  and/or worker nodes  3306  for execution. 
     At block  5708 , the search head  210  receives results. In some embodiments, the search head  210  receives results from indexers  206  as described herein at least with reference to block  610  of  FIG.  6   . In certain embodiments, the search head  210  can receive results from worker nodes  3306  as described herein at least with reference to block  3012  of  FIG.  30    or block  3808  of  FIG.  38   . Furthermore, the search head  210  can perform additional processing on the received results as described herein at least with reference to block  610  of  FIG.  6    and block  3810  of  FIG.  38   . 
     At block  5710 , the search head  210  provides the results to another data intake and query system. For example, the search head  210  can provide the results to a search head  210 , query coordinator  3304 , and/or one or more worker nodes  3306  of a primary data intake and query system. In some cases, the search head  210  stores the results to disk and communicates the results from disk to the data intake and query system. In certain cases, the search head  210  can stream the results to the other data intake and query system prior to, concurrently with, or instead of storing the results to disk. 
     As described herein, the primary data intake and query system can further process the results received from the search head  210 . Further, the results from the search head  210  can correspond to partial results of a query received by the primary data intake and query system. Accordingly, the query executed by the data intake and query system can correspond to a subquery of a query received by a primary data intake and query system. 
     It will be understood that fewer, more, or different blocks can be used as part of the routine  5700 . For example, in some embodiments, results of the query can be provided to the primary data intake and query system from the indexers and/or worker nodes  3306 . In such embodiments, block  5708  may be omitted as the search head may not receive the results (and block  5710  may be performed by the indexers  206  and/or worker nodes  3306 ). Moreover, it will be understood that one or more blocks described herein with reference to routine  5700  can be combined with one or more blocks of other routines described herein, such as the routines described herein at least with reference to  FIGS.  5 ,  6 ,  23 - 26 ,  31 ,  34 ,  38 - 45 ,  47 ,  49 ,  52 - 56 , and  59   . 
     Furthermore, it will be understood that the various blocks described herein with reference to  FIG.  57    can be implemented in a variety of orders. In some cases, the system  16  can implement some blocks concurrently or change the order as desired. 
     32.0. Task Distribution within an Execution Node 
     An execution node in a distributed execution environment, such as, but not limited to a worker node  14 , can receive and process data from multiple datasets. The datasets may correspond to data from different data sources, such as datasets from different external data systems  12  or different data intake and query systems, data associated with different DAGs, and/or different datasets from the same data source. For example, a query can include instructions to obtain different sets of data from the same (or different) data source, independently process the different sets of data, and combine the processed sets of different data, and process the combined set of data. In some embodiments, the different datasets or the processing of the different datasets can correspond to sub-DAGs of a larger DAG being executed by the execution node. 
     In some cases, an execution node may begin to process data from one dataset while ignoring data from another dataset. In doing so, the execution node can cause the query or subquery to fail. As a non-limiting example, data from different datasets can be sent to one or more buffers of the execution node. As the execution node processes the data, it can remove the data being processed from the buffer and free up additional space for additional data. However, if the execution node only processes data from one dataset, data from the other datasets will not be removed and associated buffers can fill up. 
     Once a buffer at the execution node is full, the execution node may reject incoming data or incoming data associated with datasets that are not being processed. In response, buffers at the data source used for sending data to the execution node may also fill up as the data is no longer being sent to the execution node. As the buffers at the data source fill up or after a predetermined amount of time in which data is not accepted by the execution node, the data source may determine that the execution node is not functioning or that there is some other issue associated with the execution node. As such, the data source may produce an error, stop sending results to the execution node, and/or cancel a corresponding query or subquery. 
     To address this issue, the execution node can be configured to concurrently process data from different datasets.  FIG.  58    is a block diagram illustrating an embodiment of a data path of data from different data sources  5802  in an execution node  5804 . Non-limiting examples of execution nodes  5804  are described herein at least with reference to worker nodes  14 . In some embodiments, the data sources  5802  can correspond to any source of data that is to be processed by the execution node  5804 . For example, the data source  5802  can correspond to another execution node  5804 , indexers  206 , external data sources  3318 , the query acceleration data store  3308 , common storage  4602 , an ingested data buffer  4802 , a search head  210 , and may logically correspond to different DAGs or sub-DAGs of the same DAG, etc. 
     In the illustrated embodiment, chunks of data or data chunks  5806  from different data sources (or corresponding to different datasets)  5802  are communicated to the execution node  5804 . Each data chunk  5806  can include records, events, or data that is to be processed by the execution node  5804 . For example, a data chunk  5806  can include one or more events or records that correspond to partial results received from a secondary data intake and query system. 
     In some embodiments, the data chunks  5806  received by the execution node  5804  are placed in an intake buffer  5808 . In the illustrated embodiment, the data chunks  5806  in the intake buffer  5808  include two data chunks  5806  from a first data source (each labeled “S1 Data Chunk”), two data chunks  5806  from a second data source (each labeled “S2 Data Chunk”), and one data chunk  5806  from a third data source (each labeled “S3 Data Chunk”). The data chunks  5806  in the intake buffer  5808  may correspond to partial or complete chunks of data received from the data sources  5802 . Further, the data chunks  5806  in the intake buffer  5808  can remain in the intake buffer  5808  until the entire chunk of data has been received from the data source  5802 . 
     Once the data chunk  5806  is complete it can be moved to the data chunk buffer  5810 . In some embodiments, the execution node  5804  can determine that the data chunk  5806  is complete based on an identification of a data source identifier within the data chunk  5806 . For example, each chunk of data  5806  received by the execution node  5804  can include an identifier indicating the source of the data chunk  5806 . In this way, the execution node  5804  can track the different data chunks  5806  to be processed. In some embodiments, the data source identifier can correspond to the local search identifier assigned by a secondary data intake and query system. 
     To concurrently process data chunks  5806  in the data chunk buffer  5810 , the execution node  5804  can use one or more partition generators  5812 . In some embodiments, the execution node  5804  can include a distinct partition  5812  generator for data chunks  5806  from each data source  5802 . For example, in the illustrated embodiment, the execution node  5804  receives data chunks  5806  from three data sources  5802 . As such, the execution node  5804  can include three partition generators  5812 . However, it will be understood that fewer or more partition generators  5812  can be used by the execution node  5804  to process data chunks from different data sources as desired. As a non-limiting example and with reference to the illustrated embodiment, one partition generator  5812  (labeled “S1 Partition Generator”) can generate partitions  5816  (labeled “S1 Partitions”) for the partition queue  5814  by combining S1 data chunks from the data chunk buffer  5810 . Similarly, two other partition generators  5812  (labeled “S2 Partition Generator” and “S3 Partition Generator”) can generate partitions  5816  (labeled “S2 Partitions” and “S3 Partitions”, respectively) by combining S2 data chunks and S3 data chunks, respectively, from the data chunk buffer  5810 . 
     Moreover, each partition generator  5812  can identify data chunks  5806  to be combined based on the data source identifiers. In some embodiments, such as where the execution node  5804  is to combine data chunks  5806  associated with partial results, the partition generators  5812  can use the primary search identifier, local search identifier, or mapping between the primary and local search identifier to identify data chunks  5806  to be combined to form a partition  5816 . For example, the partition generator  5812  may receive instructions to combine data chunks  5806  that have the same primary search identifier into a partition  5816 . However, the data chunks  5806  in the data chunk buffer  5810  may not have a primary search identifier included therewith. As such, the partition generator  5812  can map the primary search identifier to the local search identifier in order to identify the data chunks  5806  that are to be combined. 
     As described herein, the size of each partition  5816  or number of records placed therein can be based on resources allocated to the execution node  5804  or search. For example, the size of the partitions  5816  can be determined based on the number of processors  5818  and/or amount of memory allocated to the execution node  5804  or search and/or the size of each record. In some embodiments, the partition size can be selected to avoid having the amount of data to be processed by the execution node  5804  exceeding the amount of volatile memory available to the execution node  5804 , which may also be referred to spilling data to disk. 
     In addition to combining multiple data chunks  5806  to form a partition  5816 , a partition generator  5812  can add execution instructions to each partition  5816 . The instructions can indicate what transformation or processes are to be performed on the data of the partition  5816  (non-limiting examples: events or records that made up the data chunks  5806  used to form the partition  5816 ). In some embodiments, the instructions can be in the form of binary code executable by a processor. The partition generators  5812  can obtain the instructions for the partition based on the instructions received by the execution node  5804 . For example, the instructions generated by a query coordinator  3304  and communicated to an execution node  5804  can include the instructions for processing individual partitions  5816 . It will be understood that the instructions for each partition  5816  can vary depending on the transformation that is to be performed on the data of the partition  5816  or dataset. 
     The partitions  5816  in the partition queue  5814  can be scheduled for processing by aprocessor  5818  of the execution node  5804 . Further, the data of the partition  5816  can be processed by the processor  5818  of the execution node  5804  according to the instructions included in the partition  5816 . As mentioned, in certain embodiments, the partitions  5816  can be scheduled and processed without regard to the source identifier used to create the partition  5816 . In this way, the execution node  5804  can concurrently process data from different data sources  5802 . 
     In some embodiments, multiple execution nodes  5804  can communicate with each other to distribute partitions or tasks for execution. For example, if the partition queue  5814  in one execution node  5804  satisfies a queue threshold, it can communicate with other execution nodes  5804  to send partitions to them for execution. In some cases, the queue threshold can be based on a predetermined number or can be dynamically determined based on the partition queue sizes of other worker nodes  3306  or other means. For example, the queue threshold can be satisfied if the number of partitions  5816  in the partition queue  5814  of one execution node  5804  is 50% (or some other amount) greater than the number of partitions in the partition queue  5814  of another execution node  5804 . 
     As described herein, in some embodiments, an execution node controller, such as the query coordinator  3304 , can monitor the execution nodes  5804 . If one execution node  5804  is falling behind or satisfies a queue threshold or timing threshold (non-limiting example, is taking longer than an expected time to execute its portion of the query), the execution node controller can instruct the execution node  5804  to distribute some of its partitions  5816  or data chunks  5806  to another execution node  5804  for execution. Similarly, if one execution node  5804  has significantly fewer or no partitions to execute, the query coordinator can instruct other execution node  5804  to distribute some of their partitions  5814  or data chunks  5806  to the other execution node  5804  for execution. 
     In addition, the execution node controller can monitor the number or amount of data chunks  5806  assigned to a worker node  3306 . For example, based on the distribution of data from data sources  5802  to worker nodes  3306 , it is possible that one execution node  5804  receives a significantly larger portion of data to process than other execution node  5804  (non-limiting example: similar to the queue threshold, the number of data chunks in the intake buffer or data chunk buffer satisfy a buffer threshold). In such cases, the execution node controller can instruct the execution nodes  5804  to redistribute their data chunks  5806  or partitions  5816  in order to process the data in a more distributed fashion thereby decreasing the search execution time. Moreover, in some cases, the execution node controller can instruct the data sources  5802  to distribute their data in a different way to reduce the likelihood of sending too much data to a single execution node  5804 . 
     Although described often with reference to components of a data intake and query system, it will be understood that the functions and descriptions described herein with reference to the execution node  5804  can be used in a variety of distributed execution environments. 
     32.1. Worker Node Task Distribution Flow 
       FIG.  59    is a flow diagram illustrative of an embodiment of a routine  5900  implemented by an execution node  5804  to process a partition or task. Although described as being implemented by the execution node  5804 , it will be understood that one or more elements outlined for routine  5900  can be implemented by one or more computing devices/components in a distributed execution environment, such as, but not limited to one or more components of a data intake and query system  16 , such as the worker node  3306 , search head  210 , search process master  3302 , indexer  206 , and/or query coordinator  3304 . Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  5902 , the execution node  5804  receives chunks of data. As described herein, the chunks of data can be received over time and can include one or more records or events. As such, partial data chunks can be maintained by the execution node  5804  in an intake buffer. Further, as described herein, the data chunks can be received from different data sources and/or be associated with different datasets. The different datasets can correspond to external data systems  12 , data intake and query systems, sub DAGs of a larger DAG, or different sets of data from the same data source, etc. 
     At block  5904 , the execution node  5804  generates a task or partition. In some embodiments, the execution node  5804  can generate the partition by combining multiple chunks of data. As described herein, the size of each partition or number of records placed therein can be based on resources allocated to the execution node  5804 . In some cases, the execution node  5804  combines data chunks associated with the same dataset into the partition. For example, data chunks associated with or received from a first data source can be combined to form one partition and data chunks associated with or received from a second data source can be combined to form a different partition. Similarly, data chunks associated with a first DAG or sub-DAG can be combined to form one partition and data chunks associated with a second DAG or sub-DAG can be combined to form a different partition. 
     In certain embodiments, the execution node  5804  identifies data chunks associated with the same dataset based on a data source identifier associated with each data chunk. As described herein, in some cases, the execution node  5804  can perform a mapping function to identify related data chunks. For example, the execution node  5804  may receive an indication that data chunks with a particular primary search identifier are to be combined, and use a primary-local search identifier mapping to identify data chunks with a corresponding local search identifier for combination. 
     Moreover, as part of generating a partition, the execution node  5804  can add computer executable instructions to the combined data chunks. The added instructions can indicate what is to be done to the data or records of the partition. For example, the instructions can indicate one or more transformations to be performed on the records, such as a filtering or joining of records. In some embodiments, the execution node  5804  can receive the instructions from an execution node controller, such as, but not limited to a query coordinator  3304  of a data intake and query system. 
     In some embodiments, such as where the execution node  5804  processes data received from a secondary data intake and query system according to instructions received by a primary data intake and query system, the execution node  5804  can determine what instructions are to be included for each partition based on an association between or mapping of a primary search identifier associated with the primary data intake and query system with a local search identifier associated with the secondary data intake and query system. For example, as described herein, when generating instructions for the execution node  5804 , the primary data intake and query system may not know the identifier that will be applied to data chunks or partial results from a secondary data intake and query system. As such, the primary data intake and query system can assign a primary search identifier for data chunks or partial results that it expects to receive from a particular secondary data intake and query system. As the secondary data intake and query system processes the data according to the query or subquery, it can append or include a local search identifier to or with each chunk of data. Thus, the association or mapping can enable the execution node  5804  to determine what is to be done (using the primary search identifier) to data chunks having a particular local search identifier. 
     At block  5906 , the execution node  5804  schedules the partitions for execution by one or more processors of the execution node  5804 . The partitions can be scheduled for execution in a variety of ways. For example, the partitions can be executed in a random order, in a time-based order (e.g., first-in first out), etc. In certain embodiments, the partitions are executed without regard to the data source identifier associated therewith. That is, the processors can treat partitions associated with different data sources equally such that partitions associated with one data source are not always processed before partitions associated with a different data source. 
     At  5908 , the execution node  5804  processes the partition. As described herein, the execution node  5804  can process the partitions based on the executable instructions in the partition. It will be understood that fewer, more, or different blocks can be used as part of the routine  5900 . For example, in some embodiments, executable instructions may not be included in each partition or task. In such embodiments, the execution node  5804  can retrieve instructions for a particular partition. In some cases, the execution node  5804  can retrieve the instructions based on the primary or local search identifier, or instructions received from a controller, such as a query coordinator  3304 , etc. 
     As another example, in some embodiments, an execution node can process one partition based on instructions received from one execution node controller and then process the results of processing the partition based on instructions received from another execution node controller. For example, a secondary data intake and query system may use an execution node to process a subquery. In processing the subquery, the execution node can generate and process partitions according to instructions received from the secondary data intake and query system. Further, a primary data intake and query system may use the execution node to process the partial results of the subquery as part of a federated or multi-system query. Accordingly, the execution node can, according to instructions received from the primary data intake and query system, generate and/or process a second partition that includes the results that it generated from processing an earlier partition on behalf of the second data intake and query system. It will be understood that the second partition can include results from the execution of other partitions by the worker node or by other execution nodes. 
     Moreover, it will be understood that one or more blocks described herein with reference to routine  5900  can be combined with one or more blocks of other routines described herein, such as the routines described herein at least with reference to  FIGS.  5 ,  6 ,  23 - 26 ,  31 ,  34 ,  38 - 45 ,  47 ,  49 , and  52 - 57   . Furthermore, it will be understood that the various blocks described herein with reference to  FIG.  59    can be implemented in a variety of orders. 
     33.0. Federated Search Optimization 
     As previously described, in some embodiments, it can be beneficial to perform queries across multiple data systems, such as the data intake and query system  16  and the external data systems  12 . In some embodiments, an external data system  12  may support a different language than the data intake and query system  16 . The language of the data intake and query system  16  and of the external data systems  12  may refer to the vocabulary, syntax, and/or grammatical rules used to instruct the systems, including to request searches and queries. In some cases, the language may refer to a query language. In one non-limiting example, the data intake and query system  16  may support or be able to execute queries written in SPL and the external data system may support or be able execute queries written in Lucene or SQL. Further, the data intake and query system  16  may, in some cases, not support or be able to execute queries written in Lucene or SQL and the external data system  12  may, in some cases, not support or be able to execute queries written in SPL. 
     Because the data intake and query system  16  and the external data system  12  may support different query languages, a query that is performed across both the data intake and query system  16  and the external data system  12  may be written in multiple query languages. For example, a query provided to or generated by the data intake and query system  16  may be written in SPL, and may include or reference a portion or subquery that is written in another language, such as SQL or JSON. In some cases, the query may be suboptimal because the subquery is written in a language not supported by the system that received or generated the query. Accordingly, in some cases, it may not be possible to apply native optimization features of the data intake and query system  16  to the subquery that may be written in a different language than that supported by the data intake and query system  16 . 
     Embodiments disclosed herein include a system that can convert or translate a query or subquery from an unsupported query language to a supported query language. Once the query is converted, the system can apply its native optimization capabilities to the converted or translated query or subquery. The optimized query or subquery can then be converted or translated back to its original query language enabling the optimized translated query to be executed by an external data system that supports the original language of the query. Advantageously, in certain embodiments, by converting and optimizing the query, the computing resources for executing the query can be reduced. In some embodiments, the amount of processing nodes (e.g., worker nodes), the amount of bandwidth, the amount of compute time, and/or the amount of storage space required to execute a query may be reduced by converting and optimizing the query before execution of the query. 
       FIG.  60    is a flow diagram illustrative of an embodiment of a routine implemented  6000  by a query coordinator  3304  to optimize and execute a query involving data from an external data system  12 . The external data system  12  may include a third-party data processing and storage system  5000 , which may support a different language or query language than a data intake and query system  16 . Although described as being implemented by the query coordinator  3304 , it will be understood that one or more elements outlined for routine  6000  can be implemented by one or more computing devices/components that are associated with a data intake and query system  16 , such as the search head  210 , search process master  3302 , indexer  206 , and/or worker nodes  3306 . Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  6002 , the query coordinator  3304  receives a query, as described herein at least with reference to block  3802  of  FIG.  38   . In certain embodiments, the query may include a number of parts. At least some of the parts of the received query may themselves be queries. These additional queries that are part of the query may be referred to as “subqueries.” The query coordinator  3304  may use the result of or response to one or more subqueries to help generate a result or response to the query. In some embodiments, one or more of the subqueries may reference different data and/or different external data systems  12  than other subqueries or portions of the query. Each of the queries and/or subqueries may reference particular data to be processed and a manner of processing the data. For example, the queries may identify data fields to be accessed and whether to count, modify, delete, or provide the data to another portion of the query, and the like. 
     In some embodiments, a subquery may be written in a different language or query language than a remainder of the query or than other subqueries. For example, the query may be written in a language interpretable by the data intake and query system  16  (e.g., SPL), but the subquery may be written in a different language that is interpretable by the external data system  12  (e.g., SQL or JSON). The language interpretable by the data intake and query system  16  may not be interpretable by the external data system  12 . Similarly, the language interpretable by the external data system  12  may not be interpretable by the data intake and query system  16 . 
     In some embodiments, the subquery or at least a portion of the subquery may not be directly included in the query. Instead, in certain embodiments, the query may include an identifier or reference that indicates to the query coordinator  3304  that the query includes a subquery. The query coordinator  3304  may use the identifier or reference to determine the subquery. For example, the query coordinator  3304  may use the reference as an index to an external query configuration file, which may store one or more potential subqueries that may be referenced by a query. 
     At block  6004 , the query coordinator  3304  identifies a subquery for an external data system  12 . The query coordinator  3304  can identify the subquery based on a keyword or reference included in the query. For example, a keyword, such as “federated” or “external,” may indicate that what follows references an external data system  12 . As described above, the query may directly include the subquery to the external data system  12  or may include a reference that enables the query coordinator  3304  to determine the subquery from another location, such as an external query configuration file or other mapping that maps a keyword or reference to a subquery. For instance, the query may include, among other commands, the following: federated:my_dep_3_search_5. The query coordinator  3304  may determine from the term “federated” that what follows the ‘:’ (e.g., my_dep_3_search_5) is a reference to a subquery for querying an external data system  12 . The query coordinator  3304  may use the my_dep_3_search_5 as a reference or index to access a mapping or external query configuration file to determine the actual subquery and/or the particular external data system  12  to perform the subquery. In addition, the query coordinator  3304  may determine from the subquery and/or from the mapping or external query configuration file, the query language in which the subquery is written. For example, the query coordinator  3304  may determine that the subquery is written in SQL rather than the SPL in which the remainder of the query may be written. 
     At block  6006 , the query coordinator  3304  translates, or otherwise converts, the subquery into a query language supported by the data intake and query system  16 . As previously described, the query may be in a first query language, such as SPL, and the subquery may be in a second query language, such as SQL or JSON. The query coordinator  3304  may translate the subquery from second query language (e.g., SQL or JSON) to the first query language (e.g., SPL). The query coordinator  3304  may determine the translation of the subquery from the second query language to the first query language based on a language mapping that maps commands and command arguments or variables from the second query language to the first query language. 
     In some embodiments, a direct mapping between commands may not be possible. For example, the second query language may have a built-in command that is not available in the first query language. In some such cases, the query coordinator  3304  may determine a combination of commands or actions in the first query language that accomplishes the result of the built-in command in the second query language. The query coordinator  3304  may then replace the built-in command in the second query language with the combination of commands in the first query language. 
     In some embodiments, the query coordinator  3304  may use natural language processing and/or a machine learning process to determine a translation between a command in the second query language and a command in the first query language. The machine learning process may be a supervised or unsupervised machine learning process. Further, the machine learning process may use a set of test data to develop or refine translations between commands in the first and second query language. 
     At block  6008 , the query coordinator  3304  processes the translated subquery. In some embodiments, as part of processing the translated subquery, the query coordinator can perform one or more optimizations on the translated subquery. The query coordinator  3304  may use one or more different optimizing processes to optimize the translated subquery. For example, the query coordinator  3304  may use one or more of semantic, runtime, or infrastructure based optimizations. In certain embodiments, the query coordinator  3304  optimizes the translated subquery itself. Alternatively, or in addition, the query coordinator  3304  may optimize the distribution or assignment of the translated subquery among one or more worker nodes  3306 . Furthermore, as described herein, in some embodiments, the query coordinator  3304  can include instructions to sends results to one worker node  3306  for distribution to other worker nodes  3306  or include instructions to distribute results to multiple worker nodes  3306 . 
     In some embodiments, the query coordinator  3304  may optimize the query as a whole, or portions of the query, based at least in part on the translated subquery and/or on an optimization of the translated subquery. For example, the query coordinator  3304  may determine based on the translated subquery that another portion of the query may be modified or omitted. As another example, the query coordinator  3304  may determine that a portion of the translated subquery can be modified or omitted based on another portion of the query. In certain embodiments, translating the subquery to the same query language as the remainder of the query (e.g., the query language supported by the data intake and query system  16 ) may enable the query coordinator  3304  to recognize one or more optimizations that may be made to the query (or subquery). 
     In certain embodiments, the semantic optimization of the translated subquery may be an optimization based on the content of the translated subquery itself. The query coordinator  3304  may optimize the translated subquery by identifying superfluous portions of the subquery or alternative commands that the worker nodes  3306  or the external data system  12  may use to achieve the same result as the translated subquery. For example, a subquery may request data from two fields of a table at the external data system  12 . However, if only the data from one field is required or used by the query, the query coordinator  3304  may modify the subquery to remove the request for data from the unused field. As another example, if the subquery includes a plurality of commands that can be replaced with a single command to achieve the same result, the query coordinator  3304  may modify the subquery to replace the plurality of commands with the single command. 
     In some embodiments, the query coordinator  3304  may apply the semantic optimization to the entire query. Moreover, in some cases, the query coordinator  3304  may optimize the query based on the translated subquery. For example, the query coordinator  3304  may determine that a portion of the query is unnecessary because, for example, the translated subquery provides the requested data or the translated subquery can be optimized to make the result of the portion of the query unnecessary. As a more concrete example, suppose that the query coordinator  3304  receives a query to obtain a count of all flights across the country and that the original subquery returns a flight number and flight time for each flight at an airport within the country. Further, suppose that the query includes a command to count each flight from each airport and to add the counts. If the external data system  12  is capable of providing event count or a count of the number of flights, the query coordinator  3304  may determine that the original subquery can be replaced with a count command. By replacing the original subquery with a count command, the amount of processing and bandwidth required for the subquery may be reduced. Further, the query coordinator  3304  may determine that the count command included in the query is unnecessary because the subquery was modified to directly obtain the count from the external data system  12 . Thus, in this particular example, the query coordinator  3304  may optimize the query, based at least in part on the subquery, to remove the count command from the query resulting in additional processing improvements. In some embodiments, the query coordinator  3304  may remove the count command from the query as part of a runtime optimization. 
     In certain embodiments, the query coordinator  3304  may parse the syntax of the translated subquery, and/or the query, into a set of components or constituent parts based at least in part on the syntax and grammar of the query language of the query. The query coordinator  3304  may perform semantic optimization by modifying or replacing one or more of the components or constituent parts of the translated subquery or query. 
     In certain embodiments, the query coordinator  3304  may perform runtime optimization of the subquery or query. Runtime optimization of the translated subquery or of the query may include modifying the subquery or the query upon receipt or execution of at least a portion of the query based on the results or anticipated results of the query. The query coordinator  3304  may determine a result or predict an expected result of a portion of the query or the subquery. Based on the determined or predicted result, the query coordinator  3304  may determine whether another portion of the query or subquery can be eliminated or modified. For example, suppose that a query has a pair of subqueries that reference two different external data systems  12 , respectively. The query coordinator  3304  may determine that the second subquery can be reduced or modified based on the result or predicted result of executing the first subquery. For instance, the query coordinator  3304  may determine that a portion of the second subquery is redundant or is unnecessary based on the data obtained from the first subquery. As such, the query coordinator  3304  may modify the second subquery. As a more concrete example, suppose a user is attempting to obtain server traffic data relating to airports without flight curfews. In this particular example, the query coordinator  3304  may determine that a first external data system  12  can return the identity of airports with flight curfews in response to a first subquery. As such, in this particular example, the query coordinator  3304  may modify a second subquery that initially provided flight information for all airports to only obtain flight information for flights to or from airports without flight curfews. 
     In certain embodiments, the query coordinator  3304  performs infrastructure or infrastructure execution optimizations. Infrastructure optimizations can include any optimizations relating to the number of worker nodes  3306  and the distribution of the query or portions of the query among the number of worker nodes  3306 . In certain embodiments, the query coordinator  3304  analyzes the subquery or the query to determine characteristics or metadata of the query or subquery. The query characteristics or metadata may relate to an expected amount of data to be obtained in response to executing the subquery or various portions of the query. Further, the query characteristics or metadata may include information relating to relationships between different portions of the query. For example, the query metadata may indicate portions of the query that are reliant on the results of other portions of the query. 
     In addition, the query coordinator  3304  may determine one or more particular execution objectives for the query. In some cases, the execution objectives may be received from a user (e.g., an administrator). In other cases, the execution objectives may be predefined or determined based on default values. The execution objectives may include any objectives that may relate to the resources used to execute the query. For example, the execution objectives may relate to time, number of processors (or worker nodes  3306 ), bandwidth, memory, or any other computing resource that can be improved by a modification in a query or the distribution of a query among computing resources. 
     Based on the execution objectives, the query characteristics or metadata, and/or available computing resources (e.g., available worker nodes  3306  or available processor cores at the worker nodes  3306 ) the query coordinator  3304  may optimize the scheduling or assignment of the query. For instance, suppose the execution objective is to reduce bandwidth usage. If the query coordinator  3304  assigns a worker node  3306  or set of worker nodes  3306  a first portion of a query that serves as an input to a second portion of the query, the query coordinator  3304  may assign the second portion of the query to the same worker node  3306  or set of worker nodes  3306  to eliminate the need to communicate the data between different worker nodes resulting in reduced bandwidth requirements. Alternatively, if the execution objective is to reduce execution time, the query coordinator  3304  may assign additional worker nodes to different portions of the query despite the increased worker nodes causing, in some cases, increased bandwidth utilization due to increased cross-worker node  3306  communication. In certain embodiments, the query coordinator  3304  performs the infrastructure optimization as part of the block  6012  described below. 
     At block  6010 , the query coordinator  3304  translates the processed query into a query language supported by the external data system  12 . Translating the processed query may include translating the subquery that is to be executed by the external data system  12  while maintaining other portions of the query in the query language supported by the data intake and query system  16 . Usually, the query coordinator  3304  translates the translated subquery into the query language in which the subquery identified at the block  6004  was originally written. However, in some embodiments, the translated subquery may be translated to a different query language. For example, if it is determined during the optimization process that a different external data system  12  may more efficiently perform the query, the query coordinator  3304  may translate the subquery into the query language supported by the external data system  12  identified during the optimization process. In certain embodiments, the block  6010  may include one or more of the embodiments described with respect to the block  6006 . 
     At block  6012 , the query coordinator generates instructions for the worker nodes  3306 . Generating instructions for the worker nodes can include generating instructions for the worker nodes  3306  to execute the modified subquery and/or the optimized query. In some embodiments, generating the instructions for the worker nodes  3306  may include performing infrastructure optimization as described above. In certain embodiments, the block  6012  may include one or more of the embodiments previously described with respect to the block  5208  of  FIG.  52   . 
     At block  6014 , the query coordinator  3304  executes the query. Executing the query may include executing the subquery at the external data system  12 . In certain embodiments, the block  6014  may include one or more of the embodiments previously described with respect to the block  5210  of  FIG.  52   . 
     Advantageously, in certain embodiments, the translation of a subquery from its original language to one supported by the data intake and query system  16  for optimization and then back to the original language enables the use of computing resources of the system  100  to be reduced. In some example embodiments, the query coordinator  3304  may translate a subquery from SQL or JSON to SPL. The query coordinator  3304  may then process and/or optimize the subquery in SPL to obtained a processed subquery. Alternatively, or in addition, the query coordinator  3304  may process and/or optimize the query that includes the subquery. The processed subquery may then be translated back from SPL to SQL or JSON enabling the processed subquery to be executed at the external data system  12 . In certain embodiments, the subquery is not optimized or modified, but the translation of the subquery enables other portions of the query to be optimized. In embodiments where the subquery is not modified or optimized, the subquery may be translated back to its original language without change. Alternatively, the translated subquery is discarded and the original subquery is used or re-inserted into the modified query. 
     As described herein, in some embodiments, the external data systems  12  can process and execute the subquery similar to the manner in which the data intake and query system  16  processes and executes the query. Further, the external data systems  12  can process and execute the subquery similar to the manner in which it executes other queries received from a user or client device, except that results are communicated to one or more worker nodes  3306  of the data intake and query system  16  instead of (or in addition) to a user or client device. In some embodiments, as part of executing the subquery, the external data system  12  can assign the subquery a local search identifier and communicate the local search identifier to the worker node  3306 . The worker node  3306  can map the local search identifier with the primary search identifier received from the data intake and query system to determine how the partial results from the external data system  12  are to be processed according to the instructions received from the data intake and query system  16 . 
     It will be understood that fewer, more, or different blocks can be used as part of the routine  6000 . For example, in some embodiments, the routine  6000  can further include, monitoring nodes  3306  during query execution, allocating/deallocating resources based on the query, etc. Moreover, it will be understood that one or more blocks described herein with reference to routine  6000  can be combined with one or more blocks of other routines described herein, such as the routines described herein at least with reference to  FIGS.  5 ,  6 ,  23 - 26 ,  31 ,  34 ,  38 - 45 ,  47 ,  49 ,  52 - 57 , and  59   . 
     Furthermore, it will be understood that the various blocks described herein with reference to  FIG.  60    can be implemented in a variety of orders. In some cases, the system  16  can implement some blocks concurrently or change the order as desired. For example, the system  16  can concurrently optimize the query and generate instructions for worker nodes. In particular, the system  16  can perform infrastructure optimization while generating instructions for worker nodes, or in any order, as desired. In some cases, the results from the query of data within the data intake and query system  16  can become linked with the partial results received from the external data systems  12 . 
     34.0 Subquery Configuration File 
       FIG.  61    illustrates an example of an external query configuration file  6100  in accordance with disclosed embodiments. As previously described, in a number of embodiments, a configuration file, such as the configuration file  6100  may identify one or more queries or subqueries that can be performed at one or more external data systems  12 . In some embodiments, the data intake and query system  16  may be associated with one or more configuration files  6100 . 
     In the illustrated embodiment, the external query configuration file  6100  includes a deployment portion  6102  and a query portion  6104 . However, it will be understood that the external query configuration file  6100  can include fewer or more portions as desired. For example, the deployment portion  6102  and query portion  6104  can be combined and/or one or more entries of the deployment portion  6102  and/or query portion  6104  can be stored as one or more entries of a database, such as, a relational database like dynamoDB or Aurora DB, etc. 
     The query portion  6104  may include one or more queries or subqueries that can be referenced within a query. Each of the queries may include a reference or keyword that identifies the query within the external query configuration file  6100 . In some cases, a user or client system can provide a query to the data intake and query system  16  that incorporates one or more of the references identifying one or more of the subqueries. For example, the query may include: “federated:my_dep_1_search_1” as part of the query indicating that the query should incorporate the first subquery in the query portion  6104 . 
     In addition, the query portion  6104  can include additional information associated with the subqueries, such as, but not limited to, an identification of the external data system  12  to execute the query, a query type, estimated number of results received from the external data system  12  based on the query, the number of fields of the results received, or other information associated with the query, etc. 
     In the illustrated embodiment, the query portion  6104  includes four entries associated with four different subqueries. The first two subqueries are identified as being associated with a first external data system  12 . The other two subqueries are identified as being associated with second and third external data systems  12 , respectively. Furthermore, the subqueries associated with the third and fourth entries are in a query language different from each other and different from the subqueries associated with the first external data system  12 . 
     In the illustrated embodiment, in addition to the query, each entry in the query portion  6104  includes an identifier for the name of the external data system  12  that is to execute the query, the type of query, an estimated number of results to be received from the external data system and the number of fields in the results. However, fewer or more information can be included with each subquery as desired. 
     The deployment portion  6102  can include information related to one or more external data systems  12  associated with the primary data intake and query system  16 A (non-limiting examples: external data systems  12  that can be accessed or searched from the primary data intake and query system  16 A). The deployment portion  6102  can include location information, access/authorization information, and/or other configuration information about each external system  12  to enable the primary data intake and query system  16 A to interact with and obtain information from the external data system  12 . 
     In the illustrated embodiment, the deployment portion  6102  identifies three distinct deployments associated with the primary data intake and query system  16 A. One deployment is a Splunk data intake and query system and two deployments are third-party data storage and processing systems (Oracle, Elk). In addition, the deployment portion  6102  includes an IP address, port number, account information, password, type, and version for each external data system  12 . However, it will be understood that fewer or more information can be included as desired. As mentioned, in some embodiments, each reference to an external data system  12  in the deployment portion  6102  can be stored as one or more entries of a database. 
     As a non-limiting example, consider a query received by the data intake and query system  108  that includes, among others, the following query parameter: “federated:my_dep_1_search_2.” Based on the identified query parameter, the query coordinator  3304  can reference the query portion  6104  of the external query configuration file  6100  to determine the query to be executed on an external data system  12 . Based on the relevant entry, the query coordinator  3304  can determine that the query “SELECT COUNT (DISTINCT FlightNum) FROM airlinesdata,” is a “streaming” query, is to be executed on the “remote_deployment_2” deployment, should return fewer than 1,000,000 results and the received results or events should include two different fields. 
     With continued reference to the example, the query coordinator  3304  can use the identifier “remote_deployment_2” in the query portion  6104  to additional information about the corresponding external data system  12 . Specifically, the query coordinator  3304  can determine that the “remote_deployment_2” external data system  12  uses port 8089 and can be accessed using the “ezra_eastwood” account and password “changed” at IP address “10.224.126.105.” Moreover, the query coordinator  3304  can determine that the “remote_deployment_2” external data system  12  is a version 7 Oracle database. 
     With continued reference to the example, the query coordinator  3304  can, using the determined information, provide the identified query to the Oracle database for processing. As described herein, in certain embodiments, the query coordinator  3304  can translate the identified query from SQL to a different query language, process and/or optimize the translated query, translate the processed query back to SQL, and communicate the translated SQL to the Oracle database for execution. As mentioned, in some cases, the translated SQL query can be a modified or optimized version of the SQL query in the external query configuration file  6100  based on the other portions of the query with which the SQL query is to be executed. 
     Although illustrated as two different portions of a single file, the deployment portion  6102  and the query portion  6104  of the external query configuration file  6100  may be separate files or stored in alternative formats. Further, the external query configuration file  6100  is not limited to the illustrated format, but may include any type of data structure that can be used to store subqueries and/or access information for accessing the external data systems  12 . For example, each entry of the deployment portion  6102  and/or query portion  6104  can be implemented as a database entry of a relational database. 
     35.0. Bucket Data Distribution for Processing/Export 
     As described herein, at least with reference to  FIGS.  18 - 20 ,  26 ,  27 ,  33 ,  37 ,  46 ,  48 , and  51   , one or more indexers  206  can export records from one or more buckets (also referred to herein as bucket data) to one or more worker nodes  3306 . In some embodiments, each record can correspond to an event or a group of events stored in a data store  208 . In some cases, only a portion of different events (e.g., certain field values, keywords, etc.) or data associated with an event (e.g., data about an event stored in an inverted index) may be obtained/processed as part of a set of data identified by a query. Accordingly, during query execution, an event itself may not be obtained/processed. Instead the data extracted from one or more buckets may correspond to a portion of one or more events, a summary of the one or more events, or data associated with the one or more events. Accordingly, in some cases, reference may be made herein to processing records, which can correspond to an event or a group of events stored in a data store  208  and/or may correspond to an event that has been transformed or processed by a component of the system  16 . 
     When a particular indexer  206  processes data or exports bucket data from one or more buckets to one or more worker nodes  3306 , the indexer  206  can assign the bucket data corresponding to different buckets to one or more execution resources, such as one or more pipelines (which may also be referred to as data pipelines or data processing pipelines), or compute resources (e.g., processors, isolated execution environments, etc.). 
     In some cases, a pipeline can include one or more processing tasks to be executed on the bucket data. Further, each pipeline can be concurrently executed by one or more compute resources of an indexer, such as one or more processors, isolated execution environments, etc., in order to process/export the bucket data in parallel. Accordingly, although reference may be made herein to assigning bucket data to a pipeline, it will be understood that the bucket data is also assigned to one or more compute resources to process the data assigned to the pipeline. In addition, in some cases, multiple compute resources may be assigned to execute the pipeline (at the same or different times). Further, one compute resource can concurrently execute multiple pipelines. 
     In some cases, the assignment of bucket data to execution resources (e.g., bucket data-pipeline assignment) can be skewed such that one execution resource processes and/or exports a significantly larger amount of records compared to other execution resources. As a non-limiting example, consider a scenario in which 1) bucket data from three buckets is to be sequentially assigned to three execution resources for processing or export, 2) all bucket data from a single bucket is assigned to the same execution resource, and 3) first bucket data from the first bucket includes 1,000,000 records, second bucket data from the second bucket includes 400,000 records, and third bucket data from the third bucket includes 350,000 records. In such a scenario the first execution resource, such as a first pipeline, would process 1,350,000 records and the second execution resource would process 400,000 records. In such a scenario, the system  3301  may lose the potential benefits provided by concurrently using multiple execution resources to process/export the bucket data. 
     Moreover, in some cases, the data intake and query system  3301  may be unable to continue executing the query until the last execution resource finishes exporting all of its assigned bucket data. In some such scenarios, other compute resources assigned to execute the pipelines of the query may remain idle (not assigned to other tasks) until the last pipeline completes its data export. In certain embodiments, this can decrease compute resource utilization of the system  3301  (e.g., waste compute resources), reduce the throughput of the data intake and query system  3301 , increase the amount of time required by the indexers  206  to process and/or export data to the worker nodes  3306 , increase the amount of time required by the system  3301  to execute the query, and impair the system  3301  from being able to process/execute additional queries. 
     To address possible issues caused by the potential unequal distribution of bucket data between execution resources of an indexer  206  for processing and/or export to one or more worker nodes  3306 , the system  3301  can assign the bucket data from the buckets associated with a query to execution resources (e.g., pipelines, processors, etc.) based on a bucket distribution policy. In certain embodiments, the bucket distribution policy can be based on the content of the bucket data, such as the amount of bucket data to be exported from each bucket. In some cases, the amount of bucket data can correspond to the number of events or records to be exported. In some embodiments, the amount of data can correspond to the amount of memory used to store the bucket data (e.g., number of bytes, etc.). 
     In certain embodiments, the bucket distribution policy can indicate that the indexer  206  is to assign the bucket data to different pipelines to reduce or minimize the difference in the amount of bucket data processed by each pipeline. As a non-limiting example, if the indexer  206  is assigning bucket data that includes 30,000,000 records between five pipelines, the indexer  206  can assign the bucket data so that each pipeline processes approximately 6,000,000 records. In some cases, it may not be possible for the indexer  206  to obtain a complete equitable distribution between pipelines. For example, with continued reference to the above example, the 30,000,000 records may be unequally distributed across 13 buckets, and the indexer  206  may distribute the records between the pipelines by bucket (e.g., assign all bucket data from a particular bucket to the same pipeline). Accordingly, in some such cases, the indexer  206  can, according to the bucket distribution policy, assign the bucket data corresponding to the 13 buckets to the five pipelines so as to reduce or minimize the difference between the number of records assigned to each pipeline. For example, the indexer  206  may assign the buckets (e.g., the bucket data corresponding to the buckets) to the five pipelines as shown in the table below. Accordingly, although each pipeline may not process the exact same number of records, the indexer  206  can assign the bucket data to obtain a more equitable distribution across the pipelines. 
     
