WO2020027867A1 - Génération d'une sous-requête pour un système distinct d'entrée et d'interrogation de données - Google Patents

Génération d'une sous-requête pour un système distinct d'entrée et d'interrogation de données Download PDF

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Publication number
WO2020027867A1
WO2020027867A1 PCT/US2019/016108 US2019016108W WO2020027867A1 WO 2020027867 A1 WO2020027867 A1 WO 2020027867A1 US 2019016108 W US2019016108 W US 2019016108W WO 2020027867 A1 WO2020027867 A1 WO 2020027867A1
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WO
WIPO (PCT)
Prior art keywords
data
query
search
subquery
query system
Prior art date
Application number
PCT/US2019/016108
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English (en)
Inventor
Sourav Pal
Arindam Bhattacharjee
Original Assignee
Splunk Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US16/051,223 external-priority patent/US11243963B2/en
Priority claimed from US16/051,203 external-priority patent/US11126632B2/en
Priority claimed from US16/051,215 external-priority patent/US11615104B2/en
Priority claimed from US16/051,310 external-priority patent/US11314753B2/en
Priority claimed from US16/051,197 external-priority patent/US11663227B2/en
Priority claimed from US16/051,304 external-priority patent/US11604795B2/en
Priority claimed from US16/051,300 external-priority patent/US10977260B2/en
Priority claimed from US16/147,165 external-priority patent/US10956415B2/en
Priority claimed from US16/146,990 external-priority patent/US11023463B2/en
Application filed by Splunk Inc. filed Critical Splunk Inc.
Publication of WO2020027867A1 publication Critical patent/WO2020027867A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2471—Distributed queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/25—Integrating or interfacing systems involving database management systems
    • G06F16/256—Integrating or interfacing systems involving database management systems in federated or virtual databases

Definitions

  • 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.
  • Information technology (IT) environments can include diverse types of data systems that store large amounts of diverse data types generated by numerous devices.
  • 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.
  • raw data minimally processed or unprocessed data
  • storage capacity becomes more inexpensive and plentiful.
  • 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.
  • FIG. 1A is a block diagram of an example environment in which an embodiment may be implemented
  • FIG. IB is a block diagram of an example networked computer environment, in accordance with example embodiments.
  • FIG. 2 is a block diagram of an example data intake and query system, in accordance with example embodiments;
  • FIG. 3 is a block diagram of an example cloud-based data intake and query system, in accordance with example embodiments;
  • FIG. 4 is a block diagram of an example data intake and query system that performs searches across external data systems, in accordance with example embodiments;
  • FIG. 5A is a flowchart of an example method that illustrates how indexers process, index, and store data received from forwarders, in accordance with example embodiments;
  • FIG. 5B 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. 5C provides a visual representation of the manner in which a pipelined search language or query operates, in accordance with example embodiments
  • FIG. 6A 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. 6B provides a visual representation of an example manner in which a pipelined command language or query operates, in accordance with example embodiments
  • FIG. 7A 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. 7B illustrates an example of processing keyword searches and field searches, in accordance with disclosed embodiments
  • FIG. 7C illustrates an example of creating and using an inverted index, in accordance with example embodiments
  • FIG. 7D depicts a flowchart of example use of an inverted index in a pipelined search query, in accordance with example embodiments
  • FIG. 8A is an interface diagram of an example user interface for a search screen, in accordance with example embodiments.
  • FIG. 8B is an interface diagram of an example user interface for a data summary dialog that enables a user to select various data sources, in accordance with example embodiments;
  • FIGS. 9, 10, 11 A, 11B, 11C, 11D, 12, 13, 14, and 15 are interface diagrams of example report generation user interfaces, in accordance with example embodiments;
  • FIG. 16 is an example search query received from a client and executed by search peers, in accordance with example embodiments
  • FIG. 17A is an interface diagram of an example user interface of a key indicators view, in accordance with example embodiments.
  • FIG. 17B is an interface diagram of an example user interface of an incident review dashboard, in accordance with example embodiments.
  • FIG. 17C is a tree diagram of an example a proactive monitoring tree, in accordance with example embodiments.
  • FIG. 17D is an interface diagram of an example a user interface displaying both log data and performance data, in accordance with example embodiments;
  • FIG. 18 is a system diagram illustrating a data fabric service system architecture (“DFS system”) in which an embodiment may be implemented;
  • DFS system data fabric service system architecture
  • FIG. 19 is an operation flow diagram illustrating an example of an operation flow of a DFS system according to some embodiments of the present disclosure
  • FIG. 20 is an operation flow diagram illustrating an example of a parallel export operation performed in a DFS system according to some embodiments of the present disclosure
  • FIG. 21 is a flow diagram illustrating a method performed by the DFS system to obtain time- ordered search results according to some embodiments of the present disclosure
  • FIG. 22 is a flow diagram illustrating a method performed by a data intake and query system of a DFS system to obtain time -ordered search results according to some embodiments of the present disclosure
  • FIG. 23 is a flow diagram illustrating a method performed by nodes of a DFS system to obtain batch or reporting search results according to some embodiments of the present disclosure
  • FIG. 24 is a flow diagram illustrating a method performed by a data intake and query system of a DFS system in response to a reporting search query according to some embodiments of the present disclosure
  • FIG. 25 is a system diagram illustrating a co-located deployment of a DFS system in which an embodiment may be implemented
  • FIG. 26 is an operation flow diagram illustrating an example of an operation flow of a co located deployment of a DFS system according to some embodiments of the present disclosure
  • FIG. 27 is a cloud based system diagram illustrating a cloud deployment of a DFS system in which an embodiment may be implemented
  • FIG. 28 is a flow diagram illustrating an example of a method performed in a cloud-based
  • FIG. 29 is a flow diagram illustrating a timeline mechanism that supports rendering search results in a time -ordered visualization according to some embodiments of the present disclosure
  • FIG. 30 illustrates a timeline visualization rendered on a GUI in which an embodiment may be implemented
  • FIG. 31 illustrates a selected bin of a timeline visualization and the contents of the selected bin according to some embodiments of the present disclosure.
  • FIG. 32 is a flow diagram illustrating services of a DFS system according to some embodiments of the present disclosure.
  • FIG. 33 is a system diagram illustrating an environment for ingesting and indexing data, and performing queries on one or more datasets from one or more dataset sources;
  • FIG. 34 is a block diagram illustrating an embodiment of multiple machines, each having multiple nodes;
  • FIG. 35 is a diagram illustrating an embodiment of a DAG
  • FIG. 36 is a block diagram illustrating an embodiment of multiple partitions being used to implement various search phases of a DAG
  • FIG. 37 is a data flow diagram illustrating an embodiment of communications between various components within the environment to process and execute a query
  • FIG. 38 is a flow diagram illustrative of an embodiment of a routine to provide query results
  • FIG. 39 is a flow diagram illustrative of an embodiment of a routine to process a query
  • FIG. 40 is a flow diagram illustrative of an embodiment of a routine to generate a query processing scheme
  • FIG. 41 is a flow diagram illustrative of an embodiment of a routine to execute a query on data from multiple dataset sources
  • FIG. 42 is a flow diagram illustrative of an embodiment of a routine to execute a query on data from an external data source
  • FIG. 43 is a flow diagram illustrative of an embodiment of a routine to execute a query based on a dataset destination
  • FIG. 44 is a flow diagram illustrative of an embodiment of a routine to serialize data for communication
  • FIG. 45 is a flow diagram illustrative of an embodiment of a routine to execute a query using a query acceleration data store
  • FIG. 46 is a system diagram illustrating an environment for ingesting and indexing data, and performing queries on one or more datasets from one or more dataset sources including common storage;
  • FIG. 47 is a flow diagram illustrative of an embodiment of a routine to execute a query using common storage
  • FIG. 48 is a system diagram illustrating an environment for ingesting and indexing data, and performing queries on one or more datasets from one or more dataset sources including an ingested data buffer;
  • FIG. 49 is a flow diagram illustrative of an embodiment of a routine to execute a query using an ingested data buffer
  • FIG. 50A is a block diagram of an embodiment of an environment in which a primary data intake and query system communicates with secondary data intake and query systems to execute a query;
  • FIG. 50B is a block diagram of an embodiment of an environment in which a primary data intake and query system communicates with third-party data storage and processing systems to execute a query;
  • FIG. 51 is a data flow diagram illustrating an embodiment of communications between various components described herein to process and execute a federated query;
  • FIG. 52 is a flow diagram illustrative of an embodiment of a routine implemented by a query coordinator to execute a query involving data from a secondary data intake and query system;
  • FIGS. 53, 54, 55, and 56 are flow diagrams illustrative of embodiments of routines implemented by the query coordinator to execute a query on data from an external data system;
  • FIG. 57 is a flow diagram illustrative of an embodiment of a routine implemented by a search head to execute a query received from an external data system;
  • FIG. 58 is a block diagram illustrating an embodiment of a data path of data from different data sources in a worker node
  • FIG. 59 is a flow diagram illustrative of an embodiment of a routine implemented by a worker node to process a partition or task;
  • FIG. 60 is a flow diagram illustrative of an embodiment of a routine implemented by a query coordinator to optimize and execute a query involving data from an external data system;
  • FIG. 61 illustrates an example of an external query configuration file in accordance with disclosed embodiments.
  • FIG. 62 is a block diagram illustrating a high-level example of a hardware architecture of a computing system in which an embodiment may be implemented.
  • references to "an embodiment,” “one embodiment,” or the like, mean that the particular feature, function, structure or characteristic being described is included in at least one embodiment of the technique introduced herein. Occurrences of such phrases in this specification do not necessarily all refer to the same embodiment. On the other hand, the embodiments referred to are also not necessarily mutually exclusive.
  • a data intake and query system can index and store data in data stores of indexers, and can receive search queries causing a search of the indexers to obtain search results.
  • the data intake and query system typically has search, extraction, execution, and analytics capabilities that may be limited in scope to the data stores of the indexers (“internal data stores”).
  • the disclosed embodiments overcome these drawbacks by extending the search and analytics capabilities of a data intake and query system to include diverse data types stored in diverse data systems internal to or external from the data intake and query system.
  • an analyst can use the data intake and query system to search and analyze data from a wide variety of dataset sources, including enterprise systems and open source technologies of a big data ecosystem.
  • the term“big data” refers to large data sets that may be analyzed computationally to reveal patterns, trends, and associations, in some cases, relating to human behavior and interactions.
  • A“data source” can include a“data system,” which may refer to a system that can process and/or store data.
  • A“data storage system” may refer to a storage system that can store data such as unstructured, semi-structured, or structured data.
  • a data source can include a data system that includes a data storage system.
  • the system can improve search and analytics capabilities of previous systems by employing a search process master and query coordinators combined with a scalable network of distributed nodes communicatively coupled to diverse data systems.
  • the network of distributed nodes can act as agents of the data intake and query system to collect and process data of distributed data systems, and the search process master and coordinators can provide the processed data to the search head as search results.
