Patent Publication Number: US-2023144450-A1

Title: Multi-partitioning data for combination operations

Description:
RELATED APPLICATIONS 
     Any application referenced herein is hereby incorporated by reference in its entirety. 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. This application is a continuation of U.S. Pat. Application No. 17/086,043, filed on Oct. 30, 2020 entitled MULTI-PARTITIONING FOR COMBINATION OPERATIONS, which is a continuation of U.S. Pat. Application No. 15/714,029, filed on Sep. 25, 2017, entitled MULTI-PARTITIONING DETERMINATION FOR COMBINATION OPERATIONS, which is incorporated herein by reference in its entirety. 
    
    
     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. For example, in some cases, a user may want to use multiple partitions and distributed processing cores to combine two or more large datasets in some fashion, such as by using a join operation. However, in certain cases, combining the datasets can result in one or more significantly imbalanced partitions and a single processing core being tasked with processing a disproportionately large number of data entries as compared to other processor cores. This imbalance can result in a significant delay of the entire set of results until the processor core finishes processing the imbalanced partition. 
    
    
     
       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; 
         FIG.  9    is an example search query received from a client and executed by search peers, in accordance with example embodiments; 
         FIG.  10    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.  11    is a block diagram illustrating an embodiment of multiple machines, each having multiple nodes; 
         FIG.  12    is a diagram illustrating an embodiment of a DAG; 
         FIG.  13    is a block diagram illustrating an embodiment of partitions implementing various search phases of a DAG; 
         FIG.  14    is a data flow diagram illustrating an embodiment of communications between various components within the environment to process and execute a query; 
         FIG.  15    is a flow diagram illustrative of an embodiment of a routine to provide query results; 
         FIG.  16    is a flow diagram illustrative of an embodiment of a routine to process a query; 
         FIG.  17    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.  18    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.  19    is a flow diagram illustrative of an embodiment of a routine to process and execute a query; 
         FIG.  20    is a flow diagram illustrative of an embodiment of a multi-partition routine; 
         FIG.  21    is a diagram illustrating an embodiment of a join operation performed on two datasets; 
         FIG.  22    is a flow diagram illustrative of an embodiment of a multi-partition routine; 
         FIG.  23    is a diagram illustrating an embodiment of a join operation performed on two datasets; 
         FIG.  24    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   
   4.0. DATA INTAKE AND FABRIC SYSTEM ARCHITECTURE 
   4.1. WORKER NODES 
   4.1.1. SERIALIZATION/DESERIALIZATION   
   4.2. SEARCH PROCESS MASTER 
   4.2.1 WORKLOAD CATALOG   4.2.2 NODE MONITOR   4.2.3 DATASET COMPENSATION   
   4.3. QUERY COORDINATOR 
   4.3.1. QUERY PROCESSING   4.3.2. QUERY EXECUTION AND NODE CONTROL   4.3.3. RESULT PROCESSING   
   4.4 QUERY ACCELERATION DATA STORE   
   5.0. QUERY DATA FLOW   6.0. QUERY COORDINATOR FLOW   7.0. QUERY PROCESSING FLOW   8.0. COMMON STORAGE ARCHITECTURE   9.0. INGESTED DATA BUFFER ARCHITECTURE   10.0 COMBINING DATASETS 
   10.1 MULTI-PARTITION DETERMINATION   10.2 MULTI-PARTITION OPERATION   
   11.0. HARDWARE EMBODIMENT   12.0. 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’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 worker nodes  14 - 1  and  14 - 2  of a data intake and query system  16 , respectively (also referred to collectively and individually as worker node(s)  14 ). 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  22  (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  22 . 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  22 . 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). 
     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  22  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 . 
     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  22  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 theremaining 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  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 reprocessed 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  22  and raw data of 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, California. 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 system  100  comprises 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, a system  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’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. Pat. Application 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. System  108  includes one or more forwarders  204  that receive data from a variety of input data sources  202 , 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  202  broadly represents a distinct source of data that can be consumed by system  108 . Examples of a data sources  202  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  202  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’s preferences. 
       FIG.  3    illustrates a block diagram of an example cloud-based data intake and query system. Similar to the system of  FIG.  2   , the networked computer system  300  includes input data sources  202  and forwarders  204 . These input data sources and forwarders may be in a subscriber’s private computing environment. Alternatively, they might be directly managed by the service provider as part of the cloud service. In the example system  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, California. 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 a relational 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. Pat. Application 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’s streaming mode of operation to results from the ERP’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, a variety of triggers and ways to accomplish a search head’s switch from using streaming mode results to using reporting mode results may be appreciated by one skilled in the art. 
     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’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’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’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  202  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  536 . 
     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. 
     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. Pat. 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 (i.e., 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 515 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 “I”. 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 “I” 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 “I” 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 query  630  can be inputted by the user into a search. 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 “I” 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’s shopping application program  701  running on the user’s system. In this example, the order was not delivered to the vendor’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’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’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  1401  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  722  stored in the raw record data store. Note that while  FIG.  7 B  only illustrates four events, the raw record data store (corresponding 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  712  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’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  712  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  712  to be field searchable. In other words, the raw record data store  712  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  712  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’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’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 March, 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. Pat. Application 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. As described in greater detail in U.S. Pat. Application No. 15/665159, entitled “ MULTI-LAYER PARTITION ALLOCATION FOR QUERY EXECUTION”, filed on Jul. 31, 2017, and which is hereby incorporated by reference in its entirety for all purposes, various interfaces can be used to generate and display data models. 
     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.  9    is an example search query received from a client and executed by search peers, in accordance with example embodiments.  FIG.  9    illustrates how a search query  902  received from a client at a search head  210  can split into two phases, including: (1) subtasks  904  (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  906  to be executed by the search head when the results are ultimately collected from the indexers. 
     During operation, upon receiving search query  902 , 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  902  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  904 , and then distributes search query  904  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  906  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  906 , determine what, if any, portion of the operations of the search query are to be performed locally 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. Pat. Application 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’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 a pre-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 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 summarization table  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 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. Pat. Application 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 B , 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  1502  to another filtering step requesting the user ids for the entries in inverted index  1502  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 “carlos” would be returned to the user from the generated results table  722 . 
     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 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 (i.e. 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’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., index  722  to generate the results without any user-involvement that directs the use of the 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’ 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’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. 
     As described in greater detail U.S. Pat. Application No. U.S. Pat. Application No. 15/665159, entitled “ MULTI-LAYER PARTITION ALLOCATION FOR QUERY EXECUTION”, filed on Jul. 31, 2017, and which is hereby incorporated by reference in its entirety for all purposes, various visualizations can be included to aid in discovering security threats, to monitor virtual machines, to monitor IT environments, etc. 
     4.0. Data Intake and Fabric System Architecture 
     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  1001  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.  10   ), external data stores coupled to the data intake and query system over a network (illustrated in  FIGS.  10 ,  17 ,  18   ), common storage (illustrated in  FIGS.  17 ,  18   ), query acceleration data stores (e.g., query acceleration data store  1008  illustrated in  FIGS.  10 ,  17 ,  18   ), ingested data buffers (illustrated in  FIG.  18   ) 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. Additional embodiments are disclosed in U.S. Patent Application No. U.S. Pat. Application No. 15/665159, entitled “ MULTI-LAYER PARTITION ALLOCATION FOR QUERY EXECUTION”, filed on Jul. 31, 2017, and which is hereby incorporated by reference in its entirety for all purposes. 
     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. 
       FIG.  10    is a system diagram illustrating an environment  1000  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  1000  includes data sources  201 , client devices  404 , described in greater detail above with reference to  FIG.  4   , and external data sources  1018  communicatively coupled to a data intake and query system  1001 . The external data sources  1018  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  1001  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  202  to the indexers  206 , the indexers can  206  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  1001 . 
     In addition to forwarders  204 , indexers  206 , data stores  208 , and the search head  210 , the system  1001  further includes a search process master  1002  (in some embodiments also referred to as DFS master), one or more query coordinators  1004  (in some embodiments also referred to as search service providers), worker nodes  1006 , and a query acceleration data store  1008 . In some embodiments, a workload advisor  1010 , workload catalog  1012 , node monitor  1014 , and dataset compensation module  1016  can be included in the search process master  1002 . However, it will be understood that any one or any combination of the workload advisor  1010 , workload catalog  1012 , node monitor  1014 , and dataset compensation module  1016  can be included elsewhere in the system  1001 , such as in as a separate device or as part of a query coordinator  1004 . 
     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.  10    can differ in some respects from the functionality described previously with respect to other embodiments. For example, in the illustrated embodiment of  FIG.  10   , the search head  210  can perform some processing on the query and then communicate the query to the search process master  1002  and coordinator(s)  1004  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  1002  or query coordinator  1004 . Upon completion of the query, the search head  210  can receive the results of the query from the search process master  1002  or query coordinator  1004  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  1002  or query coordinator  1004 . 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.  10    can receive the relevant subqueries from the query coordinator  1004  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  1006  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  1006 . 
     The search head  210 , search process master  1002 , and query coordinator  1004  can be implemented using separate computer systems, processors, or virtual machines, or may alternatively comprise separate processes executing on one or more computer systems, processors, or virtual machines. In some embodiments, running the search head  210 , search process master  1002 , and/or query coordinator  1004  on the same machine can increase performance of the system  1001  by reducing communications over networks. In either case, the search process master  1002  and query coordinator  1004  can be communicatively coupled to the search head  210 . 
     The search process master  1002  and query coordinator  1004  can be used to reduce the processing demands on the search head  210 . Specifically, the search process master  1002  and coordinator  1004  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  1002  and coordinator  1004  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  1002 . In turn, the search process master  1002  can identify a query coordinator  1004  that can process the query. In some cases, if there is not a query coordinator  1004  that can handle the incoming query, the search process master  1002  can spawn an additional query coordinator  1004  to handle the query. 
     The query coordinator(s)  1004  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  1004  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  1006 , spawn the worker nodes  1006  for the different tasks, instruct different worker nodes  1006  to perform the different tasks and where to route the results of each task, monitor the worker nodes  1006  during the query, control the flow of data between the worker nodes  1006 , process the aggregate results from the worker nodes  1006 , and send the finalized results to the search head  210  or to another dataset destination. In addition, the query coordinators  1004  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 query coordinator  1004  may fail but other query coordinators  1004  for other queries can continue to operate. In addition, queries that are to be isolated from one another can use different query coordinators  1004 . 
     The worker nodes  1006  can perform the various tasks assigned to them by a query coordinator  1004 . For example, the worker nodes  1006  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  1006  and indexers  206  can be implemented using separate computer systems, processors, or virtual machines, or may alternatively comprise separate processes executing on one or more computer systems, processors, or virtual machines. 
     The query acceleration data store  1008  can be used to store datasets for accelerated access. In some cases, the worker nodes  1006  can obtain data from the indexers  206 , external data sources  1018 , or other location (e.g., common storage, ingested data buffer, etc.) and store the data in the query acceleration data store  1008 . In such embodiments, when a query is received that relates to the data stored in the query acceleration data store  1008 , the worker nodes  1006  can access the data in the query acceleration data store  1008  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  1008 , the worker nodes  1006  can begin working on the dataset obtained from the query acceleration data store  1008 , 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  1006  obtain datasets from the other dataset sources. 
     The query acceleration data store  1008  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 set  1008  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  1008  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  1008 . 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  1006  can obtain information directly from the query acceleration data store  1008 . Additionally, since the query acceleration data store  1008  can be utilized to service requests from different clients  404   a - 404   n , the query acceleration data store  1008  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  1006  can store data from any dataset source, including data from a dataset source that has not been transformed by the nodes  1006 , processed data (e.g., data that has been transformed by the nodes  1006 ), partial results, or aggregated results from a query in the query acceleration data store  1008 . In such embodiments, the results stored in the query acceleration data store  1008  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  1006 , etc. 
     It will be understood that the system  1001  can include fewer or more components as desired. For example, in some embodiments, the system  1001  does not include a search head  210 . In such embodiments, the search process master  1002  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  1010  and dataset compensation module  1016  are described as being implemented in the search process master  1002 , it will be understood that these components and their functionality can be implemented in the query coordinator  1004 . Similarly, as will be described in greater detail below, in some embodiments, the nodes  1006  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. 
     4.1. Worker Nodes 
       FIG.  11    is a block diagram illustrating an embodiment of multiple machines  1102 , each having multiple nodes  1006 - 1 ,  1006 - n  (individually and collectively referred to as node  1006  or nodes  1006 ) residing thereon. The worker nodes  1006  across the various machines  1102  can be communicatively coupled to each other, to the various components of the system  1001 , such as the indexers  206 , query coordinator  1004 , search head  210 , common storage, ingested data buffer, etc., and to the external data sources  1018 . 
     The machines  1102  can be implemented using multi-core servers or computing systems and can include an operating system layer  1104  with which the nodes  1006  interact. For example, in some embodiments, each machine  1102  can include 32, 48, 64, or more processor cores, multiple terabytes of memory, etc. 
     In the illustrated embodiment, each node  1006  includes four processors  1106 , memory  1108 , a monitoring module  1110 , and a serialization/deserialization module  1112 . It will be understood that each node  1006  can include fewer or more components as desired. Furthermore, it will be understood that the nodes  1006  can include different components and resources from each other. For example node  1006 - 1  can include fewer or more processors  1106  or memory  1108  than the node  1006 - n . 
     The processors  1106  and memory  1108  can be used by the nodes  1006  to perform the tasks assigned to it by the query coordinator  1004  and can correspond to a subset of the memory and processors of the machine  1102 . The serialization/deserialization module  1112  can be used to serialize/deserialize data for communication between components of the system  1001 , as will be described in greater detail below. 
     The monitoring module  1110  can be used to monitor the state and utilization rate of the node  1006  or processors  1106  and report the information to the search process master  1002  or query coordinator  1004 . For example, the monitoring module  1110  can indicate the number of processors in use by the node  1006 , the utilization rate of each processor, whether a processor is unavailable or not functioning, the amount of memory used by the processors  1106  or node  1006 , etc. 
     In addition, each worker node  1006  can include one or more software components or modules (“modules”) operable to carry out the functions of the system  1001  by communicating with the query coordinator  1004 , the indexers  206 , and the dataset sources. The modules can run on a programming interface of the worker nodes  1006 . 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  1006  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  1006 ). 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  1004 , the worker nodes  1006  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  1004  or other destination. Accordingly, the query coordinator  1004  can act as a manager of the worker nodes  1006 , 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  1004  to apply search queries to the distributed data systems. 
     As a non-limiting example, as part of processing a query, a node  1006  can receive instructions from a query coordinator  1004  to perform one or more tasks. For example, the node  1006  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  1006  can determine what task it is to perform in the DAG, and execute it. 
     As part of performing the assigned task, the node  1006  can determine how many processors  1106  to allocate to the different tasks. In some embodiments the node can determine that all processors  1106  are to be used for a particular task or only a subset of the processors  1106 . In certain embodiments, each processor  1106  of the node  1006  can be used as a partition to intake, process, or collect data according to a task, or to process data of a partition as part of an intake, process, or collect task. Upon completion of the task, the node  1006  can inform the query coordinator  1004  that the task has been completed. 
     When instructed to intake data, the processors  1106  of the node  1006  can be used to communicate with a dataset source (non-limiting examples: external data sources  1018 , indexers  206 , common storage, query acceleration data store  1008 , ingested data buffer, etc.). Once the node 1006 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 partitions of a node (or different nodes) can be assigned to intake data from a particular source. 
     When instructed to parse or otherwise process data, the processors  1106  of the node  1006  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  1106  of the node  1006  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  1106  of the node  1006  can be used to receive data from dataset sources or processing nodes. With continued reference to the error example, a collector partition, or processor  1106  can collect all of the errors of a certain type from one or more parsing partitions or processors  1106 . 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  1106  of the node  1006  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  1106  of the node  1006  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  1006  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, data from multiple partitions of a node (or different nodes) can be communicated to a particular destination. Furthermore, the nodes  1006  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  1004  and another copy can be communicated to the query acceleration data store  1008 . 
     The system  1001  is scalable to accommodate any number of worker nodes  1006 . As such, the system  1001  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 bid data that is greater in scope and provides deeper insights compared to existing systems. 
     4.1.1. Serializatoin/Deserialization 
     In some cases, the serialization/deserialization module  1112  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  1112  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  1112  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  1112  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  1112  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  1112  can operate on streaming data and avoid adding delay to the serialization/deserialization process. 
     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 1112 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 1112 can then generate the following event group: 
     