       
         
           
               
               
               
             
               
                   
               
               
                   
                   
                 No. of Records in 
               
               
                   
                 No. of Buckets 
                 Bucket Data of 
               
               
                 Pipeline 
                 Assigned 
                 Assigned Buckets 
               
               
                   
               
             
            
               
                 1 
                 3 
                 5.75M  
               
               
                 2 
                 2 
                 6.5M 
               
               
                 3 
                 1 
                 5.5M 
               
               
                 4 
                 4 
                 5.85M  
               
               
                 5 
                 2 
                 6.4M 
               
               
                   
               
            
           
         
       
     
     Assigning the bucket data to pipelines based on the amount of bucket data of each bucket to be exported can improve the functioning of the system  16  itself. For example, by assigning the bucket data to pipelines based on the amount of bucket data of each bucket to be exported, the system  3301  can increase parallelization of the data processing, as well as increase compute resource utilization, increase the throughput of the system  3301 , decrease the amount of time required by the indexers  206  to process and/or export data to the worker nodes  3306 , decrease query execution time, and enable the system  3301  to process/execute additional queries in less time. 
       FIGS.  62 A and  62 B  are block diagrams illustrating an embodiment of the identification of bucket data associated with a query, the allocation of execution resources of an indexer  206  to process/export the bucket data, and an assignment of the bucket data to the execution resources based on a bucket distribution policy. Although described with respect to assigning bucket data between execution resources of one indexer  206 , it will be understood that multiple indexers  206  of the system  3301  can concurrently assign respective buckets to respective execution resources in a similar manner. 
     In the illustrated embodiment of  FIG.  62 A , the indexer  206  has identified bucket data  6204 A,  6204 B,  6204 C,  6204 D,  6204 E,  6204 F (individually or collectively referred to as bucket data  6204 ) from six buckets for processing and/or exporting to one or more worker nodes  3306  and has allocated three execution resources  6202 A,  6202 B,  6202 C (individually or collectively referred to as execution resources  6202 ) based on an execution resource allocation policy to process and/or export the bucket data  6204 . As described herein, when the execution resources are implemented as a pipeline, each processor of an indexer  206  can concurrently process one or more pipelines. Accordingly, it will be understood that the three execution resources  6202 A,  6202 B,  6202 C may be executed by three processors of an indexer  206  and/or one processor of an indexer  206 . 
     As described herein, in some embodiments, the system  3301  can identify the bucket data  6204  based on a received query. In some embodiments, the query can identify a set of data and a manner of processing the set of data. For example, as described herein, the query can identify the set of data based on one or more query parameters, such as, but not limited to, a particular index (or data store partition), a time range, one or more field-value pairs, and/or one or more keyword or token values, etc. As the system  3301  processes the query, it can determine one or more subqueries. For example, the system  3301  can determine that a first portion of the query is to be executed by the one or more indexers  206  and a second portion of the query is to be executed by one or more worker nodes  3306 . Based on the different portions of the query, the system  3301  can generate a first subquery for the indexers  206  and a second subquery for the worker nodes  3306 . The first subquery can identify at least a subset of the set of data that can be obtained, and in some cases processed, by the indexers  206 . Each indexer  206  can use the first subquery to identify the relevant buckets that may include at least a portion of the set of data. In certain embodiments, an indexer  206  uses one or more query parameters to identify the relevant buckets. For example, the indexer  206  can use an identified index (or partition) and/or a time range to identify relevant buckets. 
     The indexer  206  can use one or more query parameters to identify bucket data that forms at least a portion of the set of data (or is associated with the query). For example, the indexer  206  can use an identified index (or data store partition), time range, one or more field-value pairs, and/or one or more keyword or token values, etc., to identify relevant bucket data. As described herein, in some embodiments, the bucket data can include one or more events or records. 
     In some cases, identifying the bucket data includes identifying events and a number of events from each bucket that are associated with the query. In some embodiments, the indexer  206  can identify the number of events of each bucket based on the one or more query parameters. In certain cases, the indexer  206  can use an inverted index, similar to the inverted indexes described herein at least with reference to  FIG.  5 B , associated with a particular bucket to identify the events and the number of events that satisfy the query parameters. For example, the indexer  206  can compare the time range of the query with the timestamp of the events, or the fields, field-value pairs, or keywords of the query with the corresponding information of the events to identify the events (and the number of events) that satisfy the query parameters. In certain embodiments, the indexer  206  can identify the events and the number of events based on a comparison of one or more query parameters with the event data itself (e.g., without an inverted index). 
     In the illustrated embodiment of  FIG.  62 A , the indexer  206  has identified bucket data  6204 A,  6204 B,  6204 C,  6204 D,  6204 E,  6204 F from six buckets that form at least a subset of data of the set of data identified by the query. In addition, the indexer  206  has determined the number of events or records in each bucket that is associated with the query. Specifically, in the illustrated embodiment, the indexer  206  has determined that the bucket data includes following number of records, as follows: 
     
       
         
           
               
               
               
             
               
                   
                   
               
               
                   
                 Bucket Data 
                 No. of Records 
               
               
                   
                   
               
             
            
               
                   
                 6204A 
                 65M 
               
               
                   
                 6204B 
                 110M  
               
               
                   
                 6204C 
                 20M 
               
               
                   
                 6204D 
                 30M 
               
               
                   
                 6204E 
                 70M 
               
               
                   
                 6204F 
                 50M 
               
               
                   
                   
               
            
           
         
       