  • the data intake and query system can respond to a query by executing search operations on various internal and external data sources to obtain partial search results that are harmonized and presented as search results of the query.
  • the data intake and query system can offload search and analytics operations to the distributed nodes.
  • the system enables search and analytics capabilities that can extend beyond the data stored on indexers to include external data systems, common storage, query acceleration data stores, ingested data buffers, etc.
  • the system can provide big data open stack integration to act as a big data pipeline that extends the search and analytics capabilities of a system over numerous and diverse data sources.
  • the system can extend the data execution scope of the data intake and query system to include data residing in external data systems such as MySQL, PostgreSQL, and Oracle databases; NoSQL data stores like Cassandra, Mongo DB; cloud storage like Amazon S3 and Hadoop distributed file system (HDFS); common storage; ingested data buffers; etc.
  • the system can execute search and analytics operations for all possible combinations of data types stored in various data sources.
  • the distributed processing of the system enables scalability to include any number of distributed data systems.
  • queries received by the data intake and query system can be propagated to the network of distributed nodes to extend the search and analytics capabilities of the data intake and query system over different data sources.
  • the network of distributed nodes can act as an extension of the local data intake in query system’s data processing pipeline to facilitate scalable analytics across the diverse data systems.
  • the system can extend and transform the data intake and query system to include data resources into a data fabric platform that can leverage computing assets from anywhere and access and execute on data regardless of type or origin.
  • the disclosed embodiments include services such as new search capabilities, visualization tools, and other services that are seamlessly integrated into the DFS system.
  • the disclosed techniques include new search services performed on internal data stores, external data stores, or a combination of both.
  • the search operations can provide ordered or unordered search results, or search results derived from data of diverse data systems, which can be visualized to provide new and useful insights about the data contained in a big data ecosystem.
  • the embodiments disclosed herein generally refer to an environment that includes data intake and query system including a data fabric service system architecture (“DFS system”), services, a network of distributed nodes, and distributed data systems, all interconnected over one or more networks.
  • a data fabric service system architecture (“DFS system”)
  • services a network of distributed nodes
  • distributed data systems all interconnected over one or more networks.
  • embodiments of the disclosed environment can include many computing components including software, servers, routers, client devices, and host devices that are not specifically described herein.
  • a “node” can refer to one or more devices and/or software running on devices that enable the devices to provide execute a task of the system.
  • a node can include devices running software that enable the device to execute a portion of a query.
  • FIG. 1A is a high-level system diagram of an environment 10 in which an embodiment may be implemented.
  • the environment 10 includes distributed external data systems 12-1 and 12-2 (also referred to collectively and individually as external data system(s) 12).
  • the external data systems 12 are communicatively coupled (e.g., via a LAN, WAN, etc.) to a data intake and query system 16, various examples of which are described herein at least with reference to FIGS. 1A, 2, 3, 4, 18, 25, 27, 33, 46, and 48.
  • the external data systems 12 are communicatively coupled to worker nodes 14-1 and 14-2 (also referred to collectively and individually as worker node(s) 14) of the data intake and query system 16, various examples of which are described herein at least with reference to FIGS. 18, 25, 27, 33, 46, 48, and 58.
  • the environment 10 can also include a client device 22 and applications running on the client device 22.
  • An example includes a personal computer, laptop, tablet, phone, or other computing device running a network browser application that enables a user of the client device 22 to access any of the data systems.
  • the data intake and query system 16 and the external data systems 12 can each store data obtained from various data sources.
  • the data intake and query system 16 can store data in internal data stores 20 (also referred to as an internal storage system), and the external data systems 12 can store data in respective external data stores 24 (also referred to as external storage systems).
  • the data intake and query system 16 and external data systems 12 may process and store data differently.
  • the data intake and query system 16 may store minimally processed or unprocessed data (“raw data”) in the internal data stores 20, which can be implemented as local data stores 20-1, common storage 20-2, or query acceleration data stores 20-3.
  • the external data systems 12 may store pre-processed data rather than raw data.
  • the data intake and query system 16 and the external data systems 12 can operate independent of each other in a big data ecosystem.
  • the worker nodes 14 can act as agents of the data intake and query system 16 to process data collected from the internal data stores 20 and the external data stores 24.
  • the worker nodes 14 may reside on one or more computing devices such as servers communicatively coupled to the external data systems 12. Other components of the data intake and query system 16 can finalize the results before returning the results to the client device 22. As such, the worker nodes 14 can extend the search and analytics capabilities of the data intake and query system 16 to act on diverse data systems.
  • the external data systems 12 may include one or more computing devices that can store structured, semi-structured, or unstructured data. Each external data system 12 can generate and/or collect generated data, and store the generated data in their respective external data stores 24.
  • the external data system 12-1 may include a server running a MySQL database that stores structured data objects such as time-stamped events
  • the external data system 12-2 may be a server of cloud computing services such as Amazon web services (AWS) that can provide different data types ranging from unstructured (e.g., s3) to structured (e.g., redshift).
  • AWS Amazon web services
  • the external data system 12-1 and/or 12-2 may be a data intake and query system that is separate and distinct from the data intake and query system 16, but that includes the same or similar architecture as the data intake and query system 16 and/or stores data in a similar format and/or hierarchy.
  • separate divisions of the same company may set up distinct data intake and query systems 16 that are independent from each other.
  • the internal data stores 20 are said to be internal because the data stored thereon has been processed or passed through the data intake and query system 16 in some form.
  • the external data systems 12 are said to be external to the data intake and query system 16 because the data stored at the external data stores 24 has not necessarily been processed or passed through the data intake and query system 16.
  • the data intake and query system 16 may have no control or influence over how data is processed, controlled, or managed by the external data systems 12, including other instances of a data intake and query system with the same architecture of the data intake and query system 16.
  • the external data systems 12 can process data, perform requests received from other computing systems, and perform numerous other computational tasks independent of each other and independent of the data intake and query system 16.
  • the external data system 12-1 may be a server that can process data locally that reflects correlations among the stored data.
  • the external data systems 12 may generate and/or store ever increasing volumes of data without any interaction with the data intake and query system 16. As such, each of the external data system 12 may act independently to control, manage, and process the data they contain.
  • Data stored in the internal data stores 20 and external data stores 24 may be related.
  • an online transaction could generate various forms of data stored in disparate locations and in various formats.
  • the generated data may include payment information, customer information, and information about suppliers, retailers, and the like.
  • Other examples of data generated in a big data ecosystem include application program data, system logs, network packet data, error logs, stack traces, and performance data.
  • the data can also include diagnostic information and many other types of data that can be analyzed to perform local actions, diagnose performance problems, monitor interactions, and derive other insights.
  • the volume of generated data can grow at very high rates as the number of transactions and diverse data systems grows. A portion of this large volume of data could be processed and stored by the data intake and query system 16 while other portions could be stored in any of the external data systems 12.
  • some of the external data systems 12 may pre-process the raw data based on anticipated data analysis needs, store the pre-processed data, discard some or all of theremaining raw data, or store it in a different location that data intake and query system 16 does not have access to.
  • discarding or not making the massive amounts of raw data available can result in the loss of valuable insights that could have been obtained by searching all of the raw data.
  • an event includes a portion of raw data and is associated with a specific point in time.
  • events may be derived from "time series data," where the time series data comprises a sequence of data points (e.g., performance measurements from a computer system) that are associated with successive points in time.
  • the external data systems 12 can store raw data as events that are indexed by timestamps but are also associated with predetermined data items. This structure is essentially a modification of conventional database systems that require predetermining data items for subsequent searches. These systems can be modified to retain the remaining raw data for subsequent re-processing for other predetermined data items.
  • the raw data can be divided into segments and indexed by timestamps.
  • the predetermined data items can be associated with the events indexed by timestamps.
  • the events can be searched only for the predetermined data items during search time; the events can be re -processed later in time to re-index the raw data, and generate events with new predetermined data items.
  • the data systems of the system 10 can store related data in a variety of pre-processed data and raw data in a variety of structures.
  • a number of tools are available to search and analyze data contained in these diverse data systems. As such, an analyst can use a tool to search a database of the external data system 12-1. A different tool could be used to search a cloud services application of the external data system 12-2. Yet another different tool could be used to search the internal data stores 20. Moreover, different tools can perform analytics of data stored in proprietary or open source data stores. However, existing tools cannot obtain valuable insights from data contained in a combination of the data intake and query system 16 and/or any of the external data systems 12. Examples of these valuable insights may include correlations between the structured data of the external data stores 24 and raw data of the internal data stores 20 (or external data stores 24 that store data in a similar format or hierarchy as the internal data stores 20).
  • the disclosed techniques can extend the search, extraction, execution, and analytics capabilities of data intake and query systems to seamlessly search and analyze multiple diverse data of diverse data systems in a big data ecosystem.
  • the disclosed techniques can transform a big data ecosystem into a big data pipeline between external data systems and a data intake and query system, to enable seamless search and analytics operations on a variety of data sources, which can lead to new insights that were not previously available.
  • the disclosed techniques include a data intake and query system 16 extended to search external data systems into a data fabric platform that can leverage computing assets from anywhere and access and execute on data regardless of type and origin.
  • the data intake and query system 16 facilitates implementation of both iterative searches, to read datasets multiple times in a loop, and interactive or exploratory data analysis (e.g., for repeated database-style querying of data).
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the rest of the machine data typically is not saved and is discarded during pre processing.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • machine data can have a predefined format, where data items with specific data formats are stored at predefined locations in the data.
  • the machine data may include data associated with fields in a database table.
  • 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.
  • 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.”
  • 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.
  • 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.
  • 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).
  • 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.
  • a search having criteria that specifies a field name“UserlD” may cause the system to field-search the machine data of events to identify events that have the field name“UserlD.”
  • a search having criteria that specifies a field name“UserlD” 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“UserlD” 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.
  • an extraction rule may apply to a set of events that are each associated with a particular host, source, or source type.
  • 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.
  • the data intake and query system utilizes a late-binding schema while performing queries on events.
  • a late-binding schema is applying extraction rules to events to extract values for specific fields during search time.
  • 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.
  • 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.
  • 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.
  • a user may manually define extraction rules for fields using a variety of techniques.
  • 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.
  • 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.
  • 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.
  • 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.
  • the system facilitates use of a“common information model” (CIM) across the disparate data sources (further discussed with respect to FIG. 7A).
  • CIM common information model
  • FIG. IB is a block diagram of an example networked computer environment 100, in accordance with example embodiments. Those skilled in the art would understand that FIG. IB represents one example of a networked computer system and other embodiments, such as the embodiment illustrated in FIG. 1A may use different arrangements.
  • the networked computer environment 100 includes one or more computing devices. These one or more computing devices comprise any combination of hardware and software configured to implement the various logical components described herein.
  • 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.
  • 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.
  • an environment 100 includes one or more host devices 106.
  • Host devices 106 may broadly include any number of computers, virtual machine instances, and/or data centers that are configured to host or execute one or more instances of host applications 114.
  • 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.
  • 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.