       
         
           
               
               
               
             
               
                 4 (number of events) 
               
             
            
               
                 Header_Code 
                 5 (number of fields) 
                 Data_Code “source” Data_Code “sourcetype” Data_Code “sale_type”  Data_Code “company name” Data_Code “price” 
               
               
                 Cache_Delta_Code 
                 5 (entries to add) 
                 “source” x15 “sourcetype” x16 “sale_type” x17 “company name” x18 “price” x19 
               
               
                 0 (entries to drop) 
                   
               
               
                 Event_Code 
                 5 (number of fields in event) 
                 Data_Code “ronnie.sv.splunk.com” Data_Code “access_combined” Data_Code “SALE” Data_Code “World of Cheese” Data_Code “14.95” 
               
               
                 Cache_Delta_Code 
                 5 (number of new entries) 
                 “ronnie.sv.splunk.com” x21 “access_combined” x22 “SALE” x23 “World of Cheese” x24 “14.95” x25 
               
               
                 0 (entries to drop) 
                   
               
               
                 Event_Code 
                 5 (number of fields in event) 
                 Cache_Code x21 Cache_Code x22 Data_Code “NO SALE” Cache_Code x24 Data_Code “16.75” 
               
               
                 Cache_Delta_Code 
                 2 (entries to add) 
                 “NO SALE” x26 “16.75” x27 
               
               
                 0 (entries to drop) 
                   
               
               
                 Event_Code 
                 4 (number of fields in event) 
                 Cache_Code x21 Cache_Code x22 Cache_Code x23 Cache_Code x24 
               
               
                 Event_Code 
                 5 (number of fields in event) 
                 Cache_Code x21 Cache_Code x22 Cache_Code x23 Data_Code “World of Cheese” Data_Code “20.95” 
               
               
                 Cache_Delta_Code 
                 2 (number of new entries) 
                 “World of Cheese” “20.95” 
               
               
                 1 (entry to drop) 
                 x25 
               
            
           
         
       