     
     As mentioned, in addition to identifying the bucket data  6204 , the indexer  206  has allocated execution resources  6202 A,  6202 B,  6202 C to process the bucket data  6204  based on an execution resource allocation policy. In some embodiments, the execution resource allocation policy can use a variety of factors to determine the number of execution resources to be allocated by the indexer  206 . 
     In some cases, the execution resource allocation policy can be based on the number of buckets to export (e.g., allocate three execution resources for bucket data from three buckets), the amount of bucket data (e.g., allocate more execution resources for larger quantities of records/events, memory size of bucket data, etc.), the destination of the bucket data (e.g., allocate more execution resources for export to worker nodes  3306 ), the number of worker nodes  3306  assigned to ingest the bucket data (e.g., allocate one or more execution resources per assigned worker node  3306 ), the number of processors/pipelines allocated to ingest the bucket data (e.g., allocate one or more pipelines for each processor of a worker node  3306  that is to ingest the bucket data), amount of execution resources available (e.g., allocate as many pipelines that can be supported by the available processors based on the amount of pipelines allocated to other processing tasks), based on a threshold number (e.g., allocate five or twelve execution resources for each export), an identification of a user or service level (allocate more execution resources for one user/service level than another user/service level), and/or a combination thereof. In some embodiments, the execution resource allocation policy can indicate that execution resources are to be allocated to minimize the number of execution resources allocated, minimize the execution time of the bucket data, provide a particular priority level (e.g., different levels of service are assigned a different number of execution resources), maximize parallelization up to a threshold number of execution resources or any one or any combination of the aforementioned. In some cases, the execution resource allocation policy can take into account the bucket distribution policy, etc. For example, the indexer  206  can allocate a certain number of execution resources, then assign the buckets to the execution resources based on a bucket distribution policy in such a way that one or more execution resources are deallocated or added to process the data. 
     As mentioned, the execution resource allocation policy can use multiple factors to allocate execution resources. In certain embodiments, the execution resource allocation policy can indicate that the number of execution resources allocated to process the buckets is based on the lesser of: the number of buckets associated with the query, the number of available execution resources (e.g., available pipelines or processors), and a threshold number of execution resources. For example, if there is bucket data from five buckets to be processed, seven available execution resources, and the threshold number of execution resources is six, the system can allocate five execution resources to process the bucket data. With reference to the same example but with bucket data from nine buckets to be processed, the indexer  206  can allocate six buckets (the threshold number). In the event the threshold number were eight, the indexer  206  could allocate seven execution resources (the number of available resources). Accordingly, the execution resource allocation policy can take into account a variety of factors for determining the number of execution resources to allocate to process the bucket data. 
     As another example, the execution resource allocation policy can be to reduce or minimize the total execution time or maximize parallelization. For example, if an indexer  206  identifies bucket data from three buckets for processing, the indexer  206  can allocate three execution resources to process the three buckets. In certain embodiments, the indexer  206  can allocate the three execution resources regardless of the amount of bucket data of each bucket to be processed. For example, if first bucket data includes 1,000,000 records, second bucket data includes 500,000 records, and third bucket data includes 550,000 records, the indexer  206  can allocate three execution resources even though two execution resources will be underutilized while one execution resource finishes processing the first bucket data. 
     Returning to  FIG.  62 A , the indexer  206  has allocated three execution resources  6202 A,  6202 B,  6202 C based on an execution resource allocation policy to process and/or export bucket data  6204  from six buckets. 
     In the illustrated embodiment of  FIG.  62 B , the indexer  206  has assigned the bucket data  6204  to the execution resources  6202  based on a bucket distribution policy. The bucket distribution policy can use a variety of factors to determine how the bucket data  6204  is to be assigned to execution resources  6202 . 
     In some embodiments, the bucket distribution policy can be based on the time of receipt of the bucket data. For example, the indexer  206  can assign the bucket data to the execution resources  6202  as it is received (e.g., first bucket data  6202 A assigned to first execution resource  6204 A, second bucket data  6204 B assigned to second execution resource  6202 B, and so on in a round robin fashion). In certain embodiments, the bucket distribution policy can indicate that the bucket data  6204  is to be assigned in a randomized fashion. In some embodiments, the bucket distribution policy can indicate that the bucket data  6204  is to be assigned to execution resources  6202  based on the content of the bucket data. In some such cases, the bucket distribution policy can indicate that bucket data  6204  is to be assigned to execution resources  6202  to reduce or minimize the differences between the amount of bucket data processed by the different execution resources  6202 . For example, the bucket distribution policy can indicate that bucket data  6204  is to be assigned to execution resources  6202  such that each execution resource  6202  processes approximately the same number of events, records, or bytes of data. 
     In certain embodiments, the bucket distribution policy can indicate that bucket data  6204  is to be assigned to execution resources  6202  to reduce or minimize the difference in execution time between execution resources  6202 . For example, a unit of bucket data from one bucket (e.g., a record) may generally take longer to process than a unit of bucket data from another bucket. Accordingly, the indexer  206  can use a (estimated) execution time for each unit of bucket data or the bucket data as a whole to allocate execution resources. For example, if 5 million events from one bucket and 2 million are each estimated to take 1 minute to process, then the indexer  206  can use the estimated execution time to assign the buckets to execution resources instead of (or in conjunction with) the number of events from the different buckets. In certain embodiments, the system  16  can determine the estimated execution time by applying one or more processing tasks (e.g., transformation, exports, etc.) to a subset of events of a bucket and using the result to estimate execution time of other events of the bucket or other events within the time range of the subset of events or with a similar sourcetype or other field-value of the subset of events. The estimation can be performed during the query or beforehand by the system. For example, the system  16  can, on occasion, sample bucket data from different buckets to determine the estimate execution time. The estimate can be stored in a configuration file for later use during the query. In certain cases, the estimated execution time can be determined in a manner similar to the event generation estimate described herein at least with reference to  FIG.  67    (e.g., processing a sample set of data to determine the estimated execution time and storing various estimated execution times in a lookup table or configuration file). 
     It will be understood that any one or any combination of the aforementioned methodologies can be used by a bucket distribution policy of an indexer  206  to determine how to assign bucket data to execution resources. Furthermore, different indexers  206  can use different bucket distribution policies as desired. 
     In the illustrated embodiment of  FIG.  62 B , the indexer  206  has assigned the buckets  6204  to the execution resources  6202  based on a bucket distribution policy that prioritizes an equal distribution of records of the bucket data  6204  to execution resources  6202 . Based on the bucket distribution policy, the indexer  206  assigns the bucket data  6204  to the execution resources  6202  as follows: 
     
       
         
           
               
               
               
               
             
               
                   
                   
               
               
                   
                 Execution Resource 
                 Bucket Data 
                 Total Records 
               
               
                   
                   
               
             
            
               
                   
                 6202A 
                 6204A 6204F 
                 115M 
               
               
                   
                 6202B 
                 6204B 
                 110M 
               
               
                   
                 6202C 
                 6204C 6204D 
                 120M 
               
               
                   
                   
                 6204E 
               
               
                   
                   
               
            
           
         
       
     
     While the number of buckets assigned to each execution resource varies as much as three times, the largest difference between the amount of records processed by two execution resources (execution resource  6202 A and  6202 C) is ten million with the mean number of records assigned to each execution resource  6202  being 115 million. By comparison, if the indexer  206  sequentially assigned the bucket data  6204  in a round robin fashion or based on a priority of equal distribution of buckets to execution resources  6202 , the bucket distribution may have been as follows: 
     
       
         
           
               
               
               
               
             
               
                   
                   
               
               
                   
                 Execution Resource 
                 Bucket Data 
                 Total Records 
               
               
                   
                   
               
             
            
               
                   
                 6202A 
                 6204A 6204D 
                 95M 
               
               
                   
                 6202B 
                 6204B 
                 180M  
               
               
                   
                   
                 6204E 
               
               
                   
                 6202C 
                 6204C 
                 70M 
               
               
                   
                   
                 6204F 
               
               
                   
                   
               
            
           
         
       
     
     While the mean number of records assigned to each execution resource  6202  remains 115 million, the largest number of records assigned to an execution resource (execution resource  6202 B) is 180 million and the lowest 70 million (execution resource  6202 C). As such, execution resource  6202 B would process and/or export more than twice as many records as execution resource  6202 C, which could take more than twice as long. Thus, by using a bucket distribution policy that prioritizes the equal distribution of records of the bucket data  6204  to execution resources  6202 , the indexer  206  can reduce the amount of execution time to process and/or export the bucket data to the worker nodes  3306 . 
     In some embodiments, to determine the bucket assignment, the indexer  206  can determine an average or mean number of records for each execution resource based on the total number of records to be processed and the number of execution resources  6202  allocated. The indexer  206  can then assign bucket data  6204  to the execution resources  6202  so that each execution resources  6202  is assigned a number of records that most closely approximates the average or mean. In certain embodiments, the indexer  206  can compare various combinations of bucket-execution resource assignments until an assignment is provided that results in each execution resource  6202  being assigned approximately the same number of records or results in the smallest difference between the execution resource  6202  with the most records and the execution resource  6202  with the fewest records. It will be understood that a variety of equal distribution models can be used to distribute the bucket data in a manner that increases the likelihood that each execution resource  6202  processes approximately the same amount of bucket data. 
     Furthermore, it will be understood that the bucket distribution policy and the execution resource allocation policy can be used iteratively to determine the number of execution resources  6202  allocated and the assignment of bucket data  6204  to execution resources  6202 . In some cases, the indexer  206  can iteratively use the bucket distribution policy and the execution resource allocation policy to allocate execution resources  6204  and assign bucket data to execution resources  6202 . 
     For example, consider a scenario where the execution resource allocation policy is to prioritize the parallelization of and efficient use execution resources  6202 . In such a scenario, the indexer  206  may initially allocate three execution resources  6202  to process bucket data from the following three buckets: 
     
       
         
           
               
               
               
             
               
                   
                   
               
               
                   
                   
                 No. of Records 
               
               
                   
                 Bucket Data 
                 in Bucket Data 
               
               
                   
                   
               
             
            
               
                   
                 A 
                   3M 
               
               
                   
                 B 
                 1.45M 
               
               
                   
                 C 
                  1.5M 
               
               
                   
                   
               
            
           
         
       
     
     While such an allocation would result in a highly parallelized distribution, the distribution could result in compute resources assigned to process bucket data B and C being underutilized while a compute resource assigned to process the execution resource  6202  (assigned to bucket data A) processes more than twice as many records. 
     In such a scenario, if the bucket distribution policy is to reduce or minimize differences between the amount of bucket data  6204  (e.g., number of records) assigned to execution resources  6202 , the indexer  206  may determine that an allocation of two execution resources  6202  significantly reduces the difference between the largest and smallest number of records assigned to different execution resources  6202 . Accordingly, the indexer  206  may deallocate one execution resource  6202  and assign the buckets as follows: 
     
       
         
           
               
               
               
               
             
               
                   
                   
               
               
                   
                 Execution 
                 Bucket Data 
                 No. of 
               
               
                   
                 Resource 
                 Assigned 
                 Records Assigned 
               
               
                   
                   
               
             
            
               
                   
                 1 
                 A 
                   3M 
               
               
                   
                 2 
                 B, C 
                 2.95M 
               
               
                   
                   
               
            
           
         
       
     
     Accordingly, the indexer  206  can iteratively use the execution resource allocation policy and bucket distribution policy to determine the number of execution resources to allocate and how to assign bucket data  6204  to execution resources  6204 . In some such cases, the indexer  206  can, after determining an initial execution resource allocation, determine whether an allocation of one or more additional or fewer execution resources would result in a more equitable distribution of bucket data to execution resources. If it does, the indexer  206  can deallocate one or more execution resources or allocate one or more additional execution resources. In some such embodiments, in determining whether to deallocate execution resources, the indexer  206  may determine whether a deallocation of resources increases execution time by a threshold amount (e.g., increases estimated execution time by 10%). If it does then the indexer  206  may not deallocate one or more execution resources even though doing so may result in a more equitable distribution of bucket data. Similarly, in determining whether to allocate additional execution resources, the indexer  206  can determine whether doing would decrease execution time by a threshold amount. If not, then the indexer  206  may not allocate additional execution resources. 
     As yet another example, in some embodiments, the indexer  206  can determine one or more bucket distributions, and use the bucket distributions to determine the execution resource allocation. For example, consider a scenario in which 13 buckets are to be processed/exported. The indexer  206  can determine that the buckets are most evenly distributed if four execution resources are allocated, that the buckets would be processed the fastest with thirteen execution resources, and/or that the addition of execution resources above seven does not significantly decrease the execution time (non-limiting example, does not decrease execution time by more than 10%). The execution resource allocation policy can use this information to determine the number of execution resources to allocate. As mentioned, other combinations of factors can be used to determine the number of execution resources to allocate and how to assign buckets to the execution resources. 
       FIG.  63    is a flow diagram illustrative of an embodiment of a routine  6300  implemented by an indexer  206  to assign bucket data  6202  to execution resources  6204 . Although certain blocks are described as being implemented by an indexer  206 , it will be understood that the elements outlined for routine  6300  can be implemented by one or more computing devices/components (alone or in combination) that are associated with a data intake and query system  16 , such as the search head  210 , search process master  3302 , the query coordinator  3304 , etc. Thus, the following illustrative embodiment should not be construed as limiting. Moreover, it will be understood that routine  6300  is not limited to a data intake and query system  16 , but can be used to allocate groups of data to execution resources in a variety of systems and environments. 
     At block  6302 , the indexer  206  receives a query. In some embodiments, the received query can correspond to a portion of a query received by the search head  210  or query coordinator  3304  or a subquery generated by the search head  210  and/or query coordinator  3304  as described herein at least with reference to  FIGS.  6 ,  20 ,  33 ,  37 ,  38 , and  41   . As described herein, the query received by the indexer  206  can identify a set of data and a manner of processing the set of data. In some embodiments, the set of data identified in the query received by the indexer  206  can correspond to a subset of data identified by the query received by the search head  210  and/or query coordinator  3304 . Further, as described herein, the query can indicate that the indexer  206  is to export the set of data to one or more nodes  3306  for further processing. 
     At block  6304 , the indexer  206  identifies buckets associated with the query. In some embodiments, the indexer  206  identifies the buckets associated with the query based on one or more query parameters, such as, but not limited to an identified index or partition, a time range, etc. For example, in some cases, the indexer  206  can determine that a bucket is associated with the query if the bucket is associated with an index identified in the query and/or at least partially overlaps with a time range specified in the query. In certain embodiments, the indexer  206  can use fewer or more criteria to identify buckets associated with the query. 
     At block  6306 , the indexer  206  identifies bucket data associated with the query. In some embodiments, the indexer  206  identifies bucket data associated with the query for one or more (or all) of the buckets identified at block  6304 . For example, if the indexer  206  identifies ten buckets at block  6306 , the indexer  206  can identify bucket data from the ten buckets that are associated with the query. 
     In certain embodiments, to identify the bucket data associated with the query for a particular bucket, the indexer  206  uses one or more query parameters from the query. For example, the indexer  206  can use one or more of the index(es), a time range, fields, field-value pairs, keyword or tokens, etc., identified in the query to identify bucket data associated with the query. 
     In some cases, identifying the bucket data includes identifying a number of events associated with the query. In certain embodiments, to identify the number of events (and/or the events) associated with the query, the indexer  206  can use the one or more query parameters of the query and an inverted index, similar to inverted indexes  507 ,  509 , described herein. In some cases, the indexer  206  can use the inverted indexes to determine the number of events that satisfy the query parameters. In some such embodiments, the indexer  206  can determine that the events that satisfy the query parameters form part of the set of data of the query. 
     At block  6308 , the indexer  206  determines execution resources to allocate for the query. As described herein, in some cases, the indexer  206  can determine the execution resource to allocate based on an execution resource allocation policy. As described herein, the execution resource allocation policy can indicate whether to prioritize parallelization, efficient use of execution resources, minimize number of execution resources, etc. In some embodiments, the execution resource allocation policy can indicate that the indexer  206  is to allocate execution resources based on the lesser of the number of identified buckets, the number of available execution resources or a threshold number of execution resources. In certain embodiments, the execution resource allocation policy can indicate that the indexer  206  is to allocate execution resources based on the total number of identified events. For example, the execution resource allocation policy can indicate that the average or mean number of records assigned to an execution resource should not exceed a threshold number of records and allocate sufficient execution resources to satisfy the threshold, or that a maximum threshold number of records is to be assigned to any particular execution resource and allocate the execution resources to satisfy the maximum threshold, etc. In certain embodiments, the execution resource allocation policy can indicate that the indexer  206  is to allocate execution resources based on a particular priority level to be provided for the query. Accordingly, the indexer  206  can allocate resources based on the quantity of records, content of the buckets, or bucket data, etc. 
     At block  6310 , the indexer  206  assigns buckets (e.g., bucket data of buckets) to execution resources. In some embodiments, the indexer  206  can assign the buckets to the execution resources based on the content of the bucket/bucket data and/or a bucket distribution policy. As described herein, the bucket distribution policy can indicate to the indexer  206  whether to prioritize an equitable distribution of buckets to execution resources, an equitable distribution of records or amount of bucket data or memory space of bucket data to the execution resources, etc. For example, the indexer  206  can assign buckets to execution resources so as to reduce or minimize a difference between the most and least records assigned to particular execution resources and/or to approximate a particular number of records assigned to each execution resource. Moreover, as described herein, the indexer  206  can iteratively use an execution resource allocation policy and a bucket distribution policy to allocate execution resources for the query and assign buckets to the execution resources. 
     At block  6312 , the indexer  206  processes the bucket data. In some embodiments, processing the bucket data can include performing one or more transformations on the bucket data and/or exporting the bucket data to one or more worker nodes  3306 , based on the query. 
     In embodiments where the bucket data is exported to one or more worker nodes  3306 , the indexer  206  can communicate the bucket data to the worker nodes  3306  as one or more chunks of data. In some embodiments, each chunk of data can include a particular number of records. In certain embodiments, each chunk of data (except, in some cases, the last chunk of data) can include the same number of records. In some embodiments, each chunk of data can occupy a particular amount of memory space. In certain embodiments, each chunk of data (except, in some cases, the last chunk of data) can occupy (approximately) the same amount of memory space. Further, the worker nodes  3306  can process the chunks of data based on a query they received from a search head  210  and/or query coordinator  3304 . In addition, it will be understood that the worker nodes  3306  can concurrently receive chunks of data from multiple indexers  206 . 
     It will be understood that fewer, more, or different blocks can be used as part of the routine  6300 . For example, the routine  6300  may omit blocks  6302  and  6312 . As another example, in some embodiments, a plurality of indexers  206  can concurrently perform the routine  6300 . Further, in certain embodiments, the system  16  can estimate a query execution time based on the bucket distribution. For example, the system  16  can identify the slowest execution resource across all of the indexers  206  used as part of the query. Based on the identified slowest execution resource, the system can estimate the execution time of the portion of the query received by the system that is to be executed by the indexers  206 . 
     In some embodiments, to identify the slowest execution resource, the system  16  can identify the execution resource that has the most bucket data (based on number of records, memory size) to process and/or is estimated to take the most time based to process the assigned bucket data (e.g., based on a speed of the execution resource, the number of records assigned to the execution resource, the sourcetype of the records assigned to the execution resource, and/or a measured speed of the compute resource assigned to execution resource for the particular records assigned thereto, etc.). In some cases, the system  16  can determine an estimated execution time by applying a processing task to a set of data and determining the time to process the set of data according to the processing task. The estimate can be made before or during the query processing/execution and then be applied to data that is similar to the set of data (e.g., same index, source type and/or time range, etc.). In embodiments where the estimates are made before query processing/execution, they can be stored in a configuration file for later use. 
     Moreover, it will be understood that one or more blocks described herein with reference to routine  6300  can be combined with one or more blocks of other routines described herein, such as the routines described herein at least with reference to  FIGS.  5 ,  6 ,  23 - 26 ,  31 ,  34 ,  38 - 45 ,  47 ,  49 ,  52 - 57 ,  65 - 69 ,  71 , and  73   . Furthermore, it will be understood that the various blocks described herein with reference to  FIG.  63    can be implemented in a variety of orders. For example, blocks  6304 - 6308  can be implemented concurrently, etc. 
     36.0. Partitioning and Reducing Records During Ingest at a Worker Node 
     As described herein, at least with reference to  FIGS.  18 - 20 ,  26 ,  27 ,  33 ,  37 ,  46 ,  48 , and  51   , one or more worker nodes  3306  can receive chunks of data from one or more indexers  206 . When a particular worker node  3306  receives and/or processes a chunk of data, the worker node  3306  can assign the chunk of data to different tasks or partitions for execution by one or more compute resources, such as one or more processors or isolated execution environments, etc. Although reference may be made herein to storing data in partitions of the worker nodes  3306 , it will be understood that such storing can refer to storing data in volatile memory and/or non-volatile memory with spill over to disk if needed/desired. 
     In some cases, prior to performing one or more transformations on the received data, the worker node  3306  groups and/or reduces the data to facilitate more efficient processing of the data. For example, in the event that the data received by the worker node  3306  is unstructured, the worker node  3306  can reorganize the unstructured data (e.g., assign similar data to the same partition, reduce similar data, etc.) to facilitate a more efficient processing of the data. Further, in some such embodiments, the worker node  3306  receives all of the data to be processed and assigns it to different partitions before grouping and reducing the data. For example, for unstructured data, the worker node  3306  may not attempt to group the data until it has all been received. In either case, both redistributing and reducing the data can increase the execution time of the query; more so in cases where the worker node  3306  waits to receive all of the data before attempting to group and/or reduce it. 
     To address possible issues caused by grouping and/or reducing data at ingest and/or waiting to group/reduce until all of the data has been received and assigned to partitions, a worker node  3306  can logically assign incoming data based on its content to different groups of data. Data assigned to distinct groups can be assigned to the same partition or the same group of partitions for further processing. By logically assigning incoming data to different groups based on its content, the worker node  3306  can increase the likelihood that similar records are assigned to the same partition. In addition, by logically assigning incoming data to different groups based on its content on ingest, the worker node  3306  can reduce the number of processing tasks used to redistribute and reduce the data in the different partitions, which can reduce the query execution time. 
     In addition, to address possible issues caused by redistributing and/or reducing data at ingest and/or waiting to redistribute/reduce until all of the data has been received and assigned to partitions, the worker node  3306  can combine similar data as the data is assigned to particular partitions at ingest. While the logical assignment of records based on content can increase the likelihood that records with similar data are assigned to the same group (and thus the same partition), it will be understood that combining similar records during ingest can independently improve the functioning of the system  16 . For example, by combining similar records in a partition during ingest, the worker node  3306  can reduce the amount of memory used to store all of the incoming records and possibly reduce the number of partitions used to store the incoming data. In addition, by combining similar records in a partition during ingest, the worker node  3306  can reduce the number of processing tasks used to reduce the data in the different partitions and reduce query execution time, thereby increasing throughput of the system  16 . For example, the worker node  3306  may be able to complete the data group and reduction in one processing task or fewer processing tasks than it would if it did not combine data during ingest and/or assign records to partitions based on the content of the records. 
       FIG.  64    is a block diagram illustrating an embodiment of a worker node  3306  ingesting four chunks of data (chunks 7, 8, 9, 10), assigning the records of the chunks of data to various partitions (partitions 1A, 1B, 1C, 2A, 2B, 2C, 3A, 3B), and reducing the records of the partitions. 
     As described herein, each indexer  206  can form chunks of data from bucket data associated with one or more buckets. In some cases, each chunk of data can include a similar or the same number of records. For example, each chunk of data can be assigned approximately 50,000 records or records or some other threshold number of records. Accordingly, in some embodiments, the chunks 7-10 can correspond to bucket data received from the indexers  206  and each record of a chunk of data can correspond to a record of the bucket data. 
     In certain embodiments, chunks 7-10 may each correspond to a portion of a chunk of data received from one or more indexers  206 . For example, as chunks of data are received by a worker node  3306 , the worker node  3306  may break up the chunks of data into sub-chunks of data and place the sub-chunks of data in a data buffer for assignment to one or more partitions. Accordingly, chunks 7-10 may correspond to a chunk of data received by the worker node  3306  or a sub-chunk of data generated by the worker node  3306 . Further, it will be understood that one or more chunks of data may precede chunks 7-10 and/or may follow chunks 7-10. 
     In the illustrated embodiment of  FIG.  64   , the four chunks of data (chunks 7, 8, 9, 10) each contain a plurality of records where each record includes a keyword value and a count. However, it will be understood that each record can include one or more key values, one or more field values, one or more counts, or other information, and/or can be based on the content of events/records received from the indexers  206 . Thus, the example records illustrated in  FIG.  64    should not be construed as limiting. 
     In the illustrated embodiment, chunk 7 includes eight records with the following key value and count (A:2, B:3, C:5, D:7, E:4, F:3, G:8, H:9), chunk 8 includes six records with the following key value and count (C:4, D:6, E:7, F:5, H:2, 1:8), chunk 9 includes nine records with the following key values and counts (A:7, B:5, C:4, D8, E:3, F:6, G:9, H:1, 1:2) and chunk 10 includes four records with the following key values and counts (B:4, D:3, G:2, 1:5). Although the records of each chunk are shown in alphabetical order, it will be understood that the records may be sorted in any manner or not sorted. 
     In the illustrated embodiment, the records are assigned to one of three groups (Group 1, Group 2, Group 3). However, it will be understood that fewer or more groups can be used as desired. In addition, the worker node  3306  can use a variety of methods to determine the number of logical groups. In some cases, the number of logical groups can correspond to the number of processor cores or compute resources of the worker node  3306  that are allocated to process the data. 
     In some embodiments, as the worker node  3306  ingests the chunks 7-10 and assigns the records of the chunks 7-10 to partitions, the worker node  3306  can logically group the records based on their content. In the illustrated embodiment of  FIG.  64   , the worker node  3306  groups the records of the chunks 7-10 based on the keyword value and assigns each record to one of three record groups (Group 1, Group 2, Group 3) based on the keyword value of the record. It will be understood that the worker node  3306  can logically assign records to different groups using a variety of techniques and/or content of the record. For example, the worker node  3306  can assign records based on the content of one or more keyword values, one or more fields or field values, one or more field-value pairs, counts, etc., or any combination thereof. In some embodiments, the portion of the record used to assign the record can be based on the query. For example, the worker node  3306  can use a keyword value or field value that is identified as being used in the query to reduce or summarize the records. In some embodiments, the keyword or field value identified in the query can correspond to a keyword or field value that corresponds to one or more events stored in a data store  208  (e.g., sourcetype, keyword, etc.), a field value generated during the processing of the query (e.g., count), and/or a field value that is used by the worker node  3306  to process and/or transform the records, etc. For example, if the query includes a command “group by field_name,” the worker node  3306  can use the field values of the identified field_name to logically group the records. 
     In some embodiments, to assign a record to a particular group, the worker node  3306  uses a hash code, hash value, or identifier of each record and/or applies a hash function or modulo operator to a particular keyword or field value. For example, with respect to the illustrated embodiment, the worker node  3306  can apply modulo 3 operand to the keyword value of a record to assign it to Group 1, Group 2, or Group 3. Based on the modulo three operand records with keyword values A, D, G, J, and so on, are assigned to Group 1, records with keyword values B, E, H, K, and so on, are assigned to Group 2, and records with keyword values C, F, I, L, and so on, are assigned to Group 3. In certain embodiments, the worker node  3306  uses a lookup table to assign records to a particular group. 
     In some embodiments, the logical assignment of the records to the different groups can be used by the worker node  3306  to assign the records to a particular partition or group of partitions. For example, based on the assigned group, the worker node  3306  can assign records to a particular partition or a particular group of partitions. In some cases, each group of partitions can be associated with one of the logical groups. Thus, if the records are assigned between four logical groups there can be four groups of partitions to which the record can be assigned. 
     In some embodiments, the worker node  3306  can predetermine the number of partitions to receive the records. In certain embodiments, the worker node  3306  can determine the total number of partitions to receive the records based on the number of cores of and an amount of memory allocated by the worker node  3306 . 
     In certain embodiments, the partitions can be pre-assigned to a particular group of partitions (e.g., at creation or prior to being needed to store records) or assigned during processing. For example, the total number of partitions can be equally distributed to the different groups of partitions or distributed based on an estimated amount of data to be assigned to each group. As another example, as one partition of a particular group of partitions is filled with records, another partition can be assigned to the group of partitions to accept additional records that are assigned to the group. 
     During ingest, partitions of a group of partitions can be filled sequentially. For example, as one partition of a group of partitions is filled, another partition of the group can be used to receive additional records. It will be understood that as the number of records assigned to each group of partitions varies, different groups of partitions can swap out partitions at different times. Further, at any given time the number of partitions in any particular group may vary from the number of partitions in another group of partitions. For example, with reference to  FIG.  64   , after processing Chunk 7, three partitions (partitions 1A, 1B, and 1C) are associated with Group 1 and form a first group of partitions, three partitions (partitions 2A, 2B, and 2C) are associated with Group 2 and form a second group of partitions, and two partitions (partitions 3A and 3B) are associated with Group 3 and form a third group. 
     It will be understood that in assigning records to partitions, a particular partition of a group may be unable to receive every record assigned to its group from a particular chunk. As such, records from the same chunk of data that are assigned to the same logical group may be assigned to different partitions of the corresponding group of partitions. For example, with reference to  FIG.  64   , partition 2C includes a record (K:4) that corresponds to a chunk of data that preceded Chunk 7. 
     With continued reference to  FIG.  64   , following the processing of Chunk 7, partition 1C includes three records with the following keyword values and counts (A:2, D:7, G:8), partition 2C includes four records with the following keyword values and counts (K:4, B:3, E:4, H:9), and partition 3B includes two records with the following keyword values and counts (C:5, F:3). Other than the K:4 record, the records found in partition 1C, 2C, and 3B correspond to records of chunk 7. As mentioned, the K:4 record can correspond to a record found in a chunk that preceded Chunk 7. For example, partition 2B may have been unable to accept the K:4 record due to its capacity having been reached. 
     Although reference is made to processing Chunk 7 and then processing Chunks 8-10, it will be understood that the worker node  3306  can process the chunks 7-10 serially or in parallel. For example, the worker node  3306  can assign the records of chunk 7 to one or more partitions first, then assign the records of chunk 8, and so on, or can use multiple compute resources to concurrently assign records from chunks 7-10 to one or more partitions. For example, consider a scenario in which three compute resources are used to concurrently assign records from chunks 7-10 to one or more partitions. In such a scenario, chunks 7-9 can each be assigned to a compute resource. The first compute resource that completes its task can then begin processing chunk 10. In some cases, the worker node  3306  can assign chunks of data to compute resources in a manner similar to how an indexer  206  assigns bucket data to compute resources for processing, as described herein at least with reference to  FIGS.  62  and  63   . 
     Accordingly, in some embodiments, the chunks 7-10 can be assigned to different compute resources. However in some such embodiments, each chunk is assigned to a single compute resource (e.g., an entire chunk is assigned to one compute resource). Accordingly, in such embodiments, the records of the different chunks 7-10 can be assigned to partitions in parallel, but the records within a particular chunk may be assigned serially. 
     As mentioned, as records are assigned to a particular partition of a group of partitions, they can be combined with similar records. In some cases, to determine whether records are similar, the worker node  3306  can compare the one or more keyword values and/or field values of the records. If one or more keyword and/or field values match, the records can be considered similar and can be combined. In certain embodiments, keyword and/or field values used to identify similar records can be can event field values or field values that correspond to the field values of the event(s) related to the record, as opposed to generated field values or values generated during query execution. By combining similar records from the chunks into a record in a partition, the worker node  3306  can reduce the resulting number of records in the partitions and/or the amount of memory used to store the ingested records. In some cases, this can reduce the number of partitions of a group of partitions and reduce the execution time of the query. 
     In the illustrated embodiment of  FIG.  64   , as the worker node  3306  processes the chunks 8-10 it combines similar records. For example, the worker node  3306  combines all ‘A,’ records assigned to partition 1C into a single record. Similarly, the worker node  3306  combines all ‘D’ and ‘G’ assigned to partition 1C into two records; all ‘K,’ ‘B,’ ‘E,’ and ‘H,’ records assigned to partitions 2C into four records, and ‘C,’ ‘F,’ and ‘I,’ records assigned to partition 3B into three records. When combining the records, the worker node  3306  can remove some or all of the similar records except one and aggregate at least one field value of the removed similar records to the remaining similar record. For example, if there are five similar records, the worker node  3306  can remove four records and aggregate a count field value from all five records into the remaining record, thereby reducing the total number of counts. In certain embodiments, the worker node  3306  aggregates a generated field value from the similar records. 
     With continued reference to  FIG.  64   , after chunks 8-10 are processed, partition 1C includes three records with the following keyword values and counts (A:9, D:24, G:21), partition 2C includes four records with the following keyword values and counts (K:4, B:12, E:14, H:12), and partition 3B includes three records with the following keyword values and counts (C:13, F:14, 1:15). As a non-limiting example, by combining similar records, the worker node  3306  combines the records from chunks 7-10 as shown: 
     
