Patent Publication Number: US-11392578-B1

Title: Automatically generating metadata for a metadata catalog based on detected changes to the metadata catalog

Description:
RELATED APPLICATIONS 
     Any and all applications for which a foreign or domestic priority claim is identified in the Application Data Sheet as filed with the present application are incorporated by reference under 37 CFR 1.57 and made a part of this specification. Specifically, U.S. application Ser. Nos. 16/147,129 and 15/967,587 are incorporated herein by reference in their entirety. This application also incorporates by reference U.S. application Ser. Nos. 15/665,159; 15/665,148; 15/665,187; 15/665,248; 15/665,197; 15/665,279; 15/665,302; 15/665,339; and Ser. No. 15/276,717, in their entirety. In addition, this application is being filed on Jan. 31, 2019 concurrently with U.S. patent application Ser. No. 11/238,049, entitled REVISING CATALOG METADATA BASED ON PARSING QUERIES, 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. Tools exist that allow an analyst to search data systems separately and collect results over a network for the analyst to derive insights in a piecemeal manner. However, UI tools that allow analysts to quickly search and analyze large set of raw machine data to visually identify data subsets of interest, particularly via straightforward and easy-to-understand sets of tools and search functionality do not exist. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present disclosure is illustrated by way of example, and not limitation, in the figures of the accompanying drawings, in which like reference numerals indicate similar elements. 
         FIG. 1  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. 3A  is a block diagram of one embodiment an intake system. 
         FIG. 3B  is a block diagram of another embodiment of an intake system. 
         FIG. 4  is a block diagram illustrating an embodiment of an indexing system of the data intake and query system. 
         FIG. 5  is a block diagram illustrating an embodiment of a query system of the data intake and query system. 
         FIG. 6  is a block diagram illustrating an embodiment of a metadata catalog. 
         FIG. 7  is a flow diagram depicting illustrative interactions for processing data through an intake system, in accordance with example embodiments. 
         FIG. 8  is a flowchart depicting an illustrative routine for processing data at an intake system, according to example embodiments. 
         FIG. 9  is a data flow diagram illustrating an embodiment of the data flow and communications between a variety of the components of the data intake and query system during indexing. 
         FIG. 10  is a flow diagram illustrative of an embodiment of a routine implemented by an indexing system to store data in common storage. 
         FIG. 11  is a flow diagram illustrative of an embodiment of a routine implemented by an indexing system to store data in common storage. 
         FIG. 12  is a flow diagram illustrative of an embodiment of a routine implemented by an indexing node to update a location marker in an ingestion buffer. 
         FIG. 13  is a flow diagram illustrative of an embodiment of a routine implemented by an indexing node to merge buckets. 
         FIG. 14  is a data flow diagram illustrating an embodiment of the data flow and communications between a variety of the components of the data intake and query system during execution of a query. 
         FIG. 15  is a flow diagram illustrative of an embodiment of a routine implemented by a query system to execute a query. 
         FIG. 16  is a flow diagram illustrative of an embodiment of a routine implemented by a query system to execute a query. 
         FIG. 17  is a flow diagram illustrative of an embodiment of a routine implemented by a query system to identify buckets for query execution. 
         FIG. 18  is a flow diagram illustrative of an embodiment of a routine implemented by a query system to identify search nodes for query execution. 
         FIG. 19  is a flow diagram illustrative of an embodiment of a routine implemented by a query system to hash bucket identifiers for query execution. 
         FIG. 20  is a flow diagram illustrative of an embodiment of a routine implemented by a search node to execute a search on a bucket. 
         FIG. 21  is a flow diagram illustrative of an embodiment of a routine implemented by the query system to store search results. 
         FIG. 22  is a data flow diagram illustrating an embodiment of the data flow and communications between a variety of the components of the data intake and query system to execute a query. 
         FIG. 23  is a data flow diagram illustrating an embodiment of the data flow for identifying query datasets and query configuration parameters for a particular query. 
         FIG. 24  is a flow diagram illustrative of an embodiment of a routine implemented by the query system to execute a query. 
         FIG. 25  is a flow diagram illustrative of an embodiment of a routine implemented by a query system manager to communicate query configuration parameters to a query processing component. 
         FIG. 26  is a flow diagram illustrative of an embodiment of a routine implemented by the query system to execute a query. 
         FIG. 27  is a flow diagram illustrative of an embodiment of a routine implemented by the query system to execute a query. 
         FIG. 28  is a flow diagram illustrative of an embodiment of a routine  2800  implemented by the query system to execute a query. 
         FIG. 29  is a flow diagram illustrative of an embodiment of a routine  2900  implemented by the system to generate annotations by parsing a query. 
         FIG. 30  is a flow diagram illustrative of an embodiment of a routine  2900  implemented by the system to generate annotations based on changes to a metadata catalog. 
         FIG. 31A  is a flowchart of an example method that illustrates how indexers process, index, and store data received from intake system, in accordance with example embodiments. 
         FIG. 31B  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. 31C  provides a visual representation of the manner in which a pipelined search language or query operates, in accordance with example embodiments. 
         FIG. 32A  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. 32B  provides a visual representation of an example manner in which a pipelined command language or query operates, in accordance with example embodiments. 
         FIG. 33A  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. 33B  illustrates an example of processing keyword searches and field searches, in accordance with disclosed embodiments. 
         FIG. 33C  illustrates an example of creating and using an inverted index, in accordance with example embodiments. 
         FIG. 33D  depicts a flowchart of example use of an inverted index in a pipelined search query, in accordance with example embodiments. 
         FIG. 34A  is an interface diagram of an example user interface for a search screen, in accordance with example embodiments. 
         FIG. 34B  is an interface diagram of an example user interface for a data summary dialog that enables a user to select various data sources, in accordance with example embodiments. 
         FIGS. 35, 36, 37A, 37B, 37C, 37D, 38, 39, 40, and 41  are interface diagrams of example report generation user interfaces, in accordance with example embodiments. 
         FIG. 42  is an example search query received from a client and executed by search peers, in accordance with example embodiments. 
         FIG. 43A  is an interface diagram of an example user interface of a key indicators view, in accordance with example embodiments. 
         FIG. 43B  is an interface diagram of an example user interface of an incident review dashboard, in accordance with example embodiments. 
         FIG. 43C  is a tree diagram of an example a proactive monitoring tree, in accordance with example embodiments. 
         FIG. 43D  is an interface diagram of an example a user interface displaying both log data and performance data, in accordance with example embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     Embodiments are described herein according to the following outline: 
     1.0. General Overview 
     2.0. Operating Environment
         2.1. Host Devices   2.2. Client Devices   2.3. Client Device Applications   2.4. Data Intake and Query System Overview       

     3.0. Data Intake and Query System Architecture
         3.1 Gateway   3.2. Intake System
           3.2.1 Forwarder   3.2.2 Data Retrieval Subsystem   3.2.3 Ingestion Buffer   3.2.4 Streaming Data Processors   
           3.3. Indexing System
           3.3.1. Indexing System Manager   3.3.2. Indexing Nodes
               3.3.2.1 Indexing Node Manager   3.3.2.2 Partition Manager   3.3.2.3 Indexer and Data Store   
               3.3.3. Bucket Manager   
           3.4 Query System
           3.4.1. Query System Manager   3.4.2. Search Head
               3.4.2.1 Search Master   3.4.2.2 Search Manager   
               3.4.3. Search Nodes   3.4.4. Cache Manager   3.4.5. Search Node Monitor and Catalog   
           3.5. Common Storage   3.6. Data Store Catalog   3.7. Query Acceleration Data Store   3.8. Metadata Catalog
           3.8.1. Dataset Association Records   3.8.2. Dataset Configuration Records   3.8.3. Rule Configuration Records
               3.8.4. Annotations3.8.4.1. Generating Annotations
                   3.8.4.1.1. System Annotations Based on System Use    3.8.4.1.1.1. Query Parsing    3.8.4.1.1.2. Query Execution    3.8.4.1.1.3. User Monitoring    3.8.4.1.1.4. Application Monitoring   3.8.4.1.2. System Annotations Based on Metadata Catalog Changes   
                   3.8.4.2. Example Annotations
                   3.8.4.2.1. Field Annotations   3.8.4.2.2. Inter-Field Relationship Annotations   3.8.4.2.3. Inter-Dataset Relationship Annotations   3.8.4.2.4. Dataset properties Annotations   3.8.4.2.5. Normalization Annotations   3.8.4.2.6. Unit Annotations   3.8.4.2.7. Alarm Threshold Annotations   3.8.4.2.8. Data Category Annotations   3.8.4.2.9. User/Group Annotations   3.8.4.2.10. Application Annotations   
                   
               
               

     4.0. Data Intake and Query System Functions
         4.1. Ingestion
           4.1.1 Publication to Intake Topic(s)   4.1.2 Transmission to Streaming Data Processors   4.1.3 Messages Processing   4.1.4 Transmission to Subscribers   4.1.5 Data Resiliency and Security   4.1.6 Message Processing Algorithm   
           4.2. Indexing
           4.2.1. Containerized Indexing Nodes   4.2.2. Moving Buckets to Common Storage   4.2.3. Updating Location Marker in Ingestion Buffer   4.2.4. Merging Buckets   
           4.3. Querying
           4.3.1. Containerized Search Nodes   4.3.2. Identifying Buckets for Query Execution   4.3.4. Hashing Bucket Identifiers for Query Execution   4.3.5. Mapping Buckets to Search Nodes   4.3.6. Obtaining Data for Query Execution   4.3.7. Caching Search Results   
           4.4. Querying Using Metadata Catalog
           4.4.1. Metadata Catalog Data Flow   4.4.2. Example Metadata Catalog Processing   4.4.3. Metadata Catalog Flows   
           4.5 Annotating the Metadata Catalog
           4.5.1 Annotations Based on System Use Flow   4.5.2 Annotations Based on Catalog Changes Flow   
           4.6. Data Ingestion, Indexing, and Storage Flow
           4.6.1. Input   4.6.2. Parsing   4.6.3. Indexing   
           4.7. Query Processing Flow   4.8. Pipelined Search Language   4.9. Field Extraction   4.10. Example Search Screen   4.11. Data Models   4.12. Acceleration Techniques
           4.12.1. Aggregation Technique   4.12.2. Keyword Index   4.12.3. High Performance Analytics Store
               4.10.3.1 Extracting Event Data Using Posting   
               4.12.4. Accelerating Report Generation   
           4.13. Security Features   4.14. Data Center Monitoring   4.15. IT Service Monitoring   4.16. Other Architectures       

     5.0. Terminology 
     1.0. General Overview 
     Modern data centers and other computing environments can comprise anywhere from a few host computer systems to thousands of systems configured to process data, service requests from remote clients, and perform numerous other computational tasks. During operation, various components within these computing environments often generate significant volumes of machine data. Machine data is any data produced by a machine or component in an information technology (IT) environment and that reflects activity in the IT environment. For example, machine data can be raw machine data that is generated by various components in IT environments, such as servers, sensors, routers, mobile devices, Internet of Things (IoT) devices, etc. Machine data can include system logs, network packet data, sensor data, application program data, error logs, stack traces, system performance data, etc. In general, machine data can also include performance data, diagnostic information, and many other types of data that can be analyzed to diagnose performance problems, monitor user interactions, and to derive other insights. 
     A number of tools are available to analyze machine data. In order to reduce the size of the potentially vast amount of machine data that may be generated, many of these tools typically pre-process the data based on anticipated data-analysis needs. For example, pre-specified data items may be extracted from the machine data and stored in a database to facilitate efficient retrieval and analysis of those data items at search time. However, the rest of the machine data typically is not saved and is discarded during pre-processing. As storage capacity becomes progressively cheaper and more plentiful, there are fewer incentives to discard these portions of machine data and many reasons to retain more of the data. 
     This plentiful storage capacity is presently making it feasible to store massive quantities of minimally processed machine data for later retrieval and analysis. In general, storing minimally processed machine data and performing analysis operations at search time can provide greater flexibility because it enables an analyst to search all of the machine data, instead of searching only a pre-specified set of data items. This may enable an analyst to investigate different aspects of the machine data that previously were unavailable for analysis. 
     However, analyzing and searching massive quantities of machine data presents a number of challenges. For example, a data center, servers, or network appliances may generate many different types and formats of machine data (e.g., system logs, network packet data (e.g., wire data, etc.), sensor data, application program data, error logs, stack traces, system performance data, operating system data, virtualization data, etc.) from thousands of different components, which can collectively be very time-consuming to analyze. In another example, mobile devices may generate large amounts of information relating to data accesses, application performance, operating system performance, network performance, etc. There can be millions of mobile devices that report these types of information. 
     These challenges can be addressed by using an event-based data intake and query system, such as the SPLUNK® ENTERPRISE system developed by Splunk Inc. of San Francisco, Calif. The SPLUNK® ENTERPRISE system is the leading platform for providing real-time operational intelligence that enables organizations to collect, index, and search machine data from various websites, applications, servers, networks, and mobile devices that power their businesses. The data intake and query system is particularly useful for analyzing data which is commonly found in system log files, network data, and other data input sources. Although many of the techniques described herein are explained with reference to a data intake and query system similar to the SPLUNK® ENTERPRISE system, these techniques are also applicable to other types of data systems. 
     In the data intake and query system, machine data are collected and stored as “events”. An event comprises a portion of machine data and is associated with a specific point in time. The portion of machine data may reflect activity in an IT environment and may be produced by a component of that IT environment, where the events may be searched to provide insight into the IT environment, thereby improving the performance of components in the IT environment. Events may be derived from “time series data,” where the time series data comprises a sequence of data points (e.g., performance measurements from a computer system, etc.) that are associated with successive points in time. In general, each event has a portion of machine data that is associated with a timestamp that is derived from the portion of machine data in the event. A timestamp of an event may be determined through interpolation between temporally proximate events having known timestamps or may be determined based on other configurable rules for associating timestamps with events. 
     In some instances, machine data can have a predefined format, where data items with specific data formats are stored at predefined locations in the data. For example, the machine data may include data associated with fields in a database table. In other instances, machine data may not have a predefined format (e.g., may not be at fixed, predefined locations), but may have repeatable (e.g., non-random) patterns. This means that some machine data can comprise various data items of different data types that may be stored at different locations within the data. For example, when the data source is an operating system log, an event can include one or more lines from the operating system log containing machine data that includes different types of performance and diagnostic information associated with a specific point in time (e.g., a timestamp). 
     Examples of components which may generate machine data from which events can be derived include, but are not limited to, web servers, application servers, databases, firewalls, routers, operating systems, and software applications that execute on computer systems, mobile devices, sensors, Internet of Things (IoT) devices, etc. The machine data generated by such data sources can include, for example and without limitation, server log files, activity log files, configuration files, messages, network packet data, performance measurements, sensor measurements, etc. 
     The data intake and query system uses a flexible schema to specify how to extract information from events. A flexible schema may be developed and redefined as needed. Note that a flexible schema may be applied to events “on the fly,” when it is needed (e.g., at search time, index time, ingestion time, etc.). When the schema is not applied to events until search time, the schema may be referred to as a “late-binding schema.” 
     During operation, the data intake and query system receives machine data from any type and number of sources (e.g., one or more system logs, streams of network packet data, sensor data, application program data, error logs, stack traces, system performance data, etc.). The system parses the machine data to produce events each having a portion of machine data associated with a timestamp. The system stores the events in a data store. The system enables users to run queries against the stored events to, for example, retrieve events that meet criteria specified in a query, such as criteria indicating certain keywords or having specific values in defined fields. As used herein, the term “field” refers to a location in the machine data of an event containing one or more values for a specific data item. A field may be referenced by a field name associated with the field. As will be described in more detail herein, a field is defined by an extraction rule (e.g., a regular expression) that derives one or more values or a sub-portion of text from the portion of machine data in each event to produce a value for the field for that event. The set of values produced are semantically-related (such as IP address), even though the machine data in each event may be in different formats (e.g., semantically-related values may be in different positions in the events derived from different sources). 
     As described above, the system stores the events in a data store. The events stored in the data store are field-searchable, where field-searchable herein refers to the ability to search the machine data (e.g., the raw machine data) of an event based on a field specified in search criteria. For example, a search having criteria that specifies a field name “UserID” may cause the system to field-search the machine data of events to identify events that have the field name “UserID.” In another example, a search having criteria that specifies a field name “UserID” with a corresponding field value “12345” may cause the system to field-search the machine data of events to identify events having that field-value pair (e.g., field name “UserID” with a corresponding field value of “12345”). Events are field-searchable using one or more configuration files associated with the events. Each configuration file includes one or more field names, where each field name is associated with a corresponding extraction rule and a set of events to which that extraction rule applies. The set of events to which an extraction rule applies may be identified by metadata associated with the set of events. For example, an extraction rule may apply to a set of events that are each associated with a particular host, source, or source type. When events are to be searched based on a particular field name specified in a search, the system uses one or more configuration files to determine whether there is an extraction rule for that particular field name that applies to each event that falls within the criteria of the search. If so, the event is considered as part of the search results (and additional processing may be performed on that event based on criteria specified in the search). If not, the next event is similarly analyzed, and so on. 
     As noted above, the data intake and query system utilizes a late-binding schema while performing queries on events. One aspect of a late-binding schema is applying extraction rules to events to extract values for specific fields during search time. More specifically, the extraction rule for a field can include one or more instructions that specify how to extract a value for the field from an event. An extraction rule can generally include any type of instruction for extracting values from events. In some cases, an extraction rule comprises a regular expression, where a sequence of characters form a search pattern. An extraction rule comprising a regular expression is referred to herein as a regex rule. The system applies a regex rule to an event to extract values for a field associated with the regex rule, where the values are extracted by searching the event for the sequence of characters defined in the regex rule. 
     In the data intake and query system, a field extractor may be configured to automatically generate extraction rules for certain fields in the events when the events are being created, indexed, or stored, or possibly at a later time. Alternatively, a user may manually define extraction rules for fields using a variety of techniques. In contrast to a conventional schema for a database system, a late-binding schema is not defined at data ingestion time. Instead, the late-binding schema can be developed on an ongoing basis until the time a query is actually executed. This means that extraction rules for the fields specified in a query may be provided in the query itself, or may be located during execution of the query. Hence, as a user learns more about the data in the events, the user can continue to refine the late-binding schema by adding new fields, deleting fields, or modifying the field extraction rules for use the next time the schema is used by the system. Because the data intake and query system maintains the underlying machine data and uses a late-binding schema for searching the machine data, it enables a user to continue investigating and learn valuable insights about the machine data. 
     In some embodiments, a common field name may be used to reference two or more fields containing equivalent and/or similar data items, even though the fields may be associated with different types of events that possibly have different data formats and different extraction rules. By enabling a common field name to be used to identify equivalent and/or similar fields from different types of events generated by disparate data sources, the system facilitates use of a “common information model” (CIM) across the disparate data sources (further discussed with respect to  FIG. 34A ). 
     In some embodiments, the configuration files and/or extraction rules described above can be stored in a catalog, such as a metadata catalog. In certain embodiments, the content of the extraction rules can be stored as rules or actions in the metadata catalog. For example, the identification of the data to which the extraction rule applies can be referred to a rule and the processing of the data can be referred to as an action. 
     2.0. Operating Environment 
       FIG. 1  is a block diagram of an example networked computer environment  100 , in accordance with example embodiments. It will be understood that  FIG. 1  represents one example of a networked computer system and other embodiments 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. 
     2.1. 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. 
     2.2. Client Devices 
     Client devices  102  of  FIG. 1  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. 
     2.3. Client Device Applications 
     In some embodiments, each client device  102  may host or execute one or more client applications  110  that are capable of interacting with one or more host devices  106  via one or more networks  104 . For instance, a client application  110  may be or comprise a web browser that a user may use to navigate to one or more websites or other resources provided by one or more host devices  106 . As another example, a client application  110  may comprise a mobile application or “app.” For example, an operator of a network-based service hosted by one or more host devices  106  may make available one or more mobile apps that enable users of client devices  102  to access various resources of the network-based service. As yet another example, client applications  110  may include background processes that perform various operations without direct interaction from a user. A client application  110  may include a “plug-in” or “extension” to another application, such as a web browser plug-in or extension. 
     In some embodiments, a client application  110  may include a monitoring component  112 . At a high level, the monitoring component  112  comprises a software component or other logic that facilitates generating performance data related to a client device&#39;s operating state, including monitoring network traffic sent and received from the client device and collecting other device and/or application-specific information. Monitoring component  112  may be an integrated component of a client application  110 , a plug-in, an extension, or any other type of add-on component. Monitoring component  112  may also be a stand-alone process. 
     In some embodiments, a monitoring component  112  may be created when a client application  110  is developed, for example, by an application developer using a software development kit (SDK). The SDK may include custom monitoring code that can be incorporated into the code implementing a client application  110 . When the code is converted to an executable application, the custom code implementing the monitoring functionality can become part of the application itself. 
     In some embodiments, an SDK or other code for implementing the monitoring functionality may be offered by a provider of a data intake and query system, such as a system  108 . In such cases, the provider of the system  108  can implement the custom code so that performance data generated by the monitoring functionality is sent to the system  108  to facilitate analysis of the performance data by a developer of the client application or other users. 
     In some embodiments, the custom monitoring code may be incorporated into the code of a client application  110  in a number of different ways, such as the insertion of one or more lines in the client application code that call or otherwise invoke the monitoring component  112 . As such, a developer of a client application  110  can add one or more lines of code into the client application  110  to trigger the monitoring component  112  at desired points during execution of the application. Code that triggers the monitoring component may be referred to as a monitor trigger. For instance, a monitor trigger may be included at or near the beginning of the executable code of the client application  110  such that the monitoring component  112  is initiated or triggered as the application is launched, or included at other points in the code that correspond to various actions of the client application, such as sending a network request or displaying a particular interface. 
     In some embodiments, the monitoring component  112  may monitor one or more aspects of network traffic sent and/or received by a client application  110 . For example, the monitoring component  112  may be configured to monitor data packets transmitted to and/or from one or more host applications  114 . Incoming and/or outgoing data packets can be read or examined to identify network data contained within the packets, for example, and other aspects of data packets can be analyzed to determine a number of network performance statistics. Monitoring network traffic may enable information to be gathered particular to the network performance associated with a client application  110  or set of applications. 
     In some embodiments, network performance data refers to any type of data that indicates information about the network and/or network performance. Network performance data may include, for instance, a URL requested, a connection type (e.g., HTTP, HTTPS, etc.), a connection start time, a connection end time, an HTTP status code, request length, response length, request headers, response headers, connection status (e.g., completion, response time(s), failure, etc.), and the like. Upon obtaining network performance data indicating performance of the network, the network performance data can be transmitted to a data intake and query system  108  for analysis. 
     Upon developing a client application  110  that incorporates a monitoring component  112 , the client application  110  can be distributed to client devices  102 . Applications generally can be distributed to client devices  102  in any manner, or they can be pre-loaded. In some cases, the application may be distributed to a client device  102  via an application marketplace or other application distribution system. For instance, an application marketplace or other application distribution system might distribute the application to a client device based on a request from the client device to download the application. 
     Examples of functionality that enables monitoring performance of a client device are described in U.S. patent application Ser. No. 14/524,748, entitled “UTILIZING PACKET HEADERS TO MONITOR NETWORK TRAFFIC IN ASSOCIATION WITH A CLIENT DEVICE”, filed on 27 Oct. 2014, and which is hereby incorporated by reference in its entirety for all purposes. 
     In some embodiments, the monitoring component  112  may also monitor and collect performance data related to one or more aspects of the operational state of a client application  110  and/or client device  102 . For example, a monitoring component  112  may be configured to collect device performance information by monitoring one or more client device operations, or by making calls to an operating system and/or one or more other applications executing on a client device  102  for performance information. Device performance information may include, for instance, a current wireless signal strength of the device, a current connection type and network carrier, current memory performance information, a geographic location of the device, a device orientation, and any other information related to the operational state of the client device. 
     In some embodiments, the monitoring component  112  may also monitor and collect other device profile information including, for example, a type of client device, a manufacturer, and model of the device, versions of various software applications installed on the device, and so forth. 
     In general, a monitoring component  112  may be configured to generate performance data in response to a monitor trigger in the code of a client application  110  or other triggering application event, as described above, and to store the performance data in one or more data records. Each data record, for example, may include a collection of field-value pairs, each field-value pair storing a particular item of performance data in association with a field for the item. For example, a data record generated by a monitoring component  112  may include a “networkLatency” field (not shown in the Figure) in which a value is stored. This field indicates a network latency measurement associated with one or more network requests. The data record may include a “state” field to store a value indicating a state of a network connection, and so forth for any number of aspects of collected performance data. 
     2.4. Data Intake and Query System Overview 
     The data intake and query system  108  can process and store data received data from the data sources client devices  102  or host devices  106 , and execute queries on the data in response to requests received from one or more computing devices. In some cases, the data intake and query system  108  can generate events from the received data and store the events in buckets in a common storage system. In response to received queries, the data intake and query system can assign one or more search nodes to search the buckets in the common storage. 
     In certain embodiments, the data intake and query system  108  can include various components that enable it to provide stateless services or enable it to recover from an unavailable or unresponsive component without data loss in a time efficient manner. For example, the data intake and query system  108  can store contextual information about its various components in a distributed way such that if one of the components becomes unresponsive or unavailable, the data intake and query system  108  can replace the unavailable component with a different component and provide the replacement component with the contextual information. In this way, the data intake and query system  108  can quickly recover from an unresponsive or unavailable component while reducing or eliminating the loss of data that was being processed by the unavailable component. 
     In some embodiments, the data intake and query system  108  can store the contextual information in a metadata catalog, as described herein. In certain embodiments, the contextual information can correspond to information that the data intake and query system  108  has determined or learned based on use. In some cases, the contextual information can be stored as annotations (manual annotations and/or system annotations), as described herein. 
     3.0. Data Intake and Query System Architecture 
       FIG. 2  is a block diagram of an embodiment of a data processing environment  200 . In the illustrated embodiment, the environment  200  includes data sources  202 , client devices  204   a ,  204   b  . . .  204   n  (generically referred to as client device(s)  204 ), and an application environment  205 , in communication with a data intake and query system  108  via networks  206 ,  208 , respectively. The networks  206 ,  208  may be the same network, may correspond to the network  104 , or may be different networks. Further, the networks  206 ,  208  may be implemented as 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 Internet. 
     Each data source  202  broadly represents a distinct source of data that can be consumed by the data intake and query system  108 . Examples of data sources  202  include, without limitation, data files, directories of files, data sent over a network, event logs, registries, streaming data services (examples of which can include, by way of non-limiting example, Amazon&#39;s Simple Queue Service (“SQS”) or Kinesis™ services, devices executing Apache Kafka™ software, or devices implementing the Message Queue Telemetry Transport (MQTT) protocol, Microsoft Azure EventHub, Google Cloud PubSub, devices implementing the Java Message Service (JMS) protocol, devices implementing the Advanced Message Queuing Protocol (AMQP)), performance metrics, cloud-based services (e.g., AWS, Microsoft Azure, Google Cloud, etc.), operating-system-level virtualization environments (e.g., Docker), container orchestration systems (e.g., Kubernetes), virtual machines using full virtualization or paravirtualization, or other virtualization technique or isolated execution environments. 
     As illustrated in  FIG. 2 , in some embodiments, the data sources  202  can communicate with the data to the intake system  210  via the network  206  without passing through the gateway  215 . As a non-limiting example, if the intake system  210  receives the data from a data source  202  via a forwarder  302  (described in greater detail below), the intake system  210  may receive the data via the network  206  without going through the gateway  215 . In certain embodiments, the data sources  202  can communicate the data to the intake system  210  via the network  206  using the gateway  215 . As another non-limiting example, if the intake system  210  receives the data from a data source  202  via a HTTP intake point  322  (described in greater detail below), it may receive the data via the gateway  215 . Accordingly, it will be understood that a variety of methods can be used to receive data from the data sources  202  via the network  206  or via the network  206  and the gateway  215 . 
     The client devices  204  can be implemented using one or more computing devices in communication with the data intake and query system  108 , and represent some of the different ways in which computing devices can submit queries to the data intake and query system  108 . For example, the client device  204   a  is illustrated as communicating over an Internet (Web) protocol with the data intake and query system  108 , the client device  204   b  is illustrated as communicating with the data intake and query system  108  via a command line interface, and the client device  204   n  is illustrated as communicating with the data intake and query system  108  via a software developer kit (SDK). However, it will be understood that the client devices  204  can communicate with and submit queries to the data intake and query system  108  in a variety of ways. For example, the client devices  204  can use one or more executable applications or programs from the application environment  205  to interface with the data intake and query system  108 . The application environment  205  can include tools, software modules (e.g., computer executable instructions to perform a particular function), etc., to enable application developers to create computer executable applications to interface with the data intake and query system  108 . For example, application developers can identify particular data that is of particular relevance to them. The application developers can use the application environment  205  to build a particular application to interface with the data intake and query system  108  to obtain the relevant data that they seek, process the relevant data, and display it in a manner that is consumable or easily understood by a user. The applications developed using the application environment  205  can include their own backend services, middleware logic, front-end user interface, etc., and can provide facilities for ingesting use case specific data and interacting with that data. 
     In certain embodiments, the developed applications can be executed by a computing device or in an isolated execution environment of an isolated execution environment system, such as Kubernetes, AWS, Microsoft Azure, Google Cloud, etc. In addition, some embodiments, the application environments  205  can provide one or more isolated execution environments in which to execute the developed applications. In some cases, the applications are executed in an isolated execution environment or a processing device unrelated to the application environment  205 . 
     As a non-limiting example, an application developed using the application environment  205  can include a custom web-user interface that may or may not leverage one or more UI components provided by the application environment  205 . The application could include middle-ware business logic, on a middle-ware platform of the developer&#39;s choice. Furthermore, as mentioned the applications implemented using the application environment  205  can be instantiated and execute in a different isolated execution environment or different isolated execution environment system than the data intake and query system  108 . As a non-limiting example, in embodiments where the data intake and query system  108  is implemented using a Kubernetes cluster, the applications developed using the application environment  205  can execute in a different Kubernetes cluster (or other isolated execution environment system) and interact with the data intake and query system  108  via the gateway  215 . 
     The data intake and query system  108  can process and store data received data from the data sources  202  and execute queries on the data in response to requests received from the client devices  204 . In the illustrated embodiment, the data intake and query system  108  includes a gateway  209 , an intake system  210 , an indexing system  212 , a query system  214 , common storage  216  including one or more data stores  218 , a data store catalog  220 , a metadata catalog  221 , and a query acceleration data store  222 . Although certain communication pathways are illustrated in  FIG. 2 , it will be understood that, in certain embodiments, any component of the data intake and query system  108  can interact with any other component of the data intake and query system  108 . For example, the gateway  215  can interact with one or more components of the indexing system  212  and/or one or more components of the intake system  210  can communicate with the metadata catalog  221 . Thus, data and/or commands can be communicated in a variety of ways within the data intake and query system  108 . 
     As will be described in greater detail herein, the gateway  215  can provide an interface between one or more components of the data intake and query system  108  and other systems or computing devices, such as, but not limited to, client devices  204 , the application environment  205 , one or more data sources  202 , and/or other systems  262 . In some embodiments, the gateway  215  can be implemented using an application programming interface (API). In certain embodiments, the gateway  215  can be implemented using a representational state transfer API (REST API). 
     As mentioned, the data intake and query system  108  can receive data from different sources  202 . In some cases, the data sources  202  can be associated with different tenants or customers. Further, each tenant may be associated with one or more indexes, hosts, sources, sourcetypes, or users. For example, company ABC, Inc. can correspond to one tenant and company XYZ, Inc. can correspond to a different tenant. While the two companies may be unrelated, each company may have a main index and test index associated with it, as well as one or more data sources or systems (e.g., billing system, CRM system, etc.). The data intake and query system  108  can concurrently receive and process the data from the various systems and sources of ABC, Inc. and XYZ, Inc. 
     In certain cases, although the data from different tenants can be processed together or concurrently, the data intake and query system  108  can take steps to avoid combining or co-mingling data from the different tenants. For example, the data intake and query system  108  can assign a tenant identifier for each tenant and maintain a separation between the data using the tenant identifier. In some cases, the tenant identifier can be assigned to the data at the data sources  202 , or can be assigned to the data by the data intake and query system  108  at ingest. 
     As will be described in greater detail herein, at least with reference to  FIGS. 3A and 3B , the intake system  210  can receive data from the data sources  202 , perform one or more preliminary processing operations on the data, and communicate the data to the indexing system  212 , query system  214 , or to other systems  262  (which may include, for example, data processing systems, telemetry systems, real-time analytics systems, data stores, databases, etc., any of which may be operated by an operator of the data intake and query system  108  or a third party). The intake system  210  can receive data from the data sources  202  in a variety of formats or structures. In some embodiments, the received data corresponds to raw machine data, structured or unstructured data, correlation data, data files, directories of files, data sent over a network, event logs, registries, messages published to streaming data sources, performance metrics, sensor data, image and video data, etc. The intake system  210  can process the data based on the form in which it is received. In some cases, the intake system  210  can utilize one or more rules to process data and to make the data available to downstream systems (e.g., the indexing system  212 , query system  214 , etc.). Illustratively, the intake system  210  can enrich the received data. For example, the intake system may add one or more fields to the data received from the data sources  202 , such as fields denoting the host, source, sourcetype, index, or tenant associated with the incoming data. In certain embodiments, the intake system  210  can perform additional processing on the incoming data, such as transforming structured data into unstructured data (or vice versa), identifying timestamps associated with the data, removing extraneous data, parsing data, indexing data, separating data, categorizing data, routing data based on criteria relating to the data being routed, and/or performing other data transformations, etc. 
     As will be described in greater detail herein, at least with reference to  FIG. 4 , the indexing system  212  can process the data and store it, for example, in common storage  216 . As part of processing the data, the indexing system can identify timestamps associated with the data, organize the data into buckets or time series buckets, convert editable buckets to non-editable buckets, store copies of the buckets in common storage  216 , merge buckets, generate indexes of the data, etc. In addition, the indexing system  212  can update the data store catalog  220  with information related to the buckets (pre-merged or merged) or data that is stored in common storage  216 , and can communicate with the intake system  210  about the status of the data storage. 
     As will be described in greater detail herein, at least with reference to  FIG. 5 , the query system  214  can receive queries that identify a set of data to be processed and a manner of processing the set of data from one or more client devices  204 , process the queries to identify the set of data, and execute the query on the set of data. In some cases, as part of executing the query, the query system  214  can use the data store catalog  220  to identify the set of data to be processed or its location in common storage  216  and/or can retrieve data from common storage  216  or the query acceleration data store  222 . In addition, in some embodiments, the query system  214  can store some or all of the query results in the query acceleration data store  222 . 
     As mentioned and as will be described in greater detail below, the common storage  216  can be made up of one or more data stores  218  storing data that has been processed by the indexing system  212 . The common storage  216  can be configured to provide high availability, highly resilient, low loss data storage. In some cases, to provide the high availability, highly resilient, low loss data storage, the common storage  216  can store multiple copies of the data in the same and different geographic locations and across different types of data stores (e.g., solid state, hard drive, tape, etc.). Further, as data is received at the common storage  216  it can be automatically replicated multiple times according to a replication factor to different data stores across the same and/or different geographic locations. In some embodiments, the common storage  216  can correspond to cloud storage, such as Amazon Simple Storage Service (S3) or Elastic Block Storage (EBS), Google Cloud Storage, Microsoft Azure Storage, etc. 
     In some embodiments, indexing system  212  can read to and write from the common storage  216 . For example, the indexing system  212  can copy buckets of data from its local or shared data stores to the common storage  216 . In certain embodiments, the query system  214  can read from, but cannot write to, the common storage  216 . For example, the query system  214  can read the buckets of data stored in common storage  216  by the indexing system  212 , but may not be able to copy buckets or other data to the common storage  216 . In some embodiments, the intake system  210  does not have access to the common storage  216 . However, in some embodiments, one or more components of the intake system  210  can write data to the common storage  216  that can be read by the indexing system  212 . 
     As described herein, in some embodiments, data in the data intake and query system  108  (e.g., in the data stores of the indexers of the indexing system  212 , common storage  216 , or search nodes of the query system  214 ) can be stored in one or more time series buckets. Each bucket can include raw machine data associated with a time stamp and additional information about the data or bucket, such as, but not limited to, one or more filters, indexes (e.g., TSIDX, inverted indexes, keyword indexes, etc.), bucket summaries, etc. In some embodiments, the bucket data and information about the bucket data is stored in one or more files. For example, the raw machine data, filters, indexes, bucket summaries, etc. can be stored in respective files in or associated with a bucket. In certain cases, the group of files can be associated together to form the bucket. 
     The data store catalog  220  can store information about the data stored in common storage  216 , such as, but not limited to an identifier for a set of data or buckets, a location of the set of data, tenants or indexes associated with the set of data, timing information about the data, etc. For example, in embodiments where the data in common storage  216  is stored as buckets, the data store catalog  220  can include a bucket identifier for the buckets in common storage  216 , a location of or path to the bucket in common storage  216 , a time range of the data in the bucket (e.g., range of time between the first-in-time event of the bucket and the last-in-time event of the bucket), a tenant identifier identifying a customer or computing device associated with the bucket, and/or an index (also referred to herein as a partition) associated with the bucket, etc. In certain embodiments, the data intake and query system  108  includes multiple data store catalogs  220 . For example, in some embodiments, the data intake and query system  108  can include a data store catalog  220  for each tenant (or group of tenants), each partition of each tenant (or group of indexes), etc. In some cases, the data intake and query system  108  can include a single data store catalog  220  that includes information about buckets associated with multiple or all of the tenants associated with the data intake and query system  108 . 
     The indexing system  212  can update the data store catalog  220  as the indexing system  212  stores data in common storage  216 . Furthermore, the indexing system  212  or other computing device associated with the data store catalog  220  can update the data store catalog  220  as the information in the common storage  216  changes (e.g., as buckets in common storage  216  are merged, deleted, etc.). In addition, as described herein, the query system  214  can use the data store catalog  220  to identify data to be searched or data that satisfies at least a portion of a query. In some embodiments, the query system  214  makes requests to and receives data from the data store catalog  220  using an application programming interface (“API”). 
     As will be described in greater detail herein, at least with reference to  FIGS. 6 and 22-27 , the metadata catalog  221  can store information about datasets used or supported by the data intake and query system  108  and/or one or more rules that indicate which data in a dataset to process and how to process the data from the dataset. The information about the datasets can include configuration information, such as, but not limited to the type of the dataset, access and authorization information for the dataset, location information for the dataset, physical and logical names or other identifiers for the dataset, etc. The rules can indicate how different data of a dataset is to be processed and/or how to extract fields or field values from different data of a dataset. 
     The metadata catalog  221  can also include one or more dataset association records. The dataset association records can indicate how to refer to a particular dataset (e.g., a name or other identifier for the dataset) and/or identify associations or relationships between the particular dataset and one or more rules or other datasets. In some embodiments, a dataset association record can be similar to a namespace in that it can indicate a scope of one or more datasets and the manner in which to reference the one or more datasets. As a non-limiting example, one dataset association record can identify four datasets: a “main” index dataset, a “test” index dataset, a “username” collection dataset, and a “username” lookup dataset. The dataset association record can also identify one or more rules for one or more of the datasets. For example, one rule can indicate that for data with the sourcetype “foo” from the “main” index dataset (or all datasets of the dataset association record), multiple actions are to take place, such as, extracting a field value for a “UID” field, and using the “username” lookup dataset to identify a username associated with the extracted “UID” field value. The actions of the rule can provide specific guidance as to how to extract the field value for the “UID” field from the sourcetype “foo” data in the “main” index dataset and how to perform the lookup of the username. 
     As described herein, the query system  214  can use the metadata catalog  221  to, among other things, interpret dataset identifiers in a query, verify/authenticate a user&#39;s permissions and/or authorizations for different datasets, identify additional processing as part of the query, identify one or more datasets from which to retrieve data as part of the query (also referred to herein as source datasets), determine how to extract data from datasets, identify configurations/definitions/dependencies to be used by search nodes to execute the query, etc. 
     In certain embodiments, the query system  214  can use the metadata catalog  221  to provide a stateless search service. For example, the query system  214  can use the metadata catalog  221  to dynamically determine the dataset configurations and rule configurations to be used to execute a query (also referred to herein as the query configuration parameters) and communicate the query configuration parameters to one or more search heads  504 . If the query system  214  determines that an assigned search head  504  becomes unavailable, the query system  214  can communicate the dynamically determined query configuration parameters (and query to be executed) to another search head  504  without data loss and/or with minimal or reduced time loss. 
     In some embodiments, the metadata catalog  221  can be implemented using a database system, such as, but not limited to, a relational database system (non-limiting commercial examples: DynamoDB, Aurora DB, etc.). In certain embodiments, the database system can include entries for the different datasets, rules, and/or dataset association records. Moreover, as described herein, the metadata catalog  221  can be modified over time as information is learned about the datasets associated with or managed by the data intake and query system  108 . For example, the entries in the database system can include manual or system annotations, as described herein. 
     The query acceleration data store  222  can store the results or partial results of queries, or otherwise be used to accelerate queries. For example, if a user submits a query that has no end date, the system can query system  214  can store an initial set of results in the query acceleration data store  222 . As additional query results are determined based on additional data, the additional results can be combined with the initial set of results, and so on. In this way, the query system  214  can avoid re-searching all of the data that may be responsive to the query and instead search the data that has not already been searched. 
     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 intake system  210 , indexing system  212 , query system  214 , common storage  216 , data store catalog  220 , or query acceleration data store  222 , 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 certain embodiments, one or more of the components of a data intake and query system  108  can be implemented in a remote distributed computing system. In this context, a remote distributed computing system or cloud-based service can refer to a service hosted by one more computing resources that are accessible to end users over a network, for example, by using a web browser or other application on a client device to interface with the remote computing resources. For example, a service provider may provide a data intake and query system  108  by managing computing resources configured to implement various aspects of the system (e.g., intake system  210 , indexing system  212 , query system  214 , common storage  216 , data store catalog  220 , or query acceleration data store  222 , etc.) and by providing access to the system to end users via a network. Typically, a user may pay a subscription or other fee to use such a service. Each subscribing user of the cloud-based service may be provided with an account that enables the user to configure a customized cloud-based system based on the user&#39;s preferences. When implemented as a cloud-based service, various components of the system  108  can be implemented using containerization or operating-system-level virtualization, or other virtualization technique. For example, one or more components of the intake system  210 , indexing system  212 , or query system  214  can be implemented as separate software containers or container instances. Each container instance can have certain resources (e.g., memory, processor, etc.) of the underlying host computing system assigned to it, but may share the same operating system and may use the operating system&#39;s system call interface. Each container may provide an isolated execution environment on the host system, such as by providing a memory space of the host system that is logically isolated from memory space of other containers. Further, each container may run the same or different computer applications concurrently or separately, and may interact with each other. Although reference is made herein to containerization and container instances, it will be understood that other virtualization techniques can be used. For example, the components can be implemented using virtual machines using full virtualization or paravirtualization, etc. Thus, where reference is made to “containerized” components, it should be understood that such components may additionally or alternatively be implemented in other isolated execution environments, such as a virtual machine environment. 
     3.1. Gateway 
     As described herein, the gateway  215  can provide an interface between one or more components of the data intake and query system  108  (non-limiting examples: one or more components of the intake system  210 , one or more components of the indexing system  212 , one or more components of the query system  214 , common storage  216 , the data store catalog  220 , the metadata catalog  221  and/or the acceleration data store  222 ), and other systems or computing devices, such as, but not limited to, client devices  204 , the application environment  205 , one or more data sources  202 , and/or other systems  262  (not illustrated). In some cases, one or more components of the data intake and query system  108  can include their own API. In such embodiments, the gateway  215  can communicate with the API of a component of the data intake and query system  108 . Accordingly, the gateway  215  can translate requests received from an external device into a command understood by the API of the specific component of the data intake and query system  108 . In this way, the gateway  215  can provide an interface between external devices and the API of the devices of the data intake and query system  108 . 
     In some embodiments, the gateway  215  can be implemented using an API, such as the REST API. In some such embodiments, the client devices  204  can communicate via one or more commands, such as GET, PUT, etc. However, it will be understood that the gateway  215  can be implemented in a variety of ways to enable the external devices and/or systems to interface with one or more components of the data intake and query system  108 . 
     In certain embodiments, a client device  204  can provide control parameters to the data intake and query system  108  via the gateway  215 . As a non-limiting example, using the gateway  215 , a client device  204  can provide instructions to the metadata catalog  221 , the intake system  210 , indexing system  212 , and/or the query system  214 . For example, using the gateway  215 , a client device  204  can instruct the metadata catalog  221  to add/modify/delete a dataset association record, dataset, rule, configuration, and/or action, etc. As another example, using the gateway  215 , a client device  204  can provide a query to the query system  214  and receive results. As yet another example, using the gateway  215 , a client device  204  can provide processing instructions to the intake system  210 . As yet another example, using the gateway  215 , one or more data sources  202  can provide data to the intake system  210 . In some embodiments, one or more components of the intake system  210  can receive data from a data source  202  via the gateway  215 . For example, in some embodiments, data received by the HTTP intake point  322  and/or custom intake points  332  (described in greater detail below) of the intake system  210  can be received via the gateway  215 . 
     As mentioned, upon receipt of a request or command from an external device, the gateway  215  can determine the component of the data intake and query system  108  (or service) to handle the request. In some embodiments, the request or command can include an identifier for the component associated with the request or command. In certain embodiments, the gateway  215  can determine the component to handle the request based on the type of request or services requested by the command. For example, if the request or command relates to (or includes) a query, the gateway  215  can determine that the command is to be sent to a component of the query system  214 . As another example, if the request or command includes data, such as raw machine data, metrics, or metadata, the gateway  215  can determine that the request or command is to be sent to a component of the intake system  214  (non-limiting examples: HTTP intake point  322  or other push-based publisher  320 , custom intake point  332 A or other pull-based publisher  330 , etc.) or indexing system  212  (non-limiting example: indexing node  404 , etc.). As yet another example, if the gateway  215  determines that the request or command relates to the modification of a dataset or rule, it can communicate the command or request to the metadata catalog  221 . 
     Furthermore, in some cases, the gateway  215  can translate the request or command received from the external device into a command that can be interpreted by the component of the data intake and query system  108 . For example, the request or command received by the gateway  215  may not be interpretable or understood by the component of the data intake and query system  108  that is to process the command or request. Moreover, as mentioned, in certain embodiments, one or more components of the data intake and query system  108  can use an API to interact with other components of the data intake and query system  108 . Accordingly, the gateway  215  can generate a command for the component of the data intake and query system  108  that is to process the command or request based on the received command or request and the information about the API of the component of the data intake and query system  108  (or the component itself). 
     In some cases, the gateway  215  can expose a subset of components and/or a limited number of features of the components of the data intake and query system  108  to the external devices. For example, for the query system  214 , the gateway  215 , may expose the ability to submit queries but may not expose the ability to configure certain components of the query system  214 , such as the search node catalog  510 , search node monitor  508 , and/or cache manager  516  (described in greater detail below). However, it will be understood that the gateway  215  can be configured to expose fewer or more components and/or fewer or more functions for the different components as desired. By limiting the components or commands for the components of the data intake and query system, the gateway  215  can provide improved security for the data intake and query system  108 . 
     In addition to limiting the components or functions made available to external systems, the gateway  215  can provide authentication and/or authorization functionality. For example, with each request or command received by a client device and/or data source  202 , the gateway  215  can authenticate the computing device from which the requester command was received and/or determine whether the requester has sufficient permissions or authorizations to make the request. In this way, the gateway  215  can provide additional security for the data intake and query system  108 . 
     3.2. Intake System 
     As detailed below, data may be ingested at the data intake and query system  108  through an intake system  210  configured to conduct preliminary processing on the data, and make the data available to downstream systems or components, such as the indexing system  212 , query system  214 , third party systems, etc. 
     One example configuration of an intake system  210  is shown in  FIG. 3A . As shown in  FIG. 3A , the intake system  210  includes a forwarder  302 , a data retrieval subsystem  304 , an intake ingestion buffer  306 , a streaming data processor  308 , and an output ingestion buffer  310 . As described in detail below, the components of the intake system  210  may be configured to process data according to a streaming data model, such that data ingested into the data intake and query system  108  is processed rapidly (e.g., within seconds or minutes of initial reception at the intake system  210 ) and made available to downstream systems or components. The initial processing of the intake system  210  may include search or analysis of the data ingested into the intake system  210 . For example, the initial processing can transform data ingested into the intake system  210  sufficiently, for example, for the data to be searched by a query system  214 , thus enabling “real-time” searching for data on the data intake and query system  108  (e.g., without requiring indexing of the data). Various additional and alternative uses for data processed by the intake system  210  are described below. 
     Although shown as separate components, the forwarder  302 , data retrieval subsystem  304 , intake ingestion buffer  306 , streaming data processors  308 , and output ingestion buffer  310 , in various embodiments, may reside on the same machine or be distributed across multiple machines in any combination. In one embodiment, any or all of the components of the intake system can be implemented using one or more computing devices as distinct computing devices or as one or more container instances or virtual machines across one or more computing devices. It will be appreciated by those skilled in the art that the intake system  210  may have more of fewer components than are illustrated in  FIGS. 3A and 3B . In addition, the intake system  210  could include various web services and/or peer-to-peer network configurations or inter container communication network provided by an associated container instantiation or orchestration platform. Thus, the intake system  210  of  FIGS. 3A and 3B  should be taken as illustrative. For example, in some embodiments, components of the intake system  210 , such as the ingestion buffers  306  and  310  and/or the streaming data processors  308 , may be executed by one more virtual machines implemented in a hosted computing environment. A hosted computing environment may include one or more rapidly provisioned and released computing resources, which computing resources may include computing, networking and/or storage devices. A hosted computing environment may also be referred to as a cloud computing environment. Accordingly, the hosted computing environment can include any proprietary or open source extensible computing technology, such as Apache Flink or Apache Spark, to enable fast or on-demand horizontal compute capacity scaling of the streaming data processor  308 . 
     In some embodiments, some or all of the elements of the intake system  210  (e.g., forwarder  302 , data retrieval subsystem  304 , intake ingestion buffer  306 , streaming data processors  308 , and output ingestion buffer  310 , etc.) may reside on one or more computing devices, such as servers, which may be communicatively coupled with each other and with the data sources  202 , query system  214 , indexing system  212 , or other components. In other embodiments, some or all of the elements of the intake system  210  may be implemented as worker nodes as disclosed in U.S. patent application Ser. Nos. 15/665,159, 15/665,148, 15/665,187, 15/665,248, 15/665,197, 15/665,279, 15/665,302, and 15/665,339, each of which is incorporated by reference herein in its entirety (hereinafter referred to as “the Incorporated Applications”). 
     As noted above, the intake system  210  can function to conduct preliminary processing of data ingested at the data intake and query system  108 . As such, the intake system  210  illustratively includes a forwarder  302  that obtains data from a data source  202  and transmits the data to a data retrieval subsystem  304 . The data retrieval subsystem  304  may be configured to convert or otherwise format data provided by the forwarder  302  into an appropriate format for inclusion at the intake ingestion buffer and transmit the message to the intake ingestion buffer  306  for processing. Thereafter, a streaming data processor  308  may obtain data from the intake ingestion buffer  306 , process the data according to one or more rules, and republish the data to either the intake ingestion buffer  306  (e.g., for additional processing) or to the output ingestion buffer  310 , such that the data is made available to downstream components or systems. In this manner, the intake system  210  may repeatedly or iteratively process data according to any of a variety of rules, such that the data is formatted for use on the data intake and query system  108  or any other system. As discussed below, the intake system  210  may be configured to conduct such processing rapidly (e.g., in “real-time” with little or no perceptible delay), while ensuring resiliency of the data. 
     3.2.1. Forwarder 
     The forwarder  302  can include or be executed on a computing device configured to obtain data from a data source  202  and transmit the data to the data retrieval subsystem  304 . In some implementations, the forwarder  302  can be installed on a computing device associated with the data source  202  or directly on the data source  202 . While a single forwarder  302  is illustratively shown in  FIG. 3A , the intake system  210  may include a number of different forwarders  302 . Each forwarder  302  may illustratively be associated with a different data source  202 . A forwarder  302  initially may receive the data as a raw data stream generated by the data source  202 . For example, a forwarder  302  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  202  receives the raw data and may segment the data stream into “blocks”, possibly of a uniform data size, to facilitate subsequent processing steps. The forwarder  202  may additionally or alternatively modify data received, prior to forwarding the data to the data retrieval subsystem  304 . Illustratively, the forwarder  202  may “tag” metadata for each data block, such as by specifying a source, source type, or host associated with the data, or by appending one or more timestamp or time ranges to each data block. 
     In some embodiments, a forwarder  302  may comprise a service accessible to data sources  202  via a network  206 . For example, one type of forwarder  302  may be capable of consuming vast amounts of real-time data from a potentially large number of data sources  202 . The forwarder  302  may, for example, comprise a computing device which implements multiple data pipelines or “queues” to handle forwarding of network data to data retrieval subsystems  304 . 
     3.2.2. Data Retrieval Subsystem 
     The data retrieval subsystem  304  illustratively corresponds to a computing device which obtains data (e.g., from the forwarder  302 ), and transforms the data into a format suitable for publication on the intake ingestion buffer  306 . Illustratively, where the forwarder  302  segments input data into discrete blocks, the data retrieval subsystem  304  may generate a message for each block, and publish the message to the intake ingestion buffer  306 . Generation of a message for each block may include, for example, formatting the data of the message in accordance with the requirements of a streaming data system implementing the intake ingestion buffer  306 , the requirements of which may vary according to the streaming data system. In one embodiment, the intake ingestion buffer  306  formats messages according to the protocol buffers method of serializing structured data. Thus, the intake ingestion buffer  306  may be configured to convert data from an input format into a protocol buffer format. Where a forwarder  302  does not segment input data into discrete blocks, the data retrieval subsystem  304  may itself segment the data. Similarly, the data retrieval subsystem  304  may append metadata to the input data, such as a source, source type, or host associated with the data. 
     Generation of the message may include “tagging” the message with various information, which may be included as metadata for the data provided by the forwarder  302 , and determining a “topic” for the message, under which the message should be published to the intake ingestion buffer  306 . In general, the “topic” of a message may reflect a categorization of the message on a streaming data system. Illustratively, each topic may be associated with a logically distinct queue of messages, such that a downstream device or system may “subscribe” to the topic in order to be provided with messages published to the topic on the streaming data system. 
     In one embodiment, the data retrieval subsystem  304  may obtain a set of topic rules (e.g., provided by a user of the data intake and query system  108  or based on automatic inspection or identification of the various upstream and downstream components of the data intake and query system  108 ) that determine a topic for a message as a function of the received data or metadata regarding the received data. For example, the topic of a message may be determined as a function of the data source  202  from which the data stems. After generation of a message based on input data, the data retrieval subsystem can publish the message to the intake ingestion buffer  306  under the determined topic. 
     While the data retrieval subsystem  304  is depicted in  FIG. 3A  as obtaining data from the forwarder  302 , the data retrieval subsystem  304  may additionally or alternatively obtain data from other sources, such as from the data source  202  and/or via the gateway  209 . In some instances, the data retrieval subsystem  304  may be implemented as a plurality of intake points, each functioning to obtain data from one or more corresponding data sources (e.g., the forwarder  302 , data sources  202 , or any other data source), generate messages corresponding to the data, determine topics to which the messages should be published, and to publish the messages to one or more topics of the intake ingestion buffer  306 . 