     
     By generating the group, the serialization/deserialization module  1112  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  1112  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  1112  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. 
     4.2. Search Process Master 
     As mentioned above, the search process master  1002  can perform various functions to reduce the workload of the search head  210 . For example, the search process master  1002  can parse an incoming query and allocate the query to a particular query coordinator  1004  for execution or spawn an additional query coordinator  1004  to execute the query. In addition, the search process master  1002  can track and store information regarding the system  1001 , queries, external data stores, etc., to aid the query coordinator  1004  in processing and executing a particular query. In some embodiments, the search process master  1002 . 
     In some cases, the search process master  1002  can determine whether a query coordinator  1004  should be spawned based on user information. For example, for data protection or isolation, the search process master  1002  can spawn query coordinators  1004  for different users. In addition, the search process master  1002  can spawn query coordinators  1004  if it determines that a query coordinator  1004  is over utilized. 
     In some cases, to accomplish these various tasks the search process master  1002  can include a workload advisor  1010 , workload catalog  1012 , node monitor  1014 , and dataset compensation module  1016 . Although illustrated as being a part of the search process master  1002 , 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  1004 . Furthermore, although illustrated as individual components, it will be understood that any one or any combination of the workload advisor  1010 , workload catalog  1012 , node monitor  1014 , and dataset compensation module  1016  can be implemented by the same machine, processor, or computing device. 
     As a brief introduction, the workload advisor  1010  can be used to provide resource allocation recommendations to a query coordinator  1004  for processing queries, the workload catalog  1012  can store data related to previous queries, the node monitor  1014  can receive information from the worker nodes  1006  regarding a current status and/or utilization rate of the nodes  1006 , and the dataset compensation module  1016  can be used by the query coordinator  1004  to enhance interactions with external data sources. 
     4.2.1 Workload Catalog 
     The workload catalog  1012  can store relevant information to aid the workload advisor  1010  in providing a resource allocation recommendation to a query coordinator  1004 . As queries are received and processed by the system  1001 , the workload catalog  1012  can store relevant information about the queries to improve the workload advisor’s  1010  ability to recommend the appropriate amount of resources for each query. For example, the system  1001  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  1006  used to obtain the data from each dataset source, the utilization rate of the nodes  1006  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  1006  assigned to each phase, the utilization rate of each node  1006  assigned to the particular phase, the processing performed by the query coordinator  1004  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  1012  can collect the data from the various components of the system  1001 , such as the query coordinator  1004 , worker nodes  1006 , indexers  206 , etc. For example, for each task performed by each node  1006 , the node  1006  can report relevant timing and resource utilization information to the query coordinator  1004  or directly to the workload catalog  1012 . Similarly, the query coordinator  1004  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  1012 , the workload advisor  1010  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. 
     4.2.2 Node Monitor 
     The node monitor  1014  can also store relevant information to aid the workload advisor  1010  in providing a resource allocation recommendation. For example, the node monitor  1014  can track and store information regarding any one or any combination of: total number of processors or nodes in the system  1001 , 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  1006  or processors, estimated time to complete a task by the nodes  1006  or processors, amount of available memory, total memory in the system  1001 , tasks awaiting execution by the nodes  1006  or processors, etc. 
     The node monitor  1014  can collect the relevant information by communicating with the monitoring module  1110  of each node  1006  of the system  1001 . As described above, the monitoring modules  1110  of each node  1006  can report relevant information about the node state and utilization rate. Using the information from the node monitor  1014 , the workload advisor  1010  can ascertain the general state of any particular processor, node, or the system  1001 , and determine the number of nodes  1006  or processors  1006  available for a particular task or query. 
     4.2.3 Dataset Compensation 
     As discussed above, the external data sources  1018  with which the system  1001  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  1006 . In addition, the external data sources  1018  may support parallel reads from multiple partitions. Conversely, other external data sources  1018  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  1018  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  1018  can vary significantly. As such, the system’s  1001  interaction with the different external data sources  1018  can vary significantly. 
     To aid the system  1001  in interacting with the different external data sources  1018 , the dataset compensation model  1016  can include relevant information related to each external data source  1018  with which the system  1001  can interact. For example, the dataset compensation model  1016  can include any one or any combination of: the amount of data stored in an external data source  1018 , 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  1018 , number of partitions supported by an external data source  1018 , 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  1018  can be collected in a variety of ways. In some cases, some of the information about the external data source  1018  can be received when a customer sets up the external data source  1018  for use with the system  1001 . For example, a customer can indicate the type of external data source  1018  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  1001  can determine certain characteristics about the external data store  1018 , 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  1016  can store the capabilities of the different dataset sources to aid in providing a seamless experience to users. 
     In certain cases, the system  1001  can collect relevant information about an external data source by communicating with it. For example, the query coordinator  1004  or a worker node  1006  can interact with the external data source  1018  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  1018  become available or unavailable, etc. In addition, when the system  1001  accesses the external data source  1018  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  1001  can interact with an external data source  1018  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  1001  can request endpoint information with each query that is to access the particular external data source. 
     Using the information about the external data sources  1018 , a query coordinator  1004  can determine how to interact with it and how to process data obtained from the external data source  1018 . For example, if an external data source  1018  supports parallel reads, the query coordinator  1004  can allocate multiple partitions to read the data from the external data source  1018  in parallel. In some embodiments, the query coordinator  1004  can allocate sufficient partitions or processors  1106  to establish a 1:1 relationship with the available partitions at the external data source  1018 . Similarly, if the external data source  1018  can perform some processing of the data, the query coordinator  1004  can use the information from the dataset compensation module  1016  to translate the query into commands understood by the external data source  1018  and push some processing to the external data source  1018 , thereby reducing the amount of system  1001  resources (e.g., nodes  1006 ) used to process the query. 
     Furthermore, in some cases, using the dataset compensation module  1016 , 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  1004  can intelligently interact with the external data sources  1018 . For example, if the query coordinator  1004  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  1004  can first interact with or query the external data source  1018  that includes less data and then using information gleaned from that data prepare a more narrowly tailored query for the external data source  1018  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  1004  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  1016  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  1016 , the query coordinator  1004  can instruct the nodes  1006  to first intake and process the data from the HDFS data source. Suppose that by doing so, the nodes  1006  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  1004  can instruct the nodes  1006  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  1004  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  1004  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  1006 . 
     4.3. Query Coordinator 
     The query coordinator(s)  1004  can act as the primary coordinator or controller for queries that are assigned to it by the search head  210  or search process master  1002 . 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  1002  for delivery to a client device  404 . 
     4.3.1. Query Processing 
     Upon receipt of a query, the query coordinator  1004  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  1004  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  1004  can determine whether data from the indexers  206 , external data sources  1018 , query acceleration data store  1008 , 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  1004  can identify the different entities that can perform some processing on the datasets. For example, the query coordinator  1004  can determine what portion(s) of the query can be delegated to the indexers  206 , nodes  1006 , and external data sources  1018 , and what portions of the query can be executed by the query coordinator  1004 , search process master  1002 , or search head  210 . For tasks that can be completed by the indexers  206 , the query coordinator  1004  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  1006 . For tasks that can be completed by the external data sources  1018 , the query coordinator  1004  can use the dataset compensation module  1016  to generate task instructions for the external data sources  1018  and to determine how to set up the nodes  1006  to receive data from the external data sources  1018 . 
     In addition, as part of defining the executable search process, the query coordinator  1004  can generate a logical directed acyclic graph (DAG) based on the query.  FIG.  12    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  1006 . 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.  12   , 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  1202 ,  1208 , a process vertex  1204 , collect vertices  1206 ,  1210 , a join vertex  1212 , and a branch vertex  1214 . 
     Each vertex  1202 ,  1204 ,  1206 ,  1208 ,  1210 ,  1212 ,  1214  can correspond to a search phase performed using one or more partitions or processors  1106  of one or more nodes  1006  on a particular set of data. For example, the intake, process, and collect vertices  1202 ,  1204 ,  1206  can correspond to data search phases, or transformations, on data received from a first dataset source. More specifically, the intake phase or vertex  1202  can correspond to one or more partitions that receive data from the first dataset source, the process phase  1204  can correspond to one or more partitions used to process the data received by the partitions at the intake phase  1202 , and the collect phase  1206  can correspond to one or more partitions that collect the results of the processing by the partitions in the process phase  1204 . 
     Similarly, the intake and collect vertices  1208 ,  1210  can correspond to data search phases performed using one or more partitions or processors  1106  on data received from a second dataset source. For example, the intake phase  1208  can correspond to one or more partitions that receive data from the second dataset source and the collect phase  1210  can correspond to one or more partitions that collect the results from the partitions in the intake phase  1208 . 
     The join and branch phases  1212 ,  1214  can correspond to data search phases performed using one or more partitions or processors  1106  on data received from the different branches of the DAG  2000 . For example, the join phase  1212  can correspond to one or more partitions used to combine the data received from the partitions in the collect phases  1206 ,  1210 . The branch phase  1214  can correspond to one or more partitions used to communicate results of the join phase  1212  to one or more destinations. For example, the partitions in the branch phase  1214  can communicate results of the query to the query coordinator  1004 , an external data source  1018 , accelerated data source  1008 , 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  1004  and an HDFS data store. In this example, in response to determining that the common storage do not provide processing capabilities, the query coordinator  1004  can generate vertices  1202 ,  1204 ,  1206  indicating that an intake phase  1202 , process phase  1204 , and collect phase  1206  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  1208 ,  1210  indicating that an intake phase  1208  and collect phase  1210  will be used to sufficiently process the data from the Oracle database for combination with the data from the common storage. 
     The query coordinator  1004  can further generate the join phase  1212  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  1004  and the HDFS data store, the query coordinator  1004  can generate the branch phase  1214 . As mentioned above, in each phase, the query coordinator  1004  can allocate one or more partitions to perform the particular search 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  1004  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  1004  can communicate with the workload advisor  1010 , workload catalog  1012 , and/or the node monitor  1014 . As described previously, the workload advisor  1010  can use the data collected in the workload catalog  1012  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  1010  can use the data from the node monitor module  1014  to determine the available resources in the system  1001 . Using this information, the query coordinator  1004  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  1004  can also generate the tasks and instructions for each node  1006 . The instructions can include computer executable instructions that when executed by the node  1006  cause the node 1006 to perform the task assigned to it by the query coordinator  1004 . For example, for nodes  1006  that are to be assigned to an intake phase  1202 ,  1208 , the query coordinator  1004  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  1006  that are to process data in the process phase  1204 , the query coordinator  1004  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  1006  in the collect phases  1206 ,  1210 , join phase  1212 , or branch phase  1214 , the query coordinator  1004  can generate task instructions so that the nodes  1006  are able to perform the task assigned to that particular phase. The task instructions can tell the nodes  1006  what data they are to process, how they are to process the data, where they are to route the results of the processing, either between each other or to another destination. In some cases, the query coordinator  1004  can generate the tasks and instructions for all nodes  1006  or processors  1106  and send the instructions to all of the allocated nodes  1006  or processors  1106 . Between them, the nodes  1006  or processors  1106  can determine or assign partitions to be used to help execute the different instructions and tasks. The instructions sent to the nodes  1006  or processors  1106  can include additional parameters, such as a preference to use processors  1106  partitions on the same machine for subsequent tasks. Such instructions can help reduce the amount of data communicated over the network, etc. 
     In some embodiments, to generate instructions for the dataset sources, the query coordinator  1004  can use the dataset compensation module  1016 . As described previously, the dataset compensation module  1016  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  1004  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  1004  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  1004  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  1004  can interact with one partition of the external dataset source using multiple partitions. For example, the query coordinator  1004  can allocate multiple partitions to interact with a single partition of the external dataset source. The query coordinator  1004  can break up a query or a subquery into multiple parts. Each part can be assigned to a different partition, which can communicate the subqueries to the partition of the external dataset source. Thus, unbeknownst to the external dataset source, it can concurrently process data from a single query. 
     Furthermore, the query coordinator  1004  can determine the order for conducting the search process. As mentioned above, in some embodiments, the query coordinator  1004  can determine that processing data from 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  1004  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  1004 . 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. 
     4.3.2. Query Execution and Node Control 
     Once the query is processed and the search scheme determined, the query coordinator  1004  can initiate the query execution. In some cases, in initiating the query, the query coordinator  1004  can communicate the generated task instructions to the various locations that will process the data. For example, the query coordinator  1004  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  1004  can communicate task instructions to the nodes  1006 , external data sources  1018 , query acceleration data store  1008 , common storage, and/or ingested data buffer, etc. 
     In some embodiments, rather than communicating with the various dataset sources, the query coordinator  1004  can generate task instructions for the nodes  1006  to interact with the dataset sources such that the dataset sources receive any task instructions from the nodes 1006 as opposed to the query coordinator  1004 . For example, rather than communicating the task instructions directly to a dataset source, the query coordinator  1004  can assign one or more nodes  1006  to communicate task instructions to the external data sources  1018 , indexers  206 , or query acceleration data store  1008 . In certain embodiments, the query coordinator  1004  can communicate the same search scheme or task instructions to the nodes  1006  or partitions of the nodes  1006  that have been allocated for the query. The allocated nodes  1006  or partitions of the nodes  1006  can then assign different groups to perform different portions of the search scheme. 
     Upon receipt of the task instructions, the dataset sources and nodes  1006  can begin operating in parallel. For example, if task instructions are sent to the indexers  206  and to the nodes  1006 , both can begin executing the instructions in parallel. In executing the task instructions, the nodes  1006  can organize their processors  1106  or partitions according to task instructions. For example, some of the nodes  1006  can allocate one or more partitions or processors  1106  as part of an intake phase, another partition as part of a processing phase, etc. In some cases, all partitions or processors  1106  of a node  1006  can be allocated to the same task or to different tasks. In certain embodiments, it can be beneficial to allocate partitions from the same node  1006  to different tasks to reduce network traffic between nodes  1006  or machines  1102 . 