       
         
           
               
               
               
               
             
               
                   
                   
               
               
                   
                 Keyword 
                 No. of Chunk 
                 No. of Partition 
               
               
                   
                 Value 
                 Records 
                 Records 
               
               
                   
                   
               
             
            
               
                   
                 A 
                 2 
                 1 
               
               
                   
                 B 
                 3 
                 1 
               
               
                   
                 C 
                 3 
                 1 
               
               
                   
                 D 
                 4 
                 1 
               
               
                   
                 E 
                 3 
                 1 
               
               
                   
                 F 
                 3 
                 1 
               
               
                   
                 G 
                 3 
                 1 
               
               
                   
                 H 
                 3 
                 1 
               
               
                   
                 I 
                 3 
                 1 
               
               
                   
                   
               
            
           
         
       
     
     Accordingly, by combining similar records, the worker node  3306  reduces the number of records from chunks 7-10 from 27 records to 9 records. Furthermore, in the illustrated embodiment, by combining similar records the processing of chunks 8-10 added only one new record (compared to the partitions after processing chunk 7) to any of the partitions: record 1:15 in partition 3B. It will be understood that the ratio of the reduction and the number of records added after assigning records from different chunks will depend on the similarity and timing of the data assigned to each partition. 
     In some embodiments, once the records from the chunks of data are assigned to individual partitions of the various groups of partitions, the worker node  3306  can reduce the records across the different groups of partitions. For example, with reference to  FIG.  64   , depending on when ‘G’ records (records with a keyword value of ‘G’) are received, there may exist a ‘G’ record in partition 1A and/or 1B, in addition to the G:21 record in partition 1C. Accordingly, the worker node  3306  can combine the various ‘G’ records across the partitions 1A-1C to reduce the number of records in the partitions, and potentially reduce the number of partitions. 
     In some cases, as part of reducing records across partitions of a particular group of partitions, the worker node  3306  can reassign similar records from different partitions of the group of partitions to the same partition. For example, the worker node  3306  can assign all ‘A’ records to partition 1A, all ‘D’ records to partition 1B, all ‘G’ records to partition 1C, and so on. In some cases, the worker node  3306  can reassign similar records in a way that is similar to the logical assignment of records to different groups of partitions. For example, the worker node  3306  can use a hash or modulo operand to assign the records between the group of partitions. In some embodiments, the value for the modulo operand can correspond to the number of partitions of the group of partitions. 
     In certain embodiments, the worker node  3306  can reassign records of a group of partitions to the partition that includes a largest count of a similar record. For example, if partition 1B includes an A:15 record and a D:12 record, the worker node  3306  can assign the A:9 record from partition 1C to partition 1B and assign the D:12 from partition 1B to partition 1C. It will be understood that a variety of methods can be used to reassign records between a group of partitions. 
     Once the records have been reassigned (or as they are being reassigned) to the partitions of the group of partitions, the worker node  3306  can combine the similar records in each partition of the group of partitions to reduce the total number of records across the group of partitions. With continued reference to the example above, the worker node  3306  can combine the A:15 record and A:9 record of partition 1B to become one record: A:24. Similarly, the worker node  3306  can combine the D:12 record and D24 record of partition 1C into one record: D:36. As shown, by combining records, the worker node  3306  removes one or more of the similar records and aggregates one field value of the similar records into one remaining record. However, it will be understood that the records can be combined in a number of ways using a number of keyword or field values, or other information. In certain cases, the records are combined based on the query or a processing task of the query. 
     Following the reduction of similar records across partitions of a particular group of partitions, the worker node  3306  can continue processing the partitions according to the query. As mentioned, by grouping and reducing records at ingest, the functioning of the worker node  3306  can be improved significantly. For example, grouping and reducing records at ingest can result in fewer records to be processed by the worker node  3306 , fewer partitions to store the records, less memory being allocated to store the partitions and/or records, fewer processing tasks being required to process the data, all of which can lead to improved query execution times and greater throughput by the worker node  3306  and system  16 . 
       FIG.  65    is a flow diagram illustrative of an embodiment of a routine  6500  implemented by a worker node  3306  to assign records of chunks of data to one or more partitions and combine records of the one or more partitions. Although certain blocks are described as being implemented by a worker node  3306 , it will be understood that the elements outlined for routine  6500  can be implemented by one or more computing devices/components (alone or in combination) that are associated with a data intake and query system  16 , such as the search head  210 , search process master  3302 , the query coordinator  3304 , etc. Thus, the following illustrative embodiment should not be construed as limiting. Moreover, it will be understood that routine  6500  is not limited to a data intake and query system  16 , but can be used to assign and reduce records between processors of an execution node in a variety of systems and environments. 
     At block  6502 , a worker node  3306  obtains a chunk of data. As described herein, the chunk of data can correspond to a chunk of data received from an indexer  206  or to a portion of a chunk of data received from an indexer  206 . In addition, the chunk of data can include one or more events or records. Further, each event or record may have a portion of raw machine data. In some embodiments, each event or record can include one or more keyword values, field values, etc. 
     At block  6504 , the worker node  3306  assigns a record to a record group. As described herein, the record can be assigned to one record group of a plurality of record groups. In certain cases, the number of record groups of the plurality of record groups can be based on a number of compute resources allocated by the worker node  3306  to process incoming chunks of data. For example, if three processors are allocated to process incoming chunks of data, the record can be assigned to one of three record groups. However, it will be understood that fewer or more record groups can be used. For example, the number of record groups may be greater than or less than the number of compute resources allocated to process incoming chunks, etc. 
     In some embodiments, the worker node  3306  assigns the record based on content of the record. For example, the worker node  3306  can assign the record based on any one or any combination of one or more field values or one or more keyword values of the record. In certain embodiments, the worker node  3306  assigns records to record groups such that similar records (e.g., records with at least one same keyword value or field value used to assign records to record groups) are assigned to the same record group. For example, if the worker node  3306  assigns records based on an IP address field, the worker node  3306  can assign records such that records with the same IP address are assigned to the same record group. In certain embodiments, the worker node  3306  assigns the records based on a modulo operand. In certain cases, the worker node  3306  applies the modulus operand to the keyword or field value used to assign records to record groups. For example, with reference to the IP address example, the worker node  3306  can apply the modulus operand to the IP address of a record. The output of the modulus operand can determine to which record group the record is assigned. In some embodiments, the value of the modulo operand can correspond to the number of the record groups. 
     At block  6506 , the worker node  3306  assigns the record to a partition of a group of partitions. As described herein, in some embodiments, each record group can be associated with a group of one or more partitions. Further, each group of partitions can include one or more partitions that store or hold one or more records. In some such embodiments, by assigning a record to a record group, the worker node  3306  can also assign the record to a group of partitions. 
     In some cases, the worker node  3306  assigns the record to a partition of the group of partitions based on time. For example, as the worker node  3306  ingests chunks of data, it can assign the records to a first partition of a group of partitions until the first partition is filled. Once filled, the worker node  3306  can assign records to a second partition of the group of partitions, and so on, until all of the records from the chunks of data are assigned to partitions. As such, the worker node  3306  can use or fill partitions of a particular group of partitions sequentially. Accordingly, records received during a first time and assigned to a particular group of partitions may be assigned to a first partition of the particular group of partitions and records received during a second time and assigned to the particular group of partitions may be assigned to a second partition of the particular group of partitions. 
     As described herein, it will be understood that a different number of records can be assigned to different record groups (and therefore different groups of partitions). Accordingly, different groups of partitions can have a different number of partitions and can store a different number of records. 
     At block  6508 , the worker node  3306  combines records of a particular partition. As described herein, the worker node  3306  can combine records of a particular partition as records are received or once the partition is filled. In some cases, the worker node  3306  combines similar records, such as records with at least one same keyword value or field value. In certain cases, the worker node  3306  combines records by aggregating the field value (e.g., a count) of one record with the field value (e.g., count) of a similar record. 
     In cases where a field values are aggregated and used to combine similar records, it will be understood that, in some cases, one field value can be used to determine whether records are similar, and another field value can be used in the aggregation process. For example, a sourcetype field value can be used to identify similar records and a count field or average field can be used in the aggregation process. In some such cases, the field value used to identify similar records can be an event field value corresponding to a field value of one or more events and the field value used in the aggregation process can be a generated field value generated during query execution. However, it will be understood that various types of fields and field values can be used to identify similar records and/or used in the aggregation process. 
     At block  6510 , the worker node  3306  processes the partition. In some embodiments, the worker node  3306  processes the partition based on the query. For example, the worker node  3306  can perform one or more transformations on the records based on the query, etc. 
     It will be understood that fewer, more, or different blocks can be used as part of the routine  6500 . For example, the routine  6500  may omit blocks  6502  and  6510  or omit block  6506  or  6508 . As another example, the routine  6500  can include obtaining additional chunks of data corresponding to the same or different indexers  206  and processing the additional chunks of data as described herein with reference to blocks  6504 ,  6506 , and/or  6508 . Further, it will be understood that the routine  6500  can be concurrently implemented by multiple worker nodes  3306  receiving chunks of data from multiple indexers  206 . In certain embodiments, the routine  6500  can include replacing one partition of a group of partitions that is filled with records with another partition of the group of partitions or assigning one or more first records of a chunk of data to a first partition of the group of partitions and assigning one or more second records of the chunk of data to a second partition of the group of partitions. As described herein, the records can be assigned to different partitions based at least in part on the time at which they are assigned by the worker node  3306 . 
     In some embodiments, the routine  6500  can include reassigning records to partitions of a group of partitions. For example, as described herein, the routine  6500  can include reassigning records within a group of partitions so that similar records are assigned to the same partition. In certain embodiments, after reassigning records within the group, the worker node  3306  can combine records within the partitions. For example, the worker node  3306  can combine similar records that were assigned to the same partition. 
     Moreover, it will be understood that one or more blocks described herein with reference to routine  6500  can be combined with one or more blocks of other routines described herein, such as the routines described herein at least with reference to  FIGS.  5 ,  6 ,  23 - 26 ,  31 ,  34 ,  38 - 45 ,  47 ,  49 ,  52 - 57 ,  63 ,  66 - 69 ,  71 , and  73   . Furthermore, it will be understood that the various blocks described herein with reference to  FIG.  65    can be implemented in a variety of orders. For example, blocks  6506  and  6508  can be implemented concurrently, etc. 
     37.0. Estimating Generated Records 
     In some cases, it may be administratively easier to equally allocate compute resources to execute queries. For example, the system  16  can allocate the same number of processors of an indexer  206 , the same number of worker nodes  3306 , and the same amount of memory to execute a query. In some such cases, an equal distribution of compute resources can also make it easier to determine the number of queries that can be concurrently executed. However, equally allocating compute resources to queries can result in underutilized compute resources and/or the system  16  being unable to execute certain queries. For example, compute resources may be allocated for, but used for only a portion of, a query execution. During the time that the compute resources are not used, the system  16  may not be able to be reallocate them to another query until the first query to which they are allocated is finished. Conversely, if the system  16  allocates a set number of worker nodes  3306  or partitions to execute a query, but the number of records generated during the query execution exceed the capabilities of the allocated compute resources, then the system  16  may return inaccurate results or terminate the query execution before completion. 
     To address these and other potential issues, the system  16  can determine the number of records being processed at each stage of the query to ensure that sufficient resources are allocated to handle the largest number of records. In some embodiments, the system  16  can allocate and deallocate compute resources at different stages of the query such that there are sufficient compute resources to execute the largest stage (stage with the largest number of records or that requires the most processing) and that unused compute resources in other stages of the query may be allocated to other queries. In this way, the system  16  can increase the likelihood that sufficient resources are allocated to execute the query, reduce query execution errors, and increase the throughput of the system by increasing the total number of queries being executed thereon. 
     In some embodiments, the system  16  can, as it processes the query identify the various processing tasks to be performed on the set of data identified by the query. For example, the system  16  can identify one or more transforms or extraction rules that are to be applied to the set of data. For each processing task identified, the system can determine a number of records generated by the respective processing task. In some embodiments, the system  16  can determine the number of records generated by the processing task based on the identity of the processing task, and/or certain information about the records to be processed (e.g., number of records, index, time range, sourcetype, etc.). Moreover, the determined number of records generated by a first processing task can be used to determine the number of records generated by a subsequent task (e.g., used as the number of records to be processed by the subsequent task). Using this information, the system  16  can determine which processing tasks generate the most records and allocate sufficient resources to hold/store the results as well as sufficient resources to execute the processing task(s). 
     In some embodiments, the system  16  can use a priority level to determine the amount of resources to allocate for the query. For example, the system can allocate additional resources for queries that have a higher priority level and fewer resources for queries that have a lower priority level. In this way, the system  16  can provide different levels of service based on the priority of a particular query. The priority level can be determined based on the number of records ingested by one or more worker nodes  3306  from one or more indexers  206 , an indication by a user entering the query, a user identifier (e.g., queries from one user can be identified as having a higher priority level than queries from other users), time of day, etc. 
     In addition to allocating resources, the system can use the determined number of records from the different transforms to determine an execution time of the query. For example, based on the number of record processed/generated by each processing task and the amount of resources allocated to execute the processing task, the system  16  can determine an estimated execution time for that task. In some cases, the sum of the estimate execution time for each of the processing tasks can be used to estimate the total execution time of the query or at least a portion of the query, such as the portion of the query assigned to the one or more worker nodes  3306 . 
     In some embodiments, different processing tasks and sets of data, etc. can generate significantly different numbers of records. For example, applying a particular rule or transform to data from index 1, having sourcetype 1, and within time range 1, can generate one number of records, while data from the same index and having the same sourcetype over a different time range can generate a very different number of records. Similarly, applying the same rule or transform to data from a different index or sourcetype can result in a different number of records being generated. In addition, different rules or transforms applied to the same data can result in a significantly different number of records being generated. Further, determining the records generated from each processing task of a query as the query is being processed (or executed) can delay the query execution or significantly increase the execution time of the query. 
     Accordingly, in some embodiments, the system  16  can determine a record generation estimate for various sets of data. In some cases, the record generation estimate can be generated before a query is received, processed, or executed, and can be used by the system  16  during a query processing stage to determine the number of records generated by different processing tasks of the query. In turn, the system  16  can use the determined number of generated records, to allocate resources for the query and/or estimate an execution time of individual processing tasks and/or the query (or portion thereof). 
     In certain embodiments, to determine the record generation estimate, the system  16  can obtain a sample set of data, apply a processing task to the sample set of data, determine the record generation estimate based on the results generated by the processing task, and store the record generation estimate for use with queries that include the processing task and/or use data that is similar to the sample set of data. In some cases, the sample set of data can correspond to data within a particular time range that is obtained from a particular index, and/or has a particular sourcetype. 
     Further, the system  16  can generate a record generation estimate for different sets of data by varying the sample set of data. For example, the system  16  can apply the same processing task or rule to data from different indexes and time ranges to determine a record generation estimate for different combinations of the processing task, indexes, and time ranges. Similarly, the system  16  can apply different processing tasks to the same sample set of data to determine different record generation estimates based on the processing task applied to the sample set of data. In certain embodiments, the record generation estimate can correspond to a ratio of the quantity of records generated by applying the processing task to the sample set of data and the quantity of records of the sample set of data. 
     In some embodiments, the system  16  can store multiple record generation estimates. For example, the system can store the record generation estimates in one or more lookup tables and/or one or more configuration files. In certain cases, the system  16  can store different record generation estimates based on different combinations of indexes, time ranges, source types, processing tasks, etc. A non-limiting example may include: 
     
       
         
           
               
               
               
               
               
             
               
                   
               
               
                   
                   
                   
                   
                 Record 
               
               
                   
                 Time 
                   
                 Rule/Processing 
                 Generation 
               
               
                 Index 
                 Range 
                 Sourcetype 
                 Task 
                 Estimate 
               
               
                   
               
             
            
               
                 main 
                 T0-T1 
                 apache_error 
                 Rule 1 
                 2.3 
               
               
                 main 
                 T1-T2 
                 apache_error 
                 Rule 1 
                 1.6 
               
               
                 main 
                 T0-T1 
                 access_combined 
                 Rule 2 
                 3.1 
               
               
                 main 
                 T1-T2 
                 access_combined 
                 Rule 2 
                 1.3 
               
               
                 _test 
                 T0-T1 
                 apache_error 
                 Rule 1 
                 2.4 
               
               
                 _test 
                 T0-T1 
                 apache_error 
                 Rule 1 
                 2.7 
               
               
                 _test 
                 T0-T1 
                 access_combined 
                 Rule 2 
                 3.5 
               
               
                 _test 
                 T1-T2 
                 access_combined 
                 Rule 2 
                 1.9 
               
               
                   
               
            
           
         
       
     
     As shown in the example table above, a configuration file can include different record generation estimates based on different indexes, time ranges, sourcetypes, and/or rules/processing tasks. In certain embodiments, the record generation estimate can correspond to a ratio of records generated to records processed using the processing task. In some cases, overlapping time ranges can be used. For example, one time range might be time T0-T3 and another time range (for the same or different data) can be time T1-T2 or T1-T4, etc. Further, in the illustrated embodiment, a different rule is used for the different sourcetypes. However, it will be understood that in certain embodiments, the same rule may be used for different sourcetypes, etc. 
     Although not shown in the table above, it will be understood that in some cases, the configuration file can include a record generation estimate corresponding to a sequence of different processing tasks applied to a sample set of data. In some such embodiments, the record generation estimate associated with the sequence of different processing tasks can be used to allocate resources for the sequence of processing tasks and/or estimate an execution time of the sequence of processing tasks. 
     In certain embodiments, the rule itself may include certain parameters, such as an index and/or sourcetype. As a non-limiting example, where a rule identifies a particular index and sourcetype, the configuration file may include fewer fields, such as the following: 
     
       
         
           
               
               
               
               
             
               
                   
                   
               
               
                   
                 Time 
                 Rule/Processing 
                 Record Generation 
               
               
                   
                 Range 
                 Task 
                 Estimate 
               
               
                   
                   
               
             
            
               
                   
                 T0-T1 
                 Rule 1 
                 2.3 
               
               
                   
                 T1-T2 
                 Rule 1 
                 1.6 
               
               
                   
                 T0-T1 
                 Rule 2 
                 3.1 
               
               
                   
                 T1-T2 
                 Rule 2 
                 1.3 
               
               
                   
                 T0-T1 
                 Rule 3 
                 2.4 
               
               
                   
                 T0-T1 
                 Rule 3 
                 2.7 
               
               
                   