  • 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.
  • 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.
  • a specific resource e.g., based on an HTTP request
  • 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.
  • 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.
  • 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.
  • 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.
  • Client devices 102 represent any computing device capable of interacting with one or more host devices 106 via a network 104.
  • 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.
  • 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.
  • 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.
  • 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.
  • a client application 110 may comprise a mobile application or“app.”
  • 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.
  • 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.
  • a client application 110 may include a monitoring component 112.
  • the monitoring component 112 comprises a software component or other logic that facilitates generating performance data related to a client device’s operating state, including monitoring network traffic sent and received from the client device and collecting other device and/or application-specific information.
  • Monitoring component 112 may be an integrated component of a client application 110, a plug-in, an extension, or any other type of add-on component. Monitoring component 112 may also be a stand-alone process.
  • 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).
  • SDK software development kit
  • the SDK may include custom monitoring code that can be incorporated into the code implementing a client application 110.
  • the custom code implementing the monitoring functionality can become part of the application itself.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the monitoring component 112 may monitor one or more aspects of network traffic sent and/or received by a client application 110.
  • 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.
  • 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.
  • the network performance data can be transmitted to a data intake and query system 108 for analysis.
  • 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.
  • the application may be distributed to a client device 102 via an application marketplace or other application distribution system.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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 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.
  • 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.
  • FIG. 2 is a block diagram of an example data intake and query system 108, in accordance with example embodiments.
  • the data intake and query system 108 may be or may include a data intake and query system 16.
  • System 108 includes one or more forwarders 204 that receive data from a variety of input data sources 203, and one or more indexers 206 that process and store the data in one or more data stores 208.
  • These forwarders 204 and indexers 206 can comprise separate computer systems, or may alternatively comprise separate processes executing on one or more computer systems.
  • Each data source 203 broadly represents a distinct source of data that can be consumed by system 108.
  • Examples of a data sources 203 include, without limitation, data files, directories of files, data sent over a network, event logs, registries, etc.
  • the forwarders 204 identify which indexers 206 receive data collected from a data source 203 and forward the data to the appropriate indexers. Forwarders 204 can also perform operations on the data before forwarding, including removing extraneous data, detecting timestamps in the data, parsing data, indexing data, routing data based on criteria relating to the data being routed, and/or performing other data transformations.
  • a forwarder 204 may comprise a service accessible to client devices 102 and host devices 106 via a network 104.
  • the forwarder 204 may, for example, comprise a computing device which implements multiple data pipelines or“queues” to handle forwarding of network data to indexers 206.
  • a forwarder 204 may also perform many of the functions that are performed by an indexer. For example, a forwarder 204 may perform keyword extractions on raw data or parse raw data to create events. A forwarder 204 may generate time stamps for events.
  • a forwarder 204 may perform routing of events to indexers 206.
  • Data store 208 may contain events derived from machine data from a variety of sources all pertaining to the same component in an IT environment, and this data may be produced by the machine in question or by other components in the IT environment.
  • the example data intake and query system 108 described in reference to FIG. 2 comprises several system components, including one or more forwarders, indexers, and search heads.
  • 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.
  • a user may install a software application on server computers owned by the user and configure each server to operate as one or more of a forwarder, an indexer, a search head, etc.
  • This arrangement generally may be referred to as an“on-premises” solution. That is, the system 108 is installed and operates on computing devices directly controlled by the user of the system.
  • Some users may prefer an on-premises solution because it may provide a greater level of control over the configuration of certain aspects of the system (e.g., security, privacy, standards, controls, etc.). However, other users may instead prefer an arrangement in which the user is not directly responsible for providing and managing the computing devices upon which various components of system 108 operate.
  • a cloud-based service refers to a service hosted by one more computing resources that are accessible to end users over a network, for example, by using a web browser or other application on a client device to interface with the remote computing resources.
  • a service provider may provide a cloud-based data intake and query system by managing computing resources configured to implement various aspects of the system (e.g., forwarders, indexers, search heads, etc.) and by providing access to the system to end users via a network.
  • a user may pay a subscription or other fee to use such a service.
  • Each subscribing user of the cloud-based service may be provided with an account that enables the user to configure a customized cloud-based system based on the user’s preferences.
  • FIG. 3 illustrates a block diagram of an example cloud-based data intake and query system
  • the networked computer environment 300 includes input data sources 203 and forwarders 204. These input data sources and forwarders may be in a subscriber’s private computing environment. Alternatively, they might be directly managed by the service provider as part of the cloud service.
  • one or more forwarders 204 and client devices 302 are coupled to a cloud-based data intake and query system 306 via one or more networks 304.
  • Network 304 broadly represents one or more LANs, WANs, cellular networks, intranetworks, internetworks, etc., using any of wired, wireless, terrestrial microwave, satellite links, etc., and may include the public Internet, and is used by client devices 302 and forwarders 204 to access the system 306.
  • each of the forwarders 204 may be configured to receive data from an input source and to forward the data to other components of the system 306 for further processing.
  • a cloud-based data intake and query system 306 may comprise a plurality of system instances 308.
  • each system instance 308 may include one or more computing resources managed by a provider of the cloud-based system 306 made available to a particular subscriber.
  • the computing resources comprising a system instance 308 may, for example, include one or more servers or other devices configured to implement one or more forwarders, indexers, search heads, and other components of a data intake and query system, similar to system 108.
  • a subscriber may use a web browser or other application of a client device 302 to access a web portal or other interface that enables the subscriber to configure an instance 308.
  • Each of the components of a system 108 may at times refer to various configuration files stored locally at each component. These configuration files typically may involve some level of user configuration to accommodate particular types of data a user desires to analyze and to account for other user preferences.
  • users typically may not have direct access to the underlying computing resources implementing the various system components (e.g., the computing resources comprising each system instance 308) and may desire to make such configurations indirectly, for example, using one or more web- based interfaces.
  • the techniques and systems described herein for providing user interfaces that enable a user to configure source type definitions are applicable to both on-premises and cloud-based service contexts, or some combination thereof (e.g., a hybrid system where both an on-premises environment, such as SPLUNK® ENTERPRISE, and a cloud-based environment, such as SPLUNK CLOUDTM, are centrally visible).
  • FIG. 4 shows a block diagram of an example of a data intake and query system 108 that provides transparent search facilities for data systems that are external to the data intake and query system.
  • Such facilities are available in the Splunk® Analytics for Hadoop® system provided by Splunk Inc. of San Francisco, California.
  • Splunk® Analytics for Hadoop® represents an analytics platform that enables business and IT teams to rapidly explore, analyze, and visualize data in Hadoop® and NoSQL data stores.
  • the search head 210 of the data intake and query system receives search requests from one or more client devices 404 over network connections 420.
  • the data intake and query system 108 may reside in an enterprise location, in the cloud, etc.
  • FIG. 4 illustrates that multiple client devices 404a, 404b . . . 404n may communicate with the data intake and query system 108.
  • the client devices 404 may communicate with the data intake and query system using a variety of connections. For example, one client device in FIG. 4 is illustrated as communicating over an Internet (Web) protocol, another client device is illustrated as communicating via a command line interface, and another client device is illustrated as communicating via a software developer kit (SDK).
  • Web Internet
  • SDK software developer kit
  • the search head 210 analyzes the received search request to identify request parameters. If a search request received from one of the client devices 404 references an index maintained by the data intake and query system, then the search head 210 connects to one or more indexers 206 of the data intake and query system for the index referenced in the request parameters. That is, if the request parameters of the search request reference an index, then the search head accesses the data in the index via the indexer.
  • the data intake and query system 108 may include one or more indexers 206, depending on system access resources and requirements. As described further below, the indexers 206 retrieve data from their respective local data stores 208 as specified in the search request.
  • the indexers and their respective data stores can comprise one or more storage devices and typically reside on the same system, though they may be connected via a local network connection.
  • the search head 210 can access the external data collection through an External Result Provider (ERP) process 410.
  • ERP External Result Provider
  • An external data collection may be referred to as a“virtual index” (plural,“virtual indices”).
  • An ERP process provides an interface through which the search head 210 may access virtual indices.
  • a search reference to an index of the system relates to a locally stored and managed data collection.
  • a search reference to a virtual index relates to an externally stored and managed data collection, which the search head may access through one or more ERP processes 410, 412.
  • FIG. 4 shows two ERP processes 410, 412 that connect to respective remote (external) virtual indices, which are indicated as a Hadoop or another system 414 (e.g., Amazon S3, Amazon EMR, other Hadoop® Compatible File Systems (HCFS), etc.) and a relational database management system (RDBMS) 416.
  • Other virtual indices may include other file organizations and protocols, such as Structured Query Language (SQL) and the like.
  • SQL Structured Query Language
  • An ERP process may be a computer process that is initiated or spawned by the search head 210 and is executed by the search data intake and query system 108.
  • an ERP process may be a process spawned by the search head 210 on the same or different host system as the search head 210 resides.
  • the search head 210 may spawn a single ERP process in response to multiple virtual indices referenced in a search request, or the search head may spawn different ERP processes for different virtual indices.
  • virtual indices that share common data configurations or protocols may share ERP processes.
  • all search query references to a Hadoop file system may be processed by the same ERP process, if the ERP process is suitably configured.
  • all search query references to a SQL database may be processed by the same ERP process.
  • the search head may provide a common ERP process for common external data source types (e.g., a common vendor may utilize a common ERP process, even if the vendor includes different data storage system types, such as Hadoop and SQL).
  • Common indexing schemes also may be handled by common ERP processes, such as flat text files or Weblog files.
  • the search head 210 determines the number of ERP processes to be initiated via the use of configuration parameters that are included in a search request message.
  • RDBMS assume two independent instances of such a system by one vendor, such as one RDBMS for production and another RDBMS used for development. In such a situation, it is likely preferable (but optional) to use two ERP processes to maintain the independent operation as between production and development data. Both of the ERPs, however, will belong to the same family, because the two RDBMS system types are from the same vendor.
  • the ERP processes 410, 412 receive a search request from the search head 210.
  • the search head may optimize the received search request for execution at the respective external virtual index.
  • the ERP process may receive a search request as a result of analysis performed by the search head or by a different system process.
  • the ERP processes 410, 412 can communicate with the search head 210 via conventional input/output routines (e.g., standard in / standard out, etc.). In this way, the ERP process receives the search request from a client device such that the search request may be efficiently executed at the corresponding external virtual index.
  • the ERP processes 410, 412 may be implemented as a process of the data intake and query system. Each ERP process may be provided by the data intake and query system, or may be provided by process or application providers who are independent of the data intake and query system. Each respective ERP process may include an interface application installed at a computer of the external result provider that ensures proper communication between the search support system and the external result provider.
  • the ERP processes 410, 412 generate appropriate search requests in the protocol and syntax of the respective virtual indices 414, 416, each of which corresponds to the search request received by the search head 210. Upon receiving search results from their corresponding virtual indices, the respective ERP process passes the result to the search head 210, which may return or display the results or a processed set of results based on the returned results to the respective client device.