       FIG.  13    is a block diagram illustrating an embodiment of layers of partitions implementing 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.  13   , based on task instructions received from the query coordinator  1004 , the nodes  1006  have arranged various partitions to perform different search phases on data coming from a dataset source  1302 . As described previously, the dataset source  1302  can correspond to indexers  206 , external data sources  1018 , the query acceleration data store  1008 , common storage, an ingested data buffer, or other source of data from which the nodes  1006  can receive data. 
     As referenced in  FIG.  12   , the partitions in each layer can interact with the data based on task instructions received by the query coordinator  1004 . In the illustrated embodiment of  FIG.  13   , the partitions in the intake layer  1304  can receive the data from the dataset source  1302 , which can be communicated to the partitions in the processing layer  1306  in a load-balanced fashion. The partitions in the processing layer  1306  can be used to process the data based on the task instructions, which were generated based on the query, and the results provided to the partitions in the collector layer  1308 . Similarly, upon completing their assigned task, the processors associated with the partitions in the collector layer  1308  can communicate the results of their processing to the branch layer  1310 . In the illustrated embodiment of  FIG.  13   , the branch layer  1310  communicates the results received from the partitions in the collector layer  1308  to a first dataset destination  1314  and to partitions in a storage layer  1312  for storage in a second dataset destination  1316 . 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  1306  can correspond to a map procedure and the collector layer  1308  can correspond to a reduce procedure. However, as described herein, it will be understand that various layers can correspond to map or reduce procedures. 
     In the illustrated embodiment, four partitions have been allocated to the intake layer  1304 , eight partitions have been allocated to the processing layer  1306 , five partitions have been allocated to the collector layer  1308 , one partition has been allocated to the branch layer  1310 , and three partitions have been allocated to the storage layer  1312 . However, it will be understood that fewer or more partitions can be assigned to 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  1004  can allocate separate intake, processing, and collector layers  1304 ,  1306 ,  1308  for each dataset source  1302 . 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 processors  1106  for each search phase or layer, the query coordinator  1004  can use the workload advisor  1010  and/or dataset compensation module  1016 . For example, the workload advisor  1010  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. 
     In some cases, the query coordinator  1004  can allocate partitions for the intake layer  1304  and storage layer  1312  based on information about the number of partitions available for reading from the dataset source  1302  and writing data to the dataset destination  1316 , respectively. The query coordinator  1004  can obtain the information about the dataset source  1302  or dataset destination  1316  from a number of locations, including, but not limited to, the workload catalog  1012 , the dataset compensation module  1016 , or from the dataset source  1302  or dataset destination  1316  itself. The information can inform the query coordinator  1004  as to the number of partitions available for reading from the dataset source  1302  and writing to the dataset destination  1316 . 
     In some cases, the query coordinator  1004  can allocate partitions in the intake layer  1304  or the storage layer  1312  to have a one-to-one, one-to-many, or many-to-one correspondence with partitions in the dataset source  1302  or dataset destination  1316 , respectively. The correspondence between the partitions in the intake or storage layer  1304 ,  1312  and the partitions in the dataset source or destination  1302 ,  1316 , 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  1004  determines that the dataset source  1302  (or dataset destination  1316 ) has a number of partitions that satisfies a threshold number of partitions or determines that the number of partitions of the dataset source  1302  (or dataset destination  1316 ) can be matched without overextending the nodes  1006 , the query coordinator  1004  can allocate partitions in the intake layer  1304  (or storage layer  1312 ) to have a one-to-one correspondence to partitions in the dataset source  1302  (or dataset destination  1316 ). The number of partitions that satisfy the threshold number of partitions can be determined based on the number of nodes  1006  or processors  1106  in the system  1001 , the number of available nodes  1006  in the system  1001 , scheduled usage of nodes  1006 , etc. Accordingly, the threshold number of partitions can be dynamic depending on the status of the processors  1106 , nodes  1006 , or the system  1001 . For example, if a large number of nodes  1006  are available, the threshold number of nodes can be larger, whereas, if only a relatively small number of nodes  1006  are available, the threshold number can be smaller. Similarly, if the workload advisor 10010 expects a large number of queries in the near term it can allocate fewer partitions to an individual query. Alternatively, if the workload advisor 10010 does not expect many queries in the near term it can allocate a greater number of partitions to an individual query. 
     In some cases, the query coordinator  1004  can determine whether to match the number of partitions in the dataset source  1302  or dataset destination  1316  with corresponding partitions in the intake layer  1304  or storage layer  1312 , respectively, based on the type of the dataset source  1302  or dataset destination  1316 . For example, the query coordinator  1004  can determine there should be a one-to-one correspondence of intake layer  1304  partitions to dataset source  1302  partitions (or storage layer  1312  partitions to dataset destination  1316  partitions) when the dataset source  1302  (or dataset destination  1316 ) is an external data source or ingested data buffer and that there should be a one-to-multiple correspondence when the dataset source  1302  (or dataset destination  1316 ) is indexers  206 , common storage, query acceleration data store  1008 , etc. 
     As a non-limiting example, if the dataset source  1302  is an external data source or ingested data buffer with four partitions and the query coordinator  1004  determines that it can support a one-to-one correspondence, the query coordinator  1004  can allocate four partitions to the intake layer  1304 , as illustrated in  FIG.  13   . Similarly, if the dataset destination  1316  is an external data source or ingested data buffer with three partitions and the query coordinator  1004  determines that it can support a one-to-one correspondence, the query coordinator  1004  can allocate three partitions to the storage layer  1312 , as illustrated in  FIG.  13   . As another non-limiting example, if the dataset source  1302  (or dataset destination  1316 ) is indexers  206 , common storage, or query acceleration data stores  1008  with hundreds of potential partitions, and/or the query coordinator  1004  determines that it cannot support a one-to-one correspondence, it can allocate the four partitions to the intake layer  1304  (or the three partitions to the storage layer  1312 ), as illustrated in  FIG.  13   . 
     In addition, during intake of the data from the dataset source  1302 , the query coordinator  1004  can dynamically adjust the number of partitions in the intake layer  1304 . For example, if an additional partition of the dataset source  1302  becomes available or one of the partitions becomes unavailable, the query coordinator  1004  can dynamically increase or decrease the number of partitions in the intake layer  1304 . Similarly, if the query coordinator  1004  determines that the intake layer  1304  is taking too much time and additional resources are available, it can dynamically increase the number of partitions in the intake layer  1304 . In addition, if the query coordinator  1004  determines that additional resources are available or become unavailable, it can dynamically increase or decrease the number of partitions in the intake layer  1304 . Similarly, the query coordinator can dynamically adjust the number of partitions in the storage layer  1312 . 
     Similar to the intake layer  1304  and storage layer  1312 , the query coordinator  1004  can allocate partitions to the different search layers  1306 ,  1308 ,  1310  based on information about the query and information in the workload catalog  1012 . For example, the query may include requests to process the data in a way that is resource intensive. As such, the query coordinator  1004  can allocate a larger number of partitions and/or processors  1106  to the processing layer  1306  or use multiple processing layers  1306  to process the data. In some cases, more partitions can be allocated to the search layers for queries of larger datasets. 
     In addition, during execution of the query, the query coordinator  1004  can monitor the partitions in the search layers  1306 ,  1308 ,  1310  and dynamically adjust the number of partitions in each depending on the status of the individual partitions, the status of the nodes  1006 , the status of the query, etc. In some cases, the query coordinator  1004  can determine that a significant number of results are being sent to a particular partition in the collector layer  1308 . As such, the query coordinator  1004  can allocate an additional partition to the collector layer and/or instruct that the results from the partitions in the processing layer  1306  be distributed in a different manner to reduce the load on the particular partition in the collector layer. In certain cases, the query coordinator  1004  can determine that a partition in the processing layer  1306  is not functioning or that there is significantly more data coming from the dataset source  1302  than was anticipated. Accordingly, the query coordinator  1004  can allocate an additional partition  1306  to the processing layer. Conversely, if the query coordinator  1004  determines that some of the partitions or processors  1106  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  1004  can dynamically allocate and deallocate resources to intake and process the data from the dataset source  1302  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  1008 . Based on the query, the query coordinator  1004  can generate a DAG that includes the intake layer  1304 , processing layer  1306 , collector layer  1308 , branch layer  1310 , and storage layer  1312 . Additionally, based on a determination that the external data source supports four partitions, the query coordinator  1004  allocates four partitions to the intake layer  1304 . In addition, based on the expected amount of data to be processed, the query coordinator  1004  allocates eight partitions to the processing layer  1306 , and five partitions to the collector layer  1308 . Further, based on resource availability and the determination that the dataset destination is the query acceleration data store  1008 , which can support more than a threshold number of partitions, the query coordinator  1004  allocates three partitions to the storage layer  1312 . The task instructions for each partition of each search layer can be sent to the nodes  1006 , which assign processors  1106  to the various tasks and partitions. In some cases, the processors  1106  and partitions can have a 1:1 correspondence, such that each partition corresponds to one processor. In certain embodiments, multiple partitions can be assigned to a processor  1106  or vice versa. As such, when referred to herein as a partition performing an action, it will be understood that the action can be performed by the processor  1106  assigned to that partition. 
     During execution, the partitions in the intake layer  1304  (or processors assigned to the partition) communicate with the dataset source  1302  to receive the relevant data from the partitions of the dataset source  1302 . The data is then communicated to the partitions in the processing layer  1306 . In the illustrated embodiment, each partition of the intake layer  1304  communicates data in a load-balanced fashion to two partitions in the processing layer  1306 . The partitions in the processing layer  1306  can parse the incoming data to identify events that include an error and identify the type of error. 
     The partitions in the processing layer  1306  can determine the results to the partitions in the collector layer  1308 . For example, each partition in the processing layer  1306  can apply a modulo five to the error type in order to attempt to equally separate the results between the five partitions in the collector layer  1308 . As such, for each error type, a partition in the collector layer  1308  can include the total count of errors for that type. Depending on the query, in some cases, the partitions in the collector layer  1308  can also include the event that included the particular error type. 
     The partitions in the collector layer  1308  can send the results to the partition in the branch layer  1310 . The partition in the branch layer  1310  can communicate the results to the query coordinator  1004 , which can communicate the results to the search head or client device. In addition, the branch layer  1310  can communicate the results to the partitions in the storage layer  1312 , which communicate the results in parallel to the query acceleration data store  1008 . 
     Throughout the execution of the query, the query coordinator  1004  can monitor the partitions in the intake layer  1304 , processing layer  1306 , collector layer  1308 , branch layer  1310 , and storage layer  1312 . If one partition becomes unavailable or becomes overloaded, the query coordinator  1004  can allocate additional resources. Similarly, if a partitions is not being utilized, the query coordinator  1004  can deallocated it from a layer. For example, if a partition on the external data source becomes unavailable, a corresponding partition in the intake layer  1304  may no longer receive any data. As such, the query coordinator  1004  can deallocate that partition from the intake layer  1304 . In some embodiments, any change in state of a partition can be reported to the node monitor module  1014 , which can be used by the query coordinator to allocate resources. 
     4.3.3. Result Processing 
     Once the nodes  1006  have completed processing the query or particular results of the query, they can communicate the results to the query coordinator  1004 . The query coordinator  1004  can perform any final processing. For example, in some cases, the query coordinator  1004  can collate the data from the nodes  1006 . The query coordinator  1004  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  1008 , an external data source  1018 , an ingested data buffer, etc. In addition, the query coordinator  1004  can communicate to the search process master  1002  that the query has been completed. In the event all queries assigned to the query coordinator  1004  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  1002 . 
     4.4. 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  1008 . 
     As described above, the query acceleration data store  1008  can store information (e.g., datasets) sourced from other dataset sources, such as, external data sources  1018 , 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 source  1008  (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  1006 , etc. Subsequently, the data intake and query system  1001  can cause queries directed to the particular information to utilize the query acceleration data store  1008 . 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  1018 . Since the external data sources  1018  may have potentially high latency, response times to particular queries, the query can be constrained according to characteristics of the external data sources  1018 . For example, particular external data sources  1018  may be limited in their processing speed, network bandwidth, and so on, such that the worker nodes  1006  are required to wait longer for information. As described herein, the query can therefore specify that particular information from the external data sources  1018  (or other dataset sources) be stored in the query acceleration data store  1008 . Subsequent queries that utilize this particular information can then be executed more quickly. For example, in subsequent queries the worker nodes  1006  can obtain the particular information from the query acceleration data store  1008  rather than from the external data source  1018 . 
     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  1018 . 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  1001  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  1006  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  1008 . As another example, the user’s client device or query coordinator  1004  can determine that information is to be stored in the data store  1008 . For example, the query can be analyzed by the client device or query coordinator  1004 , and based on a quantity of information being requested, the client device or query coordinator  1004  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  1001  can automatically store the requested information in the query acceleration data store  1008  without an accelerated directive in a received query. For example, the query system  1001  can automatically store data in the query acceleration data store  1008  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  1008 . 
     Upon receipt of the query, the data intake and query system  1001  (e.g., the query coordinator  1004 ) can cause the requested information from the dataset source to be stored in the query acceleration data store  1008 . Optionally, the query acceleration data store  1008  can receive the processed result associated with the query (e.g., from the worker nodes  1006 ). The query acceleration data store  1008  can then provide the processed result to the query coordinator  1004  to be relayed to the requesting client. However, to increase response times, the worker nodes  1006  can provide processed information to the query acceleration data store  1008 , and also to the query coordinator  1004 . In this way, the query acceleration data store  1008  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  1004  can relay the received processed information to the requesting client. 
     The processed result may be stored by the query acceleration data store  1008  in association with an identifier, such that the information can be easily referenced. For example, the query acceleration data store  1008  can generate a unique identifier upon receipt of information for storage by the worker nodes  1006 . For subsequent queries, the query coordinator  1004  can receive the identifier, such that the query coordinator  1004  can replace the initial portion with the unique identifier. 
     In some embodiments, the query coordinator  1004  can generate the unique identifier. For example, the query coordinator can receive information from the query acceleration data store  1008  indicating that it stored information. The query coordinator  1004  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  1008 . The query coordinator  1004  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’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  1001  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  1012 , or otherwise made available to the query coordinator  1004 . 
     Subsequently, for received queries that reference the processed information, the query coordinator  1004  can cause the worker nodes  1006  to obtain the information from the query acceleration data store  1008 . 
     For example, a subsequent query can be 
     
       
         
           
             Query 
               
             = 
               
             &lt; 
             from 
               
             
               
                 dataset 
                   
                 source 
               
             
             &gt; 
               
             | 
               
             &lt; 
             
               
                 logic 
               
             
             &gt; 
             | 
               
             &lt; 
             
               
                 subsequent_logic 
               
             
             &gt; 
           
         
       