                 T0-T1 
                 Rule 4 
                 3.5 
               
               
                   
                 T1-T2 
                 Rule 4 
                 1.9 
               
               
                   
                   
               
            
           
         
       
     
     During query processing, the system  16  can use query parameters to identify the record generation estimate for a particular processing task. For example, the system  16  can use a time range associated with a rule or processing task to determine the record generation estimate for records within the specified time frame that are processed according to the identified rule. Using the number of records to be processed and the record generation estimate, the system  16  can determine an estimated number of records that will be generated by the processing task. For example, based on the number of records to be processed by the processing task and the record generation estimate, the system  16  can determine (or estimate) the number of records that the processing task will generate during query execution. 
       FIG.  66    is a flow diagram illustrative of an embodiment of a routine  6600  implemented by a search head  210  to allocate resources and/or estimate execution time based on records generated during a processing task. Although certain blocks are described as being implemented by a search head  210 , it will be understood that the elements outlined for routine  6600  can be implemented by one or more computing devices/components (alone or in combination) that are associated with a data intake and query system  16 , such as the worker node  3306 , search process master  3302 , the query coordinator  3304 , etc. Thus, the following illustrative embodiment should not be construed as limiting. Moreover, it will be understood that routine  6600  is not limited to a data intake and query system  16 , but can be used to allocate resources and/or estimate the execution time of various records by one or more processors of a distributed execution environment. 
     At block  6602 , the system  16  receives a query. As described herein, the query can identify a set of data and a manner for processing the set of data, identify one or more dataset sources for obtaining the data, include one or more commands for executing a portion of the query by one or more indexers  206 , one or more commands for executing a portion of the query by one or more worker nodes  3306  or by an external data system  12 , etc. In certain embodiments, the search head  210  (or query coordinator  3304 ) can process the query and identify one or more subqueries (or portions of the query) for execution by one or more indexers  206 , one or more worker nodes  3306 , and/or an external data system  12 . In some such embodiments, the search head  210  may perform block  6604 ,  6606 , and/or  6608  for all or a subset of the identified subqueries (or portions of the query). 
     At block  6604 , the search head  210  identifies a processing task. In certain embodiments, the search head  210  can identify the processing task by parsing the query. For example, the search head  210  can identify one or more processing tasks based on a command identified in the query, etc. The command may be a processing task and/or may refer to, reference, or rely on a processing task. The processing task can, in some cases, correspond to a rule, such as an extraction rule, to be applied to at least a portion of the set of data of the query, such as an extraction rule, or a transform that transforms at least a portion of the set of data. Furthermore, the processing task can correspond to a processing task performed by one or more indexers  206 , by one or more worker nodes  3306 , and/or by another component of the system  16 . 
     At block  6606 , the search head  210  determines records generated by the processing task. In some embodiments, the records generated by the processing task can correspond to an estimated number of records that will be output (or generated) in response to the processing task. In certain embodiments, the search head  210  can determine the records generated by the processing task based on one or more of the number of records processed according (or input into) the processing task, the identification of the processing task, a time range of the records processed according to the processing task, a sourcetype of the records processed according to the processing task, an index associated with the records processed according to the processing task, and/or other information. 
     In some embodiments, the search head  210  can determine the number of records to be processed by the processing task based on the query and/or based on an estimated number of records received from the one or more indexers. For example, the processing task may be the first processing task of the query or a first processing task executed by the one or more worker nodes  3306  after receiving chunks of data from the one or more indexers  206 . In some such cases, the processing task may be applied to all or a subset of the set of data identified by the query. As described herein, the query can include one or more parameters to identify a set of data to be processed. In some such embodiments, the search head  210  can use the query parameters used to identify the set of data, such as, but not limited to, a time range, index, host, source, sourcetype, keyword, field, field value, etc., to determine the number of records to be processed by the processing task. 
     In certain embodiments, identifying the records to be processed by a particular processing task can be similar to the processes described herein at least with reference to blocks  6304  and  6306  of  FIG.  63   . For example, based on the query, the search head  210  can identify buckets associated with the query and bucket data of the buckets associated with the query. In some cases, this can include events or records that satisfy the query parameters. The search head  210  can determine that the number records or events that satisfy the query parameters correspond to the number of records to be processed by a particular processing task. In certain embodiments, the search head  210  can use an inverted index to determine the number of records to be processed by the processing task. 
     In some embodiments, the search head  210  can determine the number of records to be processed by the processing task based on the number of records generated by a preceding processing task. For example, if the processing task processes data after another processing task, the records output by the first processing task can be used to determine the number of records to be processed by the second processing task. 
     Similarly, a number of methods can be used to determine the number of records generated by a processing task. In certain embodiments, as described herein, the search head  210  can use a lookup table or configuration file to determine the number of records generated by the processing task. In some cases, the search head  210  can use certain characteristics of the records or processing task (e.g., time range, sourcetype, and/or index of records and/or identity of processing task) to obtain a record generation estimate from a lookup table or configuration file that stores this information for multiple combinations of processing tasks and chunks of data. For example, the configuration file can store multiple record generation estimates and for each record generation estimate, the configuration file can store a time range, sourcetype, index, processing task, etc. associated with that record generation estimate. The search head  210  can use the record generation estimate to determine the number of records generated by the processing task. In certain embodiments, the search head  210  can multiply the number of records received or processed by the processing task by the record generation estimate to determine the number of records generated by the processing task for the query. 
     In certain embodiments, the search head  210  can determine the records generated by the processing task in response to receiving the query. In some such embodiments, the search head  210  can obtain a sample data set that is similar to the set of data that is to be processed according to the processing task (e.g., similar or same time range, index, and/or sourcetype, etc.), apply the processing task to the sample set of data, determine a record generation estimate based on the number of records generated from the sample set of data, and use the record generation estimate to determine the records generated by the processing task for the query. In some embodiments, the search head  210  can determine the records generated by the query based on a user provided estimate or value. 
     At block  6608 , the search head  210  allocates compute resources and/or estimates execution time for the processing task based on the determined records. In some embodiments, the search head  210  can allocate resources by allocating worker nodes  3306 , processors of worker nodes  3306 , cores of indexers  206 , execution resources of cores, etc. Accordingly, in some embodiments, based on the determined records generated by the processing task, the search head  210  can allocate one or more processors or worker nodes  3306  to execute the query. 
     In certain embodiments, the search head  210  can use a variety of ranges to assign a size category to the determined records generated by the processing task. For example, the search head  210  can categorize the determined records generated by the processing task as in the millions, billions, or trillions. Based on the determined category, the search head  210  can allocate compute resources. For example, the search head  210  can allocate more compute resources for queries that include processing tasks that generate trillions of records and fewer compute resources for query with processing tasks that generate billions or millions of records. It will be understood that the ranges and size categories can be as coarse or granular as desired. For example, the search head  210  can include a different size category for each thousand, million, 10 million, 100 million, billion, 10 billion, and so on records as desired. 
     In some embodiments, the search head  210  can allocate the compute resources based on a priority level or prioritization factor of the query. For example, the search head  210  can allocate more compute resources for queries with a higher priority level or prioritization factor and less compute resources for queries with a lower priority level or prioritization factor. As described herein, the priority level can be based on one or more factors, such as a user identifier, query size, user indication, etc. 
     In some embodiments, the search  210  can allocate compute resources based on a combination of size category and priority level. For example, different amount of compute resources can be allocated to process tasks with a particular size category depending on the priority level. Accordingly, the search head  210  can use a variety of factors to allocate compute resources to the processing task and/or query. 
     In certain embodiments, the search head  210  can estimate an execution time for the processing task based on the number of records processed/generated and the allocation of compute resources. In certain embodiments, the search head  210  can store execution time estimates for different sets of data. In some cases, the execution time estimates can be stored in a configuration file with the record generation estimate. The execution time estimate can indicate an amount of time for one compute resource (or more) to process a record or a set of records (e.g., 1,000 records, 10,000 records) that are similar to the records to be processed according to the processing task. Using the identification of the processing task, the determined records generated by the processing task, and the compute resources allocated to the processing task, the search head  210  can estimate the execution time of the processing task. Further, the search head  210  can provide the execution time estimate to a user and/or use the execution time estimate to determine an execution time estimate of the entire query. In some embodiments, if the execution time estimate does not satisfy an execution time threshold, the search head  210  can alert a user and/or allocate additional compute resources to the query to satisfy the execution time threshold. For example, the search head  210  can increase the number of worker nodes  3306  allocated to execute the query. In some cases, the execution time threshold can be based on user input, priority level of the query, or other information. 
     It will be understood that fewer, more, or different blocks can be used as part of the routine  6600 . For example, the routine  6600  may omit block  6602 . As another example, the routine  6600  can include identifying additional processing tasks of the query (or a subquery of the query) and determining records generated by the additional processing tasks. In certain embodiments, the search head  210  performs blocks  6604  and  6606  for a subset of the processing tasks of a query. For example, the search head  210  may perform blocks  6604  and  6606  for processing tasks to be performed by the worker nodes  3306  but not the indexers  206  (or vice versa). In some cases, some processing tasks of the query may generate more records than it receives, while other processing tasks may reduce the number of records. Accordingly, in some embodiments, the search head  210  may perform blocks  6604  and  6606  for the processing tasks identified as generating more records than are received, but may or may not perform blocks  6604  and  6606  for the processing tasks identified as reducing the number of records. 
     In certain embodiments, the search head  210  can allocate resources for all or a group of processing tasks of the query based on the records generated by one or more of the processing tasks of the query. For example, the search head  210  can allocate resources for some or all processing tasks of the query based on the processing task of the query that generates the most number of records. In this way, the search  210  can increase the likelihood that sufficient resources are allocated to process the query. 
     In some embodiments, the search head  210  can individually allocate compute resources for each processing task. For example, the search head  210  can allocate a first set of compute resources for a first processing task and allocate a second set of compute resources for a second processing task. As described herein, in some embodiments, the allocation of compute resources to each task can be based on the number of records processed/generated by the processing task and/or a priority level. In some cases, the search head  210  can use a different priority level for different processing tasks. 
     In certain embodiments, the search head  210  can determine an execution time estimate for each processing task. In some cases, the sum of the execution time estimates can be used to estimate the query execution time. In certain embodiments, the processing task that takes the longest time can be used to estimate the query execution time. In certain embodiments, the sum of some processing tasks and the longest time of other processing tasks can be used together to determine the query execution time. 
     In some cases, the system  16  can initiate execution of the query based on the resources allocated to the query. During the query execution, the system  16  can monitor the execution of the query and update estimates or configuration files based on the monitoring. For example, the system  16  can determine the actual number of records generated by each processing task and update a corresponding lookup table or configuration file based on the actual number of records generated. In some such cases, the system  16  can replace a record generation estimate with the determined record generation, create a separate record for the determined record generation (with corresponding information, such as, index, processing task, sourcetype, etc.) and/or add the determined record generation to the record generation estimate. The updated configuration file can be used to determine records generated by processing tasks for future queries. Similarly, the system  16  can monitor the execution time of each processing task and update a lookup table and/or configuration file based on the monitoring. Accordingly, as queries are executed, the system  16  can update its record generation estimates and/or execution time estimates to improve estimates for future queries. 
     Moreover, it will be understood that one or more blocks described herein with reference to routine  6600  can be combined with one or more blocks of other routines described herein, such as the routines described herein at least with reference to  FIGS.  5 ,  6 ,  23 - 26 ,  31 ,  34 ,  38 - 45 ,  47 ,  49 ,  52 - 57 ,  63 ,  65 ,  67 - 69 ,  71 , and  73   . In certain embodiments, any one or any combination of  6602 - 6608  can be part of a query processing stage, as described herein. Furthermore, it will be understood that the various blocks described herein with reference to  FIG.  66    can be implemented in a variety of orders. For example, blocks  6604 - 6608  can be implemented concurrently, etc. 
     As described herein, processing tasks can modify the data in such a way so as to increase (or decrease) the number of records. For example, one record of a set of data can turn into multiple records after being processed according to the processing task. In some embodiments, one record can turn into thousands or even millions of records after being processed according to a processing task. Furthermore, it can be difficult to predict the number of records generated by a particular processing task on a particular set of data. Moreover, if a processing task results in more records than expected by the system  16 , the system  16  may be unable to complete the execution of the query or the system  16  may use significantly more time than expected to execute the query. 
       FIG.  67    is a flow diagram illustrative of an embodiment of a routine  6700  implemented by a search head  210  to determine a record generation estimate. In certain cases, the routine  6700  can be executed before a query is received or processed or during query processing or execution. In some cases, the routine  6700  can be executed when a transform, extraction rule, or other processing task is added to the system  16  and/or when additional data is stored. 
     Although certain blocks are described as being implemented by a search head  210 , it will be understood that the elements outlined for routine  6700  can be implemented by one or more computing devices/components (alone or in combination) that are associated with a data intake and query system  16 , such as the worker node  3306 , search process master  3302 , the query coordinator  3304 , etc. Thus, the following illustrative embodiment should not be construed as limiting. Moreover, it will be understood that routine  6700  is not limited to a data intake and query system  16 , but can be used to determine a record generation estimate in a variety of systems and environments. 
     At block  6702 , the search head  210  obtains a sample set of data. The sample set of data can be identified and obtained based on one or more data criteria. For example, the sample set of data can be identified and retrieved based on a determined range of time and index (or data store partition). However, it will be understood that fewer, more, or different data criteria can be used to identify the sample set of data. For example, the sample set of data can be identified based on one or more fields, field values, keywords, a host, source, or sourcetype, indexer  206 , etc. In some embodiments, the sample set of data can be retrieved in a manner similar to the manner in which data is retrieved as part of executing a query, as described herein. 
     In certain cases, the sample set of data can correspond to data from one or more buckets of an indexer  206  and/or common storage  4602 . In certain embodiments, the sample set of data can correspond to a chunk of data comprising 50,000 or 100,000 records or events. In some cases, the chunk of data can include events/records with the same or different sourcetype or other matching field values. It will be understood that the chunks of data can include fewer or more records. 
     At block  6704 , the search head  210  applies a processing task to the sample set of data. As described herein, a processing task can correspond to one or more operations to be applied to data. In some cases, a processing task can be stored as an extraction rule or transform for certain data, such as data from a particular index or having a particular sourcetype, etc. Accordingly, in some embodiments, the search head  210  can apply the processing task to the sample set of data by applying an extraction rule to the sample set of data or transforming the sample set of data. 
     At block  6706 , the search head  210  determines a number of records generated by applying the processing task to the sample set of data. As described herein, each processing task can generate a different number of records from different sets of data. Accordingly, the search head  210  can determine the number of records generated by applying processing task to the sample set of data. 
     At block  6708 , the search head  210  determines a record generation estimate. In some embodiments, the record generation estimate is based on the number of records generated and the number of events/records of the sample set of data processed using the processing task. In some embodiments, all events/records of the sample set of data are processed using the processing task. In certain embodiments, a subset of events/records of the sample set of data are processed using the processing task. For example, the sample set of data may include a set of events from a particular index within a time range without regard to sourcetype. In the event the processing task applies to a particular sourcetype, then the processing task may not be applied to all of the events/records of the sample set of data. As another example, if the processing task applies to all events of the particular index or the sample set of data only includes data from the particular sourcetype, then the processing task may be applied to all events/records of the sample set of data. 
     In certain embodiments, the record generation estimate can correspond to a ratio of the records generated and the number of events/records processed by the processing task. In some embodiments, the record generation estimate can correspond to a ratio of the records generated and the number of events/records of the sample set of data. In some embodiments, the record generation estimate can correspond to an estimated number of records generated from each event/record of the sample set of data. 
     It will be understood that fewer, more, or different blocks can be used as part of the routine  6700 . For example, the routine  6700  can include applying distinct processing tasks to the sample set of data to determine multiple record generation estimates for each processing task. In some embodiments, the routine  6700  can include sequentially applying multiple distinct processing tasks to the sample set of data and determining a record generation estimate for the sequence of processing tasks. 
     In certain embodiments, the routine  6700  can include applying the same or different processing tasks to different sample sets of data to determine additional record generation estimates. Further, in some cases, the search head  210  can perform routine  6700  at a predetermined interval or frequency, or as new data is received, or when a new processing task is added, such as a new extraction rule to a configuration file. 
     In some embodiments for each unique combination of sample set of data and processing task, the search head  210  can store the determined record generation estimate. In some embodiments, the record generation estimates can be stored in a lookup table or configuration file. In certain embodiments, some or all of the entries of the lookup table or configuration file can include an identification of the processing task and one or more characteristics of the sample set of data. For example, the lookup table or configuration file can include a time range of the sample set of data, an index of the sample set of data, a sourcetype of the sample set of data, one or more other field values or keyword values as desired. In certain embodiments, an entry of the lookup table or configuration file includes only a time range, an identification of the processing task, and the corresponding record generation estimate. 
     In addition, as described herein, the record generation estimates (or corresponding lookup tables or configuration files) can be used to allocate compute resources for a particular processing task or query and/or used to estimate the execution time of the particular processing task or the query as a whole. 
     Moreover, it will be understood that one or more blocks described herein with reference to routine  6700  can be combined with one or more blocks of other routines described herein, such as the routines described herein at least with reference to  FIGS.  5 ,  6 ,  23 - 26 ,  31 ,  34 ,  38 - 45 ,  47 ,  49 ,  52 - 57 ,  63 ,  65 ,  66 ,  68 ,  69 ,  71 , and  73   . Furthermore, it will be understood that the various blocks described herein with reference to  FIG.  67    can be implemented in a variety of orders. For example, blocks  6704 - 6708  can be implemented concurrently, etc. 
     38.0. Query-Resource Allocation and Concurrency 
     As described herein, the amount of execution resources to execute different queries can vary significantly. For example, some queries may use one processing pipeline of one indexer  206  and one processing pipelines of a search head  210  to execute, whereas another query may use several pipelines on multiple indexers  206 , multiple worker nodes  3306  (each with multiple processing cores), pipelines or cores from one or more external data systems  12 , as well as one or more pipelines of a search head  210 . Given the difference in execution resources used by each query, it can be difficult to determine whether the data intake and query system  16  has sufficient resources to execute a particular query when it is received. For example, in some cases, the system  16  may begin executing the query using execution resources of one or more indexers  206 . However, the worker nodes  3306  may have insufficient compute resources to receive and process the results from the indexers  206 . In some such cases, the system  16  may terminate the query or wait until the worker nodes  3306  become available. However, during that time, the execution resources of the indexers  206  may not be allocated to execute other queries. Further, the system  16  may wait for an undetermined amount of time before the worker nodes  3306  become available. 
     To address these and other potential issues, the system  16  can track the execution resources of its various components. As new queries are received, the system  16  can determine a query-resource allocation or the amount of execution resources of the different components of the system  16  to use to execute the query. The system can compare the query-resource allocation with the amount of execution resources available from the different components. If there are sufficient execution resources available from the various components, the system  16  can execute the query. If not, the system can place the query in a queue for later execution. Moreover, as described herein, based on the size of the query and amount of execution resources to be allocated, the system  16  can estimate a query execution time for the query. By scheduling queries based on a determined query-resource allocation and the availability of execution resources, the system  16  can be improved. For example, the system  16  can reduce the likelihood that there will be insufficient execution resources to execute a query, improve utilization of execution resources of the system  16  (e.g., increase the usage time of the compute resources), etc. As such, the system  16  can increase the number of queries being executed over a period of time (e.g., increase throughput of query executions) and decrease the wait time to execute queries. 
     As mentioned, the system  16  can track the total number of execution resources of individual components, as well as the total number of execution resources of the system  16  as a whole the total number of execution resources of portions of the system  16 , such as the group of indexers  206  or group of worker nodes  3306 . As queries are received, the system  16  can determine the number of execution resources to be used by individual components and the system  16  as a whole to execute the query and/or identify portions of the query and a query-resource allocation for each portion of the query. The system  16  can deduct the number of execution resources for the query from the total number of resources as well as from the individual resources. 
     In some embodiments, the system  16  can calculate a number of individual and indexer-wide execution resources based on the number of processor cores of each indexer  206 . For example, the system  16  may determine that each processor core can handle a certain number of queries (or pipelines) and/or that an individual indexer  206  can handle a certain number of queries (or pipelines) per core plus an additional amount. In some such embodiments, the system  16  can determine that the execution resources of one indexer  206  is number of cores*queries supported per core+offset. In some cases, the system  16  can determine that each processor can support one query from each search head  210  of a search head cluster. Thus, the number of queries supported by each core (and indexer  206 ) or the number of execution resources of each core can be based on the number of search heads  210  in a search head cluster of the system  16 . In certain cases, the system  16  can sum the execution resources from all indexers  206  to determine the total number of indexer  206  execution resources. 
     In certain embodiments, the system  16  can perform a similar determination with the worker nodes  3306 . For example, the system  16  can determine a number of queries that individual cores of a worker node  3306  can support, a number of queries that each worker node  3306  can support, and/or the number of queries that the worker nodes  3306  together can support. In some embodiments, the system  16  can specify a certain number of worker nodes  3306  to be allocated for each query that involves worker nodes  3306  or a total number of queries involving worker nodes  3306  in the aggregate that are supported by the system  16 . In some such embodiments, when a query involving worker nodes  3306  is received, the system  16  can reduce the count of additional queries that can use worker nodes  3306 . 
     In addition to tracking the number of execution resources of the different components of the system  16 , the system  16  can determine a query-resource allocation for queries to be executed by the system. As mentioned, the query-resource allocation can vary depending on the type of query, the query size (amount of events or records to be processed), the components being used, etc. For example, queries that only use indexers  206  to execute the query may have one query-resource allocation and queries that use worker nodes  3306  (alone or in conjunction with indexers  206 ) may have a different query-resource allocation. 
     In addition, the type of query to be executed can affect how the search head  210  allocates execution resources from one or more indexers  206 . For example, one type of query (also referred to herein as an “indexer search”) uses execution resources of one set of indexers  206  to obtain the set of data, perform some processing, and return the partial results to a search head  210 , which may perform some additional processing on the data. 
     In some embodiments, indexer searches can use a static number of execution resources of each indexer  206  involved in the query. For example, each indexer  206  may allocate one execution resource (or pipeline) for each indexer search. Thus, if only one indexer  206  stores or has access to the set of data of the query only one execution resource of the indexer  206  may be allocated or used for the search. Accordingly, in certain embodiments, to determine a query-resource allocation for an indexer search, the system  16  can use the total number of indexers  206  to be used for the query and the number of execution resources allocated from each indexer  206 . 
     Another type of query uses multiple sets of indexers  206  to execute a query (also referred to herein as an “intermediary search”). A first set of execution resources of indexers  206  is used to obtain the set of data of the query and may perform some processing on the set of data. One or more second sets of execution resources of the indexers  206  collate and/or perform additional processing on data obtained by the first set of indexers  206 , and provide the results to the search head  210 . In some such embodiments, the second set(s) of execution resources of the indexers  206  can act as intermediaries for the first set of execution resources to the search head  210 . In some cases, the second set of execution resources that receive data from the first set of execution resources can be a subset of the first set of execution resources (or vice versa) or a set of other execution resources (from the same or different indexers  206 ). 
     In certain embodiments, intermediary searches may use at least the same number of execution resources of indexers  206  as a corresponding indexer search plus additional execution resources for the one or more second sets of execution resources (e.g., to collate the partial results from the first set of execution resources, further process the data, and/or function as the intermediaries to the search head  210 ). In some such cases, the total number of execution resources used for an intermediary search used may correspond to the total number of execution resources used by the first set of execution resources (execution resources used to obtain the first set of data) or the number of indexers used as part of the query plus an additional amount. 
     In certain cases, the “additional amount” of execution resources can be determined based on a weighting factor. The weighting factor can correspond to the number, ratio, or amount of additional execution resources to be allocated to collate or further process data from the first set of execution resources, or otherwise act as an intermediary to the search head  210 . In some cases, the additional execution resources can be a subset of the first set of execution resources. In some embodiments, the system  16  can determine the total amount of execution resources from the indexers  206  for an intermediary search by increasing or multiplying the number of execution resources used by the first set of execution resources by the weighting factor. For example, if a weighting factor is 40% or 1.4 and one execution resource from each of ten indexers  206  will be used to obtain the initial set of data, then the system  16  can allocate fourteen execution resources of the indexers  206  for the query-resource allocation. In certain embodiments, the system  16  can round the determined number of execution resources, use a ceiling, or floor, as desired, to determine the total number of execution resources to be allocated. For example, if six execution resources are to be used by the first set of indexers  206  and the weighting factor is 40%, then the system  16  can determine that the query-resource allocation to be 8 (floor or rounding) or 9 (ceiling) execution resources. 
     In some embodiments, the system  16  can use the same weighting factor for all intermediary searches. In certain embodiments, the system  16  can use a different weighting factor for intermediary searches depending on the size of the set of data, a prioritization factor, the amount of processing to be done on the set of data, one or more query parameters or one or more characteristics of the data, such as, but not limited to, host, source, sourcetype, time range, index, fields, field values, keywords, etc. For example, the system  16  can use a larger weighting factor for larger sets of data (or sets of data that involve a large number of events or records) or queries with a higher prioritization factor, queries with more processing to be done, queries that reference a particular host, source, sourcetype, index, or keyword or field value, etc. Accordingly, in certain embodiments, to determine a query-resource allocation for an indexer search, the system  16  can use the total number of execution resources to be used to obtain the set of data and a weighting factor. 
     A third type of query can use worker nodes  3306  to process data (also referred to herein as a “worker node search”). For example, executing a worker node search may involve using execution resources of a set of indexers  206  of the system  16  to obtain a set of data, perform some processing, and export the data to worker nodes  3306 . The worker nodes  3306  may perform additional processing based on the query and provide the results of the processing to the search head  210 . In certain embodiments, some worker node searches may not use the execution resources of the indexers  206  of the system  16 . In some such embodiments, the worker nodes  3306  may obtain data from one or more external data systems  16  (which may or may not include indexers  206 ), process the data, and provide the results of the processing to the search head  210 . 
     Given that worker node searches can involve allocating execution resources from the indexers  206  and compute resources of the worker nodes  3306 , the system  16  can determine a query-resource allocation for the different portions of the worker node search. For example, the system  16  can determine a query-resource allocation for the indexer portion of the worker node search and a query-resource allocation for the worker node portion of the worker node search. 
     The system  16  can use a variety of techniques to determine the query-resource allocation for the indexer portion of a worker node search. In certain embodiments, the system  16  can use a fixed number of execution resources from each indexer  206  used in the query to determine a query-resource allocation. In some embodiments, the system  16  can use an execution resource allocation policy. As described herein, at least with reference to  FIGS.  62 A,  62 B, and  63   , the execution resource allocation policy can use a variety of factors to determine the number of execution resources to allocate. For example, the execution resource allocation policy can use the number of buckets, amount of bucket data, number of available resources, a threshold number of execution resources, the number of worker nodes  3306  or number of processors of worker nodes  3306 , etc., to determine the query-resource allocation for the indexer portion of a worker node search. 
     For worker node portions of a query, the system  16  can use a variety of techniques to determine the query-resource allocation. In some cases, the system  16  can assign the same quantity of worker nodes  3306  and/or compute resources of the worker nodes  3306  for each query. In some embodiments, the system  16  can assign worker nodes  3306  and/or compute resources of the worker nodes  3306  based on the size of the query and/or a priority level. As described herein, in some embodiments the size of the query can correspond to a number of records to be processed, the size of the records to be processed, and/or an amount of memory used to store the records, etc. As described herein, at least with reference to  FIG.  66   , the system  16  can determine the size of a query based on one or more processing tasks of the query and/or a record generation estimate. 
     In some cases, the system can determine a query-resource allocation (of the indexers  206  and/or of the worker nodes  3306 ) based on a quantity of the events or records received and/or processed by the indexers  206  and/or worker nodes  3306 . For example, the system  16  can use one or more size categories to categorize the amount of records/events to be processed by the indexers  206 /worker nodes  3306 . As described herein, at least with reference to  FIG.  69   , the size categories can be as coarse or granular as desired. For example, in some embodiments, the system  16  can use three categories for queries that have millions (or less), billions, or trillions of events. As another example, the system  16  can have a different size category for each million, ten million, hundred million, billion (or more) records. Based on the size category, the system  16  can allocate a different amount of worker nodes  3306 , a different number of compute resources of worker nodes  3306 , a different number of compute resources for each worker node  3306  allocated, and/or a different number of execution resources of the indexers  206 . 
     In some cases, the size category for the query can be based on the size of the largest number of records processed by or generated from a processing task of the query. In certain cases, the system  16  can dynamically assign compute resources to processing tasks based on the number of records processed/generated by the processing task. Accordingly, in some embodiments, a different quantity of compute resources can be allocated to different portions of the query, and/or compute resources can be dynamically allocated to different portions of the query. 