  • Client devices 404 may communicate with the data intake and query system 108 through a network interface 420, e.g., one or more LANs, WANs, cellular networks, intranetworks, and/or internetworks using any of wired, wireless, terrestrial microwave, satellite links, etc., and may include the public Internet.
  • a network interface 420 e.g., one or more LANs, WANs, cellular networks, intranetworks, and/or internetworks using any of wired, wireless, terrestrial microwave, satellite links, etc., and may include the public Internet.
  • the ERP processes described above may include two operation modes: a streaming mode and a reporting mode.
  • the ERP processes can operate in streaming mode only, in reporting mode only, or in both modes simultaneously. Operating in both modes simultaneously is referred to as mixed mode operation.
  • mixed mode operation the ERP at some point can stop providing the search head with streaming results and only provide reporting results thereafter, or the search head at some point may start ignoring streaming results it has been using and only use reporting results thereafter.
  • the streaming mode returns search results in real time, with minimal processing, in response to the search request.
  • the reporting mode provides results of a search request with processing of the search results prior to providing them to the requesting search head, which in turn provides results to the requesting client device.
  • ERP operation with such multiple modes provides greater performance flexibility with regard to report time, search latency, and resource utilization.
  • both streaming mode and reporting mode are operating simultaneously.
  • the streaming mode results e.g., the machine data obtained from the external data source
  • the search head can then process the results data (e.g., break the machine data into events, timestamp it, filter it, etc.) and integrate the results data with the results data from other external data sources, and/or from data stores of the search head.
  • the search head performs such processing and can immediately start returning interim (streaming mode) results to the user at the requesting client device; simultaneously, the search head is waiting for the ERP process to process the data it is retrieving from the external data source as a result of the concurrently executing reporting mode.
  • the ERP process initially operates in a mixed mode, such that the streaming mode operates to enable the ERP quickly to return interim results (e.g., some of the machined data or unprocessed data necessary to respond to a search request) to the search head, enabling the search head to process the interim results and begin providing to the client or search requester interim results that are responsive to the query.
  • interim results e.g., some of the machined data or unprocessed data necessary to respond to a search request
  • the ERP also operates concurrently in reporting mode, processing portions of machine data in a manner responsive to the search query.
  • the ERP may halt processing in the mixed mode at that time (or some later time) by stopping the return of data in streaming mode to the search head and switching to reporting mode only.
  • the ERP starts sending interim results in reporting mode to the search head, which in turn may then present this processed data responsive to the search request to the client or search requester.
  • the search head switches from using results from the ERP’s streaming mode of operation to results from the ERP’s reporting mode of operation when the higher bandwidth results from the reporting mode outstrip the amount of data processed by the search head in the streaming mode of ERP operation.
  • a reporting mode may have a higher bandwidth because the ERP does not have to spend time transferring data to the search head for processing all the machine data.
  • the ERP may optionally direct another processor to do the processing.
  • the streaming mode of operation does not need to be stopped to gain the higher bandwidth benefits of a reporting mode; the search head could simply stop using the streaming mode results - and start using the reporting mode results - when the bandwidth of the reporting mode has caught up with or exceeded the amount of bandwidth provided by the streaming mode.
  • the search head could simply stop using the streaming mode results - and start using the reporting mode results - when the bandwidth of the reporting mode has caught up with or exceeded the amount of bandwidth provided by the streaming mode.
  • the reporting mode can involve the ERP process (or an external system) performing event breaking, time stamping, filtering of events to match the search query request, and calculating statistics on the results.
  • the user can request particular types of data, such as if the search query itself involves types of events, or the search request may ask for statistics on data, such as on events that meet the search request.
  • the search head understands the query language used in the received query request, which may be a proprietary language.
  • One exemplary query language is Splunk Processing Language (SPL) developed by the assignee of the application, Splunk Inc.
  • SAP Splunk Processing Language
  • the search head typically understands how to use that language to obtain data from the indexers, which store data in a format used by the SPLUNK® Enterprise system.
  • the ERP processes support the search head, as the search head is not ordinarily configured to understand the format in which data is stored in external data sources such as Hadoop or SQL data systems. Rather, the ERP process performs that translation from the query submitted in the search support system’s native format (e.g., SPL if SPLUNK® ENTERPRISE is used as the search support system) to a search query request format that will be accepted by the corresponding external data system.
  • the external data system typically stores data in a different format from that of the search support system’ s native index format, and it utilizes a different query language (e.g., SQL or MapReduce, rather than SPL or the like).
  • the ERP process can operate in the streaming mode alone.
  • the search head can integrate the returned data with any data obtained from local data sources (e.g., native to the search support system), other external data sources, and other ERP processes (if such operations were required to satisfy the terms of the search query).
  • local data sources e.g., native to the search support system
  • other ERP processes if such operations were required to satisfy the terms of the search query.
  • An advantage of mixed mode operation is that, in addition to streaming mode, the ERP process is also executing concurrently in reporting mode.
  • the ERP process (rather than the search head) is processing query results (e.g., performing event breaking, timestamping, filtering, possibly calculating statistics if required to be responsive to the search query request, etc.).
  • the streaming mode will allow the search head to start returning interim results to the user at the client device before the ERP process can complete sufficient processing to start returning any search results.
  • the switchover between streaming and reporting mode happens when the ERP process determines that the switchover is appropriate, such as when the ERP process determines it can begin returning meaningful results from its reporting mode.
  • streaming mode has low latency (immediate results) and usually has relatively low bandwidth (fewer results can be returned per unit of time).
  • concurrently running reporting mode has relatively high latency (it has to perform a lot more processing before returning any results) and usually has relatively high bandwidth (more results can be processed per unit of time).
  • the ERP process does begin returning report results, it returns more processed results than in the streaming mode, because, e.g., statistics only need to be calculated to be responsive to the search request. That is, the ERP process doesn’t have to take time to first return machine data to the search head.
  • the ERP process could be configured to operate in streaming mode alone and return just the machine data for the search head to process in a way that is responsive to the search request.
  • the ERP process can be configured to operate in the reporting mode only.
  • the ERP process can be configured to operate in streaming mode and reporting mode concurrently, as described, with the ERP process stopping the transmission of streaming results to the search head when the concurrently running reporting mode has caught up and started providing results.
  • the reporting mode does not require the processing of all machine data that is responsive to the search query request before the ERP process starts returning results; rather, the reporting mode usually performs processing of chunks of events and returns the processing results to the search head for each chunk.
  • an ERP process can be configured to merely return the contents of a search result file verbatim, with little or no processing of results. That way, the search head performs all processing (such as parsing byte streams into events, filtering, etc.).
  • the ERP process can be configured to perform additional intelligence , such as analyzing the search request and handling all the computation that a native search indexer process would otherwise perform. In this way, the configured ERP process provides greater flexibility in features while operating according to desired preferences, such as response latency and resource requirements.
  • FIG. 5A is a flow chart of an example method that illustrates how indexers process, index, and store data received from forwarders, in accordance with example embodiments.
  • the data flow illustrated in FIG. 5A is provided for illustrative purposes only; those skilled in the art would understand that one or more of the steps of the processes illustrated in FIG. 5A may be removed or that the ordering of the steps may be changed.
  • one or more particular system components are described in the context of performing various operations during each of the data flow stages.
  • a forwarder is described as receiving and processing machine data during an input phase; an indexer is described as parsing and indexing machine data during parsing and indexing phases; and a search head is described as performing a search query during a search phase.
  • a forwarder is described as receiving and processing machine data during an input phase; an indexer is described as parsing and indexing machine data during parsing and indexing phases; and a search head is described as performing a search query during a search phase.
  • a forwarder receives data from an input source, such as a data source 203 shown in Fig. 2.
  • a forwarder initially may receive the data as a raw data stream generated by the input source.
  • a forwarder may receive a data stream from a log file generated by an application server, from a stream of network data from a network device, or from any other source of data.
  • a forwarder receives the raw data and may segment the data stream into“blocks”, possibly of a uniform data size, to facilitate subsequent processing steps.
  • a forwarder or other system component annotates each block generated from the raw data with one or more metadata fields.
  • These metadata fields may, for example, provide information related to the data block as a whole and may apply to each event that is subsequently derived from the data in the data block.
  • the metadata fields may include separate fields specifying each of a host, a source, and a source type related to the data block.
  • a host field may contain a value identifying a host name or IP address of a device that generated the data.
  • a source field may contain a value identifying a source of the data, such as a pathname of a file or a protocol and port related to received network data.
  • a source type field may contain a value specifying a particular source type label for the data.
  • Additional metadata fields may also be included during the input phase, such as a character encoding of the data, if known, and possibly other values that provide information relevant to later processing steps.
  • a forwarder forwards the annotated data blocks to another system component (typically an indexer) for further processing.
  • the data intake and query system allows forwarding of data from one data intake and query instance to another, or even to a third-party system.
  • the data intake and query system can employ different types of forwarders in a configuration.
  • a forwarder may contain the essential components needed to forward data.
  • a forwarder can gather data from a variety of inputs and forward the data to an indexer for indexing and searching.
  • a forwarder can also tag metadata (e.g., source, source type, host, etc.).
  • a forwarder has the capabilities of the aforementioned forwarder as well as additional capabilities.
  • the forwarder can parse data before forwarding the data (e.g., can associate a time stamp with a portion of data and create an event, etc.) and can route data based on criteria such as source or type of event.
  • the forwarder can also index data locally while forwarding the data to another indexer. 3.7.2. PARSING
  • an indexer receives data blocks from a forwarder and parses the data to organize the data into events.
  • an indexer may determine a source type associated with each data block (e.g., by extracting a source type label from the metadata fields associated with the data block, etc.) and refer to a source type configuration corresponding to the identified source type.
  • the source type definition may include one or more properties that indicate to the indexer to automatically determine the boundaries within the received data that indicate the portions of machine data for events. In general, these properties may include regular expression-based rules or delimiter rules where, for example, event boundaries may be indicated by predefined characters or character strings.
  • predefined characters may include punctuation marks or other special characters including, for example, carriage returns, tabs, spaces, line breaks, etc. If a source type for the data is unknown to the indexer, an indexer may infer a source type for the data by examining the structure of the data. Then, the indexer can apply an inferred source type definition to the data to create the events.
  • the indexer determines a timestamp for each event. Similar to the process for parsing machine data, an indexer may again refer to a source type definition associated with the data to locate one or more properties that indicate instructions for determining a timestamp for each event. The properties may, for example, instruct an indexer to extract a time value from a portion of data for the event, to interpolate time values based on timestamps associated with temporally proximate events, to create a timestamp based on a time the portion of machine data was received or generated, to use the timestamp of a previous event, or use any other rules for determining timestamps.
  • the indexer associates with each event one or more metadata fields including a field containing the timestamp determined for the event.
  • 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.
  • 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.
  • an indexer may optionally apply one or more transformations to data included in the events created at block 506.
  • 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. 5C illustrates an illustrative example of machine data can be stored in a data store in accordance with various disclosed 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.”