     
      [00429] In the above query, the query coordinator  1004  can determine that some portion of the data referenced in the query corresponds to data that is stored in the query acceleration data store  1008  (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  1008 . For example, the query coordinator  1004  can compare the query to prior queries, and any portion of data that was referenced in a prior query. The query coordinator  1004  can then instruct the worker nodes  1006  to obtain the previously stored data or the results of processing the data from the query acceleration data store  1008 . In some cases, the subsequent query can include an explicit command to obtain the data or results from the query acceleration data store  1008 . 
     Obtaining the previously stored data or results of processing the data provides multiple technical advantages. For example, the worker nodes  1006  can avoid having to reprocess the data, and instead can utilize the prior processed result. Additionally, the worker nodes  1006  can more rapidly obtain information from the query acceleration data store  1008  than, for example, the external data sources  1018 . As an example, the worker nodes  1006  may be in communication with the query acceleration data store  1008  via a direct connection (e.g., virtual networks, local area networks, wide area networks). In contrast, the worker nodes  1006  may be in communication with the external data sources  1018  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  1008  with minimal processing by the nodes  1006  or without transforming the data from the dataset source. A subsequent query can indicate that the data stored in the query acceleration data store  1008  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  1008 . The subsequent query can indicate that the results stored in the query acceleration data store  1008  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  1006  can perform any additional processing on the results obtained from the query acceleration data store  1008 , 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  1008  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  1001  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  1006  based on the processed result stored by the query acceleration data store  1008 . The result of the subsequent query can then be provided to the query coordinator  1004  to be relayed to the requesting client. 
     The query acceleration data store  1008 , 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  1008  can cause particular information to spill to disk when needed, ensuring that the data store  1008  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  1008  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  1008  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  1008  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  1008  can utilize one or more storage policies to swap datasets between low-latency memory and longer-latency memory. Additionally, the query acceleration data store  1008  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  1008  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  1008  can have faster access to at least a portion each user’s dataset. If a subsequent query is received by the data intake and query system  1001  that references a stored dataset, the query acceleration data store  1008  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  1008  can quickly provide this information to the worker nodes  1006  for processing. At a same, or similar, time, the query acceleration data store  1008  can access the longer-latency memory and obtain a remaining portion of the stored dataset. The worker nodes  1006  can then receive this remaining portion for processing. Therefore, the worker nodes  1006  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  1006  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  1008  may store the entirety of the dataset in low-latency memory. For an example dataset greater than the threshold, the data store  1008  may store a portion in low-latency memory. As the size of the dataset increases, the query acceleration data store  1008  can store an increasingly lesser sized portion in low-latency memory. In this way, the data store  1008  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  1001  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  1001  is to obtain information from an example dataset source (e.g., external data source  1018 ), process the information, and cause the query acceleration data store  1008  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.  13    illustrates a branch layer  1310 , which for the example query described above, can be utilized to provide information both to the query acceleration data store  1008  and the data destination  1314  (e.g., the requesting client). For example, subsequent to the worker nodes  1006  obtaining processed information (e.g., based on the dataset source and logic), the worker nodes  1006  can provide the processed information for storage in the query acceleration data store  1008  while continuing to process the query (e.g., apply the subsequent logic). That is, the worker nodes  1006  can bifurcate the data (e.g., at branch layer  1310 ), such that the query acceleration data store  1008  can store partial results while the worker nodes  1006  service the query and provide the completed results to the query coordinator  1004 . Optionally, another query may be received that references the partial results in the data store  1008 , and one or more worker nodes  1006  may access the data store  1008  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  1008 . For example, a first query can indicate that first information is to be obtained (e.g., from external data source  1018 , indexers  206 , common storage, and so on) and stored in the query acceleration data store  1008  as a first dataset. Additionally, a second query can indicate that second information is to obtained and stored in the data store  1008  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  1008 . 
     Furthermore, queries can reference datasets stored by the query acceleration data store  1008 , and also datasets to be obtained from another dataset source (e.g., from external data source  1018 , indexers  206 , ingested data buffer, and so on). For particular queries, the data intake and query system  1001  may be able to provide results (e.g., search results) from the query acceleration data store  1008  while datasets is being obtained from another dataset source. Similarly, the system  1001  may be able to provide results from the data store  1008  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  1008 , with the dataset being from an external data source  1018  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  1001  to provide results to a requesting client based on the stored dataset in the query acceleration data store  1008 . As an example, the second query can indicate that the system  1001  is to search for a particular name. The worker nodes  1006  can obtain stored information from the query acceleration data store  1008 , and identify instances of the particular name. 
     This access to the query acceleration data store  1008 , as described above, can be low-latency. For example, the query acceleration data store  1008  may have a portion of the stored information in low-latency memory, such as RAM or volatile memory, and the worker nodes  1006  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 1008 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  1006  interactions with the query acceleration data store  1008  can occur while information is being obtained, or processed, from the external data source  1018  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  1008 , 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  1008  for storage, for example, provided while the worker nodes  1006  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  1004  can provide an identification of a requesting user to the worker nodes  1006  and/or query acceleration data store  1008 . For example, the identification can be an authorization or authentication token associated with the user. The query acceleration data store  1008  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  1008  (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  108  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  1008  to service multitudes of users. 
     5.0. Query Data Flow 
       FIG.  14    is a data flow diagram illustrating an embodiment of communications between various components within the environment  1000  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, which can refer to the search process master  1002  and/or query coordinator  1004 . 
     At (3) the search process service processes the query. As described in greater detail above, as part of processing the query, the query coordinator  1004  can identify the dataset sources (e.g., external data sources  1018 , indexers  206 , query acceleration data store  1008 , 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  1006  to execute the query, and generate tasks for itself to process results from the nodes  1006 . 
     At (4), the query coordinator  1004  communicates the task instructions for the query to the worker nodes  1006  and/or the dataset sources  1404 . As described above, in some embodiments, the query coordinator  1004  can communicate task instructions to the dataset sources  1404 . In certain embodiments, the nodes  1006  communicate task instructions to the dataset sources  1404 . 
     At (5), the nodes  1006  and/or dataset sources  1404  process the received instructions. As described in greater detail above, the instructions for the dataset sources  1404  can include instructions for performing certain transformations on the data prior to communicating the data to the nodes  1006 , etc. As described in greater detail above, the instructions for the nodes  1006  can include instructions on how to access the relevant data, the number of search phases or layers to be generated, the number of partitions to be allocated for each search phase or layer, the tasks for the partitions in the different layer, data routing information to route data between the nodes  1006  and to the search process service  1402 , etc. As such, based on the received instructions, the nodes  1006  can assign partitions to different layer and begin executing the task instructions. 
     At (6), the nodes  1006  receive the data from the dataset source(s). As described in greater detail above, the nodes  1006  can receive the data from one or more dataset sources  1404  in parallel. In addition, the nodes  1006  can receive the data from a dataset source using one or more partitions. The data received from the dataset sources  1404  can be semi-processed data based on the processing capabilities of the dataset source  1404  or it can be unprocessed data from the dataset source  1404 . 
     At (7), the nodes  1006  process the data based on the task instructions received from the query coordinator  1004 . As described in greater detail above, the nodes can process the data using one or more layers, each having one or more partitions assigned thereto. Although not illustrated in  FIG.  37   , it will be understood that the search process service  1402  can monitor the nodes  1006  and dynamically allocate resources based on the monitoring. 
     At (8), the nodes  1006  communicate the results of the processing to the query coordinator  1004  and/or to a dataset destination  1404 . In some cases the dataset destination  1404  can be the same as the dataset source. For example, the nodes  1006  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  1008  or an external data source  1018  and then return the results of the processing to the query acceleration data store  1008  or external data source  1018 , respectively. However, in certain embodiments, the dataset destination  1404  can be different from the dataset source  1404 . For example, the nodes  1006  can obtain data from the ingested data buffer and then return the results of the processing to the query acceleration data store  1008  or an external data source  1018 . 
     At (9), the search process service  1402  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  1402  does not perform any further processing on the results and can simply forward the results to the search head  210 . In certain embodiments, nodes  1006  receive data from multiple dataset sources  1404 , etc. 
     6.0. Query Coordinator Flow 
       FIG.  15    is a flow diagram illustrative of an embodiment of a routine  1500  implemented by the query coordinator  1004  to provide query results. Although described as being implemented by the query coordinator  1004 , one skilled in the relevant art will appreciate that the elements outlined for routine  1500  can be implemented by one or more computing devices/components that are associated with the system  1001 , such as the search head  210 , search process master  1001 , indexer  206 , and/or worker nodes  1006 . Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  1502 , the query coordinator  1004  receives a query. As described in greater detail above, the query coordinator  1004  can receive the query from the search head  210 , search process master  1002 , etc. In some cases, the query coordinator  1004  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  1004  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  1001 , 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  1004  or search process master  1002   
     At block  1504 , the query coordinator  1004  processes the query. As described in greater detail above and as will be described in greater detail in  FIG.  16   , 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  1004  allocates multiple layers or search phases of partitions to execute the query. Each level of partitions can be given a different task in order to execute the query. For example, as described in greater detail above with reference to  FIGS.  12  and  13   , 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  1004  can allocate as many or as few levels of partitions to execute the query. 
     At block  1506 , the query coordinator  1004  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  1006 , monitoring the nodes  1006  during the processing of the query, or allocating/deallocating resources based on the status of the nodes and the query, and so forth, described in greater herein. 
     At block  1508 , the query coordinator  1004  receives the results. In some embodiments, the query coordinator  1004  receives the results from the nodes  1006 . For example, upon completing the query processing scheme, or as a part of it, the nodes  1006  can communicate the results of the query to the query coordinator  1004 . In certain cases, the query coordinator  1004  receives the results from the query acceleration data store, or indexers  206 , etc. In some cases, the query coordinator  1004  receives the results from one or more components of the data intake and query system  1001  depending on the dataset sources used in the query. 
     At block  1510 , the query coordinator  1004  processes the results. As described in greater detail above, in some cases, the results of a query cannot be finalized by the nodes  1006 . 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, the query coordinator  1004 , a result cannot be determined until all relevant data has been collected by the worker nodes. In such cases, the query coordinator  1004  can receive the results from the worker nodes  1006 , and then collate the results. 
     At block  1512 , the query coordinator  1004  communicates the results. In some embodiments, the query coordinator  1004  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  1004  communicates the results to the search process master  1002  or client device  404 , etc. 
     It will be understood that fewer, more, or different blocks can be used as part of the routine  1500 . In some cases, one or more blocks can be omitted. For example, in certain embodiments, the results received from nodes  1006  can be in a form that does not require any additional processing by the query coordinator  1004 . In such embodiments, the query coordinator  1004  can communicate the results without additional processing. As another example, the routine  1500  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  1500  can include reporting completion of the query to a component, such as the search process master  1002 , etc. 
     Furthermore, it will be understood that the various blocks described herein with reference to  FIG.  15    can be implemented in a variety of orders. In some cases, the query coordinator  1004  can implement some blocks concurrently or change the order as desired. For example, the query coordinator  1004  can receive ( 1508 ), process ( 1510 ), and/or communicate results ( 1512 ) concurrently or in any order, as desired. 
     7.0. Query Processing Flow 
       FIG.  16    is a flow diagram illustrative of an embodiment of a routine  1600  implemented by the query coordinator  1004  to process a query. Although described as being implemented by the query coordinator  1004 , one skilled in the relevant art will appreciate that the elements outlined for routine  1600  can be implemented by one or more computing devices/components that are associated with the system  1001 , such as the search head  210 , search process master  1001 , indexer  206 , and/or worker nodes  1006 . Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  1602 , the query coordinator  1004  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  1008 , ingested data buffer, common storage, indexers, or an external data source. In certain cases, the query coordinator  1004  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) of the location (e.g., my_index) of the relevant data and the query coordinator  1004  can use the name or identifier to determine whether that particular location is associated with the query acceleration data store  1008 , ingested data buffer, common storage, indexers  206 , or an external data source  1018 . 
     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  1001  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  1004  identifies the dataset source based on the architecture of the system  1001 . 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  1006  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  1006  in combination with the common storage. 
     At block  1604  the query coordinator  1004  obtains relevant information about the dataset sources/destinations. The query coordinator  1004  can obtain the relevant information from a variety of sources, such as the workload advisor  1010 , workload catalog  1012 , dataset compensation module  1016 , the dataset sources/destinations themselves, etc. For example, if the dataset source/destination is an external data source, the query coordinator  1004  can obtain relevant information about the external dataset source  1018  from the dataset compensation module or by communicating with the external data source  1018 . Similarly, if the dataset source/destination is an indexer  206 , common storage, query acceleration data store  1008 , ingested data buffer, etc., the query coordinator can obtain relevant information by communicating with the dataset source/destination and/or the workload advisor  1010  or workload catalog  1012 . 
     The relevant information can include, but is not limited to, information to enable the query coordinator  1004  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  1606 , the query coordinator  1004  determines processing requirement for the query. In some cases, to determine the processing requirements, the query coordinator  1004  parses the query. As described previously, the workload catalog  1012  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  1001 . For example, the query coordinator  1004  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  1608 , the query coordinator  1004  determines available resources. As described in greater detail above, the nodes  1006  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  1004  to determine the amount of resources available for the query. 
     At block  1610 , the query coordinator  1004  generates a query processing scheme. In some cases, the query coordinator  1004  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  1004  can generate instructions to be executed by the dataset sources/destinations, allocate partitions/processors for the query, generate instructions for the partitions/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  1004  can use the information from the dataset compensation module  1016 . This information can be used by the query coordinator  1004  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 that can be used to read data from the external dataset source, etc. Similarly, the query coordinator  1004  can generate instructions for other dataset sources, such as the indexers, query acceleration data store, common storage, etc. For example, the query coordinator  1004  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/partitions, the query coordinator  1004  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, the determine how to break up the processing requirements of the query into discrete or individual tasks, the query coordinator  1004  can parse the query to its different portions of the query and then determine the tasks to use to execute the different portions. 
     The query coordinator  1004  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  1004  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  1004  can finalize the results and communicate them to the search head  210 . 
     In some cases, the query coordinator  1004  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  1004  can use the physical location of the processors that will execute the query to generate the instructions. As one example, the query coordinator  1004  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 partitions for the different tasks. 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  1004  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  1006 , and the results expected from the nodes. For example, in some cases, the type of query requested may require the query coordinator  1004  to perform more or less processing. For example, a cursored search may require more processing by the query coordinator  1004  than a batch search. Accordingly, the query coordinator  1004  can generate tasks or instructions for itself based on the query requested. 
     In addition, if the nodes  1006  are unable to perform certain tasks on the data, then the query coordinator  1004  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  1004  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  1600 . 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.  16    can be implemented in a variety of orders. In some cases, the query coordinator  1004  can implement some blocks concurrently or change the order as desired. For example, the query coordinator  1004  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. 
     8.0. Common Storage Architecture 
     As discussed above, indexers  206  may in some embodiments operate both to ingest information into a data intake and query system  1001 , 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  1001  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  210 . 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  1001  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  1001  that enables parallelized searching of buckets independently of the operation of indexers  206  is shown in  FIG.  17   . The embodiment of system  1001  that is shown in  FIG.  17    substantially corresponds to embodiment of the system  1001  as shown in  FIG.  10   , and thus corresponding elements of the system  1001  will not be re-described. However, unlike the embodiment as shown in  FIG.  10   , where individual indexers  206  are assigned to maintain individual data stores  208 , the embodiment of  FIG.  17    includes a common storage  1702 . Common storage  1702  may correspond to any data storage system accessible to each of the indexers  206 . For example, common storage  1702  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  1702  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  1702  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  1702  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  1702  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  1702  or the data intake and query system  1001  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  1702  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  1702 , rather than in a data store  208  maintained by an individual indexer  206 . Thus, the common storage  1702  can render information of the data intake and query system  1001  commonly accessible to elements of that system  1001 . As will be described below, such common storage  1702  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  1001  functionalities of ingesting data and searching for data. As such, in the illustrative configuration of  FIG.  17   , worker nodes  1006  may be enabled to search for data stored within common storage  1702 . The nodes  1006  may therefore be communicatively attached (e.g., via a communication network) with the common storage  1702 , and be enabled to access buckets within the common storage  1702 . The nodes  1006  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  1006  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  1006  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  1001  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  1006 . Thus, if 10 worker nodes  1006  are available to process a query, each worker node  1006  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  1006  rather than at indexers  206 , computing resources can be allocated independently to searching operations. For example, worker nodes  1006  may be executed by a separate processor or computing device than indexers  206 , enabling computing resources available to worker nodes  1006  to scale independently of resources available to indexers  206 . 
     Operation of the data intake and query system  1001  to utilize worker nodes  1006  to search for information within common storage  1702  will now be described. As discussed above, a query can be received at the search head  210 , processed at the search process master  1002 , and passed to a query coordinator  1004  for execution. The query coordinator  1004  may generate a DAG corresponding to the query, in order to determine sequences of search phases within the query. The query coordinator  1004  may further determine based on the query whether each branch of the DAG requires searching of data within the common storage  1702  (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  1702 , 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  1702 . As discussed above with reference to  FIG.  13   , executing a search representing a branch of a DAG can include a number of phases, such as an intake phase  1304 , processing phase  1306 , and collector phase  1308 . It is therefore illustrative to discuss execution of a branch of a DAG that requires searching of the common storage  1702  with reference to such phases. As also discussed above, each phase may be carried out by a number of partitions, each of which may correspond to a worker node  1006  (e.g., a specific worker node  1006 , processor within the worker node  1006 , execution environment within a worker node  1006 , such as a virtualized computing device or software-based container, etc.). 
     When a branch requires searching within common storage  1702 , the query coordinator  1004  can select a partition (e.g., a processor within a worker node  1006 ) at random or according to a load-balancing algorithm to gather metadata regarding the information within the common storage  1702 , for use in dynamically assigning partitions (each implemented by a worker node  1006 ) to implement an intake phase  1304 . 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  1702  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  1702  that satisfy those specified parameters may be considered relevant to the query. In one embodiment, the subset of buckets is determined by the assigned partition, and returned to the query coordinator  1004 . In another embodiment, the metadata retrieved by a partition is returned to the query coordinator  1004  and used by the query coordinator  1004  to determine the subset of buckets. 
     Thereafter, the query coordinator  1004  can dynamically assign partitions to intake individual buckets within the determined subset of buckets. In one embodiment, the query coordinator  1004  attempts to maximize parallelization of the intake phase  1304 , by attempting to intake the subset of buckets with a number of partitions equal to the number of buckets in the subset (e.g., resulting in a one-to-one mapping of buckets in the subset to partitions). However, such parallelization may not be feasible or desirable, for example, where the total number of partitions is less than the number of buckets within the determined subset, where some partitions are processing other queries, or where some partitions should be left in reserve to process other queries. Accordingly, the query coordinator  1004  may interact with the workload advisor  1010  to determine a number of partitions that are to be utilized to conduct the intake phase  1304  of the query. Illustratively, the query coordinator  1004  may initially request a one-to-one correspondence between buckets and partitions, and the workload advisor  1010  may reduce the number of partitions used for the intake phase  1304  of the query, resulting in a 2-to-1, 3-to-1, or n-to-1 correspondence between buckets and partitions. Operation of the workload advisor  1010  is described in more detail above. 
     The query coordinator  1004  can then assign the partitions (e.g., those partitions identified by interaction with the workload advisor  1010 ) 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  1004  may assign all buckets as a single operation. For example, where 10 buckets are to be searched by 5 partitions, the query coordinator  1004  may assign 2 buckets to a first partitions, two buckets to a second partitions, etc. In another embodiment, the query coordinator  1004  may assign buckets iteratively. For example, where 10 buckets are to be searched by 5 partitions, the query coordinator  1004  may initially assign five buckets (e.g., one buckets to each partition), and assign additional buckets to each partition as the respective partitions complete intake of previously assigned buckets. 