     In addition, the system  16  can determine a query-resource allocation (of the indexers  206  and/or worker nodes  3306 ) based on a prioritization scheme or priority level. For example, queries as signed a higher priority level or priority level can be allocated more compute resources (or execution resources) than queries assigned a lower priority level or priority level. In some cases, the system  16  can determine the priority or priority level based on the user initiating the query, a schedule, the data being queried (e.g. based on indexes, time ranges, sourcetypes, host, sources, indexers  206 , etc.), etc. In some cases, the system  16  can use a combination of query size and priority level to determine the query-resource allocation for the query or for different portions of the query. For example, the system can determine the size category of the system and within that size category determine the amount of resources to allocate based on the priority level. In some such cases, a different size category or priority level can result in a different number of resources allocated. In certain embodiments, the system  16  can use the same priority level for an entire query and/or for different portions or processing tasks of the query. As such, different compute resources or execution resources can be allocated to different portions or processing tasks of the query. 
     In certain embodiments, the system  16  can determine the query-resource allocation based on a query execution time threshold (e.g., amount of time that the execution of the query should take) or query completion time (e.g., time by which the query is to be completed). For example, a user or the query can indicate a time by which the query is to be completed and/or an amount of time to execute the query. Based on the indicated time, the system  16  can allocate compute resources. For example, the system can allocate more compute resources to a query that is to be executed in less time compared to the same query with an indication that it can be executed over a longer period of time or more resources to a query that is to be completed sooner than to a query that is to be completed later. In addition, in some cases, the system  16  can assign different priority levels to queries based on the query execution time threshold or query completion time. In addition, as the completion time nears, the system  16  can assign a higher priority to a query. 
     Accordingly, an indexer search may use a certain amount execution resources of the indexers  206 , an intermediary search may use a different amount of execution resources of the indexers  206 , and a worker node search may use another amount of (or no) execution resources of the indexers  206  and compute resources of the worker nodes  3306 . In addition, different indexer searches may use a different number of indexers  206  and/or execution resources of indexers  206  based on the query, priority level, etc. Similarly, different intermediary searches and worker node searches can use different amount of execution resources or compute resources depending on the query, priority level, individual processing tasks, etc. 
     Based on the availability of execution resources from the indexers  206  and the worker nodes  3306  and the query-resource allocation, the system  16  can determine whether a query can be executed at that time or whether it should be placed in a queue for later execution. In addition, by determining a query execution time, as described herein, at least with reference to  FIG.  69   , and determining execution resources from different components to use to execute the query, the system  16  can dynamically schedule queries for execution. For example, based on a query (or subquery) execution time estimate, and query-resource allocation, the system  16  may determine that indexers  206 , but not worker nodes  3306  are available to execute a query at time T0, and that the worker nodes  3306  will become available at time T1. Based on that information and the system  16  determining that the subquery execution time of the indexer portion of the query is T1, the system  16  can begin executing the indexer portion of the worker node search at time T0. As the indexer portion of the query will complete at T1, the system  16  can allocate the worker nodes  3306  by that time to continue processing the query. In this way, the system  16  can increase throughput of query execution and reduce the waiting time for queries. 
     In some embodiments, the system  16  can dynamically allocate different amounts of resources during query execution. For example, if the system  16  receives a higher priority query and determines that there are insufficient execution or compute resources to execute the higher priority query using a first priority level, the system  16  can begin executing the higher priority query using a second priority level that is lower than the first level or uses fewer execution or compute resources. If the system  16  determines that additional resources will be received during the execution of the query such that it can provide the first priority level, the system  16  can add those execution or compute resources during the execution of the query. The system can similarly dynamically allocate different amounts of execution or compute resources during query execution for queries of different sizes or to more efficiently manage a scheduling queue, etc. For example, the system  16  can allocate additional resources to one query during execution to finish it in less time so as to free up execution or compute resources for more or larger queries that follow. 
       FIG.  68    is a flow diagram illustrative of an embodiment of a routine  6800  implemented by a search head  210  to schedule a query. Although certain blocks are described as being implemented by a search head  210 , it will be understood that the elements outlined for routine  6800  can be implemented by one or more computing devices/components (alone or in combination) that are associated with a data intake and query system  16 , such as the worker node  3306 , search process master  3302 , the query coordinator  3304 , etc. Thus, the following illustrative embodiment should not be construed as limiting. Moreover, it will be understood that routine  6800  is not limited to a data intake and query system  16 , but can be used to queue execution tasks in a variety of systems and environments. 
     At block  6802 , the search head  210  receives a query, as described herein, at least with reference to block  6702  of  FIG.  67   ,  FIG.  6   , and elsewhere. At block  6804 , the search head  210  determines a query-resource allocation for the query. In some embodiments, as part of determining the query-resource allocation for the query, the system  16  can determine a query-resource allocation for one or more portions of the query. The portions may refer to different sections of the query (or subqueries generated from or referenced by the query) that are to be executed by different components of the data intake and query system  16  (or an external data system  12 ) or to one or more subqueries of the query (e.g., a portion of the query or a separate query referenced by or identified by the query). For example, the system  16  can identify a first query portion that is to be executed by one or more indexers  206  (indexer portion), a second query portion that is to be executed by one or more worker nodes  3306  (worker node portion), and a third portion to be executed by a query coordinator  3305  and/or a search head  210  (results portion). Other portions can be identified as well, such as one or more portions to be executed by one or more external data systems  12  (external data system portions). In some such cases, the system  16  can determine a query-resource allocation for the different portions of the query. 
     As described herein, the system can determine the query-resource allocations for the different query portions in a variety of ways. In some cases, the system  16  determines query-resource allocations for different query portions in different ways. For example, the system  16  can determine a query-resource allocation for an indexer portion differently than the way in which it determines a query-resource allocation for a worker node portion. 
     In some embodiments, the system  16  can use the type of query (indexer search, intermediary search, worker node search), the amount of the bucket data, the number of indexers  206 , and/or a priority level to determine the query-resource allocation for indexer search portions. As described herein, the system  16  can allocate more execution or compute resources for queries with the higher priority level than for queries with the lower priority level. 
     As mentioned, the query type can affect the manner in which the system  16  allocates execution resources for an indexer portion of a query. As described herein, for the indexer search portion of an indexer search, the system  16  can determine the number of execution resources to allocate based on the number of indexers  206  to be used to execute the indexer portion of the query. For example, the system  16  can allocate a predetermined number of execution resources for each indexer  206  for the indexer portion of the query. In some such embodiments, the system  16  can determine the query-resource allocation for the indexer portion by aggregating the predetermined number of execution resources allocated from each indexer  206  for the query. In addition, as described herein, for an intermediary search, the system  16  can allocate execution resources for an indexer portion based on the number of indexers  206  to be used to execute the query and a weighting factor. 
     In certain embodiments, for an indexer portion of a worker node search, the system  16  can allocate execution resources based on an execution resource allocation policy. In some such embodiments, the system  16  can allocate execution resources based on the lesser of the number of buckets to be exported from an indexer  206 , the number of available cores or pipelines of the indexer  206 , or a threshold number of cores or pipelines. 
     For worker node portions of a query, the system  16  can use a variety of techniques to determine the query-resource allocation. In some cases, the system  16  can assign the same quantity of worker nodes  3306  and/or compute resources of the worker nodes  3306  for each query. In some embodiments, the system  16  can assign worker nodes  3306  and/or compute resources of the worker nodes  3306  based on the size of the query and/or a priority level. As described herein, in some embodiments the size of the query can correspond to a number of records to be processed, the size of the records to be processed, and/or an amount of memory used to store the records, etc. 
     In certain embodiments, the system  16  can determine the query-resource allocation for a worker node portion based on the processing task that processes and/or generates the largest number of records. In certain embodiments, the system can determine query-resource allocation for each processing task of a worker node portion of the query. As described herein, the system  16  can allocate more compute resources for larger queries (or larger processing tasks) and fewer compute resources for smaller queries (or smaller processing tasks). 
     Additionally, as described herein, the system  16  can determine a query-resource allocation based on a priority level, query execution time threshold, and/or query completion time. In some such embodiments, the system  16  can allocate more compute resources for queries with a higher priority level, queries that are to be executed within less time or sooner than for queries with a lower priority level, queries that are to executed in more time or later, etc. In addition, as described herein, the system can assign a priority level based on the query execution time threshold or the query completion time. 
     In some embodiments, the system  16  can determine a range of query-resource allocations for the query and/or the portions of the query. For example, the system  16  can indicate that a particular number of execution or compute resources is preferred, but that a different number of execution or compute resources are acceptable to execute the query or that at least a certain number of execution or compute resources are to be allocated. For example, for an indexer portion of the worker node search, the system  16  can indicate that 12 execution resources from each indexer  206  is preferred, but also indicate that the query can be executed if at least three execution resources from each indexer  206  can be allocated. Similarly, the system  16  can indicate that the query has a first priority level, but that if there are insufficient resources to execute the query at the first priority level, then it can be executed at a second priority level (with fewer execution or compute resources). In this way, the system  16  can provide flexibility in scheduling the query for execution. At block  6806 , the search head  210  determines execution or compute resource availability for one or more portions of the query. In some cases, the search head  210  determines the execution or compute resource availability for the different portions of the query based on the total number of execution or compute resources of the components that will be used to execute that portion of the query and the amount of execution or compute resources of those components that are allocated to other queries. For example, if a first portion of the query corresponds to an indexer portion of the query (indexer search, intermediary search, or worker node search), the search head  210  can determine the total amount of execution resources available from the indexers  206  to execute the query based on the total number of execution resources of the indexers  206  and the amount of execution resources of the indexers  206  allocated to other queries. Similarly, for a worker node portion of a query, the search head  210  can determine the total amount of compute resources available by the worker nodes  3306  to execute the query based on the total number of compute resources of the worker nodes  3306  and the amount of execution resources of the indexers  206  allocated to other queries. As another example, for worker node portions of the query, the system  16  may indicate a fixed number of worker node searches are supported. In some such embodiments, for worker node portions of worker node searches, the system  16  can determine the compute resource availability of the worker nodes  3306  based on the number of worker node searches being executed or scheduled for execution compared to the number of worker node searches that are supported. 
     It will be understood, that in some embodiments, the search head  210  can determine an execution or compute resource availability for only one portion of the query. For example, if the query is an indexer search or an intermediary search and does not use other components of the data intake and query system  16 , such as the worker nodes  3306 , the search head  210  may determine an execution resource availability for only the indexer portion of the indexer search for intermediary search. 
     In certain embodiments, the search head  210  can also determine an execution or compute resource availability for other portions of the query, such as a results portion of the query or an external data system portion of the query, etc. 
     At block  6808 , the search head  210  schedules the query. The search head  210  can schedule the query based on the determined the execution or compute resource availability and the query-resource allocation. For example, if the search head  210  determines that there are sufficient execution or compute resources for the different portions of the query to satisfy the query-resource allocations for those portions, the search head  210  can schedule the query for execution at that time. However, if the search head  210  determines that there are insufficient execution or compute resources for the different portions of the query to satisfy the query-resource allocations, the search head  210  can schedule the query for execution at a future time. In some cases, the search head  210  places the query in a queue for execution at a future time, and in some cases, determines the time at which the query is to be executed. 
     In some embodiments, the search head  210  can use the range of query-resource allocations to schedule the query. For example, if the search head  210  determines that there are insufficient execution or compute resources to execute the query using a preferred query-resource allocation, but there are sufficient execution or compute resources to execute the query using an alternate query-resource allocation, the search head  210  can schedule the query for execution using the alternate query-resource allocation. 
     In certain embodiments, the search head  210  can use the query execution time threshold and/or query completion time to schedule the query. For example, if there are sufficient resources to execute a query upon receipt, but the query completion time is later than a query completion time of a query in a queue, the search head  210  can place the query in the queue. As another example, if there are sufficient resources to execute the query within a particular time, but that time does not satisfy the query execution time threshold, the search head  210  can place the query in a queue until there are sufficient resources available to execute the query within the query execution time threshold. 
     Furthermore, as described herein, in some embodiments, the search head  210  can allocate different amounts of execution or compute resources to different portions of the query at different times. For example, during a worker node portion of the query, the search head  210  can assign a different number of execution or compute resources to execute different processing tasks of the worker node portion. As another example, during execution, if the search head  210  is unable to provide the preferred number of execution or compute resources for the query and additional resources become available during execution, the search head  210  can assign additional execution or compute resources to the query. In this way, the search head  210  can dynamically allocate and assign execution or compute resources to execute the query. In some embodiments, the search head  210  may not dynamically allocate execution or compute resources during execution of the query. For example, based on an initial query-resource allocation, the search head  210  can schedule and execute the query. 
     It will be understood that fewer, more, or different blocks can be used as part of the routine  6800 . In some cases, one or more blocks can be omitted. For example, block  6802  can be omitted. In certain embodiments, the block  6808  can be replaced with executing the query based on the query-resource allocation. 
     Moreover, it will be understood that one or more blocks described herein with reference to routine  6800  can be combined with one or more blocks of other routines described herein, such as the routines described herein at least with reference to  FIGS.  5 ,  6 ,  23 - 26 ,  31 ,  34 ,  38 - 45 ,  47 ,  49 ,  52 - 57 ,  63 ,  65 ,  66 ,  67 ,  69 ,  71 , and  73   . In certain embodiments, any one or any combination of  6802 - 6810  can be part of a query processing stage, as described herein. Furthermore, it will be understood that the various blocks described herein with reference to  FIG.  68    can be implemented in a variety of orders. For example, blocks  6804  and  6806  can be implemented concurrently, etc. 
     39.0. Search Time Estimate 
     Queries executed by the data intake and query system  16  can vary significantly in size, the amount of data processed, and the time it takes to execute the query. In some cases, queries executed by the data intake and query system  16  can take hours, or days, or longer. Queries that take significant amounts of time can reduce the query execution throughput of the system  16  and/or reduce the amount of queries that can be executed by the data intake and query system  16 . In some cases, when a user enters a query, they do not know how much time the query will take. Accordingly, if the query being executed is time sensitive, the system  16  may be unable to determine whether the query will be finished by a particular time. Similarly, if the user provides a query completion time, the system  16  may be unable to determine whether the query can be completed by the query completion time. This can increase the difficulty of scheduling queries for execution and executing those queries. 
     Accordingly, in some cases, the system  16  can be improved by estimating the query execution time of a query before it is executed. By determining the query execution time, the system  16  can enable a user to modify the query so that it can be executed in less time. For example, the user may determine that a smaller set of data can be used for the query, can increase the priority level of the query, and/or terminate other queries. 
     However, it can be difficult to determine the query execution time for a query to be executed by the data intake and query system  16 . For example, as described herein, a query executed by the data intake and query system  16  can include different portions executed by different components of the data intake and query system  16 . In addition, executing the query can include processing data by one or more indexers  206 , one or more worker nodes  3306 , search heads  210 , and/or one or more external data systems  12 . The complexity of the system  16  can make it difficult to determine the query execution time of the query. 
     To address these and other potential issues, the system  16  can identify different portions of the query or different subqueries, which can be executed by different components of the data intake and query system  16 . As described herein, at least with reference to  FIG.  68   , one portion of a query or subquery can be executed by one or more indexers  206 , a second portion of the query can be executed by one or more worker nodes  3306 , a third portion of a query can be executed by an external data system  12 , and a fourth portion of the query can be executed by a search head  210 . The system  16  can determine a query execution time for each of the portions of the query and use the execution times of the different portions to determine the query execution time as a whole. 
     Determining the execution time for each query portion can vary depending on the amount of data processed during the query portion and the components of the data intake and query system  16  (or external data system  12 ) executing the query portion. In certain embodiments, the search head  210  can determine the execution time for a query portion to be executed by the one or more indexers  206  based on the number of buckets, amount of bucket data, and number of execution resources allocated to process the bucket data. For example, as described herein, at least with reference to  FIG.  63   , the system can allocate bucket data to execution resources for processing and/or export. Based on the bucket distribution, the system  16  can identify which of the execution resources will take the longest time to process the data. In some embodiments, the system  16  can determine that the query portion execution time for the query portion executed by the indexers  206  corresponds to the execution time of the slowest execution resource. 
     In certain embodiments, the search head  210  can determine the query execution time for a worker node portion of a query based on the amount of data received by the worker nodes  3306 , the number of processing tasks executed by the worker nodes  3306 , the amount of records processed/generated by each processing task of the worker nodes  3306 , and/or the query-resource allocation of the worker node portion of a query, etc. 
     As described herein, at least with reference to  FIGS.  66 ,  67 , and  68   , a query can include multiple processing tasks for execution by the worker nodes  3306 . Accordingly, in certain cases, the system  16  can identify the processing tasks for execution by the worker nodes  3306 , determine the number of records processed/generated by the worker nodes  3306 , determine a query-resource allocation for the processing task, and based on the query-resource allocation and the number of records to be processed/generated, determine an execution time for a particular processing task. 
     In some embodiments, the search head  210  can estimate the execution time for a processing task of a worker node portion of a query based on a heuristically-determined data model that indicates an amount of time to process a particular number of records using a particular number of compute resources. For example, the search head  210  can compare the number of records to be processed/generated by a processing task and the number of compute resources allocated for the processing task with the heuristically-determined data model to determine the execution time for the particular processing task of the worker node portion of the query. 
     In certain embodiments, the system  16  can combine the execution time of the processing tasks to determine the query execution time for the worker node portion of a query. In certain embodiments, the system  16  can combine the execution time of the different processing tasks by summing the execution time of the different processing tasks or summing the processing tasks that are to be executed sequentially. 
     In some cases, the search head  210  can determine the query execution time for external data systems in a manner that is similar to the way in which subquery execution times are determines for the indexers  206 . For example, as described herein, in some cases, the external data system  12  can be another data intake and query system  16 . In some such embodiments, the search head  210  can use an estimate provided by the external data system  12  to determine the query execution time for the subquery executed by the external data system  12 . 
     In embodiments where the external data system is not another data intake and query system  16 , the search head  210  can cause one or more worker nodes  3306  to interact with the external data system  12  to determine a query execution time. In some embodiments, a query execution time may be provided in a configuration file or the external data system may be able to provide a query execution time based on the query that it receives from a worker node  3306 . 
     In some cases, the system  16  can also determine the execution time for portions of the query executed by the query coordinator  3304  and/or the search head  210 . In some cases, the system can also take into account certain time that is required to communicate data between components of the data intake and query system  16  (e.g., between indexers  206 /worker nodes  3306 , worker nodes  3306 /query coordinator, query coordinator/search process master/search head  210 , etc.). 
     Once the system  16  determines the query execution time for the various portions of the query, it can determine the query execution time for the entire query. In certain embodiments, the system  16  determines the query execution time for the query by summing the determined query execution time of each portion of the query. As mentioned, the system  16  can, in some cases, include other time requirement of the query when determining the query execution time. For example, the system  16  can include the time required to communicate data between different components of the system  16 . 
     Once determined, the system  16  can provide the query execution time to the user. In some cases, the system  16  can use the query execution time to schedule queries for execution, modify priority levels, etc. For example, the system  16  can determine that by scheduling two smaller queries concurrently, it will have more execution resources available for a larger query and can therefore process all of the queries in less time or in a more efficient manner. 
       FIG.  69    is a flow diagram illustrative of an embodiment of a routine  6900  implemented by a search head  210  to determine a query execution time for a query. Although certain blocks are described as being implemented by a search head  210 , it will be understood that the elements outlined for routine  6900  can be implemented by one or more computing devices/components (alone or in combination) that are associated with a data intake and query system  16 , such as the worker node  3306 , search process master  3302 , the query coordinator  3304 , etc. Thus, the following illustrative embodiment should not be construed as limiting. Moreover, it will be understood that routine  6900  is not limited to a data intake and query system  16 , but can be used to determine query execution time estimates in a variety of systems and environments. 
     At block  6902 , the search head  210  receives a query, as described herein, at least with reference to block  6702  of  FIG.  67   ,  FIG.  6   , and elsewhere. At block  6904 , the search head  210  identifies one or more query portions. In some embodiments, the query portions can correspond to portions of the query or subqueries to be executed by different portions of the data intake and query system  16 , as described herein at least with reference to  FIGS.  6 ,  41 ,  42 ,  52 - 56 ,  60 , and  68   . For example, one query portion can correspond to the portion of the query (or a generated or identified subquery) to be executed by the indexers  206  and another query portion can correspond to the portion of the query (or a generated or identified subquery) to be executed by the worker nodes  3306 , an external data system  12 , the search head  210 , or query coordinator  3304 , etc. In certain embodiments, the search head  210  can determine the query portions and the components that are to execute the query portions based on the syntax and semantics of the query. In certain embodiments, the search head  210  can determine that indexers  206  are to extract the set of data identified by the query, the worker nodes  3306  are to process and/or transform the extracted data, and the query coordinator  3304  and/or search head  210  are to collate and finalize the results of the query, etc. 
     At block  6906 , the search head  210  determines an execution time for one or more portions of the query. As described herein, the search head  210  can determine an execution time for an indexer portion of the query based on one or more of the amount of bucket data, the number of buckets, the number of allocated execution resources, the type of query, etc. 
     In addition, the search head  210  can determine an execution time for a worker node portion of the query based on the processing tasks (number and/or type) to be executed by the worker nodes, the amount of records processed/generated by the worker nodes  3306 , and the amount of compute resources of the worker nodes  3306  allocated to the worker node portion of the query. 
     As mentioned, in some embodiments, the search head  210  can estimate the execution time for a particular processing task based on a comparison of the number of records and allocation of compute resources with a heuristically-determined data model that indicates an amount of time to process a particular number of records using a particular number of compute resources. 
     In addition, the search head  210  can determine an execution time for a results portion of the query and/or one or more data transport portions of the query (e.g., time to transport data between different components of the system  16 . 
     Further, the search head  210  can determine an execution time for an external data system  12 . In some cases, the search head  210  can determine the execution time for the external data system  12  similar to the way in which the search head  210  determines the execution time for the indexers  206 . For example, if the external data system  12  is another data intake and query system  16 , the search head  210  can communicate the subquery for the other data intake and query system  16  and the other data intake and query system  16  can provide an execution time. In certain cases, the search head  210  can determine an execution time for the external data system  12  based on a predetermined (or provided) estimate or based on a previously measured execution time. For example, the system  16  can communicate a subquery to the external data system  12  and measure the amount of time to receive results from the external data system  12 . 
     At block  6908 , the search head  210  determines a query execution time for the query. In some embodiments, the search head  210  can determine the query execution time for the query based on the query execution time for the different query portions. In certain embodiments, the search head  210  can determine the query execution time by adding the execution time of the different query portions. In some cases, some parts of the query may be performed concurrently or in parallel. For example, the indexers  206  may execute an indexer portion of the query concurrently with one or more external data systems  12 . Similarly, as worker nodes  3306  receive data from the indexers  206  and/or external data system  12 , they can begin processing the data concurrently with the indexers  206  and/or external data systems  12 . The search head  210  can take into account any concurrent processing between the different components of the data intake and query system  16  or external data systems  12  as it determines the query execution time for the query. 
     In certain embodiments, the search head  210  can also use additional information to determine the query execution time. For example, different portions of the query may take a predetermined amount of time or may not vary significantly between queries, such as, but not limited to, communicating chunks of data from the indexers  206  to the worker nodes  3306 , communicating results from the worker nodes  3306  to the query coordinator  3304 , and/or communicating results from the query coordinator to the search process master or search head  210 , etc. Accordingly, the search head  210  can use the estimated time corresponding to communicating information between components of the data intake and query system  16  to determine the query execution time. 
     It will be understood that fewer, more, or different blocks can be used as part of the routine  3800 . In some cases, one or more blocks can be omitted. For example, in certain embodiments, the results received from nodes  3306  can be in a form that does not require any additional processing by the query coordinator  3304 . In some such embodiments, the query coordinator  3304  can communicate the results without additional processing. As another example, the routine  3800  can include monitoring worker nodes  3306  during execution of the query or query processing scheme, allocating or deallocating resources during the execution of the query, etc. Based on any reallocations, the system  16  can determine an updated execution time of the query. Similarly, routine  3800  can include reporting completion of the query to a component, such as the search process master  3302 , etc. 
     Moreover, it will be understood that one or more blocks described herein with reference to routine  6900  can be combined with one or more blocks of other routines described herein, such as the routines described herein at least with reference to  FIGS.  5 ,  6 ,  23 - 26 ,  31 ,  34 ,  38 - 45 ,  47 ,  49 ,  52 - 57 ,  63 ,  65 - 67 ,  68 ,  71 , and  73   . Furthermore, it will be understood that the various blocks described herein with reference to  FIG.  69    can be implemented in a variety of orders. For example, blocks  6904 - 6908  can be implemented concurrently, etc. 
     40.0. Processing High Cardinality Records with Related Fields 
     The worker nodes  3306  can receive a variety of records from the indexers  206 . In some cases, the worker nodes  3306  receive relatively large records that include multiple sub-records. In certain cases, one record can include thousands, millions, or even more sub-records. For example, executing a “stats dc (field A) by field B” command, or other command that identifies an association between multiple data fields, on a set of data can result in records with hundreds of thousands or more sub-records per record. 
     Large records can impede the worker nodes  3306  ability to store the records in partitions and process the partitions. For example, a worker node  3306  may have a limited amount of memory to store partitions and if the worker node  3306  receives many large records to store in the partition, it may run out of memory space, generate a memory error, or be unable to assign additional records to additional partitions. This can reduce system performance, result in the failure of the query to complete and/or result in the loss of data. 
     To address this and other potential issues, the system  16  can generate multiple records from a large record, assign the generated records to one or more partitions, and then combine similar records across the partitions. By breaking a large record into smaller records, the system  16  can be improved. For example, the system  16  can reduce the amount of memory used by a particular partition, reduce the likelihood of or avoid running out of memory for a particular partition, concurrently process the generated records in less time, and increase the throughput of the system  16 . 
       FIG.  70    is a block diagram illustrating an example of an embodiment in which individual records from multiple chunks of data are used to generate multiple records, which are stored in multiple partitions. The illustrated embodiment further illustrates the combination of similar records across multiple partitions and the reduction of those records. 
     In the illustrated embodiment, three chunks (Chunk 1, Chunk 2, and Chunk 3) are to be processed by a worker node  3306 . In some cases, Chunks 1, 2, 3 can correspond to chunks of data or portions of chunks received by a worker node  3306  from one or more indexers  206  in response to a query. For example, an indexer  206  may send a chunk of 50,000 records to a worker node  3306 . The worker node  3306  may break up the 50,000 records into groups or sub-chunks (e.g., each with 50 or 100 records) to facilitate processing. Accordingly, Chunks 1, 2, and 3 can correspond to different chunks of data received from one or more indexers  206  or sub-chunks of the chunks of data received from the one or more indexers  206 . In some cases, Chunks 1, 2, and 3 correspond to chunks received from the same indexer  206  or chunks received from different indexers  206  (e.g., Chunk 1 from indexer 1, Chunk 2 from indexer 2, and/or Chunk 3 from indexer 3). 
     As described herein, each chunk of data can include many records. In the illustrated embodiment, three records from each chunk are shown. As described herein, in some embodiments, each record of a chunk can correspond to one or more events or portions of event(s) that have been processed or transformed by the indexers  206  based on a query. For example, each record may be generated based on or include a portion of an event stored in a data store  208 . 
     The field values of a record can depend on a field value of a corresponding event and/or a field value generated during query execution. For example, some of the field values of each record can depend on the data of the corresponding event (e.g., Field 1 and Field 2), while others may depend on data that is generated as events or records are processed (e.g., Count Field). In the illustrated embodiment, the field values of Field 1 and Field 2 of each record of the Chunks 1, 2, 3, can be based on the data in a corresponding event, and the field value of the Count Field can be based on data generated as events/records are processed by the system  16 . 
     As described herein, the records of each chunk can be based on the commands of the query. In the illustrated embodiment, the records of Chunks 1, 2, and 3 can be based on a query that includes a command that indicates a relationship between two fields, such as, but not limited to, “stats DC (Field 1) by Field 2.” However, it will be understood that a variety of commands can result in records similar to those shown in the illustrated embodiment or that otherwise result in large records or records with a large number of sub-records. In certain cases, the command indicates a relationship between two fields where one or both fields have high cardinality field values. 
     In some cases, one or more (or all) of the records of a chunk of data can include multiple sub-records. In some such embodiments, sub-records of a record can share the same field value for some fields and different field values for other fields. For example, the sub-records of a record can share the same field value for one field, different field values for a second field, and the same or different field values for a third field, etc. 
     In the illustrated embodiment of  FIG.  70   , each of the Chunks 1, 2, 3 includes three records. The Records 1, 2, and 3, of Chunks 1 and 3 and Records 1 and 2 of Chunk 2 each include multiple sub-records. The Record 3 of Chunk 2 includes only one record (or one sub-record). The following table summarizes the number of sub-records per record in the illustrated embodiment, however, it will be understood that other records can have different numbers of sub-records depending on the query: 
     