     One illustrative set of intake points implementing the data retrieval subsystem  304  is shown in  FIG. 3B . Specifically, as shown in  FIG. 3B , the data retrieval subsystem  304  of  FIG. 3A  may be implemented as a set of push-based publishers  320  or a set of pull-based publishers  330 . The illustrative push-based publishers  320  operate on a “push” model, such that messages are generated at the push-based publishers  320  and transmitted to an intake ingestion buffer  306  (shown in  FIG. 3B  as primary and secondary intake ingestion buffers  306 A and  306 B, which are discussed in more detail below). As will be appreciated by one skilled in the art, “push” data transmission models generally correspond to models in which a data source determines when data should be transmitted to a data target. A variety of mechanisms exist to provide “push” functionality, including “true push” mechanisms (e.g., where a data source independently initiates transmission of information) and “emulated push” mechanisms, such as “long polling” (a mechanism whereby a data target initiates a connection with a data source, but allows the data source to determine within a timeframe when data is to be transmitted to the data source). 
     As shown in  FIG. 3B , the push-based publishers  320  illustratively include an HTTP intake point  322  and a data intake and query system (DIQS) intake point  324 . The HTTP intake point  322  can include a computing device configured to obtain HTTP-based data (e.g., as JavaScript Object Notation, or JSON messages) to format the HTTP-based data as a message, to determine a topic for the message (e.g., based on fields within the HTTP-based data), and to publish the message to the primary intake ingestion buffer  306 A. Similarly, the DIQS intake point  324  can be configured to obtain data from a forwarder  324 , to format the forwarder data as a message, to determine a topic for the message, and to publish the message to the primary intake ingestion buffer  306 A. In this manner, the DIQS intake point  324  can function in a similar manner to the operations described with respect to the data retrieval subsystem  304  of  FIG. 3A . 
     In addition to the push-based publishers  320 , one or more pull-based publishers  330  may be used to implement the data retrieval subsystem  304 . The pull-based publishers  330  may function on a “pull” model, whereby a data target (e.g., the primary intake ingestion buffer  306 A) functions to continuously or periodically (e.g., each n seconds) query the pull-based publishers  330  for new messages to be placed on the primary intake ingestion buffer  306 A. In some instances, development of pull-based systems may require less coordination of functionality between a pull-based publisher  330  and the primary intake ingestion buffer  306 A. Thus, for example, pull-based publishers  330  may be more readily developed by third parties (e.g., other than a developer of the data intake a query system  108 ), and enable the data intake and query system  108  to ingest data associated with third party data sources  202 . Accordingly,  FIG. 3B  includes a set of custom intake points  332 A through  332 N, each of which functions to obtain data from a third-party data source  202 , format the data as a message for inclusion in the primary intake ingestion buffer  306 A, determine a topic for the message, and make the message available to the primary intake ingestion buffer  306 A in response to a request (a “pull”) for such messages. 
     While the pull-based publishers  330  are illustratively described as developed by third parties, push-based publishers  320  may also in some instances be developed by third parties. Additionally or alternatively, pull-based publishers may be developed by the developer of the data intake and query system  108 . To facilitate integration of systems potentially developed by disparate entities, the primary intake ingestion buffer  306 A may provide an API through which an intake point may publish messages to the primary intake ingestion buffer  306 A. Illustratively, the API may enable an intake point to “push” messages to the primary intake ingestion buffer  306 A, or request that the primary intake ingestion buffer  306 A “pull” messages from the intake point. Similarly, the streaming data processors  308  may provide an API through which ingestions buffers may register with the streaming data processors  308  to facilitate pre-processing of messages on the ingestion buffers, and the output ingestion buffer  310  may provide an API through which the streaming data processors  308  may publish messages or through which downstream devices or systems may subscribe to topics on the output ingestion buffer  310 . Furthermore, any one or more of the intake points  322  through  332 N may provide an API through which data sources  202  may submit data to the intake points. Thus, any one or more of the components of  FIGS. 3A and 3B  may be made available via APIs to enable integration of systems potentially provided by disparate parties. 
     The specific configuration of publishers  320  and  330  shown in  FIG. 3B  is intended to be illustrative in nature. For example, the specific number and configuration of intake points may vary according to embodiments of the present application. In some instances, one or more components of the intake system  210  may be omitted. For example, a data source  202  may in some embodiments publish messages to an intake ingestion buffer  306 , and thus an intake point  332  may be unnecessary. Other configurations of the intake system  210  are possible. 
     3.2.3. Ingestion Buffer 
     The intake system  210  is illustratively configured to ensure message resiliency, such that data is persisted in the event of failures within the intake system  310 . Specifically, the intake system  210  may utilize one or more ingestion buffers, which operate to resiliently maintain data received at the intake system  210  until the data is acknowledged by downstream systems or components. In one embodiment, resiliency is provided at the intake system  210  by use of ingestion buffers that operate according to a publish-subscribe (“pub-sub”) message model. In accordance with the pub-sub model, data ingested into the data intake and query system  108  may be atomized as “messages,” each of which is categorized into one or more “topics.” An ingestion buffer can maintain a queue for each such topic, and enable devices to “subscribe” to a given topic. As messages are published to the topic, the ingestion buffer can function to transmit the messages to each subscriber, and ensure message resiliency until at least each subscriber has acknowledged receipt of the message (e.g., at which point the ingestion buffer may delete the message). In this manner, the ingestion buffer may function as a “broker” within the pub-sub model. A variety of techniques to ensure resiliency at a pub-sub broker are known in the art, and thus will not be described in detail herein. In one embodiment, an ingestion buffer is implemented by a streaming data source. As noted above, examples of streaming data sources include (but are not limited to) Amazon&#39;s Simple Queue Service (“SQS”) or Kinesis™ services, devices executing Apache Kafka™ software, or devices implementing the Message Queue Telemetry Transport (MQTT) protocol. Any one or more of these example streaming data sources may be utilized to implement an ingestion buffer in accordance with embodiments of the present disclosure. 
     With reference to  FIG. 3A , the intake system  210  may include at least two logical ingestion buffers: an intake ingestion buffer  306  and an output ingestion buffer  310 . As noted above, the intake ingestion buffer  306  can be configured to receive messages from the data retrieval subsystem  304  and resiliently store the message. The intake ingestion buffer  306  can further be configured to transmit the message to the streaming data processors  308  for processing. As further described below, the streaming data processors  308  can be configured with one or more data transformation rules to transform the messages, and republish the messages to one or both of the intake ingestion buffer  306  and the output ingestion buffer  310 . The output ingestion buffer  310 , in turn, may make the messages available to various subscribers to the output ingestion buffer  310 , which subscribers may include the query system  214 , the indexing system  212 , or other third-party devices (e.g., client devices  102 , host devices  106 , etc.). 
     Both the input ingestion buffer  306  and output ingestion buffer  310  may be implemented on a streaming data source, as noted above. In one embodiment, the intake ingestion buffer  306  operates to maintain source-oriented topics, such as topics for each data source  202  from which data is obtained, while the output ingestion buffer operates to maintain content-oriented topics, such as topics to which the data of an individual message pertains. As discussed in more detail below, the streaming data processors  308  can be configured to transform messages from the intake ingestion buffer  306  (e.g., arranged according to source-oriented topics) and publish the transformed messages to the output ingestion buffer  310  (e.g., arranged according to content-oriented topics). In some instances, the streaming data processors  308  may additionally or alternatively republish transformed messages to the intake ingestion buffer  306 , enabling iterative or repeated processing of the data within the message by the streaming data processors  308 . 
     While shown in  FIG. 3A  as distinct, these ingestion buffers  306  and  310  may be implemented as a common ingestion buffer. However, use of distinct ingestion buffers may be beneficial, for example, where a geographic region in which data is received differs from a region in which the data is desired. For example, use of distinct ingestion buffers may beneficially allow the intake ingestion buffer  306  to operate in a first geographic region associated with a first set of data privacy restrictions, while the output ingestion buffer  308  operates in a second geographic region associated with a second set of data privacy restrictions. In this manner, the intake system  210  can be configured to comply with all relevant data privacy restrictions, ensuring privacy of data processed at the data intake and query system  108 . 
     Moreover, either or both of the ingestion buffers  306  and  310  may be implemented across multiple distinct devices, as either a single or multiple ingestion buffers. Illustratively, as shown in  FIG. 3B , the intake system  210  may include both a primary intake ingestion buffer  306 A and a secondary intake ingestion buffer  306 B. The primary intake ingestion buffer  306 A is illustratively configured to obtain messages from the data retrieval subsystem  304  (e.g., implemented as a set of intake points  322  through  332 N). The secondary intake ingestion buffer  306 B is illustratively configured to provide an additional set of messages (e.g., from other data sources  202 ). In one embodiment, the primary intake ingestion buffer  306 A is provided by an administrator or developer of the data intake and query system  108 , while the secondary intake ingestion buffer  306 B is a user-supplied ingestion buffer (e.g., implemented externally to the data intake and query system  108 ). 
     As noted above, an intake ingestion buffer  306  may in some embodiments categorize messages according to source-oriented topics (e.g., denoting a data source  202  from which the message was obtained). In other embodiments, an intake ingestion buffer  306  may in some embodiments categorize messages according to intake-oriented topics (e.g., denoting the intake point from which the message was obtained). The number and variety of such topics may vary, and thus are not shown in  FIG. 3B . In one embodiment, the intake ingestion buffer  306  maintains only a single topic (e.g., all data to be ingested at the data intake and query system  108 ). 
     The output ingestion buffer  310  may in one embodiment categorize messages according to content-centric topics (e.g., determined based on the content of a message). Additionally or alternatively, the output ingestion buffer  310  may categorize messages according to consumer-centric topics (e.g., topics intended to store messages for consumption by a downstream device or system). An illustrative number of topics are shown in  FIG. 3B , as topics  342  through  352 N. Each topic may correspond to a queue of messages (e.g., in accordance with the pub-sub model) relevant to the corresponding topic. As described in more detail below, the streaming data processors  308  may be configured to process messages from the intake ingestion buffer  306  and determine which topics of the topics  342  through  352 N into which to place the messages. For example, the index topic  342  may be intended to store messages holding data that should be consumed and indexed by the indexing system  212 . The notable event topic  344  may be intended to store messages holding data that indicates a notable event at a data source  202  (e.g., the occurrence of an error or other notable event). The metrics topic  346  may be intended to store messages holding metrics data for data sources  202 . The search results topic  348  may be intended to store messages holding data responsive to a search query. The mobile alerts topic  350  may be intended to store messages holding data for which an end user has requested alerts on a mobile device. A variety of custom topics  352 A through  352 N may be intended to hold data relevant to end-user-created topics. 
     As will be described below, by application of message transformation rules at the streaming data processors  308 , the intake system  210  may divide and categorize messages from the intake ingestion buffer  306 , partitioning the message into output topics relevant to a specific downstream consumer. In this manner, specific portions of data input to the data intake and query system  108  may be “divided out” and handled separately, enabling different types of data to be handled differently, and potentially at different speeds. Illustratively, the index topic  342  may be configured to include all or substantially all data included in the intake ingestion buffer  306 . Given the volume of data, there may be a significant delay (e.g., minutes or hours) before a downstream consumer (e.g., the indexing system  212 ) processes a message in the index topic  342 . Thus, for example, searching data processed by the indexing system  212  may incur significant delay. 
     Conversely, the search results topic  348  may be configured to hold only messages corresponding to data relevant to a current query. Illustratively, on receiving a query from a client device  204 , the query system  214  may transmit to the intake system  210  a rule that detects, within messages from the intake ingestion buffer  306 A, data potentially relevant to the query. The streaming data processors  308  may republish these messages within the search results topic  348 , and the query system  214  may subscribe to the search results topic  348  in order to obtain the data within the messages. In this manner, the query system  214  can “bypass” the indexing system  212  and avoid delay that may be caused by that system, thus enabling faster (and potentially real time) display of search results. 
     While shown in  FIGS. 3A and 3B  as a single output ingestion buffer  310 , the intake system  210  may in some instances utilize multiple output ingestion buffers  310 . 
     3.2.4. Streaming Data Processors 
     As noted above, the streaming data processors  308  may apply one or more rules to process messages from the intake ingestion buffer  306 A into messages on the output ingestion buffer  310 . These rules may be specified, for example, by an end user of the data intake and query system  108  or may be automatically generated by the data intake and query system  108  (e.g., in response to a user query). 
     Illustratively, each rule may correspond to a set of selection criteria indicating messages to which the rule applies, as well as one or more processing sub-rules indicating an action to be taken by the streaming data processors  308  with respect to the message. The selection criteria may include any number or combination of criteria based on the data included within a message or metadata of the message (e.g., a topic to which the message is published). In one embodiment, the selection criteria are formatted in the same manner or similarly to extraction rules, discussed in more detail below. For example, selection criteria may include regular expressions that derive one or more values or a sub-portion of text from the portion of machine data in each message to produce a value for the field for that message. When a message is located within the intake ingestion buffer  308  that matches the selection criteria, the streaming data processors  308  may apply the processing rules to the message. Processing sub-rules may indicate, for example, a topic of the output ingestion buffer  310  into which the message should be placed. Processing sub-rules may further indicate transformations, such as field or unit normalization operations, to be performed on the message. Illustratively, a transformation may include modifying data within the message, such as altering a format in which the data is conveyed (e.g., converting millisecond timestamps values to microsecond timestamp values, converting imperial units to metric units, etc.), or supplementing the data with additional information (e.g., appending an error descriptor to an error code). In some instances, the streaming data processors  308  may be in communication with one or more external data stores (the locations of which may be specified within a rule) that provide information used to supplement or enrich messages processed at the streaming data processors  308 . For example, a specific rule may include selection criteria identifying an error code within a message of the primary ingestion buffer  306 A, and specifying that when the error code is detected within a message, that the streaming data processors  308  should conduct a lookup in an external data source (e.g., a database) to retrieve the human-readable descriptor for that error code, and inject the descriptor into the message. In this manner, rules may be used to process, transform, or enrich messages. 
     The streaming data processors  308  may include a set of computing devices configured to process messages from the intake ingestion buffer  306  at a speed commensurate with a rate at which messages are placed into the intake ingestion buffer  306 . In one embodiment, the number of streaming data processors  308  used to process messages may vary based on a number of messages on the intake ingestion buffer  306  awaiting processing. Thus, as additional messages are queued into the intake ingestion buffer  306 , the number of streaming data processors  308  may be increased to ensure that such messages are rapidly processed. In some instances, the streaming data processors  308  may be extensible on a per topic basis. Thus, individual devices implementing the streaming data processors  308  may subscribe to different topics on the intake ingestion buffer  306 , and the number of devices subscribed to an individual topic may vary according to a rate of publication of messages to that topic (e.g., as measured by a backlog of messages in the topic). In this way, the intake system  210  can support ingestion of massive amounts of data from numerous data sources  202 . 
     In some embodiments, an intake system may comprise a service accessible to client devices  102  and host devices  106  via a network  104 . For example, one type of forwarder 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 may, for example, comprise a computing device which implements multiple data pipelines or “queues” to handle forwarding of network data to indexers. A forwarder may also perform many of the functions that are performed by an indexer. For example, a forwarder may perform keyword extractions on raw data or parse raw data to create events. A forwarder may generate time stamps for events. Additionally or alternatively, a forwarder may perform routing of events to indexers. Data store  212  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. 
     3.2. Indexing System 
       FIG. 4  is a block diagram illustrating an embodiment of an indexing system  212  of the data intake and query system  108 . The indexing system  212  can receive, process, and store data from multiple data sources  202 , which may be associated with different tenants, users, etc. Using the received data, the indexing system can generate events that include a portion of machine data associated with a timestamp and store the events in buckets based on one or more of the timestamps, tenants, indexes, etc., associated with the data. Moreover, the indexing system  212  can include various components that enable it to provide a stateless indexing service, or indexing service that is able to rapidly recover without data loss if one or more components of the indexing system  212  become unresponsive or unavailable. 
     In the illustrated embodiment, the indexing system  212  includes an indexing system manager  402  and one or more indexing nodes  404 . However, it will be understood that the indexing system  212  can include fewer or more components. For example, in some embodiments, the common storage  216  or data store catalog  220  can form part of the indexing system  212 , etc. 
     As described herein, each of the components of the indexing system  212  can be implemented using one or more computing devices as distinct computing devices or as one or more container instances or virtual machines across one or more computing devices. For example, in some embodiments, the indexing system manager  402  and indexing nodes  404  can be implemented as distinct computing devices with separate hardware, memory, and processors. In certain embodiments, the indexing system manager  402  and indexing nodes  404  can be implemented on the same or across different computing devices as distinct container instances, with each container having access to a subset of the resources of a host computing device (e.g., a subset of the memory or processing time of the processors of the host computing device), but sharing a similar operating system. In some cases, the components can be implemented as distinct virtual machines across one or more computing devices, where each virtual machine can have its own unshared operating system but shares the underlying hardware with other virtual machines on the same host computing device. 
     3.3.1 Indexing System Manager 
     As mentioned, the indexing system manager  402  can monitor and manage the indexing nodes  404 , and can be implemented as a distinct computing device, virtual machine, container, container of a pod, or a process or thread associated with a container. In certain embodiments, the indexing system  212  can include one indexing system manager  402  to manage all indexing nodes  404  of the indexing system  212 . In some embodiments, the indexing system  212  can include multiple indexing system managers  402 . For example, an indexing system manager  402  can be instantiated for each computing device (or group of computing devices) configured as a host computing device for multiple indexing nodes  404 . 
     The indexing system manager  402  can handle resource management, creation/destruction of indexing nodes  404 , high availability, load balancing, application upgrades/rollbacks, logging and monitoring, storage, networking, service discovery, and performance and scalability, and otherwise handle containerization management of the containers of the indexing system  212 . In certain embodiments, the indexing system manager  402  can be implemented using Kubernetes or Swarm. 
     In some cases, the indexing system manager  402  can monitor the available resources of a host computing device and request additional resources in a shared resource environment, based on workload of the indexing nodes  404  or create, destroy, or reassign indexing nodes  404  based on workload. Further, the indexing system manager  402  system can assign indexing nodes  404  to handle data streams based on workload, system resources, etc. 
     3.3.2. Indexing Nodes 
     The indexing nodes  404  can include one or more components to implement various functions of the indexing system  212 . In the illustrated embodiment, the indexing node  404  includes an indexing node manager  406 , partition manager  408 , indexer  410 , data store  412 , and bucket manager  414 . As described herein, the indexing nodes  404  can be implemented on separate computing devices or as containers or virtual machines in a virtualization environment. 
     In some embodiments, an indexing node  404 , and can be implemented as a distinct computing device, virtual machine, container, container of a pod, or a process or thread associated with a container, or using multiple-related containers. In certain embodiments, such as in a Kubernetes deployment, each indexing node  404  can be implemented as a separate container or pod. For example, one or more of the components of the indexing node  404  can be implemented as different containers of a single pod, e.g., on a containerization platform, such as Docker, the one or more components of the indexing node can be implemented as different Docker containers managed by synchronization platforms such as Kubernetes or Swarm. Accordingly, reference to a containerized indexing node  404  can refer to the indexing node  404  as being a single container or as one or more components of the indexing node  404  being implemented as different, related containers or virtual machines. 
     3.3.2.1. Indexing Node Manager 
     The indexing node manager  406  can manage the processing of the various streams or partitions of data by the indexing node  404 , and can be implemented as a distinct computing device, virtual machine, container, container of a pod, or a process or thread associated with a container. For example, in certain embodiments, as partitions or data streams are assigned to the indexing node  404 , the indexing node manager  406  can generate one or more partition manager(s)  408  to manage each partition or data stream. In some cases, the indexing node manager  406  generates a separate partition manager  408  for each partition or shard that is processed by the indexing node  404 . In certain embodiments, the partition can correspond to a topic of a data stream of the ingestion buffer  310 . Each topic can be configured in a variety of ways. For example, in some embodiments, a topic may correspond to data from a particular data source  202 , tenant, index/partition, or sourcetype. In this way, in certain embodiments, the indexing system  212  can discriminate between data from different sources or associated with different tenants, or indexes/partitions. For example, the indexing system  212  can assign more indexing nodes  404  to process data from one topic (associated with one tenant) than another topic (associated with another tenant), or store the data from one topic more frequently to common storage  216  than the data from a different topic, etc. 
     In some embodiments, the indexing node manager  406  monitors the various shards of data being processed by the indexing node  404  and the read pointers or location markers for those shards. In some embodiments, the indexing node manager  406  stores the read pointers or location marker in one or more data stores, such as but not limited to, common storage  216 , DynamoDB, S3, or another type of storage system, shared storage system, or networked storage system, etc. As the indexing node  404  processes the data and the markers for the shards are updated by the intake system  210 , the indexing node manager  406  can be updated to reflect the changes to the read pointers or location markers. In this way, if a particular partition manager  408  becomes unresponsive or unavailable, the indexing node manager  406  can generate a new partition manager  408  to handle the data stream without losing context of what data is to be read from the intake system  210 . Accordingly, in some embodiments, by using the ingestion buffer  310  and tracking the location of the location markers in the shards of the ingestion buffer, the indexing system  212  can aid in providing a stateless indexing service. 
     In some embodiments, the indexing node manager  406  is implemented as a background process, or daemon, on the indexing node  404  and the partition manager(s)  408  are implemented as threads, copies, or forks of the background process. In some cases, an indexing node manager  406  can copy itself, or fork, to create a partition manager  408  or cause a template process to copy itself, or fork, to create each new partition manager  408 , etc. This may be done for multithreading efficiency or for other reasons related to containerization and efficiency of managing indexers  410 . In certain embodiments, the indexing node manager  406  generates a new process for each partition manager  408 . In some cases, by generating a new process for each partition manager  408 , the indexing node manager  408  can support multiple language implementations and be language agnostic. For example, the indexing node manager  408  can generate a process for a partition manager  408  in python and create a second process for a partition manager  408  in golang, etc. 
     3.3.2.2. Partition Manager 
     As mentioned, the partition manager(s)  408  can manage the processing of one or more of the partitions or shards of a data stream processed by an indexing node  404  or the indexer  410  of the indexing node  404 , and can be implemented as a distinct computing device, virtual machine, container, container of a pod, or a process or thread associated with a container. 
     In some cases, managing the processing of a partition or shard can include, but it not limited to, communicating data from a particular shard to the indexer  410  for processing, monitoring the indexer  410  and the size of the data being processed by the indexer  410 , instructing the indexer  410  to move the data to common storage  216 , and reporting the storage of the data to the intake system  210 . For a particular shard or partition of data from the intake system  210 , the indexing node manager  406  can assign a particular partition manager  408 . The partition manager  408  for that partition can receive the data from the intake system  210  and forward or communicate that data to the indexer  410  for processing. 
     In some embodiments, the partition manager  408  receives data from a pub-sub messaging system, such as the ingestion buffer  310 . As described herein, the ingestion buffer  310  can have one or more streams of data and one or more shards or partitions associated with each stream of data. Each stream of data can be separated into shards and/or other partitions or types of organization of data. In certain cases, each shard can include data from multiple tenants, indexes/partition, etc. In some cases, each shard can correspond to data associated with a particular tenant, index/partition, source, sourcetype, etc. Accordingly, the indexing system  212  can include a partition manager  408  for individual tenants, indexes/partitions, sources, sourcetypes, etc. In this way, the indexing system  212  can manage and process the data differently. For example, the indexing system  212  can assign more indexing nodes  404  to process data from one tenant than another tenant, or store buckets associated with one tenant or partition/index more frequently to common storage  216  than buckets associated with a different tenant or partition/index, etc. 
     Accordingly, in some embodiments, a partition manager  408  receives data from one or more of the shards or partitions of the ingestion buffer  310 . The partition manager  408  can forward the data from the shard to the indexer  410  for processing. In some cases, the amount of data coming into a shard may exceed the shard&#39;s throughput. For example, 4 MB/s of data may be sent to an ingestion buffer  310  for a particular shard, but the ingestion buffer  310  may be able to process only 2 MB/s of data per shard. Accordingly, in some embodiments, the data in the shard can include a reference to a location in storage where the indexing system  212  can retrieve the data. For example, a reference pointer to data can be placed in the ingestion buffer  310  rather than putting the data itself into the ingestion buffer. The reference pointer can reference a chunk of data that is larger than the throughput of the ingestion buffer  310  for that shard. In this way, the data intake and query system  108  can increase the throughput of individual shards of the ingestion buffer  310 . In such embodiments, the partition manager  408  can obtain the reference pointer from the ingestion buffer  310  and retrieve the data from the referenced storage for processing. In some cases, the referenced storage to which reference pointers in the ingestion buffer  310  may point can correspond to the common storage  216  or other cloud or local storage. In some implementations, the chunks of data to which the reference pointers refer may be directed to common storage  216  from intake system  210 , e.g., streaming data processor  308  or ingestion buffer  310 . 
     As the indexer  410  processes the data, stores the data in buckets, and generates indexes of the data, the partition manager  408  can monitor the indexer  410  and the size of the data on the indexer  410  (inclusive of the data store  412 ) associated with the partition. The size of the data on the indexer  410  can correspond to the data that is actually received from the particular partition of the intake system  210 , as well as data generated by the indexer  410  based on the received data (e.g., inverted indexes, summaries, etc.), and may correspond to one or more buckets. For instance, the indexer  410  may have generated one or more buckets for each tenant and/or partition associated with data being processed in the indexer  410 . 
     Based on a bucket roll-over policy, the partition manager  408  can instruct the indexer  410  to convert editable groups of data or buckets to non-editable groups or buckets and/or copy the data associated with the partition to common storage  216 . In some embodiments, the bucket roll-over policy can indicate that the data associated with the particular partition, which may have been indexed by the indexer  410  and stored in the data store  412  in various buckets, is to be copied to common storage  216  based on a determination that the size of the data associated with the particular partition satisfies a threshold size. In some cases, the bucket roll-over policy can include different threshold sizes for different partitions. In other implementations the bucket roll-over policy may be modified by other factors, such as an identity of a tenant associated with indexing node  404 , system resource usage, which could be based on the pod or other container that contains indexing node  404 , or one of the physical hardware layers with which the indexing node  404  is running, or any other appropriate factor for scaling and system performance of indexing nodes  404  or any other system component. 
     In certain embodiments, the bucket roll-over policy can indicate data is to be copied to common storage  216  based on a determination that the amount of data associated with all partitions (or a subset thereof) of the indexing node  404  satisfies a threshold amount. Further, the bucket roll-over policy can indicate that the one or more partition managers  408  of an indexing node  404  are to communicate with each other or with the indexing node manager  406  to monitor the amount of data on the indexer  410  associated with all of the partitions (or a subset thereof) assigned to the indexing node  404  and determine that the amount of data on the indexer  410  (or data store  412 ) associated with all the partitions (or a subset thereof) satisfies a threshold amount. Accordingly, based on the bucket roll-over policy, one or more of the partition managers  408  or the indexing node manager  406  can instruct the indexer  410  to convert editable buckets associated with the partitions (or subsets thereof) to non-editable buckets and/or store the data associated with the partitions (or subset thereof) in common storage  216 . 
     In certain embodiments, the bucket roll-over policy can indicate that buckets are to be converted to non-editable buckets and stored in common storage based on a collective size of buckets satisfying a threshold size. In some cases, the bucket roll-over policy can use different threshold sizes for conversion and storage. For example, the bucket roll-over policy can use a first threshold size to indicate when editable buckets are to be converted to non-editable buckets (e.g., stop writing to the buckets) and a second threshold size to indicate when the data (or buckets) are to be stored in common storage  216 . In certain cases, the bucket roll-over policy can indicate that the partition manager(s)  408  are to send a single command to the indexer  410  that causes the indexer  410  to convert editable buckets to non-editable buckets and store the buckets in common storage  216 . 
     Based on an acknowledgement that the data associated with a partition (or multiple partitions as the case may be) has been stored in common storage  216 , the partition manager  408  can communicate to the intake system  210 , either directly, or through the indexing node manager  406 , that the data has been stored and/or that the location marker or read pointer can be moved or updated. In some cases, the partition manager  408  receives the acknowledgement that the data has been stored from common storage  216  and/or from the indexer  410 . In certain embodiments, which will be described in more detail herein, the intake system  210  does not receive communication that the data stored in intake system  210  has been read and processed until after that data has been stored in common storage  216 . 
     The acknowledgement that the data has been stored in common storage  216  can also include location information about the data within the common storage  216 . For example, the acknowledgement can provide a link, map, or path to the copied data in the common storage  216 . Using the information about the data stored in common storage  216 , the partition manager  408  can update the data store catalog  220 . For example, the partition manager  408  can update the data store catalog  220  with an identifier of the data (e.g., bucket identifier, tenant identifier, partition identifier, etc.), the location of the data in common storage  216 , a time range associated with the data, etc. In this way, the data store catalog  220  can be kept up-to-date with the contents of the common storage  216 . 
     Moreover, as additional data is received from the intake system  210 , the partition manager  408  can continue to communicate the data to the indexer  410 , monitor the size or amount of data on the indexer  410 , instruct the indexer  410  to copy the data to common storage  216 , communicate the successful storage of the data to the intake system  210 , and update the data store catalog  220 . 
     As a non-limiting example, consider the scenario in which the intake system  210  communicates data from a particular shard or partition to the indexing system  212 . The intake system  210  can track which data it has sent and a location marker for the data in the intake system  210  (e.g., a marker that identifies data that has been sent to the indexing system  212  for processing). 
     As described herein, the intake system  210  can retain or persistently make available the sent data until the intake system  210  receives an acknowledgement from the indexing system  212  that the sent data has been processed, stored in persistent storage (e.g., common storage  216 ), or is safe to be deleted. In this way, if an indexing node  404  assigned to process the sent data becomes unresponsive or is lost, e.g., due to a hardware failure or a crash of the indexing node manager  406  or other component, process, or daemon, the data that was sent to the unresponsive indexing node  404  will not be lost. Rather, a different indexing node  404  can obtain and process the data from the intake system  210 . 
     As the indexing system  212  stores the data in common storage  216 , it can report the storage to the intake system  210 . In response, the intake system  210  can update its marker to identify different data that has been sent to the indexing system  212  for processing, but has not yet been stored. By moving the marker, the intake system  210  can indicate that the previously-identified data has been stored in common storage  216 , can be deleted from the intake system  210  or, otherwise, can be allowed to be overwritten, lost, etc. 
     With reference to the example above, in some embodiments, the indexing node manager  406  can track the marker used by the ingestion buffer  310 , and the partition manager  408  can receive the data from the ingestion buffer  310  and forward it to an indexer  410  for processing (or use the data in the ingestion buffer to obtain data from a referenced storage location and forward the obtained data to the indexer). The partition manager  408  can monitor the amount of data being processed and instruct the indexer  410  to copy the data to common storage  216 . Once the data is stored in common storage  216 , the partition manager  408  can report the storage to the ingestion buffer  310 , so that the ingestion buffer  310  can update its marker. In addition, the indexing node manager  406  can update its records with the location of the updated marker. In this way, if partition manager  408  become unresponsive or fails, the indexing node manager  406  can assign a different partition manager  408  to obtain the data from the data stream without losing the location information, or if the indexer  410  becomes unavailable or fails, the indexing node manager  406  can assign a different indexer  410  to process and store the data. 
     3.3.2.3. Indexer and Data Store 
     As described herein, the indexer  410  can be the primary indexing execution engine, and can be implemented as a distinct computing device, container, container within a pod, etc. For example, the indexer  410  can tasked with parsing, processing, indexing, and storing the data received from the intake system  210  via the partition manager(s)  408 . Specifically, in some embodiments, the indexer  410  can parse the incoming data to identify timestamps, generate events from the incoming data, group and save events into buckets, generate summaries or indexes (e.g., time series index, inverted index, keyword index, etc.) of the events in the buckets, and store the buckets in common storage  216 . 
     In some cases, one indexer  410  can be assigned to each partition manager  408 , and in certain embodiments, one indexer  410  can receive and process the data from multiple (or all) partition mangers  408  on the same indexing node  404  or from multiple indexing nodes  404 . 
     In some embodiments, the indexer  410  can store the events and buckets in the data store  412  according to a bucket creation policy. The bucket creation policy can indicate how many buckets the indexer  410  is to generate for the data that it processes. In some cases, based on the bucket creation policy, the indexer  410  generates at least one bucket for each tenant and index (also referred to as a partition) associated with the data that it processes. For example, if the indexer  410  receives data associated with three tenants A, B, C, each with two indexes X, Y, then the indexer  410  can generate at least six buckets: at least one bucket for each of Tenant A::Index X, Tenant A::Index Y, Tenant B::Index X, Tenant B::Index Y, Tenant C::Index X, and Tenant C::Index Y. Additional buckets may be generated for a tenant/partition pair based on the amount of data received that is associated with the tenant/partition pair. However, it will be understood that the indexer  410  can generate buckets using a variety of policies. For example, the indexer  410  can generate one or more buckets for each tenant, partition, source, sourcetype, etc. 
     In some cases, if the indexer  410  receives data that it determines to be “old,” e.g., based on a timestamp of the data or other temporal determination regarding the data, then it can generate a bucket for the “old” data. In some embodiments, the indexer  410  can determine that data is “old,” if the data is associated with a timestamp that is earlier in time by a threshold amount than timestamps of other data in the corresponding bucket (e.g., depending on the bucket creation policy, data from the same partition and/or tenant) being processed by the indexer  410 . For example, if the indexer  410  is processing data for the bucket for Tenant A::Index X having timestamps on 4/23 between 16:23:56 and 16:46:32 and receives data for the Tenant A::Index X bucket having a timestamp on 4/22 or on 4/23 at 08:05:32, then it can determine that the data with the earlier timestamps is “old” data and generate a new bucket for that data. In this way, the indexer  410  can avoid placing data in the same bucket that creates a time range that is significantly larger than the time range of other buckets, which can decrease the performance of the system as the bucket could be identified as relevant for a search more often than it otherwise would. 
     The threshold amount of time used to determine if received data is “old,” can be predetermined or dynamically determined based on a number of factors, such as, but not limited to, time ranges of other buckets, amount of data being processed, timestamps of the data being processed, etc. For example, the indexer  410  can determine an average time range of buckets that it processes for different tenants and indexes. If incoming data would cause the time range of a bucket to be significantly larger (e.g., 25%, 50%, 75%, double, or other amount) than the average time range, then the indexer  410  can determine that the data is “old” data, and generate a separate bucket for it. By placing the “old” bucket in a separate bucket, the indexer  410  can reduce the instances in which the bucket is identified as storing data that may be relevant to a query. For example, by having a smaller time range, the query system  214  may identify the bucket less frequently as a relevant bucket then if the bucket had the large time range due to the “old” data. Additionally, in a process that will be described in more detail herein, time-restricted searches and search queries may be executed more quickly because there may be fewer buckets to search for a particular time range. In this manner, computational efficiency of searching large amounts of data can be improved. Although described with respect detecting “old” data, the indexer  410  can use similar techniques to determine that “new” data should be placed in a new bucket or that a time gap between data in a bucket and “new” data is larger than a threshold amount such that the “new” data should be stored in a separate bucket. 
     Once a particular bucket satisfies a size threshold, the indexer  410  can store the bucket in or copy the bucket to common storage  216 . In certain embodiments, the partition manager  408  can monitor the size of the buckets and instruct the indexer  410  to copy the bucket to common storage  216 . The threshold size can be predetermined or dynamically determined. 
     In certain embodiments, the partition manager  408  can monitor the size of multiple, or all, buckets associated with the partition being managed by the partition manager  408 , and based on the collective size of the buckets satisfying a threshold size, instruct the indexer  410  to copy the buckets associated with the partition to common storage  216 . In certain cases, one or more partition managers  408  or the indexing node manager  406  can monitor the size of buckets across multiple, or all partitions, associated with the indexing node  404 , and instruct the indexer to copy the buckets to common storage  216  based on the size of the buckets satisfying a threshold size. 
     As described herein, buckets in the data store  412  that are being edited by the indexer  410  can be referred to as hot buckets or editable buckets. For example, the indexer  410  can add data, events, and indexes to editable buckets in the data store  412 , etc. Buckets in the data store  412  that are no longer edited by the indexer  410  can be referred to as warm buckets or non-editable buckets. In some embodiments, once the indexer  410  determines that a hot bucket is to be copied to common storage  216 , it can convert the hot (editable) bucket to a warm (non-editable) bucket, and then move or copy the warm bucket to the common storage  216 . Once the warm bucket is moved or copied to common storage  216 , the indexer  410  can notify the partition manager  408  that the data associated with the warm bucket has been processed and stored. As mentioned, the partition manager  408  can relay the information to the intake system  210 . In addition, the indexer  410  can provide the partition manager  408  with information about the buckets stored in common storage  216 , such as, but not limited to, location information, tenant identifier, index identifier, time range, etc. As described herein, the partition manager  408  can use this information to update the data store catalog  220 . 
     3.3.3. Bucket Manager 
     The bucket manager  414  can manage the buckets stored in the data store  412 , and can be implemented as a distinct computing device, virtual machine, container, container of a pod, or a process or thread associated with a container. In some cases, the bucket manager  414  can be implemented as part of the indexer  410 , indexing node  404 , or as a separate component of the indexing system  212 . 
     As described herein, the indexer  410  stores data in the data store  412  as one or more buckets associated with different tenants, indexes, etc. In some cases, the contents of the buckets are not searchable by the query system  214  until they are stored in common storage  216 . For example, the query system  214  may be unable to identify data responsive to a query that is located in hot (editable) buckets in the data store  412  and/or the warm (non-editable) buckets in the data store  412  that have not been copied to common storage  216 . Thus, query results may be incomplete or inaccurate, or slowed as the data in the buckets of the data store  412  are copied to common storage  216 . 
     To decrease the delay between processing and/or indexing the data and making that data searchable, the indexing system  212  can use a bucket roll-over policy that instructs the indexer  410  to convert hot buckets to warm buckets more frequently (or convert based on a smaller threshold size) and/or copy the warm buckets to common storage  216 . While converting hot buckets to warm buckets more frequently or based on a smaller storage size can decrease the lag between processing the data and making it searchable, it can increase the storage size and overhead of buckets in common storage  216 . For example, each bucket may have overhead associated with it, in terms of storage space required, processor power required, or other resource requirement. Thus, more buckets in common storage  216  can result in more storage used for overhead than for storing data, which can lead to increased storage size and costs. In addition, a larger number of buckets in common storage  216  can increase query times, as the opening of each bucket as part of a query can have certain processing overhead or time delay associated with it. 
     To decrease search times and reduce overhead and storage associated with the buckets (while maintaining a reduced delay between processing the data and making it searchable), the bucket manager  414  can monitor the buckets stored in the data store  412  and/or common storage  216  and merge buckets according to a bucket merge policy. For example, the bucket manager  414  can monitor and merge warm buckets stored in the data store  412  before, after, or concurrently with the indexer copying warm buckets to common storage  216 . 
     The bucket merge policy can indicate which buckets are candidates for a merge or which bucket to merge (e.g., based on time ranges, size, tenant/partition or other identifiers), the number of buckets to merge, size or time range parameters for the merged buckets, and/or a frequency for creating the merged buckets. For example, the bucket merge policy can indicate that a certain number of buckets are to be merged, regardless of size of the buckets. As another non-limiting example, the bucket merge policy can indicate that multiple buckets are to be merged until a threshold bucket size is reached (e.g., 750 MB, or 1 GB, or more). As yet another non-limiting example, the bucket merge policy can indicate that buckets having a time range within a set period of time (e.g., 30 sec, 1 min., etc.) are to be merged, regardless of the number or size of the buckets being merged. 
     In addition, the bucket merge policy can indicate which buckets are to be merged or include additional criteria for merging buckets. For example, the bucket merge policy can indicate that only buckets having the same tenant identifier and/or partition are to be merged, or set constraints on the size of the time range for a merged bucket (e.g., the time range of the merged bucket is not to exceed an average time range of buckets associated with the same source, tenant, partition, etc.). In certain embodiments, the bucket merge policy can indicate that buckets that are older than a threshold amount (e.g., one hour, one day, etc.) are candidates for a merge or that a bucket merge is to take place once an hour, once a day, etc. In certain embodiments, the bucket merge policy can indicate that buckets are to be merged based on a determination that the number or size of warm buckets in the data store  412  of the indexing node  404  satisfies a threshold number or size, or the number or size of warm buckets associated with the same tenant identifier and/or partition satisfies the threshold number or size. It will be understood, that the bucket manager  414  can use any one or any combination of the aforementioned or other criteria for the bucket merge policy to determine when, how, and which buckets to merge. 
     Once a group of buckets is merged into one or more merged buckets, the bucket manager  414  can copy or instruct the indexer  406  to copy the merged buckets to common storage  216 . Based on a determination that the merged buckets are successfully copied to the common storage  216 , the bucket manager  414  can delete the merged buckets and the buckets used to generate the merged buckets (also referred to herein as unmerged buckets or pre-merged buckets) from the data store  412 . 
     In some cases, the bucket manager  414  can also remove or instruct the common storage  216  to remove corresponding pre-merged buckets from the common storage  216  according to a bucket management policy. The bucket management policy can indicate when the pre-merged buckets are to be deleted or designated as able to be overwritten from common storage  216 . 
     In some cases, the bucket management policy can indicate that the pre-merged buckets are to be deleted immediately, once any queries relying on the pre-merged buckets are completed, after a predetermined amount of time, etc. In some cases, the pre-merged buckets may be in use or identified for use by one or more queries. Removing the pre-merged buckets from common storage  216  in the middle of a query may cause one or more failures in the query system  214  or result in query responses that are incomplete or erroneous. Accordingly, the bucket management policy, in some cases, can indicate to the common storage  216  that queries that arrive before a merged bucket is stored in common storage  216  are to use the corresponding pre-merged buckets and queries that arrive after the merged bucket is stored in common storage  216  are to use the merged bucket. 
     Further, the bucket management policy can indicate that once queries using the pre-merged buckets are completed, the buckets are to be removed from common storage  216 . However, it will be understood that the bucket management policy can indicate removal of the buckets in a variety of ways. For example, per the bucket management policy, the common storage  216  can remove the buckets after on one or more hours, one day, one week, etc., with or without regard to queries that may be relying on the pre-merged buckets. In some embodiments, the bucket management policy can indicate that the pre-merged buckets are to be removed without regard to queries relying on the pre-merged buckets and that any queries relying on the pre-merged buckets are to be redirected to the merged bucket. 
     In addition to removing the pre-merged buckets and merged bucket from the data store  412  and removing or instructing common storage  216  to remove the pre-merged buckets from the data store(s)  218 , the bucket manger  414  can update the data store catalog  220  or cause the indexer  410  or partition manager  408  to update the data store catalog  220  with the relevant changes. These changes can include removing reference to the pre-merged buckets in the data store catalog  220  and/or adding information about the merged bucket, including, but not limited to, a bucket, tenant, and/or partition identifier associated with the merged bucket, a time range of the merged bucket, location information of the merged bucket in common storage  216 , etc. In this way, the data store catalog  220  can be kept up-to-date with the contents of the common storage  216 . 
     3.4. Query System 
       FIG. 5  is a block diagram illustrating an embodiment of a query system  214  of the data intake and query system  108 . The query system  214  can receive, process, and execute queries from multiple client devices  204 , which may be associated with different tenants, users, etc. Similarly, the query system  214  can execute the queries on data from the intake system  210 , indexing system  212 , common storage  216 , acceleration data store  222 , or other system. Moreover, the query system  214  can include various components that enable it to provide a stateless or state-free search service, or search service that is able to rapidly recover without data loss if one or more components of the query system  214  become unresponsive or unavailable. 
     In the illustrated embodiment, the query system  214  includes one or more query system managers  502  (collectively or individually referred to as query system manager  502 ), one or more search heads  504  (collectively or individually referred to as search head  504  or search heads  504 ), one or more search nodes  506  (collectively or individually referred to as search node  506  or search nodes  506 ), a search node monitor  508 , and a search node catalog  510 . However, it will be understood that the query system  214  can include fewer or more components as desired. For example, in some embodiments, the common storage  216 , data store catalog  220 , or query acceleration data store  222  can form part of the query system  214 , etc. 
     As described herein, each of the components of the query system  214  can be implemented using one or more computing devices as distinct computing devices or as one or more container instances or virtual machines across one or more computing devices. For example, in some embodiments, the query system manager  502 , search heads  504 , and search nodes  506  can be implemented as distinct computing devices with separate hardware, memory, and processors. In certain embodiments, the query system manager  502 , search heads  504 , and search nodes  506  can be implemented on the same or across different computing devices as distinct container instances, with each container having access to a subset of the resources of a host computing device (e.g., a subset of the memory or processing time of the processors of the host computing device), but sharing a similar operating system. In some cases, the components can be implemented as distinct virtual machines across one or more computing devices, where each virtual machine can have its own unshared operating system but shares the underlying hardware with other virtual machines on the same host computing device. 
     3.4.1. Query System Manager 
     As mentioned, the query system manager  502  can monitor and manage the search heads  504  and search nodes  506 , and can be implemented as a distinct computing device, virtual machine, container, container of a pod, or a process or thread associated with a container. For example, the query system manager  502  can determine which search head  504  is to handle an incoming query or determine whether to generate an additional search node  506  based on the number of queries received by the query system  214  or based on another search node  506  becoming unavailable or unresponsive. Similarly, the query system manager  502  can determine that additional search heads  504  should be generated to handle an influx of queries or that some search heads  504  can be de-allocated or terminated based on a reduction in the number of queries received. 
     In certain embodiments, the query system  214  can include one query system manager  502  to manage all search heads  504  and search nodes  506  of the query system  214 . In some embodiments, the query system  214  can include multiple query system managers  502 . For example, a query system manager  502  can be instantiated for each computing device (or group of computing devices) configured as a host computing device for multiple search heads  504  and/or search nodes  506 . 
     Moreover, the query system manager  502  can handle resource management, creation, assignment, or destruction of search heads  504  and/or search nodes  506 , high availability, load balancing, application upgrades/rollbacks, logging and monitoring, storage, networking, service discovery, and performance and scalability, and otherwise handle containerization management of the containers of the query system  214 . In certain embodiments, the query system manager  502  can be implemented using Kubernetes or Swarm. For example, in certain embodiments, the query system manager  502  may be part of a sidecar or sidecar container that allows communication between various search nodes  506 , various search heads  504 , and/or combinations thereof. 
     In some cases, the query system manager  502  can monitor the available resources of a host computing device and/or request additional resources in a shared resource environment, based on workload of the search heads  504  and/or search nodes  506  or create, destroy, or reassign search heads  504  and/or search nodes  506  based on workload. Further, the query system manager  502  system can assign search heads  504  to handle incoming queries and/or assign search nodes  506  to handle query processing based on workload, system resources, etc. 
     3.4.2. Search Head 
     As described herein, the search heads  504  can manage the execution of queries received by the query system  214 . For example, the search heads  504  can parse the queries to identify the set of data to be processed and the manner of processing the set of data, identify the location of the data (non-limiting examples: intake system  210 , common storage  216 , acceleration data store  222 , etc.), identify tasks to be performed by the search head and tasks to be performed by the search nodes  506 , distribute the query (or sub-queries corresponding to the query) to the search nodes  506 , apply extraction rules to the set of data to be processed, aggregate search results from the search nodes  506 , store the search results in the query acceleration data store  222 , return search results to the client device  204 , etc. 
     As described herein, the search heads  504  can be implemented on separate computing devices or as containers or virtual machines in a virtualization environment. In some embodiments, the search heads  504  may be implemented using multiple-related containers. In certain embodiments, such as in a Kubernetes deployment, each search head  504  can be implemented as a separate container or pod. For example, one or more of the components of the search head  504  can be implemented as different containers of a single pod, e.g., on a containerization platform, such as Docker, the one or more components of the indexing node can be implemented as different Docker containers managed by synchronization platforms such as Kubernetes or Swarm. Accordingly, reference to a containerized search head  504  can refer to the search head  504  as being a single container or as one or more components of the search head  504  being implemented as different, related containers. 
     In the illustrated embodiment, the search head  504  includes a search master  512  and one or more search managers  514  to carry out its various functions. However, it will be understood that the search head  504  can include fewer or more components as desired. For example, the search head  504  can include multiple search masters  512 . 
     3.4.2.1. Search Master 
     The search master  512  can manage the execution of the various queries assigned to the search head  504 , and can be implemented as a distinct computing device, virtual machine, container, container of a pod, or a process or thread associated with a container. For example, in certain embodiments, as the search head  504  is assigned a query, the search master  512  can generate one or more search manager(s)  514  to manage the query. In some cases, the search master  512  generates a separate search manager  514  for each query that is received by the search head  504 . In addition, once a query is completed, the search master  512  can handle the termination of the corresponding search manager  514 . 
     In certain embodiments, the search master  512  can track and store the queries assigned to the different search managers  514 . Accordingly, if a search manager  514  becomes unavailable or unresponsive, the search master  512  can generate a new search manager  514  and assign the query to the new search manager  514 . In this way, the search head  504  can increase the resiliency of the query system  214 , reduce delay caused by an unresponsive component, and can aid in providing a stateless searching service. 
     In some embodiments, the search master  512  is implemented as a background process, or daemon, on the search head  504  and the search manager(s)  514  are implemented as threads, copies, or forks of the background process. In some cases, a search master  512  can copy itself, or fork, to create a search manager  514  or cause a template process to copy itself, or fork, to create each new search manager  514 , etc., in order to support efficient multithreaded implementations 
     3.4.2.2. Search Manager 
     As mentioned, the search managers  514  can manage the processing and execution of the queries assigned to the search head  504 , and can be implemented as a distinct computing device, virtual machine, container, container of a pod, or a process or thread associated with a container. In some embodiments, one search manager  514  manages the processing and execution of one query at a time. In such embodiments, if the search head  504  is processing one hundred queries, the search master  512  can generate one hundred search managers  514  to manage the one hundred queries. Upon completing an assigned query, the search manager  514  can await assignment to a new query or be terminated. 
     As part of managing the processing and execution of a query, and as described herein, a search manager  514  can parse the query to identify the set of data and the manner in which the set of data is to be processed (e.g., the transformations that are to be applied to the set of data), determine tasks to be performed by the search manager  514  and tasks to be performed by the search nodes  506 , identify search nodes  506  that are available to execute the query, map search nodes  506  to the set of data that is to be processed, instruct the search nodes  506  to execute the query and return results, aggregate and/or transform the search results from the various search nodes  506 , and provide the search results to a user and/or to the query acceleration data store  222 . 
     In some cases, to aid in identifying the set of data to be processed, the search manager  514  can consult the data store catalog  220  (depicted in  FIG. 2 ). As described herein, the data store catalog  220  can include information regarding the data stored in common storage  216 . In some cases, the data store catalog  220  can include bucket identifiers, a time range, and a location of the buckets in common storage  216 . In addition, the data store catalog  220  can include a tenant identifier and partition identifier for the buckets. This information can be used to identify buckets that include data that satisfies at least a portion of the query. 
     As a non-limiting example, consider a search manager  514  that has parsed a query to identify the following filter criteria that is used to identify the data to be processed: time range: past hour, partition: _sales, tenant: ABC, Inc., keyword: Error. Using the received filter criteria, the search manager  514  can consult the data store catalog  220 . Specifically, the search manager  514  can use the data store catalog  220  to identify buckets associated with the _sales partition and the tenant ABC, Inc. and that include data from the past hour. In some cases, the search manager  514  can obtain bucket identifiers and location information from the data store catalog  220  for the buckets storing data that satisfies at least the aforementioned filter criteria. In certain embodiments, if the data store catalog  220  includes keyword pairs, it can use the keyword: Error to identify buckets that have at least one event that include the keyword Error. 
     Using the bucket identifiers and/or the location information, the search manager  514  can assign one or more search nodes  506  to search the corresponding buckets. Accordingly, the data store catalog  220  can be used to identify relevant buckets and reduce the number of buckets that are to be searched by the search nodes  506 . In this way, the data store catalog  220  can decrease the query response time of the data intake and query system  108 . 
     In some embodiments, the use of the data store catalog  220  to identify buckets for searching can contribute to the statelessness of the query system  214  and search head  504 . For example, if a search head  504  or search manager  514  becomes unresponsive or unavailable, the query system manager  502  or search master  512 , as the case may be, can spin up or assign an additional resource (new search head  504  or new search manager  514 ) to execute the query. As the bucket information is persistently stored in the data store catalog  220 , data lost due to the unavailability or unresponsiveness of a component of the query system  214  can be recovered by using the bucket information in the data store catalog  220 . 
     In certain embodiments, to identify search nodes  506  that are available to execute the query, the search manager  514  can consult the search node catalog  510 . As described herein, the search node catalog  510  can include information regarding the search nodes  506 . In some cases, the search node catalog  510  can include an identifier for each search node  506 , as well as utilization and availability information. For example, the search node catalog  510  can identify search nodes  506  that are instantiated but are unavailable or unresponsive. In addition, the search node catalog  510  can identify the utilization rate of the search nodes  506 . For example, the search node catalog  510  can identify search nodes  506  that are working at maximum capacity or at a utilization rate that satisfies utilization threshold, such that the search node  506  should not be used to execute additional queries for a time. 
     In addition, the search node catalog  510  can include architectural information about the search nodes  506 . For example, the search node catalog  510  can identify search nodes  506  that share a data store and/or are located on the same computing device, or on computing devices that are co-located. 
     Accordingly, in some embodiments, based on the receipt of a query, a search manager  514  can consult the search node catalog  510  for search nodes  506  that are available to execute the received query. Based on the consultation of the search node catalog  510 , the search manager  514  can determine which search nodes  506  to assign to execute the query. 
     The search manager  514  can map the search nodes  506  to the data that is to be processed according to a search node mapping policy. The search node mapping policy can indicate how search nodes  506  are to be assigned to data (e.g., buckets) and when search nodes  506  are to be assigned to (and instructed to search) the data or buckets. 
     In some cases, the search manager  514  can map the search nodes  506  to buckets that include data that satisfies at least a portion of the query. For example, in some cases, the search manager  514  can consult the data store catalog  220  to obtain bucket identifiers of buckets that include data that satisfies at least a portion of the query, e.g., as a non-limiting example, to obtain bucket identifiers of buckets that include data associated with a particular time range. Based on the identified buckets and search nodes  506 , the search manager  514  can dynamically assign (or map) search nodes  506  to individual buckets according to a search node mapping policy. 
     In some embodiments, the search node mapping policy can indicate that the search manager  514  is to assign all buckets to search nodes  506  as a single operation. For example, where ten buckets are to be searched by five search nodes  506 , the search manager  514  can assign two buckets to a first search node  506 , two buckets to a second search node  506 , etc. In another embodiment, the search node mapping policy can indicate that the search manager  514  is to assign buckets iteratively. For example, where ten buckets are to be searched by five search nodes  506 , the search manager  514  can initially assign five buckets (e.g., one buckets to each search node  506 ), and assign additional buckets to each search node  506  as the respective search nodes  506  complete the execution on the assigned buckets. 
     Retrieving buckets from common storage  216  to be searched by the search nodes  506  can cause delay or may use a relatively high amount of network bandwidth or disk read/write bandwidth. In some cases, a local or shared data store associated with the search nodes  506  may include a copy of a bucket that was previously retrieved from common storage  216 . Accordingly, to reduce delay caused by retrieving buckets from common storage  216 , the search node mapping policy can indicate that the search manager  514  is to assign, preferably assign, or attempt to assign the same search node  506  to search the same bucket over time. In this way, the assigned search node  506  can keep a local copy of the bucket on its data store (or a data store shared between multiple search nodes  506 ) and avoid the processing delays associated with obtaining the bucket from the common storage  216 . 
     In certain embodiments, the search node mapping policy can indicate that the search manager  514  is to use a consistent hash function or other function to consistently map a bucket to a particular search node  506 . The search manager  514  can perform the hash using the bucket identifier obtained from the data store catalog  220 , and the output of the hash can be used to identify the search node  506  assigned to the bucket. In some cases, the consistent hash function can be configured such that even with a different number of search nodes  506  being assigned to execute the query, the output will consistently identify the same search node  506 , or have an increased probability of identifying the same search node  506 . 
     In some embodiments, the query system  214  can store a mapping of search nodes  506  to bucket identifiers. The search node mapping policy can indicate that the search manager  514  is to use the mapping to determine whether a particular bucket has been assigned to a search node  506 . If the bucket has been assigned to a particular search node  506  and that search node  506  is available, then the search manager  514  can assign the bucket to the search node  506 . If the bucket has not been assigned to a particular search node  506 , the search manager  514  can use a hash function to identify a search node  506  for assignment. Once assigned, the search manager  514  can store the mapping for future use. 