  • TSIDX time series index
  • the depiction of machine data and associated metadata as rows and columns in the table of FIG. 5C 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.
  • machine data can be stored in a compressed or encrypted formatted.
  • 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.
  • certain metadata e.g., host 536, source 537, source type 538, and timestamps 535 can be generated for each event, and associated with a corresponding portion of machine data 539 when storing the event data in a data store, e.g., data store 208.
  • a data store e.g., data store 208.
  • Any of the metadata can be extracted from the corresponding machine data, or supplied or defined by an entity, such as a user or computer system.
  • the metadata fields can become part of or stored with the event. Note that while the time-stamp metadata field can be extracted from the raw data of each event, the values for the other metadata fields may be determined by the indexer based on information it receives pertaining to the source of the data separate from the machine data.
  • the machine data within an event can be maintained in its original condition.
  • 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.
  • the port of machine data in an event can be processed or otherwise altered.
  • all the raw machine data contained in an event can be preserved and saved in its original form.
  • 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.
  • the first three rows of the table represent events 531, 532, and 533 and are related to a server access log that records requests from multiple clients processed by a server, as indicated by entry of“access.log” in the source column 537.
  • each of the events 531-534 is associated with a discrete request made from a client device.
  • the raw machine data generated by the server and extracted from a server access log can include the IP address of the client 540, the user id of the person requesting the document 541, the time the server finished processing the request 542, the request line from the client 543, the status code returned by the server to the client 545, the size of the object returned to the client (in this case, the gif file requested by the client) 546 and the time spent to serve the request in microseconds 544.
  • all the raw machine data retrieved from the server access log is retained and stored as part of the corresponding events, 1221, 1222, and 1223 in the data store.
  • Event 534 is associated with an entry in a server error log, as indicated by“error.log” in the source column 537 that records errors that the server encountered when processing a client request. Similar to the events related to the server access log, all the raw machine data in the error log file pertaining to event 534 can be preserved and stored as part of the event 534.
  • Saving minimally processed or unprocessed machine data in a data store associated with metadata fields in the manner similar to that shown in FIG. 5C 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.
  • 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.
  • an indexer can optionally generate a keyword index to facilitate fast keyword searching for events.
  • the indexer identifies a set of keywords in each event.
  • the indexer includes the identified keywords in an index, which associates each stored keyword with reference pointers to events containing that keyword (or to locations within events where that keyword is located, other location identifiers, etc.).
  • the indexer can access the keyword index to quickly identify events containing the keyword.
  • 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.
  • the indexer stores the events with an associated timestamp in a data store 208.
  • Timestamps enable a user to search for events based on a time range.
  • 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.
  • buckets containing the most recent events can be stored in flash memory rather than on a hard disk.
  • each bucket may be associated with an identifier, a time range, and a size constraint.
  • Each indexer 206 may be responsible for storing and searching a subset of the events contained in a corresponding data store 208.
  • the indexers can analyze events for a query in parallel. For example, using map-reduce techniques, each indexer returns partial responses for a subset of events to a search head that combines the results to produce an answer for the query.
  • an indexer may further optimize the data retrieval process by searching buckets corresponding to time ranges that are relevant to a query.
  • each indexer has a home directory and a cold directory.
  • the home directory of an indexer stores hot buckets and warm buckets
  • the cold directory of an indexer stores cold buckets.
  • a hot bucket is a bucket that is capable of receiving and storing events.
  • a warm bucket is a bucket that can no longer receive events for storage but has not yet been moved to the cold directory.
  • a cold bucket is a bucket that can no longer receive events and may be a bucket that was previously stored in the home directory.
  • the home directory may be stored in faster memory, such as flash memory, as events may be actively written to the home directory, and the home directory may typically store events that are more frequently searched and thus are accessed more frequently.
  • the cold directory may be stored in slower and/or larger memory, such as a hard disk, as events are no longer being written to the cold directory, and the cold directory may typically store events that are not as frequently searched and thus are accessed less frequently.
  • an indexer may also have a quarantine bucket that contains events having potentially inaccurate information, such as an incorrect time stamp associated with the event or a time stamp that appears to be an unreasonable time stamp for the corresponding event.
  • the quarantine bucket may have events from any time range; as such, the quarantine bucket may always be searched at search time.
  • an indexer may store old, archived data in a frozen bucket that is not capable of being searched at search time.
  • 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.
  • events and buckets can also be replicated across different indexers and data stores to facilitate high availability and disaster recovery as described in U.S. Patent No. 9,130,971, entitled“SITE- BASED SEARCH AFFINITY”, issued on 8 September 2015, and in U.S. Patent No. 14/266,817, entitled “MULTI-SITE CLUSTERING”, issued on 1 September 2015, each of which is hereby incorporated by reference in its entirety for all purposes.
  • the indexer indexes semi-processed, or cooked data (e.g., data that has been parsed and/or had some fields determined for it), and stores the results in common storage.
  • semi-processed, or cooked data e.g., data that has been parsed and/or had some fields determined for it
  • FIG. 5B is a block diagram of an example data store 501 that includes a directory for each index (or partition) that contains a portion of data managed by an indexer.
  • FIG. 5B further illustrates details of an embodiment of an inverted index 507B and an event reference array 515 associated with inverted index 507B.
  • the data store 501 can correspond to a data store 208 that stores events managed by an indexer 206 or can correspond to a different data store associated with an indexer 206.
  • the data store 501 includes a _main directory 503 associated with a _main index and a _test directory 505 associated with a _test index.
  • the data store 501 can include fewer or more directories.
  • multiple indexes can share a single directory or all indexes can share a common directory.
  • the data store 501 can be implemented as multiple data stores storing different portions of the information shown in FIG. 5B.
  • a single index or partition can span multiple directories or multiple data stores, and can be indexed or searched by multiple corresponding indexers.
  • the index-specific directories 503 and 505 include inverted indexes 507A, 507B and 509A, 509B, respectively.
  • the inverted indexes 507A...507B, and 509A...509B can be keyword indexes or field- value pair indexes described herein and can include less or more information that depicted in FIG. 5B.
  • each inverted index 507A...507B, and 509A...509B can correspond to a distinct time-series bucket that is managed by the indexer 206 and that contains events corresponding to the relevant index (e.g., _main index, _test index).
  • each inverted index can correspond to a particular range of time for an index.
  • Additional files, such as high performance indexes for each time-series bucket of an index, can also be stored in the same directory as the inverted indexes 507A...507B, and 509A...509B.
  • inverted index 507A...507B, and 509A...509B can correspond to multiple time-series buckets or inverted indexes 507A...507B, and 509A...509B can correspond to a single time-series bucket.
  • Each inverted index 507A...507B, and 509A...509B can include one or more entries, such as keyword (or token) entries or field-value pair entries.
  • the inverted indexes 507A...507B, and 509A...509B can include additional information, such as a time range 523 associated with the inverted index or an index identifier 525 identifying the index associated with the inverted index 507A...507B, and 509A...509B.
  • each inverted index 507A...507B, and 509A...509B can include less or more information than depicted.
  • Token entries can include a token 511A (e.g.,“error,”“itemID,” etc.) and event references 511B indicative of events that include the token.
  • a token 511A e.g.,“error,”“itemID,” etc.
  • event references 511B indicative of events that include the token.
  • 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.”
  • the error token entry includes the identifiers 3, 5, 6, 8, 11, and 12 corresponding to events managed by the indexer 206 and associated with the index _main 503 that are located in the time-series bucket associated with the inverted index 507B.
  • some token entries can be default entries, automatically determined entries, or user specified entries.
  • the indexer 206 can identify each word or string in an event as a distinct token and generate a token entry for it. In some cases, the indexer 206 can identify the beginning and ending of tokens based on punctuation, spaces, as described in greater detail herein. In certain cases, the indexer 206 can rely on user input or a configuration file to identify tokens for token entries 511, etc. It will be understood that any combination of token entries can be included as a default, automatically determined, a or included based on user-specified criteria.
  • field-value pair entries such as field- value pair entries 513 shown in inverted index
  • a field-value pair 513A and event references 513B indicative of events that include a field value that corresponds to the field- value pair.
  • a field-value pair entry would include the field-value pair sourcetype::sendmail and a unique identifier, or event reference, for each event stored in the corresponding time-series bucket that includes a sendmail sourcetype.
  • the field- value pair entries 513 can be default entries, automatically determined entries, or user specified entries.
  • the field-value pair entries for the fields host, source, sourcetype can be included in the inverted indexes 507A...507B, and 509A...509B as a default.
  • all of the inverted indexes 507A...507B, and 509A...509B can include field-value pair entries for the fields host, source, sourcetype.
  • the field-value pair entries for the IP_address field can be user specified and may only appear in the inverted index 507B based on user- specified criteria.
  • the indexer can automatically identify field-value pairs and create field-value pair entries. For example, based on the indexers review of events, it can identify IP_address as a field in each event and add the IP_address field-value pair entries to the inverted index 507B. It will be understood that any combination of field-value pair entries can be included as a default, automatically determined, or included based on user-specified criteria.
  • Each unique identifier 517, or event reference can correspond to a unique event located in the time series bucket. However, the same event reference can be located in multiple entries. For example if an event has a sourcetype splunkd, host wwwl and token“warning,” then the unique identifier for the event will appear in the field- value pair entries sourcetype:: splunkd and host:: wwwl, as well as the token entry “warning.” With reference to the illustrated embodiment of FIG.
  • the event reference 3 is found in the field-value pair entries 513 host::hostA, source ::sourceB, sourcetype: :sourcetypeA, and IP_address::9l.205.l89.l5 indicating that the event corresponding to the event reference 3 is from host A, sourceB, of sourcetype A, and includes 91.205.189.15 in the event data.
  • the unique identifier is located in only one field-value pair entry for a particular field.
  • 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.
  • the event references 517 can be used to locate the events in the corresponding bucket.
  • the inverted index can include, or be associated with, an event reference array 515.
  • the event reference array 515 can include an array entry 517 for each event reference in the inverted index 507B.
  • Each array entry 517 can include location information 519 of the event corresponding to the unique identifier (non limiting example: seek address of the event), a timestamp 521 associated with the event, or additional information regarding the event associated with the event reference, etc.
  • the event reference 50lBor 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.
  • the event reference 1 in the illustrated embodiment of FIG. 5B 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.
  • 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.
  • 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.
  • entry origin e.g., default, automatically generated, user-specified, etc.
  • entry type e.g., field-value pair entry, token entry, etc.
  • the entries are sorted first by entry type and then alphabetically.
  • the indexers can receive filter criteria indicating data that is to be categorized and categorization criteria indicating how the data is to be categorized.
  • Example filter criteria can include, but is not limited to, indexes (or partitions), hosts, sources, sourcetypes, time ranges, field identifier, keywords, etc.