       
         
           
               
               
               
             
               
                   
               
               
                   
                   
                 No. of 
               
               
                 Chunk No. 
                 Record No. 
                 Sub-Records 
               
               
                   
               
             
            
               
                 1 
                 1 
                 3 
               
               
                 1 
                 2 
                 2 
               
               
                 1 
                 3 
                 5 
               
               
                 2 
                 1 
                 8 
               
               
                 2 
                 2 
                 8 
               
               
                 2 
                 3 
                 1 
               
               
                 3 
                 1 
                 4 
               
               
                 3 
                 2 
                 4 
               
               
                 3 
                 3 
                 5 
               
               
                   
               
            
           
         
       
     
     In the illustrated embodiment of  FIG.  70   , for the records with sub-records, each sub-record of a record shares the same field value for Field 1 and has a different field value for Field 2. In addition, each sub-record in the illustrated embodiment includes a Count Field which may or may not match the count for another sub-record of the same record. However, it will be understood that other records can have different numbers of fields and/or different combinations of matching fields, depending on the query 
     As described herein, each record from a chunk of data is typically assigned to a partition as one record. In some embodiments, each partition is configured to store approximately the same number of records or use approximately the same amount of memory. In certain embodiments, the worker node  3306  may be able to vary the amount of records per partition or the amount of memory used per partition to accommodate related records or to complete a task. 
     In the illustrated embodiment, each of the Partitions 1, 2, 3 is configured to hold six records. Accordingly, if the three records from Chunk 1 and Chunk 2 are assigned to Partition 1, Partition 1 would store six records with a total of twenty-seven sub-records. Alternatively, if only records with field value A for Field 1 are assigned to Partition 1, then Partition 1 would store the records  7002  (Record 1 of Chunk 1),  7004  (Record 1 of Chunk 2), and  7006  (Record 1 of Chunk 3), totaling 15 sub-records, plus potentially three more records from additional chunks of data. Given that Partition 1 is configured to store six records, such a large amount of data or large number of sub-records compared to the Partition 1&#39;s configuration may exceed the storage limits of the partition and result in some of the records from Chunks 1, 2, or 3 not being processed. 
     To avoid this scenario, the worker node  3306  can generate a record from each sub-record of a chunk of data. In the illustrated embodiment of  FIG.  70   , the worker node  3306  has generated a record for each sub-record of records  7002 ,  7004 ,  7006 , (sub-records of Chunks 1, 2, 3 with field value A for Field 1). Although not illustrated, the worker node  3306  can generate a record for each sub-record with field value B or C for Field 1. However, for simplicity, only records generated from records with field value A for Field 1 are shown. Accordingly, in the illustrated embodiment, the worker nodes  3306  generates three records from record  7002 , eight records from record  7004 , and four records from the record  7004 . 
     In some cases, the worker node  3306  can assign the generated records to the same partition (or group of partitions) based on the shared field value. In the illustrated embodiment of  FIG.  70   , the worker node  3306  assigns the 15 generated records with field value A for Field 1 to one of Partitions 1, 2, and 3. 
     In some cases, the worker nodes  3306  can assign the generated records to one of the group of partitions based on the time of assignment and/or the content of the partitions. For example, as one partition is filled with records, another partition can be assigned to accept additional records. In the illustrated embodiment, each of the Partitions 1, 2, 3 can hold up to six records. Accordingly, as shown at  7008 , the worker node  3306  assigns the three records generated from the record  7002  and the first three records of the eight records generated from the record  7004  to Partition 1. With Partition 1 filled to capacity, the worker node  3306  assigns the remaining five records generated from the record  7004  to Partition 2. Finally, the worker node  3306  assigns the first record of the four records generated from the record  7006  to Partition 2 (meeting its capacity) and the last three records generated from the record  7006  to Partition 3. 
     As illustrated above, the sub-records from different chunks of data can be assigned to the same partition and sub-records from the same chunk of data can be assigned to different partitions. For example, as mentioned, Partition 1 includes records generated from sub-records of Chunks 1 and 2 and Partition 2 includes records generated from sub-records of Chunks 2 and 3. Similarly, records generated from record  7002  are assigned to Partition 1, records generated from record  7004  are assigned to Partitions 1 and 2, and records generated from record  7006  are assigned to Partitions 2 and 3. 
     In some cases, given the mixing of records from different chunks of data to the same partition, the worker node  3306  can parse the different partitions and combine similar records. For example, the worker node  3306  can combine records with the same event field values (field values that correspond to the field values of the event(s) related to the record). In some embodiments, the worker node  3306  can combine records by aggregating one or more field values of similar records (e.g., aggregating a count field value or other generated field value of records with the same event field values). By combining similar records, the worker node  3306  can reduce the amount of memory used by each partition and reduce the amount of processing (and therefore execution time) of later stages, or in some cases eliminate one or more processing stages. 
     As shown with reference to  7008  and  7010 , in the illustrated embodiment of  FIG.  70   , the worker node  3306  combines similar records in Partition 1, similar records in Partition 2, and similar records in Partition 3. As all of the generated records share the at least the same field value for one field (value ‘A’ for Field 1), similar records in this instance can refer to records that share the same field value for at least two fields (e.g., the value ‘A’ for Field 1 and the same field value for Field 2). For example, Partition 1 includes multiple records with the following field values for Fields 1 and 2: A:0 (Records 1 and 4) and A:1 (Records 2 and 5). Thus, worker node  3306  combines the A:0 records by aggregating the Count Field for all A:0. Similarly, the worker node  3306  combines the A:1 records of Partition 1 and the A:3 records of Partition 2 (Records 1 and 6). As Partition 3 does not include any similar records, the worker node  3306  does not combine any of Partition 3&#39;s records. 
     As the worker node  3306  may not be able to assign all generated records to the same partition, some similar records may be found across the group of partitions. For example, with reference to the illustrated embodiment, Partitions 1, 2, 3 each include an A:6 record (Records 3, 4, and 2, respectively). Accordingly, the worker node  3306  can reassign records to different partitions so that all similar records are found in the same partition. 
     The worker node  3306  can reassign records to different partitions in a variety of ways. In some embodiments, the worker node  3306  can use a modulo operand or hash code to reassign records. For example, the worker node  3306  can apply a modulo operand to one or more of the field values of the records. If the field values are the same, the records can be assigned to the same partition. 
     In some cases, the worker node  3306  uses a field value that is different from the already determined matching field value(s) of the sub-records. For example, with reference to the illustrated embodiment of  FIG.  70   , the worker node  3306  can use the Field 2 field value of each record to reassign the records given that it is already determined that the records of Partitions 1, 2, 3 have the same field value for Field 1. 
     In certain embodiments, the worker node  3306  can use a generated field value to reassign the records (e.g., a field value generated during the processing of the events or records). For example, the worker node  3306  can assign similar records to the partition with the highest (lowest, or some other amount) count for the similar record. In the illustrated embodiment of  FIG.  70   , the worker node  3306  has reassigned the records based on the Count Field value. Specifically, with reference to  7010 , the worker node  3306  determines that between Partitions 1, 2, and  3  there are multiple A:4, A:6, and A:7 records. In addition, the worker node  3306  determines that Partition 2 has the highest count of A:6 records and Partition 3 has the highest count of A:4 and A:7 records. Accordingly, the worker node  3306  assigns the A:4 and A:7 records from Partition 2 to Partition 3 and the A:6 records from Partitions 1 and 3 to Partition 2, as shown at  7012 . 
     In some cases, following the reassignment of records to the different partitions, each partition can include a set of records that does not overlap with the set of records of the other partitions. For example, the combination of Field 1 field values and Field 2 field values in one partition may not be found in another partition. With reference to  7012 , Partition 1 includes all A:0 (Record 1), A:1 (Record 2), and A:2 (Record 3) records, Partition 2 includes all A:3 (Record 1), A:5 (Record 2), and A:6 (Records 3-5) records, and Partition 3 includes all A:4 (Records 1 and 3) and A:7 (Record 2 and 4) records. In certain embodiments, the records can be reassigned such that the partitions include the records in a particular order (e.g., Partition 1 can include the A:0-A:2 records, Partition 2 can include the A:3-A:5 records, and Partition 3 can include A:6 and A:7 records) as desired. 
     Based on the reassignment of records, the worker node  3306  can (again) combine similar records similar records within each partition. With reference to  7012  and  7014 , the worker node  3306  combines the three A:6 records of Partition 2, the two A:4 records of Partition 3, and the two A:7 records of Partition 3 by aggregating the Count Field value of the similar records. 
     The worker node  3306  can continue to process the partitions based on the query. In the illustrated embodiment of  FIG.  70   , based on the query, the worker node  3306  determines a count for the number of distinct records with the same Field 1 field value that remain in the partitions at  7014  and/or a count of the number of unique combinations of the Field 1 field value and the Field 2 field value. The results of the processing are shown at  7016 . Specifically, each of Partitions 1, 2, 3 has been reduced to a single record for each Field 1 field value that indicates the number of unique Field 2 field values for the Field 1 field value. 
     It will be understood that the example embodiment shown in  FIG.  70    is a simplified example. For example, for simplicity, the example shown in  FIG.  70    may only illustrate a subset of the number of records in a chunk of data (e.g., each chunk of data may include thousands, millions, or more records), a subset of the number of sub-records of a record of a chunk of data (e.g., each record may include thousands, millions, or more sub-records), a subset of the number of records generated from a record of a chunk of data (e.g., thousands, millions, or more records can be generated from a single record), a subset of the number of records per partition (e.g., each partition may include thousands, millions, or more records), a subset of the number of partitions (e.g., a worker node  3306  may process hundreds, thousands, or millions of partitions, etc.). Furthermore, for simplicity, the example shown in  FIG.  70    does now show that multiple field values for Field 1 can be assigned to the same partition (e.g., Partition 1 can include records with a field value of B for Field 1) or that multiple cores in a worker node  3306  can be working concurrently to process Partitions 1, 2, 3 or that each core of the worker node  3306  can be processing its own set of partitions. Furthermore, the example of  FIG.  70    does not show that multiple worker nodes  3306  can be working concurrently to process chunks of data received by the indexers  206 . Accordingly, the example shown in  FIG.  70    should not be construed as limiting. 
     In some embodiments, the expansion of records, assignment to different partitions, and the combination of similar records can be performed by the worker node  3306  based on one or more factors. In some cases, worker node  3306  can perform these processes based on a query parameter identified or referenced by the query. For example, if the query includes a command “stats DC (Field 1) by Field 2,” or other command that identifies an association between two fields, the worker node  3306  can perform one or more of these processes. As yet another example, the worker node  3306  can perform these processes based on an identification of a particular index, host, source, or sourcetype. In certain cases, the worker node  3306  can perform these processes based on a determined size of the records of the chunks it receives. For example, if the worker node  3306  determines that each record uses up a threshold size of memory or includes a threshold number of sub-records, the worker node  3306  can determine that it is to generate multiple records from one record and process the generated records as described herein. 
       FIG.  71    is a flow diagram illustrative of an embodiment of a routine  7100  implemented by a worker node  3306  to expand and reduce records from one or more chunks of data. Although certain blocks are described as being implemented by a worker node  3306 , it will be understood that the elements outlined for routine  7100  can be implemented by one or more computing devices/components (alone or in combination) that are associated with a data intake and query system  16 , such as an indexer  206 , search head  210 , search process master  3302 , query coordinator  3304 , etc. Thus, the following illustrative embodiment should not be construed as limiting. Moreover, it will be understood that routine  7100  is not limited to a data intake and query system  16 , but can be used to process high cardinality records in a variety of systems and environments. 
     At block  7102 , the worker node  3306  obtains a chunk of data. In some embodiments, the worker node  3306  can obtain a chunk of data as described herein at least with reference to block  6502  of  FIG.  65   . As described herein the chunk of data can correspond to a chunk of data received from an indexer  206  or a chunk (or sub-chunk) of data generated from a chunk of data received from the indexer  206 . In addition, the records in the chunk of data can be based on the query and the set of data identified by the query. 
     As described herein, in some cases, based on the query (non-limiting example: a “stats DC by” command or other command that identifies a relationship between two fields and/or relevant records have high cardinality field values for one or both fields) and the set of data identified by the query, one or more records of the chunk of data can include multiple sub-records. In some cases, a record can include thousands or millions of sub-records. In certain cases, each sub-record of a record can share the same field value for at least one field. The sub-records may or may not share field values of other fields. For example, the record can be identified as a field value “A” record indicating that all sub-records have the field value “A” for the same field (but may have different field values for other fields). Further, in some cases, the shared field value can correspond to a field value of one or more events stored in a data store  208  of the system  16 . In certain embodiments, each record or each sub-record can correspond to one event stored in a data store  208 . In some cases, each record or each sub-record can correspond to multiple events stored in a data store  208 . 
     At block  7104 , the worker node  3306  generates a plurality of records from a record of the chunk of data. In some cases, the worker node  3306  generates a record for one or more sub-records of the record (or each sub-record) of the chunk of data. In certain embodiments, similar to the sub-records, each generated record can share the same field value for at least one field (the same field value that is shared by sub-records of the same record). Other field values of generated records may or may not be the same. In some cases, the worker node  3306  generates a record for each sub-record of a record from the chunk of data. In some such embodiments, the number of records generated from a record can correspond to the number of sub-records of the record. For example, if a record has 100,000 sub-records, the worker node  3306  can generate 100,000 records from the record. 
     At block  7106 , the worker node  3306  assigns the generated records to one or more partitions. In some embodiments, the worker nodes  3306  assigns a generated record to a partition as it is generated. In some such embodiments, the assignment may be based on the time of the assignment or based on a first generated first assigned type assignment. In certain embodiments, the worker node  3306  assigns generated records (records generated from the same record) to the same partition until the partition is filled. Once the partition is filled, the worker nodes  3306  can assign generated records to a subsequent partition. In certain embodiments, each partition can be allocated up to approximately the same number of records or use up to approximately the same amount of memory. 
     In some cases, the worker nodes  3306  assigns the generated records based on a hash code or modulus of one of the fields of the generated records or sub-records. For example, in some cases, the worker nodes  3306  can assign the generated records to a partition based on a modulus or hash of a field value of the field that is different from the field with the shared field value. In some such cases, this type of assignment may be similar to the assignment of records described herein at least with reference to  FIG.  65   . In some embodiments, by assigning the generated records based on hash code or modulus, the worker nodes  3306  can facilitate the combination of similar records. 
     As described herein, based on the assignment of generated records to partitions, one partition can receive records from multiple chunks of data and/or records generated from the same chunk of data can be assigned to different partitions. Accordingly, in some embodiments, similar records can be assigned to the same partition. In some such embodiments, the similar records may correspond to sub-records of records from different chunks of data. As described herein, in some cases, similar records can correspond to records that share the same field value for one or more fields. In certain embodiments, as all of the generated records may share the same field value for one field, the similar records in this case may correspond to records that share the same field value for at least two fields. 
     At block  7108 , the worker node  3306  combines records of a partition of the one or more partitions. As described herein, the worker node  3306  can combine similar records, such as records with the same field value for at least two fields. The two fields may correspond to event fields or fields that are based on content of one or more events stored in a data store  208  as opposed to field values generated during query execution (e.g., a count field value). In some embodiments, the worker node  3306  can combine similar records by aggregating one or more field values of the records. For example, if the records include a count field, the worker node  3306  can aggregate the count field values. Further, in combining the records, the worker node  3306  can reduce the similar records to a single record. For example, three similar records can be reduced to one record. 
     At block  7110 , the worker node  3306  combines records across the one or more partitions. In some cases, to combine records across the one or more partitions, the worker node  3306  can reassign records. In certain embodiments, the worker node  3306  reassigns records such that similar records are assigned to the same partition. For example, the worker node  3306  can use a hash function or modulo on the field value of a particular field of each record in the partitions. In certain cases, the particular field is different from the field with the same field value across all records generated from the same record of a chunk of data. Based on the results of the hash function or modulo, the worker node  3306  can reassign the record to a particular partition. In this way, records with the same field value for the particular field can be assigned to the same partition. In certain embodiments, the worker node  3306  can identify the partition with the highest count for a particular similar record and assign all others records that are similar to the particular similar record to that partition. 
     In addition, based on the reassignment, the worker node  3306  can combine records of the partitions. In some cases this combination of records of the partition can be similar to the combination of records described herein at least with reference to block  7108 . 
     At block  7112 , the worker node  3306  processes the one or more partitions. In some embodiments, the worker node  3306  can continue to process the partitions based on the query. In some embodiments, the command that led to the initiation of routine  7100  can include additional processing tasks. For example, as described herein, in some cases, the worker node  3306  can combine all records with the same field value of the first field in a partition. In some such embodiments, to combine the records with the same field value in the partition, the worker node  3306  can count the number of records with the same field value of the first field and generated a record that identifies the field value of the first field and the count of the remaining records that included the same field value of the first field. In some such embodiments, the resulting partition can include one record for each unique field value of the first field and a count corresponding to the number of unique combinations of the first field value of the first field and field values of a second field. 
     It will be understood that fewer, more, or different blocks can be used as part of the routine  7100 . In some cases, one or more blocks can be omitted. For example, block  7108  can be omitted. In some such embodiments, the worker node  3306  may reassign records before attempting to combine similar records. As another example, in some cases, blocks  7106  and  7108  can be combined. For example, as the worker nodes  3306  assigns records to one or more partitions, the received records can be combined with similar records that are already assigned to the partition. In some embodiments, the combination blocks  7106  and  7108  can be similar to the combination described herein at least with reference to  FIG.  65   . 
     Moreover, it will be understood that one or more blocks described herein with reference to routine  7100  can be combined with one or more blocks of other routines described herein, such as the routines described herein at least with reference to  FIGS.  5 ,  6 ,  23 - 26 ,  31 ,  34 ,  38 - 45 ,  47 ,  49 ,  52 - 57 ,  63 ,  65 - 69 , and  73   . In certain embodiments, any one or any combination of  7102 - 7110  can be part of a query execution stage, as described herein. Furthermore, it will be understood that the various blocks described herein with reference to  FIG.  71    can be implemented in a variety of orders. For example, blocks  7104 - 7108  can be implemented concurrently, etc. 
     41.0. Pushing Processing Tasks 
     Queries executed by the system  16  can create different demands on different components of the system  16 . For example, based on the query parameters and syntax, certain processing tasks may be performed on different components of the system  16 . For example, some queries may use minimal indexers  206  but use a significant number of worker nodes  3306  to execute a query (e.g., queries that include multiple commands that expand the number of records). As another example, one query may result in minimal processing by a query coordinator  3304  or search head  210 , while another query may result in significant processing being done by the query coordinator  3304  or search head  210 . 
     In addition, depending on the query, some components may be able to concurrently execute commands or processing tasks of the query, while other components may execute the command or processing task by itself or sequentially. Executing a command of a query using one component as opposed to multiple components concurrently can negatively impact the system  16 . For example, the component can create a bottleneck for query execution, increase the query execution time, and reduce the overall throughput of the system  16 . In addition, using a single component to execute a command can increase the likelihood of a memory error or result in the systems  16 &#39;s inability to process some of the data. 
     To address this and other potential issues, the system  16  can, in some cases, assign processing tasks (or parts of a processing task) that would be executed by one component to other components, or assign a supplemental processing task to the other components. For example, if based on a query, a search head  210  is to perform a particular processing task, the system  16  can assign that processing task to the one or more worker nodes  3306 , assign a portion of the processing task to the one or more worker nodes  3306 , and/or assign a supplemental processing task to the one or more worker nodes  3306 . In some cases, this may be referred to as pushing or pushing down a processing task. In certain embodiments pushing or pushing down a processing task can refer to the reassignment, partial reassignment, or assignment of a supplemental processing task from one component to another group of components that execute processing tasks prior to the one component. As such, pushing or pushing down a processing task can refer to moving or executing processing tasks earlier in a pipeline or DAG. 
     By pushing a processing task, the system  16  can reduce the strain on a component or reduce the likelihood that the component will create a bottleneck. In addition, the system  16  can more evenly distribute the processing tasks to the components of the system  16 , thereby increasing the parallelized execution of the query, increasing the query execution throughput, and decreasing the query execution time. As such pushing processing tasks can improve the functioning of the system  16  itself, as well as improve the functioning of distributed systems. 
     As a non-limiting example, based on the query, the system  16  may determine that the search head  210  is to analyze all of the records of multiple partitions to identify a particular subset of the records for additional processing or as query results. In some such cases, analyzing the records of the partitions may result in the search head  210  analyzing millions or billions of results do identify a relatively small subset of the records for further processing. For example, it may be that the search head  210  is to analyze 50,000,000 records across 100 partitions to identify the top 10,000 records. Analyzing significantly more records than will be used for further processing can take a significant amount of time and create a bottle neck at the search head  210 . 
     To reduce the bottleneck, the system  16  can push the command of identifying the top 10,000 records to the worker nodes  3306  to perform on each of the 100 partitions. As such, rather than the worker nodes  3306  sending all of the records from each partition to the search head  210 , the worker nodes  3306  can send at most 10,000 records from each of the partitions. As such, the search head  210  can analyze 1,000,000 records to identify the top 10,000 records (rather than analyzing 50,000,000 records). Accordingly, the system  16  can significantly reduce the amount of processing to be performed by the search head  210 . In addition, using the worker nodes  3306 , the system  16  can parallelize some of the processing that was to be done by the search head  210 , thereby reducing the execution time of the processing task and the query execution time. 
       FIG.  72    is a block diagram illustrating an example of an embodiment of the system  16  assigning a processing task to one or more worker nodes  3306  from a search head  210  and/or a query coordinator  3304 . As described herein, pushing the processing task to one or more worker nodes  3306  can refer to reassigning the processing task, assigning a portion of the processing task, or assigning a supplemental processing task to the one or more worker nodes  3306 . In some embodiments, by pushing the processing task, the system  16  can reduce the amount of processing performed by the search head  210 , remove or reduce potential bottleneck at the search head  210 , increase the parallelized execution of the query, and reduce the query execution time. 
     In some embodiments, the system  16  can determine to push a processing task based on one or more query parameters. For example, the system  16  can identify a particular command of a query that can be pushed to other components (e.g., head, tail, etc.). In certain embodiments, the system  16  can determine to push a processing task based on a sequence of commands or the syntax of the query. For example, if a particular sequence of commands is included in the query (e.g., sort . . . | . . . head/tail . . . ), then the system  16  can determine that a command can be pushed. In some cases, the system  16  can determine whether to push a processing task based on a field identified in the query. For example, for some fields or field-command combinations (e.g., host, source, sourcetype), the system  16  may be able to push a command but for other fields or command-field combination (e.g., “sort count”), the system  16  may be unable to push a command. 
     In the illustrated embodiment, based on a query, the worker nodes  3306  generate Partitions 1, 2, 3, 4. In some cases, the Partitions 1, 2, 3, 4 can correspond to the results or partial results that the worker nodes  3306  are ready to communicate to the search head  210  and/or query coordinator  3304  after performing one or more processing tasks on the records (or earlier versions of the records). It will be understood that the worker nodes  3306  can generate fewer or more partitions depending on the query. 
     As shown, each of the Partitions 1, 2, 3, 4 includes a number of records. Specifically, Partition 1 includes eight records, Partition 2 includes six records, Partition 3 includes nine records and Partition 4 includes five records. It will be understood that the Partitions 1, 2, 3, 4 can include fewer or more records. As described herein, in some embodiments, a partition can include thousands, millions, or more records. 
     In the illustrated embodiment, each record of the partitions includes a keyword value for a keyword field and a count value for a count field. In addition, the records within and across the partitions are sorted by the keyword value. It will be understood that the records of each partition may include any number of fields and/or field values and be in any order. Moreover, it will be understood that the records between the Partitions 1-4 may not be sorted as shown. 
     In the illustrated embodiment, the query includes a command (e.g., “|head 4”) indicating that following one or more processing tasks, the search head  210  is to provide the top four results as a final result. For example, the system  16  may initially determine that the search head  210  is to analyze all of the record of Partitions 1, 2, 3, 4. However, rather than having the search head  210  analyze all 28 records of the four partitions, the system  16  can push the command to the worker nodes  3306  such that only the top four results from each partition are sent to the search head  210  for further processing. In the illustrated embodiment, the worker nodes  3306  send the records with the following field keyword and count values to the search head  210 , as intermediate results, as shown at  7202 . 
     