     In certain cases, the search node mapping policy can indicate that the search manager  514  is to use architectural information about the search nodes  506  to assign buckets. For example, if the identified search node  506  is unavailable or its utilization rate satisfies a threshold utilization rate, the search manager  514  can determine whether an available search node  506  shares a data store with the unavailable search node  506 . If it does, the search manager  514  can assign the bucket to the available search node  506  that shares the data store with the unavailable search node  506 . In this way, the search manager  514  can reduce the likelihood that the bucket will be obtained from common storage  216 , which can introduce additional delay to the query while the bucket is retrieved from common storage  216  to the data store shared by the available search node  506 . 
     In some instances, the search node mapping policy can indicate that the search manager  514  is to assign buckets to search nodes  506  randomly, or in a simple sequence (e.g., a first search nodes  506  is assigned a first bucket, a second search node  506  is assigned a second bucket, etc.). In other instances, as discussed, the search node mapping policy can indicate that the search manager  514  is to assign buckets to search nodes  506  based on buckets previously assigned to a search nodes  506 , in a prior or current search. As mentioned above, in some embodiments each search node  506  may be associated with a local data store or cache of information (e.g., in memory of the search nodes  506 , such as random access memory [“RAM” ], disk-based cache, a data store, or other form of storage). Each search node  506  can store copies of one or more buckets from the common storage  216  within the local cache, such that the buckets may be more rapidly searched by search nodes  506 . The search manager  514  (or cache manager  516 ) can maintain or retrieve from search nodes  506  information identifying, for each relevant search node  506 , what buckets are copied within local cache of the respective search nodes  506 . In the event that the search manager  514  determines that a search node  506  assigned to execute a search has within its data store or local cache a copy of an identified bucket, the search manager  514  can preferentially assign the search node  506  to search that locally-cached bucket. 
     In still more embodiments, according to the search node mapping policy, search nodes  506  may be assigned based on overlaps of computing resources of the search nodes  506 . For example, where a containerized search node  506  is to retrieve a bucket from common storage  216  (e.g., where a local cached copy of the bucket does not exist on the search node  506 ), such retrieval may use a relatively high amount of network bandwidth or disk read/write bandwidth. Thus, assigning a second containerized search node  506  instantiated on the same host computing device might be expected to strain or exceed the network or disk read/write bandwidth of the host computing device. For this reason, in some embodiments, according to the search node mapping policy, the search manager  514  can assign buckets to search nodes  506  such that two containerized search nodes  506  on a common host computing device do not both retrieve buckets from common storage  216  at the same time. 
     Further, in certain embodiments, where a data store that is shared between multiple search nodes  506  includes two buckets identified for the search, the search manager  514  can, according to the search node mapping policy, assign both such buckets to the same search node  506  or to two different search nodes  506  that share the data store, such that both buckets can be searched in parallel by the respective search nodes  506 . 
     The search node mapping policy can indicate that the search manager  514  is to use any one or any combination of the above-described mechanisms to assign buckets to search nodes  506 . Furthermore, the search node mapping policy can indicate that the search manager  514  is to prioritize assigning search nodes  506  to buckets based on any one or any combination of: assigning search nodes  506  to process buckets that are in a local or shared data store of the search nodes  506 , maximizing parallelization (e.g., assigning as many different search nodes  506  to execute the query as are available), assigning search nodes  506  to process buckets with overlapping timestamps, maximizing individual search node  506  utilization (e.g., ensuring that each search node  506  is searching at least one bucket at any given time, etc.), or assigning search nodes  506  to process buckets associated with a particular tenant, user, or other known feature of data stored within the bucket (e.g., buckets holding data known to be used in time-sensitive searches may be prioritized). Thus, according to the search node mapping policy, the search manager  514  can dynamically alter the assignment of buckets to search nodes  506  to increase the parallelization of a search, and to increase the speed and efficiency with which the search is executed. 
     It will be understood that the search manager  514  can assign any search node  506  to search any bucket. This flexibility can decrease query response time as the search manager can dynamically determine which search nodes  506  are best suited or available to execute the query on different buckets. Further, if one bucket is being used by multiple queries, the search manager  515  can assign multiple search nodes  506  to search the bucket. In addition, in the event a search node  506  becomes unavailable or unresponsive, the search manager  514  can assign a different search node  506  to search the buckets assigned to the unavailable search node  506 . 
     As part of the query execution, the search manager  514  can instruct the search nodes  506  to execute the query (or sub-query) on the assigned buckets. As described herein, the search manager  514  can generate specific queries or sub-queries for the individual search nodes  506 . The search nodes  506  can use the queries to execute the query on the buckets assigned thereto. 
     In some embodiments, the search manager  514  stores the sub-queries and bucket assignments for the different search nodes  506 . Storing the sub-queries and bucket assignments can contribute to the statelessness of the query system  214 . For example, in the event an assigned search node  506  becomes unresponsive or unavailable during the query execution, the search manager  514  can re-assign the sub-query and bucket assignments of the unavailable search node  506  to one or more available search nodes  506  or identify a different available search node  506  from the search node catalog  510  to execute the sub-query. In certain embodiments, the query system manager  502  can generate an additional search node  506  to execute the sub-query of the unavailable search node  506 . Accordingly, the query system  214  can quickly recover from an unavailable or unresponsive component without data loss and while reducing or minimizing delay. 
     During the query execution, the search manager  514  can monitor the status of the assigned search nodes  506 . In some cases, the search manager  514  can ping or set up a communication link between it and the search nodes  506  assigned to execute the query. As mentioned, the search manager  514  can store the mapping of the buckets to the search nodes  506 . Accordingly, in the event a particular search node  506  becomes unavailable for his unresponsive, the search manager  514  can assign a different search node  506  to complete the execution of the query for the buckets assigned to the unresponsive search node  506 . 
     In some cases, as part of the status updates to the search manager  514 , the search nodes  506  can provide the search manager with partial results and information regarding the buckets that have been searched. In response, the search manager  514  can store the partial results and bucket information in persistent storage. Accordingly, if a search node  506  partially executes the query and becomes unresponsive or unavailable, the search manager  514  can assign a different search node  506  to complete the execution, as described above. For example, the search manager  514  can assign a search node  506  to execute the query on the buckets that were not searched by the unavailable search node  506 . In this way, the search manager  514  can more quickly recover from an unavailable or unresponsive search node  506  without data loss and while reducing or minimizing delay. 
     As the search manager  514  receives query results from the different search nodes  506 , it can process the data. In some cases, the search manager  514  processes the partial results as it receives them. For example, if the query includes a count, the search manager  514  can increment the count as it receives the results from the different search nodes  506 . In certain cases, the search manager  514  waits for the complete results from the search nodes before processing them. For example, if the query includes a command that operates on a result set, or a partial result set, e.g., a stats command (e.g., a command that calculates one or more aggregate statistics over the results set, e.g., average, count, or standard deviation, as examples), the search manager  514  can wait for the results from all the search nodes  506  before executing the stats command. 
     As the search manager  514  processes the results or completes processing the results, it can store the results in the query acceleration data store  222  or communicate the results to a client device  204 . As described herein, results stored in the query acceleration data store  222  can be combined with other results over time. For example, if the query system  212  receives an open-ended query (e.g., no set end time), the search manager  515  can store the query results over time in the query acceleration data store  222 . Query results in the query acceleration data store  222  can be updated as additional query results are obtained. In this manner, if an open-ended query is run at time B, query results may be stored from initial time A to time B. If the same open-ended query is run at time C, then the query results from the prior open-ended query can be obtained from the query acceleration data store  222  (which gives the results from time A to time B), and the query can be run from time B to time C and combined with the prior results, rather than running the entire query from time A to time C. In this manner, the computational efficiency of ongoing search queries can be improved. 
     3.4.3. Search Nodes 
     As described herein, the search nodes  506  can be the primary query execution engines for the query system  214 , and can be implemented as distinct computing devices, virtual machines, containers, container of a pods, or processes or threads associated with one or more containers. Accordingly, each search node  506  can include a processing device and a data store, as depicted at a high level in  FIG. 5 . Depending on the embodiment, the processing device and data store can be dedicated to the search node (e.g., embodiments where each search node is a distinct computing device) or can be shared with other search nodes or components of the data intake and query system  108  (e.g., embodiments where the search nodes are implemented as containers or virtual machines or where the shared data store is a networked data store, etc.). 
     In some embodiments, the search nodes  506  can obtain and search buckets identified by the search manager  514  that include data that satisfies at least a portion of the query, identify the set of data within the buckets that satisfies the query, perform one or more transformations on the set of data, and communicate the set of data to the search manager  514 . Individually, a search node  506  can obtain the buckets assigned to it by the search manager  514  for a particular query, search the assigned buckets for a subset of the set of data, perform one or more transformation on the subset of data, and communicate partial search results to the search manager  514  for additional processing and combination with the partial results from other search nodes  506 . 
     In some cases, the buckets to be searched may be located in a local data store of the search node  506  or a data store that is shared between multiple search nodes  506 . In such cases, the search nodes  506  can identify the location of the buckets and search the buckets for the set of data that satisfies the query. 
     In certain cases, the buckets may be located in the common storage  216 . In such cases, the search nodes  506  can search the buckets in the common storage  216  and/or copy the buckets from the common storage  216  to a local or shared data store and search the locally stored copy for the set of data. As described herein, the cache manager  516  can coordinate with the search nodes  506  to identify the location of the buckets (whether in a local or shared data store or in common storage  216 ) and/or obtain buckets stored in common storage  216 . 
     Once the relevant buckets (or relevant files of the buckets) are obtained, the search nodes  506  can search their contents to identify the set of data to be processed. In some cases, upon obtaining a bucket from the common storage  216 , a search node  306  can decompress the bucket from a compressed format, and accessing one or more files stored within the bucket. In some cases, the search node  306  references a bucket summary or manifest to locate one or more portions (e.g., records or individual files) of the bucket that potentially contain information relevant to the search. 
     In some cases, the search nodes  506  can use all of the files of a bucket to identify the set of data. In certain embodiments, the search nodes  506  use a subset of the files of a bucket to identify the set of data. For example, in some cases, a search node  506  can use an inverted index, bloom filter, or bucket summary or manifest to identify a subset of the set of data without searching the raw machine data of the bucket. In certain cases, the search node  506  uses the inverted index, bloom filter, bucket summary, and raw machine data to identify the subset of the set of data that satisfies the query. 
     In some embodiments, depending on the query, the search nodes  506  can perform one or more transformations on the data from the buckets. For example, the search nodes  506  may perform various data transformations, scripts, and processes, e.g., a count of the set of data, etc. 
     As the search nodes  506  execute the query, they can provide the search manager  514  with search results. In some cases, a search node  506  provides the search manager  514  results as they are identified by the search node  506 , and updates the results over time. In certain embodiments, a search node  506  waits until all of its partial results are gathered before sending the results to the search manager  504 . 
     In some embodiments, the search nodes  506  provide a status of the query to the search manager  514 . For example, an individual search node  506  can inform the search manager  514  of which buckets it has searched and/or provide the search manager  514  with the results from the searched buckets. As mentioned, the search manager  514  can track or store the status and the results as they are received from the search node  506 . In the event the search node  506  becomes unresponsive or unavailable, the tracked information can be used to generate and assign a new search node  506  to execute the remaining portions of the query assigned to the unavailable search node  506 . 
     3.4.4. Cache Manager 
     As mentioned, the cache manager  516  can communicate with the search nodes  506  to obtain or identify the location of the buckets assigned to the search nodes  506 , and can be implemented as a distinct computing device, virtual machine, container, container of a pod, or a process or thread associated with a container. 
     In some embodiments, based on the receipt of a bucket assignment, a search node  506  can provide the cache manager  516  with an identifier of the bucket that it is to search, a file associated with the bucket that it is to search, and/or a location of the bucket. In response, the cache manager  516  can determine whether the identified bucket or file is located in a local or shared data store or is to be retrieved from the common storage  216 . 
     As mentioned, in some cases, multiple search nodes  506  can share a data store. Accordingly, if the cache manager  516  determines that the requested bucket is located in a local or shared data store, the cache manager  516  can provide the search node  506  with the location of the requested bucket or file. In certain cases, if the cache manager  516  determines that the requested bucket or file is not located in the local or shared data store, the cache manager  516  can request the bucket or file from the common storage  216 , and inform the search node  506  that the requested bucket or file is being retrieved from common storage  216 . 
     In some cases, the cache manager  516  can request one or more files associated with the requested bucket prior to, or in place of, requesting all contents of the bucket from the common storage  216 . For example, a search node  506  may request a subset of files from a particular bucket. Based on the request and a determination that the files are located in common storage  216 , the cache manager  516  can download or obtain the identified files from the common storage  216 . 
     In some cases, based on the information provided from the search node  506 , the cache manager  516  may be unable to uniquely identify a requested file or files within the common storage  216 . Accordingly, in certain embodiments, the cache manager  516  can retrieve a bucket summary or manifest file from the common storage  216  and provide the bucket summary to the search node  506 . In some cases, the cache manager  516  can provide the bucket summary to the search node  506  while concurrently informing the search node  506  that the requested files are not located in a local or shared data store and are to be retrieved from common storage  216 . 
     Using the bucket summary, the search node  506  can uniquely identify the files to be used to execute the query. Using the unique identification, the cache manager  516  can request the files from the common storage  216 . Accordingly, rather than downloading the entire contents of the bucket from common storage  216 , the cache manager  516  can download those portions of the bucket that are to be used by the search node  506  to execute the query. In this way, the cache manager  516  can decrease the amount of data sent over the network and decrease the search time. 
     As a non-limiting example, a search node  506  may determine that an inverted index of a bucket is to be used to execute a query. For example, the search node  506  may determine that all the information that it needs to execute the query on the bucket can be found in an inverted index associated with the bucket. Accordingly, the search node  506  can request the file associated with the inverted index of the bucket from the cache manager  516 . Based on a determination that the requested file is not located in a local or shared data store, the cache manager  516  can determine that the file is located in the common storage  216 . 
     As the bucket may have multiple inverted indexes associated with it, the information provided by the search node  506  may be insufficient to uniquely identify the inverted index within the bucket. To address this issue, the cache manager  516  can request a bucket summary or manifest from the common storage  216 , and forward it to the search node  506 . The search node  506  can analyze the bucket summary to identify the particular inverted index that is to be used to execute the query, and request the identified particular inverted index from the cache manager  516  (e.g., by name and/or location). Using the bucket manifest and/or the information received from the search node  506 , the cache manager  516  can obtain the identified particular inverted index from the common storage  216 . By obtaining the bucket manifest and downloading the requested inverted index instead of all inverted indexes or files of the bucket, the cache manager  516  can reduce the amount of data communicated over the network and reduce the search time for the query. 
     In some cases, when requesting a particular file, the search node  506  can include a priority level for the file. For example, the files of a bucket may be of different sizes and may be used more or less frequently when executing queries. For example, the bucket manifest may be a relatively small file. However, if the bucket is searched, the bucket manifest can be a relatively valuable file (and frequently used) because it includes a list or index of the various files of the bucket. Similarly, a bloom filter of a bucket may be a relatively small file but frequently used as it can relatively quickly identify the contents of the bucket. In addition, an inverted index may be used more frequently than raw data of a bucket to satisfy a query. 
     Accordingly, to improve retention of files that are commonly used in a search of a bucket, the search node  506  can include a priority level for the requested file. The cache manager  516  can use the priority level received from the search node  506  to determine how long to keep or when to evict the file from the local or shared data store. For example, files identified by the search node  506  as having a higher priority level can be stored for a greater period of time than files identified as having a lower priority level. 
     Furthermore, the cache manager  516  can determine what data and how long to retain the data in the local or shared data stores of the search nodes  506  based on a bucket caching policy. In some cases, the bucket caching policy can rely on any one or any combination of the priority level received from the search nodes  506  for a particular file, least recently used, most recent in time, or other policies to indicate how long to retain files in the local or shared data store. 
     In some instances, according to the bucket caching policy, the cache manager  516  or other component of the query system  214  (e.g., the search master  512  or search manager  514 ) can instruct search nodes  506  to retrieve and locally cache copies of various buckets from the common storage  216 , independently of processing queries. In certain embodiments, the query system  214  is configured, according to the bucket caching policy, such that one or more buckets from the common storage  216  (e.g., buckets associated with a tenant or partition of a tenant) or each bucket from the common storage  216  is locally cached on at least one search node  506 . 
     In some embodiments, according to the bucket caching policy, the query system  214  is configured such that at least one bucket from the common storage  216  is locally cached on at least two search nodes  506 . Caching a bucket on at least two search nodes  506  may be beneficial, for example, in instances where different queries both require searching the bucket (e.g., because the at least search nodes  506  may process their respective local copies in parallel). In still other embodiments, the query system  214  is configured, according to the bucket caching policy, such that one or more buckets from the common storage  216  or all buckets from the common storage  216  are locally cached on at least a given number n of search nodes  506 , wherein n is defined by a replication factor on the system  108 . For example, a replication factor of five may be established to ensure that five copies of a bucket are locally cached across different search nodes  506 . 
     In certain embodiments, the search manager  514  (or search master  512 ) can assign buckets to different search nodes  506  based on time. For example, buckets that are less than one day old can be assigned to a first group of search nodes  506  for caching, buckets that are more than one day but less than one week old can be assigned to a different group of search nodes  506  for caching, and buckets that are more than one week old can be assigned to a third group of search nodes  506  for caching. In certain cases, the first group can be larger than the second group, and the second group can be larger than the third group. In this way, the query system  214  can provide better/faster results for queries searching data that is less than one day old, and so on, etc. It will be understood that the search nodes can be grouped and assigned buckets in a variety of ways. For example, search nodes  506  can be grouped based on a tenant identifier, index, etc. In this way, the query system  212  can dynamically provide faster results based any one or any number of factors. 
     In some embodiments, when a search node  506  is added to the query system  214 , the cache manager  516  can, based on the bucket caching policy, instruct the search node  506  to download one or more buckets from common storage  216  prior to receiving a query. In certain embodiments, the cache manager  516  can instruct the search node  506  to download specific buckets, such as most recent in time buckets, buckets associated with a particular tenant or partition, etc. In some cases, the cache manager  516  can instruct the search node  506  to download the buckets before the search node  506  reports to the search node monitor  508  that it is available for executing queries. It will be understood that other components of the query system  214  can implement this functionality, such as, but not limited to the query system manager  502 , search node monitor  508 , search manager  514 , or the search nodes  506  themselves. 
     In certain embodiments, when a search node  506  is removed from the query system  214  or becomes unresponsive or unavailable, the cache manager  516  can identify the buckets that the removed search node  506  was responsible for and instruct the remaining search nodes  506  that they will be responsible for the identified buckets. In some cases, the remaining search nodes  506  can download the identified buckets from common storage  516  or retrieve them from the data store associated with the removed search node  506 . 
     In some cases, the cache manager  516  can change the bucket-search node  506  assignments, such as when a search node  506  is removed or added. In certain embodiments, based on a reassignment, the cache manager  516  can inform a particular search node  506  to remove buckets to which it is no longer assigned, reduce the priority level of the buckets, etc. In this way, the cache manager  516  can make it so the reassigned bucket will be removed more quickly from the search node  506  than it otherwise would without the reassignment. In certain embodiments, the search node  506  that receives the new for the bucket can retrieve the bucket from the now unassigned search node  506  and/or retrieve the bucket from common storage  216 . 
     3.4.5. Search Node Monitor and Catalog 
     The search node monitor  508  can monitor search nodes and populate the search node catalog  510  with relevant information, and can be implemented as a distinct computing device, virtual machine, container, container of a pod, or a process or thread associated with a container. 
     In some cases, the search node monitor  508  can ping the search nodes  506  over time to determine their availability, responsiveness, and/or utilization rate. In certain embodiments, each search node  506  can include a monitoring module that provides performance metrics or status updates about the search node  506  to the search node monitor  508 . For example, the monitoring module can indicate the amount of processing resources in use by the search node  506 , the utilization rate of the search node  506 , the amount of memory used by the search node  506 , etc. In certain embodiments, the search node monitor  508  can determine that a search node  506  is unavailable or failing based on the data in the status update or absence of a state update from the monitoring module of the search node  506 . 
     Using the information obtained from the search nodes  506 , the search node monitor  508  can populate the search node catalog  510  and update it over time. As described herein, the search manager  514  can use the search node catalog  510  to identify search nodes  506  available to execute a query. In some embodiments, the search manager  214  can communicate with the search node catalog  510  using an API. 
     As the availability, responsiveness, and/or utilization change for the different search nodes  506 , the search node monitor  508  can update the search node catalog  510 . In this way, the search node catalog  510  can retain an up-to-date list of search nodes  506  available to execute a query. 
     Furthermore, as search nodes  506  are instantiated (or at other times), the search node monitor  508  can update the search node catalog  510  with information about the search node  506 , such as, but not limited to its computing resources, utilization, network architecture (identification of machine where it is instantiated, location with reference to other search nodes  506 , computing resources shared with other search nodes  506 , such as data stores, processors, I/O, etc.), etc. 
     3.5. Common Storage 
     Returning to  FIG. 2 , the common storage  216  can be used to store data indexed by the indexing system  212 , and can be implemented using one or more data stores  218 . 
     In some systems, the same computing devices (e.g., indexers) operate both to ingest, index, store, and search data. The use of an indexer to both ingest and search information may be beneficial, for example, because an indexer may have ready access to information that it has ingested, and can quickly access that information for searching purposes. However, use of an indexer to both ingest and search information may not be desirable in all instances. As an illustrative example, consider an instance in which ingested data is organized into buckets, and each indexer is responsible for maintaining buckets within a data store corresponding to the indexer. Illustratively, a set of ten indexers may maintain 100 buckets, distributed evenly across ten data stores (each of which is managed by a corresponding indexer). Information may be distributed throughout the buckets according to a load-balancing mechanism used to distribute information to the indexers during data ingestion. In an idealized scenario, information responsive to a query would be spread across the 100 buckets, such that each indexer may search their corresponding ten buckets in parallel, and provide search results to a search head. 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. As one example, consider a query in which responsive information exists within ten buckets, all of which are included in a single data store associated with a single indexer. In such an instance, a bottleneck may be created at the single indexer, and the effects of parallelized searching across the indexers may be minimized. To increase the speed of operation of search queries in such cases, it may therefore be desirable to store data indexed by the indexing system  212  in common storage  216  that can be accessible to any one or multiple components of the indexing system  212  or the query system  214 . 
     Common storage  216  may correspond to any data storage system accessible to the indexing system  212  and the query system  214 . For example, common storage  216  may correspond to a storage area network (SAN), network attached storage (NAS), other network-accessible storage system (e.g., a hosted storage system, such as Amazon S3 or EBS provided by Amazon, Inc., Google Cloud Storage, Microsoft Azure Storage, etc., which may also be referred to as “cloud” storage), or combination thereof. The common storage  216  may include, for example, hard disk drives (HDDs), solid state storage devices (SSDs), or other substantially persistent or non-transitory media. Data stores  218  within common storage  216  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 containerized or virtualized storage device hosted by an underlying physical storage device. In some embodiments, the common storage  216  may also be referred to as a shared storage system or shared storage environment as the data stores  218  may store data associated with multiple customers, tenants, etc., or across different data intake and query systems  108  or other systems unrelated to the data intake and query systems  108 . 
     The common storage  216  can be configured to provide high availability, highly resilient, low loss data storage. In some cases, to provide the high availability, highly resilient, low loss data storage, the common storage  216  can store multiple copies of the data in the same and different geographic locations and across different types of data stores (e.g., solid state, hard drive, tape, etc.). Further, as data is received at the common storage  216  it can be automatically replicated multiple times according to a replication factor to different data stores across the same and/or different geographic locations. 
     In one embodiment, common storage  216  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  216  may be physically co-located with the indexing system  212  or the query system  214  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  218  is grouped into buckets, each of which is commonly accessible to the indexing system  212  and query system  214 . The size of each bucket may be selected according to the computational resources of the common storage  216  or the data intake and query system  108  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. Further, as mentioned, in some embodiments, some buckets can be merged to create larger buckets. 
     As described herein, each bucket can include one or more files, such as, but not limited to, one or more compressed or uncompressed raw machine data files, metadata files, filter files, indexes files, bucket summary or manifest files, etc. In addition, each bucket can store events including raw machine data associated with a timestamp. 
     As described herein, the indexing nodes  404  can generate buckets during indexing and communicate with common storage  216  to store the buckets. For example, data may be provided to the indexing nodes  404  from one or more ingestion buffers of the intake system  210 . The indexing nodes  404  can process the information and store it as buckets in common storage  216 , rather than in a data store maintained by an individual indexer or indexing node. Thus, the common storage  216  can render information of the data intake and query system  108  commonly accessible to elements of the system  108 . As described herein, the common storage  216  can enable parallelized searching of buckets to occur independently of the operation of indexing system  212 . 
     As noted above, it may be beneficial in some instances to separate data indexing and searching. Accordingly, as described herein, the search nodes  506  of the query system  214  can search for data stored within common storage  216 . The search nodes  506  may therefore be communicatively attached (e.g., via a communication network) with the common storage  216 , and be enabled to access buckets within the common storage  216 . 
     Further, as described herein, because the search nodes  506  in some instances are not statically assigned to individual data stores  218  (and thus to buckets within such a data store  218 ), the buckets searched by an individual search node  506  may be selected dynamically, to increase the parallelization with which the buckets can be searched. For example, consider an instance where information is stored within 100 buckets, and a query is received at the data intake and query system  108  for information within ten buckets. Unlike a scenario in which buckets are statically assigned to an indexer, which could result in a bottleneck if the ten relevant buckets are associated with the same indexer, the ten buckets holding relevant information may be dynamically distributed across multiple search nodes  506 . Thus, if ten search nodes  506  are available to process a query, each search node  506  may be assigned to retrieve and search within one bucket greatly increasing parallelization when compared to the low-parallelization scenarios (e.g., where a single indexer  206  is required to search all ten buckets). 
     Moreover, because searching occurs at the search nodes  506  rather than at the indexing system  212 , indexing resources can be allocated independently to searching operations. For example, search nodes  506  may be executed by a separate processor or computing device than indexing nodes  404 , enabling computing resources available to search nodes  506  to scale independently of resources available to indexing nodes  404 . Additionally, the impact on data ingestion and indexing due to above-average volumes of search query requests is reduced or eliminated, and similarly, the impact of data ingestion on search query result generation time also is reduced or eliminated. 
     As will be appreciated in view of the above description, the use of a common storage  216  can provide many advantages within the data intake and query system  108 . Specifically, use of a common storage  216  can enable the system  108  to decouple functionality of data indexing by indexing nodes  404  with functionality of searching by search nodes  506 . Moreover, because buckets containing data are accessible by each search node  506 , a search manager  514  can dynamically allocate search nodes  506  to buckets at the time of a search in order to increase parallelization. Thus, use of a common storage  216  can substantially improve the speed and efficiency of operation of the system  108 . 
     3.6. Data Store Catalog 
     The data store catalog  220  can store information about the data stored in common storage  216 , and can be implemented using one or more data stores. In some embodiments, the data store catalog  220  can be implemented as a portion of the common storage  216  and/or using similar data storage techniques (e.g., local or cloud storage, multi-tiered storage, etc.). In another implementation, the data store catalog  22 —may utilize a database, e.g., a relational database engine, such as commercially-provided relational database services, e.g., Amazon&#39;s Aurora. In some implementations, the data store catalog  220  may use an API to allow access to register buckets, and to allow query system  214  to access buckets. In other implementations, data store catalog  220  may be implemented through other means, and maybe stored as part of common storage  216 , or another type of common storage, as previously described. In various implementations, requests for buckets may include a tenant identifier and some form of user authentication, e.g., a user access token that can be authenticated by authentication service. In various implementations, the data store catalog  220  may store one data structure, e.g., table, per tenant, for the buckets associated with that tenant, one data structure per partition of each tenant, etc. In other implementations, a single data structure, e.g., a single table, may be used for all tenants, and unique tenant IDs may be used to identify buckets associated with the different tenants. 
     As described herein, the data store catalog  220  can be updated by the indexing system  212  with information about the buckets or data stored in common storage  216 . For example, the data store catalog can store an identifier for a sets of data in common storage  216 , a location of the sets of data in common storage  216 , tenant or indexes associated with the sets of data, timing information about the sets of data, etc. In embodiments where the data in common storage  216  is stored as buckets, the data store catalog  220  can include a bucket identifier for the buckets in common storage  216 , a location of or path to the buckets in common storage  216 , a time range of the data in the bucket (e.g., range of time between the first-in-time event of the bucket and the last-in-time event of the bucket), a tenant identifier identifying a customer or computing device associated with the bucket, and/or an index or partition associated with the bucket, etc. 
     In certain embodiments, the data store catalog  220  can include an indication of a location of a copy of a bucket found in one or more search nodes  506 . For example, as buckets are copied to search nodes  506 , the query system  214  can update the data store catalog  220  with information about which search nodes  506  include a copy of the buckets. This information can be used by the query system  214  to assign search nodes  506  to buckets as part of a query. 
     In certain embodiments, the data store catalog  220  can function as an index or inverted index of the buckets stored in common storage  216 . For example, the data store catalog  220  can provide location and other information about the buckets stored in common storage  216 . In some embodiments, the data store catalog  220  can provide additional information about the contents of the buckets. For example, the data store catalog  220  can provide a list of sources, sourcetypes, or hosts associated with the data in the buckets. 
     In certain embodiments, the data store catalog  220  can include one or more keywords found within the data of the buckets. In such embodiments, the data store catalog can be similar to an inverted index, except rather than identifying specific events associated with a particular host, source, sourcetype, or keyword, it can identify buckets with data associated with the particular host, source, sourcetype, or keyword. 
     In some embodiments, the query system  214  (e.g., search head  504 , search master  512 , search manager  514 , etc.) can communicate with the data store catalog  220  as part of processing and executing a query. In certain cases, the query system  214  communicates with the data store catalog  220  using an API. As a non-limiting example, the query system  214  can provide the data store catalog  220  with at least a portion of the query or one or more filter criteria associated with the query. In response, the data store catalog  220  can provide the query system  214  with an identification of buckets that store data that satisfies at least a portion of the query. In addition, the data store catalog  220  can provide the query system  214  with an indication of the location of the identified buckets in common storage  216  and/or in one or more local or shared data stores of the search nodes  506 . 
     Accordingly, using the information from the data store catalog  220 , the query system  214  can reduce (or filter) the amount of data or number of buckets to be searched. For example, using tenant or partition information in the data store catalog  220 , the query system  214  can exclude buckets associated with a tenant or a partition, respectively, that is not to be searched. Similarly, using time range information, the query system  214  can exclude buckets that do not satisfy a time range from a search. In this way, the data store catalog  220  can reduce the amount of data to be searched and decrease search times. 
     As mentioned, in some cases, as buckets are copied from common storage  216  to search nodes  506  as part of a query, the query system  214  can update the data store catalog  220  with the location information of the copy of the bucket. The query system  214  can use this information to assign search nodes  506  to buckets. For example, if the data store catalog  220  indicates that a copy of a bucket in common storage  216  is stored in a particular search node  506 , the query system  214  can assign the particular search node to the bucket. In this way, the query system  214  can reduce the likelihood that the bucket will be retrieved from common storage  216 . In certain embodiments, the data store catalog  220  can store an indication that a bucket was recently downloaded to a search node  506 . The query system  214  for can use this information to assign search node  506  to that bucket. 
     3.7. Query Acceleration Data Store 
     With continued reference to  FIG. 2 , the query acceleration data store  222  can be used to store query results or datasets for accelerated access, and can be implemented as, a distributed in-memory database system, storage subsystem, local or networked storage (e.g., cloud storage), 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). In some embodiments, to increase efficiency and response times, the accelerated data store  222  can maintain particular datasets in the low-latency memory, and other datasets in the longer-latency memory. For example, in some embodiments, 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  222  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. 
     In certain embodiments, the search manager  514  or search nodes  506  can store query results in the query acceleration data store  222 . In some embodiments, the query results can correspond to partial results from one or more search nodes  506  or to aggregated results from all the search nodes  506  involved in a query or the search manager  514 . In such embodiments, the results stored in the query acceleration data store  222  can be served at a later time to the search head  504 , combined with additional results obtained from a later query, transformed or further processed by the search nodes  506  or search manager  514 , etc. For example, in some cases, such as where a query does not include a termination date, the search manager  514  can store initial results in the acceleration data store  222  and update the initial results as additional results are received. At any time, the initial results, or iteratively updated results can be provided to a client device  204 , transformed by the search nodes  506  or search manager  514 , etc. 
     As described herein, a user can indicate in a query that particular datasets or results are to be stored in the query acceleration data store  222 . 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 for the datasets stored in the acceleration data store  222 ), the search nodes  506  can obtain information directly from the query acceleration data store  222 . 
     Additionally, since the query acceleration data store  222  can be utilized to service requests from different client devices  204 , the query acceleration data store  222  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 some cases, data from the intake system  210  (e.g., ingested data buffer  310 , etc.) can be stored in the acceleration data store  222 . In such embodiments, the data from the intake system  210  can be transformed by the search nodes  506  or combined with data in the common storage  216   
     Furthermore, in some cases, if the query system  214  receives a query that includes a request to process data in the query acceleration data store  222 , as well as data in the common storage  216 , the search manager  514  or search nodes  506  can begin processing the data in the query acceleration data store  222 , while also obtaining and processing the other data from the common storage  216 . In this way, the query system  214  can rapidly provide initial results for the query, while the search nodes  506  obtain and search the data from the common storage  216 . 
     It will be understood that the data intake and query system  108  can include fewer or more components as desired. For example, in some embodiments, the system  108  does not include an acceleration data store  222 . Further, it will be understood that in some embodiments, the functionality described herein for one component can be performed by another component. For example, the search master  512  and search manager  514  can be combined as one component, etc. 
     3.8. Metadata Catalog 
       FIG. 6  is a block diagram illustrating an embodiment of a metadata catalog  221 . The metadata catalog  221  can be implemented using one or more data stores, databases, computing devices, or the like. In some embodiments, the metadata catalog  221  is implemented using one or more relational databases, such as, but not limited to, Dynamo DB and/or Aurora DB. 
     As described herein, the metadata catalog  221  can store information about datasets and/or rules used or supported by the data intake and query system  108 . Furthermore, the metadata catalog  221  can be used to, among other things, interpret dataset identifiers in a query, verify/authenticate a user&#39;s permissions and/or authorizations for different datasets, identify additional processing as part of the query, identify one or more source datasets from which to retrieve data as part of the query, determine how to extract data from datasets, identify configurations/definitions/dependencies to be used by search nodes to execute the query, etc. 
     In certain embodiments, the query system  214  can use the metadata catalog  221  to dynamically determine the dataset configurations and rule configurations to be used to execute the query (also referred to herein as the query configuration parameters). In certain embodiments, the query system  214  can use the dynamically determined query configuration parameters to provide a stateless search experience. For example, if the query system  214  determines that search heads  504  are to be used to process a query or if an assigned search head  504  becomes unavailable, the query system  214  can communicate the dynamically determined query configuration parameters (and query to be executed) to another search head  504  without data loss and/or with minimal or reduced time loss. 
     In the illustrated embodiment, the metadata catalog  221  stores one or more dataset association records  602 , one or more dataset configuration records  604 , and one or more rule configuration records  606 . It will be understood, that the metadata catalog  221  can store more or less information as desired. Although shown in the illustrated embodiment as belonging to different folders or files, it will be understood, that the various dataset association records  602 , dataset configuration records  604 , and rule configuration records  606  can be stored in the same file, directory, and/or database. For example, in certain embodiments, the metadata catalog  221  can include one or more entries in a database for each dataset association record  602 , dataset (or dataset configuration record  604 ), and/or rule (or rule configuration record  606 ). Moreover, in certain embodiments, the dataset configuration records  604  and/or the rule configuration records  606  can be included as part of the dataset association records  602 . 
     In some cases, the metadata catalog  221  may not store separate dataset association records  602 . Rather the datasets association records  602  shown in  FIG. 6  can be considered logical associations between one or more dataset configuration records  604  and/or one or more rule configuration records  606 . In some such embodiments, the logical association can be determined based on an identifier or entry of each dataset configuration record  604  and/or rule configuration record  606 . For example, the dataset configuration records  604  and rule configuration records  606  that begin with “shared,” can be considered part of the “shared” dataset association record  602 A (even if separate data structure does not physically or logically exist on a data store) and the dataset configuration records  604  and rule configuration records  606  that begin with “trafficTeam,” can be considered part of the “trafficTeam” dataset association record  602 N. 
     In some embodiments, a user can modify the metadata catalog  221  via the gateway  215 . For example, the gateway  215  can receive instruction from client device  204  to add/modify/delete dataset association records  602 , dataset configuration records  604 , and/or rule configuration records  606 . The information received via the gateway  215  can be used by the metadata catalog  221  to create, modify, or delete a dataset association record  602 , dataset configuration record  604 , and/or a rule configuration record  606 . However, it will be understood that the metadata catalog  221  can be modified in a variety of ways and/or without using the gateway  215 . 
     3.8.1. Dataset Association Records 
     As described herein, the dataset association records  602  can indicate how to refer to one or more datasets (e.g., provide a name or other identifier for the datasets), identify associations or relationships between a particular dataset and one or more rules or other datasets and/or indicate the scope or definition of a dataset. Accordingly, a dataset association record  602  can include or identify one or more datasets  608  and/or rules  610 . 
     In certain embodiments, a dataset association record  602  can provide a mechanism to avoid conflicts in dataset and/or rule identifiers. For example, different dataset association records  602  can use the same name to refer to different datasets, however, the data intake and query system  108  can differentiate the datasets with the same name based on the dataset association record  602  with which the different datasets are associated. Accordingly, in some embodiments, a dataset can be identified using a logical identifier or name and/or a physical identifier or name. The logical identifier may refer to a particular dataset in the context of a particular dataset association record  602 . The physical identifier may be used by the metadata catalog  221  and/or the data intake and query system  108  to uniquely identify the dataset from other datasets supported or used by the data intake and query system  108 . 
     In some embodiments, the data intake and query system  108  can determine a physical identifier for a dataset using an identifier of the dataset association record  602  with which the dataset is associated. In some embodiments, the physical name can correspond to a combination of the logical name and the name of the dataset association record  602 . In certain embodiments, the data intake and query system  108  can determine the physical name for a dataset by appending the name of the dataset association record  602  to the name of the dataset. For example, if the name of the dataset is “main” and it is associated with or part of the “shared” dataset association record  602 , the data intake and query system  108  can generate a physical name for the dataset as “shared.main” or “shared_main.” In this way, if another dataset association record  602  “test” includes a “main” dataset, the “main” dataset from the “shared” dataset association record will not conflict with the “main” dataset from the “test” dataset association record (identified as “test.main” or “test_main”). It will be understood that a variety of ways can be used to generate or determine a physical name for a dataset. For example, the data intake and query system  108  can concatenate the logical name and the name of the dataset association record  602 , use a different identifier, etc. 
     In some embodiments, the dataset association records  602  can also be used to limit or restrict access to datasets and/or rules. For example, if a user uses one dataset association record  602  they may be unable to access or use datasets and/or rules from another dataset association record  602 . In some such embodiments, if a query identifies a dataset association record  602  for use but references datasets or rules of another dataset association record  602 , the data intake and query system  108  can indicate an error. 
     In certain embodiments, datasets and/or rules can be imported from one dataset association record  602  to another dataset association record  602 . Importing a dataset and/or rule can enable a dataset association record  602  to use the referenced dataset and/or rule. In certain embodiments, when importing a dataset and/or rule  610 , the imported dataset and/or rule  610  can be given a different name for use in the dataset association record  602 . For example, a “main” dataset in one dataset association record can be imported to another dataset association record and renamed “traffic.” However, it will be understood that in some embodiments, the imported dataset  608  and/or rule  610  can retain the same name. 
     Accordingly, in some embodiments, the logical identifier for a dataset can vary depending on the dataset association record  602  used, but the physical identifier for the dataset may not change. For example, if the “main” dataset from the “shared” dataset association record is imported by the “test” dataset association record and renamed as “traffic,” the same dataset may be referenced as “main” when using the “shared” dataset association record and may be referenced as “traffic” when using the “test” dataset association record. However, in either case, the data intake and query system  108  can recognize that, regardless of the logical identifier used, both datasets refer to the “shared.main” dataset. 
     In some embodiments, one or more datasets and/or rules can be imported automatically. For example, consider a scenario where a rule from the “main” dataset association record  602  is imported by the “test” dataset association record and references dataset “users.” In such a scenario, even if the dataset “users” is not explicitly imported by the “test” dataset association record  602 , the “users” dataset can be imported by the “test” dataset association record  602 . In this way, the data intake and query system  108  can reduce the likelihood that an error occurs when an imported dataset and/or rule references a dataset and/or rule that was not explicitly imported. 
     In certain cases, when a dataset and/or rule is automatically imported, the data intake and query system  108  can provide limited functionality with respect to the automatically imported dataset and/or rule. For example, by explicitly importing a dataset and/or rule, a user may be able to reference the dataset and/or rule in a query, whereas if the dataset and/or rule is automatically imported, a user may not be able to reference the dataset and/or rule the query. However, the data intake and query system  108  may be able to reference the automatically imported dataset and/or rule in order to execute a query without errors. 
     Datasets of a dataset association record  602  can be associated with a dataset type. A dataset type can be used to differentiate how to interact with the dataset. In some embodiments, datasets of the same type can have similar characteristics or be interacted with in a similar way. For example, index datasets and metrics interactions datasets may be searchable, collection datasets may be searchable via a lookup dataset, view datasets may include query parameters or a query, etc. Non-limiting examples of dataset types include, but are not limited to: index (or partition), view, lookup, collections, metrics interactions, action service, interactions, four hexagonal coordinate systems, etc. 
     In some cases, the datasets may or may not refer to other datasets. In certain embodiments, a dataset may refer to no other datasets, one other dataset, or multiple datasets. A dataset that does not refer to another dataset may be referred to herein as a non-referential dataset, a dataset that refers to one dataset may be referred to as a single reference dataset, and a dataset that refers to multiple datasets may be referred to as a multi-reference dataset. 
     In certain embodiments, some datasets can include data of the data intake and query system  108 . Some such datasets may also be referred to herein as source datasets. For example, index or partition datasets can include data stored in buckets as described herein. Similarly, collection datasets can include collected data. As yet another example metrics interactions datasets can include metrics data. In some cases, a source dataset may not refer to another dataset or otherwise identified as a non-referential dataset or non-referential source dataset. However, it will be understood that in certain embodiments, a source dataset can be a single reference dataset (or single reference source dataset) and/or a multi-reference dataset (or multi-reference source dataset). 
     In some embodiments, certain datasets can be used to reference data in a particular source dataset. Some such datasets may be referred to herein as source reference datasets. For example, a source dataset may include certain restrictions that preclude it from making its data searchable generally. In some such cases, a source reference dataset can be used to access the data of the source dataset. For example, a collection dataset may not make its data searchable except via a lookup dataset. As such, the collection dataset may be referred to as a source dataset and the lookup dataset may be referred to as a source reference dataset. In some embodiments, a source reference dataset can correspond to or be paired with a particular source dataset. In certain embodiments, each source reference dataset references only one other (source) dataset. In such embodiments, the source reference dataset can be referred to as a single reference dataset or single source reference dataset. However, it will be understood that source reference datasets can be configured in a variety of ways and/or may reference multiple datasets (and be referred to as a multi-reference dataset or multi-source reference dataset). 
     In certain embodiments, a dataset can include one or more query parameters. Some such datasets may be referred to as query datasets. For example a view dataset can include a query that identifies a set of data and how to process the set of data and/or one or more query parameters. When referenced, the data intake and query system  108  can incorporate the query parameters of the query dataset into a query to be processed/executed by the query system  214 . Similar to a query, a query dataset can reference one dataset (single reference dataset or single reference query dataset) or multiple datasets (multi-reference dataset or multi-reference query dataset) and/or include an instruction to access one or more datasets (e.g., from, lookup, search, etc.). Moreover, the query dataset can include multiple query parameters to process the data from the one or more datasets (e.g., union, stats, count by, sort by, where, etc.) 
     As mentioned, in some cases, a dataset  608  in a dataset association record  602  can be imported or inherited from another dataset association record  602 . In some such cases, if the dataset association record  602  includes an imported dataset  608 , it can identify the dataset  608  as an imported dataset and/or it can identify the dataset  608  as having the same dataset type as the corresponding dataset  608  from the other dataset association record  602 . 
     Rules of a dataset association record  602  can identify types of data and one or more actions that are to be performed on the identified types of data. The rule can identify the data in a variety of ways. In some embodiments, the rule can use a field-value pair, index, or other metadata to identify data that is to be processed according to the actions of the rule. For example, a rule can indicate that the data intake and query system  108  is to perform three processes or extraction rules on data from the “main” index dataset (or multiple or all datasets of a dataset association record  602 ) with a field-value pair “sourcetype:foo.” In certain cases, a rule can apply to one or more datasets of a dataset association record  602 . In some cases, a rule can apply to all datasets of dataset association record  602 . For example, the rule  610 A can apply to all datasets of the shared dataset association record  602 A or to all index type datasets of the shared dataset association record  602 A, etc. 
     The actions of a rule can indicate a particular process that is to be applied to the data. Similar to dataset types, each action can have an action type. Action of the same type can have a similar characteristic or perform a similar process on the data. Non-limiting examples of action types include regex, aliasing, auto-lookup, and calculated field. 
     Regex actions can indicate a particular extraction rule that is to be used to extract a particular field value from a field of the identified data. Auto-lookup actions can indicate a particular lookup that is to take place using data extracted from an event to identify related information stored elsewhere. For example, an auto-lookup can indicate that when a UID value is extracted from an event, it is to be compared with a data collection that relates UIDs to usernames to identify the username associated with the UID. Aliasing actions can indicate how to relate fields from different data. For example, one sourcetype may include usernames in a “customer” field and another sourcetype may include usernames in a “user” field. An aliasing action can associate the two field names together or associate both field names with another field name, such as “username.” Calculated field actions can indicate how to calculate a field from data in an event. For example, a calculated field may indicate that an average is to be calculated from the various numbers in an event and assigned to the field name “score_avg.” It will be understood that additional actions can be used to process or extract information from the data as desired. 
     In the illustrated embodiment of  FIG. 6 , two dataset association records  602 A,  602 N (also referred to herein as dataset association record(s)  602 ), two dataset configuration records  604 A,  604 N (also referred to herein as dataset configuration record(s)  604 ), and two rule configuration records  606 A,  606 N (also referred to herein as rule configuration record(s)  606 ) are shown. However, it will be understood that fewer or more dataset association records  602  dataset configuration records  604 , and/or rule definitions  606  can be included in the metadata catalog  221 . 
     As mentioned, each dataset association record  602  can include a name (or other identifier) for the dataset association record  602 , an identification of one or more datasets  608  associated with the dataset association record  602 , and one or more rules  610 . As described herein, the datasets  608  of a dataset association record  602  can be native to the dataset association record  602  or imported from another dataset association record  602 . Similarly, rules of a dataset association record  602  can be native to the dataset association record  602  and/or imported from another dataset association record  602 . 
     In the illustrated embodiment, the name of the dataset association record  602 A is “shared” and includes the “main” dataset  608 A, “metrics” dataset  608 B, “users” dataset  608 C, and “users-col” dataset  608 D. In addition, the “main” dataset  608 A and “metrics” dataset  608 B are index datasets, the “users” dataset  608 C is a lookup dataset associated with the collection “users-col” dataset  608 D. Moreover, in the illustrated embodiment, the “main” dataset  608 A, “metrics” dataset  608 B, and “users-col” dataset  608 D are non-referential source datasets and the “users” dataset  608 C is a source reference dataset (and single reference dataset) that references the “users-col” dataset  608 D. 
     In addition, in the illustrated embodiment, the dataset association record  602 A includes the “X” rule  610 A associated with the “main” dataset  608 A and “metrics” dataset  608 B. The “X” rule  610 A uses a field-value pair “sourcetype:foo” to identify data that is to be processed according to an “auto lookup” action  612 A, “regex” action  612 B, and “aliasing” action  612 C. Accordingly, in some embodiments, when data from the “main” dataset  608 A is accessed, the actions  612 A,  612 B,  612 C of the “X” rule  610 A are applied to data of the sourcetype “foo.” 
     Similar to the dataset association record  602 A, the dataset association record  602 N includes a name (“trafficTeam”) and various native index datasets  608 E,  608 F (“main” and “metrics,” respectively), a collection dataset  608 G (“threats-col”) and a lookup dataset  608 H (“threats”), and a native rule  610 C (“Y”). In addition, the dataset association record  602  includes a view dataset  608 I (“threats-encountered”). The “threats-encountered” dataset  608 I includes a query (shown in the dataset configuration record  604 N) “| from traffic | lookup threats sig OUTPUT threat | where threat=*| stats count by threat” that references two other datasets  608 J,  608 H (“traffic” and “threats”). Thus, when the “threats-encountered” dataset  608 I is referenced, the data intake and query system  108  can process and execute the identified query. Moreover, in the illustrated embodiment, the “main” dataset  608 E, “metrics” dataset  608 E, and “threats-col” dataset  608 G are non-referential source datasets, the “threats” dataset  608 H is a single source reference dataset (source reference and single reference dataset) that references the “threats-col” dataset  608 G, and the “threats-encountered dataset”  608 I is a multi-reference query dataset. 
     The dataset association record  602 N also includes an imported “traffic” dataset  608 J and an imported “shared.X” rule  610 B. In the illustrated embodiment, the “traffic” dataset  608 J corresponds to the “main” dataset  608 A from the “shared” dataset association record  602 A. As described herein, in some embodiments, to associate the “main” dataset  608 A (from the “shared” dataset association record  602 A) with the “traffic” dataset  608 J (from the “trafficTeam” dataset association record  602 N), the name of the dataset association record  602 A (“shared”) is placed in front of the name of the dataset  608 A (“main”). However it will be understood that a variety of ways can be used to associate a dataset  608  from one dataset association record  602  with the dataset  608  from another dataset association record  602 . As described herein, by importing the dataset “main” dataset  608 A, a user using the dataset association record  602  and can reference the “main” dataset  608 A and/or access the data in the “main” dataset  608 A. 
     Similar to the “main” dataset  608 A, the “X” rule  610 A is also imported by the “trafficTeam” dataset association record  602 N as the “shared.X” rule  610 B. As described herein, by importing “X” rule  610 A, a user using the “trafficTeam” dataset association record  602 N can use the “X” rule  610 A. Furthermore, in some embodiments, if the “X” rule  610 A (or a dataset) references other datasets, such as, the “users” dataset  608 C and the “users-col” dataset  608 D, these datasets can be automatically imported by the “trafficTeam” dataset association record  602 N. However, a user may not be able to reference these automatically imported rules (datasets) in a query. 
     3.8.2. Dataset Configuration Records 
     The dataset configuration records  604  can include the configuration and/or access information for the datasets associated with the dataset association records  602  or otherwise used or supported by the data intake and query system  108 . In certain embodiments, the metadata catalog  221  includes the dataset configuration records  604  for all of the datasets  608  used or supported by the data intake and query system  108  in one or more files or entries. In some embodiments, the metadata catalog  221  includes a separate file, record, or entry for each dataset  608  or dataset configuration record  604 . 
     The dataset configuration record  604  for each dataset  608  can identify a physical and/or logical name for the dataset, a dataset type, authorization information indicating users or credentials that have to access the dataset, access information (e.g., IP address, end point, indexer information), and/or location information (e.g., physical location of data) to enable access to the data of the dataset, etc. Furthermore, depending on the dataset type, each dataset configuration record  604  can indicate custom fields or characteristics associated with the dataset. In some embodiments, index, metrics, lookup, and collection datasets may include location information, while view datasets do not. For example, in some cases view datasets may not have data except that which is access via an index, metrics, lookup, and collection datasets. Accordingly, the content and information for the dataset association records  604  can vary depending on the dataset type. 
     In the illustrated embodiment, the “shared.main” dataset configuration record  604 A for the “shared.main” dataset  608 A indicates that it is an index data type, and includes authorization information indicating the entities that have access to the “shared.main” dataset  608 A, access information that enables the data intake and query system  108  to access the data of the “shared.main” dataset  608 A, and location information that indicates the location where the data is located. In some cases, the location information and access information can overlap or be combined. In addition, the dataset configuration record  604 A includes a retention period indicating the length of time in which data associated with the “shared.main” dataset  608 A is to be retained by the data intake and query system  108 . In some embodiments, because “shared.main” is imported into the “trafficTeam” dataset association record  602 N as the dataset “traffic,” it may also be identified as the “trafficTeam.traffic” dataset  608 J. Accordingly, in some such embodiments, the dataset configuration record  604 A may include an additional identifier for “trafficTeam.traffic” or as is shown in the illustrated embodiment, it may indicate that the “trafficTeam.traffic” dataset is a dependent dataset. 
     Similarly, in the illustrated embodiment, the “trafficTeam.threats-encountered” dataset configuration record  604 N for the “trafficTeam.threats-encountered” dataset  608 I indicates that it is a view type of dataset and includes authorization information indicating the entities that have access to it. In addition, the dataset configuration record  604 N includes the query for the “trafficTeam.threats-encountered” dataset  608 I. 
     The dataset configuration record  604  can also include additional information or metadata (also referred to herein as annotations). The annotations can correspond to user annotations added by a user or to system annotations that are automatically generated by the system. 
     In the illustrated embodiment of  FIG. 6 , the dataset configuration record  604 A includes a system annotation  614  that indicates the number of identified fields of the “shared.main” dataset ( 4 ), a system annotations  616  that identify the fields of the “shared.main” dataset (sig, IP_addr, userID, error), and a system annotation  618  that identifies the datasets that depend on the “shared.main” dataset (“trafficTeam.traffic” and “trafficTeam.threats-encountered”). In the illustrated embodiment, the dependent datasets annotation  618  includes reference to the “trafficTeam.traffic” dataset  608 J even though it is only an identifier to import the “shared.main” dataset to the dataset association record  602 N. However, in some embodiments, datasets that only import another dataset or are merely identifiers for another dataset may not be identified as dependent datasets and/or may not be included as part of a system annotation. 
     With further reference to the illustrated embodiment of  FIG. 6 , the dataset configuration record  604 N includes a user annotation  620  that identifies a group associated with the dataset “trafficTeam.threats-encountered” (also referred to herein as “threats-encountered”). This annotation can be used by the system to determine which group is responsible for the dataset  604 N and/or should be charged for its use. The dataset configuration record  604 N also includes a system annotation  622  that identifies the datasets on which the “threats-encountered” dataset depends (“trafficTeam.traffic,” which is also “shared.main” and “trafficTeam.threats”), a system annotation  624  that identifies the number of times the “threats-encountered” dataset has been used and/or accessed. In some embodiments, because trafficTeam.traffic merely imports “shared.main” it may not be considered a related dataset or may be omitted from the dependency dataset annotation  622 . It will be understood that fewer or more annotations can be included in the dataset configuration record  604 N. For example, the dataset configuration record  604 N can include the identity and number of fields used by the “threats-encountered” dataset. 
     It will be understood that more or less information or annotations can be included in each dataset configuration record  604 . For example, the dataset configuration records  604  can indicate whether the dataset is a non-referential, single reference or multi-reference dataset and/or identify any datasets that it references (by the physical or logical identifier of the datasets or other mechanism), is dependent on or that depend on it, its usage, etc. As another example, the dataset configuration records  604  can identify one or more rules associated with the dataset. Additional information regarding example annotations that can be generated and/or included in dataset configuration records  604  or in the metadata catalog  221  are described herein. 