     In some instances, buckets may be assigned to partitions randomly, or in a simple sequence (e.g., a first partitions is assigned a first bucket, a second partitions is assigned a second bucket, etc.). In other instances, the query coordinator  1004  may assign buckets to partitions based on buckets previously assigned to a partitions, in a prior or current search. Illustratively, in some embodiments each worker node  1006  may be associated with a local cache of information (e.g., in memory of the partitions, such as random access memory [“RAM”] or disk-based cache). Each worker node  1006  may store copies of one or more buckets from the common storage  1702  within the local cache, such that the buckets may be more rapidly searched by partitions implemented on the worker node  1006 . The query coordinator  1004  may maintain or retrieve from worker nodes  1006  information identifying, for each relevant node  1006 , what buckets are copied within local cache of the respective nodes  1006 . Where a partition assigned to execute a search is implemented by a worker node  1006  that has within its local cache a copy of a bucket determined to be potentially relevant to the search, that partition may be preferentially assigned to search that locally-cached bucket. In some instances, local cache information can further be used to determine the partitions to be used to conduct a search. For example, partitions corresponding to worker nodes  1006  that have locally-cached copies of buckets potentially relevant to a search may be preferentially selected by the query coordinator  1004  or workload advisor  1010  to execute the intake phase  1304  of a search. In some instances, the query coordinator  1004  or other component of the system  1001  (e.g., the search process master  1002 ) may instruct worker nodes  1006  to retrieve and locally cache copies of various buckets from the common storage  1702 , independently of processing queries. In one embodiment, the system  1001  is configured such that each bucket from the common storage  1702  is locally cached on at least one worker node  1006 . In another embodiment, the system  1001  is configured such that at least one bucket from the common storage  1702  is locally cached on at least two worker nodes  1006 . Caching a bucket on at least two worker nodes  1006  may be beneficial, for example, in instances where different queries both require searching the bucket (e.g., because the at least two worker nodes  3006  may process their respective local copies in parallel). In still other embodiments, the system  1001  is configured such that all buckets from the common storage  1702  are locally cached on at least a given number n of worker nodes  1006 , wherein n is defined by a replication factor on the system  1001 . 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  1006 , 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  1004  may attempt to assign buckets with overlapping time ranges to the same partition, such that information within the buckets can be sorted at the partition. Where the buckets assigned to different partitions are non-overlapping in time, the query coordinator  1004  may sort information from different partitions according to an absolute ordering of the buckets processed by the different partitions. That is, if all timestamps in all buckets processed by a first worker node  1006  occur prior to all timestamps in all buckets processed by a second worker node  1006 , query coordinator  1004  can quickly determine (e.g., without referencing timestamps of information) that all information identified by the first worker node  1006  in response to a search occurs in time prior to information identified by the second worker node  1006  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 based on overlaps of computing resources of the partitions. For example, where a partition is required to retrieve a bucket from common storage  1702  (e.g., where a local cached copy of the bucket does not exist on the worker node  1006  implementing the partition), such retrieval may use a relatively high amount of network bandwidth or disk read/write bandwidth on the worker node  1006  implementing the partition. Thus, assigning a second partition of the same worker node  1006  might be expected to strain or exceed the network or disk read/write bandwidth of the worker node  1006 . For this reason, it may be preferential to assign buckets to partitions such that two partitions within a common worker node  1006  are not both required to retrieve buckets from the common storage  1702 . Illustratively, it may be preferential to evenly assign all buckets containing potentially relevant information among the different worker nodes  1006  used to implement the intake phase  1304 . For similar reasons, where a given worker node  1006  has within its local cache two buckets that potentially include relevant information, it may be preferential to assign both such buckets to different partitions implemented by the same worker node  1006 , such that both buckets can be search in parallel on the worker node  1006  by the respective partitions. In some instances, commonality of computing resources between partitions can further be used to determine the partitions to be used to conduct an intake phase  1304 . For example, the query coordinator  1004  may preferentially select partitions that are implemented by different worker nodes  1006  (e.g., in order to maximize network or disk read/write bandwidth) to implement an intake phase  1304 . However, where a worker node  1006  has locally cached multiple buckets with information potentially relevant to the search, the query coordinator  1004  may preferentially multiple partitions on that worker node  1006  (e.g., up to a number of partitions equal to the number of potentially-relevant buckets stored at the worker node  1006 ). 
     The above mechanisms for assigning buckets to partitions may be combined based on priorities of each potential outcome. For example, the query coordinator  1004  may give an initial priority to distributing assigned partitions across a maximum number of different worker nodes  1006 , but a higher priority to assigning partitions to process buckets with overlapping timestamps. The query coordinator  1004  may give yet a higher priority to assigning partitions to process buckets that have been locally cached. The query coordinator  1004  may still further give higher priority to ensuring that each partition is searching at least one bucket for information responsive to a query at any given time. Thus, the query coordinator  1004  can dynamically alter the assignment of buckets to partitions 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  1702 , the intake phase  1304  may be carried out according to bucket-to-partition mapping discussed above, as determined by the query coordinator  1004 . Specifically, after assigning at least one bucket to each partition to be used during the intake phase  1304 , each partition may begin to retrieve its assigned bucket. Retrieval may include, for example, downloading the bucket from the common storage  1702 , or locating a copy of the bucket in a local cache of a worker node  1006  implementing the partition. Thereafter, each partition 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. 
     Thereafter, the search proceeds to the processing phase  1306 , where the portions of buckets identified during the intake phase  1304  are searched to locate information responsive to the search. Illustratively, the searching that occurs during the processing phase  1306  may be predicted to be more processor (e.g., CPU) intensive than that which occurred during the intake phase  1304 . As such, the number of partitions used to conduct the processing phase  1306  may vary from that of the intake phase  1304 . For example, during or after the conclusion of the intake phase  1304 , each partition implementing that phase  1304  may communicate to the query coordinator  1004  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  1004  may thereafter determine from that information (e.g., based on interactions with the workload advisor  1010 ) the partitions to be used to conduct the processing phase  1306 . In other embodiments, the query coordinator  1004  may select partitions to be used to conduct the processing phase  1306  prior to implementation of the intake phase  1304  (e.g., contemporaneously with selecting partitions to conduct the intake phase  1304 ). The partitions selected for conducting the processing phase  1306  may include one or more partitions that previously conducted the intake phase  1304 . However, because the processing phase  1306  may be expected to be more resource intensive than the intake phase  1304  (e.g., with respect to use of processing cycles), the number of partitions selected for conducting the processing phase  1306  may exceed the number of partitions that previously conducted the intake phase  1304 . To minimize network communications, the additional partitions selected to conduct the processing phase  1306  may be preferentially selected to be collocated on a worker node  1006  with a partition that previously conducted the intake phase  1304 , such that portions of buckets to be processed by the additional partitions can be received from a partition on that worker node  1006 , rather than being transmitted across a network. 
     At the processing phase  1306 , the partitions may parse the portions of buckets located during the intake phase  1304  in order to identify information relative to a search. For example, the 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  1306  can correspond to implementing a map function. Where the search requires that results be time-ordered, the processing phase  1306  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  1004 . For example, where the branch of the DAG currently being processed includes a collection node, the search may proceed to a collector phase  1308 . The collector phase  1308  may be executed by one or more partitions selected by the query coordinator  1004  (e.g., based on the information identified during the processing phase  1306 ), and operate to aggregate information identified during the processing phase  1306  (e.g., according to a reduce function). Where the processing phase  1306  represents a top-node of a branch of the DAG being executed, the information located by each partition during the processing phase  1306  may be transmitted to the query coordinator  1004 , where any additional nodes of the DAG are completed, and search results are transmitted to a data destination  1316 . 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  1702 . 
     As will be appreciated in view of the above description, the use of a common storage  1702  can provide many advantages within the data intake and query system  1001 . Specifically, use of a common storage  1702  can enable the system  1001  to decouple functionality of data ingestion, as implemented by indexers  206 , with functionality of searching, as implemented by partitions of worker nodes  1006 . Moreover, because buckets containing data are accessible by each worker node  1006 , a query coordinator  1004  can dynamically allocate partitions to buckets at the time of a search in order to maximize parallelization. Thus, use of a common storage  1702  can substantially improve the speed and efficiency of operation of the system  1001 . 
     9.0. Ingested Data Buffer Architecture 
     One embodiment of the system  1001  that enables worker nodes  1006  to search not-yet-indexed information is shown in  FIG.  18   . 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  1001 , 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  1001 , rather than waiting for the data to be processed by the indexers  206  and saved into a data store  208 . 
     The embodiment of  FIG.  18    is similar to that of  FIG.  17   , and corresponding elements will not be re-described. However, unlike the embodiment of  FIG.  17   , the embodiment of  FIG.  18    includes an ingested data buffer  1802 . The ingested data buffer  1802  of  FIG.  18    operates to receive information obtained by the forwarders  204  from the data sources  202 , and make such information available for searching to both indexers  206  and worker nodes  1006 . As such, the ingested data buffer  1802  may represent a computing device or computing system in communication with both the indexers  206  and the worker nodes  1006  via a communication network. 
     In one embodiment, the ingested data buffer  1802  operates according to a publish-subscribe (“pub-sub”) messaging model. For example, each data source  202  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  1001 , including indexers  206  and worker nodes  1006  (or partitions within worker nodes  1006 ) may subscribe to a topic representing desired information (e.g., information of a particular data source  202 ) 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  1802 . A variety of implementations of the pub-sub messaging model are known in the art, and may be usable within the ingested data buffer  1802 . As will be appreciated based on the description below, use of a pub-sub messaging model can provide many benefits to the system  1001 , including the ability to search data quickly after the data is received at the ingested data buffer  1802  (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  1802 , operation of the indexer  206  may be modified to receive information from the buffer  1802 . Specifically, each indexer  206  may be configured to subscribe to one or more topics on the ingested data buffer  1802  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  1802 . In accordance with the pub-sub messaging model, the ingested data buffer  1802  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  1001  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.  10  and  17   , etc.). 
     As discussed above, the ingested data buffer  1802  is also in communication with the worker nodes  1006 . As such, the data intake and query system  1001  can be configured to utilize the worker nodes  1006  to search data from the ingested data buffer  1802  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  210 , processed at the search process master  1002 , and passed to a query coordinator  1004  for execution. The query coordinator  1004  may generate a DAG corresponding to the query, in order to determine sequences of search phases within the query. The query coordinator  1004  may further determine based on the query whether any branch of the DAG requires searching of data within the ingested data buffer  1802 . For example, the query coordinator  1004  may determine that at least one branch of the query requires searching of data within the ingested data buffer  1802  by identifying, within the query, a topic of the ingested data buffer  1802  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  1802 , 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  1802 . As discussed above with reference to  FIG.  13   , executing a search representing a branch of a DAG can include a number of phases, such as an intake phase  1304 , processing phase  1306 , and collector phase  1308 . It is therefore illustrative to discuss execution of a branch of a DAG that requires searching of the common storage  1702  with reference to such phases. As also discussed above, each phase may be carried out by a number of partitions, each of which may correspond to a worker node  1006  (e.g., a specific worker node  1006 , processor within the worker node  1006 , execution environment within a worker node  1006 , 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  1306  may occur with respect to a first set of information while the intake phase  1304  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  1001 ), and each sequence may occur at least partially concurrently with one or more other sequences. Moreover, because the ingested data buffer  1802  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.  18    provides a highly scalable, highly resilient, high availability architecture for searching information received at the system  1001 . 
     When a branch requires searching within ingested data buffer  1802 , the query coordinator  1004  can select a partition (e.g., a processor within a worker node  1006 ) at random or according to a load-balancing algorithm to gather metadata regarding the topic specified within the query from the ingested data buffer  1802 . Metadata regarding a topic may include, for example, a number of message queues within the ingested data buffer  1802  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  1802 ). In some instances, the ingested data buffer  1802  may implement a single message queue for a topic. In other instances, the ingested data buffer  1802  may implement multiple message queues (e.g., across multiple computing devices) to aid in load-balancing operation of the ingested data buffer  1802  with respect to the topic. The selected partition can determine the number of message queues maintained at the ingested data buffer  1802  for a topic, and return this information to the query coordinator. 
     Thereafter, the query coordinator  1004  can dynamically assign partitions to conduct an intake phase  1304 , by retrieving individual message queues of the topic within the ingested data buffer  1802 . In one embodiment, the query coordinator  1004  attempts to maximize parallelization of the intake phase  1304 , by attempting to retrieve messages from the message queues with a number of partitions equal to the number of message queues for the topic maintained at the ingested data buffer  1802  (e.g., resulting in a one-to-one mapping of message queues in the topic to partitions). However, such parallelization may not be feasible or desirable, for example, where the total number of partitions is less than the number of message queues, where some partitions are processing other queries, or where some partitions should be left in reserve to process other queries. Accordingly, the query coordinator  1004  may interact with the workload advisor  1010  to determine a number of partitions that are to be utilized to intake messages from the message queues during the intake phase  1304 . Illustratively, the query coordinator  1004  may initially request a one-to-one correspondence between message queues and partitions, and the workload advisor  1010  may reduce the number of partitions used to read the message queues, resulting in a 2-to-1, 3-to-1, or n-to-1 correspondence between message queues and partitions. Operation of the workload advisor  1010  is described in more detail above. When a greater than 1-to-1 correspondence exists between queues and partitions (e.g., 2-to-1, 3-to-1, etc.), the message queues may be evenly assigned among different worker nodes  1006  used to implement the intake phase  1304 , to maximize network or read/write bandwidth available to partitions conducting the intake phase  1304 . 
     During the intake phase  1304 , each partition used during the intake phase  1304  can subscribe to those message queues assigned to the partition. Illustratively, where partitions are assigned in a 1-to-1 correspondence with message queues for a topic in the ingested data buffer  1802 , each partition may subscribe to one corresponding message queue. Thereafter, in accordance with the pub-sub messaging model, the partition can receive from the ingested data buffer  1802  messages publishes within those respective message queues. However, to ensure message resiliency, a partition 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 partition may, during the intake phase  1304  act as an aggregator of messages published to a respective message queue of the ingested data buffer  1802 , to define a collection of data to be processed during an instance of the processing phase  1306 . For example, the partition 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 for further processing during a processing phase  1306  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  1802  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  1802 . For example, it is possible the information arrives at the ingested data buffer  1802  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  1306  (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  1802  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  1006 . For example, a longer time window may reduce computing resources used by a partition, by enabling a larger collection of messages to be processed at a single instance of the processing phase  1306 . However, the longer time window may also 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 defined according to a time-window, other embodiments of the present disclosure may utilize additional or alternative collection techniques. For example, a partition may be configured to include no more than a threshold number of messages or a threshold amount of data in a collection, regardless of a time-window for collection. As another example, a partition may be configured during the intake phase  1304  not to aggregate messages, but rather to pass each message to a processing phase 1306 immediately or substantially immediately. Thus, embodiments related to time-windowing of messages are illustrative in nature. 
     In some embodiments, the partitions, during the intake phase  1304  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 prior to the search process proceeding to the processing phase  1306 . 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 from a respective message queue, the search can proceed to the processing phase  1306 , where one or more partitions are utilize to search the messages for information relevant to the search query. Illustratively, the searching that occurs during the processing phase  1306  may be predicted to be more processor (e.g., CPU) intensive than that which occurred during the intake phase  1304 . As such, the number of partitions used to conduct the processing phase  1306  may vary from that of the intake phase  1304 . For example, during or after the conclusion of the intake phase  1304 , each partition implementing that phase  1304  may communicate to the query coordinator  1004  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  1004  may thereafter determine from that information (e.g., based on interactions with the workload advisor  1010 ) the partitions to be used to conduct the processing phase  1306 . In other embodiments, the query coordinator  1004  may select partitions to be used to conduct the processing phase  1306  prior to implementation of the intake phase  1304  (e.g., contemporaneously with selecting partitions to conduct the intake phase  1304 ). The partitions selected for conducting the processing phase  1306  may include one or more partitions that previously conducted the intake phase  1304 . However, because the processing phase  1306  may be expected to be more resource intensive than the intake phase  1304  (e.g., with respect to use of processing cycles), the number of partitions selected for conducting the processing phase  1306  may exceed the number of partitions that previously conducted the intake phase  1304 . To minimize network communications, the additional partitions selected to conduct the processing phase  1306  may be preferentially selected to be collocated on a worker node  1006  with a partition that previously conducted the intake phase  1304 , such that portions of buckets to be processed by the additional partitions can be received from a partition on that worker node  1006 , rather than being transmitted across a network. 
     At the processing phase  1306 , the partitions may parse the collections of messages generated during the intake phase  1304  in order to identify information relative to a search. For example, the may parse individual messages 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  1306  can correspond to implementing a map function. Where the search requires that results be time-ordered, the processing phase  1306  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  1004 . For example, where the branch of the DAG currently being processed includes a collection node, the search may proceed to a collector phase  1308 . The collector phase  1308  may be executed by one or more partitions selected by the query coordinator  1004  (e.g., based on the information identified during the processing phase  1306 ), and operate to aggregate information identified during the processing phase  1306  (e.g., according to a reduce function). Where the processing phase  1306  represents a top-node of a branch of the DAG being executed, the information located by each partition during the processing phase  1306  may be transmitted to the query coordinator  1004 , where any additional nodes of the DAG are completed, and search results are transmitted to a data destination  1316 . 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  1702 . 
     Subsequent to these phases, a set of search results corresponding to each collection of messages (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  1802 . For example, the search head  210  may notify the query coordinator  1004  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  1004  can thereafter notify partitions used to ingest messages making up the set of information that the search results have been transmitted. The partitions can then acknowledge to the ingested data buffer  300  receipt of the messages. In accordance with the pub-sub messaging model, the ingested data buffer  1802  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  1802  and search results based on that message being passed to a data destination (e.g., a worker node  1006  fails, causing a copy of the message maintained at the worker node  1006  to be lost), the query coordinator  1004  can detect the failure (e.g., based on heartbeat information from a worker node  1006 ), and cause the worker node  1006  to be restarted, or a new worker node  1006  to replace the failed worker node  1006 . Because the message has not yet been acknowledged to the ingested data buffer  1802 , the message is expected to still exist within a message queue of the ingested data buffer  1802 , and thus, the restarted or new worker node  1006  can retrieve and process the message as described below. Thus, by delaying acknowledgement of a message, failures of worker nodes  1006  during the process described above can be expected not to result in data loss within the data intake and query system  1001 . 
     In some embodiments, the ingested data buffer  1802  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.  18    may enable a client to define a long-running search that locates codes within messages of the ingested data buffer  1802  (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  1702 ), 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  1802 . In this manner, the information maintained at the ingested data buffer  1802  may be readily annotated or transformed by searches executed at the system  1001 . Any number of types of processing or transformation may be applied to information of the ingested data buffer  1802  to produce search results, and any of such search results may be republished to the ingested data buffer  1802 , 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  1802  can provide many advantages within the data intake and query system  1001 . Specifically, use of a ingested data buffer  1802  can enable the system  1001  to utilize worker nodes  1006  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  1802  can make messages available to both indexers  206  and worker nodes  1006 , searching of not-yet-indexed information by worker nodes  1006  can be expected not to detrimentally effect the operation of the indexers  206 . Still further, because the ingested data buffer  1802  can operate according to a pub-sub messaging model, the system  1001  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  1001 . Thus, use of an ingested data buffer  1802  can substantially improve the speed, efficiency, and reliability of operation of the system  1001 . 
     As described in greater detail in greater detail in U.S. Pat. Application No. U.S. Pat. Application No. 15/665159, entitled “ MULTI-LAYER PARTITION ALLOCATION FOR QUERY EXECUTION”, filed on Jul. 31, 2017, and which is hereby incorporated by reference in its entirety for all purposes, the various architectures of the system described herein can be used to define query processing schemes and execute queries based on workload monitoring, process and execute queries corresponding to data in external data sources, common storage, ingested data buffers, acceleration data stores, indexers, etc., allocate partitions based on identified dataset sources or dataset destinations, dynamically generate subqueries for external data sources or indexers, serialize/deserialize data for communication, accelerate query processing using the acceleration data store  1008 , etc. 
     10.0 Combining Datasets 
     In some cases, a query can indicate that two or more datasets are to be combined in some fashion, such as by using a join or a union operation. Combining datasets can result in significantly larger number of data entries than the sum of the datasets to be combined. 
     Accordingly, executing a query on large datasets, in some cases, the system can allocate more partitions for combination or expansion operations than for mapping or reducing operations to avoid partitions having too many data entries. For example, in certain cases, the system can automatically allocate five, ten, or twenty, times (or more) more partitions for combination or expansion operations than for mapping or reducing operations. Mapping operations generally operate on a set of results or partitions. Reducing operations generally reduce a set of results to a smaller set of results, which can result in fewer partitions being used. Combination or expansion operations generally increase a set of results to a larger set of results, which can result in more partitions being used. 
     However, while assigning more partitions can result in smaller partitions overall, some partitions may have a disproportionate number of the data entries from the datasets. For example, if the data entries are partitioned based on a particular field value or field-value pairs, one or more field values or field-value pairs may occur significantly more frequently than others. As such, the partition assigned to store that field-value pair can end up having a disproportionate number of data entries than the other partitions. Similarly, a processor core tasked with processing the data entries of that partition may end up having significantly more processing to do as compared to other processor cores. This imbalance can result in a significant delay of the entire set of results until the processor core finishes processing the imbalanced partition. 
     For example, consider the following search which, includes a join command that is to be executed by a distributed system having sixty cores:  
     