       
         
           
               
               
             
               
                   
               
               
                 Partition 
                 Keyword Value:Count 
               
               
                   
               
             
            
               
                 1 
                 C:5, D:7, G:8, H:9 
               
               
                 2 
                 J:6, K:7, L:5, N:8 
               
               
                 3 
                 O:7, R:8, T:6, U:9 
               
               
                 4 
                 X:4, Y:3, AA:5, BB:3 
               
               
                   
               
            
           
         
       
     
     Accordingly, the system  16  has reduced the number of records to be analyzed by the search head  210  almost by half (from 28 to 16). As such, the search heard  210  is able to more quickly identify the top four results from the received records (G:8, H:9, N:8, U:9) and provide them as a final result, as shown at  7204 . Although described has providing the top results, it will be understood that other commands can be pushed to different components of the system  16 . In addition, it will be understood that the pushing of commands can be implemented in a variety of distributed systems where a particular processing task is assigned to one component and where the system assigns a portion of the processing task or a supplemental processing task to a group of other components such that the processing load of the initially assigned component is reduced. 
     Furthermore, in some cases, the system  16  can push the command to the worker nodes  3306  so that the worker nodes  3306  provide the final results to the search head  210 . For example, rather than sending the intermediate results  7202  to the search head  210 , the worker nodes  3306  can assign the intermediate results to one or more partitions and perform the same process on the partition(s) that hold the intermediate results  7202 . The worker nodes  3306  can iteratively process the records until the final results  7204  are determined. The worker nodes  3306  can then provide the final results to the search head  210 . 
       FIG.  73    is a flow diagram illustrative of an embodiment of a routine  7300  implemented by the system  16  to push a processing task from one component to one or more different components. As described herein, pushing the processing task can refer to reassigning the processing task, assigning a portion of the processing task, or assigning a supplemental processing task to one or more components different from the component that would otherwise execute the processing task. Although certain blocks are described as being implemented by the system  16 , it will be understood that the elements outlined for routine  7300  can be implemented by one or more computing devices/components (alone or in combination) that are associated with a data intake and query system  16 , such as an indexer  206 , search head  210 , search process master  3302 , query coordinator  3304 , worker nodes  3306 , etc. Thus, the following illustrative embodiment should not be construed as limiting. Moreover, it will be understood that routine  7300  is not limited to a data intake and query system  16 , but can be used to push processing tasks in a variety of systems and environments. 
     At block  7302 , the system  16  obtains one or more partitions. In some embodiments, one or more worker nodes  3306  obtain the partitions based on one or more processing tasks executed by the worker nodes  3306  on a plurality of partitions. As described herein, the worker nodes  3306  can receive chunks of data from the indexers  206  and store records from the chunks of data into one or more partitions. As described herein, the record of the partitions can include one or more field values for one or more fields. Some of the fields can correspond to fields of events stored in a data store  208  and other fields can correspond to fields generated based on the query (e.g., count field, etc.) 
     The worker nodes  3306  can then process the partitions (including their records) based on a query. In some cases, processing the partitions includes executing one or more processing tasks on the records of the different partitions. In certain cases, the worker nodes  3306  provide the results of the processing tasks to a search head  210 . Accordingly, in some embodiments, the partitions can correspond to the partitions that the worker nodes  3306  have processed. In some such embodiments, the worker nodes  3306  may be ready to communicate the records of the one or more partitions to the search head  210 . 
     As mentioned, the partitions can be generated or obtained based on one or more query parameters, including one or more commands, the syntax of the query, the set of data identified by the query, etc. In addition, the query can identify a processing task that is to be executed by one component of the system  16 . In certain cases, the processing task can be to provide a particular quantity of records as a result of the query. Further, the processing task can be designated for execution by one component of the system  16 . Based on the identification of the processing task and the query (e.g., a sequence of processing tasks, etc.), the system  16  can determine that the processing task is to be pushed to a different set of components. For example, the system  16  can determine that the processing task is to be pushed to the worker nodes  3306  from a search head  210 . Accordingly, the system  16  can assign the worker nodes  3306  to execute the processing task, a portion of the processing task or a processing task that supplements the processing task of the other component. 
     At block  7304 , the worker nodes  3306  obtain one or more records from each of the one or more partitions. In some embodiments, the worker nodes  3306  obtain the one or more records based on the assignment determined by the system  16 . In some cases, the worker nodes  3306  obtain a particular quantity of records from each partition based on the query. In certain embodiments, the worker nodes  3306  obtain the records from the partitions based on the query. For example, if the query indicates that 100 results are to be obtained as a final result, the worker nodes  3306  can obtain 100 results from each of the partitions. In some cases, the worker nodes  3306  provide the results from each partition to the search head  210  for further processing. In certain embodiments, the worker nodes  3306  provide the results to another partition. In some such embodiments, the aggregated records from each of the partitions can be referred to as a set of records. 
     At block  7306 , the system  16  obtains records from a set of records. As mentioned, the set of records can correspond to records obtained from each partition. In some such embodiments, as described herein, the system  16  can obtain the same quantity of records from each partition. In certain embodiments, the records obtained from the set of records can based on the query. For example, if the query indicates that 100 results are to be obtained as a final result, the search head  210  (or worker nodes  3306 ) can obtain 100 results from the set of results. As mentioned, in some cases the set of records can reside in one or more partitions associated with the worker nodes  3306  or with the search head  210 . Accordingly, in some cases, the worker nodes  3306  can obtain the records from the set of records. In certain cases, the search head  210  can obtain the records from the set of records. 
     At block  7308 , the system  16  displays query results. In some case, the results can correspond to the records obtained from the set of records. In certain embodiments, a search head  210  can further process the records obtained from the set of records to determine the query results. In certain embodiments, the results can be based on the query. 
     It will be understood that fewer, more, or different blocks can be used as part of the routine  7300 . In some cases, one or more blocks can be omitted. Moreover, it will be understood that one or more blocks described herein with reference to routine  7300  can be combined with one or more blocks of other routines described herein, such as the routines described herein at least with reference to  FIGS.  5 ,  6 ,  23 - 26 ,  31 ,  34 ,  38 - 45 ,  47 ,  49 ,  52 - 57 ,  63 ,  65 - 69 , and  71   . In certain embodiments, any one or any combination of  7302 - 7310  can be part of a query execution stage, as described herein. Furthermore, it will be understood that the various blocks described herein with reference to  FIG.  7300    can be implemented in a variety of orders. For example, blocks  7304  and  7306  can be implemented concurrently, etc. 
     42.0. Hardware and Isolated Execution Environment Embodiment 
       FIG.  74    is a block diagram illustrating a high-level example of a hardware architecture of a computing system in which an embodiment may be implemented. For example, the hardware architecture of a computing system  72  can be used to implement any one or more of the functional components described herein (e.g., indexer, data intake and query system, search head, data store, server computer system, edge device, etc.). In some embodiments, one or multiple instances of the computing system  72  can be used to implement the techniques described herein, where multiple such instances can be coupled to each other via one or more networks. 
     The illustrated computing system  72  includes one or more processing devices  74 , one or more memory devices  76 , one or more communication devices  78 , one or more input/output (I/O) devices  80 , and one or more mass storage devices  82 , all coupled to each other through an interconnect  84 . The interconnect  84  may be or include one or more conductive traces, buses, point-to-point connections, controllers, adapters, and/or other conventional connection devices. Each of the processing devices  74  controls, at least in part, the overall operation of the processing of the computing system  72  and can be or include, for example, one or more general-purpose programmable microprocessors, digital signal processors (DSPs), mobile application processors, microcontrollers, application-specific integrated circuits (ASICs), programmable gate arrays (PGAs), or the like, or a combination of such devices. 
     Each of the memory devices  76  can be or include one or more physical storage devices, which may be in the form of random access memory (RAM), read-only memory (ROM) (which may be erasable and programmable), flash memory, miniature hard disk drive, or other suitable type of storage device, or a combination of such devices. Each mass storage device  82  can be or include one or more hard drives, digital versatile disks (DVDs), flash memories, or the like. Each memory device  76  and/or mass storage device  82  can store (individually or collectively) data and instructions that configure the processing device(s)  74  to execute operations to implement the techniques described above. 
     Each communication device  78  may be or include, for example, an Ethernet adapter, cable modem, Wi-Fi adapter, cellular transceiver, baseband processor, Bluetooth or Bluetooth Low Energy (BLE) transceiver, or the like, or a combination thereof. Depending on the specific nature and purpose of the processing devices  74 , each I/O device  80  can be or include a device such as a display (which may be a touch screen display), audio speaker, keyboard, mouse or other pointing device, microphone, camera, etc. Note, however, that such I/O devices  80  may be unnecessary if the processing device  74  is embodied solely as a server computer. 
     In the case of a client device (e.g., edge device), the communication devices(s)  78  can be or include, for example, a cellular telecommunications transceiver (e.g., 3G, LTE/4G, 5G), Wi-Fi transceiver, baseband processor, Bluetooth or BLE transceiver, or the like, or a combination thereof. In the case of a server, the communication device(s)  78  can be or include, for example, any of the aforementioned types of communication devices, a wired Ethernet adapter, cable modem, DSL modem, or the like, or a combination of such devices. 
     A software program or algorithm, when referred to as “implemented in a computer-readable storage medium,” includes computer-readable instructions stored in a memory device (e.g., memory device(s)  76 ). A processor (e.g., processing device(s)  74 ) is “configured to execute a software program” when at least one value associated with the software program is stored in a register that is readable by the processor. In some embodiments, routines executed to implement the disclosed techniques may be implemented as part of OS software (e.g., MICROSOFT WINDOWS® and LINUX®) or a specific software application, algorithm component, program, object, module, or sequence of instructions referred to as “computer programs.” 
     43.0. Terminology 
     Computer programs typically comprise one or more instructions set at various times in various memory devices of a computing device, which, when read and executed by at least one processor (e.g., processing device(s)  74 ), will cause a computing device to execute functions involving the disclosed techniques. In some embodiments, a carrier containing the aforementioned computer program product is provided. The carrier is one of an electronic signal, an optical signal, a radio signal, or a non-transitory computer-readable storage medium (e.g., the memory device(s)  76 ). 
     Any or all of the features and functions described above can be combined with each other, except to the extent it may be otherwise stated above or to the extent that any such embodiments may be incompatible by virtue of their function or structure, as will be apparent to persons of ordinary skill in the art. Unless contrary to physical possibility, it is envisioned that (i) the methods/steps described herein may be performed in any sequence and/or in any combination, and (ii) the components of respective embodiments may be combined in any manner. 
     Although the subject matter has been described in language specific to structural features and/or acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as examples of implementing the claims, and other equivalent features and acts are intended to be within the scope of the claims. 
     Conditional language, such as, among others, “can,” “could,” “might,” or “may,” unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments include, while other embodiments do not include, certain features, elements and/or steps. Thus, such conditional language is not generally intended to imply that features, elements and/or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without user input or prompting, whether these features, elements and/or steps are included or are to be performed in any particular embodiment. 
     Unless the context clearly requires otherwise, throughout the description and the claims, the words “comprise,” “comprising,” and the like are to be construed in an inclusive sense, as opposed to an exclusive or exhaustive sense, e.g., in the sense of “including, but not limited to.” As used herein, the terms “connected,” “coupled,” or any variant thereof means any connection or coupling, either direct or indirect, between two or more elements; the coupling or connection between the elements can be physical, logical, or a combination thereof. Additionally, the words “herein,” “above,” “below,” and words of similar import, when used in this application, refer to this application as a whole and not to any particular portions of this application. Where the context permits, words using the singular or plural number may also include the plural or singular number respectively. The word “or” in reference to a list of two or more items, covers all of the following interpretations of the word: any one of the items in the list, all of the items in the list, and any combination of the items in the list. Likewise the term “and/or” in reference to a list of two or more items, covers all of the following interpretations of the word: any one of the items in the list, all of the items in the list, and any combination of the items in the list. 
     Conjunctive language such as the phrase “at least one of X, Y and Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to convey that an item, term, etc. may be either X, Y or Z, or any combination thereof. Thus, such conjunctive language is not generally intended to imply that certain embodiments require at least one of X, at least one of Y and at least one of Z to each be present. Further, use of the phrase “at least one of X, Y or Z” as used in general is to convey that an item, term, etc. may be either X, Y or Z, or any combination thereof. 
     In some embodiments, certain operations, acts, events, or functions of any of the algorithms described herein can be performed in a different sequence, can be added, merged, or left out altogether (e.g., not all are necessary for the practice of the algorithms). In certain embodiments, operations, acts, functions, or events can be performed concurrently, e.g., through multi-threaded processing, interrupt processing, or multiple processors or processor cores or on other parallel architectures, rather than sequentially. 
     Systems and modules described herein may comprise software, firmware, hardware, or any combination(s) of software, firmware, or hardware suitable for the purposes described. Software and other modules may reside and execute on servers, workstations, personal computers, computerized tablets, PDAs, and other computing devices suitable for the purposes described herein. Software and other modules may be accessible via local computer memory, via a network, via a browser, or via other means suitable for the purposes described herein. Data structures described herein may comprise computer files, variables, programming arrays, programming structures, or any electronic information storage schemes or methods, or any combinations thereof, suitable for the purposes described herein. User interface elements described herein may comprise elements from graphical user interfaces, interactive voice response, command line interfaces, and other suitable interfaces. 
     Further, processing of the various components of the illustrated systems can be distributed across multiple machines, networks, and other computing resources. In certain embodiments, one or more of the components of the data intake and query system  16  can be implemented in a remote distributed computing system. In this context, a remote distributed computing system or cloud-based service can refer to a service hosted by one more computing resources that are accessible to end users over a network, for example, by using a web browser or other application on a client device to interface with the remote computing resources. For example, a service provider may provide a data intake and query system  16  by managing computing resources configured to implement various aspects of the system (e.g., search head  210 , indexers  206 , worker nodes  3306 , common storage  4602 , ingested data buffer  4802 , search process master  3302 , query coordinator  3304 , acceleration data store  3308 , etc.) and by providing access to the system to end users via a network. 
     When implemented as a cloud-based service, various components of the system  108  can be implemented using containerization or operating-system-level virtualization, or other virtualization technique. For example, one or more components of the system  16  (e.g., search head  210 , indexers  206 , worker nodes  3306 , ingested data buffer  4802 , search process master  3302 , query coordinator  3304 , etc.) can be implemented as separate software containers or container instances. Each container instance can have certain resources (e.g., memory, processor, etc.) of the underlying host computing system assigned to it, but may share the same operating system and may use the operating system&#39;s system call interface. Each container may provide an isolated execution environment on the host system, such as by providing a memory space of the host system that is logically isolated from memory space of other containers. Further, each container may run the same or different computer applications concurrently or separately, and may interact with each other. Although reference is made herein to containerization and container instances, it will be understood that other virtualization techniques can be used. For example, the components can be implemented using virtual machines using full virtualization or paravirtualization, etc. Thus, where reference is made to “containerized” components, it should be understood that such components may additionally or alternatively be implemented in other isolated execution environments, such as a virtual machine environment. 
     Likewise, the data repositories shown can represent physical and/or logical data storage, including, e.g., storage area networks or other distributed storage systems. Moreover, in some embodiments the connections between the components shown represent possible paths of data flow, rather than actual connections between hardware. While some examples of possible connections are shown, any of the subset of the components shown can communicate with any other subset of components in various implementations. 
     Embodiments are also described above with reference to flow chart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products. Each block of the flow chart illustrations and/or block diagrams, and combinations of blocks in the flow chart illustrations and/or block diagrams, may be implemented by computer program instructions. Such instructions may be provided to a processor of a general purpose computer, special purpose computer, specially-equipped computer (e.g., comprising a high-performance database server, a graphics subsystem, etc.) or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor(s) of the computer or other programmable data processing apparatus, create means for implementing the acts specified in the flow chart and/or block diagram block or blocks. These computer program instructions may also be stored in a non-transitory computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the acts specified in the flow chart and/or block diagram block or blocks. The computer program instructions may also be loaded to a computing device or other programmable data processing apparatus to cause operations to be performed on the computing device or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computing device or other programmable apparatus provide steps for implementing the acts specified in the flow chart and/or block diagram block or blocks. 
     Any patents and applications and other references noted above, including any that may be listed in accompanying filing papers, are incorporated herein by reference. Aspects of the invention can be modified, if necessary, to employ the systems, functions, and concepts of the various references described above to provide yet further implementations of the invention. These and other changes can be made to the invention in light of the above Detailed Description. While the above description describes certain examples of the invention, and describes the best mode contemplated, no matter how detailed the above appears in text, the invention can be practiced in many ways. Details of the system may vary considerably in its specific implementation, while still being encompassed by the invention disclosed herein. As noted above, particular terminology used when describing certain features or aspects of the invention should not be taken to imply that the terminology is being redefined herein to be restricted to any specific characteristics, features, or aspects of the invention with which that terminology is associated. In general, the terms used in the following claims should not be construed to limit the invention to the specific examples disclosed in the specification, unless the above Detailed Description section explicitly defines such terms. Accordingly, the actual scope of the invention encompasses not only the disclosed examples, but also all equivalent ways of practicing or implementing the invention under the claims. 
     To reduce the number of claims, certain aspects of the invention are presented below in certain claim forms, but the applicant contemplates other aspects of the invention in any number of claim forms. Any claims intended to be treated under 35 U.S.C. § 112(f) will begin with the words “means for,” but use of the term “for” in any other context is not intended to invoke treatment under 35 U.S.C. § 112(f). Accordingly, the applicant reserves the right to pursue additional claims after filing this application, in either this application or in a continuing application.