     Although not illustrated in  FIG. 6 , it will be understood that the metadata catalog  221  can include a separate dataset configuration record  604  for the datasets  608 B,  608 C,  608 D,  608 E,  608 F,  608 G,  608 H, and  608 J. 
     In some embodiments, some datasets may not have a separate dataset configuration record  604 . For example, imported datasets and/or view datasets may not include a separate dataset configuration record  604 . In certain embodiments, view datasets can include a query identified in a dataset association record  602 , but may not have a separate dataset configuration record  604  like index, metrics, collection, and/or lookup datasets. 
     In some embodiments, the dataset configuration record  604  for the “traffic” dataset  608 J (or other imported datasets) can indicate that the “traffic” dataset  608 J is an imported version of the “shared.main” dataset  608 A. In certain cases, the dataset configuration record  604  for the “traffic” dataset  608 J can include a reference to the dataset configuration record  604  for the “shared.main” dataset  608 A and/or can include all of the configuration information for the “shared.main” dataset  608 A. In certain embodiments, the metadata catalog  221  may omit a separate dataset configuration record  604  for the “traffic” dataset  608 J because that dataset is an imported dataset of the “main” dataset  608 A from the “share” dataset association record  602 A. 
     As described herein, although the dataset association records  602 A,  602 N each include a “main” dataset  608 B,  608 E and a “metrics” dataset  608 B,  608 F, the data intake and query system  108  can differentiate between the datasets from the different dataset association records based on the dataset association record  602  associated with the datasets. For example, the metadata catalog  221  can include separate dataset configuration records  604  for the “shared.main” dataset  608 A, “trafficTeam.main” dataset  608 E, “shared.metrics” dataset  608 B, and the “trafficTeam.metrics” dataset  608 F. 
     3.8.3. Rule Configuration Records 
     The rule configuration records  606  can include the rules, actions, and instructions for executing the rules and actions for the rules referenced of the dataset association records  602  or otherwise used or supported by the data intake and query system  108 . In some embodiments, the metadata catalog  221  includes a separate file or entry for each rule configuration record  606 . In certain embodiments, the metadata catalog  221  includes the rule configuration records  606  for all of the rules  610  in one or more files or entries. 
     In the illustrated embodiment, a rule configuration records  606 N is shown for the “shared.X” rule  610 A. The rule configuration record  606 N can include the specific parameters and instructions for the “shared.X” rule  610 A. For example, the rule configuration record  606 N can identify the data that satisfies the rule (sourcetype:foo of the “main” dataset  608 A). In addition, the rule configuration record  606 N can include the specific parameters and instructions for the actions associated with the rule. For example, for the “regex” action  612 B, the rule configuration record  606 N can indicate how to parse data with a sourcetype “foo” to identify a field value for a “customerID” field, etc. With continued reference to the example, for the “aliasing” action  612 C, the rule configuration record  606 N can indicate that the “customerID” field corresponds to a “userNumber” field in data with a sourcetype “roo.” Similarly, for the “auto-lookup” action  612 A, the rule configuration record  606 N can indicate that the field value for the “customerID” field can be used to lookup a customer name using the “users” dataset  608 C and “users-col” dataset  608 D. 
     It will be understood that more or less information can be included in each rule configuration record  606 . For example, the rule configuration records  606  can identify the datasets or dataset association records  602  to which the rule applies, indicate whether a rule is imported, indicate include authorizations and/or access information to use the rule, etc. 
     Similar to the dataset configuration records  604 , the metadata catalog  221  can include rule configuration records  606  for the various rules  610  of the dataset association table  602  or other rules supported for use by the data intake and query system  108 . For example, the metadata catalog  221  can include rule configuration record  606  for the “shared.X” rule  610 A and the “trafficTeam.Y” rule  610 C. 
     As described herein, the dataset association records  602 , dataset configuration records  604 , and/or rule configuration records  606  can be used by the system  108  to interpret dataset identifiers in a query, verify/authenticate a user&#39;s permissions and/or authorizations for different datasets, identify additional processing as part of the query, identify one or more source datasets from which to retrieve data as part of the query, determine how to extract data from datasets, identify configurations/definitions/dependencies to be used by search nodes to execute the query, etc. 
     In certain embodiments, the dataset association records  602 , dataset configuration records  604 , and/or rule configuration records  606  can be used to identify primary datasets and secondary datasets. The primary datasets can include datasets that are to be used to execute the query. The secondary datasets can correspond to datasets that are directly or indirectly referenced by the query but are not used to execute the query. Similarly, the dataset association records  602 , dataset configuration records  604 , and/or rule configuration records  606  can be used to identify rules (or primary rules) that are to be used to execute the query. 
     3.8.4. Annotations 
     In some embodiments, the system  108  stores data without type or as unstructured data. Thus, the system  108  may not “know” or have insight (e.g., include a table or other stored information) into the content of the data. For example, the system  108  may not have any insight into what fields (e.g., IP address, error code, userID, etc.) can be found in which datasets or what rules are related to what datasets. While it may be advantageous for a variety of reasons to store data without type or as unstructured data and use late binding schema to query the data, this can result in longer query times and the use of greater processing resources during query processing and execution. To decrease query times and/or processing resources used during a query, the system  108  can dynamically add information or metadata (also referred to herein as annotations) to the metadata catalog as it is learned. 
     In some embodiments, the annotations can be added to the dataset configuration records  604 , the rule configuration records  606  or as a separate annotation entry in the metadata catalog  221 , or elsewhere in the system  108 . For example, as changes are made to the metadata catalog  221  or as queries are executed on the data, the system  108  can infer information or learn about the datasets and rules and update the dataset configuration records  604  and rule configuration records  606  with this information. In the illustrated embodiment of  FIG. 6 , dynamically generated annotations  614 ,  616 ,  618 ,  622 ,  624  are included as part of the dataset configuration records  604 A,  604 N. However, as mentioned, the annotations can be stored as a separate entry or data structure. For example, the system  108  can update or create an annotation entry for each annotation and store the annotations in a database, such as a relational database or table of the metadata catalog  221 , or elsewhere in the system  108 . When stored in a separate data structure, the annotations can identify any datasets or fields to which they are associated or related. 
     The updated datasets configuration records  604  (or annotation entries) can be used by the system  108  to propagate annotations to related datasets, protect datasets from deletion, improve portability, and make recommendations to a user and/or process additional queries as they are received, etc. In this way, the system  108  can provide an incrementally evolving schema or map of the data and can enable more efficient queries and/or reduce the amount of processing resources used during query execution. 
     3.8.4.1. Generating Annotations 
     In some cases, the annotations can be added to the metadata catalog  221  (in dataset configuration records  604  or as annotation entries) manually by a user or automatically by the system  108 . 
     It will be understood that a user can manually add a variety of annotations (also referred to herein as “user annotations”) to the metadata catalog  221 , which can be used by the system  108  to dynamically make user recommendations, improve query processing, and/or search time. For example, a user can add or revise a dataset configuration record  604  to the metadata catalog  221  for a dataset. As part of adding/revising the dataset configuration record, the user can add annotations about the capabilities of the dataset source associated with the dataset (e.g., speed, bandwidth, parallelization, size, etc.), one or more fields of the dataset and one or more relationships between the fields, one or more datasets related to the new/revised dataset, users or groups associated with the dataset, units or preferred units for data from the dataset, etc. 
     In certain embodiments, the annotations can be added automatically by the system  108  in response to monitoring system  108  use and/or based on detected changes to the metadata catalog  221  (also referred to herein as “system annotations”). To generate the various system annotations, the system  108  can use one or more processes, threads, containers, isolated execution environments, etc. (generically referred to as processes). In some cases, the system  108  can use multiple processes to generate system annotations. For example, a separate process can be used to generate annotations based on parsing a query, monitoring query execution, monitoring user/groups, monitoring applications, etc. Similarly, separate processes can be used to generate annotations based on detected changes to the metadata catalog  221 . For example, separate processes can be used to generate annotations in response to detecting the addition or removal of a field, dataset, unit or preferred unit, field-dataset relationship, inter-field relationship, inter-dataset relationship, etc. 
     Moreover, the various processes can communicate with each other to generate the system annotations. For example, consider the scenario where one process is used to generate annotations based on parsing a query and another process is used to generate annotations based on the identification of a new field or new field-dataset relationship in the metadata catalog  221 . If the process that parses the query identifies and generates an annotation based on a new field for a dataset, it can alert the process that generates annotations based on new fields added to the dataset. In this way, the system  108  can effectively increase its knowledge or understanding of the data stored thereon, and use this understanding to facilitate more effective searching of the data. 
     3.8.4.1.1. System Annotations Based on System Use 
     A variety of system annotations can be generated based on monitoring system use. As non-limiting examples, system annotations can be automatically added to the metadata catalog  221  in response to parsing a query, executing a query, tracking user interactions with the system  108 , tracking the use of different applications executing in the system  108 , or other system use monitoring, etc. 
     The system annotations generated based on monitoring system use can be used for a variety of functions. For example, the system annotations generated based on monitoring system use can be used to track field use, dataset use, suggest fields or datasets to a user (e.g., frequently/infrequently used fields or datasets, related fields or datasets, similar fields or datasets, datasets that satisfy the criteria of another dataset, such as datasets that satisfy the field criteria of a view dataset, etc.), display similar datasets, suggest applications, identify groups or individuals responsible for the use of a particular dataset (e.g., determine charge back distribution), cost-based optimizations (e.g., when querying data from multiple datasets, how to prioritize which dataset to obtain first), propagate annotations to related datasets or fields, etc. 
     3.8.4.1.1.1. Query Parsing 
     In some cases, the system  108  can parse and extract metadata from queries to generate system annotations. The queries can correspond to queries entered by a user in a user interface or queries that form part of a dataset, such as a view dataset. 
     In some embodiments, the system  108  can use the syntax and semantics of a query to extract metadata from the query. For example, based on the known syntax of a query processing language, the system  108  can identify query commands and locations where information can be extracted, such as dataset names or identifiers, field names or identifiers, etc. Based on the syntax and semantics of the query, the system  108  can identify relationships between the datasets and fields of the query. Furthermore, the system  108  can iteratively parse the identified datasets to identify additional datasets, fields, relationships, etc. For example, the system  108  can use the dataset identifiers to identify and parse the corresponding dataset configuration records  604  to identify additional datasets, fields, and/or rules. 
     As a non-limiting example, with reference to the query “| from traffic | lookup threats sig OUTPUT threat | where threat=* | stats count by threat” of the “threats-encountered” dataset  604 N, the system  108  can, based on a knowledge of the commands for the query language used, determine that “from,” “lookup,” “OUTPUT,” “where,” “stats,” and “count by” are query commands. In addition, the system  108  can, based on the known syntax or semantics of the query language, determine that the words following the “from,” and “lookup” commands are dataset names or identifiers and the words following “where,” “stats,” “count by,” and “OUTPUT” are field names or identifiers. Similarly, the system  108  can determine that the second word following the “lookup” command is a field name or identifier. Accordingly, the system  108  can determine that the “threats-encountered” dataset  604 N references datasets “trafficTeam.traffic” and “trafficTeam.threats” and fields “threat” and “sig.” In addition, based on the dataset association records  602  or a dataset configuration record  604 , the system  108  can determine that “trafficTeam.traffic” is the “shared.main” dataset imported from the dataset association record  602 A. 
     In addition to identifying the identity of datasets and fields of the query, the system  108  can extract other metadata from the query, such as, but not limited to, field-dataset relationships, inter-dataset relationships, inter-field relationships, etc. In certain embodiments, the system  108  can identify relationships between the fields and datasets of the query. For example, based on the presence and placement of the field names “sig” and “threat” in the query, the system  108  can determine that the dataset “trafficTeam.traffic” (and “shared.main”) includes a field “sig,” and the dataset “trafficTeam.threats” includes the fields “sig” and “threat.” 
     In some embodiments, the system  108  can determine inter-field relationships. For example, given that the field “sig” is included in both “trafficTeam.traffic” and “trafficTeam.threats,” the system  108  can determine that there is a relationship between “sig” in “trafficTeam.traffic” (or “shared.main”) and “sig” in “trafficTeam.threats” (e.g., that the two “sigs” correspond to each other). 
     Moreover, in some cases, the system  108  can determine inter-dataset relationships. In some embodiments, based on the presence of the “trafficTeam.traffic” and “trafficTeam.threats” datasets in the query of the “threats-encountered” dataset  604 N, the system  108  can determine that the “threats-encountered” dataset  604 N is related to and dependent on the “trafficTeam.traffic” and “trafficTeam.threats” datasets. For example, if the datasets “traffic” and “threats” do not exist or are not defined, the “threats-encountered” dataset may return an error or be unable to function properly. In addition, the system  108  can identify a relationship between the “traffic” and “threats” datasets. For example, given that the “traffic” and “threats” datasets both have the same field “sig,” the system  108  can identify a foreign key relationship between them—similar to the inter-field relationship discussed above. 
     As additional datasets are identified, the system  108  can parse the corresponding dataset configuration records  604  to identify additional relationships. For example, the system  108  can determine that the “trafficTeam.threats” dataset is dependent on a “trafficTeam.threats-col” dataset, and that the “trafficTeam.traffic” (or “shared.main” dataset) is related to a rule “X,” which is dependent on dataset “shared.users,” which in turn depends on a dataset “shared.users-col.” Accordingly, the system  108  can iteratively parse the dataset configurations to determine the relationships between the various rules and datasets of the system  108 . Another non-limiting example of parsing a query and extracting information about the datasets and rules referenced by the query is given with reference to  FIG. 23 . 
     Based on the extracted metadata of the query (e.g., identity of fields and datasets, field-dataset relationships, inter-field relationships, inter-dataset relationships, etc.), the system  108  can generate one or more annotations. In some embodiments, the system  108  can generate an annotation for each piece of extracted metadata and/or each identified relationship. In certain embodiments, the system  108  can generate one or more annotations for any one or any combination of the identified fields and datasets, the identified field-dataset relationships, the identified inter-field relationships, and/or the identified inter-dataset relationships, etc. 
     As described herein, the annotations generated from the extracted metadata of the query can be used to track field use, dataset use, suggest fields or datasets to a user (e.g., frequently/infrequently used fields or datasets, related fields or datasets, similar fields or datasets, datasets that satisfy the criteria of another dataset, such as datasets that satisfy the field criteria of a view dataset, etc.), display similar datasets, suggest applications, identify groups or individuals responsible for the use of a particular dataset (e.g., determine charge back distribution), propagate annotations to related datasets or fields, etc. 
     3.8.4.1.1.2. Query Execution 
     In some embodiments, the system  108  can monitor system use during query execution. For example, during query execution, the system  108  can track which dataset is being accessed, the amount of data of the dataset being retrieved from a dataset source (e.g., the total number of data entries being retrieved, the number of data entries by field that are retrieved, the total amount of data being retrieved, etc.), the amount of processing resources used to retrieve data from the dataset source, the amount of time taken to obtain the data from the dataset source, the speed at which data is retrieved from the dataset source, whether a dataset source supports parallelization (e.g., whether the system  108  can extract data from the dataset source in parallel or serially), etc. 
     Based on the information that is tracked during query execution, the system  108  can generate one or more annotations. For example, based on the information, the system  108  can generate or update annotations about the speed and size of a dataset or dataset source (e.g., the number of data entries in the dataset, the number of data entries for each known field of the dataset, total size of the dataset or dataset source, etc.), the connectivity or latency with a dataset source, etc. In some embodiments, the system  108  can generate an annotation for each statistic that is monitored or generate an annotation for a group or all of the statistics being tracked. As described herein, the annotations can be stored as part of a dataset configuration record  604  or other annotation entry. 
     The annotations generated based on monitoring the system  108  during query execution can be used to track the speed and size of datasets and the capabilities of dataset sources. The system  108  can further use this information to generate cost-based optimizations during query execution. Consider the scenario where a query indicates that data from dataset A and dataset B are to be joined. The system  108  can use the annotations generated from monitoring the system  108  during query execution to determine which dataset to access first. For example, the annotations may indicate that for field  1 , dataset A has significantly more data entries or is slower than dataset B. Thus, if the query includes a join of field  1 , the system  108  can access dataset B first and use the information from dataset B to refine the data that is requested from dataset A. As another example, if another query indicates that field  2  of datasets A and B are to be used for a join and the annotations indicate that dataset B has significantly more data entries than dataset A, the system  108  can pull data from dataset A first and use it to refine the query for dataset B. Furthermore, the system  108  can use a combination of annotations to determine which dataset to access first. For example, if dataset B has significantly more data for field  3  than dataset A, but dataset A is significantly slower, the system  108  may determine that it will take less time and be more efficient to pull data from dataset B first and use that to refine the query for dataset A. 
     3.8.4.1.1.3. User Monitoring 
     In some embodiments, the system  108  can monitor users as they interact with the system  108 . For example, the system  108  can monitor which users use the system  108 , the duration of use, the frequency of use, which datasets are created, accessed, modified, or used by the user, which applications are used by the user, typical fields that are used by the user, etc. Similarly, if a user is part of a group, the system  108  can monitor the collective actions of the users of the group. This information can be used to generate user/group annotations. As described herein, the annotations can be stored as part of a dataset configuration record  604  or other annotation entry. 
     The system  108  can use the user/group annotations to track usage of the system  108  by user or group. Furthermore, the system  108  can use the user/group annotations to suggest fields, datasets, applications, etc. to the user or the group. For example, the system  108  can identify fields or datasets that are related to or similar to fields or datasets typically used by the user. As another example, if users with similar characteristics to the current user use certain fields, applications, or datasets, the system  108  can recommend these fields, application, or datasets to the user, etc. In this way, the system  108  can improve the users understanding of the data in the system  108  and enhance the user&#39;s ability to user or query data in the system. 
     3.8.4.1.1.4. Application Monitoring 
     In certain embodiments, the system can monitor applications used on the system  108 . For example, the system  108  can monitor which applications are available on the system  108 , which datasets or dataset sources are used by the application, the frequency of use of applications, an identification of applications that are frequently used together, an identity of users or user types that use particular applications, etc. This information can be used to generate annotations. 
     The system  108  can use the annotations generated by monitoring applications to track the usage of the applications and to make suggestions to users. For example, if multiple users of a group frequently use one or more applications, the system  108  can recommend the applications to other users of the group. As another example, if one user of a group begins using and spends significant time on one application compared to time spent on other applications before beginning use of the “new” application, the system  108  can recommend the “new” application to other members of the group. In this way, the system  108  can propagate knowledge about the system  108  and applications to various users and improve their understanding of the system  108  and how to use it effectively. 
     3.8.4.1.2. System Annotations Based on Changes to the Metadata Catalog 
     As mentioned, in some embodiments, system  108  annotations can be added automatically to the metadata catalog  221  in response to changes in the metadata catalog  221 . The changes may be the result of a manual change by a user, such as a user annotation, or an automated change by the system  108 , such as a system  108  annotation. For example, when a user adds or revises information about a first dataset, the system  108  can compare information about the first dataset with other information of other datasets to identify potential relationships or similarities. If a relationship or similarity is detected, the system  108  can add an annotation to the dataset configuration record  604  (or annotation entry) of the first dataset as well as to the dataset configuration records  604  of the other identified datasets. As another example, if the system  108  updates information for the first dataset based on a query, the system  108  can identify other datasets that are related to the first dataset and update metadata of the other identified datasets. In this way, as the system  108  is used, it can learn about the datasets, and use the information to improve search time or search capabilities. As described herein, in some cases, the system  108  can use one or more processes to identify the change to the metadata catalog  221  and generate additional annotations based on the change. 
     In some embodiments, based on the addition of a dataset, the system  108  can identify fields of the dataset, related datasets (datasets on which the dataset depends), similar datasets (e.g., datasets with similar fields), dataset to which the new dataset can be mapped (e.g., view datasets to which the new dataset can be mapped), etc. In certain embodiments, if the added dataset is a view dataset that includes a query, the system  108  can process the query as described above to generate one or more annotations. 
     In certain embodiments, based on the addition of a field-dataset relationship annotation or the identification of a field of a dataset, the system  108  can determine a total number of fields of the dataset, identify similar datasets and/or datasets to which the dataset can be mapped, and generate corresponding annotations. For example, based on the addition of the field “userID” to the dataset “Logons,” the system  108  can identify other datasets with a “userID” field. If found, the system  108  can generate an annotation for the dataset “Logons” and/or the other dataset to indicate a similar field is located in each dataset. As another example, based on the addition of the field “userID” to the dataset “Logons,” the system  108  can identify view datasets that use the field “userID” to generate a view or interface. If the view dataset uses additional fields that are also found in the “Logons” dataset, the system  108  can generate an annotation for the dataset “Logons” and/or the other dataset to indicate that the view dataset may be related or usable with the “Logons” dataset or that the “Logon” dataset may be mapped to the view dataset. 
     In certain embodiments, based on the addition of an inter-field relationship or inter-dataset annotation, the system  108  can identify additional inter-field and inter-dataset relationships. For example if dataset A is dependent on dataset B and dataset B is dependent on dataset C, the system  108  can determine that dataset A is dependent on dataset B and generate an additional inter-dataset annotation indicating A&#39;s dependency on C. As another example, if field “userID” of dataset B is related to field “ID” of dataset C and a new relationship between field “ID” of dataset C and field “UID” of dataset D, the system  108  can determine that “userID” of dataset B is related to “UID” of dataset D. 
     In addition, based on the addition of an inter-dataset annotation, the system  108  can propagate one or more annotations. For example, if an alarm threshold, unit, or preferred unit is associated with a metrics dataset A and an inter-dataset relationship annotation is added that relates metrics dataset A with metrics dataset B, the system  108  can propagate the alarm threshold, unit, and/or preferred unit to metrics dataset B. Specifically, if an annotation for metric cpu_speed of dataset A indicates that the units are Hz and the preferred units are GHz, the system  108  can propagate the Hz and GHz units/preferred units to a corresponding cpu_speed metric of dataset B. Similarly, if a data category annotation for a dataset or field of a dataset indicates that the information is confidential, then based on an inter-field relationship that indicates another field is derived from the confidential field or an inter-dataset relationship that indicates another dataset uses the confidential information, the system  108  can propagate the data category annotation to the related field or dataset. 
     In some embodiments, based on the addition of an inter-dataset annotation, the system  108  can generate an annotation indicating that the dataset that is depended on should not be deleted so long as the dependent dataset exists or an annotation indicating that if the dataset that is depended on is deleted then the dependent dataset should also be deleted. The system can also use an inter-dataset annotation to generate an annotation that indicates the total number (and identity) of datasets that depend on a particular dataset, or the total number (and identity) of datasets on which the particular dataset depends. 
     In certain embodiments, based on an update to the field use for a field, the system  108  can compare the field use of the field with the field use of other fields and determine the popularity of the fields. Based on the popularity, the system  108  can generate one or more annotations indicating the popularity of the fields. Similarly, the system  108  can use the dataset use and application use to generate annotations indicating the popularity of different datasets and applications, respectively. In addition, using the user or group information, the system  108  can determine the popularity of fields, datasets, and/or applications for a particular user or group. 
     In certain embodiments, based on a change/addition of a unit or preferred unit for a dataset, the system  108  can identify related datasets and generate annotations for the units and preferred units for the related datasets. Similarly, the system  108  can generate annotations for one or more datasets or fields in response to change/additions of alarm thresholds or data category (e.g., use restrictions) annotations to a related dataset or field. 
     3.8.4.2. Example Annotations 
     As mentioned, the metadata catalog  221  can include annotations or information about the datasets, fields, users, or applications of the system  108  and can be revised as additional information is learned. Non-limiting examples of annotations that can be added to the dataset configuration records  604 , other configurations, annotation tables or entries, or other locations of the metadata catalog  221  or system  108 , include but are not limited to, the identification and use of fields in a dataset, number of fields in a dataset, related fields, related datasets, number (and identity) of dependent datasets, number (and identity) of datasets depended on, capabilities of a dataset or related dataset source or provider, the identification of datasets with similar configurations or fields, units or preferred units of data obtained from a dataset, alarm thresholds, data categories (e.g., restrictions), users or groups, applications, popular field, datasets, and applications (in total or by user or group), etc. In certain cases, the annotations can be added as the system  108  monitors system use (e.g., processing queries, monitoring query execution, user interaction, etc.) or as the system  108  detects changes to the metadata catalog  221  (e.g., one manual/automated change can lead to another automated change), etc. 
     3.8.4.2.1. Field Annotations 
     The metadata catalog  221  can store various annotations about the fields found in datasets. For example, the metadata catalog  221  can include an identification of the dataset associated with a field (or field-dataset relationship), the number of fields of a dataset (or field count), an identification of all fields of a dataset, the frequency of use of the different fields, users of the field, etc. As described herein, the information about the fields of a dataset can be stored as part of a dataset configuration record  604  or as part of a separate data structure. When stored as a separate data structure, the data structure can identify the datasets that include the field or are otherwise associated with or related to the field. 
     The number and identity of fields of a dataset can be identified in a variety of ways. In some cases, a user can manually include or add a field to the metadata catalog  221  (e.g., the dataset configuration record  604  or an annotation entry). For example, the user may add or relate a regex rule to a dataset. The regex rule can define how to extract field values for the field from the dataset. Based on the information in the regex rule, the system  108  can identify the field and increment the number of fields associated with the dataset. 
     In some embodiments, the system  108  can parse a query to identify fields of a dataset. As described herein, in parsing the query, the system  108  can identify phrases or use the syntax of the query to identify (and count the number of) fields. For example, with reference to the query “| from traffic | lookup threats sig OUTPUT threat | where threat=* | stats count by threat.” of threats-encountered dataset  604 N, the system  108  can, based on the query language used, identify “from” and “lookup” as commands and determine that the words after “from” and “lookup,” respectively, identify a dataset and the words after “threats” and “OUTPUT,” respectively, identify a field. Accordingly, the system  108  can infer that the dataset “traffic” has a field “sig” and a dataset “threats” has fields “sig” and “threat.” In some embodiments, based on this inference, the system  108  can update the dataset configuration record  604  of the dataset “traffic” or generate a field-dataset relationship annotation in the metadata catalog  221  with field information that identifies “sig” as a field associated with dataset “traffic.” Similarly, the system  108  can update the metadata catalog  221  with a field-dataset annotation that identifies “sig” and “threat” as fields of the dataset “threats.” Additionally, the system  108  can identify other fields in a query based on the syntax of the query. With each new field, the system  108  can update the corresponding dataset configuration record  604  and/or update a table that stores field information of fields in the system  108 . 
     As queries are executed or the fields are used, the system  108  can further revise the dataset configuration records  604  or field entries to reflect the use of the fields over time. In this way, the system  108  can track the fields in the system  108 , the relationship of the fields to datasets, and the frequency of use of the fields. 
     The system  108  can use the metadata related to the fields for a variety of functions. In some cases, the system  108  can use the metadata related to the fields to make field recommendations to a user, identify datasets with similar fields, suggest datasets for use together, identify datasets with a particular field, etc. 
     In some embodiments, as a user is typing a query related to a dataset, the system  108  can use the identified fields of the dataset to indicate to the user which fields are known about that dataset. In this way, the system  108  can provide insight into the content of a dataset as the user enters a query. Moreover, based on information of which fields are used most frequently, the system  108  can recommend or more prominently display a field to the user. For example, if the system  108  has determined (and the dataset configuration record  604  indicates) that the dataset “main” has at least three fields: “userID,” “IP address,” and “errorCode,” then as the user is typing out the query “from main | group by . . . ” the system  108  can display “userID,” “IP address,” and “errorCode.” Furthermore, if the system  108  has determined that “userID” is the most frequently used field (in total, by the user, or by a group associated with the user) related to “main” and/or most frequently used after “group by,” then the system  108  can suggest “userID” first or place it more prominently relative to the other fields. In this way, the system  108  can aid the user in crafting a query for the system  108  to execute based on information that the system  108  has iteratively learned about the data. 
     In certain cases, the system  108  can use the annotations related to the fields to identify datasets with similar fields and suggest use of datasets for views for queries. For example, if a first dataset with fields: “userID,” “productID,” and “viewTime,” is used to generate a view (or mapped to a view dataset), the system  108  can use the dataset configuration records  604  to compare the fields of the first dataset with fields of other datasets. If a second dataset is identified that includes the fields “userID,” “productID,” and “viewTime,” the system  108  can recommend the second dataset to the user for viewing and/or annotate the dataset configuration records  604  of the first and second dataset to indicate the existence of another dataset with similar fields. As another example, if a first dataset is a view dataset that uses the fields “userID,” “productID,” and “viewTime” from a second dataset to generate a view or UI, the system  108  can identify other datasets with the fields “userID,” “productID,” and “viewTime,” and suggest the identified datasets to the user of the view dataset. In this way, the system  108  can track similar datasets and identify potentially related datasets. 
     In some cases, a user may want to execute a query using a particular field. As the user enters a field identifier, the system  108  can suggest or identify datasets that include the particular field. In this way, the system  108  can aid the user in understanding the content of the data based on information that the system has iteratively learned about the data. 
     In certain embodiments, the system  108  can use the number of fields to estimate a size of a particular dataset. 
     3.8.4.2.2. Inter-Field Relationship Annotations 
     The metadata catalog  221  can store information about relationships between fields of datasets. In certain embodiments, the relationships can correspond to one field being derived from another field, fields with matching, corresponding, or correlating field values, etc. As described herein, annotations about the relationship between fields of datasets can be stored as part of a dataset configuration record  604  or as part of a separate data structure. When stored as a separate data structure, the data structure can identify the datasets that include the field or are otherwise associated with or related to the field. 
     In some cases, when storing the inter-field relationship annotations, the system  108  can store an ID for the relationship (e.g., name or unique name for the relationship), identifiers for the datasets associated with the related fields, and identifiers for the fields of the datasets that are related. In addition, the system  108  can store a relationship type. In some embodiments, the relationship type may be an exact relationship, such that field values of the different fields match (e.g., the field value for a “UID” field of one dataset matches the field value for an “ID” field of another dataset). In certain embodiments, the relationship type may be correlated, such as a field value of “time” in one dataset that is the most recent in time and before a field value of “_time” in another dataset. In some embodiments, the relationship type may be a complex relationship, such as the combination of field values from multiple fields in one dataset to one field value of one field in another dataset. 
     The relationships between fields can be identified in a variety of ways. In some cases, a user can manually include or add an inter-field relationship annotation to the metadata catalog  221  (e.g., the dataset configuration record  604  or an annotation entry). 
     In some embodiments, the system  108  can parse a query or dataset to identify relationships between fields. Similar to the identification of fields described herein, the system  108  can use the syntax of the query to identify relationships between fields. For example, with continued reference to the query of the threats-encountered dataset  604 N, based on the identification of a “sig” field in the datasets “traffic” and “threats,” the system  108  can determine that there is a relationship or foreign-key relationship between the “sig” field of “traffic” and the “sig” field of “threats.” In some such cases, based on the existence of the “sig” field in both datasets and its use in the same query, the system  108  can determine that field values in the “sig” field of “traffic” match the field values in the “sig” field of “threats.” As such, the system  108  can identify and store information about the relationship in the metadata catalog  221 . 
     As another example, based on a query or parsing a dataset, the system  108  can identify fields derived from other fields. For example, a query may initially refer to a field “salary.” Field values of the field “salary,” may be transformed and/or combined with other data as part of the query and later referenced as the field “sum.” In some such cases, by parsing the syntax of the query, the system  108  can identify the relationship between “sum” and “salary” and identify “sum” as a field derived from “salary.” As such, the system  108  can identify and store information about the relationship in the metadata catalog  221 . 
     In certain embodiments, the system  108  can identify inter-field relationships based on changes to the metadata catalog  221 . For example if the metadata catalog  221  identifies a relationship between fields A and B (e.g., field B is derived from field A) and a new inter-field relationship annotation is added indicating a relationship between fields B and C (e.g., field C is derived from field B), the system  108  can determine and generate an inter-field relationship annotation for fields A and C (e.g., field C is derived from field A). 
     The system  108  can use the inter-field relationship annotations to propagate additional annotations. With continued reference to the “sum” and “salary” field example above, if the “salary” field is indented as personally identifiable information or is otherwise subject to restrictions, the system  108  can use the relationship information to also mark the “sum” field as PII or restricted. As another example, if units or preferred units are identified for one field, the system  108  can use the identification of related fields to automatically identify units or preferred units for the field. By iteratively learning and storing information about relationships between fields, the system  108  can iteratively learn about the various connections between fields and improve compliance with data restrictions. 
     3.8.4.2.3. Inter-Dataset Relationship Annotations 
     The metadata catalog  221  can store annotations about relationships between datasets. In some embodiments, a dataset configuration record  604  of a first dataset can include the number and/or identification of related datasets, such as datasets that depend on the first dataset or datasets on which the first dataset depends. For example, if a first dataset refers to or uses data from a second dataset, the dataset configuration record  604  of the first dataset and the second dataset can identify the first dataset as being dependent on the second dataset. In certain embodiments, certain metrics data may be identified as being related to certain raw machine data datasets. As such the dataset configuration records  604  of the metrics data and raw machine data datasets can identify each other as being related. As described herein, in some cases, fields of different datasets may be related or correspond to each other. As such, based on the relationship between the fields, the metadata catalog  221  can identify the datasets as being related. As described herein, annotations about the relationship between fields of datasets can be stored as part of a dataset configuration record  604  or as part of a separate data structure. When stored as a separate data structure, the data structure can identify the datasets that include the field or are otherwise associated with or related to the field. 
     The relationships between datasets can be identified in a variety of ways. In some cases, a user can manually include or add a relationship between datasets to a dataset configuration record  604  and/or an annotation entry. 
     In certain embodiments, the system  108  can parse a query or dataset to identify relationships between datasets. For example, with continued reference to the threat-encountered dataset  604 N, the system  108  can parse the query “| from traffic | lookup threats sig OUTPUT threat | where threat=* | stats count by threat.” In parsing the query, the system  108  can use the syntax of the query language to identify datasets and relationships. For example, “from” and “lookup” can be commands and words following those commands can identify datasets. Accordingly, the system  108  can identify the datasets “trafficTeam.traffic” (which is the “shared.main” dataset imported from dataset association record  602 A) and “trafficTeam.threats” from the query. Furthermore, the system  108  can determine that the threats-encountered dataset  604 N is dependent on the “trafficTeam.traffic” and “trafficTeam.threats” datasets given that those datasets are used in the threats-encountered query. In other words, without the datasets “trafficTeam.traffic” and “trafficTeam.threats,” the “threats-encountered” dataset would not function properly or would return an error. 
     In addition, the system  108  can identify a relationship between the “trafficTeam.traffic” and “trafficTeam.threats” datasets. For example, given that the “trafficTeam.traffic” and “trafficTeam.threats” datasets both have the same field “sig,” the system  108  can identify a foreign key relationship between them and store a corresponding annotation—similar to the inter-field relationship field annotation. 
     In certain embodiments, the system  108  can identify inter-dataset relationships based on changes to the metadata catalog  221 . For example, if the metadata catalog  221  identifies a relationship between dataset A and B (non-limiting examples: (1) dataset B depends from dataset A, (2) dataset A can be mapped to dataset B, (3) dataset A and B have similar fields) and a new inter-field relationship annotation is added indicating a relationship between datasets B and C (non-limiting examples: (1) dataset C depends from dataset B, (2) dataset C can be mapped to dataset B, (3) dataset B can be mapped to dataset C), the system  108  can determine and generate an inter-dataset relationship annotation for datasets A and C (non-limiting examples: (1) dataset C depends from dataset A, (2) dataset A and C have similar fields, (3) dataset A can be mapped to dataset C). 
     The inter-dataset relationship annotations can be used for a variety of functions. In some cases, the system  108  can use the inter-dataset relationship annotations to generate additional annotations (e.g., additional inter-dataset relationships as described above), to propagate annotations from one dataset to another dataset (e.g., if units or preferred units are identified for dataset one then the units or preferred units may also be used for related dataset two), to lock datasets from or identify datasets for deletion (e.g., if dataset one depends on dataset two then dataset two should not be deleted or if dataset one depends on dataset two and dataset two is to be deleted then dataset one should also be deleted). 
     In certain embodiments, the system  108  can use the inter-dataset relationship annotations to propagate annotations from one dataset to another. For example, if dataset one is annotated as containing restricted information, the system  108  can use the inter-dataset relationship annotations to identify and annotate other datasets that depend from dataset one. As another example, if data from one dataset is annotated with a particular unit or preferred unit (e.g., MB instead of bytes), the system  108  can use the inter-dataset relationship annotations to identify other datasets that can be similarly annotated. Similarly, alarm thresholds for one dataset may be propagated to related datasets, etc. 
     3.8.4.2.4. Dataset Properties Annotations 
     The metadata catalog  221  can store annotations about the properties of a dataset, such as, but not limited to, an (estimated) size of a dataset, the usage of the dataset, and/or the capabilities of the dataset or dataset source. In some embodiments as users interact with the datasets, the system  108  can track when a dataset is used, the frequency of its use, the users or groups that use the dataset, etc. In addition, as a dataset is used, the metadata catalog  221  can estimate its size as it learns about the number of fields in the dataset and/or track the amount of data obtained from the dataset. In some cases, as data is extracted from datasets or dataset sources, the system  108  can monitor the performance of the dataset or dataset source. For example, the system  108  can monitor the speed of the dataset source, its bandwidth, network connectivity, etc. Based on this information, the system  108  can determine a cost to access a particular dataset. The cost may refer to time, computing resources, etc. This information can be stored as an annotation entry or as part of a dataset configuration record  604  as described herein. 
     Using the usage annotations, the system  108  can make recommendations to a user. For example, based on the frequency of use of dataset one or the number of datasets that refer to or depend from dataset one, the system  108  can recommend that dataset one be used for a particular query by the user. 
     Using the estimated size, speed, cost, or capability of a dataset, the system  108  can allocate resources for a query that depends on the dataset. For example, the system  108  can allocate more resources if it determines that the dataset is relatively large, slow, or supports parallelization, or allocate fewer resources if it determines that the dataset is relatively small or fast or does not support parallelization, etc. In addition, the system  108  can use the capabilities of the dataset to perform cost-based optimizations. For example, if, based on a query, the system  108  is to join data from dataset A and dataset B, based on the size, speed, etc. of the datasets, the system  108  can determine which dataset to access first. If, for example, dataset A is smaller or faster than dataset B, the system  108  can determine that dataset A should be accessed first and the results of dataset A can be used to refine the query to dataset B. 
     3.8.4.2.5. Normalization Annotations 
     The metadata catalog  221  can store normalization annotations about the datasets. In some cases, datasets may not be explicitly related, but may include similar data or fields. In some such cases, the system  108  can analyze the datasets to identify similar datasets or dataset that include similar data or fields. 
     In some cases, the metadata catalog  221  can identify similar datasets by comparing fields of datasets. As field annotations are added to the metadata catalog  221 , as described herein, the system  108  can compare the fields of one dataset with the fields of another dataset. If a threshold number of fields are the same, then the system  108  can generate a normalization annotation (or inter-dataset relationship annotation) indicating that the datasets include similar data. The threshold number can be based on the total number of fields in one or both datasets or the number of fields used in another dataset, such as a view dataset. 
     In certain embodiments, as datasets are added, such as a view dataset that references dataset  1 , the fields used by the view dataset can be compared with the fields of other datasets in the metadata catalog  221 . If dataset  2  includes the same or similar fields to those used by the view dataset from dataset  1 , the system  108  can generate a normalization annotation (or inter-dataset relationship annotation) indicating the similarity of dataset  2  to dataset  1  and/or indicate that dataset  2  could be used with the view dataset. 
     The normalization annotations can be used by the system  108  to make suggestions to a user about which datasets can be used with other datasets, such as view datasets, or to suggest that a user review a dataset. For example, as a user views an interface resulting from multiple fields from dataset  1  being mapped to a view dataset, the system  108  can recommend to the user that dataset  2  may provide additional results that may be helpful to the user&#39;s analysis of dataset  1 . 
     3.8.4.2.6. Unit Annotations 
     The metadata catalog  221  can store unit annotations about the datasets or fields of the datasets. In some cases, the system  108  can identify the unit annotations based on user input and/or based on analysis of related datasets. In certain embodiments, a user can indicate that data from a particular dataset or field has a particular unit and/or has a preferred unit. For example, a user can indicate that the unit for a particular metric is Hz and/or that the preferred unit for the metric is MHz or GHz. The unit and/or preferred unit can be stored by the system  108  as a unit annotation. As described herein, the unit annotation can be stored as part of a dataset configuration record  604  and/or annotation entry. 
     In some embodiments, the system  108  can determine unit annotations based on changes to the metadata catalog  221 . For example, if datasets A and B (or a field or metric of dataset A and B) are related and a new annotation is added indicating a preferred unit for dataset A (or a metric or field of dataset A), the system  108  can automatically determine and generate an annotation for dataset B (or a metric or field of dataset B) indicating the same preferred unit. 
     The unit annotations can be used by the system  108  to convert and/or display the data in a particular way. For example, if the unit annotation for a field or metric is identified as a byte and the preferred unit is a gigabyte, the system  108  can convert the bytes from the dataset to gigabytes and display the data as a gigabyte. Furthermore, the system  108  can propagate a unit annotation from one dataset to other datasets. In certain embodiments, the system  108  can identify fields or datasets related to the annotated field or dataset and propagate the unit annotation to the identified field or dataset. 
     3.8.4.2.7. Alarm Threshold Annotations 
     The metadata catalog  221  can store alarm threshold annotations about the datasets or fields of the datasets. In some cases, the system  108  can identify the alarm threshold annotations based on user input or based on previous user actions. For example, a user can indicate that when a particular metric or value satisfies a threshold, a person should be alerted or an alarm sounded. 
     In some embodiments, the system  108  can determine alarm threshold annotations based on changes to the metadata catalog  221 . For example, if datasets A and B (or a field or metric of dataset A and B) are related and a new annotation is added indicating an alarm threshold for dataset A (or a metric or field of dataset A), the system  108  can automatically determine and generate an annotation for dataset B (or a metric or field of dataset B) indicating the same alarm threshold. 
     The alarm threshold annotations can be used by the system  108  to generate alarms or automatically execute a query. For example, based on an alarm threshold being satisfied, the system  108  can execute a query that surfaces information related to the alarm threshold. In addition, the system  108  can propagate the alarm thresholds to related datasets or fields. 
     3.8.4.2.8. Data Category Annotations 
     The metadata catalog  221  can store data category annotations about the datasets or fields of the datasets. In some cases, the system  108  can identify the data category (or use restriction) annotations based on user input. For example, a user can indicate that a particular field or dataset includes personally identifiable information, should be separately tracked or monitored, etc. Based on the identification, the system  108  can store a data category annotation for that field or dataset. 
     In some embodiments, the system  108  can determine data category annotations based on changes to the metadata catalog  221 . For example, if datasets A and B (or a field or metric of dataset A and B) are related and a new annotation is added indicating a data category for dataset A (or a metric or field of dataset A), the system  108  can automatically determine and generate an annotation for dataset B (or a metric or field of dataset B) indicating the same data category. For instance, consider a scenario where dataset A includes a “social_security_num” field and a data category annotation indicating that the field is PII, and dataset B includes an “ID” field. If the metadata catalog is updated to reflect that the “ID” field is derived from the “social_security_num” field, then the system can automatically propagate the data category for the “social_security_num” field to the “ID” field. 
     The data category annotations can be used by the system  108  to track how certain data is being used and/or for compliance purposes. For example, the system can monitor PII data and generate alerts if it is not properly stored or processed. 
     3.8.4.2.9. User/Group Annotations 
     The metadata catalog  221  can store user or group annotations. In some cases, the system  108  can identify the user/group annotations based on user input. For example, a user can indicate that a particular user or group is associated with a particular dataset. In certain embodiments, the system  108  can generate the user/group annotations based on usage information. For example, the system  108  can track which datasets are accessed by which users or groups of users. This information can be stored as user/group annotations. As yet another example, if a particular user or group is the most frequent user of a dataset, the system  108  can relate the user or group to the dataset and generate a user/group annotation. 
     The user/group annotations can be used by the system  108  to determine how usage time should be allocated between parties. For example, if twenty users have access to a dataset, the system  108  can track which of the users or groups used the dataset most frequently and should be charged for the usage. 
     3.8.4.2.10. Application Annotations 
     The metadata catalog  221  can store application annotations. In certain embodiments, the system  108  can generate the application annotations based on usage information. For example, the system  108  can track which applications are used by which users and with what datasets. This information can be stored as application annotations as part of a dataset configuration record  604  or annotation entry. 
     The application annotations can be used by the system  108  to make recommendations to users. For example, if a threshold number of users frequently use three applications and a different user frequently uses two of the three applications, the system  108  can recommend the third application to the user. 
     4.0. Data Intake and Query System Functions 
     As described herein, the various components of the data intake and query system  108  can perform a variety of functions associated with the intake, indexing, storage, and querying of data from a variety of sources. It will be understood that any one or any combination of the functions described herein can be combined as part of a single routine or method. For example, a routine can include any one or any combination of one or more data ingestion functions, one or more indexing functions, and/or one or more searching functions. 
     4.1 Ingestion 
     As discussed above, ingestion into the data intake and query system  108  can be facilitated by an intake system  210 , which functions to process data according to a streaming data model, and make the data available as messages on an output ingestion buffer  310 , categorized according to a number of potential topics. Messages may be published to the output ingestion buffer  310  by a streaming data processors  308 , based on preliminary processing of messages published to an intake ingestion buffer  306 . The intake ingestion buffer  304  is, in turn, populated with messages by one or more publishers, each of which may represent an intake point for the data intake and query system  108 . The publishers may collectively implement a data retrieval subsystem  304  for the data intake and query system  108 , which subsystem  304  functions to retrieve data from a data source  202  and publish the data in the form of a message on the intake ingestion buffer  304 . A flow diagram depicting an illustrative embodiment for processing data at the intake system  210  is shown at  FIG. 7 . While the flow diagram is illustratively described with respect to a single message, the same or similar interactions may be used to process multiple messages at the intake system  210 . 
     4.1.1 Publication to Intake Topic(s) 
     As shown in  FIG. 7 , processing of data at the intake system  210  can illustratively begin at ( 1 ), where a data retrieval subsystem  304  or a data source  202  publishes a message to a topic at the intake ingestion buffer  306 . Generally described, the data retrieval subsystem  304  may include either or both push-based and pull-based publishers. Push-based publishers can illustratively correspond to publishers which independently initiate transmission of messages to the intake ingestion buffer  306 . Pull-based publishes can illustratively correspond to publishers which await an inquiry by the intake ingestion buffer  306  for messages to be published to the buffer  306 . The publication of a message at ( 1 ) is intended to include publication under either push- or pull-based models. 
     As discussed above, the data retrieval subsystem  304  may generate the message based on data received from a forwarder  302  and/or from one or more data sources  202 . In some instances, generation of a message may include converting a format of the data into a format suitable for publishing on the intake ingestion buffer  306 . Generation of a message may further include determining a topic for the message. In one embodiment, the data retrieval subsystem  304  selects a topic based on a data source  202  from which the data is received, or based on the specific publisher (e.g., intake point) on which the message is generated. For example, each data source  202  or specific publisher may be associated with a particular topic on the intake ingestion buffer  306  to which corresponding messages are published. In some instances, the same source data may be used to generate multiple messages to the intake ingestion buffer  306  (e.g., associated with different topics). 
     4.1.2 Transmission to Streaming Data Processors 
     After receiving a message from a publisher, the intake ingestion buffer  306 , at ( 2 ), determines subscribers to the topic. For the purposes of example, it will be associated that at least one device of the streaming data processors  308  has subscribed to the topic (e.g., by previously transmitting to the intake ingestion buffer  306  a subscription request). As noted above, the streaming data processors  308  may be implemented by a number of (logically or physically) distinct devices. As such, the streaming data processors  308 , at ( 2 ), may operate to determine which devices of the streaming data processors  308  have subscribed to the topic (or topics) to which the message was published. 
     Thereafter, at ( 3 ), the intake ingestion buffer  306  publishes the message to the streaming data processors  308  in accordance with the pub-sub model. This publication may correspond to a “push” model of communication, whereby an ingestion buffer determines topic subscribers and initiates transmission of messages within the topic to the subscribers. While interactions of  FIG. 7  are described with reference to such a push model, in some embodiments, a pull model of transmission may additionally or alternatively be used. Illustratively, rather than an ingestion buffer determining topic subscribers and initiating transmission of messages for the topic to a subscriber (e.g., the streaming data processors  308 ), an ingestion buffer may enable a subscriber to query for unread messages for a topic, and for the subscriber to initiate transmission of the messages from the ingestion buffer to the subscriber. Thus, an ingestion buffer (e.g., the intake ingestion buffer  306 ) may enable subscribers to “pull” messages from the buffer. As such, interactions of  FIG. 7  (e.g., including interactions ( 2 ) and ( 3 ) as well as ( 9 ), ( 10 ), ( 16 ), and ( 17 ) described below) may be modified to include pull-based interactions (e.g., whereby a subscriber queries for unread messages and retrieves the messages from an appropriate ingestion buffer). 
     4.1.3 Messages Processing 
     On receiving a message, the streaming data processors  308 , at ( 4 ), analyze the message to determine one or more rules applicable to the message. As noted above, rules maintained at the streaming data processors  308  can generally include selection criteria indicating messages to which the rule applies. This selection criteria may be formatted in the same manner or similarly to extraction rules, discussed in more detail below, and may include any number or combination of criteria based on the data included within a message or metadata of the message, such as regular expressions based on the data or metadata. 
     On determining that a rule is applicable to the message, the streaming data processors  308  can apply to the message one or more processing sub-rules indicated within the rule. Processing sub-rules may include modifying data or metadata of the message. Illustratively, processing sub-rules may edit or normalize data of the message (e.g., to convert a format of the data) or inject additional information into the message (e.g., retrieved based on the data of the message). For example, a processing sub-rule may specify that the data of the message be transformed according to a transformation algorithmically specified within the sub-rule. Thus, at ( 5 ), the streaming data processors  308  applies the sub-rule to transform the data of the message. 
     In addition or alternatively, processing sub-rules can specify a destination of the message after the message is processed at the streaming data processors  308 . The destination may include, for example, a specific ingestion buffer (e.g., intake ingestion buffer  306 , output ingestion buffer  310 , etc.) to which the message should be published, as well as the topic on the ingestion buffer to which the message should be published. For example, a particular rule may state that messages including metrics within a first format (e.g., imperial units) should have their data transformed into a second format (e.g., metric units) and be republished to the intake ingestion buffer  306 . At such, at ( 6 ), the streaming data processors  308  can determine a target ingestion buffer and topic for the transformed message based on the rule determined to apply to the message. Thereafter, the streaming data processors  308  publishes the message to the destination buffer and topic. 
     For the purposes of illustration, the interactions of  FIG. 7  assume that, during an initial processing of a message, the streaming data processors  308  determines (e.g., according to a rule of the data processor) that the message should be republished to the intake ingestion buffer  306 , as shown at ( 7 ). The streaming data processors  308  further acknowledges the initial message to the intake ingestion buffer  306 , at ( 8 ), thus indicating to the intake ingestion buffer  306  that the streaming data processors  308  has processed the initial message or published it to an intake ingestion buffer. The intake ingestion buffer  306  may be configured to maintain a message until all subscribers have acknowledged receipt of the message. Thus, transmission of the acknowledgement at ( 8 ) may enable the intake ingestion buffer  306  to delete the initial message. 
     It is assumed for the purposes of these illustrative interactions that at least one device implementing the streaming data processors  308  has subscribed to the topic to which the transformed message is published. Thus, the streaming data processors  308  is expected to again receive the message (e.g., as previously transformed the streaming data processors  308 ), determine whether any rules apply to the message, and process the message in accordance with one or more applicable rules. In this manner, interactions ( 2 ) through ( 8 ) may occur repeatedly, as designated in  FIG. 7  by the iterative processing loop  402 . By use of iterative processing, the streaming data processors  308  may be configured to progressively transform or enrich messages obtained at data sources  202 . Moreover, because each rule may specify only a portion of the total transformation or enrichment of a message, rules may be created without knowledge of the entire transformation. For example, a first rule may be provided by a first system to transform a message according to the knowledge of that system (e.g., transforming an error code into an error descriptor), while a second rule may process the message according to the transformation (e.g., by detecting that the error descriptor satisfies alert criteria). Thus, the streaming data processors  308  enable highly granulized processing of data without requiring an individual entity (e.g., user or system) to have knowledge of all permutations or transformations of the data. 
     After completion of the iterative processing loop  402 , the interactions of  FIG. 7  proceed to interaction ( 9 ), where the intake ingestion buffer  306  again determines subscribers of the message. The intake ingestion buffer  306 , at ( 10 ), the transmits the message to the streaming data processors  308 , and the streaming data processors  308  again analyze the message for applicable rules, process the message according to the rules, determine a target ingestion buffer and topic for the processed message, and acknowledge the message to the intake ingestion buffer  306 , at interactions ( 11 ), ( 12 ), ( 13 ), and ( 15 ). These interactions are similar to interactions ( 4 ), ( 5 ), ( 6 ), and ( 8 ) discussed above, and therefore will not be re-described. However, in contrast to interaction ( 13 ), the streaming data processors  308  may determine that a target ingestion buffer for the message is the output ingestion buffer  310 . Thus, the streaming data processors  308 , at ( 14 ), publishes the message to the output ingestion buffer  310 , making the data of the message available to a downstream system. 
       FIG. 7  illustrates one processing path for data at the streaming data processors  308 . However, other processing paths may occur according to embodiments of the present disclosure. For example, in some instances, a rule applicable to an initially published message on the intake ingestion buffer  306  may cause the streaming data processors  308  to publish the message out ingestion buffer  310  on first processing the data of the message, without entering the iterative processing loop  402 . Thus, interactions ( 2 ) through ( 8 ) may be omitted. 
     In other instances, a single message published to the intake ingestion buffer  306  may spawn multiple processing paths at the streaming data processors  308 . Illustratively, the streaming data processors  308  may be configured to maintain a set of rules, and to independently apply to a message all rules applicable to the message. Each application of a rule may spawn an independent processing path, and potentially a new message for publication to a relevant ingestion buffer. In other instances, the streaming data processors  308  may maintain a ranking of rules to be applied to messages, and may be configured to process only a highest ranked rule which applies to the message. Thus, a single message on the intake ingestion buffer  306  may result in a single message or multiple messages published by the streaming data processors  308 , according to the configuration of the streaming data processors  308  in applying rules. 
     As noted above, the rules applied by the streaming data processors  308  may vary during operation of those processors  308 . For example, the rules may be updated as user queries are received (e.g., to identify messages whose data is relevant to those queries). In some instances, rules of the streaming data processors  308  may be altered during the processing of a message, and thus the interactions of  FIG. 7  may be altered dynamically during operation of the streaming data processors  308 . 
     While the rules above are described as making various illustrative alterations to messages, various other alterations are possible within the present disclosure. For example, rules in some instances be used to remove data from messages, or to alter the structure of the messages to conform to the format requirements of a downstream system or component. Removal of information may be beneficial, for example, where the messages include private, personal, or confidential information which is unneeded or should not be made available by a downstream system. In some instances, removal of information may include replacement of the information with a less confidential value. For example, a mailing address may be considered confidential information, whereas a postal code may not be. Thus, a rule may be implemented at the streaming data processors  308  to replace mailing addresses with a corresponding postal code, to ensure confidentiality. Various other alterations will be apparent in view of the present disclosure. 