       
         
           
               
            
               
                 “search index=dogfood2007 | fields _time, source | fields - _raw | join usetime=f 
               
               
                        left=L right=R where L._time=R._time [search index=dogfood2007 | fields _time, 
               
               
                        sourcetype | fields - _raw] | stats count” 
               
            
           
         
       
     
     As indicated in the search command, the index dogfood2007 is being searched for data entries with fields _time and source. The search command also includes an instruction to join two datasets, L and R, based at least in part on the values in the _time field. Supposing that the dogfood2007 index contains approximately 500 million data entries the above join returns results in &gt;264 billion data entries. 
     In some scenarios, the distributed system partitions the 500 million data entries based on the field value of each entry that corresponds to the field that is the subject of the join. For example, one core is tasked with processing the data entries that include an identical or similar _time field value. However, such a distribution can result in imbalanced partitions in cases where one or more field values are highly repetitive and/or have high cardinality. 
     Consider an instance in which one of the _time field values in the above search has ~380,000 repetitions. Since the above search involves a self join (joining different datasets that originated from the same data source or dataset), one of the partitions would contain 380,000 field value repetitions on both sides (from the L and R datasets) that are to be joined. The joining of the two sets of 380,000 field values would result in ~144 billion results in the partition. The processor core assigned to process the data entries in that partition would be tasked to process the ~144 billion search, which could result in days of search execution time. 
     Thus, although sixty cores are assigned for the above search, fifty-nine of the cores would no longer be utilized after completing the processing on their relatively smaller partitions, while one core would continue to run for many hours to compute the ~144 billion result output. 
     To improve the distribution of the data entries that have a high number of repetitive field values and high cardinality, the system can determine and apply a seed value to such data entries such that the data entries are distributed between multiple partitions, enabling multiple processor cores to process them in parallel. 
     As the additional partitioning can result in additional processing resources, the system can perform an preliminary review to determine whether to implement the multi-partition operation. This additional processing can be completed by a master node, such as by a search head, query coordinator, or by one or more of the distributed processor cores executing the search. Furthermore, in some embodiments, the additional processing can be performed during the query processing stage and/or during the query execution stage. 
     In some embodiments, prior to executing the query, the system can determine whether a multi-partition operation is to be used. For example, the system can perform a semantic analysis of the query itself to determine the likelihood of a significantly imbalanced partition. The semantic analysis can include a review of the query command itself. For example, if the system determines that the query command does not include a combination operation, such as a join operation, the system can determine not to implement the multi-partition operation. In some cases, the system can determine not to implement the multi-partition operation if the query command indicates a combination based on a field that was previously used in a reduction operation of the query or is a subset of the fields used in a reduction operation. 
     Further, if the system determines that the query command indicates that the combination is based on a field that was not previously used in a reduction operation or that the operation just prior to the instruction to combine was a combination or expansion operation, then the system can determine that the multi-partition operation may be used. 
     If, following the pre-execution analysis, the system determines that an imbalanced partition is possible, then it can monitor the execution of the query or instruct the distributed processor cores to monitor the execution of the query. 
     During execution of the query, the system can identify the field that is to be used to combine the different datasets, and determine the number of data entries in each dataset that have the same field value for the identified field. Using the number of data entries from each dataset that have the same field value for the field used in the combination, the system can determine whether to implement the multi-partition operation. 
     In some embodiments, if the data entries from each dataset that have the same field value satisfies a data entries quantity threshold, then the system can implement the multi-partition operation. As a non-limiting example, the system can combine the number of data entries in each dataset, such as by multiplying the number of data entries that have the same field value in a first dataset with the number of data entries that have the same field value in a second dataset. If the product exceeds a data entries quantity threshold, then the system can implement the multi-partition operation. 
     Following the multi-partition operation, the system can perform a similar analysis on each of the partitions involved in the multi-partition operation. If the combination of the data entries from the datasets in a partition exceeds the data entries quantity threshold or the memory usage for the sub-partition exceeds a memory level threshold, the system can implement the multi-partition operation for the affected partition. The system can continue to perform the multi-partition operation until the combination of data entries from the datasets in each partition or sub-partition does not satisfy the data entries quantity threshold and the memory level threshold. 
     10.1 Multi-Partition Determination 
       FIG.  19    is a flow diagram illustrative of an embodiment of a routine  1900  implemented by the system to process and execute a query. One skilled in the relevant art will appreciate that the elements outlined for routine  1900  can be implemented by one or more computing devices/components that are associated with the system  1000 , such as the search head  210 , search process master  1002 , query coordinator  1004  and/or worker nodes  1006 , or any combination thereof. Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  1902 , the system receives a query. The system can receive the query similar in a manner similar to that described above with reference to block  3802  of  FIG.  38     
     At decision block  1904 , the system determines whether the query is susceptible to a significant partition imbalance, such as an imbalance that could result in a processor core spending significant amounts of additional time (non-limiting examples: hours or days) processing the partition while other processor cores assigned to the same query have completed processing their partitions. For example, as part of decision block  1904 , the system can analyze the syntax or semantics of the query to determine whether the query is susceptible to a significant partition imbalance. The system can make this determination in a variety of ways. 
     In some embodiments, the system can determine whether the query is susceptible to a significant partition imbalance based on whether the query includes a reduction operation prior to the combination operation and/or whether the datasets are to be combined using a field that is to be used in a reduction operation prior to the combination operation. Some reduction operations can include, but are not limited to, stats commands, such as, countby, count, etc. or mathematical operations, such as mean, median, average, etc. Examples of combination operations can include, but are not limited to inner joint, outer join (left outer, right outer, full outer), union, etc. 
     In certain embodiments, such as when no reduction operation has been performed on the datasets or the field used in the combination operation does not correspond to a field used in a prior reduction operation, the system can determine that the query is susceptible to a significantly imbalanced partition. In certain embodiments, such as when the field used in the combination operation corresponds to a field used in a prior reduction operation (e.g., field in the combination is the same field or a subset of the fields used in a prior reduction operation), the system can determine that the query is not susceptible to a significantly imbalanced partition. 
     As a non-limiting example, if the query indicates that two datasets are to be joined based on the field “_time,” the system can determine whether the query includes a reduction operation using the field “_time” that is prior to the join. For example, the reduction operation can use the field “_time” alone or in combination with other fields, such that the field “_time” in the join is a subset of the fields used in the reduction operation. In some embodiments, upon determining that the query includes a reduction operation using the field “_time” prior to the join, the system can determine that the query is not susceptible to a significantly imbalanced partition. Conversely, in certain embodiments, upon determining that the query does not include any reduction operations, any reduction operations prior to the join, or a reduction operation prior to the join that uses the field “_time,” the system can determine that the query is susceptible to a significantly imbalanced partition. 
     In some embodiments, the system can determine that the query is susceptible to a significant partition imbalance if one of the datasets includes a combination or expansion operation just prior to the combination operation. In some circumstances, this determination can be made even if the field in the combination operation matches a field in an earlier reduction operation. For example, if the _time field is used to reduce two datasets, an expansion operation is performed on one of the datasets (with or without the _time field), and the datasets are then to be combined based on the _time field (or any other field in some embodiments), the system can determine that the query is susceptible to a significant partition imbalance. 
     In certain embodiments, the system can review an inverted index to determine whether the query is susceptible to a significant partition imbalance. As described herein, inverted indexes can include information about data entries or events that are stored by the system, such as, but not limited to, relevant fields associated with different data entries or events, field-value pairs of various data entries or events, a count of the field-value pairs for data entries or events in different data stores or time series buckets, etc. 
     Accordingly, if the field to be used in the combination operation is included in an inverted index, the system can review the inverted indexes associated with the datasets that are to be combined. For example, the system can review field-value pairs in the relevant inverted indexes and the quantity of each field-value pair. The system can then use the quantity of the field-value pairs to determine whether the query is susceptible to a significant partition imbalance. For example, if the quantity of a given field-value pair in the inverted indexes associated with the datasets satisfies the data entries quantity threshold, the system can determine that the query is susceptible to a significant partition imbalance. 
     In the event that the system determines that the query is susceptible to a significant partition imbalance, the routine moves to block  1906  and the system monitors the query during execution to determine a number of matching field-value pair data entries in datasets that are to be combined based on the field corresponding to the matching field-value pair data entries. 
     The matching field-value pair data entries can correspond to data entries that have a matching field-value pair (i.e., a combination of a field and field value for that field). It will be understood that each dataset can include a large number of matching field-value pair data entries for many different field-value pairs. Furthermore, it will be understood that a single data entry can be a matching field-value pair data entry for different fields and field-value pairs. For example, if a data entry includes the field-value pairs “_time::1:34:00” and “IP_addr::192.168.1.4,” then it can belong to one group of matching field-value pair data entries with the field-value pair “_time::1:34:00” and to another group of matching field-value pair data entries with the field-value pair “IP_addr::192.168.1.4.” Further, although reference is made herein to a data entry including a field-value pair, in some embodiments, the data entry itself may not expressly identify the field. Rather, the data entry may include a field value that corresponds to a field designated by the system. For example, the data entry may include the value “192.168.1.4,” which the system identifies as the field value for an IP address field. 
     As part of monitoring the query during execution, the system can identify the fields that are to be used in a combination operation of the datasets. The system can also identify the number of matching field-value pairs data entries corresponding to the identified field in each of the datasets to be combined in the combination operation. The system can determine whether the matching field-value pair data entries in the different datasets satisfies a data entries quantity threshold. 
     At decision block  1908 , the system determines whether to implement a multi-partition operation for a particular field-value pair. In certain embodiments, the system can determine whether to implement the multi-partition operation based on whether the matching field-value pair data entries in the datasets satisfy a data entries quantity threshold. In some cases, the system can combine the quantity of matching field-value pair data entries from the datasets to determine if the data entries quantity threshold is satisfied. In certain instances, the system can combine the quantity of matching field-value pair data entries by multiplying or adding the number of matching field-value pair data entries from each dataset that is to be combined. 
     In some embodiments, the data entries quantity threshold can be based on the processing power/speed of the individual processing cores, the number of available cores for the query, a timing preference for completing the query, etc. For example, the data entries quantity threshold can be larger for processing cores with more processing power/speed and smaller for processing cores with less processing power/speed. In some cases, the data entries quantity threshold can be larger for when fewer cores are used for a particular query or smaller when more cores are used for the particular query. In certain embodiments, the data entries quantity threshold can be smaller for queries that are to be completed in less time than for queries that can be completed in more time. In certain embodiments, the data entries quantity threshold can be one million, five, million, ten million or more data entries. 
     In certain embodiments, the system can use the inverted indexes, as described above, to determine whether to implement a multi-partition operation. Thus the inverted indexes can be used to determine whether the query is susceptible to a significantly imbalanced partition and/or whether to implement a multi-partition operation for a particular field-value pair. 
     In the event the system determines to implement the multi-partition operation for a particular field-value pair, then the routine moves to block  1910  and the system implements the multi-partition operation. Some embodiments of the re-partition operation are described in greater detail below with reference to  FIGS.  20  and  22   . 
     In some embodiments, the multi-partition operation includes partitioning matching field-value pair data entries from the different datasets into multiple partitions, such that each partition includes a group or subset of the combined matching field-value pair data entries from the different datasets (e.g., each partition can include a group of matching field-value pair data entries from each of the datasets to be combined). By partitioning matching field-value pair data entries, the system can reduce the size of each partition, as well as the amount of time and processing power to process the partition. 
     In certain cases, as part of the multi-partition operation, the system can determine whether to perform a second multi-partition operation on one or more of the partitions formed as a result of the first multi-partition operation. In some cases, the system can determine to perform the second multi-partition operation based on the quantity of data entries in the particular partition, as described above. For example, if the quantity of data entries in the particular partition, or a combination of the data entries, satisfies the data entries quantity threshold, then the system can perform a multi-partition operation on that partition, effectively creating sub-partitions or replacement partitions for that partition. 
     In some embodiments, the system can also perform a second multi-partition operation on one or more of the partitions based on a combined size of the data entries in that partition. For example, if the amount of memory used or required to store the data entries, or the data entries following a combination operation, in a partition satisfies a memory level threshold, then the system can perform a multi-partition operation on that partition. In some cases, the system may have limited amounts of memory that can be used for each processor core. Accordingly, to avoid exceeding that amount, the system can use a memory level threshold. 
     The memory level threshold can correspond to an acceptable amount of memory that the combination of the first subgroup and the second group can use. The threshold can vary depending on the number of processor cores in use on a system, the total amount of memory on the device, the total amount of available memory, etc. If the amount of memory required to store the combination satisfies or exceeds the threshold, the system can repeat the multi-partition operation until the combination of matching field-value pair data entries in a partition/sub-partition satisfy the data entries quantity threshold and the memory level threshold. By performing the multi-partition operation at block  1910 , the system can avoid a significantly imbalanced partition, thereby reducing the overall runtime of the query. 
     Following the multi-partition operation, the system continues with the query execution, such as by combining, or continuing to combine, the datasets as illustrated in block  1912 . In some embodiments the query execution can include combining groups of data entries of the datasets in different partitions that were not part of the multi-partition operation or performing the multi-partition operation for other field-value pairs. 
     In addition, as shown in  FIG.  19   , in the event that the system determines that the query is not susceptible to a significant partition imbalance or determines not to implement the multi-partition operation for a given field-value pair or combination operation, the system moves to block 1912 and continues query execution without performing the multi-partition operation for that particular field-value pair or combination operation, respectively. 
     It will be understood that fewer, more, or different blocks can be used as part of the routine  1900 . In some cases, one or more blocks can be omitted. For example, decision blocks  1904  and  1908 , and the corresponding no decision paths, can be replaced with a “determine that query is susceptible to an imbalance” block and a “determine to implement multi-partition operation” block, respectively. 
     As yet another example, for a combination operation, the system can analyze each field-value pair of the datasets that are to be combined to determine whether the multi-partition operation is to performed. Further, if the query includes multiple combination operations, the system can analyze each combination operation to determine whether to perform the multi-partition operation for relevant data entries of the datasets. Accordingly, during execution of a query, the multi-partition operation may not be performed at all or may be performed one or more times for a single query, one or more times for a single combination operation (e.g., for different field-value pairs), or one or more times for a single field-value pair. 
     Furthermore, it will be understood that the various blocks described herein with reference to  FIG.  19    can be implemented in a variety of orders. In some cases, the system can implement some blocks concurrently or change the order as desired. For example, the system can continue with a query execution for some field-value pairs, while concurrently executing the multi-partition operation for other field-value pairs as desired. In addition, it will be understood that any of the blocks described herein with reference to routine  1900  can be combined with any of routines  2000  or  2200 , or be combined with or form part of routines  1500 ,  1600 . For example, in some cases, the decision block  1904 , or a similar block, can form part processing a query, as described in greater detail with reference to block  1504  of  FIG.  15   . In certain embodiments, the instructions to monitor the query can be generated as part of the query processing scheme, as described in greater detail with reference to block  1610  of  FIG.  16   , and included in the DAG communicated to the worker nodes  1006 , as described above. 
     10.2 Multi-Partition Operation 
     As described above, in some instances a query includes instructions to combine multiple datasets based on one or more fields. Each dataset may include multiple field-value pairs that correspond to the one or more fields used to combine the datasets. In some cases, these field-value pairs can be used to assign matching field-value pair data entries of the datasets to different partitions. For example, matching field-value pair data entries from the different datasets can be assigned to the same partition. However, as there may exist a large number of matching field-value-pair data entries assigned to the same partition, the system can determine that at least some of the matching field-value pair data entries should be further partitioned. In such cases, the system can allocate the data that was to be assigned to the single partition to multiple partitions. 
     Accordingly,  FIG.  20    is a flow diagram illustrative of an embodiment of a multi-partition routine  2000  implemented by the system on matching field-value pair data entries. In some embodiments, the multi-partition routine  2000  can correspond to the multi-partition operation referenced in block  1910  of  FIG.  19   . One skilled in the relevant art will appreciate that the elements outlined for routine  2000  can be implemented by one or more computing devices/components that are associated with the  1000 , such as the search head  210 , search process master  1002 , query coordinator  1004  and/or worker nodes  1006 , or any combination thereof. Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  2002 , the system identifies a first group of data entries from a first dataset and a second group of data entries from a second dataset. The first group of data entries can correspond to data entries of the first dataset that have a field-value pair that corresponds to a field that is being used to combine the first dataset with a second dataset. Similarly, the second group of data entries can correspond to data entries of the second dataset that have a field-value pair that matches the field-value pair of the data entries of the first group. 
     As mentioned above, the datasets that are to be combined can correspond to the same original data source or dataset that may have been processed differently or can correspond to different original data sources or datasets. 
     At block  2004 , the system assigns data entries of the first group to a plurality of partitions. The allocated partitions can correspond to partitions that are being used to process other matching field-value pair data entries or other data entries of the datasets, or they can correspond to separate partitions that are used to process just the subgroups of the first group. 
     In some embodiments, the system assigns each data entry of the first group to one of the allocated partitions. In certain embodiments, the system assigns the data entries of the first group to a partition in a random or pseudo-random fashion. By randomly assigning the data entries to the different partitions, the system can reduce overhead as compared to sequentially assigning the data entries to the different partitions. However, it will be understood that the system can sequentially assign the data entries of the first group to the different partitions, or use other mechanisms to assign the data entries of the first group to the different partitions as desired. Once the system assigns the data entries of the first group to the plurality of partitions, each partition can include a first subgroup of data entries that correspond to a subset of the first group. 
     In some embodiments, the system can calculate a seed value, and use the seed value to partition the data entries of the first group. In some embodiments, the seed value can be determined based on the first group of data entries and the second group of data entries from the second dataset. In certain cases, the system can calculate the seed value based on the number of data entries in the first group of data entries and the number of data entries in the second group of data entries. Furthermore, in the event more than two datasets are to be combined, the system can use the number of data entries in the additional datasets to determine the seed value.. 
     In certain embodiments, to calculate the seed value, the system uses a data entries quantity threshold. For example, the seed value can be determined by dividing the number of data entries after combining the number of data entries in the first group and the number of data entries in the second group by the data entries quantity threshold. In certain embodiments, following the division, the system can round up the quotient to determine the seed value. In some embodiments, the seed value can be calculated as: 
     
       
         