  • the indexer identifies relevant inverted indexes to be searched. For example, if the filter criteria includes a set of partitions, the indexer can identify the inverted indexes stored in the directory corresponding to the particular partition as relevant inverted indexes. Other means can be used to identify inverted indexes associated with a partition of interest. For example, in some embodiments, the indexer can review an entry in the inverted indexes, such as an index-value pair entry 513 to determine if a particular inverted index is relevant. If the filter criteria does not identify any partition, then the indexer can identify all inverted indexes managed by the indexer as relevant inverted indexes.
  • the indexer can identify inverted indexes corresponding to buckets that satisfy at least a portion of the time range as relevant inverted indexes. For example, if the time range is last hour then the indexer can identify all inverted indexes that correspond to buckets storing events associated with timestamps within the last hour as relevant inverted indexes.
  • 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.
  • the indexer can focus the processing to only a subset of the total number of inverted indexes that the indexer manages.
  • the indexer can review them using any additional filter criteria to identify events that satisfy the filter criteria.
  • the indexer can determine that any events identified using the relevant inverted indexes satisfy an index filter criterion. For example, if the filter criteria includes a partition main, then the indexer can determine that any events identified using inverted indexes within the partition main directory (or otherwise associated with the partition main) satisfy the index filter criterion.
  • the indexer can determine that that any events identified using a particular inverted index satisfies a time range filter criterion. For example, if a time range filter criterion is for the last hour and a particular inverted index corresponds to events within a time range of 50 minutes ago to 35 minutes ago, the indexer can determine that any events identified using the particular inverted index satisfy the time range filter criterion. Conversely, if the particular inverted index corresponds to events within a time range of 59 minutes ago to 62 minutes ago, the indexer can determine that some events identified using the particular inverted index may not satisfy the time range filter criterion.
  • the indexer can identify event references (and therefore events) that satisfy the filter criteria. For example, if the token“error” is a filter criterion, the indexer can track all event references within the token entry“error.” Similarly, the indexer can identify other event references located in other token entries or field-value pair entries that match the filter criteria. The system can identify event references located in all of the entries identified by the filter criteria. For example, if the filter criteria include the token“error” and field-value pair sourcetype::web_ui, the indexer can track the event references found in both the token entry“error” and the field-value pair entry sourcetype::web_ui.
  • the system can identify event references located in at least one of the entries corresponding to the multiple values and in all other entries identified by the filter criteria.
  • the indexer can determine that the events associated with the identified event references satisfy the filter criteria.
  • the indexer can further consult a timestamp associated with the event reference to determine whether an event satisfies the filter criteria. For example, if an inverted index corresponds to a time range that is partially outside of a time range filter criterion, then the indexer can consult a timestamp associated with the event reference to determine whether the corresponding event satisfies the time range criterion. In some embodiments, to identify events that satisfy a time range, the indexer can review an array, such as the event reference arrayl6l4 that identifies the time associated with the events. Furthermore, as mentioned above using the known location of the directory in which the relevant inverted indexes are located (or other index identifier), the indexer can determine that any events identified using the relevant inverted indexes satisfy the index filter criterion.
  • the indexer reviews an extraction rule.
  • the filter criteria includes a field name that does not correspond to a field- value pair entry in an inverted index
  • the indexer can review an extraction rule, which may be located in a configuration file, to identify a field that corresponds to a field-value pair entry in the inverted index.
  • the filter criteria includes a field name“sessionID” and the indexer determines that at least one relevant inverted index does not include a field-value pair entry corresponding to the field name sessionID, the indexer can review an extraction rule that identifies how the sessionID field is to be extracted from a particular host, source, or sourcetype (implicitly identifying the particular host, source, or sourcetype that includes a sessionID field). The indexer can replace the field name“sessionID” in the filter criteria with the identified host, source, or sourcetype.
  • 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.
  • 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 Sl and the“sessionID” field is found in source S2, the source S2 can replace Sl in the filter criteria, be appended such that the filter criteria includes source Sl and source S2, or be excluded based on the presence of the filter criterion source Sl. If the identified host, source, or sourcetype is included in the filter criteria, the indexer can then identify a field-value pair entry in the inverted index that includes a field value corresponding to the identity of the particular host, source, or sourcetype identified using the extraction rule.
  • the system such as the indexer
  • the 206 can categorize the results based on the categorization criteria.
  • the categorization criteria can include categories for grouping the results, such as any combination of partition, source, sourcetype, or host, or other categories or fields as desired.
  • the indexer can use the categorization criteria to identify categorization criteria-value pairs or categorization criteria values by which to categorize or group the results.
  • the categorization criteria-value pairs can correspond to one or more field-value pair entries stored in a relevant inverted index, one or more index-value pairs based on a directory in which the inverted index is located or an entry in the inverted index (or other means by which an inverted index can be associated with a partition), or other criteria-value pair that identifies a general category and a particular value for that category.
  • the categorization criteria values can correspond to the value portion of the categorization criteria-value pair.
  • the categorization criteria-value pairs can correspond to one or more field-value pair entries stored in the relevant inverted indexes.
  • the categorization criteria- value pairs can correspond to field-value pair entries of host, source, and sourcetype (or other field-value pair entry as desired). For instance, if there are ten different hosts, four different sources, and five different sourcetypes for an inverted index, then the inverted index can include ten host field-value pair entries, four source field-value pair entries, and five sourcetype field-value pair entries. The indexer can use the nineteen distinct field-value pair entries as categorization criteria-value pairs to group the results.
  • the indexer can identify the location of the event references associated with the events that satisfy the filter criteria within the field-value pairs, and group the event references based on their location. As such, the indexer can identify the particular field value associated with the event corresponding to the event reference. For example, if the categorization criteria include host and sourcetype, the host field- value pair entries and sourcetype field-value pair entries can be used as categorization criteria-value pairs to identify the specific host and sourcetype associated with the events that satisfy the filter criteria.
  • categorization criteria-value pairs can correspond to data other than the field- value pair entries in the relevant inverted indexes.
  • the inverted indexes may not include partition field-value pair entries. Rather, the indexer can identify the categorization criteria-value pair associated with the partition based on the directory in which an inverted index is located, information in the inverted index, or other information that associates the inverted index with the partition, etc. As such a variety of methods can be used to identify the categorization criteria-value pairs from the categorization criteria.
  • the indexer can generate groupings based on the events that satisfy the filter criteria.
  • the categorization criteria includes a partition and sourcetype
  • 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.
  • the indexer can generate up to thirty groups for the results that satisfy the filter criteria. Each group can be associated with a unique combination of categorization criteria-value pairs (e.g., unique combinations of partition value sourcetype value, and host value).
  • the indexer can count the number of events associated with each group based on the number of events that meet the unique combination of categorization criteria for a particular group (or match the categorization criteria-value pairs for the particular group). With continued reference to the example above, the indexer can count the number of events that meet the unique combination of partition, sourcetype, and host for a particular group.
  • Each indexer communicates the groupings to the search head.
  • the search head can aggregate the groupings from the indexers and provide the groupings for display.
  • the groups are displayed based on at least one of the host, source, sourcetype, or partition associated with the groupings.
  • the search head can further display the groups based on display criteria, such as a display order or a sort order as described in greater detail above.
  • display criteria such as a display order or a sort order as described in greater detail above.
  • the indexer 206 identifies _main directory 503 and can ignore
  • inverted partition 507B is a relevant partition based on its location within the _main directory 503 and the time range associated with it. For sake of simplicity in this example, the indexer 206 determines that no other inverted indexes in the _main directory 503, such as inverted index 507A satisfy the time range criterion.
  • the indexer reviews the token entries 511 and the field-value pair entries 513 to identify event references, or events, that satisfy all of the filter criteria.
  • the indexer can review the error token entry and identify event references 3, 5, 6, 8, 11, 12, indicating that the term“error” is found in the corresponding events. Similarly, the indexer can identify event references 4, 5, 6, 8, 9, 10, 11 in the field-value pair entry sourcetype:: source typeC and event references 2, 5, 6, 8, 10, 11 in the field-value pair entry host::hostB. As the filter criteria did not include a source or an IP_address field-value pair, the indexer can ignore those field- value pair entries.
  • the indexer can identify events (and corresponding event references) that satisfy the time range criterion using the event reference array 1614 (e.g., event references 2, 3, 4, 5, 6, 7, 8, 9, 10). Using the information obtained from the inverted index 507B (including the event reference array 515), the indexer 206 can identify the event references that satisfy all of the filter criteria (e.g., event references 5, 6, 8).
  • the indexer 206 can group the event references using the received categorization criteria (source). In doing so, the indexer can determine that event references 5 and 6 are located in the field- value pair entry source: :sourceD (or have matching categorization criteria-value pairs) and event reference 8 is located in the field-value pair entry source: :sourceC. Accordingly, the indexer can generate a sourceC group having a count of one corresponding to reference 8 and a sourceD group having a count of two corresponding to references 5 and 6. This information can be communicated to the search head. In turn the search head can aggregate the results from the various indexers and display the groupings. As mentioned above, in some embodiments, the groupings can be displayed based at least in part on the categorization criteria, including at least one of host, source, sourcetype, or partition.
  • a change to any of the filter criteria or categorization criteria can result in different groupings.
  • the indexer would then generate up to 24 groupings corresponding to the 24 different combinations of the categorization criteria-value pairs, including host (hostA, hostB), source (sourceA, sourceB, sourceC, sourceD), and sourcetype (sourcetypeA, sourcetypeB, sourcetypeC).
  • host hostA
  • hostB source
  • source source
  • sourcetype sourcetypeB
  • sourcetypeC sourcetypeC
  • 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 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)
  • each group has a unique combination of categorization criteria-value pairs or categorization criteria values.
  • the indexer communicates the groups to the search head for aggregation with results received from other indexers.
  • the indexer can include the categorization criteria-value pairs for each group and the count.
  • the indexer can include more or less information.
  • the indexer can include the event references associated with each group and other identifying information, such as the indexer or inverted index used to identify the groups.
  • Group 1 (hostA, sourceA, sourcetypeC): 1 (event reference 4)
  • Group 3 (hostB, sourceD, sourcetypeC): 1 (event references 10)
  • the indexer communicates the groups to the search head for aggregation with results received from other indexers.
  • the indexer can review multiple inverted indexes associated with an partition or review the inverted indexes of multiple partitions, and categorize the data using any one or any combination of partition, host, source, sourcetype, or other category, as desired.
  • the indexer can provide additional information regarding the group. For example, the indexer can perform a targeted search or sampling of the events that satisfy the filter criteria and the categorization criteria for the selected group, also referred to as the filter criteria corresponding to the group or filter criteria associated with the group.
  • the indexer relies on the inverted index.
  • the indexer can identify the event references associated with the events that satisfy the filter criteria and the categorization criteria for the selected group and then use the event reference array 515 to access some or all of the identified events.
  • the categorization criteria values or categorization criteria- value pairs associated with the group become part of the filter criteria for the review.
  • the indexer identifies the event references associated with the group using the filter criteria and the categorization criteria for the group (e.g., categorization criteria values or categorization criteria-value pairs unique to the group). Together, the filter criteria and the categorization criteria for the group can be referred to as the filter criteria associated with the group. Using the filter criteria associated with the group, the indexer identifies event references 4, 5, 6, 8, 10, 11.