     4.1.4 Transmission to Subscribers 
     As discussed above, the rules applied by the streaming data processors  308  may eventually cause a message containing data from a data source  202  to be published to a topic on an output ingestion buffer  310 , which topic may be specified, for example, by the rule applied by the streaming data processors  308 . The output ingestion buffer  310  may thereafter make the message available to downstream systems or components. These downstream systems or components are generally referred to herein as “subscribers.” For example, the indexing system  212  may subscribe to an indexing topic  342 , the query system  214  may subscribe to a search results topic  348 , a client device  102  may subscribe to a custom topic  352 A, etc. In accordance with the pub-sub model, the output ingestion buffer  310  may transmit each message published to a topic to each subscriber of that topic, and resiliently store the messages until acknowledged by each subscriber (or potentially until an error is logged with respect to a subscriber). As noted above, other models of communication are possible and contemplated within the present disclosure. For example, rather than subscribing to a topic on the output ingestion buffer  310  and allowing the output ingestion buffer  310  to initiate transmission of messages to the subscriber  702 , the output ingestion buffer  310  may be configured to allow a subscriber  702  to query the buffer  310  for messages (e.g., unread messages, new messages since last transmission, etc.), and to initiate transmission of those messages form the buffer  310  to the subscriber  702 . In some instances, such querying may remove the need for the subscriber  702  to separately “subscribe” to the topic. 
     Accordingly, at ( 16 ), after receiving a message to a topic, the output ingestion buffer  310  determines the subscribers to the topic (e.g., based on prior subscription requests transmitted to the output ingestion buffer  310 ). At ( 17 ), the output ingestion buffer  310  transmits the message to a subscriber  402 . Thereafter, the subscriber may process the message at ( 18 ). Illustrative examples of such processing are described below, and may include (for example) preparation of search results for a client device  204 , indexing of the data at the indexing system  212 , and the like. After processing, the subscriber can acknowledge the message to the output ingestion buffer  310 , thus confirming that the message has been processed at the subscriber. 
     4.1.5 Data Resiliency and Security 
     In accordance with embodiments of the present disclosure, the interactions of  FIG. 7  may be ordered such that resiliency is maintained at the intake system  210 . Specifically, as disclosed above, data streaming systems (which may be used to implement ingestion buffers) may implement a variety of techniques to ensure the resiliency of messages stored at such systems, absent systematic or catastrophic failures. Thus, the interactions of  FIG. 7  may be ordered such that data from a data source  202  is expected or guaranteed to be included in at least one message on an ingestion system until confirmation is received that the data is no longer required. 
     For example, as shown in  FIG. 7 , interaction ( 8 )—wherein the streaming data processors  308  acknowledges receipt of an initial message at the intake ingestion buffer  306 —can illustratively occur after interaction ( 7 )—wherein the streaming data processors  308  republishes the data to the intake ingestion buffer  306 . Similarly, interaction ( 15 )—wherein the streaming data processors  308  acknowledges receipt of an initial message at the intake ingestion buffer  306 —can illustratively occur after interaction ( 14 )—wherein the streaming data processors  308  republishes the data to the intake ingestion buffer  306 . This ordering of interactions can ensure, for example, that the data being processed by the streaming data processors  308  is, during that processing, always stored at the ingestion buffer  306  in at least one message. Because an ingestion buffer  306  can be configured to maintain and potentially resend messages until acknowledgement is received from each subscriber, this ordering of interactions can ensure that, should a device of the streaming data processors  308  fail during processing, another device implementing the streaming data processors  308  can later obtain the data and continue the processing. 
     Similarly, as shown in  FIG. 7 , each subscriber  402  may be configured to acknowledge a message to the output ingestion buffer  310  after processing for the message is completed. In this manner, should a subscriber  402  fail after receiving a message but prior to completing processing of the message, the processing of the subscriber  402  can be restarted to successfully process the message. Thus, the interactions of  FIG. 7  can maintain resiliency of data on the intake system  108  commensurate with the resiliency provided by an individual ingestion buffer  306 . 
     While message acknowledgement is described herein as an illustrative mechanism to ensure data resiliency at an intake system  210 , other mechanisms for ensuring data resiliency may additionally or alternatively be used. 
     As will be appreciated in view of the present disclosure, the configuration and operation of the intake system  210  can further provide high amounts of security to the messages of that system. Illustratively, the intake ingestion buffer  306  or output ingestion buffer  310  may maintain an authorization record indicating specific devices or systems with authorization to publish or subscribe to a specific topic on the ingestion buffer. As such, an ingestion buffer may ensure that only authorized parties are able to access sensitive data. In some instances, this security may enable multiple entities to utilize the intake system  210  to manage confidential information, with little or no risk of that information being shared between the entities. The managing of data or processing for multiple entities is in some instances referred to as “multi-tenancy.” 
     Illustratively, a first entity may publish messages to a first topic on the intake ingestion buffer  306 , and the intake ingestion buffer  306  may verify that any intake point or data source  202  publishing to that first topic be authorized by the first entity to do so. The streaming data processors  308  may maintain rules specific to the first entity, which the first entity may illustrative provide through authenticated session on an interface (e.g., GUI, API, command line interface (CLI), etc.). The rules of the first entity may specify one or more entity-specific topics on the output ingestion buffer  310  to which messages containing data of the first entity should be published by the streaming data processors  308 . The output ingestion buffer  310  may maintain authorization records for such entity-specific topics, thus restricting messages of those topics to parties authorized by the first entity. In this manner, data security for the first entity can be ensured across the intake system  210 . Similar operations may be performed for other entities, thus allowing multiple entities to separately and confidentially publish data to and retrieve data from the intake system. 
     4.1.6 Message Processing Algorithm 
     With reference to  FIG. 8 , an illustrative algorithm or routine for processing messages at the intake system  210  will be described in the form of a flowchart. The routine begins at block b 102 , where the intake system  210  obtains one or more rules for handling messages enqueued at an intake ingestion buffer  306 . As noted above, the rules may, for example, be human-generated, or may be automatically generated based on operation of the data intake and query system  108  (e.g., in response to user submission of a query to the system  108 ). 
     At block  804 , the intake system  210  obtains a message at the intake ingestion buffer  306 . The message may be published to the intake ingestion buffer  306 , for example, by the data retrieval subsystem  304  (e.g., working in conjunction with a forwarder  302 ) and reflect data obtained from a data source  202 . 
     At block  806 , the intake system  210  determines whether any obtained rule applies to the message. Illustratively, the intake system  210  (e.g., via the streaming data processors  308 ) may apply selection criteria of each rule to the message to determine whether the message satisfies the selection criteria. Thereafter, the routine varies according to whether a rule applies to the message. If no rule applies, the routine can continue to block  814 , where the intake system  210  transmits an acknowledgement for the message to the intake ingestion buffer  306 , thus enabling the buffer  306  to discard the message (e.g., once all other subscribers have acknowledged the message). In some variations of the routine, a “default rule” may be applied at the intake system  210 , such that all messages are processed as least according to the default rule. The default rule may, for example, forward the message to an indexing topic  342  for processing by an indexing system  212 . In such a configuration, block  806  may always evaluate as true. 
     In the instance that at least one rule is determined to apply to the message, the routine continues to block  808 , where the intake system  210  (e.g., via the streaming data processors  308 ) transforms the message as specified by the applicable rule. For example, a processing sub-rule of the applicable rule may specify that data or metadata of the message be converted from one format to another via an algorithmic transformation. As such, the intake system  210  may apply the algorithmic transformation to the data or metadata of the message at block  808  to transform the data or metadata of the message. In some instances, no transformation may be specified within intake system  210 , and thus block  808  may be omitted. 
     At block  810 , the intake system  210  determines a destination ingestion buffer to which to publish the (potentially transformed) message, as well as a topic to which the message should be published. The destination ingestion buffer and topic may be specified, for example, in processing sub-rules of the rule determined to apply to the message. In one embodiment, the destination ingestion buffer and topic may vary according to the data or metadata of the message. In another embodiment, the destination ingestion buffer and topic may be fixed with respect to a particular rule. 
     At block  812 , the intake system  210  publishes the (potentially transformed) message to the determined destination ingestion buffer and topic. The determined destination ingestion buffer may be, for example, the intake ingestion buffer  306  or the output ingestion buffer  310 . Thereafter, at block  814 , the intake system  210  acknowledges the initial message on the intake ingestion buffer  306 , thus enabling the intake ingestion buffer  306  to delete the message. 
     Thereafter, the routine returns to block  804 , where the intake system  210  continues to process messages from the intake ingestion buffer  306 . Because the destination ingestion buffer determined during a prior implementation of the routine may be the intake ingestion buffer  306 , the routine may continue to process the same underlying data within multiple messages published on that buffer  306  (thus implementing an iterative processing loop with respect to that data). The routine may then continue to be implemented during operation of the intake system  210 , such that data published to the intake ingestion buffer  306  is processed by the intake system  210  and made available on an output ingestion buffer  310  to downstream systems or components. 
     While the routine of  FIG. 8  is described linearly, various implementations may involve concurrent or at least partially parallel processing. For example, in one embodiment, the intake system  210  is configured to process a message according to all rules determined to apply to that message. Thus for example if at block  806  five rules are determined to apply to the message, the intake system  210  may implement five instances of blocks  808  through  814 , each of which may transform the message in different ways or publish the message to different ingestion buffers or topics. These five instances may be implemented in serial, parallel, or a combination thereof. Thus, the linear description of  FIG. 8  is intended simply for illustrative purposes. 
     While the routine of  FIG. 8  is described with respect to a single message, in some embodiments streaming data processors  308  may be configured to process multiple messages concurrently or as a batch. Similarly, all or a portion of the rules used by the streaming data processors  308  may apply to sets or batches of messages. Illustratively, the streaming data processors  308  may obtain a batch of messages from the intake ingestion buffer  306  and process those messages according to a set of “batch” rules, whose criteria and/or processing sub-rules apply to the messages of the batch collectively. Such rules may, for example, determine aggregate attributes of the messages within the batch, sort messages within the batch, group subsets of messages within the batch, and the like. In some instances, such rules may further alter messages based on aggregate attributes, sorting, or groupings. For example, a rule may select the third messages within a batch, and perform a specific operation on that message. As another example, a rule may determine how many messages within a batch are contained within a specific group of messages. Various other examples for batch-based rules will be apparent in view of the present disclosure. Batches of messages may be determined based on a variety of criteria. For example, the streaming data processors  308  may batch messages based on a threshold number of messages (e.g., each thousand messages), based on timing (e.g., all messages received over a ten minute window), or based on other criteria (e.g., the lack of new messages posted to a topic within a threshold period of time). 
     4.2. Indexing 
       FIG. 9  is a data flow diagram illustrating an embodiment of the data flow and communications between a variety of the components of the data intake and query system  108  during indexing. Specifically,  FIG. 9  is a data flow diagram illustrating an embodiment of the data flow and communications between an ingestion buffer  310 , an indexing node manager  406  or partition manager  408 , an indexer  410 , common storage  216 , and the data store catalog  220 . However, it will be understood, that in some of embodiments, one or more of the functions described herein with respect to  FIG. 9  can be omitted, performed in a different order and/or performed by a different component of the data intake and query system  108 . Accordingly, the illustrated embodiment and description should not be construed as limiting. 
     At ( 1 ), the indexing node manager  406  activates a partition manager  408  for a partition. As described herein, the indexing node manager  406  can activate a partition manager  408  for each partition or shard that is processed by an indexing node  404 . In some embodiments, the indexing node manager  406  can activate the partition manager  408  based on an assignment of a new partition to the indexing node  404  or a partition manager  408  becoming unresponsive or unavailable, etc. 
     In some embodiments, the partition manager  408  can be a copy of the indexing node manager  406  or a copy of a template process. In certain embodiments, the partition manager  408  can be instantiated in a separate container from the indexing node manager  406 . 
     At ( 2 ), the ingestion buffer  310  sends data and a buffer location to the indexing node  212 . As described herein, the data can be raw machine data, performance metrics data, correlation data, JSON blobs, XML data, data in a data model, report data, tabular data, streaming data, data exposed in an API, data in a relational database, etc. The buffer location can correspond to a marker in the ingestion buffer  310  that indicates the point at which the data within a partition has been communicated to the indexing node  404 . For example, data before the marker can correspond to data that has not been communicated to the indexing node  404 , and data after the marker can correspond to data that has been communicated to the indexing node. In some cases, the marker can correspond to a set of data that has been communicated to the indexing node  404 , but for which no indication has been received that the data has been stored. Accordingly, based on the marker, the ingestion buffer  310  can retain a portion of its data persistently until it receives confirmation that the data can be deleted or has been stored in common storage  216 . 
     At ( 3 ), the indexing node manager  406  tracks the buffer location and the partition manager  408  communicates the data to the indexer  410 . As described herein, the indexing node manager  406  can track (and/or store) the buffer location for the various partitions received from the ingestion buffer  310 . In addition, as described herein, the partition manager  408  can forward the data received from the ingestion buffer  310  to the indexer  410  for processing. In various implementations, as previously described, the data from ingestion buffer  310  that is sent to the indexer  410  may include a path to stored data, e.g., data stored in common store  216  or another common store, which is then retrieved by the indexer  410  or another component of the indexing node  404 . 
     At ( 4 ), the indexer  410  processes the data. As described herein, the indexer  410  can perform a variety of functions, enrichments, or transformations on the data as it is indexed. For example, the indexer  410  can parse the data, identify events from the data, identify and associate timestamps with the events, associate metadata or one or more field values with the events, group events (e.g., based on time, partition, and/or tenant ID, etc.), etc. Furthermore, the indexer  410  can generate buckets based on a bucket creation policy and store the events in the hot buckets, which may be stored in data store  412  of the indexing node  404  associated with that indexer  410  (see  FIG. 4 ). 
     At ( 5 ), the indexer  410  reports the size of the data being indexed to the partition manager  408 . In some cases, the indexer  410  can routinely provide a status update to the partition manager  408  regarding the data that is being processed by the indexer  410 . 
     The status update can include, but is not limited to the size of the data, the number of buckets being created, the amount of time since the buckets have been created, etc. In some embodiments, the indexer  410  can provide the status update based on one or more thresholds being satisfied (e.g., one or more threshold sizes being satisfied by the amount of data being processed, one or more timing thresholds being satisfied based on the amount of time the buckets have been created, one or more bucket number thresholds based on the number of buckets created, the number of hot or warm buckets, number of buckets that have not been stored in common storage  216 , etc.). 
     In certain cases, the indexer  410  can provide an update to the partition manager  408  regarding the size of the data that is being processed by the indexer  410  in response to one or more threshold sizes being satisfied. For example, each time a certain amount of data is added to the indexer  410  (e.g., 5 MB, 10 MB, etc.), the indexer  410  can report the updated size to the partition manager  408 . In some cases, the indexer  410  can report the size of the data stored thereon to the partition manager  408  once a threshold size is satisfied. 
     In certain embodiments, the indexer  408  reports the size of the date being indexed to the partition manager  408  based on a query by the partition manager  408 . In certain embodiments, the indexer  410  and partition manager  408  maintain an open communication link such that the partition manager  408  is persistently aware of the amount of data on the indexer  410 . 
     In some cases, a partition manager  408  monitors the data processed by the indexer  410 . For example, the partition manager  408  can track the size of the data on the indexer  410  that is associated with the partition being managed by the partition manager  408 . In certain cases, one or more partition managers  408  can track the amount or size of the data on the indexer  410  that is associated with any partition being managed by the indexing node manager  406  or that is associated with the indexing node  404 . 
     At ( 6 ), the partition manager  408  instructs the indexer  410  to copy the data to common storage  216 . As described herein, the partition manager  408  can instruct the indexer  410  to copy the data to common storage  216  based on a bucket roll-over policy. As described herein, in some cases, the bucket roll-over policy can indicate that one or more buckets are to be rolled over based on size. Accordingly, in some embodiments, the partition manager  408  can instruct the indexer  410  to copy the data to common storage  216  based on a determination that the amount of data stored on the indexer  410  satisfies a threshold amount. The threshold amount can correspond to the amount of data associated with the partition that is managed by the partition manager  408  or the amount of data being processed by the indexer  410  for any partition. 
     In some cases, the partition manager  408  can instruct the indexer  410  to copy the data that corresponds to the partition being managed by the partition manager  408  to common storage  216  based on the size of the data that corresponds to the partition satisfying the threshold amount. In certain embodiments, the partition manager  408  can instruct the indexer  410  to copy the data associated with any partition being processed by the indexer  410  to common storage  216  based on the amount of the data from the partitions that are being processed by the indexer  410  satisfying the threshold amount. 
     In some embodiments, ( 5 ) and/or ( 6 ) can be omitted. For example, the indexer  410  can monitor the data stored thereon. Based on the bucket roll-over policy, the indexer  410  can determine that the data is to be copied to common storage  216 . Accordingly, in some embodiments, the indexer  410  can determine that the data is to be copied to common storage  216  without communication with the partition manager  408 . 
     At ( 7 ), the indexer  410  copies and/or stores the data to common storage  216 . As described herein, in some cases, as the indexer  410  processes the data, it generates events and stores the events in hot buckets. In response to receiving the instruction to move the data to common storage  216 , the indexer  410  can convert the hot buckets to warm buckets, and copy or move the warm buckets to the common storage  216 . 
     As part of storing the data to common storage  216 , the indexer  410  can verify or obtain acknowledgements that the data is stored successfully. In some embodiments, the indexer  410  can determine information regarding the data stored in the common storage  216 . For example, the information can include location information regarding the data that was stored to the common storage  216 , bucket identifiers of the buckets that were copied to common storage  216 , as well as additional information, e.g., in implementations in which the ingestion buffer  310  uses sequences of records as the form for data storage, the list of record sequence numbers that were used as part of those buckets that were copied to common storage  216 . 
     At ( 8 ), the indexer  410  reports or acknowledges to the partition manager  408  that the data is stored in the common storage  216 . In various implementations, this can be in response to periodic requests from the partition manager  408  to the indexer  410  regarding which buckets and/or data have been stored to common storage  216 . The indexer  410  can provide the partition manager  408  with information regarding the data stored in common storage  216  similar to the data that is provided to the indexer  410  by the common storage  216 . In some cases, ( 8 ) can be replaced with the common storage  216  acknowledging or reporting the storage of the data to the partition manager  408 . 
     At ( 9 ), the partition manager  408  updates the data store catalog  220 . As described herein, the partition manager  408  can update the data store catalog  220  with information regarding the data or buckets stored in common storage  216 . For example, the partition manager  408  can update the data store catalog  220  to include location information, a bucket identifier, a time range, and tenant and partition information regarding the buckets copied to common storage  216 , etc. In this way, the data store catalog  220  can include up-to-date information regarding the buckets stored in common storage  216 . 
     At ( 10 ), the partition manager  408  reports the completion of the storage to the ingestion buffer  310 , and at ( 11 ), the ingestion buffer  310  updates the buffer location or marker. Accordingly, in some embodiments, the ingestion buffer  310  can maintain its marker until it receives an acknowledgement that the data that it sent to the indexing node  404  has been indexed by the indexing node  404  and stored to common storage  216 . In addition, the updated buffer location or marker can be communicated to and stored by the indexing node manager  406 . In this way, a data intake and query system  108  can use the ingestion buffer  310  to provide a stateless environment for the indexing system  212 . For example, as described herein, if an indexing node  404  or one of its components (e.g., indexing node manager  486 , partition manager  408 , indexer) becomes unavailable or unresponsive before data from the ingestion buffer  310  is copied to common storage  216 , the indexing system  212  can generate or assign a new indexing node  404  (or component), to process the data that was assigned to the now unavailable indexing node  404  (or component) while reducing, minimizing, or eliminating data loss. 
     At ( 12 ), a bucket manager  414 , which may form part of the indexer  410 , the indexing node  404 , or indexing system  212 , merges multiple buckets into one or more merged buckets. As described herein, to reduce delay between processing data and making that data available for searching, the indexer  410  can convert smaller hot buckets to warm buckets and copy the warm buckets to common storage  216 . However, as smaller buckets in common storage  216  can result in increased overhead and storage costs, the bucket manager  414  can monitor warm buckets in the indexer  410  and merge the warm buckets into one or more merged buckets. 
     In some cases, the bucket manager  414  can merge the buckets according to a bucket merge policy. As described herein, the bucket merge policy can indicate which buckets are candidates for a merge (e.g., based on time ranges, size, tenant/partition or other identifiers, etc.), the number of buckets to merge, size or time range parameters for the merged buckets, a frequency for creating the merged buckets, etc. 
     At ( 13 ), the bucket manager  414  stores and/or copies the merged data or buckets to common storage  216 , and obtains information about the merged buckets stored in common storage  216 . Similar to ( 7 ), the obtained information can include information regarding the storage of the merged buckets, such as, but not limited to, the location of the buckets, one or more bucket identifiers, tenant or partition identifiers, etc. At ( 14 ), the bucket manager  414  reports the storage of the merged data to the partition manager  408 , similar to the reporting of the data storage at ( 8 ). 
     At ( 15 ), the indexer  410  deletes data from the data store (e.g., data store  412 ). As described herein, once the merged buckets have been stored in common storage  216 , the indexer  410  can delete corresponding buckets that it has stored locally. For example, the indexer  410  can delete the merged buckets from the data store  412 , as well as the pre-merged buckets (buckets used to generate the merged buckets). By removing the data from the data store  412 , the indexer  410  can free up additional space for additional hot buckets, warm buckets, and/or merged buckets. 
     At ( 16 ), the common storage  216  deletes data according to a bucket management policy. As described herein, once the merged buckets have been stored in common storage  216 , the common storage  216  can delete the pre-merged buckets stored therein. In some cases, as described herein, the common storage  216  can delete the pre-merged buckets immediately, after a predetermined amount of time, after one or more queries relying on the pre-merged buckets have completed, or based on other criteria in the bucket management policy, etc. In certain embodiments, a controller at the common storage  216  handles the deletion of the data in common storage  216  according to the bucket management policy. In certain embodiments, one or more components of the indexing node  404  delete the data from common storage  216  according to the bucket management policy. However, for simplicity, reference is made to common storage  216  performing the deletion. 
     At ( 17 ), the partition manager  408  updates the data store catalog  220  with the information about the merged buckets. Similar to ( 9 ), the partition manager  408  can update the data store catalog  220  with the merged bucket information. The information can include, but is not limited to, the time range of the merged buckets, location of the merged buckets in common storage  216 , a bucket identifier for the merged buckets, tenant and partition information of the merged buckets, etc. In addition, as part of updating the data store catalog  220 , the partition manager  408  can remove reference to the pre-merged buckets. Accordingly, the data store catalog  220  can be revised to include information about the merged buckets and omit information about the pre-merged buckets. In this way, as the search managers  514  request information about buckets in common storage  216  from the data store catalog  220 , the data store catalog  220  can provide the search managers  514  with the merged bucket information. 
     As mentioned previously, in some of embodiments, one or more of the functions described herein with respect to  FIG. 9  can be omitted, performed in a variety of orders and/or performed by a different component of the data intake and query system  108 . For example, the partition manager  408  can ( 9 ) update the data store catalog  220  before, after, or concurrently with the deletion of the data in the ( 15 ) indexer  410  or ( 16 ) common storage  216 . Similarly, in certain embodiments, the indexer  410  can ( 12 ) merge buckets before, after, or concurrently with ( 7 )-( 11 ), etc. 
     4.2.1. Containerized Indexing Nodes 
       FIG. 10  is a flow diagram illustrative of an embodiment of a routine  1000  implemented by the indexing system  212  to store data in common storage  216 . Although described as being implemented by the indexing system  212 , it will be understood that the elements outlined for routine  1000  can be implemented by one or more computing devices/components that are associated with the data intake and query system  108 , such as, but not limited to, the indexing manager  402 , the indexing node  404 , indexing node manager  406 , the partition manager  408 , the indexer  410 , the bucket manager  414 , etc. Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  1002 , the indexing system  212  receives data. As described herein, the system  312  can receive data from a variety of sources in various formats. For example, as described herein, the data received can be machine data, performance metrics, correlated data, etc. 
     At block  1004 , the indexing system  212  stores the data in buckets using one or more containerized indexing nodes  404 . As described herein, the indexing system  212  can include multiple containerized indexing nodes  404  to receive and process the data. The containerized indexing nodes  404  can enable the indexing system  212  to provide a highly extensible and dynamic indexing service. For example, based on resource availability and/or workload, the indexing system  212  can instantiate additional containerized indexing nodes  404  or terminate containerized indexing nodes  404 . Further, multiple containerized indexing nodes  404  can be instantiated on the same computing device, and share the resources of the computing device. 
     As described herein, each indexing node  404  can be implemented using containerization or operating-system-level virtualization, or other virtualization technique. For example, the indexing node  404 , or one or more components of the indexing node  404  can be implemented as separate containers or container instances. Each container instance can have certain resources (e.g., memory, processor, etc.) of the underlying computing system assigned to it, but may share the same operating system and may use the operating system&#39;s system call interface. Further, each container may run the same or different computer applications concurrently or separately, and may interact with each other. It will be understood that other virtualization techniques can be used. For example, the containerized indexing nodes  404  can be implemented using virtual machines using full virtualization or paravirtualization, etc. 
     In some embodiments, the indexing node  404  can be implemented as a group of related containers or a pod, and the various components of the indexing node  404  can be implemented as related containers of a pod. Further, the indexing node  404  can assign different containers to execute different tasks. For example, one container of a containerized indexing node  404  can receive the incoming data and forward it to a second container for processing, etc. The second container can generate buckets for the data, store the data in buckets, and communicate the buckets to common storage  216 . A third container of the containerized indexing node  404  can merge the buckets into merged buckets and store the merged buckets in common storage. However, it will be understood that the containerized indexing node  404  can be implemented in a variety of configurations. For example, in some cases, the containerized indexing node  404  can be implemented as a single container and can include multiple processes to implement the tasks described above by the three containers. Any combination of containerization and processed can be used to implement the containerized indexing node  404  as desired. 
     In some embodiments, the containerized indexing node  404  processes the received data (or the data obtained using the received data) and stores it in buckets. As part of the processing, the containerized indexing node  404  can determine information about the data (e.g., host, source, sourcetype), extract or identify timestamps, associated metadata fields with the data, extract keywords, transform the data, identify and organize the data into events having raw machine data associated with a timestamp, etc. In some embodiments, the containerized indexing node  404  uses one or more configuration files and/or extraction rules to extract information from the data or events. 
     In addition, as part of processing and storing the data, the containerized indexing node  404  can generate buckets for the data according to a bucket creation policy. As described herein, the containerized indexing node  404  can concurrently generate and fill multiple buckets with the data that it processes. In some embodiments, the containerized indexing node  404  generates buckets for each partition or tenant associated with the data that is being processed. In certain embodiments, the indexing node  404  stores the data or events in the buckets based on the identified timestamps. 
     Furthermore, containerized indexing node  404  can generate one or more indexes associated with the buckets, such as, but not limited to, one or more inverted indexes, TSIDXs, keyword indexes, etc. The data and the indexes can be stored in one or more files of the buckets. In addition, the indexing node  404  can generate additional files for the buckets, such as, but not limited to, one or more filter files, a bucket summary, or manifest, etc. 
     At block  1006 , the indexing node  404  stores buckets in common storage  216 . As described herein, in certain embodiments, the indexing node  404  stores the buckets in common storage  216  according to a bucket roll-over policy. In some cases, the buckets are stored in common storage  216  in one or more directories based on an index/partition or tenant associated with the buckets. Further, the buckets can be stored in a time series manner to facilitate time series searching as described herein. Additionally, as described herein, the common storage  216  can replicate the buckets across multiple tiers and data stores across one or more geographical locations. 
     Fewer, more, or different blocks can be used as part of the routine  1000 . In some cases, one or more blocks can be omitted. For example, in some embodiments, the containerized indexing node  404  or an indexing system manager  402  can monitor the amount of data received by the indexing system  212 . Based on the amount of data received and/or a workload or utilization of the containerized indexing node  404 , the indexing system  212  can instantiate an additional containerized indexing node  404  to process the data. 
     In some cases, the containerized indexing node  404  can instantiate a container or process to manage the processing and storage of data from an additional shard or partition of data received from the intake system. For example, as described herein, the containerized indexing node  404  can instantiate a partition manager  408  for each partition or shard of data that is processed by the containerized indexing node  404 . 
     In certain embodiments, the indexing node  404  can delete locally stored buckets. For example, once the buckets are stored in common storage  216 , the indexing node  404  can delete the locally stored buckets. In this way, the indexing node  404  can reduce the amount of data stored thereon. 
     As described herein, the indexing node  404  can merge buckets and store merged buckets in the common storage  216 . In some cases, as part of merging and storing buckets in common storage  216 , the indexing node  404  can delete locally storage pre-merged buckets (buckets used to generate the merged buckets) and/or the merged buckets or can instruct the common storage  216  to delete the pre-merged buckets. In this way, the indexing node  404  can reduce the amount of data stored in the indexing node  404  and/or the amount of data stored in common storage  216 . 
     In some embodiments, the indexing node  404  can update a data store catalog  220  with information about pre-merged or merged buckets stored in common storage  216 . As described herein, the information can identify the location of the buckets in common storage  216  and other information, such as, but not limited to, a partition or tenant associated with the bucket, time range of the bucket, etc. As described herein, the information stored in the data store catalog  220  can be used by the query system  214  to identify buckets to be searched as part of a query. 
     Furthermore, it will be understood that the various blocks described herein with reference to  FIG. 10  can be implemented in a variety of orders, or can be performed concurrently. For example, the indexing node  404  can concurrently convert buckets and store them in common storage  216 , or concurrently receive data from a data source and process data from the data source, etc. 
     4.2.2. Moving Buckets to Common Storage 
       FIG. 11  is a flow diagram illustrative of an embodiment of a routine  1000  implemented by the indexing node  404  to store data in common storage  216 . Although described as being implemented by the indexing node  404 , it will be understood that the elements outlined for routine  1000  can be implemented by one or more computing devices/components that are associated with the data intake and query system  108 , such as, but not limited to, the indexing manager  402 , the indexing node manager  406 , the partition manager  408 , the indexer  410 , the bucket manager  414 , etc. Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  1102 , the indexing node  404  receives data. As described herein, the indexing node  404  can receive data from a variety of sources in various formats. For example, as described herein, the data received can be machine data, performance metrics, correlated data, etc. 
     Further, as described herein, the indexing node  404  can receive data from one or more components of the intake system  210  (e.g., the ingesting buffer  310 , forwarder  302 , etc.) or other data sources  202 . In some embodiments, the indexing node  404  can receive data from a shard or partition of the ingestion buffer  310 . Further, in certain cases, the indexing node  404  can generate a partition manager  408  for each shard or partition of a data stream. In some cases, the indexing node  404  receives data from the ingestion buffer  310  that references or points to data stored in one or more data stores, such as a data store  218  of common storage  216 , or other network accessible data store or cloud storage. In such embodiments, the indexing node  404  can obtain the data from the referenced data store using the information received from the ingestion buffer  310 . 
     At block  1104 , the indexing node  404  stores data in buckets. In some embodiments, the indexing node  404  processes the received data (or the data obtained using the received data) and stores it in buckets. As part of the processing, the indexing node  404  can determine information about the data (e.g., host, source, sourcetype), extract or identify timestamps, associated metadata fields with the data, extract keywords, transform the data, identify and organize the data into events having raw machine data associated with a timestamp, etc. In some embodiments, the indexing node  404  uses one or more configuration files and/or extraction rules to extract information from the data or events. 
     In addition, as part of processing and storing the data, the indexing node  404  can generate buckets for the data according to a bucket creation policy. As described herein, the indexing node  404  can concurrently generate and fill multiple buckets with the data that it processes. In some embodiments, the indexing node  404  generates buckets for each partition or tenant associated with the data that is being processed. In certain embodiments, the indexing node  404  stores the data or events in the buckets based on the identified timestamps. 
     Furthermore, indexing node  404  can generate one or more indexes associated with the buckets, such as, but not limited to, one or more inverted indexes, TSIDXs, keyword indexes, bloom filter files, etc. The data and the indexes can be stored in one or more files of the buckets. In addition, the indexing node  404  can generate additional files for the buckets, such as, but not limited to, one or more filter files, a buckets summary, or manifest, etc. 
     At block  1106 , the indexing node  404  monitors the buckets. As described herein, the indexing node  404  can process significant amounts of data across a multitude of buckets, and can monitor the size or amount of data stored in individual buckets, groups of buckets or all the buckets that it is generating and filling. In certain embodiments, one component of the indexing node  404  can monitor the buckets (e.g., partition manager  408 ), while another component fills the buckets (e.g., indexer  410 ). 
     In some embodiments, as part of monitoring the buckets, the indexing node  404  can compare the individual size of the buckets or the collective size of multiple buckets with a threshold size. Once the threshold size is satisfied, the indexing node  404  can determine that the buckets are to be stored in common storage  216 . In certain embodiments, the indexing node  404  can monitor the amount of time that has passed since the buckets have been stored in common storage  216 . Based on a determination that a threshold amount of time has passed, the indexing node  404  can determine that the buckets are to be stored in common storage  216 . Further, it will be understood that the indexing node  404  can use a bucket roll-over policy and/or a variety of techniques to determine when to store buckets in common storage  216 . 
     At block  1108 , the indexing node  404  converts the buckets. In some cases, as part of preparing the buckets for storage in common storage  216 , the indexing node  404  can convert the buckets from editable buckets to non-editable buckets. In some cases, the indexing node  404  convert hot buckets to warm buckets based on the bucket roll-over policy. The bucket roll-over policy can indicate that buckets are to be converted from hot to warm buckets based on a predetermined period of time, one or more buckets satisfying a threshold size, the number of hot buckets, etc. In some cases, based on the bucket roll-over policy, the indexing node  404  converts hot buckets to warm buckets based on a collective size of multiple hot buckets satisfying a threshold size. The multiple hot buckets can correspond to any one or any combination of randomly selected hot buckets, hot buckets associated with a particular partition or shard (or partition manager  408 ), hot buckets associated with a particular tenant or partition, all hot buckets in the data store  412  or being processed by the indexer  410 , etc. 
     At block  1110 , the indexing node  404  stores the converted buckets in a data store. As described herein, the indexing node  404  can store the buckets in common storage  216  or other location accessible to the query system  214 . In some cases, the indexing node  404  stores a copy of the buckets in common storage  416  and retains the original bucket in its data store  412 . In certain embodiments, the indexing node  404  stores a copy of the buckets in common storage and deletes any reference to the original buckets in its data store  412 . 
     Furthermore, as described herein, in some cases, the indexing node  404  can store the one or more buckets based on the bucket roll-over policy. In addition to indicating when buckets are to be converted from hot buckets to warm buckets, the bucket roll-over policy can indicate when buckets are to be stored in common storage  216 . In some cases, the bucket roll-over policy can use the same or different policies or thresholds to indicate when hot buckets are to be converted to warm and when buckets are to be stored in common storage  216 . 
     In certain embodiments, the bucket roll-over policy can indicate that buckets are to be stored in common storage  216  based on a collective size of buckets satisfying a threshold size. As mentioned, the threshold size used to determine that the buckets are to be stored in common storage  216  can be the same as or different from the threshold size used to determine that editable buckets should be converted to non-editable buckets. Accordingly, in certain embodiments, based on a determination that the size of the one or more buckets have satisfied a threshold size, the indexing node  404  can convert the buckets to non-editable buckets and store the buckets in common storage  216 . 
     Other thresholds and/or other factors or combinations of thresholds and factors can be used as part of the bucket roll-over policy. For example, the bucket roll-over policy can indicate that buckets are to be stored in common storage  216  based on the passage of a threshold amount of time. As yet another example, bucket roll-over policy can indicate that buckets are to be stored in common storage  216  based on the number of buckets satisfying a threshold number. 
     It will be understood that the bucket roll-over policy can use a variety of techniques or thresholds to indicate when to store the buckets in common storage  216 . For example, in some cases, the bucket roll-over policy can use any one or any combination of a threshold time period, threshold number of buckets, user information, tenant or partition information, query frequency, amount of data being received, time of day or schedules, etc., to indicate when buckets are to be stored in common storage  216  (and/or converted to non-editable buckets). In some cases, the bucket roll-over policy can use different priorities to determine how to store the buckets, such as, but not limited to, minimizing or reducing time between processing and storage to common storage  216 , maximizing or increasing individual bucket size, etc. Furthermore, the bucket roll-over policy can use dynamic thresholds to indicate when buckets are to be stored in common storage  216 . 
     As mentioned, in some cases, based on an increased query frequency, the bucket roll-over policy can indicate that buckets are to be moved to common storage  216  more frequently by adjusting one more thresholds used to determine when the buckets are to be stored to common storage  216  (e.g., threshold size, threshold number, threshold time, etc.). 
     In addition, the bucket roll-over policy can indicate that different sets of buckets are to be rolled-over differently or at different rates or frequencies. For example, the bucket roll-over policy can indicate that buckets associated with a first tenant or partition are to be rolled over according to one policy and buckets associated with a second tenant or partition are to be rolled over according to a different policy. The different policies may indicate that the buckets associated with the first tenant or partition are to be stored more frequently to common storage  216  than the buckets associated with the second tenant or partition. Accordingly, the bucket roll-over policy can use one set of thresholds (e.g., threshold size, threshold number, and/or threshold time, etc.) to indicate when the buckets associated with the first tenant or partition are to be stored in common storage  216  and a different set of thresholds for the buckets associated with the second tenant or partition. 
     As another non-limiting example, consider a scenario in which buckets from a partition _main are being queried more frequently than bucket from the partition _test. The bucket roll-over policy can indicate that based on the increased frequency of queries for buckets from partition _main, buckets associated with partition _main should be moved more frequently to common storage  216 , for example, by adjusting the threshold size used to determine when to store the buckets in common storage  216 . In this way, the query system  214  can obtain relevant search results more quickly for data associated with the _main partition. Further, if the frequency of queries for buckets from the _main partition decreases, the data intake and query system  108  can adjust the threshold accordingly. In addition, the bucket roll-over policy may indicate that the changes are only for buckets associated with the partition _main or that the changes are to be made for all buckets, or all buckets associated with a particular tenant that is associated with the partition _main, etc. 
     Furthermore, as mentioned, the bucket roll-over policy can indicate that buckets are to be stored in common storage  216  at different rates or frequencies based on time of day. For example, the data intake and query system  108  can adjust the thresholds so that the buckets are moved to common storage  216  more frequently during working hours and less frequently during non-working hours. In this way, the delay between processing and making the data available for searching during working hours can be reduced, and can decrease the amount of merging performed on buckets generated during non-working hours. In other cases, the data intake and query system  108  can adjust the thresholds so that the buckets are moved to common storage  216  less frequently during working hours and more frequently during non-working hours. 
     As mentioned, the bucket roll-over policy can indicate that based on an increased rate at which data is received, buckets are to be moved to common storage more (or less) frequently. For example, if the bucket roll-over policy initially indicates that the buckets are to be stored every millisecond, as the rate of data received by the indexing node  404  increases, the amount of data received during each millisecond can increase, resulting in more data waiting to be stored. As such, in some cases, the bucket roll-over policy can indicate that the buckets are to be stored more frequently in common storage  216 . Further, in some cases, such as when a collective bucket size threshold is used, an increased rate at which data is received may overburden the indexing node  404  due to the overhead associated with copying each bucket to common storage  216 . As such, in certain cases, the bucket roll-over policy can use a larger collective bucket size threshold to indicate that the buckets are to be stored in common storage  216 . In this way, the bucket roll-over policy can reduce the ratio of overhead to data being stored. 
     Similarly, the bucket roll-over policy can indicate that certain users are to be treated differently. For example, if a particular user is logged in, the bucket roll-over policy can indicate that the buckets in an indexing node  404  are to be moved to common storage  216  more or less frequently to accommodate the user&#39;s preferences, etc. Further, as mentioned, in some embodiments, the data intake and query system  108  may indicate that only those buckets associated with the user (e.g., based on tenant information, indexing information, user information, etc.) are to be stored more or less frequently. 
     Furthermore, the bucket roll-over policy can indicate whether, after copying buckets to common storage  216 , the locally stored buckets are to be retained or discarded. In some cases, the bucket roll-over policy can indicate that the buckets are to be retained for merging. In certain cases, the bucket roll-over policy can indicate that the buckets are to be discarded. 
     Fewer, more, or different blocks can be used as part of the routine  1000 . In some cases, one or more blocks can be omitted. For example, in certain embodiments, the indexing node  404  may not convert the buckets before storing them. As another example, the routine  1000  can include notifying the data source, such as the intake system, that the buckets have been uploaded to common storage, merging buckets and uploading merged buckets to common storage, receiving identifying information about the buckets in common storage  216  and updating a data store catalog  220  with the received information, etc. 
     Furthermore, it will be understood that the various blocks described herein with reference to  FIG. 11  can be implemented in a variety of orders, or can be performed concurrently. For example, the indexing node  404  can concurrently convert buckets and store them in common storage  216 , or concurrently receive data from a data source and process data from the data source, etc. 
     4.2.3. Updating Location Marker in Ingestion Buffer 
       FIG. 12  is a flow diagram illustrative of an embodiment of a routine  1200  implemented by the indexing node  404  to update a location marker in an ingestion buffer, e.g., ingestion buffer  310 . Although described as being implemented by the indexing node  404 , it will be understood that the elements outlined for routine  1200  can be implemented by one or more computing devices/components that are associated with the data intake and query system  108 , such as, but not limited to, the indexing manager  402 , the indexing node manager  406 , the partition manager  408 , the indexer  410 , the bucket manager  414 , etc. Thus, the following illustrative embodiment should not be construed as limiting. Moreover, although the example refers to updating a location marker in ingestion buffer  310 , other implementations can include other ingestion components with other types of location tracking that can be updated in a similar manner as the location marker. 
     At block  1202 , the indexing node  404  receives data. As described in greater detail above with reference to block  1102 , the indexing node  404  can receive a variety of types of data from a variety of sources. 
     In some embodiments, the indexing node  404  receives data from an ingestion buffer  310 . As described herein, the ingestion buffer  310  can operate according to a pub-sub messaging service. As such, the ingestion buffer  310  can communicate data to the indexing node  404 , and also ensure that the data is available for additional reads until it receives an acknowledgement from the indexing node  404  that the data can be removed. 
     In some cases, the ingestion buffer  310  can use one or more read pointers or location markers to track the data that has been communicated to the indexing node  404  but that has not been acknowledged for removal. As the ingestion buffer  310  receives acknowledgments from the indexing node  404 , it can update the location markers. In some cases, such as where the ingestion buffer  310  uses multiple partitions or shards to provide the data to the indexing node  404 , the ingestion buffer  310  can include at least one location marker for each partition or shard. In this way, the ingestion buffer  310  can separately track the progress of the data reads in the different shards. 
     In certain embodiments, the indexing node  404  can receive (and/or store) the location markers in addition to or as part of the data received from the ingestion buffer  310 . Accordingly, the indexing node  404  can track the location of the data in the ingestion buffer  310  that the indexing node  404  has received from the ingestion buffer  310 . In this way, if an indexer  410  or partition manager  408  becomes unavailable or fails, the indexing node  404  can assign a different indexer  410  or partition manager  408  to process or manage the data from the ingestion buffer  310  and provide the indexer  410  or partition manager  408  with a location from which the indexer  410  or partition manager  408  can obtain the data. 
     At block  1204 , the indexing node  404  stores the data in buckets. As described in greater detail above with reference to block  1104  of  FIG. 11 , as part of storing the data in buckets, the indexing node  404  can parse the data, generate events, generate indexes of the data, compress the data, etc. In some cases, the indexing node  404  can store the data in hot or warm buckets and/or convert hot buckets to warm buckets based on the bucket roll-over policy. 
     At block  1206 , the indexing node  404  stores buckets in common storage  216 . As described herein, in certain embodiments, the indexing node  404  stores the buckets in common storage  216  according to the bucket roll-over policy. In some cases, the buckets are stored in common storage  216  in one or more directories based on an index/partition or tenant associated with the buckets. Further, the buckets can be stored in a time series manner to facilitate time series searching as described herein. Additionally, as described herein, the common storage  216  can replicate the buckets across multiple tiers and data stores across one or more geographical locations. In some cases, in response to the storage, the indexing node  404  receives an acknowledgement that the data was stored. Further, the indexing node  404  can receive information about the location of the data in common storage, one or more identifiers of the stored data, etc. The indexing node  404  can use this information to update the data store catalog  220 . 
     At block  1208 , the indexing node  404  notifies an ingestion buffer  310  that the data has been stored in common storage  216 . As described herein, in some cases, the ingestion buffer  310  can retain location markers for the data that it sends to the indexing node  404 . The ingestion buffer  310  can use the location markers to indicate that the data sent to the indexing node  404  is to be made persistently available to the indexing system  212  until the ingestion buffer  310  receives an acknowledgement from the indexing node  404  that the data has been stored successfully. In response to the acknowledgement, the ingestion buffer  310  can update the location marker(s) and communicate the updated location markers to the indexing node  404 . The indexing node  404  can store updated location markers for use in the event one or more components of the indexing node  404  (e.g., partition manager  408 , indexer  410 ) become unavailable or fail. In this way, the ingestion buffer  310  and the location markers can aid in providing a stateless indexing service. 
     Fewer, more, or different blocks can be used as part of the routine  1200 . In some cases, one or more blocks can be omitted. For example, in certain embodiments, the indexing node  404  can update the data store catalog  220  with information about the buckets created by the indexing node  404  and/or stored in common storage  216 , as described herein. 
     Furthermore, it will be understood that the various blocks described herein with reference to  FIG. 12  can be implemented in a variety of orders. In some cases, the indexing node  404  can implement some blocks concurrently or change the order as desired. For example, the indexing node  404  can concurrently receive data, store other data in buckets, and store buckets in common storage. 
     4.2.4. Merging Buckets 
       FIG. 13  is a flow diagram illustrative of an embodiment of a routine  1300  implemented by the indexing node  404  to merge buckets. Although described as being implemented by the indexing node  404 , it will be understood that the elements outlined for routine  1300  can be implemented by one or more computing devices/components that are associated with the data intake and query system  108 , such as, but not limited to, the indexing manager  402 , the indexing node manager  406 , the partition manager  408 , the indexer  410 , the bucket manager  414 , etc. Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  1302 , the indexing node  404  stores data in buckets. As described herein, the indexing node  404  can process various types of data from a variety of sources. Further, the indexing node  404  can create one or more buckets according to a bucket creation policy and store the data in the store the data in one or more buckets. In addition, in certain embodiments, the indexing node  404  can convert hot or editable buckets to warm or non-editable buckets according to a bucket roll-over policy. 
     At block  1304 , the indexing node  404  stores buckets in common storage  216 . As described herein, the indexing node  404  can store the buckets in common storage  216  according to the bucket roll-over policy. In some cases, the buckets are stored in common storage  216  in one or more directories based on an index/partition or tenant associated with the buckets. Further, the buckets can be stored in a time series manner to facilitate time series searching as described herein. Additionally, as described herein, the common storage  216  can replicate the buckets across multiple tiers and data stores across one or more geographical locations. 
     At block  1306 , the indexing node  404  updates the data store catalog  220 . As described herein, in some cases, in response to the storage, the indexing node  404  receives an acknowledgement that the data was stored. Further, the indexing node  404  can receive information about the location of the data in common storage, one or more identifiers of the stored data, etc. The received information can be used by the indexing node  404  to update the data store catalog  220 . In addition, the indexing node  404  can provide the data store catalog  220  with any one or any combination of the tenant or partition associated with the bucket, a time range of the events in the bucket, one or more metadata fields of the bucket (e.g., host, source, sourcetype, etc.), etc. In this way, the data store catalog  220  can store up-to-date information about the buckets in common storage  216 . Further, this information can be used by the query system  214  to identify relevant buckets for a query. 
     In some cases, the indexing node  404  can update the data store catalog  220  before, after, or concurrently with storing the data to common storage  216 . For example, as buckets are created by the indexing node  404 , the indexing node  404  can update the data store catalog  220  with information about the created buckets, such as, but not limited to, a partition or tenant associated with the bucket, a time range or initial time (e.g., time of earliest-in-time timestamp), etc. In addition, the indexing node  404  can include an indication that the bucket is a hot bucket or editable bucket and that the contents of the bucket are not (yet) available for searching or in the common storage  216 . 
     As the bucket is filled with events or data, the indexing node  404  can update the data store catalog  220  with additional information about the bucket (e.g., updated time range based on additional events, size of the bucket, number of events in the bucket, certain keywords or metadata from the bucket, such as, but not limited to a host, source, or sourcetype associated with different events in the bucket, etc.). Further, once the bucket is uploaded to common storage  216 , the indexing node  404  can complete the entry for the bucket, such as, by providing a completed time range, location information of the bucket in common storage  216 , completed keyword or metadata information as desired, etc. 
     The information in the data store catalog  220  can be used by the query system  214  to execute queries. In some cases, based on the information in the data store catalog  220  about buckets that are not yet available for searching, the query system  214  can wait until the data is available for searching before completing the query or inform a user that some data that may be relevant has not been processed or that the results will be updated. Further, in some cases, the query system  214  can inform the indexing system  212  about the bucket, and the indexing system  212  can cause the indexing node  404  to store the bucket in common storage  216  sooner than it otherwise would without the communication from the query system  214 . 
     In addition, the indexing node  404  can update the data store catalog  220  with information about buckets to be merged. For example, once one or more buckets are identified for merging, the indexing node  404  can update an entry for the buckets in the data store catalog  220  indicating that they are part of a merge operation and/or will be replaced. In some cases, as part of the identification, the data store catalog  220  can provide information about the entries to the indexing node  404  for merging. As the entries may have summary information about the buckets, the indexing node  404  can use the summary information to generate a merged entry for the data store catalog  220  as opposed to generating the summary information from the merged data itself. In this way, the information from the data store catalog  220  can increase the efficiency of a merge operation by the indexing node  404 . 
     At block  1308 , the indexing node  404  merges buckets. In some embodiments, the indexing node  404  can merge buckets according to a bucket merge policy. As described herein, the bucket merge policy can indicate which buckets to merge, when to merge buckets and one or more parameters for the merged buckets (e.g., time range for the merged buckets, size of the merged buckets, etc.). For example, the bucket merge policy can indicate that only buckets associated with the same tenant identifier and/or partition can be merged. As another example, the bucket merge policy can indicate that only buckets that satisfy a threshold age (e.g., have existed or been converted to warm buckets for more than a set period of time) are eligible for a merge. Similarly, the bucket merge policy can indicate that each merged bucket must be at least 750 MB or no greater than 1 GB, or cannot have a time range that exceeds a predetermined amount or is larger than 75% of other buckets. The other buckets can refer to one or more buckets in common storage  216  or similar buckets (e.g., buckets associated with the same tenant, partition, host, source, or sourcetype, etc.). In certain cases, the bucket merge policy can indicate that buckets are to be merged based on a schedule (e.g., during non-working hours) or user login (e.g., when a particular user is not logged in), etc. In certain embodiments, the bucket merge policy can indicate that bucket merges can be adjusted dynamically. For example, based on the rate of incoming data or queries, the bucket merge policy can indicate that buckets are to be merged more or less frequently, etc. In some cases, the bucket merge policy can indicate that due to increased processing demands by other indexing nodes  404  or other components of an indexing node  404 , such as processing and storing buckets, that bucket merges are to occur less frequently so that the computing resources used to merge buckets can be redirected to other tasks. It will be understood that a variety of priorities and policies can be used as part of the bucket merge policy. 
     At block  1310 , the indexing node  404  stores the merged buckets in common storage  216 . In certain embodiments, the indexing node  404  can store the merged buckets based on the bucket merge policy. For example, based on the bucket merge policy indicating that merged buckets are to satisfy a size threshold, the indexing node  404  can store a merged bucket once it satisfies the size threshold. Similarly, the indexing node  404  can store the merged buckets after a predetermined amount of time or during non-working hours, etc., per the bucket merge policy. 
     In response to the storage of the merged buckets in common storage  216 , the indexing node  404  can receive an acknowledgement that the merged buckets have been stored. In some cases, the acknowledgement can include information about the merged buckets, including, but not limited to, a storage location in common storage  216 , identifier, etc. 
     At block  1312 , the indexing node  404  updates the data store catalog  220 . As described herein, the indexing node  404  can store information about the merged buckets in the data store catalog.  220 . The information can be similar to the information stored in the data store catalog  220  for the pre-merged buckets (buckets used to create the merged buckets). For example, in some cases, the indexing node  404  can store any one or any combination of the following in the data store catalog: the tenant or partition associated with the merged buckets, a time range of the merged bucket, the location information of the merged bucket in common storage  216 , metadata fields associated with the bucket (e.g., host, source, sourcetype), etc. As mentioned, the information about the merged buckets in the data store catalog  220  can be used by the query system  214  to identify relevant buckets for a search. Accordingly, in some embodiments, the data store catalog  220  can be used in a similar fashion as an inverted index, and can include similar information (e.g., time ranges, field-value pairs, keyword pairs, location information, etc.). However, instead of providing information about individual events in a bucket, the data store catalog  220  can provide information about individual buckets in common storage  216 . 
     In some cases, the indexing node  404  can retrieve information from the data store catalog  220  about the pre-merged buckets and use that information to generate information about the merged bucket(s) for storage in the data store catalog  220 . For example, the indexing node  404  can use the time ranges of the pre-merged buckets to generate a merged time range, identify metadata fields associated with the different events in the pre-merged buckets, etc. In certain embodiments, the indexing node  404  can generate the information about the merged buckets for the data store catalog  220  from the merged data itself without retrieving information about the pre-merged buckets from the data store catalog  220 . 
     In certain embodiments, as part of updating the data store catalog  220  with information about the merged buckets, the indexing node  404  can delete the information in the data store catalog  220  about the pre-merged buckets. For example, once the merged bucket is stored in common storage  216 , the merged bucket can be used for queries. As such, the information about the pre-merged buckets can be removed so that the query system  214  does not use the pre-merged buckets to execute a query. 
     Fewer, more, or different blocks can be used as part of the routine  1300 . In some cases, one or more blocks can be omitted. For example, in certain embodiments, the indexing node  404  can delete locally stored buckets. In some cases, the indexing node  404  deletes any buckets used to form merged buckets and/or the merged buckets. In this way, the indexing node  404  can reduce the amount of data stored in the indexing node  404 . 
     In certain embodiments, the indexing node  404  can instruct the common storage  216  to delete buckets or delete the buckets in common storage according to a bucket management policy. For example, the indexing node  404  can instruct the common storage  216  to delete any buckets used to generate the merged buckets. Based on the bucket management policy, the common storage  216  can remove the buckets. As described herein, the bucket management policy can indicate when buckets are to be removed from common storage  216 . For example, the bucket management policy can indicate that buckets are to be removed from common storage  216  after a predetermined amount of time, once any queries relying on the pre-merged buckets are completed, etc. 
     By removing buckets from common storage  216 , the indexing node  404  can reduce the size or amount of data stored in common storage  216  and improve search times. For example, in some cases, large buckets can increase search times as there are fewer buckets for the query system  214  to search. By another example, merging buckets after indexing allows optimal or near-optimal bucket sizes for search (e.g., performed by query system  214 ) and index (e.g., performed by indexing system  212 ) to be determined independently or near-independently. 
     Furthermore, it will be understood that the various blocks described herein with reference to  FIG. 13  can be implemented in a variety of orders. In some cases, the indexing node  404  can implement some blocks concurrently or change the order as desired. For example, the indexing node  404  can concurrently merge buckets while updating an ingestion buffer  310  about the data stored in common storage  216  or updating the data store catalog  220 . As another example, the indexing node  404  can delete data about the pre-merged buckets locally and instruct the common storage  216  to delete the data about the pre-merged buckets while concurrently updating the data store catalog  220  about the merged buckets. In some embodiments, the indexing node  404  deletes the pre-merged bucket data entries in the data store catalog  220  prior to instructing the common storage  216  to delete the buckets. In this way, the data indexing node  404  can reduce the risk that a query relies on information in the data store catalog  220  that does not reflect the data stored in the common storage  216 . 
     4.3. Querying 
       FIG. 14  is a data flow diagram illustrating an embodiment of the data flow and communications between a variety of the components of the data intake and query system  108  during execution of a query. Specifically,  FIG. 14  is a data flow diagram illustrating an embodiment of the data flow and communications between the indexing system  212 , the data store catalog  220 , a search head  504 , a search node monitor  508 , search node catalog  510 , search nodes  506 , common storage  216 , and the query acceleration data store  222 . However, it will be understood, that in some of embodiments, one or more of the functions described herein with respect to  FIG. 14  can be omitted, performed in a different order and/or performed by a different component of the data intake and query system  108 . Accordingly, the illustrated embodiment and description should not be construed as limiting. 