           
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     However, it will be understood that the seed value can be determined in a number of ways to reduce the number of entries in a partition so as to not satisfy the data entries quantity threshold. 
     In some cases, the system can use the seed value to allocate partitions and to assign the data entries of the first group to the different partitions. For example, the system can allocate a number of partitions equal to the seed value. In addition, the system can randomly or sequentially assign the data entries of the first group to the different partitions using the seed value, for example, by modulating a randomly generated number by the seed value and using the result to assign a particular data entry to a partition. 
     At block  2006 , for at least one partition, the system combines the data entries of the first subgroup of the first group with the data entries of the second group. In certain embodiments, the system performs block  2006  for all partitions. The system can access the entries of the second group for combination with the data entries of the subgroup of the first group in a variety of ways. In some embodiments, the system uses multiple processors or nodes to combine the data entries in the different partitions. In certain embodiments, a distinct processor is used to combine the data entries for each partition. 
     In some embodiments, a copy of each data entry of the second group can be assigned to each partition. For example, the system can make a copy of each entry of the second group and assign it to each allocated partition. In certain embodiments, each core processing a partition can read a memory location that stores the data entries of the second group. The system can use the data read from the relevant memory location to combine the first subgroup with the second subgroup. 
     In certain embodiments, the system can assign the second group to the allocated partitions, similar to the manner in which the data entries of the first group are assigned, such that each partition includes a second subgroup of data entries that correspond to the second group. For each partition, the system can generate a copy of each data entry of the second subgroup, reassign the copies (or the original) to the other partitions, and reform the second subgroup to include the data entries assigned from other partitions. The system can then combine the reformed second subgroup with the first subgroup. 
     The combination of the first subgroup with the second group can result in a larger number of data entries than the sum of the first subgroup and second group. In some cases, the resultant number of data entries can correspond to the product of the number of entries in the first subgroup and the number of entries in the second group. In certain embodiments, the system can combine the first subgroup with the second group based on the matching field value. For example, each data entry in the combined subgroup can correspond to a unique combination of a data entry of the first subgroup and a data entry of the second group. 
     It will be understood that fewer, more, or different blocks can be used as part of the routine  2000 . In addition, it will be understood that any of the blocks described herein with reference to routine  2000  can be combined with any of routines  1900  or  2200 . In some cases, one or more blocks can be omitted or repeated. For example, the system can determine that the second group is smaller than the first group and assign the first group to the different partitions based on that determination. As another example, the system can perform additional operations on the data entries of the combined subgroup or the combined first and second dataset. As described herein, the instruction to combine the datasets can be part of a combination operation that is only one operation of a query. Accordingly, following the combination operation, the system can perform additional operations on the data entries that resulted from the combination operation. 
     As yet another example, in addition to combining the subgroups and groups described above, the routine  2000  can also combine other groups of the datasets and/or perform other tasks to complete the execution of the query. As described above, in some cases, the system does not perform a multi-partition operation for all data entries. Thus, routine  2000  can further include the system combining the first and second datasets, with blocks  2002 ,  2004 , and  2006  being performed on a subset of the data entries of the datasets. 
     As a non-limiting example, the system can partition datasets using matching field-value pairs that correspond to a field that is used to combine the datasets. Further, the system can perform the routine  2000  on a subset of the partitions, such as the partitions that include matching field-value pair data entries that, when combined, satisfy the data entries quantity threshold. In some cases, the system only performs the routine  2000  on the partitions that include matching field-value pair data entries that, when combined, satisfy the data entries quantity threshold. 
     As described above, the routine  2000  can be repeated multiple times for a particular field-value pair (e.g., in the event the combination of events of the subgroup of the first group and the second group satisfies a data entries quantity threshold) or for a particular combination operation (e.g., for multiple field-value pairs in the datasets to be combined). In addition, if the query includes multiple combination operations, the system can repeat the routine  2000  for one or more field-value pairs in each combination operation. 
     Furthermore, it will be understood that the various blocks described herein with reference to  FIG.  20    can be implemented in a variety of orders. In some cases, the system can concurrently assign data entries of the first group to the different partitions, while concurrently combining the data entries of the subgroup of the first group with the data entries of the second group. In addition, the system can concurrently implement the routine  2000  for multiple field-value pairs as part of a combination operation. 
     Similarly, the system can implement the routine  2000  for one or more field-value pairs, while concurrently combining other field-value pairs without routine  2000 . In some embodiments, when combining multiple datasets as part of a combination operation, the system can determine that data entries assigned to one partition are to be assigned to multiple partitions, while data entries assigned to a second partition are not to be assigned to multiple partitions. For example, the data entries assigned to the second partition may not satisfy the data entries quantity threshold. As such, the multi-partition operation may not be used for that partition. However, the system can combine the data entries in the second partition, while concurrently using the routine  2000  to combine the data entries of the first partition. 
       FIG.  21    is a diagram illustrating an embodiment of a join operation performed on two datasets. It will be understood that the although the datasets in the illustrated example are relatively small, the datasets used by the system can be significantly larger and include millions or even billions of data entries. Accordingly, the illustrated example should be not construed as limiting. 
     In the illustrated embodiment, Dataset 1 and Dataset 2, illustrated at  2102 , are to be joined based on the field time. For purposes of this example, the data entries quantity threshold is five. In addition, in the illustrated example, the data entries of Dataset 1 include field values for the fields time and source and the data entries in Dataset 2 include field values for the fields time and source type. It will be understood that the illustrated data entries are examples only and should not be construed as limiting. 
     As described in greater detail above with reference to block  1906  of  FIG.  19   , the system can monitor the number of field-value pairs in each dataset that correspond to the field being used in the join operation. As part of the monitoring, the system can determine whether the matching field-value pair data entries in the different datasets satisfy the data entries quantity threshold. When the system analyzes the field-value pair time::1, it determines that the combination of the matching field-value pair entries for Dataset 1 and Dataset 2 is six, which satisfies the data entries quantity threshold of five. Accordingly, the system can proceed to implement a multi-partition operation on the data entries with a field-value pair of time::1. 
     In this example, the system determines a seed value of two based on the quantity of matching field-value pair data entries in each dataset and the data entries quantity threshold. Using the seed value, the system randomly assigns a seed to each matching field-value pair data entry in Dataset 2 as illustrated by the seeded Dataset 2  2104 . In some cases, Dataset 2 can be selected for seeding based on a determination that the Dataset 1 has fewer matching field-value pair data entries than Dataset 2. However, it will be understood that Dataset 2 can be selected for seeding in a variety of ways, such as randomly, because it has more matching field-value pair data entries than Dataset 1, etc. 
     Using the seeding, the system allocates the matching field-value pair data entries of Dataset 2 to Partition 1 and Partition 2 as illustrated at  2106 . In some cases, the number of partitions used corresponds to the seed value. For example, as the seed value is two in this example, the system uses two partitions and allocates the matching field-value pair data entries of Dataset 2 based on the number of partitions. In addition, as illustrated, in some embodiments, the partitions maintain a separation between the data from Dataset 1 and Dataset 2, or otherwise identify the matching field-value pair data entries based on the dataset from which they came. 
     As illustrated at  2108 , the system makes the matching field-value pair data entries of Dataset 1 available to each of the partitions. In some cases, this can be done by copying the matching field-value pair data entries of Dataset 1 to each partition, enabling the processor cores that process the different partitions to access the matching field-value pair data entries in a read-only fashion, or partitioning, duplicating, and repartitioning the matching field-value pair data entries of Dataset 2 as described in greater detail below with reference to  FIG.  22   . 
     As illustrated at  2110 , in each partition, the system joins the matching field-value pair data entries from the different datasets. Although not illustrated in this example, it will be understood that the join of the matching field-value pair data entries in Partition 1 and Partition 2 can occur before, after, or concurrently with each other and/or with the join performed on the other data entries of the datasets. For example, in addition to Partition 1 and Partition 2, used to join the matching field-value pair data entries for time 1, an additional one or more partitions can be concurrently used to join the matching field-value pair data entries for time 2, 3, and 4. As illustrated, given that the combination of matching field-value pair data entries for time 4 satisfies the data entries quantity threshold, the system can generate multiple partitions to process the matching field-value pair data entries for time 4. One example of partitioning the matching field-value pair data entries for time 4 is described below with reference to  FIG.  23   . 
     In some embodiments, the seeds used to assign the different data entries to the different partitions (e.g., 0.1 and 0.2) can remain with the data entries. In this way the system can track the different subgroups of the first group. In certain embodiments, the seeds can be removed following the combination operation, and/or as part of or after a subsequent operation. In some cases, the seeds can remain until a reduction operation is performed using the data entries. 
       FIG.  22    is a flow diagram illustrative of an embodiment of a multi-partition routine  2200  implemented by the system to partition matching field-value pair data entries. One skilled in the relevant art will appreciate that the elements outlined for routine  2200  can be implemented by one or more computing devices/components that are associated with the  1000 , such as the search head  210 , search process master  1002 , query coordinator  1004  and/or worker nodes  1006 , or any combination thereof. Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  2202 , the system identifies a first group and a second group associated with the multi-partition operation. As mentioned, the first group of data entries can correspond to data entries of the first dataset that have a field-value pair that corresponds to a field that is being used to combine the first dataset with a second dataset. Similarly, the second group of data entries can correspond to data entries of the second dataset that have a field-value pair that matches the field-value pair of the data entries of the first group. 
     At block  2204 , the system identifies the first group as the a partitioning group. In some embodiments, the system identifies the first group as the partitioning group based on a determination that the second group of data entries has fewer data entries than the first group of data entries. However, it will be understood that the system can identify the partition group in a variety of ways. In some cases, the system can identify the first group as the partitioning group based on a determination that it is the same size as or larger than the second group or based on a default setting. In some embodiments, the system determines the quantity of the first group and the second group using a stats command or other command that provides a count of the number of data entries in the first group and the second group. In some embodiments, the command can be executed as a background process and without the knowledge of the user. Using the data, the system can determine that the second group has fewer data entries then the first group. 
     At block  2206 , the system assigns each data entry of the first group and each data entry of the second group to one of a plurality of partitions. The assignment of the data entries from the different groups can be accomplished similar to the manner described above. For example, the system can calculate a seed value as described in greater detail above. Further, the system can use the seed value to allocate partitions and/or assign the data entries of the first and second groups to the partitions. 
     Once the first and second groups have been partitioned and the partitions include the first and second subgroups of the first and second groups, the system can perform blocks  2208 ,  2210 ,  2212 , and  2214  for at least one partition. However, in certain embodiments, the system performs blocks  2208 ,  2210 ,  2212 , and  2214  on each partition. In addition, in some embodiments, the system can use one or more processors to perform blocks  2208 ,  2210 ,  2212 , and  2214  on the different partitions. In some cases, a distinct processor can be assigned to perform blocks  2208 ,  2210 ,  2212 , and  2214  on each partition. 
     At block  2208 , the system duplicates the second subgroup. In certain cases, the system duplicates the second subgroup based on the identification of the first group as the partitioning group. In some cases, the system can duplicate each data entry of the second subgroup based on the number of partitions that hold a subgroup of the second group. For example, if seven partitions hold a subgroup of the second group, the system can generate six duplicates for each data entry of the second subgroup such that a total of seven identical data entries exist. 
     At block  2210 , the system reassigns the data entries of the second subgroup, or assigns duplicates of the second subgroup, to the other partitions. In some cases the system reassigns the data entries so that each partition includes the data entries corresponding to the second group. In some cases, as the system generates the duplicates for each data entry it can also assign it to a partition. In certain embodiments, the duplicates can be assigned in a sequential manner such that for partition 1, the first duplicate of a data entry is assigned to partition 2, the second duplicate of a data entry is assigned to partition 3, and so on. However, it will be understood that the data entries can be assigned in any manner as desired. For example, in some cases, all of the original data entries of the second group can be assigned to one partition, all of the first duplicates in each partition can be assigned to a second partition, and so on. 
     At block  2212 , the system reforms the second subgroup to include one or more data entries assigned to it from other partitions. As the system re-assigns data entries, assigns duplicate data entries, or repartitions the second group of data entries so that each partition includes a set of the second group, the second subgroup in each partition can be reformed to include the data entries assigned from other partitions. Further, once the repartitioning is complete, each partition can include a complete set of the second group. Accordingly, in some embodiments, the reformed second subgroup can correspond to, or be the same as, the second group of data entries. 
     At block  2214 , the system generates a combined subgroup based the first subgroup and the reformed second group. As described above, the system can combine the first subgroup and the reformed second group in a variety of ways as desired, or depending on the combination operation to be performed by based on the query. In some cases, the number of data entries in the combined subgroup can correspond to the product of the number of data entries in the first subgroup and the number of entries in the reformed second subgroup or second group. In certain embodiments, the system can combine the first subgroup with the second group based on the matching field value such that data entries in the combined subgroup includes the field value, at least one value from a data entry in the first subgroup, and at least one value from a data entry in the second group or reformed second subgroup. Furthermore, the system can generate the combined subgroup by generating a data entry for each unique combination of a data entry in the first group with a data entry in the second group or reformed second subgroup. 
     It will be understood that fewer, more, or different blocks can be used as part of the routine  2200 . In some cases, the routine can include additional blocks for performing additional functions on the partitions that include the combined subgroup. For example, following the combination operation that generates the different operations, the node can perform a reduction operation that results in fewer data entries and/or reduces the number of partitions used to hold the data entries. Similarly, the node can perform an expansion operation that results in more data entries and/or increases the number of partitions used to hold the data entries. In certain cases, the system can retain the seed values assigned to the different data entries following the combination operation, or discard the seed value as part of a subsequent operation. In certain embodiments, the system can discard the seed value assigned to the different data entries, as part of or after a reduction operation. 
     In some cases, one or more blocks can be omitted or repeated. For example, blocks  2208 ,  2210 , or  2212  can be combined into a single block. In addition, the system can also combine data entries of partitions that were not subject to the seeding or repartitioning described above. The combination of data entries of partitions that were not subject to the seeding or repartitioning can be done before, after, or concurrently with the blocks of routine  2200 . In addition, it will be understood that any of the blocks described herein with reference to routine  2200  can be combined with any of routines  1900  or  2000 . 
     As described above, the routine  2200  can be repeated multiple times for a particular field-value pair (e.g., in the event the combination of events of the subgroup of the first group and the second group satisfies a data entries quantity threshold) or for a particular combination operation (e.g., for multiple field-value pairs in the datasets to be combined). In addition, if the query includes multiple combination operations, the system can repeat the routine  2200  for one or more field-value pairs in each combination operation. 
     Furthermore, it will be understood that the various blocks described herein with reference to  FIG.  22    can be implemented in a variety of orders. In some cases, the system can concurrently duplicate the second group, assign duplicate entries to other partitions, and reform the second subgroup. 
       FIG.  23    is a diagram illustrating an embodiment of a join operation of Dataset 1 and Dataset 2 described above with reference to  FIG.  3    for the field-value pair time::4. As discussed, Dataset 1 and Dataset 2 are to be joined based on the field time with a data entries quantity threshold of five. When the system analyzes the field-value pairs for time 4, it determines that the combination of the matching field-value pair entries for Dataset 1 and Dataset 2 is twelve, which satisfies the data entries quantity threshold of five. Accordingly, the system can proceed to implement a multi-partition operation on the data entries with a field-value pair of time::4. 
     Based on the quantity of matching field-value pair data entries for time 4 in Datasets 1 and 2 and the data entries quantity threshold, the system determines a seed value of three. 
     In this example, using the seed value, the system randomly assigns each matching field-value pair data entry in Dataset 1 and Dataset 2 to a partition as illustrated at  2304 , and allocates the matching field-value pair data entries of Dataset 1 and Dataset 2 to Partition 1, Partition 2, or Partition 3 based on the assignment, as illustrated at  2306 . As discussed above, the number of partitions can correspond to the seed value. Further, as illustrated, in some embodiments, the partitions can retain information indicating the dataset from which each matching field-value pair data entry came. 
     As shown at  2308 , the system duplicates the matching field-value pair data entries from Dataset 1 in each partition. In some cases, Dataset 1 can be selected for duplication based on a determination that the Dataset 1 has fewer matching field-value pair data entries than Dataset 2. However, it will be understood that Dataset 1 can be selected for duplication in a variety of ways, such as by random selection, etc. 
     In the illustrated embodiment, the system also seeds the duplicate matching field-value pair data entries, or duplicate data entries, for assignment to the other partitions. In some cases, the system can sequentially seed the duplicate data entries for assignment to the other partitions. However, it will be understood that the system can allocate the matching field-value pair data entries that correspond to Dataset 1 for assignment to the different partitions in a variety of ways as discussed above. 
     As shown at  2310 , the system repartitions the matching field-value pair data entries that correspond to Dataset 1 so that each partition includes matching field-value pair data entries that correspond to Dataset 1. As described above, this can be done by repartitioning duplicate data entries from each partition to another partition or otherwise reassigning the matching field-value pair data entries that correspond to Dataset 1 to the different partitions. 
     In addition, as shown at  2310 , the system determines that the number of matching field-value pair data entries in Partition 2 satisfies the data entries quantity threshold. Accordingly, the system determines a seed value (2), and assigns the matching field-value pair data entries in Partition 2 to one of two partitions, Partition 2.1 and Partition 2.2, as illustrated at  2312 . 
     As shown at  2314 , the system allocates the matching field-value pair data entries in Partition 2 to the Partitions 2.1 and 2.2, and based on a determination that the number of matching field-value pair data entries in the Dataset 2 portion of Partition 2 is less than the number of matching field-value pair data entries in the Dataset 1 portion of Partition 2, the system duplicates the matching field-value pair data entries in the Dataset 2 portion of Partitions 2.1 and 2.2. 
     As shown at  2316 , the system reallocates the matching field-value pair data entries that correspond to the Dataset 2 portion of Partition 2 such that Partitions 2.1 and 2.2 each include the matching field-value pair data entries that correspond to the Dataset 2 portion of Partition 2. 
     As illustrated at  2318 , in each of the Partitions 1, 2.1, 2.2, and 3, the system joins the matching field-value pair data entries from the different datasets. Although not illustrated in this example, it will be understood that the join of the matching field-value pair data entries in the Partitions 1, 2.1, 2.2, and 3, can occur before, after, or concurrently with each other and/or with the join performed on the other data entries of the datasets. For example, as discussed above with reference to  FIG.  3   , one or more partitions can be concurrently used to join the matching field-value pair data entries for time 1, 2, and 3. 
     Although  FIGS.  19 - 23    have been described with reference to the system  1000 , it will be understood that the concepts described herein can be used in any distributed data processing system where datasets are to be combined in some fashion. 
     11.0. Hardware Embodiment 
       FIG.  24    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.” 
     12.0 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, i.e., 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. Two or more components of a system can be combined into fewer components. Various components of the illustrated systems can be implemented in one or more virtual machines, rather than in dedicated computer hardware systems and/or computing devices. 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. For example, while only one aspect of the invention is recited as a means-plus-function claim under 35 U.S.C sec. 112(f) (AIA), other aspects may likewise be embodied as a means-plus-function claim, or in other forms, such as being embodied in a computer-readable medium. 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.