  • the indexer can determine that it will analyze a sample of the events associated with the event references 4, 5, 6, 8, 10, 11.
  • the sample can include analyzing event data associated with the event references 5, 8, 10.
  • the indexer can use the event reference array 1616 to access the event data associated with the event references 5, 8, 10. Once accessed, the indexer can compile the relevant information and provide it to the search head for aggregation with results from other indexers. By identifying events and sampling event data using the inverted indexes, the indexer can reduce the amount of actual data this is analyzed and the number of events that are accessed in order to generate the summary of the group and provide a response in less time.
  • FIG. 6A 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.
  • a search head receives a search query from a client.
  • the search head analyzes the search query to determine what portion(s) of the query can be delegated to indexers and what portions of the query can be executed locally by the search head.
  • the search head distributes the determined portions of the query to the appropriate indexers.
  • a search head cluster may take the place of an independent search head where each search head in the search head cluster coordinates with peer search heads in the search head cluster to schedule jobs, replicate search results, update configurations, fulfill search requests, etc.
  • the search head (or each search head) communicates with a master node (also known as a cluster master, not shown in Fig. 2) that provides the search head with a list of indexers to which the search head can distribute the determined portions of the query.
  • the master node maintains a list of active indexers and can also designate which indexers may have responsibility for responding to queries over certain sets of events.
  • a search head may communicate with the master node before the search head distributes queries to indexers to discover the addresses of active indexers.
  • the indexers to which the query was distributed search data stores associated with them for events that are responsive to the query.
  • the indexer searches for events that match the criteria specified in the query. These criteria can include matching keywords or specific values for certain fields.
  • the searching operations at block 608 may use the late-binding schema to extract values for specified fields from events at the time the query is processed.
  • one or more rules for extracting field values may be specified as part of a source type definition in a configuration file.
  • the indexers may then either send the relevant events back to the search head, or use the events to determine a partial result, and send the partial result back to the search head.
  • the search head combines the partial results and/or events received from the indexers to produce a final result for the query.
  • 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.
  • 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.
  • the final result can include one or more calculated values derived from the matching events.
  • results generated by the system 108 can be returned to a client using different techniques.
  • 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.
  • certain results are stored as“search jobs” and the client may retrieve the results by referring the search jobs.
  • the search head can also perform various operations to make the search more efficient. For example, before the search head begins execution of a query, the search head can determine a time range for the query and a set of common keywords that all matching events include. The search head may then use these parameters to query the indexers to obtain a superset of the eventual results. Then, during a filtering stage, the search head can perform field-extraction operations on the superset to produce a reduced set of search results. This speeds up queries, which may be particularly helpful for queries that are performed on a periodic basis.
  • a query coordinator analyzes the query, identifies dataset sources to be accessed, generates subqueries for execution by dataset sources, such as indexers, collects partial results to produce a final result and returns the final results to the search head for delivery to a client device or delivers the final results to the client device without the search head.
  • results from dataset sources, such as the indexers are communicated to nodes, which further process the data, and communicate the results of the processing to the query coordinator, etc.
  • the search head spawns a search process, which communicates the query to a search process master.
  • the search process master can communicate the query to the query coordinator for processing and execution.
  • the indexers are not involved in search operations or only search some data, such as data in hot buckets, etc.
  • nodes can perform the search functionality described herein with respect to indexers.
  • nodes can use late -binding schema to extract values for specified fields from events at the time the query is processed and/or use one or more rules specified as part of a source type definition in a configuration file for extracting field values, etc.
  • nodes can perform search operations on data in common storage or found in other dataset sources, such as external data stores, query acceleration data stores, ingested data buffers, etc.
  • 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.
  • 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 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.
  • 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.
  • a query can be formulated as a command sequence defined in a command line of a search UI.
  • 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”.
  • 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.
  • 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).
  • SPL Splunk Processing Language
  • 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.
  • 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.
  • 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.
  • 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.
  • a single query can include a search command and search term expressions, as well as data-analysis expressions.
  • 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).
  • 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.
  • 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.
  • 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. 6B provides a visual representation of the manner in which a pipelined command language or query operates in accordance with the disclosed embodiments.
  • the command or query 630 can be inputted by the user into a search field.
  • the query comprises a search, the results of which are piped to two commands (namely, command 1 and command 2) that follow the search step.
  • Disk 622 represents the event data in the raw record data store.
  • a search step will precede other queries in the pipeline in order to generate a set of events at block 640.
  • 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).
  • 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.
  • the second intermediate results table 626 shows fewer columns, representing the result of the top command,“top user” which summarizes the events into a list of the top 10 users and displays the user, count, and percentage.
  • 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.
  • the“fields - percent” part of command 630 removes the column that shows the percentage, thereby, leaving a final results table 628 without a percentage column.
  • other query languages such as the Structured Query Language (“SQL”), can be used to create a query.
  • each stage can correspond to a search phase or layer in a DAG. The processing performed in each stage can be handled by one or more partitions allocated to each stage.
  • the search head 210 allows users to search and visualize events generated from machine data received from homogenous data sources.
  • the search head 210 also allows users to search and visualize events generated from machine data received from heterogeneous data sources.
  • the search head 210 includes various mechanisms, which may additionally reside in an indexer 206, for processing a query.
  • a query language may be used to create a query, such as any suitable pipelined query language.
  • Splunk Processing Language SPL
  • 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.
  • SQL Structured Query Language
  • search head 210 uses extraction rules to extract values for fields in the events being searched.
  • the search head 210 obtains extraction rules that specify how to extract a value for fields from an event.
  • Extraction rules can comprise regex rules that specify how to extract values for the fields corresponding to the extraction rules.
  • 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.
  • the query itself can specify one or more extraction rules.
  • the search head 210 can apply the extraction rules to events that it receives from indexers
  • Indexers 206 may apply the extraction rules to events in an associated data store 208. Extraction rules can be applied to all the events in a data store or to a subset of the events that have been filtered based on some criteria (e.g., event time stamp values, etc.). Extraction rules can be used to extract one or more values for a field from events by parsing the portions of machine data in the events and examining the data for one or more patterns of characters, numbers, delimiters, etc., that indicate where the field begins and, optionally, ends.
  • 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.
  • a query coordinator or nodes use extraction rules to extract values for fields in the events being searched.
  • the query coordinator or nodes obtain extraction rules that specify how to extract a value for fields from an event, etc., and apply the extraction rules to events that it receives from indexers, common storage, ingested data buffers, query acceleration data stores, or other dataset sources.
  • FIG. 7A 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.
  • a user submits an order for merchandise using a vendor’s shopping application program 701 running on the user’s system.
  • the order was not delivered to the vendor’s server due to a resource exception at the destination server that is detected by the middleware code 702.
  • the user then sends a message to the customer support server 703 to complain about the order failing to complete.
  • the three systems 701, 702, and 703 are disparate systems that do not have a common logging format.
  • the order application 701 sends log data 704 to the data intake and query system in one format
  • the middleware code 702 sends error log data 705 in a second format
  • the support server 703 sends log data 706 in a third format.
  • the vendor can uniquely obtain an insight into user activity, user experience, and system behavior.
  • the search head 210 allows the vendor’ s administrator to search the log data from the three systems that one or more indexers 206 are responsible for searching, thereby obtaining correlated information, such as the order number and corresponding customer ID number of the person placing the order.
  • the system also allows the administrator to see a visualization of related events via a user interface. The administrator can query the search head 210 for customer ID field value matches across the log data from the three systems that are stored at the one or more indexers 206.
  • the customer ID field value exists in the data gathered from the three systems, but the customer ID field value may be located in different areas of the data given differences in the architecture of the systems. There is a semantic relationship between the customer ID field values generated by the three systems.
  • the search head 210 requests events from the one or more indexers 206 to gather relevant events from the three systems.
  • the search head 210 then applies extraction rules to the events in order to extract field values that it can correlate.
  • the search head may apply a different extraction rule to each set of events from each system when the event format differs among systems.
  • the user interface can display to the administrator the events corresponding to the common customer ID field values 707, 708, and 709, thereby providing the administrator with insight into a customer’s experience.
  • query results can be returned to a client, a search head, or any other system component for further processing.
  • query results may include a set of one or more events, a set of one or more values obtained from the events, a subset of the values, statistics calculated based on the values, a report containing the values, a visualization (e.g., a graph or chart) generated from the values, and the like.
  • the search system enables users to run queries against the stored data to retrieve events that meet criteria specified in a query, such as containing certain keywords or having specific values in defined fields.
  • FIG. 7B illustrates the manner in which keyword searches and field searches are processed in accordance with disclosed embodiments.
  • search bar 710 that includes only keywords (also known as
  • tokens e.g., the keyword“error” or“warning”
  • the query search engine of the data intake and query system searches for those keywords directly in the event data 711 of the events 713, 714, 715, 719 stored in the raw record data store.
  • FIG. 7B only illustrates four events, the raw record data store (which may to data store 208 in FIG. 2) may contain records for millions of events.
  • an indexer can optionally generate a keyword index to facilitate fast keyword searching for event data.
  • the indexer includes the identified keywords in an index, which associates each stored keyword with reference pointers to events containing that keyword (or to locations within events where that keyword is located, other location identifiers, etc.).
  • the indexer can access the keyword index to quickly identify events containing the keyword. For example, if the keyword‘ ⁇ TTR” was indexed by the indexer at index time, and the user searches for the keyword‘ ⁇ TTR”, events 713 to 715 will be identified based on the results returned from the keyword index.
  • 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.
  • the data intake and query system would nevertheless be able to retrieve the events by searching the event data for the keyword in the raw record data store directly as shown in FIG. 7B. For example, if a user searches for the keyword “frank”, and the name“frank” has not been indexed at index time, the DATA INTAKE AND QUERY system will search the event data directly and return the first event 713.
  • the search engine will need to search through all the records in the data store to service the search.
  • a user’s search will also include fields.
  • the term“field” refers to a location in the event data containing one or more values for a specific data item. Often, a field is a value with a fixed, delimited position on a line, or a name and value pair, where there is a single value to each field name. A field can also be multivalued, that is, it can appear more than once in an event and have a different value for each appearance, e.g., email address fields. Fields are searchable by the field name or field name-value pairs. Some examples of fields are“clientip” for IP addresses accessing a web server, or the“From” and“To” fields in email addresses.
  • This search query finds events with“status” fields that have a value of“404.”
  • the search engine does not look for events with any other“status” value. It also does not look for events containing other fields that share“404” as a value.
  • 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.
  • the data store may contain events where the“user_name” value always appears by itself after the timestamp as illustrated by the following string:“Nov 15 09:33:22 johnmedlock.”
  • the data intake and query system advantageously allows for search time field extraction.
  • 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.
  • search head 210 uses extraction rules to extract values for the fields associated with a field or fields in the event data being searched.