     Further, it will be understood that the various functions described herein with respect to  FIG. 14  can be performed by one or more distinct components of the data intake and query system  108 . For example, for simplicity, reference is made to a search head  504  performing one or more functions. However, it will be understood that these functions can be performed by one or more components of the search head  504 , such as, but not limited to, the search master  512  and/or the search manager  514 . Similarly, reference is made to the indexing system  212  performing one or more functions. However, it will be understood that the functions identified as being performed by the indexing system  212  can be performed by one or more components of the indexing system  212 . 
     At ( 1 ) and ( 2 ), the indexing system  212  monitors the storage of processed data and updates the data store catalog  220  based on the monitoring. As described herein, one or more components of the indexing system  212 , such as the partition manager  408  and/or the indexer  410  can monitor the storage of data or buckets to common storage  216 . As the data is stored in common storage  216 , the indexing system  212  can obtain information about the data stored in the common storage  216 , such as, but not limited to, location information, bucket identifiers, tenant identifier (e.g., for buckets that are single tenant) etc. The indexing system  212  can use the received information about the data stored in common storage  216  to update the data store catalog  220 . 
     Furthermore, as described herein, in some embodiments, the indexing system  212  can merge buckets into one or more merged buckets, store the merged buckets in common storage  216 , and update the data store catalog to  220  with the information about the merged buckets stored in common storage  216 . 
     At ( 3 ) and ( 4 ), the search node monitor  508  monitors the search nodes  506  and updates the search node catalog  510 . As described herein, the search node monitor  508  can monitor the availability, responsiveness, and/or utilization rate of the search nodes  506 . Based on the status of the search nodes  506 , the search node monitor  508  can update the search node catalog  510 . In this way, the search node catalog  510  can retain information regarding a current status of each of the search nodes  506  in the query system  214 . 
     At ( 5 ), the search head  504  receives a query and generates a search manager  514 . As described herein, in some cases, a search master  512  can generate the search manager  514 . For example, the search master  512  can spin up or instantiate a new process, container, or virtual machine, or copy itself to generate the search manager  514 , etc. As described herein, in some embodiments, the search manager  514  can perform one or more of functions described herein with reference to  FIG. 14  as being performed by the search head  504  to process and execute the query. 
     The search head  504  ( 6 A) requests data identifiers from the data store catalog  220  and ( 6 B) requests an identification of available search nodes from the search node catalog  510 . As described, the data store catalog  220  can include information regarding the data stored in common storage  216  and the search node catalog  510  can include information regarding the search nodes  506  of the query system  214 . Accordingly, the search head  504  can query the respective catalogs to identify data or buckets that include data that satisfies at least a portion of the query and search nodes available to execute the query. In some cases, these requests can be done concurrently or in any order. 
     At ( 7 A), the data store catalog  220  provides the search head  504  with an identification of data that satisfies at least a portion of the query. As described herein, in response to the request from the search head  504 , the data store catalog  220  can be used to identify and return identifiers of buckets in common storage  216  and/or location information of data in common storage  216  that satisfy at least a portion of the query or at least some filter criteria (e.g., buckets associated with an identified tenant or partition or that satisfy an identified time range, etc.). 
     In some cases, as the data store catalog  220  can routinely receive updates by the indexing system  212 , it can implement a read-write lock while it is being queried by the search head  504 . Furthermore, the data store catalog  220  can store information regarding which buckets were identified for the search. In this way, the data store catalog  220  can be used by the indexing system  212  to determine which buckets in common storage  216  can be removed or deleted as part of a merge operation. 
     At ( 7 B), the search node catalog  510  provides the search head  504  with an identification of available search nodes  506 . As described herein, in response to the request from the search head  504 , the search node catalog  510  can be used to identify and return identifiers for search nodes  506  that are available to execute the query. 
     At ( 8 ) the search head  504  maps the identified search nodes  506  to the data according to a search node mapping policy. In some cases, per the search node mapping policy, the search head  504  can dynamically map search nodes  506  to the identified data or buckets. As described herein, the search head  504  can map the identified search nodes  506  to the identified data or buckets at one time or iteratively as the buckets are searched according to the search node mapping policy. In certain embodiments, per the search node mapping policy, the search head  504  can map the identified search nodes  506  to the identified data based on previous assignments, data stored in a local or shared data store of one or more search heads  506 , network architecture of the search nodes  506 , a hashing algorithm, etc. 
     In some cases, as some of the data may reside in a local or shared data store between the search nodes  506 , the search head  504  can attempt to map that was previously assigned to a search node  506  to the same search node  506 . In certain embodiments, to map the data to the search nodes  506 , the search head  504  uses the identifiers, such as bucket identifiers, received from the data store catalog  220 . In some embodiments, the search head  504  performs a hash function to map a bucket identifier to a search node  506 . In some cases, the search head  504  uses a consistent hash algorithm to increase the probability of mapping a bucket identifier to the same search node  506 . 
     In certain embodiments, the search head  504  or query system  214  can maintain a table or list of bucket mappings to search nodes  506 . In such embodiments, per the search node mapping policy, the search head  504  can use the mapping to identify previous assignments between search nodes and buckets. If a particular bucket identifier has not been assigned to a search node  506 , the search head  504  can use a hash algorithm to assign it to a search node  506 . In certain embodiments, prior to using the mapping for a particular bucket, the search head  504  can confirm that the search node  506  that was previously assigned to the particular bucket is available for the query. In some embodiments, if the search node  506  is not available for the query, the search head  504  can determine whether another search node  506  that shares a data store with the unavailable search node  506  is available for the query. If the search head  504  determines that an available search node  506  shares a data store with the unavailable search node  506 , the search head  504  can assign the identified available search node  506  to the bucket identifier that was previously assigned to the now unavailable search node  506 . 
     At ( 9 ), the search head  504  instructs the search nodes  506  to execute the query. As described herein, based on the assignment of buckets to the search nodes  506 , the search head  504  can generate search instructions for each of the assigned search nodes  506 . These instructions can be in various forms, including, but not limited to, JSON, DAG, etc. In some cases, the search head  504  can generate sub-queries for the search nodes  506 . Each sub-query or instructions for a particular search node  506  generated for the search nodes  506  can identify the buckets that are to be searched, the filter criteria to identify a subset of the set of data to be processed, and the manner of processing the subset of data. Accordingly, the instructions can provide the search nodes  506  with the relevant information to execute their particular portion of the query. 
     At ( 10 ), the search nodes  506  obtain the data to be searched. As described herein, in some cases the data to be searched can be stored on one or more local or shared data stores of the search nodes  506 . In some embodiments, the data to be searched is located in the intake system  210  and/or the acceleration data store  222 . In certain embodiments, the data to be searched is located in the common storage  216 . In such embodiments, the search nodes  506  or a cache manager  516  can obtain the data from the common storage  216 . 
     In some cases, the cache manager  516  can identify or obtain the data requested by the search nodes  506 . For example, if the requested data is stored on the local or shared data store of the search nodes  506 , the cache manager  516  can identify the location of the data for the search nodes  506 . If the requested data is stored in common storage  216 , the cache manager  516  can obtain the data from the common storage  216 . As another example, if the requested data is stored in the intake system  210  and/or the acceleration data store  222 , the cache manager  516  can obtain the data from the intake system  210  and/or the acceleration data store  222 . 
     As described herein, in some embodiments, the cache manager  516  can obtain a subset of the files associated with the bucket to be searched by the search nodes  506 . For example, based on the query, the search node  506  can determine that a subset of the files of a bucket are to be used to execute the query. Accordingly, the search node  506  can request the subset of files, as opposed to all files of the bucket. The cache manager  516  can download the subset of files from common storage  216  and provide them to the search node  506  for searching. 
     In some embodiments, such as when a search node  506  cannot uniquely identify the file of a bucket to be searched, the cache manager  516  can download a bucket summary or manifest that identifies the files associated with the bucket. The search node  506  can use the bucket summary or manifest to uniquely identify the file to be used in the query. The common storage  216  can then obtain that uniquely identified file from common storage  216 . 
     At ( 11 ), the search nodes  506  search and process the data. As described herein, the sub-queries or instructions received from the search head  504  can instruct the search nodes  506  to identify data within one or more buckets and perform one or more transformations on the data. Accordingly, each search node  506  can identify a subset of the set of data to be processed and process the subset of data according to the received instructions. This can include searching the contents of one or more inverted indexes of a bucket or the raw machine data or events of a bucket, etc. In some embodiments, based on the query or sub-query, a search node  506  can perform one or more transformations on the data received from each bucket or on aggregate data from the different buckets that are searched by the search node  506 . 
     At ( 12 ), the search head  504  monitors the status of the query of the search nodes  506 . As described herein, the search nodes  506  can become unresponsive or fail for a variety of reasons (e.g., network failure, error, high utilization rate, etc.). Accordingly, during execution of the query, the search head  504  can monitor the responsiveness and availability of the search nodes  506 . In some cases, this can be done by pinging or querying the search nodes  506 , establishing a persistent communication link with the search nodes  506 , or receiving status updates from the search nodes  506 . In some cases, the status can indicate the buckets that have been searched by the search nodes  506 , the number or percentage of remaining buckets to be searched, the percentage of the query that has been executed by the search node  506 , etc. In some cases, based on a determination that a search node  506  has become unresponsive, the search head  504  can assign a different search node  506  to complete the portion of the query assigned to the unresponsive search node  506 . 
     In certain embodiments, depending on the status of the search nodes  506 , the search manager  514  can dynamically assign or re-assign buckets to search nodes  506 . For example, as search nodes  506  complete their search of buckets assigned to them, the search manager  514  can assign additional buckets for search. As yet another example, if one search node  506  is 95% complete with its search while another search node  506  is less than 50% complete, the query manager can dynamically assign additional buckets to the search node  506  that is 95% complete or re-assign buckets from the search node  506  that is less than 50% complete to the search node that is 95% complete. In this way, the search manager  514  can improve the efficiency of how a computing system performs searches through the search manager  514  increasing parallelization of searching and decreasing the search time. 
     At ( 13 ), the search nodes  506  send individual query results to the search head  504 . As described herein, the search nodes  506  can send the query results as they are obtained from the buckets and/or send the results once they are completed by a search node  506 . In some embodiments, as the search head  504  receives results from individual search nodes  506 , it can track the progress of the query. For example, the search head  504  can track which buckets have been searched by the search nodes  506 . Accordingly, in the event a search node  506  becomes unresponsive or fails, the search head  504  can assign a different search node  506  to complete the portion of the query assigned to the unresponsive search node  506 . By tracking the buckets that have been searched by the search nodes and instructing different search node  506  to continue searching where the unresponsive search node  506  left off, the search head  504  can reduce the delay caused by a search node  506  becoming unresponsive, and can aid in providing a stateless searching service. 
     At ( 14 ), the search head  504  processes the results from the search nodes  506 . As described herein, the search head  504  can perform one or more transformations on the data received from the search nodes  506 . For example, some queries can include transformations that cannot be completed until the data is aggregated from the different search nodes  506 . In some embodiments, the search head  504  can perform these transformations. 
     At ( 15 ), the search head  504  stores results in the query acceleration data store  222 . As described herein, in some cases some, all, or a copy of the results of the query can be stored in the query acceleration data store  222 . The results stored in the query acceleration data store  222  can be combined with other results already stored in the query acceleration data store  222  and/or be combined with subsequent results. For example, in some cases, the query system  214  can receive ongoing queries, or queries that do not have a predetermined end time. In such cases, as the search head  504  receives a first set of results, it can store the first set of results in the query acceleration data store  222 . As subsequent results are received, the search head  504  can add them to the first set of results, and so forth. In this way, rather than executing the same or similar query data across increasingly larger time ranges, the query system  214  can execute the query across a first time range and then aggregate the results of the query with the results of the query across the second time range. In this way, the query system can reduce the amount of queries and the size of queries being executed and can provide query results in a more time efficient manner. 
     At ( 16 ), the search head  504  terminates the search manager  514 . As described herein, in some embodiments a search head  504  or a search master  512  can generate a search manager  514  for each query assigned to the search head  504 . Accordingly, in some embodiments, upon completion of a search, the search head  504  or search master  512  can terminate the search manager  514 . In certain embodiments, rather than terminating the search manager  514  upon completion of a query, the search head  504  can assign the search manager  514  to a new query. 
     As mentioned previously, in some of embodiments, one or more of the functions described herein with respect to  FIG. 14  can be omitted, performed in a variety of orders and/or performed by a different component of the data intake and query system  108 . For example, the search head  504  can monitor the status of the query throughout its execution by the search nodes  506  (e.g., during ( 10 ), ( 11 ), and ( 13 )). Similarly, ( 1 ) and ( 2 ) can be performed concurrently, ( 3 ) and ( 4 ) can be performed concurrently, and all can be performed before, after, or concurrently with ( 5 ). Similarly, steps ( 6 A) and ( 6 B) and steps ( 7 A) and ( 7 B) can be performed before, after, or concurrently with each other. Further, ( 6 A) and ( 7 A) can be performed before, after, or concurrently with ( 7 A) and ( 7 B). As yet another example, ( 10 ), ( 11 ), and ( 13 ) can be performed concurrently. For example, a search node  506  can concurrently receive one or more files for one bucket, while searching the content of one or more files of a second bucket and sending query results for a third bucket to the search head  504 . Similarly, the search head  504  can ( 8 ) map search nodes  506  to buckets while concurrently ( 9 ) generating instructions for and instructing other search nodes  506  to begin execution of the query. In some cases, such as when the set of data is from the intake system  210  or the acceleration data store  222 , ( 6 A) and ( 7 A) can be omitted. Furthermore, in some such cases, the data may be obtained ( 10 ) from the intake system  210  and/or the acceleration data store  222 . 
     4.3.1. Containerized Search Nodes 
       FIG. 15  is a flow diagram illustrative of an embodiment of a routine  1500  implemented by the query system  214  to execute a query. Although described as being implemented by the search head  504 , it will be understood that the elements outlined for routine  1500  can be implemented by one or more computing devices/components that are associated with the data intake and query system  108 , such as, but not limited to, the query system manager  502 , the search head  504 , the search master  512 , the search manager  514 , the search nodes  506 , etc. Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  1502 , the search manager  514  receives a query. As described in greater detail above, the search manager  514  can receive the query from the search head  504 , search master  512 , etc. In some cases, the search manager  514  can receive the query from a client device  204 . The query can be in a query language as described in greater detail above. In some cases, the query received by the search manager  514  can correspond to a query received and reviewed by the search head  504 . For example, the search head  504  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  108 , has correct semantics and syntax, etc. In some cases, the search head  504  can use a search master  512  to receive search queries, and in some cases, spawn the search manager  514  to process and execute the query. 
     At block  1504 , the search manager  514  identifies one or more containerized search nodes, e.g., search nodes  506 , to execute the query. As described herein, the query system  214  can include multiple containerized search nodes  506  to execute queries. One or more of the containerized search nodes  506  can be instantiated on the same computing device, and share the resources of the computing device. In addition, the containerized search nodes  506  can enable the query system  214  to provide a highly extensible and dynamic searching service. For example, based on resource availability and/or workload, the query system  214  can instantiate additional containerized search nodes  506  or terminate containerized search nodes  506 . Furthermore, the query system  214  can dynamically assign containerized search nodes  506  to execute queries on data in common storage  216  based on a search node mapping policy. 
     As described herein, each search node  506  can be implemented using containerization or operating-system-level virtualization, or other virtualization technique. For example, the containerized search node  506 , or one or more components of the search node  506  can be implemented as separate containers or container instances. Each container instance can have certain resources (e.g., memory, processor, etc.) of the underlying computing system assigned to it, but may share the same operating system and may use the operating system&#39;s system call interface. Further, each container may run the same or different computer applications concurrently or separately, and may interact with each other. It will be understood that other virtualization techniques can be used. For example, the containerized search nodes  506  can be implemented using virtual machines using full virtualization or paravirtualization, etc. 
     In some embodiments, the search node  506  can be implemented as a group of related containers or a pod, and the various components of the search node  506  can be implemented as related containers of a pod. Further, the search node  506  can assign different containers to execute different tasks. For example one container of a containerized search node  506  can receive and query instructions, a second container can obtain the data or buckets to be searched, and a third container of the containerized search node  506  can search the buckets and/or perform one or more transformations on the data. However, it will be understood that the containerized search node  506  can be implemented in a variety of configurations. For example, in some cases, the containerized search node  506  can be implemented as a single container and can include multiple processes to implement the tasks described above by the three containers. Any combination of containerization and processed can be used to implement the containerized search node  506  as desired. 
     In some cases, the search manager  514  can identify the search nodes  506  using the search node catalog  510 . For example, as described herein a search node monitor  508  can monitor the status of the search nodes  506  instantiated in the query system  514  and monitor their status. The search node monitor can store the status of the search nodes  506  in the search node catalog  510 . 
     In certain embodiments, the search manager  514  can identify search nodes  506  using a search node mapping policy, previous mappings, previous searches, or the contents of a data store associated with the search nodes  506 . For example, based on the previous assignment of a search node  506  to search data as part of a query, the search manager  514  can assign the search node  506  to search the same data for a different query. As another example, as search nodes  506  search data, it can cache the data in a local or shared data store. Based on the data in the cache, the search manager  514  can assign the search node  506  to search the again as part of a different query. 
     In certain embodiments, the search manager  514  can identify search nodes  506  based on shared resources. For example, if the search manager  514  determines that a search node  506  shares a data store with a search node  506  that previously performed a search on data and cached the data in the shared data store, the search manager  514  can assign the search node  506  that share the data store to search the data stored therein as part of a different query. 
     In some embodiments, the search manager  514  can identify search nodes  506  using a hashing algorithm. For example, as described herein, the search manager  514  based can perform a hash on a bucket identifier of a bucket that is to be searched to identify a search node to search the bucket. In some implementations, that hash may be a consistent hash, to increase the chance that the same search node will be selected to search that bucket as was previously used, thereby reducing the chance that the bucket must be retrieved from common storage  216 . 
     It will be understood that the search manger  514  can identify search nodes  506  based on any one or any combination of the aforementioned methods. Furthermore, it will be understood that the search manager  514  can identify search nodes  506  in a variety of ways. 
     At  1506 , the search manager  514  instructs the search nodes  506  to execute the query. As described herein, the search manager  514  can process the query to determine portions of the query that it will execute and portions of the query to be executed by the search nodes  506 . Furthermore, the search manager  514  can generate instructions or sub-queries for each search node  506  that is to execute a portion of the query. In some cases, the search manager  514  generates a DAG for execution by the search nodes  506 . The instructions or sub-queries can identify the data or buckets to be searched by the search nodes  506 . In addition, the instructions or sub-queries may identify one or more transformations that the search nodes  506  are to perform on the data. 
     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 search manager  514  can receive partial results from the search nodes  506 , process the partial results, perform one or more transformation on the partial results or aggregated results, etc. Further, in some embodiments, the search manager  514  provide the results to a client device  204 . In some embodiments, the search manager  514  can combine the results with results stored in the accelerated data store  222  or store the results in the accelerated data store  222  for combination with additional search results. 
     In some cases, the search manager  514  can identify the data or buckets to be searched by, for example, using the data store catalog  220 , and map the buckets to the search nodes  506  according to a search node mapping policy. As described herein, the data store catalog  220  can receive updates from the indexing system  212  about the data that is stored in common storage  216 . The information in the data store catalog  220  can include, but is not limited to, information about the location of the buckets in common storage  216 , and other information that can be used by the search manager  514  to identify buckets that include data that satisfies at least a portion of the query. 
     In certain cases, as part of executing the query, the search nodes  506  can obtain the data to be searched from common storage  216  using the cache manager  516 . The obtained data can be stored on a local or shared data store and searched as part of the query. In addition, the data can be retained on the local or shared data store based on a bucket caching policy as described herein. 
     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 search manager  514  can implement some blocks concurrently or change the order as desired. For example, the search manager  514  an concurrently identify search nodes  506  to execute the query and instruct the search nodes  506  to execute the query. As described herein, in some embodiments, the search manager  514  can instruct the search nodes  506  to execute the query at once. In certain embodiments, the search manager  514  can assign a first group of buckets for searching, and dynamically assign additional groups of buckets to search nodes  506  depending on which search nodes  506  complete their searching first or based on an updated status of the search nodes  506 , etc. 
     4.3.2. Identifying Buckets and Search Nodes for Query 
       FIG. 16  is a flow diagram illustrative of an embodiment of a routine  1600  implemented by the query system  214  to execute a query. Although described as being implemented by the search manager  514 , it will be understood that the elements outlined for routine  1600  can be implemented by one or more computing devices/components that are associated with the data intake and query system  108 , such as, but not limited to, the query system manager  502 , the search head  504 , the search master  512 , the search manager  514 , the search nodes  506 , etc. Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  1602 , the search manager  514  receives a query, as described in greater detail herein at least with reference to block  1502  of  FIG. 15 . 
     At block  1604 , the search manager  514  identifies search nodes to execute the query, as described in greater detail herein at least with reference to block  1504  of  FIG. 15 . However, it will be noted, that in certain embodiments, the search nodes  506  may not be containerized. 
     At block  1606 , the search manager  514  identifies buckets to query. As described herein, in some cases, the search manager  514  can consult the data store catalog  220  to identify buckets to be searched. In certain embodiments, the search manager  514  can use metadata of the buckets stored in common storage  216  to identify the buckets for the query. For example, the search manager  514  can compare a tenant identifier and/or partition identifier associated with the query with the tenant identifier and/or partition identifier of the buckets. The search manager  514  can exclude buckets that have a tenant identifier and/or partition identifier that does not match the tenant identifier and/or partition identifier associated with the query. Similarly, the search manager can compare a time range associate with the query with the time range associated with the buckets in common storage  216 . Based on the comparison, the search manager  514  can identify buckets that satisfy the time range associated with the query (e.g., at least partly overlap with the time range from the query). 
     At  1608 , the search manager  514  executes the query. As described herein, at least with reference to  1506  of  FIG. 15 , in some embodiments, as part of executing the query, the search manager  514  can process the search query, identify tasks for it to complete and tasks for the search nodes  506 , generate instructions or sub-queries for the search nodes  506  and instruct the search nodes  506  to execute the query. Further, the search manager  514  can aggregate the results from the search nodes  506  and perform one or more transformations on the data. 
     Fewer, more, or different blocks can be used as part of the routine  1600 . In some cases, one or more blocks can be omitted. For example, as described herein, the search manager  514  can map the search nodes  506  to certain data or buckets for the search according to a search node mapping policy. Based on the search node mapping policy, search manager  514  can instruct the search nodes to search the buckets to which they are mapped. Further, as described herein, in some cases, the search node mapping policy can indicate that the search manager  514  is to use a hashing algorithm, previous assignment, network architecture, cache information, etc., to map the search nodes  506  to the buckets. 
     As another example, the routine  1600  can include storing the search results in the accelerated data store  222 . Furthermore, as described herein, the search nodes  506  can store buckets from common storage  216  to a local or shared data store for searching, etc. 
     In addition, it will be understood that the various blocks described herein with reference to  FIG. 16  can be implemented in a variety of orders, or implemented concurrently. For example, the search manager  514  can identify search nodes to execute the query and identify bucket for the query concurrently or in any order. 
     4.3.3. Identifying Buckets for Query Execution 
       FIG. 17  is a flow diagram illustrative of an embodiment of a routine  1700  implemented by the query system  214  to identify buckets for query execution. Although described as being implemented by the search manager  514 , it will be understood that the elements outlined for routine  1700  can be implemented by one or more computing devices/components that are associated with the data intake and query system  108 , such as, but not limited to, the query system manager  502 , the search head  504 , the search master  512 , the search manager  514 , the search nodes  506 , etc. Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  1702 , the data intake and query system  108  maintains a catalog of bucket in common storage  216 . As described herein, the catalog can also be referred to as the data store catalog  220 , and can include information about the buckets in common storage  216 , such as, but not limited to, location information, metadata fields, tenant and partition information, time range information, etc. Further, the data store catalog  220  can be kept up-to-date based on information received from the indexing system  212  as the indexing system  212  processes and stores data in the common storage  216 . 
     At block  1704 , the search manager  514  receives a query, as described in greater detail herein at least with reference to block  1502  of  FIG. 15 . 
     At block  1706 , the search manager  514  identifies buckets to be searched as part of the query using the data store catalog  220 . As described herein, the search manager  514  can use the data store catalog  220  to filter the universe of buckets in the common storage  216  to buckets that include data that satisfies at least a portion of the query. For example, if a query includes a time range of 4/23/18 from 03:30:50 to 04:53:32, the search manager  514  can use the time range information in the data store catalog to identify buckets with a time range that overlaps with the time range provided in the query. In addition, if the query indicates that only a _main partition is to be searched, the search manager  514  can use the information in the data store catalog to identify buckets that satisfy the time range and are associated with the _main partition. Accordingly, depending on the information in the query and the information stored in the data store catalog  220  about the buckets, the search manager  514  can reduce the number of buckets to be searched. In this way, the data store catalog  220  can reduce search time and the processing resources used to execute a query. 
     At block  1708 , the search manager  514  executes the query, as described in greater detail herein at least with reference to block  1608  of  FIG. 16 . 
     Fewer, more, or different blocks can be used as part of the routine  1700 . In some cases, one or more blocks can be omitted. For example, as described herein, the search manager  514  can identify and map search nodes  306  to the buckets for searching or store the search results in the accelerated data store  222 . Furthermore, as described herein, the search nodes  506  can store buckets from common storage  216  to a local or shared data store for searching, etc. In addition, it will be understood that the various blocks described herein with reference to  FIG. 16  can be implemented in a variety of orders, or implemented concurrently. 
     4.3.4. Identifying Search Nodes for Query Execution 
       FIG. 18  is a flow diagram illustrative of an embodiment of a routine  1800  implemented by the query system  214  to identify search nodes for query execution. Although described as being implemented by the search manager  514 , it will be understood that the elements outlined for routine  1800  can be implemented by one or more computing devices/components that are associated with the data intake and query system  108 , such as, but not limited to, the query system manager  502 , the search head  504 , the search master  512 , the search manager  514 , the search nodes  506 , etc. Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  1802 , the query system  214  maintains a catalog of instantiated search nodes  506 . As described herein, the catalog can also be referred to as the search node catalog  510 , and can include information about the search nodes  506 , such as, but not limited to, availability, utilization, responsiveness, network architecture, etc. Further, the search node catalog  510  can be kept up-to-date based on information received by the search node monitor  508  from the search nodes  506 . 
     At block  1804 , the search manager  514  receives a query, as described in greater detail herein at least with reference to block  1502  of  FIG. 15 . At block  1806 , the search manager  514  identifies available search nodes using the search node catalog  220 , as described in greater detail herein at least with reference to block  1504  of  FIG. 15  and block  1604  of  FIG. 16 . 
     At block  1808 , the search manager  514  instructs the search nodes  506  to execute the query, as described in greater detail herein at least with reference to block  1506  of  FIG. 15  and block  1608  of  FIG. 16 . 
     Fewer, more, or different blocks can be used as part of the routine  1800 . In some cases, one or more blocks can be omitted. For example, in certain embodiments, the search manager can identify buckets in common storage  216  for searching. In addition, it will be understood that the various blocks described herein with reference to  FIG. 18  can be implemented in a variety of orders, or implemented concurrently. 
     4.3.5. Hashing Bucket Identifiers for Query Execution 
       FIG. 19  is a flow diagram illustrative of an embodiment of a routine  1900  implemented by the query system  214  to hash bucket identifiers for query execution. Although described as being implemented by the search manager  514 , it will be understood that the elements outlined for routine  1900  can be implemented by one or more computing devices/components that are associated with the data intake and query system  108 , such as, but not limited to, the query system manager  502 , the search head  504 , the search master  512 , the search manager  514 , the search nodes  506 , etc. Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  1902 , the search manager  514  receives a query, as described in greater detail herein at least with reference to block  1502  of  FIG. 15 . 
     At block  1904 , the search manager  514  identifies bucket identifiers associated with buckets to be searched as part of the query. The bucket identifiers can correspond to an alphanumeric identifier or other identifier that can be used to uniquely identify the bucket from other buckets stored in common storage  216 . In some embodiments, the unique identifier may incorporate one or more portions of a tenant identifier, partition identifier, or time range of the bucket or a random or sequential (e.g., based on time of storage, creation, etc.) alphanumeric string, etc. As described herein, the search manager  514  can parse the query to identify buckets to be searched. In some cases, the search manager  514  can identify buckets to be searched and an associated bucket identifier based on metadata of the buckets and/or using a data store catalog  220 . However, it will be understood that the search manager  514  can use a variety of techniques to identify buckets to be searched. 
     At block  1906 , the search manager  514  performs a hash function on the bucket identifiers. The search manager can, in some embodiments, use the output of the hash function to identify a search node  506  to search the bucket. For example, as a non-limiting example, consider a scenario in which a bucket identifier is  4149  and the search manager  514  identified ten search nodes to process the query. The search manager  514  could perform a modulo ten operation on the bucket identifier to determine which search node  506  is to search the bucket. Based on this example, the search manager  514  would assign the ninth search node  506  to search the bucket, e.g., because the value  4149  modulo ten is 9, so the bucket having the identifier  4149  is assigned to the ninth search node. In some cases, the search manager can use a consistent hash to increase the likelihood that the same search node  506  is repeatedly assigned to the same bucket for searching. In this way, the search manager  514  can increase the likelihood that the bucket to be searched is already located in a local or shared data store of the search node  506 , and reduce the likelihood that the bucket will be downloaded from common storage  216 . It will be understood that the search manager can use a variety of techniques to map the bucket to a search node  506  according to a search node mapping policy. For example, the search manager  514  can use previous assignments, network architecture, etc., to assign buckets to search nodes  506  according to the search node mapping policy. 
     At block  1908 , the search manager  514  instructs the search nodes  506  to execute the query, as described in greater detail herein at least with reference to block  4906  of  FIG. 49  and block  1608  of  FIG. 16 . 
     Fewer, more, or different blocks can be used as part of the routine  1900 . In some cases, one or more blocks can be omitted. In addition, it will be understood that the various blocks described herein with reference to  FIG. 19  can be implemented in a variety of orders, or implemented concurrently. 
     4.3.6. Obtaining Data for Query Execution 
       FIG. 20  is a flow diagram illustrative of an embodiment of a routine  2000  implemented by a search node  506  to execute a search on a bucket. Although reference is made to downloading and searching a bucket, it will be understood that this can refer to downloading and searching one or more files associated within a bucket and does not necessarily refer to downloading all files associated with the bucket. 
     Further, although described as being implemented by the search node  506 , it will be understood that the elements outlined for routine  2000  can be implemented by one or more computing devices/components that are associated with the data intake and query system  108 , such as, but not limited to, the query system manager  502 , the search head  504 , the search master  512 , search manager  514 , cache manager  516 , etc. Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  2002 , the search node  506  receives instructions for a query or sub-query. As described herein, a search manager  514  can receive and parse a query to determine the tasks to be assigned to the search nodes  506 , such as, but not limited to, the searching of one or more buckets in common storage  216 , etc. The search node  506  can parse the instructions and identify the buckets that are to be searched. In some cases, the search node  506  can determine that a bucket that is to be searched is not located in the search nodes local or shared data store. 
     At block  2004 , the search node  506  obtains the bucket from common storage  216 . As described herein, in some embodiments, the search node  506  obtains the bucket from common storage  216  in conjunction with a cache manager  516 . For example, the search node  506  can request the cache manager  516  to identify the location of the bucket. The cache manager  516  can review the data stored in the local or shared data store for the bucket. If the cache manager  516  cannot locate the bucket in the local or shared data store, it can inform the search node  506  that the bucket is not stored locally and that it will be retrieved from common storage  216 . As described herein, in some cases, the cache manager  516  can download a portion of the bucket (e.g., one or more files) and provide the portion of the bucket to the search node  506  as part of informing the search node  506  that the bucket is not found locally. The search node  506  can use the downloaded portion of the bucket to identify any other portions of the bucket that are to be retrieved from common storage  216 . 
     Accordingly, as described herein, the search node  506  can retrieve all or portions of the bucket from common storage  216  and store the retrieved portions to a local or shared data store. 
     At block  2006 , the search node  506  executes the search on the portions of the bucket stored in the local data store. As described herein, the search node  506  can review one or more files of the bucket to identify data that satisfies the query. In some cases, the search nodes  506  searches an inverted index to identify the data. In certain embodiments, the search node  506  searches the raw machine data, uses one or more configuration files, regex rules, and/or late binding schema to identify data in the bucket that satisfies the query. 
     Fewer, more, or different blocks can be used as part of the routine  2000 . For example, in certain embodiments, the routine  2000  includes blocks for requesting a cache manager  516  to search for the bucket in the local or shared storage, and a block for informing the search node  506  that the requested bucket is not available in the local or shared data store. As another example, the routine  2000  can include performing one or more transformations on the data, and providing partial search results to a search manager  514 , etc. In addition, it will be understood that the various blocks described herein with reference to  FIG. 20  can be implemented in a variety of orders, or implemented concurrently. 
     4.3.7. Caching Search Results 
       FIG. 21  is a flow diagram illustrative of an embodiment of a routine  2100  implemented by the query system  212  to store search results. Although described as being implemented by the search manager  514 , it will be understood that the elements outlined for routine  2100  can be implemented by one or more computing devices/components that are associated with the data intake and query system  108 , such as, but not limited to, the query system manager  502 , the search head  504 , the search master  512 , the search nodes  506 , etc. Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  2102 , the search manager  514  receives a query, as described in greater detail herein at least with reference to block  4902  of  FIG. 49 , and at block  2104 , the search manager  514  executes the query, as described in greater detail herein at least with reference to block  1608  of  FIG. 16 . For example, as described herein, the search manager  514  can identify buckets for searching assign the buckets to search nodes  506 , and instruct the search nodes  506  to search the buckets. Furthermore, the search manager can receive partial results from each of the buckets, and perform one or more transformations on the received data. 
     At block  2106 , the search manager  514  stores the results in the accelerated data store  222 . As described herein, the results can be combined with results previously stored in the accelerated data store  222  and/or can be stored for combination with results to be obtained later in time. In some cases, the search manager  514  can receive queries and determine that at least a portion of the results are stored in the accelerated data store  222 . Based on the identification, the search manager  514  can generate instructions for the search nodes  506  to obtain results to the query that are not stored in the accelerated data store  222 , combine the results in the accelerated data store  222  with results obtained by the search nodes  506 , and provide the aggregated search results to the client device  204 , or store the aggregated search results in the accelerated data store  222  for further aggregation. By storing results in the accelerated data store  222 , the search manager  514  can reduce the search time and computing resources used for future searches that rely on the query results. 
     Fewer, more, or different blocks can be used as part of the routine  2100 . In some cases, one or more blocks can be omitted. For example, in certain embodiments, the search manager  514  can consult a data store catalog  220  to identify buckets, consult a search node catalog  510  to identify available search nodes, map buckets to search nodes  506 , etc. Further, in some cases, the search nodes  506  can retrieve buckets from common storage  216 . In addition, it will be understood that the various blocks described herein with reference to  FIG. 21  can be implemented in a variety of orders, or implemented concurrently. 
     4.4. Querying Using Metadata Catalog 
     As described herein, the metadata catalog  221  can be used to stored information related to various datasets  608  and/or rules  610  used by the data intake and query system to process data. In some embodiments, the metadata catalog  221  can be used to process and/or execute queries received by the data intake and query system  108 . 
     4.4.1 Metadata Catalog Data Flow 
       FIG. 22  is a data flow diagram illustrating an embodiment of the data flow and communications between a variety of the components of the data intake and query system  108  during execution of a query. Specifically,  FIG. 22  is a data flow diagram illustrating an embodiment of the data flow and communications between the metadata catalog  221 , the query system manager  502 , and the search head  504 . However, it will be understood, that in some of embodiments, one or more of the functions described herein with respect to  FIG. 22  can be omitted, performed in a different order and/or performed by the same or a different component of the data intake and query system  108 . For example, in some embodiments, the steps identified as being performed by the query system manager  502  and search head  504  can be performed by the same component (e.g., the query system manager  502 , the search head  504 , or another component of the data intake and query system  108 ). In some such embodiments, ( 4 ′) can be omitted. 
     Furthermore, in some embodiments, the data flow diagram illustrated at  FIG. 22  can be performed prior to ( 5 ) of the data flow diagram illustrated in  FIG. 14 . For example, ( 5 ) of  FIG. 14  references receiving a query at the search head  504 . In some embodiments, the query received at the search head  504  can correspond to the system query communicated to the search head  504  by the query system manager  502  at ( 4 ′) of  FIG. 22 . 
     At ( 1 ′), a query system manager  502  receives and processes a user query. The user query can correspond to a query received from a client device  204  and can include one or more query parameters. In some cases, the user query can be received via the gateway  215  and/or via the network  208 . The query can identify (and the query parameters can include) a set of data and manner processing the set of data. In certain embodiments the set of data of a query can include multiple datasets. For example, the set of data of the query can include one or more source datasets, source reference datasets and/or query datasets. In turn a dataset can include one or more queries (or subqueries). For example, a query dataset can be identified as at least a portion of the set of data of a received query, and can include a query (or subquery) that identifies a set of data and a manner of processing the set of data. As another example, the query dataset could reference one or more additional query datasets that in turn include one or more subqueries. 
     Furthermore, the query can include at least one dataset identifier and/or dataset association record identifier. In some embodiments, the dataset identifier can be a logical identifier of a dataset. In certain embodiments, the dataset identifier and/or dataset association record identifier can follow a particular query parameter, such as “from” “datasetID,” “moduleID,” etc. In some embodiments, the dataset identifier and/or dataset association record identifier can be included as a parameter of a command received by the query system manager  502 . For example, in some embodiments, the data intake and query system  108  can receive the query as one parameter and the dataset identifier and/or the dataset association record as another parameter. 
     As part of processing the user query, the query system manager  502  can identify the dataset identifier(s) and/or the dataset association record identifier. In some embodiments, the query system manager  502  can parse the query to identify the dataset identifier and/or dataset association record identifier. For example, the query system manager  502  can identify “from” (or some other query parameter) in the query and determine that the subsequent string is the dataset identifier. Furthermore, it will be understood that the query system manager  502  can identify multiple dataset identifier(s) and/or dataset association record identifier(s) as part of processing the user query. 
     At ( 2 ′), the query system manager  502  communicates with the metadata catalog  221  to authenticate the datasets identified in the query (and other datasets parsed during the query processing), identify primary datasets (e.g. datasets with configurations used to execute the query), secondary datasets (datasets referenced directly or indirectly by the query but that do not include configurations used to execute the query) and/or identify query configuration parameters. 
     In some embodiments, upon identifying a dataset association record  602  associated with the query, the query system manager  502  uses the dataset association record  602  to identify additional information associated with the user query, such as one or more datasets and/or rules. In some embodiments, using the dataset association record, the query system manager  502  can determine whether a user associated with the query has the authorizations and/or permissions to access the datasets identified in the query. 
     Once the query system manager  502  identifies the dataset of the dataset association record  602  referenced in the query, the query system manager  502  can determine whether the identified dataset identifies one or more additional datasets (e.g., is a single or multi-reference dataset), includes additional query parameters, is a source dataset, a secondary dataset, and/or a primary dataset that will be used by the data intake and query system to execute the query. 
     In the event, the dataset is a single or multi-reference dataset, with each additional dataset identified, the query system manager  502  can recursively review information about the dataset to determine whether it is a non-referential, single, or multi-reference dataset, a secondary dataset, and/or a primary dataset until it has identified any dataset referenced directly or indirectly by the query (e.g., all primary and secondary datasets). For example, as described in herein, the dataset identifier used in the user query may refer to a dataset that is from another dataset association record. Based on the determination that the dataset is imported, the query system manager  502  can review the other dataset association record to identify any additional datasets, identify configuration parameter (e.g., access information, dataset type, etc.) of the imported dataset, and/or determine whether the referenced dataset was imported from a third dataset. The query system manager  502  can continue to review the dataset association records  206  until it has identified the dataset association record where the dataset is native. 
     As another example, the dataset identifier in the user query may refer to a multi-reference dataset, such as a query dataset that refers to one or more source datasets, source reference datasets, and/or other query datasets. Accordingly, the query system manager  502  can recursively review the datasets referred to in the multi-reference dataset until it identifies datasets that do not rely on any other datasets (e.g., non-referential datasets) and/or identifies the source datasets that include the data that forms at least a portion of the set of data or other primary datasets. 
     With each new dataset identified from the dataset association records, the query system manager  502  can authenticate the dataset. As part of authenticating the datasets, the query system manager  502  can determine whether the dataset referred to is imported by the dataset association record and/or whether the user has the proper credentials, authorizations, and/or permissions to access the dataset. 
     In addition to identifying additional datasets, the query system manager  502  can identify additional query parameters. For example, one or more datasets, such as a query dataset, may include additional query parameters. Accordingly, as the query system manager  502  parses the various datasets, it can identify additional query parameters that are to be processed and/or executed. 
     Furthermore, as the query system manager  502  parses the dataset association records  602 , it can identify one or more rules that are to be used to process data from one or more datasets. As described herein, the rules can be imported by different dataset association records  602 . Accordingly, the query system manager  502  can recursively parse the rules to identify the dataset association record  602  from which the rule originated. Furthermore, as the query system manager  502  parses the dataset association records  602  and identifies additional rules, it can determine whether the user has the proper credentials permissions etc. to access the identified rules. In addition, the query system manager  502  can identify one or more datasets associated with the rules (e.g., that reference, use, are referenced by, or used by, the additional rules). As described herein, in some embodiments these datasets may not be explicitly imported in a dataset association record, but may be automatically included as part of the query processing process. 
     In addition to identifying the various datasets and/or rules associated with the query, the query system manager  502  can identify the configurations associated with the datasets and rules associated with the query. In some embodiments, the query system manager  502  can use the dataset configuration records  604  and/or rule configuration records  606  to identify the relevant configurations for the datasets and/or rules associated with the query. For example, the query system manager  502  can refer to the dataset configuration records  604  to identify the dataset types of the various datasets associated with the query. In some embodiments, based on the dataset type, the query system manager  502  can determine how to interact with or generate commands for the dataset. For example, for a lookup dataset, the query system manager may generate a “lookup” command, for an “index” dataset, the query system manager may generate a “search” command, and for a metrics interaction dataset, the query system manager may generate an “mstats” command. 
     As described herein, in some embodiments, the dataset configuration records  604  and rule configuration records  606  can include a physical identifier for the datasets and/or rules. Accordingly, in some embodiments, the query system manager  502  can obtain the physical identifiers for each of the datasets and/or rules associated with the query. In certain embodiments, the query system manager  502  can determine the physical identifiers for each of the datasets and/or rules associated with the query based on the logical name and dataset association record  602  associated with the dataset or rule. For example, in certain embodiments, the physical identifier can correspond to a combination of the logical identifier of the dataset and the logical identifier of the associated dataset association record. 
     In some embodiments, when identifying the rule configuration records  606  and/or dataset configuration records  604 , the query system manager  502  can obtain a subset of the dataset configuration records  604  and/or rule configuration records  606  in the metadata catalog  221  and/or a subset of the dataset configuration records  604  and/or rule configuration records  606  associated with the dataset association records  602  identified by the query or referenced while processing the query. In certain embodiments, the query system manager  502  obtains only the dataset configuration records  604  and/or rule configuration records  606  that are needed to process the query or only the primary dataset configuration records  604  and primary rule configuration records  606 . For example, if the dataset association record  602  reference three datasets and two rules, but the query only uses one of the datasets and one of the rules, the query system manager  502  can obtain the dataset configuration record  604  of the dataset referenced and the rule configuration record  606  in the query but not the dataset configuration records  604  and rule configuration records  606  of the datasets and rule not referenced in or used by the query. 
     At ( 3 ′), the query system manager  502  generates a system query and/or groups query configuration parameters. The query configuration parameters can include the dataset configuration records  604  corresponding to the primary datasets and/or the rule configuration records  606  corresponding to the rules associated with the query or primary rules. 
     In some embodiments, the system query can be based on the user query, one or more primary or secondary datasets, the physical name of a primary dataset(s), the dataset type of the primary dataset(s), additional query parameters identified from the datasets, and/or based on information about the search head  504 , etc. In certain embodiments, the system query corresponds to the user query modified to be compatible with the search head  504 . For example, in some embodiments, the search head  504  may not be able to process one or more commands in the system query. Accordingly, the query system manager  502  can replace the commands unsupported by the search head  504  with commands that are supported by the search head  504 . 
     In some embodiments, as the system query parses the dataset association records  602  and/or dataset configuration records  604 , it identifies the datasets to be included in the query. In certain embodiments, the query system manager  502  identifies the datasets to be included based on the dataset identifier(s) included in the query. For example, if the query identifies a source dataset or source reference dataset, the query system manager  602  can include an identifier for the source dataset or source reference dataset in the system query. Similarly, if the query identifies a single or multi-reference dataset, the query system manager  602  can include an identifier for the single or multi-reference dataset in the system query and/or may include an identifier for one or more (primary) datasets referenced by the single or multi-reference dataset in the system query 
     In some embodiments, the query system manager  502  identifies the datasets to be included based on the dataset identifier(s) included in the query and/or one or more query parameters of a dataset referenced by the query. For example, if the query identifies (or references) a query dataset, the query system manager  502  can include the query parameters (including any referenced primary datasets) of the query dataset in the query. As another example, the query system manager  502  can recursively parse the query parameters (including any referenced datasets) of the query dataset to identify primary datasets and instructions for processing data from (or referenced by) the primary datasets, and include the identified primary datasets and instructions for processing the data in the query. Similarly, if a query dataset references one or more single reference or multi-reference datasets, the query system manager  502  can recursively process the single reference or multi-reference datasets referenced by the query dataset until it identifies the query parameters referenced by any dataset referenced by the query dataset and the primary datasets that include (or reference) the data to be processed according to the identified query parameters. 
     In certain embodiments, the system query replaces any logical dataset identifier of the user query (such as a query dataset) with the physical dataset identifier of a primary dataset or source dataset identified from the metadata catalog  221 . For example, if the logical name of a dataset is “main” and the dataset association record  602  is “test,” the query system manager  504  can replace “main” with “test.main” or “test__main,” as the case may be. Accordingly, the query system manager  502  can generate the system query based on the physical identifier of the primary datasets or source datasets. 
     In some embodiments, the query system manager  502  generates the system query based on the dataset type of one or more primary datasets, source datasets, or other datasets to be referenced in the system query. For example, datasets of different types may be interacted with using different commands and/or procedures. Accordingly, the query system manager  502  can include the command associated with the dataset type of the dataset in the query. For example, if the dataset type is an index type, the query system manager  502  can replace a “from” command with a “search” command. Similarly, if the dataset type is a lookup type, the query system manager  502  can replace the “from” command with a “lookup” command. As yet another example, if the dataset type is a metrics interactions type, the query system manager  502  can replace the “from” command with an “mstats” command. As yet another example, if the dataset type is a view dataset, the query system manager  502  can replace the “from” and dataset identifier with a query identified by the view dataset. Accordingly, in certain embodiments, the query system manager  502  can generate the system query based on the dataset type of one or more primary datasets. 
     In certain embodiments, the query system manager  502  does not include identifiers for any secondary datasets used to parse the user query. In some cases, as the query system manager  502  parses the dataset referenced by a query, it can determine whether a dataset associated with the query will be used to execute the query. If not, the dataset can be omitted from the system query. For example, if a query dataset includes query parameters, which reference two source datasets, the query system manager  502  can include the query parameters and identifiers for the two source dataset in the system query. Having included the content of the query dataset in the query, the query system manager  502  can determine that no additional information or configurations from the query dataset will be used by the query or to execute the query. Accordingly, the query system manager  502  can determine that the query dataset is a secondary dataset and omit it from the query. 
     In some embodiments, the query system manager  502  includes only datasets (or source datasets or source reference datasets) explicitly referenced in the user query or in a query parameter of another dataset in the system query. For example, if the user query references a “main” source dataset, the “main” source dataset will only be included in the query. As another example, if the user query (or a query parameter of another dataset, such as a query dataset) includes a “main” source dataset and a “test” source reference dataset, only the “main” source dataset and “test” source reference dataset, will be included in the system query. However, it will be understood that the query system manager  502  can use a variety of techniques to determine whether to include a dataset in the system query. 
     In certain embodiments, the query system manager  502  can identify query configuration parameters (configuration parameters associated with the query) based on the primary datasets and/or rules associated with the query. For example, as the query system manager  502  parses the dataset configuration records  604  of the datasets referenced (directly or indirectly) by the user query it can determine whether the dataset configuration records  604  are to be used to execute the system query. 
     In some cases, to determine whether the dataset configuration record  604  is to be used to execute the query, the query system manager  502  can parse a generated system query. In parsing the system query, the query system manager  502  can determine that the datasets referenced in the system query will be used to execute the system query. Accordingly, the query system manager  502  can obtain the dataset configuration records  604  corresponding to the datasets referenced in the system query. For example, if a system query references the “test.main” dataset, the query system manager  502  can obtain the dataset configuration record  604  of the “test.main” dataset. 
     In addition, in some cases, the query system manager can identify any datasets referenced by the datasets in the system query and obtain the dataset configuration records  604  of the datasets referenced by the datasets in the system query. For example, if the system query references a “users” source reference dataset, the query system manager  502  can identify the source dataset referenced by the “users” source reference dataset and obtain the corresponding dataset configuration records  604 , as well as the dataset configuration record  604  for the “users” source reference dataset. 
     In certain embodiments, the query system manager  502  can identify and obtain dataset configuration records  604  for any source dataset(s) and source reference dataset(s) referenced (directly or indirectly) by the query. 
     In some embodiments, the query system manager  502  can identify and obtain rules configurations  606  for any rules referenced by: the (system or otherwise) query, a dataset included in the system (or other generated) query, a dataset for which a dataset configuration record  604  is obtained as part of the query configuration parameters, and/or a dataset association record referenced (directly or indirectly) by the user query. In some cases, the query system manager  502  includes all rules associated with the dataset association record(s) associated with the query in the query configuration parameters. In certain cases, the query system manager  502  includes a subset of the rules associated with the dataset a dataset association record(s) associated with the query. For example, the query system manager  502  can include rule configuration records  606  for only the rules referenced by or associated with a dataset that is also being included in the query configuration parameters. 
     As described herein, the query system manager  502  can obtain the dataset configuration records  604  and/or rule configuration records  606  from the metadata catalog  221  based on a dynamic parsing of the user query. Accordingly, in some embodiments, the query system manager  502  can dynamically identify the query configuration parameters to be used to process and execute the query. 
     At ( 4 ′), the query system manager  502  communicates the system query and/or query configuration parameters to the search head  504 . As described herein, in some embodiments, the query system manager can communicate the system query to the search head  504 . In certain embodiments, the query system manager  502  can communicate the query configuration parameters to the search head  504 . Accordingly, the query system manager  502  can communicate either the system query, the query configuration parameters, or both. 
     In certain embodiments, by dynamically determining and communicating the query configuration parameters to the search head  504 , the query system manager  502  can provide a stateless search experience. For example, if the search head  504  becomes unavailable, the query system manager  502  can communicate the dynamically determined query configuration parameters (and/or query to be executed) to another search head  504  without data loss and/or with minimal or reduced time loss. 
     4.4.2 Example Metadata Catalog Processing 
       FIG. 23  is a data flow diagram illustrating an embodiment of the data flow for identifying primary datasets, secondary datasets, and query configuration parameters for a particular query  2302 . In the illustrated embodiment, the query system manager  502  receives the query  2302 , which includes the following query parameters “| from threats-encountered | sort-count | head 10.” In addition, “trafficTeam” is identified as the identifier of a dataset association record  602 N associated with the query  2302 . 
     Based on the identification of “trafficTeam” as the dataset association record identifier, the query system manager  502  ( 1 ) determines that the “trafficTeam” dataset association record  602 N is associated with the query, is to be searched, and/or determines a portion of the physical name for datasets (or dataset configuration records  604 ) to be searched. 
     In addition, based on the query  2302 , the query system manager  502  identifies “threats-encountered” as a logical dataset identifier. For example, the query system manager  502  can determine that a dataset identifier follows the “from” command. Accordingly, at ( 2 ), the query system manager  502  parses the “threats-encountered” dataset  608 I (or associated dataset configuration record  604 ). As part of parsing the “threats-encountered” dataset  608 I, the query system manager  502  determines that the “threats-encountered” dataset  608 I is a multi-reference query dataset that references two additional datasets  608 J and  608 H (“traffic” and “threats”). In some embodiments, the query system manager  502  can identify the related datasets  608 J and  608 H based on a system annotation in the dataset configuration record  604 N and/or based on parsing the query of the dataset configuration record  604 N. Based on the identification of the additional datasets, the query system manager  502  parses the “traffic” dataset  608 J and the “threats” dataset  608 H (or associated dataset configuration record  604 ) at ( 3 A) and ( 3 B), respectively. Based on parsing the “threats” dataset  608 H (or association dataset configuration record  604 ), the query system manager  502  determines that the “threats” dataset  608 H is a single source reference dataset that references or relies on the “threats-col” dataset  608 G. In certain cases, the query system manager  502  can identify the “threats-col” dataset  608 G based on an annotation in the dataset configuration record  604  associated with the “threats” dataset  608 H. Accordingly, at ( 4 A) query system manager  502  parses the “threats-col” dataset  608 G (or associated dataset configuration record  604 ). Based on parsing the “threats-col” dataset  608 G, the query system manager  502  determines that the “threats-col” dataset  608 G is a non-referential source dataset. 
     Based on parsing the “traffic” dataset  608 J, the query system manager  502  determines that the “traffic” dataset  608 J is an imported dataset that corresponds to the “main” dataset  608 A of the “shared” dataset association record  602 A, which may also be referred to as the “shared.main” dataset  608 A. In some cases, the query system manager  502  can identify the “shared.main” dataset  608 A based on the definition of the “traffic” dataset  608 J in the dataset association record  602 N or based on an annotation in a dataset configuration record  604  associated with the dataset “traffic”  608 H. Accordingly, at ( 4 B), the query system manager  502  parses the “shared.main” dataset  608 A (or associated dataset configuration record  604 ). Based on parsing the “shared.main” dataset  608 A, the query system manager  502  determines that the “shared.main” dataset  608 A is a non-referential source dataset. In some embodiments, based on parsing the “shared.main” dataset  608 A, the query system manager  502  can determine that the rule “shared.X”  610 A is related to the “shared.main” dataset  608 A and begin parsing the rule “shared.X”  610 A based on the identification. This may be done in place of or concurrently with step ( 4 C) and ( 5 ) described below. 
     As part of parsing the “traffic” dataset  608 J, the query system manager  502  also determines that the “shared.X” rule  610 B is associated with the “traffic” dataset  608 J (e.g., based on its presence in the dataset association record  602 N and/or based on another indication of a relationship, such as an annotation in a rule configuration record  606  for the “shared.X” rule  610 B or an annotation in a dataset configuration record  604  for the “shared.main” dataset  608 A), and at ( 4 C), parses the “shared.X” rule  610 B (which may include parsing the rule configuration record  606  of the “shared.X” rule  610 B). Based on parsing the “shared.X” rule  610 B, the query system manager  502  determines that the “shared.X” rule  610 B is imported from the “shared” dataset association record  602 A and at ( 5 ) parses the “X” rule  610 A of the dataset association record  602 A. Based on parsing the “X” rule  610 A (or associated rule configuration record  606 ), the query system manager  502  determines that the “X” rule  610 A references the “users” dataset  608 C, and at ( 6 ) parses the “users” dataset  608 C (or associated dataset configuration record  604 ). Based on parsing the “users” dataset  608 C, the query system manager  502  determines that the “users” dataset  608 C references the “users-col” dataset  608 D and at ( 7 ) parses the “users-col” dataset  608 D. Based on parsing the “users-col” dataset  608 D, the query system manger  502  determines that the “users-col” dataset  608 D is a non-referential source dataset. 
     In some embodiments, each time the query system manager  502  identifies a new dataset, it can identify the dataset as a dataset associated with the query. As the query system manager  502  processes the dataset, it can determine whether the dataset is a primary dataset or a secondary dataset. For example, if a view dataset merely references other datasets or includes additional query parameters and the configurations of the view dataset will not be used (or needed) to execute the query parameters or access the referenced datasets, it can be identified as a secondary dataset and omitted as a primary dataset. With reference to the illustrated embodiment, the query system manager  502  may identify “threats-encountered” dataset  608 I as being associated with the query based on its presence in the user query  2302 . However, once the query system manager  502  determines that the “threats-encountered” dataset  608 I adds additional query parameters to the query  2302 , but does not include data and/or will not be used to execute the query, it can identify the “threats-encountered” dataset  608 I as secondary dataset but not a primary dataset (and may or may not keep the query parameters). 
     As described herein, in some cases, the query system manager  502  determines the physical names of the primary datasets based on dataset association records  602 A,  602 N. For example, the query system manager  502  can use the names or identifiers of the dataset association records  602 A,  602 N to determine the physical names of the primary datasets and/or rules associated with the query. Using the physical names of the primary datasets and/or rules associated with the query, the query system manager  502  ( 8 ) identifies the dataset configuration records  604  from various dataset configuration records  604  and rule configuration records  606  from various rule configuration records  606  for inclusion as query configuration parameters  2306 . In some embodiments, the query system manager  502  can determine the dataset types of the primary datasets and other query configuration parameters associated with the primary datasets and rules associated with the query using the dataset configuration records  604  and rule configuration records  606 . 
     In the illustrated embodiment, the query system manager  502  can determine that the datasets  608 B,  608 E, and  608 F are not datasets associated with the query as they were not referenced (directly or indirectly) by the query  2302 . Conversely, in the illustrated embodiment, the query system manager  502  determines that datasets  608 A,  608 C,  608 D,  608 G,  608 H,  608 I, and  608 J are datasets associated with the query as they were referenced (directly or indirectly) by the query  2302 . 
     In addition, in the illustrated embodiment, the query system manager  502  determines that the “shared.main,” “shared.users,” “shared.users-col,” “trafficTeam.threats,” and “trafficTeam.threat-col” datasets  608 A,  608 C,  608 D,  608 H,  608 G, respectively, are primary datasets as they will be used to execute or process the system query  2304  and that the “trafficTeam.threats-encountered” dataset  608 I and “trafficTeam.traffic” dataset  608 J are secondary datasets as they will not be used to process/execute the query. Moreover, the query system manager  502  determines that the rule “shared.X” is associated with the query and/or will be used to process/execute the system query. 
     As mentioned, although, the “threats-encountered” and “traffic” datasets  608 I,  608 J, respectively, were identified as part of the processing, the query system manager  502  determines not to include them as primary datasets as they are not source datasets or will not be used to execute the system query. Rather, the “threats-encountered” and “traffic” datasets  608 I,  608 J were used to identify other datasets and query parameters. For example, the “threats-encountered” dataset  608 I is a view dataset that includes additional query parameters that reference two other datasets, and the “traffic” dataset  608 J is merely the name of the “shared.main” dataset  608 A imported into the “trafficTeam” dataset association record  602 N. 
     Based on the acquired information, the query system manager  502  ( 9 ) generates the system query  2304  and/or the query configuration parameters  2306  for the query. With reference to the system query  2304 , the query system manager  502  has included query parameters identified from the “threats-encountered dataset” in the system query  2304  and replaced the logical identifiers of datasets in the query with physical identifiers of the datasets (e.g., replaced “threats-encountered” with “shared.main” and “trafficTeam.threats”). In addition, the query system manager  502  includes commands specific to the dataset type of the datasets in the query (e.g., “from” replaced with “search” for the “shared.main” dataset  608 A and “lookup” included for the lookup “trafficTeam.threats” dataset  608 H). Accordingly, the system query  2304  is configured to be communicated to the search head  504  for processing and execution. 
     Moreover, based on the information from the metadata catalog  221 , the query system manager  502  is able to generate the query configuration parameters  2306  for the query to be executed by the data intake and query system  108 . In some embodiments, the query configuration parameters  2306  include dataset configuration records  604  (or portions thereof) associated with: datasets identified in the query  2304 , datasets referenced by the datasets identified in the query  2304 , and/or datasets referenced by a rule or rule configuration record  606  included (or identified for inclusion) in the query configuration parameters  2306 . In certain embodiments, the query configuration parameters  2306  include dataset configuration records  604  (or portions thereof) associated with the primary datasets. In some cases, when including dataset configuration records  604 , the query system manager  502  may omit certain portions of the dataset configuration records  604 . For example, the query system manager  502  may omit one or more annotations, such as the annotations identifying relationships between datasets or fields, etc. In certain embodiments, the query system manager  502  includes a reference to the various dataset configuration records  604  rather than a copy of the dataset configuration records  604 . 
     In some embodiments, the query configuration parameters  2306  includes rule configuration records  606  of rules associated with: the query (referenced directly or indirectly), datasets identified in the query  2304 , and/or datasets (or dataset configuration records  604 ) identified in the query configuration parameters  2306 . 
     In some cases, the query system manager  502  can iteratively identify dataset configuration records  604  and/or rules configurations  606  for inclusion in the query configuration parameters  2306 . As a non-limiting example, the query system manager  502  can include a first dataset configuration record  604  in the query configuration parameters  2306  (e.g., of a dataset referenced in the query to be executed). The query system manager  502  can then include dataset configuration records  604  or rule configuration records  606  of any datasets referenced by the first dataset (or corresponding configuration  604 ). The query system manager  502  can iteratively include dataset and rule configuration records  604 ,  606  corresponding to datasets or rules referenced by an already included rule or dataset (or corresponding configurations  604 ,  606 ) until the relevant dataset and rule configuration records  606  are included in the query configuration parameters  2306 . In certain embodiments, only configurations corresponding to primary datasets and primary rules are included in the query configuration parameters  2306 . Less or additional information or configurations can be included in the query configuration parameters  2306 . 
     As another non-limiting example and with reference to the illustrated embodiment, the query system manager  502  can include the “shared.main” dataset configuration record  604  and “trafficTeam.threats” dataset configuration record  604  in the query configuration parameters  2306  based on their presence in the query  2304 . Based on a determination that the “trafficTeam.threats-col” dataset configuration record  604  is referenced by the “trafficTeam.threats” dataset (or corresponding configuration  604 ), the query system manager  502  can include the “trafficTeam.threats-col” dataset configuration record  604  in the query configuration parameters  2306 . 
     Based on a determination that the “shared.X” rule is referenced by the “shared.main” dataset  608 A or a determination that the “shared.X” rule is included in the dataset association record  602 N, the query system manager  502  can include the “shared.X” rule configuration record  606  in the query configuration parameters  2306 . Furthermore, based on a determination that the “shared.users” dataset  608 C is referenced by the “shared.X” rule (inclusive of any action of the “shared.X” rule or corresponding configuration  606 ), the query system manager  502  can include the “shared.users” dataset  608 C in the query configuration parameters  2306 . Similarly, the query system manager  502  can include the “shared.users-col” dataset  608 D in the query configuration parameters  2306  based on a determination that it is referenced by the “shared.users” dataset  608 C. 
     In the illustrated embodiment, the query system manager  502  determines that the datasets “shared.main,” “shared.users,” “shared.users-col,” “trafficTeam.threats,” and “trafficTeam.threat-col” are primary datasets. Accordingly, the query system manager  502  includes the dataset configuration records  604  corresponding to the identified primary datasets as part of the query configuration parameters  2306 . Similarly, the query system manager  502  determines that the “shared.X” rule is associated with the query and/or will be used to process/execute the query and includes the corresponding rule configuration record  606  as part of the query configuration parameters  2306 . 
     In the illustrated embodiment, the query to be executed by the data intake and query system  108  corresponds to the system query  2304 , however, it will be understood that in other embodiments, the query system manager  502  may identify the query configuration parameters  2306  for the query and may not translate the user query to the system query  2304 . Thus, the query configuration parameters  2306  can be used to execute a system query, a user query, or some other query generated from the user query  2302 . 
     As mentioned, in some embodiments, the metadata catalog  221  may not store separate dataset association records  602 . Rather, the datasets association records  602  illustrated in  FIG. 23  can be considered a logical association between one or more dataset configuration records  604  and/or one or more rule configuration records  606 . In certain embodiments, the datasets  608  and/or rules  610  of each dataset association record  602  may be references to dataset configuration records  604  and/or rule configuration records  606 . Accordingly, in some embodiments, rather than moving from or parsing different portions of a dataset association record  602 , it will be understood that the query system manager  502  can parse different dataset configuration records  604  and/or rule configuration records  606  based on the identified physical identifier for the dataset or rule. For example, ( 2 ) may refer to parsing the “trafficTeam.threats-encountered” dataset configuration record  604 , ( 3 A) and ( 3 B) may refer to parsing the “trafficTeam.traffic” and “trafficTeam.threats” dataset configuration records  604 , respectively, ( 4 A) and ( 4 B) may refer to parsing the “trafficTeam.threats-col” and “shared.main,” dataset configuration records  604 , respectively, ( 4 C) may refer to parsing the “trafficTeam.shared.X” (or “shared.X”) rule configuration record  606 , ( 5 ) may refer to parsing the “shared.X” rule configuration record  606  (or be combined with ( 4 C)), ( 6 ) may refer to parsing the “shared.users” dataset configuration record  604 , and ( 7 ) may refer to parsing the “shared.users-col” dataset configuration record  604 . Thus, as the query system manager  502  parses different datasets  608  or rules  610 , it can do so using the dataset configuration records  604  and rule configuration records  606 , respectively. Moreover, in some such embodiments ( 8 ) may be omitted (or considered as part of each parsing step) as the query system manager  502  references the relevant dataset configuration records  604  and rule configuration records  606  throughout the review or parsing process. Based on the review of the various dataset configuration records  604  and rule configuration records  606 , the query system manager  502  can ( 9 ) generate the system query  2304  and/or the query configuration parameters  2306 . 
     Furthermore, when parsing the dataset configuration records  604  or rule configuration records  606 , the system can use one or more annotations to identify related datasets. For example, the system can determine that the “threats-encountered” dataset  608 I depends on and/or is related to the “traffic” dataset  608 J and “threats” dataset  608 H based on one or more annotations, such as an inter-dataset relationship annotation, in the dataset configuration record  604 N. In some embodiments, using the annotations in the dataset configuration records  604 , the system can more quickly traverse between the different datasets and identify the primary datasets for the query  2302 . 
     In certain embodiments, as the system parses the query  2302 , it can extract metadata and generate additional annotations for one or more dataset configuration records  604  and rule configuration records  606 . For example, the query  2302  can be referred to as a dataset “job.” Based on its reference to “threats-encountered,” the system can determine that the dataset “job” is dependent on “threats-encountered” and generate an annotation based on the determined relationship. The system can generate one or more additional annotations for the dataset “job” as described herein. In some embodiments the annotations can be stored for future use or reference. For example, for each query that is entered, the system can generate a dataset configuration record  606  and store the annotations generated for the query. 
     In addition, if the system has not already generated annotations for other datasets referenced by the query (e.g., when the various datasets are added to the metadata catalog  221 ), then the system can generate annotations as it traverses the datasets as part of parsing the query  2302 . For example, as described herein, the system can generate annotations for the dataset configuration record  604 N indicating that the “threats-encountered” dataset  608 I is dependent on the “traffic” and “threats” datasets  608 H,  608 J, respectively. As also described herein, the system can determine a relationship between the field “sig” of the “traffic” dataset  608 J and the field “sig” of the “threats” dataset  608 H. Likewise, the system can determine inter-dataset relationships between the “traffic” and “main” datasets  608 J and  608 A, the “threats-col” and “threats” datasets  608 G and  608 H, and the “users” and “users-col” datasets  608 C and  608 D. In a similar way, the system can determine a rule-dataset relationship between rule “X”  610 A and dataset “users”  608 C, etc. The system can use the various determined relationships to generate annotations for corresponding dataset and rule configuration records  604 ,  606 , respectively. In some embodiments, the generated annotations can be used to more efficiently parse and execute the query if it is executed again, to generate suggestions for the user, and/or to enable the user to gain a greater understanding of the data associated with, stored by, or managed by the system. 
     4.4.3. Metadata Catalog Flows 
       FIG. 24  is a flow diagram illustrative of an embodiment of a routine  2400  implemented by the query system  214  to execute a query. Although described as being implemented by the query system  214 , it will be understood that the elements outlined for routine  2500  can be implemented by one or more computing devices/components that are associated with the data intake and query system  108 , such as, but not limited to, the query system manager  502 , the search head  504 , the search master  512 , the search manager  514 , the search nodes  506 , etc. Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  2402 , a query system  214  receives a search query. As described herein, the query system manager  502  can receive the query in a variety of ways. For example, the query can be received via the gateway  215  and/or network  208 . The query can identify a set of data processing set of data. In addition, in some embodiments, the query can include one or more commands to obtain data from a dataset, one or more dataset identifiers, and/or a dataset association record identifier. 
     At block  2404 , query system  214  identifies one or more primary datasets. As described herein, the primary datasets can include one or more source datasets and/or one or more datasets that are to be used to execute the query. In some embodiments, to identify the source datasets, the query system  214  parses the query to identify the dataset identifier(s) and/or the dataset association record identifier. In certain embodiments, the query system  214  uses the dataset identifier(s) and/or the dataset association record identifier to identify the one or more primary datasets. 
     In some embodiments, the query system  214  can iteratively process the dataset association record  602  associated with the identified dataset association record identifier to identify datasets associated with the query and then identify primary datasets. For example, as described herein, the query system  214  can parse one or more datasets of the dataset association record. For each dataset that is parsed, the query system  214  can determine whether the dataset is a source dataset or will otherwise be used to execute the query. If the query system  214  determines that the dataset is a source dataset or will otherwise be used to execute the query, it can include the dataset as a primary dataset. 
     In certain embodiments, the query system  214  can use the dataset associated with the dataset identifier to identify primary datasets. For example, the query system  214  can parse the dataset (or corresponding dataset configuration record  604 ) to determine whether the dataset includes at least a portion of the set of data of the query (or is a source dataset), includes one or more query parameters to be included as part of the query, references additional datasets (e.g., as part of a query parameter and/or as part of being imported), and/or will be used (or its configuration parameters will be used) to execute the query. 
     Based on the parsing the query system  214  can determine whether the dataset is a primary dataset. In some embodiments, if the query system determines that the dataset includes at least a portion of the set of data of the query, it can identify the dataset as a source dataset and a primary dataset. In certain embodiments, if the dataset (or its configuration parameters) will be used to execute the query, the query system  214  can determine that the dataset is a primary dataset. In some cases, if the dataset references other datasets (e.g., is a single or multi-reference dataset), the query system  214  can parse the referenced datasets to determine whether they are primary datasets. The query system  214  can iteratively process the datasets until any dataset referenced by the query or referenced by another dataset that was referenced by the (directly or indirectly) query, has been processed. In each case, the query system  214  can determine whether the dataset is a primary dataset. In certain embodiments, if the dataset includes one or more query parameters and/or references one or more additional datasets but does not include at least a portion of the set of data or will not be used as part of the query, the query system  214  can determine that the dataset is not a primary dataset or is a secondary dataset. 
     In certain embodiments, the query system  214  can also identify primary rules, such as rules that will be used to process at least a portion of the set of data or process data from a primary dataset. In some embodiments, the query system  214  identifies the primary rules similar to identifying primary datasets. For example, the query system  214  can identify one or more rules in the query and/or one or more rules associated with a dataset that is referenced in the query or is referenced by another dataset that is referenced (directly or indirectly) by the query. 
     At  2406 , the query system  214  generates query configuration parameters. In some cases, the query system  214  can generate the query configuration parameters based on one or more identified primary datasets and/or primary rules. In certain embodiments, the query system  214  can generate the query configuration parameters based on one or more dataset and/or query configurations from a metadata catalog  221 . For example, as described herein, the metadata catalog  221  can include one or more dataset configuration records  604 . In certain embodiments, the query system  214  includes the dataset configuration records  604  associated with the primary datasets in the query configuration parameters. In certain embodiments, the query configuration parameters can include rule configuration records  606  associated with the primary rules. 
     At  2408 , the query system  214  executes the query. In some embodiments, the query system  214  executes the query based on the query configuration parameters. For example, the query configuration parameters can indicate how to access the source datasets, how to process data from the source datasets, etc. As described herein, the query system  214  can dynamically determine the query configuration parameters for the query. In certain embodiments, the query system  214  determines the configurations to execute the query using only the query configuration parameters identified at block  2406 . Furthermore, the query system  214  can execute the query, as described herein at least with reference to  FIGS. 14-21 . 
     Fewer, more, or different blocks can be used as part of the routine  2400 . For example, in some embodiments, the query system  214  can generate a system query from a user query. 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. 24  can be implemented in a variety of orders, or can be performed concurrently. For example, the indexing node  404  can concurrently identify source datasets and obtain query configuration parameters. 
       FIG. 25  is a flow diagram illustrative of an embodiment of a routine  2500  implemented by a query system manager  502  to communicate query configuration parameters to a query processing component. Although described as being implemented by the query system manager  502 , it will be understood that the elements outlined for routine  2500  can be implemented by one or more computing devices/components that are associated with the data intake and query system  108 , such as, but not limited to, query system  214 , the search head  504 , the search master  512 , the search manager  514 , the search nodes  506 , etc. Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  2502 , the query system manager  502  receives a search query, as described in greater detail above at least with reference to block  2402  of  FIG. 24  and ( 1 ′) of  FIG. 22 . At block  2504 , query system manager  502  identifies primary datasets, as described herein at least with reference to block  2404  of  FIG. 24  and ( 2 ′) of  FIG. 22 . At  2506 , the query system manager  502  obtains query configuration parameters, as described in greater detail above at least with reference to block  2406  of  FIG. 24  and ( 2 ′) of  FIG. 22 . 
     At  2508 , the query system manager  502  communicates the query configuration parameters to a query processing component, such as the search head  504 . As described herein, the query processing component can process and execute the query using the received query configuration parameters. Further, as described herein, in some embodiments, the query configuration parameters communicated to the query processing component include only the query configuration parameters of the primary dataset and primary rules, which, in some embodiments, form a subset of the dataset configuration records  604  and rule configuration records  606  of the metadata catalog  221  and, in certain embodiments, form a subset of the dataset configuration records  604  and rule configuration records  606  associated with the dataset association record(s) associated with the query. 
     In some embodiments, the query processing component does not store query configuration parameters. Accordingly, the search head  504  may be otherwise unable to process and execute the query without the query configuration parameters received from the query system manager  502 . Similarly, in some embodiments, the indexers and/or search nodes do not include query configuration parameters. Accordingly, in some such embodiments, without the query configuration parameters received from the query system manager  502 , the query system  214  would be unable to process and execute the query. Furthermore, by dynamically determining and providing the query configuration parameters to the query processing component, the query system  214  can provide a stateless query system. For example, if the query system  214  determines that multiple query processing components are to be used to process the query or if an assigned query processing component becomes unavailable, the query system can communicate the query configuration parameters to another query processing component without data loss. 
     Fewer, more, or different blocks can be used as part of the routine  2500 . 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. 25  can be implemented in a variety of orders, or can be performed concurrently. For example, the indexing node  404  can concurrently identify source datasets and obtain query configuration parameters. 
       FIG. 26  is a flow diagram illustrative of an embodiment of a routine  2600  implemented by the query system  214  to execute a query. Although described as being implemented by the search head  504 , it will be understood that the elements outlined for routine  2600  can be implemented by one or more computing devices/components that are associated with the data intake and query system  108 , such as, but not limited to, the query system manager  502 , the search master  512 , the search manager  514 , the search nodes  506 , etc. Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  2602 , the search head  504  receives a query. In some embodiments the query received by the search head  504  can be a system query generated by a query system manager  502 . In certain embodiments, the query received by the search head  504  can correspond to a query received by the query system  214  and/or a query received by the data intake and query system  108 . 
     At block  2604 , the search head  504  receives query configuration parameters. As described herein, in some embodiments, the query system manager  502  dynamically identifies the query configuration parameters to be used to process and execute query. The query configuration parameters can include dataset configuration records  604  associated with primary datasets and/or rule configuration records  606  associated with primary rules. In some such embodiments, the search head  504  does not store query configuration parameters locally. In certain embodiments, the query configuration parameters are concurrently received with the query. Furthermore, as described herein, in some embodiments, the query configuration parameters are dynamically generated at query time, or in other words are not determined prior to receipt of the query. In certain embodiments, the query configuration parameters correspond to a subset of the configuration parameters associated with a dataset association record and/or a metadata catalog  221 . 
     In certain embodiments, by dynamically receiving the query configuration parameters associated with a query (or concurrently with the query), the query system  214  can provide a stateless search experience. For example, if the search head  504  becomes unavailable, the query system manager  502  can communicate the dynamically determined query configuration parameters (and/or query to be executed) to another search head  504  without data loss and/or with minimal or reduced time loss. At block  2606 , the search head  504  executes the query, as described herein at least with reference to block  2408  of  FIG. 24  and  FIGS. 14-21 . 
     Fewer, more, or different blocks can be used as part of the routine  2600 . 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. 26  can be implemented in a variety of orders, or can be performed concurrently. 
       FIG. 27  is a flow diagram illustrative of an embodiment of a routine  2700  implemented by the query system  214  to execute a query. Although described as being implemented by the query system manager  502 , it will be understood that the elements outlined for routine  2700  can be implemented by one or more computing devices/components that are associated with the data intake and query system  108 , such as, but not limited to, the search head  504 , the search master  512 , the search manager  514 , the search nodes  506 , etc. Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  2702 , the query system manager  502  receives a user query, as described herein at least with reference to ( 1 ′) of  FIG. 22  and block  2502  of  FIG. 25 . At block  2704 , the query system manager  502  identifies one or more dataset association records. In some embodiments, the query system manager  502  identifies the more dataset association records by parsing the user query and/or via command received with the user query. 
     In certain embodiments, as described herein, the dataset association records identify a subset of datasets of a plurality of datasets in a metadata catalog  221  and/or one or more rules for processing data from at least one dataset of the subset of datasets. In certain embodiments, the datasets of a dataset association record include source datasets, datasets that reference additional datasets, and/or datasets that reference one or more rules. In some embodiments, if the dataset references another dataset or rule, the query system manager  502  can recursively analyze the referenced datasets and rules until it identifies the primary datasets and primary rules. In certain embodiments, the query system manager  502  parses multiple dataset association records to identify primary datasets and/or primary rules. 
     At block  2706 , the query system manager  502  generates a system query. In some embodiments, the query system manager  502  generates a system query based on the dataset association records identified at block  2704 . For example, using the dataset association records, the query system manager  502  can determine a physical identifier for primary datasets and primary rules. The query system manager  502  can use the physical dataset identifiers to generate the system query. For example, the query system manager  502  can reference the physical dataset identifiers in the system query and/or remove all logical dataset identifiers from the user query. In addition, as described herein, in some embodiments, datasets of a dataset association record  602  may reference one or more query parameters. Accordingly, in certain embodiments, the query system manager  502  can include the query parameters referenced by a dataset in the system query. 
     Furthermore, using the dataset association records, the query system manager can identify one or more rules related to the source datasets. As described herein, in certain embodiments, the query system manager  502  analyzes multiple dataset association records to identify datasets associated with the query. 
     At block  2708 , the query system manager  502  communicates the system query to a query execution component of the data intake and query system. In certain embodiments, query system manager  502  communicates the system query to a search head  504 , as described herein at least with reference to the ( 4 ′) of  FIG. 22 . Furthermore, the query execution component can process and execute the system query, as described herein at least with reference to  FIGS. 14-21 . 
     Fewer, more, or different blocks can be used as part of the routine  2700 . For example, the query system manager  502  can generate query configuration parameters and communicate the query configuration parameters to the query execution component. 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. 27  can be implemented in a variety of orders, or can be performed concurrently. 
       FIG. 28  is a flow diagram illustrative of an embodiment of a routine  2800  implemented by the query system  214  to execute a query. Although described as being implemented by the query system manager  502 , it will be understood that the elements outlined for routine  2800  can be implemented by one or more computing devices/components that are associated with the data intake and query system  108 , such as, but not limited to, the search head  504 , the search master  512 , the search manager  514 , the search nodes  506 , etc. Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  2802 , the query system manager  502  receives a user query, as described in herein at least with reference to block  2702  of  FIG. 27 . At block  2804 , the query system manager  502  identifies one or more datasets for a system query. As described herein, the query system manager  502  can identify the one or more datasets in a variety of ways. For example, the query system manager  502  can parse a query and iteratively parse one or more dataset association records  602 , datasets, and/or dataset configuration records  604  associated with the query to identify query parameters (including datasets) that are to be included in the system query. 
     In some embodiments, a dataset to be included in a query can corresponds to a dataset referenced in the user query. For example, a user query may identify a “main” dataset (associated with a “test” dataset association record) and the query system manager  502  can determine that the “test.main” dataset is to be included in the system query. 
     In certain embodiments, a dataset to be included in the system query can correspond to a dataset referenced by another datasets, such as a query dataset, an imported dataset, or another dataset etc. For example, a user query may reference a “findme” query dataset and the query system manager  502  can determine that a “myapp.test” dataset referenced (directly or indirectly) by the “findme” query dataset is to be included in the system query. In some such cases, the dataset to be included in the query can correspond to a query parameter of a query in a query dataset. 
     In certain cases, the dataset to be included in the system query can include a source dataset and/or a source reference dataset. For example, the system query can include a dataset that includes at least a portion of the data of the set of data to be searched and/or include a dataset that refers to or is used to access the data of the set of data that is to be searched. 
     At block  2806  the query system manager  502  identifies a dataset type of the source datasets, as described herein at least with reference to ( 2 ′) of  FIG. 22  and  FIG. 23 . For example, the query system manager  502  can use one or more dataset association records  602  and/or dataset configuration records  604  to identify a dataset type of the datasets to be included in the query. In certain cases, the query system manager  502  can parse the identified dataset configuration records  604  to identify the dataset type of the source datasets. 
     At block  2808 , the query system manager  502  generates a system query, as described herein at least with reference to ( 3 ′) of  FIG. 22  and  FIG. 23 . In some embodiments, different commands can be associated with different datasets. For example, an index dataset can be associated with a “search” command, a lookup dataset can be associated with a “lookup,” command, a metrics interaction dataset can be associated with an “mstats,” command, etc. Accordingly, based on the dataset type, the query system manager  502  can determine a command to be used to search or retrieve data from the datasets identified for inclusion in the system query. The query system manager  502  can include the determined commands for the identified source dataset in the system query. Furthermore, in some embodiments, the query system manager  502  can determine a physical identifier for the datasets to be included in the system query and include the physical identifier for the datasets in the system query. In certain embodiments, the query system manager  502  can identify one or more query parameters of with a dataset associated with the query and include the query parameters in the system query. 
     At block  2810 , the query system manager  502  communicates the system query to a query execution component of the data intake and query system, as described herein at least with reference to block  2708  of  FIG. 27 . 
     Fewer, more, or different blocks can be used as part of the routine  2800 . For example, the query system manager  502  can generate query configuration parameters and communicate the query configuration parameters to the query execution component. 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. 28  can be implemented in a variety of orders, or can be performed concurrently. 
     4.5. Annotating the Metadata Catalog 
     As described herein, the metadata catalog  221  can be automatically annotated based on system use or based on detected changes to the metadata catalog  221 . The system can use the annotations to improve the query times, reduce the amount of processing used to execute a query, generate suggestions for improved queries, and generate an understanding of the dataset associated with, stored by, or managed by the system, etc. 
     4.5.1. Annotations Based on Query Processing Flow 
       FIG. 29  is a flow diagram illustrative of an embodiment of a routine  2900  implemented by the system  108  to generate an annotation based on processing a query. Although described as being implemented by the system  108 , it will be understood that the elements outlined for routine  2900  can be implemented by one or more computing devices/components that are associated with the data intake and query system  108 , such as, but not limited to, an indexing node  404 , indexing system manager  402 , the query system manager  502 , the search head  504 , the search master  512 , the search manager  514 , the search nodes  506 , etc. Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  2902 , the system identifies a query. The system can identify a query in a variety of ways. In some embodiments, the system can identify a query by parsing a dataset configuration record  604  that includes a query, such as a dataset configuration record  604  associated with a view dataset. In certain embodiments, the system can identify a query by receiving a query from a user via a user interface. For example, a user may enter a query for execution by the system  108  via the user interface. 
     At block  2904 , the system identifies one or more datasets associated with the query. As described herein, the system can identify the datasets associated with the query by parsing the query. In parsing the query, the system can use known syntax and semantics of the query language to identify datasets explicitly recited in the query. For example, the system can use the syntax and semantics of the query language to identify commands in the query and locations following commands where datasets are identified. The system can use the datasets explicitly identified in the query to identify additional datasets associated with the query. For example, the system can parse the dataset configuration records  604  of the identified datasets to identify additional datasets associated with the query The system can iteratively process the datasets until any dataset referenced by the query or referenced by another dataset that was referenced by the (directly or indirectly) query, has been parsed. 
     At block  2906 , the system identifies one or more data fields associated with the query. The system can identify the one or more data fields similar to the manner in which it identifies datasets. For example, the system can identify an initial group of data fields based on the syntax and semantics of the query. The system can identify additional data fields based on the datasets identified at bloc  2904 . For example, if a field is referenced by a dataset in connection with the query (e.g., a view dataset that is associated with the query identifies the fields as part of a query of the view dataset), the system can identify the fields as being associated with the query. In certain embodiments, some fields associated with the identified datasets are not identified as being associated with the query. For example, if a dataset configuration record  604  associated with an identified dataset indicates that the dataset has four fields, but only one of those fields is referenced by or will be used to execute the query, the system can identify the one field as associated with the query and determine that the other three fields are not associated with the query. 
     At block  2908 , the system generates one or more annotations. The annotations can be based on the identified dataset and/or fields. As described herein, at least with reference to  FIG. 6 , the system can generate a variety of annotations. In some cases, the annotations can correspond to the identified datasets and fields. For example, the system can generate one more annotations identifying the one or more fields or datasets identified at block  2904  and  2906 . 
     In certain embodiments, the annotations can correspond to one or more determined relationships between the identified fields and datasets. For example, as described herein, the system can generate an annotation identifying one or more field-dataset relationships, inter-field relationships, inter-dataset relationships, etc. In some embodiments, the system can determine an inter-dataset relationship based on the (direct or indirect) reference of one dataset to another dataset or multiple datasets having the similar or same field. In certain embodiments, the system can determine an inter-field relationship based on the presence of a field in multiple datasets or based on one field being derived from another field following one or more transformations of data. Additional examples of annotations derived from parsing the query are described herein at least with reference to  FIG. 6 . 
     In some embodiments, the annotations can correspond to one or more users. For example, the system can generate an annotation identifying the user (and/or group associated with the user) associated with the query. 
     At block  2910 , the system updates a metadata catalog  221  based at least in part on the generated annotations. In some embodiments, the generated annotations are stored and associated with one or more dataset or rule configuration records  604 ,  606 . In certain embodiments, the system  108  can associate each annotation with a dataset. The annotation can be stored in the dataset configuration record  604  of the dataset or otherwise reference the dataset. For example, if an annotation identifies a field or field-dataset relationship, the system  108  can store the annotation and indicate the dataset related to the identified field. As another example, if an annotation identifies an inter-field relationship, the system can store the annotation indicating the related fields and associated datasets. Similarly, if an annotation identifies an inter-dataset relationship, the system can store the annotation indicating the related datasets. 
     Fewer, more, or different blocks can be used as part of the routine  2900 . In some cases, one or more blocks can be omitted. For example, in some cases, the routine  2900  can exclude block  2904  or  2906 . In some embodiments, the blocks of routine  2900  can be combined with any one or any combination of blocks described herein with reference to at least  FIGS. 10-13, 15-21, 24-28, 30, and 31A . For example, the system can execute routine  2900  as part of processing or executing a query or can use the annotations generated as a result of routine  2900  to process additional queries. Similarly, in response to updating the metadata catalog  221 , the system can execute one or more blocks of routine  3000  to generate one or more additional annotations. 
     In some embodiments, the routine  2900  further includes using the annotations to generate additional annotations, propagating one or more annotations from one dataset to another dataset, suggesting one or more datasets or fields to a user or group. 
     In certain embodiments, the routine  2900  further includes monitoring the system during execution of the query and generating one or more annotations based on the monitoring. For example, the system can monitor the amount of data obtained from a dataset, the time to obtain data from a dataset source, an estimated size of a dataset source, etc. and generate corresponding annotations. Moreover, the routine  2900  can further include generating one or more cost-based optimizations based on the annotations. In some cases, the system can generate the cost-based optimizations as part of processing a query for execution. 
     Furthermore, it will be understood that the various blocks described herein with reference to  FIG. 29  can be implemented in a variety of orders, or can be performed concurrently. For example, the system can concurrently identify datasets, identify fields, generate annotations, and update the metadata catalog  221 . For example, as each new dataset is identified, the system can generate annotations associated with the dataset and update the metadata catalog  221  for that dataset while concurrently identifying additional datasets and/or fields. 
     4.5.2. Annotations Based on Catalog Changes Flow 
       FIG. 30  is a flow diagram illustrative of an embodiment of a routine  3000  implemented by the system  108  to generate an annotation based on a change to the metadata catalog  221 . Although described as being implemented by the system  108 , it will be understood that the elements outlined for routine  3000  can be implemented by one or more computing devices/components that are associated with the data intake and query system  108 , such as, but not limited to, the query system manager  502 , the search head  504 , the search master  512 , the search manager  514 , the search nodes  506 , etc. Thus, the following illustrative embodiment should not be construed as limiting. 
     At block  3002 , the system identifies a change to the metadata catalog  221  based at least in part on a first annotation. As described herein at least with reference to  FIG. 6 , the metadata catalog  221  can identify a variety of datasets and store dataset configuration records  604  for the different datasets. The dataset configuration records  604  can include or be associated with annotations that correspond to the dataset. Similarly, the annotations can identify a dataset and/or field with which they are associated. Accordingly, the identified change can correspond to the addition of the first annotation to the metadata catalog  221  or a change made to the first annotation. 
     As described herein, the annotations of the metadata catalog  221  (including the first annotation) can correspond to user annotations added by a user or system annotations generated by the system. In some cases, the first annotation can correspond to an annotation generated in response to parsing a query, such as an annotation generated as a result of routine  2900 , an annotation generated in response to monitoring the execution of a query, an annotation generated in response to monitoring a user or program executing in the system, or an annotation generated in response to another annotation. 
     In addition, as described herein, the annotations can identify any one or any combination of field-dataset relationships, inter-field relationships, inter-dataset relationships, number of fields in a dataset, field usage (total, by dataset, by user, by group, etc.), popular fields of a dataset, dataset usage (total, by user, by group, etc.), popular datasets, alarm thresholds, data categories, users or groups associated with a dataset or field, units or preferred units associated with a dataset or field, among others. 
     At block  3004 , in response to identifying the change, the system generates a second annotation for the metadata catalog  221  based at least in part on the first annotation. 
     As described herein, the second annotation can be generated for the same dataset that is associated with the first annotation or a second dataset. Accordingly, in some embodiments, as part of generating a second annotation, the system identifies a second dataset that is related to the first dataset and to which the second annotation will be associated. 
     For example if the first annotation is a field-dataset relationship annotation, the second annotation can be an annotation that indicates the number of fields for the first dataset (updated based on the new field-dataset relationship annotation), or an inter-dataset relationship annotation identifying a second dataset that is similar to the first dataset or a second dataset to which the first dataset can be mapped. As another example, if the first annotation is an inter-field or inter-dataset relationship annotation, the second annotation can be another inter-field or inter-dataset relationship annotation, a propagated alarm threshold annotation, a propagated data category annotation, or a propagated unit or preferred unit annotation. As another example, if the first annotation is a field, dataset, or application annotation, the second annotation can be a number of uses or popularity annotation. As yet another example, if the first annotation is an alarm threshold, unit/preferred unit, or data category annotation associated with the first dataset, the second annotation can be an alarm threshold, unit/preferred unit, or data category annotation associated with a second dataset that is related to the first dataset. Additional examples of second annotations that can be generated based on first annotations is described herein at least with reference to  FIG. 6 . 
     At block  3006 , the system updates the metadata catalog  221  based at least in part on the second annotation. Similar to block  2910  of routine  2900 , the generated annotation can be stored and associated with one or more dataset or rule configuration records  604 ,  606 . 
     Fewer, more, or different blocks can be used as part of the routine  3000 . In some cases, one or more blocks can be omitted. For example, the blocks of routine  3000  can be combined with any one or any combination of blocks described herein with reference to at least  FIGS. 10-13, 15-21, 24-29, and 31A . For example, system  108  can execute routine  3000  in response to updating the metadata catalog  221  as described herein at least with reference to  FIG. 29 . 
     In some embodiments, routine  3000  can be repeated based on generated annotations. For example, if a first annotation is generated the first time system executes routine  3000 , the system  108  can execute routine  3000  based on the first annotation to generate a second annotation, until the system  108  determines that no more annotations are to be generated. In some embodiments, the system  108  can use multiple processes (e.g., processes, threads, or isolated execution environments) to execute multiple versions of routine  3000  concurrently. For example, one process can monitor for changes related to fields in the metadata catalog  221  and generate annotations based on the detected field changes, another process can monitor for changes related to datasets or inter-dataset relationships in the metadata catalog  221  and generate annotations based on the detected changes, and so forth. In this way, the system can concurrently generate different annotations for the metadata catalog  221 . 
     Furthermore, it will be understood that the various blocks described herein with reference to  FIG. 30  can be implemented in a variety of orders, or can be performed concurrently. For example, the system can concurrently identify datasets, generate multiple annotations, and update the metadata catalog  221 . For example, multiple annotations may be concurrently generated based on the first annotation. 
     4.6. Data Ingestion, Indexing, and Storage Flow 
       FIG. 31A  is a flow diagram of an example method that illustrates how a data intake and query system  108  processes, indexes, and stores data received from data sources  202 , in accordance with example embodiments. The data flow illustrated in  FIG. 31A  is provided for illustrative purposes only; it will be understood that one or more of the steps of the processes illustrated in  FIG. 31A  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, the intake system  210  is described as receiving and processing machine data during an input phase; the indexing system  212  is described as parsing and indexing machine data during parsing and indexing phases; and a query system  214  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. 
     4.6.1. Input 
     At block  3102 , the intake system  210  receives data from an input source, such as a data source  202  shown in  FIG. 2 . The intake system  210  initially may receive the data as a raw data stream generated by the input source. For example, the intake system  210  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, the intake system  210  receives the raw data and may segment the data stream into messages, possibly of a uniform data size, to facilitate subsequent processing steps. The intake system  210  may thereafter process the messages in accordance with one or more rules, as discussed above for example with reference to  FIGS. 7 and 8 , to conduct preliminary processing of the data. In one embodiment, the processing conducted by the intake system  210  may be used to indicate one or more metadata fields applicable to each message. For example, the intake system  210  may include metadata fields within the messages, or publish the messages to topics indicative of a metadata field. These metadata fields may, for example, provide information related to a message as a whole and may apply to each event that is subsequently derived from the data in the message. For example, the metadata fields may include separate fields specifying each of a host, a source, and a source type related to the message. 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. 
     At block  504 , the intake system  210  publishes the data as messages on an output ingestion buffer  310 . Illustratively, other components of the data intake and query system  108  may be configured to subscribe to various topics on the output ingestion buffer  310 , thus receiving the data of the messages when published to the buffer  310 . 
     4.6.2. Parsing 
     At block  3106 , the indexing system  212  receives messages from the intake system  210  (e.g., by obtaining the messages from the output ingestion buffer  310 ) and parses the data of the message to organize the data into events. In some embodiments, to organize the data into events, the indexing system  212  may determine a source type associated with each message (e.g., by extracting a source type label from the metadata fields associated with the message, 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 indexing system  212  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 indexing system  212 , the indexing system  212  may infer a source type for the data by examining the structure of the data. Then, the indexing system  212  can apply an inferred source type definition to the data to create the events. 
     At block  3108 , the indexing system  212  determines a timestamp for each event. Similar to the process for parsing machine data, an indexing system  212  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 the indexing system  212  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  3110 , the indexing system  212  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  3104 , 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  3112 , the indexing system  212  may optionally apply one or more transformations to data included in the events created at block  3106 . 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. 31C  illustrates an illustrative example of how 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. 31C  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  3136 , source  3137 , source type  3138  and timestamps  3135  can be generated for each event, and associated with a corresponding portion of machine data  3139  when storing the event data in a data store, e.g., data store  212 . 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 timestamp metadata field can be extracted from the raw data of each event, the values for the other metadata fields may be determined by the indexing system  212  or indexing node  404  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. 31C , the first three rows of the table represent events  3131 ,  3132 , and  3133  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  3136 . 
     In the example shown in  FIG. 31C , each of the events  3131 - 3333  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  3140 , the user id of the person requesting the document  3141 , the time the server finished processing the request  3142 , the request line from the client  3143 , the status code returned by the server to the client  3145 , the size of the object returned to the client (in this case, the gif file requested by the client)  3146  and the time spent to serve the request in microseconds  3144 . As seen in  FIG. 31C , all the raw machine data retrieved from the server access log is retained and stored as part of the corresponding events,  3131 - 3333  in the data store. 
     Event  3134  is associated with an entry in a server error log, as indicated by “error.log” in the source column  3137  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  3134  can be preserved and stored as part of the event  3134 . 
     Saving minimally processed or unprocessed machine data in a data store associated with metadata fields in the manner similar to that shown in  FIG. 31C  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. 
     4.6.3. Indexing 
     At blocks  3114  and  3116 , the indexing system  212  can optionally generate a keyword index to facilitate fast keyword searching for events. To build a keyword index, at block  3114 , the indexing system  212  identifies a set of keywords in each event. At block  3116 , the indexing system  212  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 the data intake and query system  108  subsequently receives a keyword-based query, the query system  214  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  3118 , the indexing system  212  stores the events with an associated timestamp in a local data store  212  and/or common storage  216 . 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. 
     The indexing system  212  may be responsible for storing the events contained in various data stores  218  of common storage  216 . By distributing events among the data stores in common storage  216 , the query system  214  can analyze events for a query in parallel. For example, using map-reduce techniques, each search node  506  can return 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, the indexing system  212  may further optimize the data retrieval process by enabling search nodes  506  to search buckets corresponding to time ranges that are relevant to a query. 
     In some embodiments, each indexing node  404  (e.g., the indexer  410  or data store  412 ) of the indexing system  212  has a home directory and a cold directory. The home directory stores hot buckets and warm buckets, and the cold directory 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 indexing node  404  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 indexing node  404  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. 
     In some embodiments, an indexing node  404  may not include a cold directory and/or cold or frozen buckets. For example, as warm buckets and/or merged buckets are copied to common storage  216 , they can be deleted from the indexing node  404 . In certain embodiments, one or more data stores  218  of the common storage  216  can include a home directory that includes warm buckets copied from the indexing nodes  404  and a cold directory of cold or frozen buckets as described above. 
     Moreover, events and buckets can also be replicated across different indexing nodes  404  and data stores  218  of the common storage  216 . 
       FIG. 31B  is a block diagram of an example data store  3101  that includes a directory for each index (or partition) that contains a portion of data stored in the data store  3101 .  FIG. 31B  further illustrates details of an embodiment of an inverted index  3107 B and an event reference array  3115  associated with inverted index  3107 B. 
     The data store  3101  can correspond to a data store  218  that stores events in common storage  216 , a data store  412  associated with an indexing node  404 , or a data store associated with a search peer  506 . In the illustrated embodiment, the data store  3101  includes a _main directory  3103  associated with a _main partition and a _test directory  3105  associated with a _test partition. However, the data store  3101  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  3101 , it will be understood that the data store  3101  can be implemented as multiple data stores storing different portions of the information shown in  FIG. 31B . For example, a single index or partition can span multiple directories or multiple data stores, and can be indexed or searched by multiple search nodes  506 . 
     Furthermore, although not illustrated in  FIG. 31B , it will be understood that, in some embodiments, the data store  3101  can include directories for each tenant and sub-directories for each partition of each tenant, or vice versa. Accordingly, the directories  3101  and  3103  illustrated in  FIG. 31B  can, in certain embodiments, correspond to sub-directories of a tenant or include sub-directories for different tenants. 
     In the illustrated embodiment of  FIG. 31B , the partition-specific directories  3103  and  3105  include inverted indexes  3107 A,  3107 B (generically referred to as inverted index  3107  or inverted indexes  3017 ) and  3109 A,  3109 B (generically referred to as inverted index  3109  or inverted indexes  3019 ), respectively. The inverted indexes  3107  and  3109  can be keyword indexes or field-value pair indexes described herein and can include less or more information than depicted in  FIG. 31B . 
     In some embodiments, the inverted indexes  3107  and  3109  can correspond to distinct time-series buckets stored in common storage  216 , a search node  506 , or an indexing node  404  and that contains events corresponding to the relevant partition (e.g., _main partition, _test partition). As such, each inverted index can correspond to a particular range of time for a partition. Additional files, such as high performance indexes for each time-series bucket of a partition, can also be stored in the same directory as the inverted indexes  3107  and  3109 . In some embodiments inverted index  3107  and  3109  can correspond to multiple time-series buckets or inverted indexes  3107  and  3109  can correspond to a single time-series bucket. 
     Each inverted index  3107  and  3109  can include one or more entries, such as keyword (or token) entries or field-value pair entries. Furthermore, in certain embodiments, the inverted indexes  3107  and  3109  can include additional information, such as a time range  3123  associated with the inverted index or a partition identifier  3125  identifying the partition associated with the inverted index  3107  and  3109 . However, each inverted index  3107  and  3109  can include less or more information than depicted. 
     Token entries, such as token entries  3111  illustrated in inverted index  3107 B, can include a token  3111 A (e.g., “error,” “itemID,” etc.) and event references  3111 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. 31B , the error token entry includes the identifiers  3 ,  5 ,  6 ,  8 ,  11 , and  12  corresponding to events located in the time-series bucket associated with the inverted index  3107 B that is stored in common storage  216 , a search node  506 , or an indexing node  404  and is associated with the partition _main  3103 . 
     In some cases, some token entries can be default entries, automatically determined entries, or user specified entries. In some embodiments, the indexing system  212  can identify each word or string in an event as a distinct token and generate a token entry for the identified word or string. In some cases, the indexing system  212  can identify the beginning and ending of tokens based on punctuation, spaces, as described in greater detail herein. In certain cases, the indexing system  212  can rely on user input or a configuration file to identify tokens for token entries  3111 , etc. It will be understood that any combination of token entries can be included as a default, automatically determined, or included based on user-specified criteria. 
     Similarly, field-value pair entries, such as field-value pair entries  3113  shown in inverted index  3107 B, can include a field-value pair  3113 A and event references  3113 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 can 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  3113  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, and sourcetype can be included in the inverted indexes  3107  and  3109  as a default. As such, all of the inverted indexes  3107  and  3109  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  3107 B based on user-specified criteria. As another non-limiting example, as the indexing system  212  indexes the events, it can automatically identify field-value pairs and create field-value pair entries. For example, based on the indexing system&#39;s  212  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  3107 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. 
     With reference to the event reference array  3115 , each unique identifier  3117 , 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 of an inverted index. 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. 31B  and the event that corresponds to the event reference  3 , the event reference  3  is found in the field-value pair entries  3113  host::hostA, source::sourceB, sourcetype::sourcetypeA, and IP_address::91.205.189.15 indicating that the event corresponding to the event references 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. 31B , 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  3117  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  3115 . The event reference array  3115  can include an array entry  3117  for each event reference in the inverted index  3107 B. Each array entry  3117  can include location information  3119  of the event corresponding to the unique identifier (non-limiting example: seek address of the event), a timestamp  3121  associated with the event, or additional information regarding the event associated with the event reference, etc. 
     For each token entry  3111  or field-value pair entry  3113 , the event reference  3101 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. 31B  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. 31B , the entries are sorted first by entry type and then alphabetically. 
     As a non-limiting example of how the inverted indexes  3107  and  3109  can be used during a data categorization request command, the query system  214  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, tenant and/or user identifiers, keywords, etc. 
     Using the filter criteria, the query system  214  identifies relevant inverted indexes to be searched. For example, if the filter criteria includes a set of partitions (also referred to as indexes), the query system  214  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 query system  214  can review an entry in the inverted indexes, such as a partition-value pair entry  3113  to determine if a particular inverted index is relevant. If the filter criteria does not identify any partition, then the query system  214  can identify all inverted indexes managed by the query system  214  as relevant inverted indexes. 
     Similarly, if the filter criteria includes a time range, the query system  214  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 query system  214  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 query system  214  can focus the processing to only a subset of the total number of inverted indexes in the data intake and query system  108 . 
     Once the relevant inverted indexes are identified, the query system  214  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 query system  214  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 query system  214  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 query system  214  can determine 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 query system  214  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 query system  214  can determine that some events identified using the particular inverted index may not satisfy the time range filter criterion. 
     Using the inverted indexes, the query system  214  can identify event references (and therefore events) that satisfy the filter criteria. For example, if the token “error” is a filter criterion, the query system  214  can track all event references within the token entry “error.” Similarly, the query system  214  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 query system  214  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 query system  214  can determine that the events associated with the identified event references satisfy the filter criteria. 
     In some cases, the query system  214  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 query system  214  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 query system  214  can review an array, such as the event reference array  2115  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 partition identifier), the query system  214  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 query system  214  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 query system  214  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 query system  214  determines that at least one relevant inverted index does not include a field-value pair entry corresponding to the field name sessionID, the query system  214  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 query system  214  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 S 1  and the “sessionID” field is found in source S 2 , the source S 2  can replace S 1  in the filter criteria, be appended such that the filter criteria includes source S 1  and source S 2 , or be excluded based on the presence of the filter criterion source S 1 . If the identified host, source, or sourcetype is included in the filter criteria, the query system  214  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 query system  214  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 query system  214  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 partition-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 query system  214  can use the nineteen distinct field-value pair entries as categorization criteria-value pairs to group the results. 
     Specifically, the query system  214  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 query system  214  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 query system  214  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 query system  214  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 query system  214  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 query system  214  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 query system  214  can count the number of events that meet the unique combination of partition, sourcetype, and host for a particular group. 
     The query system  214 , such as the search head  504  can aggregate the groupings from the buckets, or search nodes  506 , 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 query system  214  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. 31B , consider a request received by the query system  214  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, a search node  506  of the query system  214  that is associated with the data store  3101  identifies _main directory  3103  and can ignore _test directory  3105  and any other partition-specific directories. The search node  506  determines that inverted index  3107 B is a relevant index based on its location within the _main directory  3103  and the time range associated with it. For sake of simplicity in this example, the search node  506  determines that no other inverted indexes in the _main directory  3103 , such as inverted index  3107 A satisfy the time range criterion. 
     Having identified the relevant inverted index  3107 B, the search node  506  reviews the token entries  3111  and the field-value pair entries  3113  to identify event references, or events that satisfy all of the filter criteria. 
     With respect to the token entries  3111 , the search node  506  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 search node  506  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 search node  506  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 search node  506  can identify events (and corresponding event references) that satisfy the time range criterion using the event reference array  3115  (e.g., event references  2 ,  3 ,  4 ,  5 ,  6 ,  7 ,  8 ,  9 ,  10 ). Using the information obtained from the inverted index  3107 B (including the event reference array  3115 ), the search node  506  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 search node  506  can group the event references using the received categorization criteria (source). In doing so, the search node  506  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 search node  506  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  504 . In turn the search head  504  can aggregate the results from the various search nodes  506  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, consider a request received by a search node  506  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 can result in the search node  506  identifying event references  1 - 12  as satisfying the filter criteria. The search node  506  can 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 search node  506  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 search node  506  communicates the groups to the search head  504  for aggregation with results received from other search nodes  506 . In communicating the groups to the search head  504 , the search node  506  can include the categorization criteria-value pairs for each group and the count. In some embodiments, the search node  506  can include more or less information. For example, the search node  506  can include the event references associated with each group and other identifying information, such as the search node  506  or inverted index used to identify the groups. 
     As another non-limiting example, consider a request received by an search node  506  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 can result in the search node 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 search node  506  communicates the groups to the search head  504  for aggregation with results received from other search node  506   s . As will be understand there are myriad ways for filtering and categorizing the events and event references. For example, the search node  506  can review multiple inverted indexes associated with a 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 search node  506  can provide additional information regarding the group. For example, the search node  506  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 search node  506  relies on the inverted index. For example, the search node  506  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  3115  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. 31B  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  504  communicates with the search node  506  to provide additional information regarding the group. 
     In some embodiments, the search node  506  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 search node  506  identifies event references  4 ,  5 ,  6 ,  8 ,  10 ,  11 . 
     Based on a sampling criteria, discussed in greater detail above, the search node  506  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 search node  506  can use the event reference array  3115  to access the event data associated with the event references  5 ,  8 ,  10 . Once accessed, the search node  506  can compile the relevant information and provide it to the search head  504  for aggregation with results from other search nodes. By identifying events and sampling event data using the inverted indexes, the search node 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. 
     4.7. Query Processing Flow 
       FIG. 32A  is a flow diagram illustrating an embodiment of a routine implemented by the query system  214  for executing a query. At block  3202 , a search head  504  receives a search query. At block  3204 , the search head  504  analyzes the search query to determine what portion(s) of the query to delegate to search nodes  506  and what portions of the query to execute locally by the search head  504 . At block  3206 , the search head distributes the determined portions of the query to the appropriate search nodes  506 . In some embodiments, a search head cluster may take the place of an independent search head  504  where each search head  504  in the search head cluster coordinates with peer search heads  504  in the search head cluster to schedule jobs, replicate search results, update configurations, fulfill search requests, etc. In some embodiments, the search head  504  (or each search head) consults with a search node catalog  510  that provides the search head with a list of search nodes  506  to which the search head can distribute the determined portions of the query. A search head  504  may communicate with the search node catalog  510  to discover the addresses of active search nodes  506 . 
     At block  3208 , the search nodes  506  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 search node  506  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  3208  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 search nodes  506  may then either send the relevant events back to the search head  504 , or use the events to determine a partial result, and send the partial result back to the search head  504 . 
     At block  3210 , the search head  504  combines the partial results and/or events received from the search nodes  506  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  504  can also perform various operations to make the search more efficient. For example, before the search head  504  begins execution of a query, the search head  504  can determine a time range for the query and a set of common keywords that all matching events include. The search head  504  may then use these parameters to query the search nodes  506  to obtain a superset of the eventual results. Then, during a filtering stage, the search head  504  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. 
     4.8. 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. 32B  provides a visual representation of the manner in which a pipelined command language or query operates in accordance with the disclosed embodiments. The query  3230  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  3222  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  3240 . For example, the query can comprise search terms “sourcetype=syslog ERROR” at the front of the pipeline as shown in  FIG. 32B . Intermediate results table  3224  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  3230 . 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  3242 , 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  3226  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  3244 , 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. 32B , the “fields—percent” part of command  3230  removes the column that shows the percentage, thereby, leaving a final results table  3228  without a percentage column. In different embodiments, other query languages, such as the Structured Query Language (“SQL”), can be used to create a query. 
     4.9. Field Extraction 
     The query system  214  allows users to search and visualize events generated from machine data received from homogenous data sources. The query system  214  also allows users to search and visualize events generated from machine data received from heterogeneous data sources. The query system  214  includes various components for processing a query, such as, but not limited to a query system manager  502 , one or more search heads  504  having one or more search masters  512  and search managers  514 , and one or more search nodes  506 . 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, a search head  504  (e.g., a search master  512  or search manager  514 ) can use extraction rules to extract values for fields in the events being searched. The search head  504  can obtain 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  504  can apply the extraction rules to events that it receives from search nodes  506 . The search nodes  506  may apply the extraction rules to events in an associated data store or common storage  216 . Extraction rules can be applied to all the events in a data store or common storage  216  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. 
       FIG. 33A  is a diagram of an example scenario where a common customer identifier is found among log data received from three disparate data sources, in accordance with example embodiments. In this example, a user submits an order for merchandise using a vendor&#39;s shopping application program  3301  running on the user&#39;s system. In this example, the order was not delivered to the vendor&#39;s server due to a resource exception at the destination server that is detected by the middleware code  3302 . The user then sends a message to the customer support server  3303  to complain about the order failing to complete. The three systems  3301 ,  3302 , and  3303  are disparate systems that do not have a common logging format. The order application  3301  sends log data  3304  to the data intake and query system  108  in one format, the middleware code  3302  sends error log data  3305  in a second format, and the support server  3303  sends log data  3306  in a third format. 
     Using the log data received at the data intake and query system  108  from the three systems, the vendor can uniquely obtain an insight into user activity, user experience, and system behavior. The query system  214  allows the vendor&#39;s administrator to search the log data from the three systems, 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 query system  214  for customer ID field value matches across the log data from the three systems that are stored in common storage  216 . 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 query system  214  requests events from the one or more data stores  218  to gather relevant events from the three systems. The search head  504  then applies extraction rules to the events in order to extract field values that it can correlate. The search head  504  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  3307 ,  3308 , and  3309 , thereby providing the administrator with insight into a customer&#39;s experience. 
     Note that query results can be returned to a client, a search head  504 , 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 query system  214  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. 33B  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  3310  that includes only keywords (also known as “tokens”), e.g., the keyword “error” or “warning”, the query system  214  of the data intake and query system  108  can search for those keywords directly in the event data  3311  stored in the raw record data store. Note that while  FIG. 33B  only illustrates four events  3312 ,  3313 ,  3314 ,  3315 , the raw record data store (corresponding to data store  212  in  FIG. 2 ) may contain records for millions of events. 
     As disclosed above, the indexing system  212  can optionally generate a keyword index to facilitate fast keyword searching for event data. The indexing system  212  can include 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 the query system  214  subsequently receives a keyword-based query, the query system  214  can access the keyword index to quickly identify events containing the keyword. For example, if the keyword “HTTP” was indexed by the indexing system  212  at index time, and the user searches for the keyword “HTTP”, the events  3312 ,  3313 , and  3314 , 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 indexing system  212 , the data intake and query system  108  may 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. 33B . For example, if a user searches for the keyword “frank”, and the name “frank” has not been indexed at search time, the query system  214  can search the event data directly and return the first event  3312 . Note that whether the keyword has been indexed at index time or search time or not, in both cases the raw data with the events  3311  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 query system  214  can search through the records in the data store to service the search. 
     In most cases, however, in addition to keywords, a user&#39;s search will also include fields. The term “field” refers to a location in the event data containing one or more values for a specific data item. Often, a field is a value with a fixed, delimited position on a line, or a name and value pair, where there is a single value to each field name. A field can also be multivalued, that is, it can appear more than once in an event and have a different value for each appearance, e.g., email address fields. Fields are searchable by the field name or field name-value pairs. Some examples of fields are “clientip” for IP addresses accessing a web server, or the “From” and “To” fields in email addresses. 
     By way of further example, consider the search, “status=404”. This search query finds events with “status” fields that have a value of “404.” When the search is run, the query system  214  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  108  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, a search head  504  of the query system  214  can use extraction rules to extract values for the fields associated with a field or fields in the event data being searched. The search head  504  can obtain 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. 33B  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  108  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 query system  214  may, in one or more embodiments, need to locate configuration file  3316  during the execution of the search as shown in  FIG. 33B . 
     Configuration file  3316  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 can 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  3316 . 
     In some embodiments, the indexing system  212  can 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  3316 . 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  504  can apply the extraction rules derived from configuration file  3316  to event data that it receives from search nodes  506 . The search nodes  506  may apply the extraction rules from the configuration file to events in an associated data store or common storage  216 . 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  3316  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  3315  also contains “clientip” field, however, the “clientip” field is in a different format from events  3312 ,  3313 , and  3314 . 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  3317  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 can pertain to only a particular type of event. If a particular field, e.g., “clientip” occurs in multiple types of events, each of those types of events can have its own corresponding extraction rule in the configuration file  3316  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  3316  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 system  214  can first locate the configuration file  3316  to retrieve extraction rule  3317  that allows it to extract values associated with the “clientip” field from the event data  3320  “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 system  214  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. 33B , the events  3312 ,  3313 , and  3314  would be returned in response to the user query. In this manner, the query system  214  can service queries containing field criteria in addition to queries containing keyword criteria (as explained above). 
     In some embodiments, the configuration file  3316  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 data stores in common storage  216 , wherein various indexing nodes  404  may be responsible for storing the events in the common storage  216  and various search nodes  506  may be responsible for searching the events contained in common storage  216 . 
     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  108  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  108  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  3316  allows the record data store to be field searchable. In other words, the raw record data store can be searched using keywords as well as fields, wherein the fields are searchable name/value pairings that distinguish one event from another and can be defined in configuration file  3316  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. 33B . 
     It should also be noted that any events filtered out by performing a search-time field extraction using a configuration file  3316  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 can pipeline the results of the compare step to an aggregate function by asking the query system  214  to count the number of events where the “clientip” field equals “127.0.0.1.” 
     4.10. Example Search Screen 
       FIG. 34A  is an interface diagram of an example user interface for a search screen  3400 , in accordance with example embodiments. Search screen  3400  includes a search bar  3402  that accepts user input in the form of a search string. It also includes a time range picker  3412  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 preceding time window to search for real-time events. Search screen  3400  also initially displays a “data summary” dialog as is illustrated in  FIG. 34B  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  3400  in  FIG. 34A  can display the results through search results tabs  3404 , wherein search results tabs  3404  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. 34A  displays a timeline graph  3405  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  3408  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  3406 . The field picker  3406  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  3406  includes field names that reference fields present in the events in the search results. The field picker may display any Selected Fields  3420  that a user has pre-selected for display (e.g., host, source, sourcetype) and may also display any Interesting Fields  3422  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  3424 . 
     Each field name in the field picker  3406  has a value type identifier to the left of the field name, such as value type identifier  3426 . 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  3428 . 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  3408  will be updated with events in the search results that have the field that is reference by the field name “host.” 
     4.11. Data Models 
     A data model is a hierarchically structured search-time mapping of semantic knowledge about one or more datasets. It encodes the domain knowledge used to build a variety of specialized searches of those datasets. Those searches, in turn, can be used to generate reports. 
     A data model is composed of one or more “objects” (or “data model objects”) that define or otherwise correspond to a specific set of data. An object is defined by constraints and attributes. An object&#39;s constraints are search criteria that define the set of events to be operated on by running a search having that search criteria at the time the data model is selected. An object&#39;s attributes are the set of fields to be exposed for operating on that set of events generated by the search criteria. 
     Objects in data models can be arranged hierarchically in parent/child relationships. Each child object represents a subset of the dataset covered by its parent object. The top-level objects in data models are collectively referred to as “root objects.” 
     Child objects have inheritance. Child objects inherit constraints and attributes from their parent objects and may have additional constraints and attributes of their own. Child objects provide a way of filtering events from parent objects. Because a child object may provide an additional constraint in addition to the constraints it has inherited from its parent object, the dataset it represents may be a subset of the dataset that its parent represents. For example, a first data model object may define a broad set of data pertaining to e-mail activity generally, and another data model object may define specific datasets within the broad dataset, such as a subset of the e-mail data pertaining specifically to e-mails sent. For example, a user can simply select an “e-mail activity” data model object to access a dataset relating to e-mails generally (e.g., sent or received), or select an “e-mails sent” data model object (or data sub-model object) to access a dataset relating to e-mails sent. 
     Because a data model object is defined by its constraints (e.g., a set of search criteria) and attributes (e.g., a set of fields), a data model object can be used to quickly search data to identify a set of events and to identify a set of fields to be associated with the set of events. For example, an “e-mails sent” data model object may specify a search for events relating to e-mails that have been sent, and specify a set of fields that are associated with the events. Thus, a user can retrieve and use the “e-mails sent” data model object to quickly search source data for events relating to sent e-mails, and may be provided with a listing of the set of fields relevant to the events in a user interface screen. 
     Examples of data models can include electronic mail, authentication, databases, intrusion detection, malware, application state, alerts, compute inventory, network sessions, network traffic, performance, audits, updates, vulnerabilities, etc. Data models and their objects can be designed by knowledge managers in an organization, and they can enable downstream users to quickly focus on a specific set of data. A user iteratively applies a model development tool (not shown in  FIG. 34A ) to prepare a query that defines a subset of events and assigns an object name to that subset. A child subset is created by further limiting a query that generated a parent subset. 
     Data definitions in associated schemas can be taken from the common information model (CIM) or can be devised for a particular schema and optionally added to the CIM. Child objects inherit fields from parents and can include fields not present in parents. A model developer can select fewer extraction rules than are available for the sources returned by the query that defines events belonging to a model. Selecting a limited set of extraction rules can be a tool for simplifying and focusing the data model, while allowing a user flexibility to explore the data subset. Development of a data model is further explained in U.S. Pat. Nos. 8,788,525 and 8,788,526, both entitled “DATA MODEL FOR MACHINE DATA FOR SEMANTIC SEARCH”, both issued on 22 Jul. 2014, U.S. Pat. No. 8,983,994, entitled “GENERATION OF A DATA MODEL FOR SEARCHING MACHINE DATA”, issued on 17 Mar. 2015, U.S. Pat. No. 9,128,980, entitled “GENERATION OF A DATA MODEL APPLIED TO QUERIES”, issued on 8 Sep. 2015, and U.S. Pat. No. 9,589,012, entitled “GENERATION OF A DATA MODEL APPLIED TO OBJECT QUERIES”, issued on 7 Mar. 2017, each of which is hereby incorporated by reference in its entirety for all purposes. 
     A data model can also include reports. One or more report formats can be associated with a particular data model and be made available to run against the data model. A user can use child objects to design reports with object datasets that already have extraneous data pre-filtered out. In some embodiments, the data intake and query system  108  provides the user with the ability to produce reports (e.g., a table, chart, visualization, etc.) without having to enter SPL, SQL, or other query language terms into a search screen. Data models are used as the basis for the search feature. 
     Data models may be selected in a report generation interface. The report generator supports drag-and-drop organization of fields to be summarized in a report. When a model is selected, the fields with available extraction rules are made available for use in the report. The user may refine and/or filter search results to produce more precise reports. The user may select some fields for organizing the report and select other fields for providing detail according to the report organization. For example, “region” and “salesperson” are fields used for organizing the report and sales data can be summarized (subtotaled and totaled) within this organization. The report generator allows the user to specify one or more fields within events and apply statistical analysis on values extracted from the specified one or more fields. The report generator may aggregate search results across sets of events and generate statistics based on aggregated search results. Building reports using the report generation interface is further explained in U.S. patent application Ser. No. 14/503,335, entitled “GENERATING REPORTS FROM UNSTRUCTURED DATA”, filed on 30 Sep. 2014, and which is hereby incorporated by reference in its entirety for all purposes. Data visualizations also can be generated in a variety of formats, by reference to the data model. Reports, data visualizations, and data model objects can be saved and associated with the data model for future use. The data model object may be used to perform searches of other data. 
       FIGS. 35-41  are interface diagrams of example report generation user interfaces, in accordance with example embodiments. The report generation process may be driven by a predefined data model object, such as a data model object defined and/or saved via a reporting application or a data model object obtained from another source. A user can load a saved data model object using a report editor. For example, the initial search query and fields used to drive the report editor may be obtained from a data model object. The data model object that is used to drive a report generation process may define a search and a set of fields. Upon loading of the data model object, the report generation process may enable a user to use the fields (e.g., the fields defined by the data model object) to define criteria for a report (e.g., filters, split rows/columns, aggregates, etc.) and the search may be used to identify events (e.g., to identify events responsive to the search) used to generate the report. That is, for example, if a data model object is selected to drive a report editor, the graphical user interface of the report editor may enable a user to define reporting criteria for the report using the fields associated with the selected data model object, and the events used to generate the report may be constrained to the events that match, or otherwise satisfy, the search constraints of the selected data model object. 
     The selection of a data model object for use in driving a report generation may be facilitated by a data model object selection interface.  FIG. 35  illustrates an example interactive data model selection graphical user interface  3500  of a report editor that displays a listing of available data models  3501 . The user may select one of the data models  3502 . 
       FIG. 36  illustrates an example data model object selection graphical user interface  3600  that displays available data objects  3601  for the selected data object model  3502 . The user may select one of the displayed data model objects  3602  for use in driving the report generation process. 
     Once a data model object is selected by the user, a user interface screen  3700  shown in  FIG. 37A  may display an interactive listing of automatic field identification options  3701  based on the selected data model object. For example, a user may select one of the three illustrated options (e.g., the “All Fields” option  3702 , the “Selected Fields” option  3703 , or the “Coverage” option (e.g., fields with at least a specified % of coverage)  3704 ). If the user selects the “All Fields” option  3702 , all of the fields identified from the events that were returned in response to an initial search query may be selected. That is, for example, all of the fields of the identified data model object fields may be selected. If the user selects the “Selected Fields” option  3703 , only the fields from the fields of the identified data model object fields that are selected by the user may be used. If the user selects the “Coverage” option  3704 , only the fields of the identified data model object fields meeting a specified coverage criteria may be selected. A percent coverage may refer to the percentage of events returned by the initial search query that a given field appears in. Thus, for example, if an object dataset includes 10,000 events returned in response to an initial search query, and the “avg_age” field appears in  854  of those 10,000 events, then the “avg_age” field would have a coverage of 8.54% for that object dataset. If, for example, the user selects the “Coverage” option and specifies a coverage value of 2%, only fields having a coverage value equal to or greater than 2% may be selected. The number of fields corresponding to each selectable option may be displayed in association with each option. For example, “97” displayed next to the “All Fields” option  3702  indicates that 97 fields will be selected if the “All Fields” option is selected. The “3” displayed next to the “Selected Fields” option  3703  indicates that 3 of the 97 fields will be selected if the “Selected Fields” option is selected. The “49” displayed next to the “Coverage” option  3704  indicates that 49 of the 97 fields (e.g., the 49 fields having a coverage of 2% or greater) will be selected if the “Coverage” option is selected. The number of fields corresponding to the “Coverage” option may be dynamically updated based on the specified percent of coverage. 
       FIG. 37B  illustrates an example graphical user interface screen  3705  displaying the reporting application&#39;s “Report Editor” page. The screen may display interactive elements for defining various elements of a report. For example, the page includes a “Filters” element  3706 , a “Split Rows” element  3707 , a “Split Columns” element  3708 , and a “Column Values” element  3709 . The page may include a list of search results  3711 . In this example, the Split Rows element  3707  is expanded, revealing a listing of fields  3710  that can be used to define additional criteria (e.g., reporting criteria). The listing of fields  3710  may correspond to the selected fields. That is, the listing of fields  3710  may list only the fields previously selected, either automatically and/or manually by a user.  FIG. 37C  illustrates a formatting dialogue  3712  that may be displayed upon selecting a field from the listing of fields  3710 . The dialogue can be used to format the display of the results of the selection (e.g., label the column for the selected field to be displayed as “component”). 
       FIG. 37D  illustrates an example graphical user interface screen  3705  including a table of results  3713  based on the selected criteria including splitting the rows by the “component” field. A column  3714  having an associated count for each component listed in the table may be displayed that indicates an aggregate count of the number of times that the particular field-value pair (e.g., the value in a row for a particular field, such as the value “BucketMover” for the field “component”) occurs in the set of events responsive to the initial search query. 
       FIG. 38  illustrates an example graphical user interface screen  3800  that allows the user to filter search results and to perform statistical analysis on values extracted from specific fields in the set of events. In this example, the top ten product names ranked by price are selected as a filter  3801  that causes the display of the ten most popular products sorted by price. Each row is displayed by product name and price  3802 . This results in each product displayed in a column labeled “product name” along with an associated price in a column labeled “price”  3806 . Statistical analysis of other fields in the events associated with the ten most popular products have been specified as column values  3803 . A count of the number of successful purchases for each product is displayed in column  3804 . These statistics may be produced by filtering the search results by the product name, finding all occurrences of a successful purchase in a field within the events and generating a total of the number of occurrences. A sum of the total sales is displayed in column  3805 , which is a result of the multiplication of the price and the number of successful purchases for each product. 
     The reporting application allows the user to create graphical visualizations of the statistics generated for a report. For example,  FIG. 39  illustrates an example graphical user interface  3900  that displays a set of components and associated statistics  3901 . The reporting application allows the user to select a visualization of the statistics in a graph (e.g., bar chart, scatter plot, area chart, line chart, pie chart, radial gauge, marker gauge, filler gauge, etc.), where the format of the graph may be selected using the user interface controls  3902  along the left panel of the user interface  3900 .  FIG. 32  illustrates an example of a bar chart visualization  4000  of an aspect of the statistical data  3901 .  FIG. 41  illustrates a scatter plot visualization  4100  of an aspect of the statistical data  3901 . 
     4.12. Acceleration Techniques 
     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  108  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 using multiple search nodes  506 ; (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. 
     4.12.1. Aggregation Technique 
     To facilitate faster query processing, a query can be structured such that multiple search nodes  506  perform the query in parallel, while aggregation of search results from the multiple search nodes  506  is performed at the search head  504 . For example,  FIG. 42  is an example search query received from a client and executed by search nodes  506 , in accordance with example embodiments.  FIG. 42  illustrates how a search query  4202  received from a client at a search head  504  can split into two phases, including: (1) subtasks  4204  (e.g., data retrieval or simple filtering) that may be performed in parallel by search nodes  506  for execution, and (2) a search results aggregation operation  4206  to be executed by the search head  504  when the results are ultimately collected from the search nodes  506 . 
     During operation, upon receiving search query  4202 , a search head  504  determines that a portion of the operations involved with the search query may be performed locally by the search head  504 . The search head  504  modifies search query  4202  by substituting “stats” (create aggregate statistics over results sets received from the search nodes  506  at the search head  504 ) with “prestats” (create statistics by the search node  506  from local results set) to produce search query  4204 , and then distributes search query  4204  to distributed search nodes  506 , which are also referred to as “search peers” or “peer search nodes.” 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  504  may distribute the full search query to the search peers, 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 search nodes  506  are responsible for producing the results and sending them to the search head  504 . After the search nodes  506  return the results to the search head  504 , the search head  504  aggregates the received results  4206  to form a single search result set. By executing the query in this manner, the system effectively distributes the computational operations across the search nodes  506  while minimizing data transfers. 
     4.12.2. Keyword Index 
     As described herein, the data intake and query system  108  can construct and maintain one or more keyword indexes 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 indexing node  404  first identifies a set of keywords. Then, the indexing node  404  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 the query system  214  subsequently receives a keyword-based query, the indexer can access the keyword index to quickly identify events containing the keyword. 
     4.12.3. High Performance Analytics Store 
     To speed up certain types of queries, some embodiments of data intake and query 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 summarization table for the common storage  216 , one or more data stores  218  of the common storage  216 , buckets cached on a search node  506 , etc. The different summarization tables can include entries for the events in the common storage  216 , certain data stores  218  in the common storage  216 , or data stores associated with a particular search node  506 , etc. 
     The summarization table can be populated by running a periodic query that scans a set of events to find instances of a specific field-value combination, or alternatively instances of all field-value combinations for a specific field. A periodic query can be initiated by a user, or can be scheduled to occur automatically at specific time intervals. A periodic query can also be automatically launched in response to a query that asks for a specific field-value combination. 
     In some cases, when the summarization tables may not cover all of the events that are relevant to a query, the system can use the summarization tables to obtain partial results for the events that are covered by summarization tables, but may also have to search through other events that are not covered by the summarization tables to produce additional results. These additional results can then be combined with the partial results to produce a final set of results for the query. The summarization table and associated techniques are described in more detail in U.S. Pat. No. 8,682,925, entitled “DISTRIBUTED HIGH PERFORMANCE ANALYTICS STORE”, issued on 25 Mar. 2014, U.S. Pat. No. 9,128,985, entitled “SUPPLEMENTING A HIGH PERFORMANCE ANALYTICS STORE WITH EVALUATION OF INDIVIDUAL EVENTS TO RESPOND TO AN EVENT QUERY”, issued on 8 Sep. 2015, and U.S. patent application Ser. No. 14/815,973, entitled “GENERATING AND STORING SUMMARIZATION TABLES FOR SETS OF SEARCHABLE EVENTS”, filed on 1 Aug. 2015, each of which is hereby incorporated by reference in its entirety for all purposes. 
     To speed up certain types of queries, e.g., frequently encountered queries or computationally intensive queries, some embodiments of data intake and query 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 certain embodiments, the query system  214  can 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 the indexing system  212  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 various compression techniques to reduce its storage size. 
     Further, note that the term “reference value” (also referred to as a “posting value”) as used herein is a value that references the location of a source record in the field searchable data store. In some embodiments, the reference value may include additional information about each record, such as timestamps, record size, meta-data, or the like. Each reference value may be a unique identifier which may be used to access the event data directly in the field searchable data store. In some embodiments, the reference values may be ordered based on each event record&#39;s timestamp. For example, if numbers are used as identifiers, they may be sorted so event records having a later timestamp always have a lower valued identifier than event records with an earlier timestamp, or vice-versa. Reference values are often included in inverted indexes for retrieving and/or identifying event records. 
     In one or more embodiments, an inverted index is generated in response to a user-initiated collection query. The term “collection query” as used herein refers to queries that include commands that generate summarization information and inverted indexes (or summarization tables) from event records stored in the field searchable data store. 
     Note that a collection query is a special type of query that can be user-generated and is used to create an inverted index. A collection query is not the same as a query that is used to call up or invoke a pre-existing inverted index. In one or more embodiments, 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. 32B , a set of events can be generated at block  3240  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. 33C  illustrates the manner in which an inverted index is created and used in accordance with the disclosed embodiments. As shown in  FIG. 33C , an inverted index  3322  can be created in response to a user-initiated collection query using the event data  3323  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  3322  being generated from the event data  3323  as shown in  FIG. 33C . Each entry in inverted index  3322  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 one or more search nodes  506  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. 33C , prior to running the collection query that generates the inverted index  3322 , 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  3322  is scheduled to run periodically, one or more search nodes  506  can periodically search through the relevant buckets to update inverted index  3322  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  3322 ) 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. 33C  rather than viewing the fields within the inverted index  3322 , a user may want to generate a count of all client requests from IP address “127.0.0.1.” In this case, the query system  214  can simply return a result of “4” rather than including details about the inverted index  3322  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  3322  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 one or more data stores  218  of common storage  216 , an indexing node  404 , or a search node  506 . The specific inverted indexes can include entries for the events in the one or more data stores  218  or data store associated with the indexing nodes  404  or search node  506 . In some embodiments, if one or more of the queries is a stats query, a search node  506  can generate a partial result set from previously generated summarization information. The partial result sets may be returned to the search head  504  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 a search node  506  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. 
     4.12.3.1. 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. 31C , 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  3322  to another filtering step requesting the user ids for the entries in inverted index  3322  where the server response time is greater than “0.0900” microseconds. The query system  214  can use the reference values stored in inverted index  3322  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  3325 . 
     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 query system  214  can again use the reference values stored in inverted index  3322  to retrieve the event data from the field searchable data store and, further, extract the object size field values from the associated events  3331 ,  3332 ,  3333  and  3334 . 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&#39;s query may request the average object size (size of the requested gif) requested by clients from IP address “127.0.0.1.” without any reference to or use of inverted index  3322 . The query system  214 , in this case, can automatically determine that an inverted index  3322  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 query system  214  can 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 query system  214  can automatically use the pre-generated inverted index, e.g., index  3322  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  108  includes an intake system  210  that receives data from a variety of input data sources, and an indexing system  212  that processes and stores the data in one or more data stores or common storage  216 . By distributing events among the data stores  218  of common storage  213 , the query system  214  can analyze events for a query in parallel. In some embodiments, the data intake and query system  108  can 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  504  can correlate and synthesize data from across the various buckets and search nodes  506 . 
     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, a search node  506  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 search nodes  506 . 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 search node  506  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 query system  214  automatically determines that using an inverted index can expedite the processing of the query, the search nodes  506  can 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. 
       FIG. 33D  is a flow diagram illustrating an embodiment of a routine implemented by one or more computing devices of the data intake and query system for using an inverted index in a pipelined search query to determine a set of event data that can be further limited by filtering or processing. For example, the routine can be implemented by any one or any combination of the search head  504 , search node  506 , search master  512 , or search manager  514 , etc. However, for simplicity, reference below is made to the query system  214  performing the various steps of the routine. 
     At block  3342 , a query is received by a data intake and query system  108 . In some embodiments, the query can be received 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  3344 , 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, a query system  214  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 some embodiments, the query system  214  employs the inverted index separate from the raw record data store to generate responses to the received queries. 
     At block  3346 , the query system  214  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  3354 . 
     If, however, the query does contain further filtering and processing commands, then at block  3348 , the query system  214  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  3350 . 
     If, however, the query references fields that are not extracted in the inverted index, the query system  214  can access event data pointed to by the reference values in the inverted index to retrieve any further information required at block  3356 . 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  3358 . 
     4.12.4. Accelerating Report Generation 
     In some embodiments, a data server system such as the data intake and query system  108  can accelerate the process of periodically generating updated reports based on query results. To accelerate this process, a summarization engine can automatically examine 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 may only include 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 system  214  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. 
     4.13. Security Features 
     The data intake and query system  108  provides various schemas, dashboards, and visualizations that simplify developers&#39; tasks to create applications with additional capabilities. One such application is the an enterprise security application, such as SPLUNK® ENTERPRISE SECURITY, which performs monitoring and alerting operations and includes analytics to facilitate identifying both known and unknown security threats based on large volumes of data stored by the data intake and query system  108 . 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  108  searching and reporting capabilities, the enterprise security application provides a top-down and bottom-up view of an organization&#39;s security posture. 
     The enterprise security application leverages the data intake and query system  108  search-time normalization techniques, saved searches, and correlation searches to provide visibility into security-relevant threats and activity and generate notable events for tracking. The enterprise security application enables the security practitioner to investigate and explore the data to find new or unknown threats that do not follow signature-based patterns. 
     Conventional Security Information and Event Management (SIEM) systems lack the infrastructure to effectively store and analyze large volumes of security-related data. Traditional SIEM systems typically use fixed schemas to extract data from pre-defined security-related fields at data ingestion time and store the extracted data in a relational database. This traditional data extraction process (and associated reduction in data size) that occurs at data ingestion time inevitably hampers future incident investigations that may need original data to determine the root cause of a security issue, or to detect the onset of an impending security threat. 
     In contrast, the enterprise security application system stores large volumes of minimally-processed security-related data at ingestion time for later retrieval and analysis at search time when a live security threat is being investigated. To facilitate this data retrieval process, the enterprise security application provides pre-specified schemas for extracting relevant values from the different types of security-related events and enables a user to define such schemas. 
     The enterprise security application can process many types of security-related information. In general, this security-related information can include any information that can be used to identify security threats. For example, the security-related information can include network-related information, such as IP addresses, domain names, asset identifiers, network traffic volume, uniform resource locator strings, and source addresses. The process of detecting security threats for network-related information is further described in U.S. Pat. No. 8,826,434, entitled “SECURITY THREAT DETECTION BASED ON INDICATIONS IN BIG DATA OF ACCESS TO NEWLY REGISTERED DOMAINS”, issued on 2 Sep. 2014, U.S. Pat. No. 9,215,240, entitled “INVESTIGATIVE AND DYNAMIC DETECTION OF POTENTIAL SECURITY-THREAT INDICATORS FROM EVENTS IN BIG DATA”, issued on 15 Dec. 2015, U.S. Pat. No. 9,173,801, entitled “GRAPHIC DISPLAY OF SECURITY THREATS BASED ON INDICATIONS OF ACCESS TO NEWLY REGISTERED DOMAINS”, issued on 3 Nov. 2015, U.S. Pat. No. 9,248,068, entitled “SECURITY THREAT DETECTION OF NEWLY REGISTERED DOMAINS”, issued on 2 Feb. 2016, U.S. Pat. No. 9,426,172, entitled “SECURITY THREAT DETECTION USING DOMAIN NAME ACCESSES”, issued on 23 Aug. 2016, and U.S. Pat. No. 9,432,396, entitled “SECURITY THREAT DETECTION USING DOMAIN NAME REGISTRATIONS”, issued on 30 Aug. 2016, each of which is hereby incorporated by reference in its entirety for all purposes. Security-related information can also include malware infection data and system configuration information, as well as access control information, such as login/logout information and access failure notifications. The security-related information can originate from various sources within a data center, such as hosts, virtual machines, storage devices and sensors. The security-related information can also originate from various sources in a network, such as routers, switches, email servers, proxy servers, gateways, firewalls and intrusion-detection systems. 
     During operation, the enterprise security application facilitates detecting “notable events” that are likely to indicate a security threat. A notable event represents one or more anomalous incidents, the occurrence of which can be identified based on one or more events (e.g., time stamped portions of raw machine data) fulfilling pre-specified and/or dynamically-determined (e.g., based on machine-learning) criteria defined for that notable event. Examples of notable events include the repeated occurrence of an abnormal spike in network usage over a period of time, a single occurrence of unauthorized access to system, a host communicating with a server on a known threat list, and the like. These notable events can be detected in a number of ways, such as: (1) a user can notice a correlation in events and can manually identify that a corresponding group of one or more events amounts to a notable event; or (2) a user can define a “correlation search” specifying criteria for a notable event, and every time one or more events satisfy the criteria, the application can indicate that the one or more events correspond to a notable event; and the like. A user can alternatively select a pre-defined correlation search provided by the application. Note that correlation searches can be run continuously or at regular intervals (e.g., every hour) to search for notable events. Upon detection, notable events can be stored in a dedicated “notable events index,” which can be subsequently accessed to generate various visualizations containing security-related information. Also, alerts can be generated to notify system operators when important notable events are discovered. 
     The enterprise security application provides various visualizations to aid in discovering security threats, such as a “key indicators view” that enables a user to view security metrics, such as counts of different types of notable events. For example,  FIG. 43A  illustrates an example key indicators view  4300  that comprises a dashboard, which can display a value  4301 , for various security-related metrics, such as malware infections  4302 . It can also display a change in a metric value  4303 , which indicates that the number of malware infections increased by 63 during the preceding interval. Key indicators view  4300  additionally displays a histogram panel  4304  that displays a histogram of notable events organized by urgency values, and a histogram of notable events organized by time intervals. This key indicators view is described in further detail in pending U.S. patent application Ser. No. 13/956,338, entitled “KEY INDICATORS VIEW”, filed on 31 Jul. 2013, and which is hereby incorporated by reference in its entirety for all purposes. 
     These visualizations can also include an “incident review dashboard” that enables a user to view and act on “notable events.” These notable events can include: (1) a single event of high importance, such as any activity from a known web attacker; or (2) multiple events that collectively warrant review, such as a large number of authentication failures on a host followed by a successful authentication. For example,  FIG. 43B  illustrates an example incident review dashboard  4310  that includes a set of incident attribute fields  4311  that, for example, enables a user to specify a time range field  4312  for the displayed events. It also includes a timeline  4313  that graphically illustrates the number of incidents that occurred in time intervals over the selected time range. It additionally displays an events list  4314  that enables a user to view a list of all of the notable events that match the criteria in the incident attributes fields  4311 . To facilitate identifying patterns among the notable events, each notable event can be associated with an urgency value (e.g., low, medium, high, critical), which is indicated in the incident review dashboard. The urgency value for a detected event can be determined based on the severity of the event and the priority of the system component associated with the event. 
     4.14. Data Center Monitoring 
     As mentioned above, the data intake and query platform provides various features that simplify the developer&#39;s task to create various applications. One such application is a virtual machine monitoring application, such as SPLUNK® APP FOR VMWARE® that provides operational visibility into granular performance metrics, logs, tasks and events, and topology from hosts, virtual machines and virtual centers. It empowers administrators with an accurate real-time picture of the health of the environment, proactively identifying performance and capacity bottlenecks. 
     Conventional data-center-monitoring systems lack the infrastructure to effectively store and analyze large volumes of machine-generated data, such as performance information and log data obtained from the data center. In conventional data-center-monitoring systems, machine-generated data is typically pre-processed prior to being stored, for example, by extracting pre-specified data items and storing them in a database to facilitate subsequent retrieval and analysis at search time. However, the rest of the data is not saved and discarded during pre-processing. 
     In contrast, the virtual machine monitoring application stores large volumes of minimally processed machine data, such as performance information and log data, at ingestion time for later retrieval and analysis at search time when a live performance issue is being investigated. In addition to data obtained from various log files, this performance-related information can include values for performance metrics obtained through an application programming interface (API) provided as part of the vSphere Hypervisor™ system distributed by VMware, Inc. of Palo Alto, Calif. For example, these performance metrics can include: (1) CPU-related performance metrics; (2) disk-related performance metrics; (3) memory-related performance metrics; (4) network-related performance metrics; (5) energy-usage statistics; (6) data-traffic-related performance metrics; (7) overall system availability performance metrics; (8) cluster-related performance metrics; and (9) virtual machine performance statistics. Such performance metrics are described in U.S. patent application Ser. No. 14/167,316, entitled “CORRELATION FOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCE METRICS OF COMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOG DATA FROM THAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan. 2014, and which is hereby incorporated by reference in its entirety for all purposes. 
     To facilitate retrieving information of interest from performance data and log files, the virtual machine monitoring application provides pre-specified schemas for extracting relevant values from different types of performance-related events, and also enables a user to define such schemas. 
     The virtual machine monitoring application additionally provides various visualizations to facilitate detecting and diagnosing the root cause of performance problems. For example, one such visualization is a “proactive monitoring tree” that enables a user to easily view and understand relationships among various factors that affect the performance of a hierarchically structured computing system. This proactive monitoring tree enables a user to easily navigate the hierarchy by selectively expanding nodes representing various entities (e.g., virtual centers or computing clusters) to view performance information for lower-level nodes associated with lower-level entities (e.g., virtual machines or host systems). Example node-expansion operations are illustrated in  FIG. 43C , wherein nodes  4333  and  4334  are selectively expanded. Note that nodes  4331 - 4339  can be displayed using different patterns or colors to represent different performance states, such as a critical state, a warning state, a normal state or an unknown/offline state. The ease of navigation provided by selective expansion in combination with the associated performance-state information enables a user to quickly diagnose the root cause of a performance problem. The proactive monitoring tree is described in further detail in U.S. Pat. No. 9,185,007, entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATE SORTING”, issued on 10 Nov. 2015, and U.S. Pat. No. 9,426,045, also entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATE SORTING”, issued on 23 Aug. 2016, each of which is hereby incorporated by reference in its entirety for all purposes. 
     The virtual machine monitoring application also provides a user interface that enables a user to select a specific time range and then view heterogeneous data comprising events, log data, and associated performance metrics for the selected time range. For example, the screen illustrated in  FIG. 43D  displays a listing of recent “tasks and events” and a listing of recent “log entries” for a selected time range above a performance-metric graph for “average CPU core utilization” for the selected time range. Note that a user is able to operate pull-down menus  4342  to selectively display different performance metric graphs for the selected time range. This enables the user to correlate trends in the performance-metric graph with corresponding event and log data to quickly determine the root cause of a performance problem. This user interface is described in more detail in U.S. patent application Ser. No. 14/167,316, entitled “CORRELATION FOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCE METRICS OF COMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOG DATA FROM THAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan. 2014, and which is hereby incorporated by reference in its entirety for all purposes. 
     4.15. IT Service Monitoring 
     As previously mentioned, the data intake and query platform provides various schemas, dashboards and visualizations that make it easy for developers to create applications to provide additional capabilities. One such application is an IT monitoring application, such as SPLUNK® IT SERVICE INTELLIGENCE™, which performs monitoring and alerting operations. The IT monitoring application also includes analytics to help an analyst diagnose the root cause of performance problems based on large volumes of data stored by the data intake and query system  108  as correlated to the various services an IT organization provides (a service-centric view). This differs significantly from conventional IT monitoring systems that lack the infrastructure to effectively store and analyze large volumes of service-related events. Traditional service monitoring systems typically use fixed schemas to extract data from pre-defined fields at data ingestion time, wherein the extracted data is typically stored in a relational database. This data extraction process and associated reduction in data content that occurs at data ingestion time inevitably hampers future investigations, when all of the original data may be needed to determine the root cause of or contributing factors to a service issue. 
     In contrast, an IT monitoring application system stores large volumes of minimally-processed service-related data at ingestion time for later retrieval and analysis at search time, to perform regular monitoring, or to investigate a service issue. To facilitate this data retrieval process, the IT monitoring application enables a user to define an IT operations infrastructure from the perspective of the services it provides. In this service-centric approach, a service such as corporate e-mail may be defined in terms of the entities employed to provide the service, such as host machines and network devices. Each entity is defined to include information for identifying all of the events that pertains to the entity, whether produced by the entity itself or by another machine, and considering the many various ways the entity may be identified in machine data (such as by a URL, an IP address, or machine name). The service and entity definitions can organize events around a service so that all of the events pertaining to that service can be easily identified. This capability provides a foundation for the implementation of Key Performance Indicators. 
     One or more Key Performance Indicators (KPI&#39;s) are defined for a service within the IT monitoring application. Each KPI measures an aspect of service performance at a point in time or over a period of time (aspect KPI&#39;s). Each KPI is defined by a search query that derives a KPI value from the machine data of events associated with the entities that provide the service. Information in the entity definitions may be used to identify the appropriate events at the time a KPI is defined or whenever a KPI value is being determined. The KPI values derived over time may be stored to build a valuable repository of current and historical performance information for the service, and the repository, itself, may be subject to search query processing. Aggregate KPIs may be defined to provide a measure of service performance calculated from a set of service aspect KPI values; this aggregate may even be taken across defined timeframes and/or across multiple services. A particular service may have an aggregate KPI derived from substantially all of the aspect KPI&#39;s of the service to indicate an overall health score for the service. 
     The IT monitoring application facilitates the production of meaningful aggregate KPI&#39;s through a system of KPI thresholds and state values. Different KPI definitions may produce values in different ranges, and so the same value may mean something very different from one KPI definition to another. To address this, the IT monitoring application implements a translation of individual KPI values to a common domain of “state” values. For example, a KPI range of values may be 1-100, or 50-275, while values in the state domain may be ‘critical,’ ‘warning,’ ‘normal,’ and ‘informational’. Thresholds associated with a particular KPI definition determine ranges of values for that KPI that correspond to the various state values. In one case, KPI values 95-100 may be set to correspond to ‘critical’ in the state domain. KPI values from disparate KPI&#39;s can be processed uniformly once they are translated into the common state values using the thresholds. For example, “normal 80% of the time” can be applied across various KPI&#39;s. To provide meaningful aggregate KPI&#39;s, a weighting value can be assigned to each KPI so that its influence on the calculated aggregate KPI value is increased or decreased relative to the other KPI&#39;s. 
     One service in an IT environment often impacts, or is impacted by, another service. The IT monitoring application can reflect these dependencies. For example, a dependency relationship between a corporate e-mail service and a centralized authentication service can be reflected by recording an association between their respective service definitions. The recorded associations establish a service dependency topology that informs the data or selection options presented in a GUI, for example. (The service dependency topology is like a “map” showing how services are connected based on their dependencies.) The service topology may itself be depicted in a GUI and may be interactive to allow navigation among related services. 
     Entity definitions in the IT monitoring application can include informational fields that can serve as metadata, implied data fields, or attributed data fields for the events identified by other aspects of the entity definition. Entity definitions in the IT monitoring application can also be created and updated by an import of tabular data (as represented in a CSV, another delimited file, or a search query result set). The import may be GUI-mediated or processed using import parameters from a GUI-based import definition process. Entity definitions in the IT monitoring application can also be associated with a service by means of a service definition rule. Processing the rule results in the matching entity definitions being associated with the service definition. The rule can be processed at creation time, and thereafter on a scheduled or on-demand basis. This allows dynamic, rule-based updates to the service definition. 
     During operation, the IT monitoring application can recognize notable events that may indicate a service performance problem or other situation of interest. These notable events can be recognized by a “correlation search” specifying trigger criteria for a notable event: every time KPI values satisfy the criteria, the application indicates a notable event. A severity level for the notable event may also be specified. Furthermore, when trigger criteria are satisfied, the correlation search may additionally or alternatively cause a service ticket to be created in an IT service management (ITSM) system, such as a systems available from ServiceNow, Inc., of Santa Clara, Calif. 
     SPLUNK® IT SERVICE INTELLIGENCE™ provides various visualizations built on its service-centric organization of events and the KPI values generated and collected. Visualizations can be particularly useful for monitoring or investigating service performance. The IT monitoring application provides a service monitoring interface suitable as the home page for ongoing IT service monitoring. The interface is appropriate for settings such as desktop use or for a wall-mounted display in a network operations center (NOC). The interface may prominently display a services health section with tiles for the aggregate KPI&#39;s indicating overall health for defined services and a general KPI section with tiles for KPI&#39;s related to individual service aspects. These tiles may display KPI information in a variety of ways, such as by being colored and ordered according to factors like the KPI state value. They also can be interactive and navigate to visualizations of more detailed KPI information. 
     The IT monitoring application provides a service-monitoring dashboard visualization based on a user-defined template. The template can include user-selectable widgets of varying types and styles to display KPI information. The content and the appearance of widgets can respond dynamically to changing KPI information. The KPI widgets can appear in conjunction with a background image, user drawing objects, or other visual elements, that depict the IT operations environment, for example. The KPI widgets or other GUI elements can be interactive so as to provide navigation to visualizations of more detailed KPI information. 
     The IT monitoring application provides a visualization showing detailed time-series information for multiple KPI&#39;s in parallel graph lanes. The length of each lane can correspond to a uniform time range, while the width of each lane may be automatically adjusted to fit the displayed KPI data. Data within each lane may be displayed in a user selectable style, such as a line, area, or bar chart. During operation a user may select a position in the time range of the graph lanes to activate lane inspection at that point in time. Lane inspection may display an indicator for the selected time across the graph lanes and display the KPI value associated with that point in time for each of the graph lanes. The visualization may also provide navigation to an interface for defining a correlation search, using information from the visualization to pre-populate the definition. 
     The IT monitoring application provides a visualization for incident review showing detailed information for notable events. The incident review visualization may also show summary information for the notable events over a time frame, such as an indication of the number of notable events at each of a number of severity levels. The severity level display may be presented as a rainbow chart with the warmest color associated with the highest severity classification. The incident review visualization may also show summary information for the notable events over a time frame, such as the number of notable events occurring within segments of the time frame. The incident review visualization may display a list of notable events within the time frame ordered by any number of factors, such as time or severity. The selection of a particular notable event from the list may display detailed information about that notable event, including an identification of the correlation search that generated the notable event. 
     The IT monitoring application provides pre-specified schemas for extracting relevant values from the different types of service-related events. It also enables a user to define such schemas. 
     4.16. Other Architectures 
     In view of the description above, it will be appreciate that the architecture disclosed herein, or elements of that architecture, may be implemented independently from, or in conjunction with, other architectures. For example, the Incorporated Applications disclose a variety of architectures wholly or partially compatible with the architecture of the present disclosure. 
     Generally speaking one or more components of the data intake and query system  108  of the present disclosure can be used in combination with or to replace one or more components of the data intake and query system  108  of the Incorporated Applications. For example, depending on the embodiment, the operations of the forwarder  204  and the ingestion buffer  4802  of the Incorporated Applications can be performed by or replaced with the intake system  210  of the present disclosure. The parsing, indexing, and storing operations (or other non-searching operations) of the indexers  206 ,  230  and indexing cache components  254  of the Incorporated Applications can be performed by or replaced with the indexing nodes  404  of the present disclosure. The storage operations of the data stores  208  of the Incorporated Applications can be performed using the data stores  412  of the present disclosure (in some cases with the data not being moved to common storage  216 ). The storage operations of the common storage  4602 , cloud storage  256 , or global index  258  can be performed by the common storage  216  of the present disclosure. The storage operations of the query acceleration data store  3308  can be performed by the query acceleration data store  222  of the present disclosure. 
     As continuing examples, the search operations of the indexers  206 ,  230  and indexing cache components  254  of the Incorporated Applications can be performed by or replaced with the indexing nodes  404  in some embodiments or by the search nodes  506  in certain embodiments. For example, in some embodiments of certain architectures of the Incorporated Applications (e.g., one or more embodiments related to  FIGS. 2, 3, 4, 18, 25, 27, 33, 46 ), the indexers  206 ,  230  and indexing cache components  254  of the Incorporated Applications may perform parsing, indexing, storing, and at least some searching operations, and in embodiments of some architectures of the Incorporated Applications (e.g., one more embodiments related to  FIG. 48 ), indexers  206 ,  230  and indexing cache components  254  of the Incorporated Applications perform parsing, indexing, and storing operations, but do not perform searching operations. Accordingly, in some embodiments, some or all of the searching operations described as being performed by the indexers  206 ,  230  and indexing cache components  254  of the Incorporated Applications can be performed by the search nodes  506 . For example, in embodiments described in the Incorporated Applications in which worker nodes  214 ,  236 ,  246 ,  3306  perform searching operations in place of the indexers  206 ,  230  or indexing cache components  254 , the search nodes  506  can perform those operations. In certain embodiments, some or all of the searching operations described as being performed by the indexers  206 ,  230  and indexing cache components  254  of the Incorporated Applications can be performed by the indexing nodes  404 . For example, in embodiments described in the Incorporated Applications in which the indexers  206 ,  230  and indexing cache components  254  perform searching operations, the indexing nodes  404  can perform those operations. 
     As a further example, the query operations performed by the search heads  210 ,  226 ,  244 , daemons  210 ,  232 ,  252 , search master  212 ,  234 ,  250 , search process master  3302 , search service provider  216 , and query coordinator  3304  of the Incorporated Applications, can be performed by or replaced with any one or any combination of the query system manager  502 , search head  504 , search master  512 , search manager  514 , search node monitor  508 , and/or the search node catalog  510 . For example, these components can handle and coordinate the intake of queries, query processing, identification of available nodes and resources, resource allocation, query execution plan generation, assignment of query operations, combining query results, and providing query results to a user or a data store. 
     In certain embodiments, the query operations performed by the worker nodes  214 ,  236 ,  246 ,  3306  of the Incorporated Applications can be performed by or replaced with the search nodes  506  of the present disclosure. In some embodiments, the intake or ingestion operations performed by the worker nodes  214 ,  236 ,  246 ,  3306  of the Incorporated Applications can be performed by or replaced with one or more components of the intake system  210 . 
     Furthermore, it will be understood that some or all of the components of the architectures of the Incorporated Applications can be replaced with components of the present disclosure. For example, in certain embodiments, the intake system  210  can be used in place of the forwarders  204  and/or ingestion buffer  4802  of one or more architectures of the Incorporated Applications, with all other components of the one or more architecture of the Incorporated Applications remaining the same. As another example, in some embodiments the indexing nodes  404  can replace the indexer  206  of one or more architectures of the Incorporated Applications with all other components of the one or more architectures of the Incorporated Applications remaining the same. Accordingly, it will be understood that a variety of architectures can be designed using one or more components of the data intake and query system  108  of the present disclosure in combination with one or more components of the data intake and query system  108  of the Incorporated Applications. 
     Illustratively, the architecture depicted at  FIG. 2  of the Incorporated Applications may be modified to replace the forwarder  204  of that architecture with the intake system  210  of the present disclosure. In addition, in some cases, the indexers  206  of the Incorporated Applications can be replaced with the indexing nodes  404  of the present disclosure. In such embodiments, the indexing nodes  404  can retain the buckets in the data stores  412  that they create rather than store the buckets in common storage  216 . Further, in the architecture depicted at  FIG. 2  of the Incorporated Applications, the indexing nodes  404  of the present disclosure can be used to execute searches on the buckets stored in the data stores  412 . In some embodiments, in the architecture depicted at  FIG. 2  of the Incorporated Applications, the partition manager  408  can receive data from one or more forwarders  204  of the Incorporated Applications. As additional forwarders  204  are added or as additional data is supplied to the architecture depicted at  FIG. 2  of the Incorporated Applications, the indexing node  406  can spawn additional partition manager  408  and/or the indexing manager system  402  can spawn additional indexing nodes  404 . In addition, in certain embodiments, the bucket manager  414  may merge buckets in the data store  414  or be omitted from the architecture depicted at  FIG. 2  of the Incorporated Applications. 
     Furthermore, in certain embodiments, the search head  210  of the Incorporated Applications can be replaced with the search head  504  of the present disclosure. In some cases, as described herein, the search head  504  can use the search master  512  and search manager  514  to process and manager the queries. However, rather than communicating with search nodes  506  to execute a query, the search head  504  can, depending on the embodiment, communicate with the indexers  206  of the Incorporated Applications or the search nodes  404  to execute the query. 
     Similarly the architecture of  FIG. 3  of the Incorporated Applications may be modified in a variety of ways to include one or more components of the data intake and query system  108  described herein. For example, the architecture of  FIG. 3  of the Incorporated Applications may be modified to include an intake system  210  in accordance with the present disclosure within the cloud-based data intake and query system  1006  of the Incorporated Applications, which intake system  210  may logically include or communicate with the forwarders  204  of the Incorporated Applications. In addition, the indexing nodes  404  described herein may be utilized in place of or to implement functionality similar to the indexers described with reference to  FIG. 3  of the Incorporated Applications. In addition, the architecture of  FIG. 3  of the Incorporated Applications may be modified to include common storage  216  and/or search nodes  506 . 
     With respect to the architecture of  FIG. 4  of the Incorporated Applications, the intake system  210  described herein may be utilized in place of or to implement functionality similar to either or both the forwarders  204  or the ERP processes  410  through  412  of the Incorporated Applications. Similarly, the indexing nodes  506  and the search head  504  described herein may be utilized in place of or to implement functionality similar to the indexer  206  and search head  210 , respectively. In some cases, the search manager  514  described herein can manage the communications and interfacing between the indexer  210  and the ERP processes  410  through  412 . 
     With respect to the flow diagrams and functionality described in  FIGS. 5A-5C, 6A, 6B, 7A-7D, 8A, 8B, 9, 10, 11A-11D, 12-16, and 17A-17D  of the Incorporated Applications, it will be understood that the processing and indexing operations described as being performed by the indexers  206  can be performed by the indexing nodes  404 , the search operations described as being performed by the indexers  206  can be performed by the indexing nodes  404  or search nodes  506  (depending on the embodiment), and/or the searching operations described as being performed by the search head  210 , can be performed by the search head  504  or other component of the query system  214 . 
     With reference to  FIG. 18  of the Incorporated Applications, the indexing nodes  404  and search heads  504  described herein may be utilized in place of or to implement functionality similar to the indexers  206  and search head  210 , respectively. Similarly, the search master  512  and search manager  514  described herein may be utilized in place of or to implement functionality similar to the master  212  and the search service provider  216 , respectively, described with respect to  FIG. 18  of the Incorporated Applications. Further, the intake system  210  described herein may be utilized in place of or to implement ingestion functionality similar to the ingestion functionality of the worker nodes  214  of the Incorporated Applications. Similarly, the search nodes  506  described herein may be utilized in place of or to implement search functionality similar to the search functionality of the worker nodes  214  of the Incorporated Applications. 
     With reference to  FIG. 25  of the Incorporated Applications, the indexing nodes  404  and search heads  504  described herein may be utilized in place of or to implement functionality similar to the indexers  236  and search heads  226 , respectively. In addition, the search head  504  described herein may be utilized in place of or to implement functionality similar to the daemon  232  and the master  234  described with respect to  FIG. 25  of the Incorporated Applications. The intake system  210  described herein may be utilized in place of or to implement ingestion functionality similar to the ingestion functionality of the worker nodes  214  of the Incorporated Applications. Similarly, the search nodes  506  described herein may be utilized in place of or to implement search functionality similar to the search functionality of the worker nodes  234  of the Incorporated Applications. 
     With reference to  FIG. 27  of the Incorporated Applications, the indexing nodes  404  or search nodes  506  described herein may be utilized in place of or to implement functionality similar to the index cache components  254 . For example, the indexing nodes  404  may be utilized in place of or to implement parsing, indexing, storing functionality of the index cache components  254 , and the search nodes  506  described herein may be utilized in place of or to implement searching or caching functionality similar to the index cache components  254 . In addition, the search head  504  described herein may be utilized in place of or to implement functionality similar to the search heads  244 , daemon  252 , and/or the master  250  described with respect to  FIG. 27  of the Incorporated Applications. The intake system  210  described herein may be utilized in place of or to implement ingestion functionality similar to the ingestion functionality of the worker nodes  246  described with respect to  FIG. 27  of the Incorporated Applications. Similarly, the search nodes  506  described herein may be utilized in place of or to implement search functionality similar to the search functionality of the worker nodes  234  described with respect to  FIG. 27  of the Incorporated Applications. In addition, the common storage  216  described herein may be utilized in place of or to implement functionality similar to the functionality of the cloud storage  256  and/or global index  258  described with respect to  FIG. 27  of the Incorporated Applications. 
     With respect to the architectures of  FIGS. 33, 46, and 48  of the Incorporated Applications, the intake system  210  described herein may be utilized in place of or to implement functionality similar to the forwarders  204 . In addition, the indexing nodes  404  of the present disclosure can perform the functions described as being performed by the indexers  206  (e.g., parsing, indexing, storing, and in some embodiments, searching) of the architectures of  FIGS. 33, 46, and 48  of the Incorporated Applications; the operations of the acceleration data store  3308  of the architectures of  FIGS. 33, 46, and 48  of the Incorporated Applications can be performed by the acceleration data store  222  of the present application; and the operations of the search head  210 , search process maser  3302 , and query coordinator  3304  of the architectures of  FIGS. 33, 46, and 48  of the Incorporated Applications can be performed by the search head  504 , search node catalog  510 , and or search node monitor  508  of the present application. For example, the functionality of the workload catalog  3312  and node monitor  3314  of the architectures of  FIGS. 33, 46, and 48  of the Incorporated Applications can be performed by the search node catalog  510  and search node monitor  508 ; the functionality of the search head  210  and other components of the search process master  3302  of the architectures of  FIGS. 33, 46, and 48  of the Incorporated Applications can be performed by the search head  504  or search master  512 ; and the functionality of the query coordinator  3304  of the architectures of  FIGS. 33, 46, and 48  of the Incorporated Applications can be performed by the search manager  514 . 
     In addition, in some embodiments, the searching operations described as being performed by the worker nodes  3306  of the architectures of  FIGS. 33, 46, and 48  of the Incorporated Applications can be performed by the search nodes  506  of the present application and the intake or ingestion operations performed by the worker nodes  3306  of the architectures of  FIGS. 33, 46, and 48  of the Incorporated Applications can be performed by the intake system  210 . However, it will be understood that in some embodiments, the search nodes  506  can perform the intake and search operations described in the Incorporated Applications as being performed by the worker nodes  3306 . Furthermore, the cache manager  516  can implement one or more of the caching operations described in the Incorporated Applications with reference to the architectures of  FIGS. 33, 46, and 48  of the Incorporated Applications. 
     With respect to  FIGS. 46 and 48  of the Incorporated Applications, the common storage  216  of the present application can be used to provide the functionality with respect to the common storage  2602  of the architecture of  FIGS. 46 and 48  of the Incorporated Applications. With respect to the architecture of  FIG. 48  of the Incorporated Applications, the intake system  210  described herein may be utilized in place of or to implement operations similar to the forwarders  204  and ingested data buffer  4802 , and may in some instances implement all or a portion of the operations described in that reference with respect to worker nodes  3306 . Thus, the architecture of the present disclosure, or components thereof, may be implemented independently from or incorporated within architectures of the prior disclosures. 
     5.0 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, 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. 
     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 or an isolated execution environment, 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.