  • the search head 210 obtains extraction rules that specify how to extract a value for certain fields from an event.
  • Extraction rules can comprise regex rules that specify how to extract values for the relevant fields.
  • 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.
  • the query itself can specify one or more extraction rules.
  • the data intake and query system determines if the query references a“field.” For example, a query may request a list of events where the“clientip” field equals“127.0.0.1.” If the query itself does not specify an extraction rule and if the field is not a metadata field, e.g., time, host, source, source type, etc., then in order to determine an extraction rule, the search engine may, in one or more embodiments, need to locate configuration file 712 during the execution of the search as shown in FIG. 7B.
  • a“field For example, a query may request a list of events where the“clientip” field equals“127.0.0.1.” If the query itself does not specify an extraction rule and if the field is not a metadata field, e.g., time, host, source, source type, etc., then in order to determine an extraction rule, the search engine may, in one or more embodiments, need to locate configuration file 712 during the execution of the search as shown in FIG. 7B.
  • Configuration file 712 may contain extraction rules for all the various fields that are not metadata fields, e.g., the“clientip” field.
  • the extraction rules may be inserted into the configuration file in a variety of ways.
  • 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.
  • 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.
  • a user may be able to dynamically create custom fields by highlighting portions of a sample event that should be extracted as fields using a graphical user interface. The system would then generate a regular expression that extracts those fields from similar events and store the regular expression as an extraction rule for the associated field in the configuration file 712.
  • the search head 210 can apply the extraction rules derived from configuration file 1402 to event data that it receives from indexers 206.
  • Indexers 206 may apply the extraction rules from the configuration file to events in an associated data store 208.
  • Extraction rules can be applied to all the events in a data store, or to a subset of the events that have been filtered based on some criteria (e.g., event time stamp values, etc.).
  • Extraction rules can be used to extract one or more values for a field from events by parsing the event data and examining the event data for one or more patterns of characters, numbers, delimiters, etc., that indicate where the field begins and, optionally, ends.
  • the extraction rule in configuration file 712 will also need to define the type or set of events that the rule applies to. Because the raw record data store will contain events from multiple heterogeneous sources, multiple events may contain the same fields in different locations because of discrepancies in the format of the data generated by the various sources. Furthermore, certain events may not contain a particular field at all. For example, event 719 also contains“clientip” field, however, the“clientip” field is in a different format from events 713-715.
  • the configuration file will also need to specify the set of events that an extraction rule applies to, e.g., extraction rule 716 specifies a rule for filtering by the type of event and contains a regular expression for parsing out the field value. Accordingly, each extraction rule will pertain to only a particular type of event. If a particular field, e.g.,“clientip” occurs in multiple events, each of those types of events would need its own corresponding extraction rule in the configuration file 712 and each of the extraction rules would comprise a different regular expression to parse out the associated field value.
  • the most common way to categorize events is by source type because events generated by a particular source can have the same format.
  • the field extraction rules stored in configuration file 712 perform search-time field extractions. For example, for a query that requests a list of events with source type“access_combined” where the“clientip” field equals“127.0.0.1,” the query search engine would first locate the configuration file 712 to retrieve extraction rule 716 that would allow it to extract values associated with the“clientip” field from the event data 720“where the source type is“access_combined.
  • the query search engine can then execute the field criteria by performing the compare operation to filter out the events where the“clientip” field equals“127.0.0.1.” In the example shown in FIG. 7B, events 713-715 would be returned in response to the user query. In this manner, the search engine can service queries containing field criteria in addition to queries containing keyword criteria (as explained above).
  • the configuration file can be created during indexing. It may either be manually created by the user or automatically generated with certain predetermined field extraction rules. As discussed above, the events may be distributed across several indexers, wherein each indexer may be responsible for storing and searching a subset of the events contained in a corresponding data store. In a distributed indexer system, each indexer would need to maintain a local copy of the configuration file that is synchronized periodically across the various indexers.
  • the configuration file 712 allows the record data store to be field searchable.
  • the raw record data store can be searched using keywords as well as fields, wherein the fields are searchable name/value pairings that distinguish one event from another and can be defined in configuration file 1402 using extraction rules.
  • a keyword search does not need the configuration file and can search the event data directly as shown in FIG. 7B.
  • any events filtered out by performing a search-time field extraction using a configuration file can be further processed by directing the results of the filtering step to a processing step using a pipelined search language.
  • a user could pipeline the results of the compare step to an aggregate function by asking the query search engine to count the number of events where the“clientip” field equals“127.0.0.1.”
  • the data is stored in a dataset source, which may be an indexer (or data store controlled by an indexer) or may be a different type of dataset source, such as a common storage or external data source.
  • a query coordinator or node can request events from the indexers or other dataset source, apply extraction rules and correlate, automatically discover certain custom fields, etc., as described above.
  • FIG. 8A is an interface diagram of an example user interface for a search screen 800, in accordance with example embodiments.
  • Search screen 800 includes a search bar 802 that accepts user input in the form of a search string. It also includes a time range picker 812 that enables the user to specify a time range for the search. For historical searches (e.g., searches based on a particular historical time range), the user can select a specific time range, or alternatively a relative time range, such as“today,”“yesterday” or “last week.” For real-time searches (e.g., searches whose results are based on data received in real-time), the user can select the size of a time window to search for real-time events.
  • Search screen 800 also initially displays a“data summary” dialog as is illustrated in FIG. 8B that enables the user to select different sources for the events, such as by selecting specific hosts and log files.
  • search screen 800 in FIG. 8A can display the results through search results tabs 804, wherein search results tabs 804 includes: an“events tab” that displays various information about events returned by the search; a“statistics tab” that displays statistics about the search results; and a“visualization tab” that displays various visualizations of the search results.
  • search results tabs 804 includes: an“events tab” that displays various information about events returned by the search; a“statistics tab” that displays statistics about the search results; and a“visualization tab” that displays various visualizations of the search results.
  • the events tab illustrated in FIG. 8A displays a timeline graph 805 that graphically illustrates the number of events that occurred in one-hour intervals over the selected time range.
  • the events tab also displays an events list 808 that enables a user to view the machine data in each of the returned events.
  • the events tab additionally displays a sidebar that is an interactive field picker 806.
  • the field picker 806 may be displayed to a user in response to the search being executed and allows the user to further analyze the search results based on the fields in the events of the search results.
  • the field picker 806 includes field names that reference fields present in the events in the search results.
  • the field picker may display any Selected Fields 820 that a user has pre-selected for display (e.g., host, source, sourcetype) and may also display any Interesting Fields 822 that the system determines may be interesting to the user based on pre specified criteria (e.g., action, bytes, categoryid, clientip, date_hour, date_mday, date_minute, etc.).
  • the field picker also provides an option to display field names for all the fields present in the events of the search results using the All Fields control 824.
  • Each field name in the field picker 806 has a value type identifier to the left of the field name, such as value type identifier 826.
  • a value type identifier identifies the type of value for the respective field, such as an“a” for fields that include literal values or a“#” for fields that include numerical values.
  • Each field name in the field picker also has a unique value count to the right of the field name, such as unique value count 828.
  • the unique value count indicates the number of unique values for the respective field in the events of the search results.
  • Each field name is selectable to view the events in the search results that have the field referenced by that field name. For example, a user can select the“host” field name, and the events shown in the events list 808 will be updated with events in the search results that have the field that is reference by the field name“host.”
  • a data model is a hierarchically structured search-time mapping of semantic knowledge about one or more datasets. It encodes the domain knowledge used to build a variety of specialized searches of those datasets. Those searches, in turn, can be used to generate reports.
  • a data model is composed of one or more“objects” (or“data model objects”) that define or otherwise correspond to a specific set of data.
  • An object is defined by constraints and attributes.
  • An object’s constraints are search criteria that define the set of events to be operated on by running a search having that search criteria at the time the data model is selected.
  • An object’s attributes are the set of fields to be exposed for operating on that set of events generated by the search criteria.
  • Objects in data models can be arranged hierarchically in parent/child relationships. Each child object represents a subset of the dataset covered by its parent object.
  • the top-level objects in data models are collectively referred to as "root objects.”
  • Child objects have inheritance. Child objects inherit constraints and attributes from their parent objects and may have additional constraints and attributes of their own. Child objects provide a way of filtering events from parent objects. Because a child object may provide an additional constraint in addition to the constraints it has inherited from its parent object, the dataset it represents may be a subset of the dataset that its parent represents. For example, a first data model object may define a broad set of data pertaining to e- mail activity generally, and another data model object may define specific datasets within the broad dataset, such as a subset of the e-mail data pertaining specifically to e-mails sent.
  • 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.
  • 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.
  • 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.
  • 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. 8A) 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.
  • CIM 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. Patent Nos. 8,788,525 and 8,788,526, both entitled “DATA MODEL FOR MACHINE DATA FOR SEMANTIC SEARCH”, both issued on 22 July 2014, U.S. Patent No.
  • 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.
  • 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.
  • FIGS. 9-15 are interface diagrams of example report generation user interfaces, in accordance with example embodiments.
  • the report generation process may be driven by a predefined data model object, such as a data model object defined and/or saved via a reporting application or a data model object obtained from another source.
  • a user can load a saved data model object using a report editor.
  • 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.
  • 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 fields e.g., the fields defined by the data model object
  • criteria for a report e.g., filters, split rows/columns, aggregates, etc.
  • 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,
  • FIG. 9 illustrates an example interactive data model selection graphical user interface 900 of a report editor that displays a listing of available data models 901. The user may select one of the data models 902.
  • FIG. 10 illustrates an example data model object selection graphical user interface 1000 that displays available data objects 1001 for the selected data object model 902. The user may select one of the displayed data model objects 1002 for use in driving the report generation process.
  • 11A may display an interactive listing of automatic field identification options 1101 based on the selected data model object. For example, a user may select one of the three illustrated options (e.g., the“All Fields” option 1102, the“Selected Fields” option 1103, or the“Coverage” option (e.g., fields with at least a specified % of coverage) 1104). If the user selects the“All Fields” option 1102, all of the fields identified from the events that were returned in response to an initial search query may be selected. That is, for example, all of the fields of the identified data model object fields may be selected. If the user selects the“Selected Fields” option 1103, only the fields from the fields of the identified data model object fields that are selected by the user may be used.
  • the“All Fields” option 1102 the“Selected Fields” option 1103
  • the“Selected Fields” option 1103 only the fields from the fields of the identified data model object fields that are selected by the user may be used.
  • 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.
  • “97” displayed next to the“All Fields” option 1102 indicates that 97 fields will be selected if the“All Fields” option is selected.
  • The“3” displayed next to the“Selected Fields” option 1103 indicates that 3 of the 97 fields will be selected if the“Selected Fields” option is selected.
  • The“49” displayed next to the“Coverage” option 1104 indicates that 49 of the 97 fields (e.g., the 49 fields having a coverage of 2% or greater) will be selected if the“Coverage” option is selected.
  • the number of fields corresponding to the“Coverage” option may be dynamically updated based on the specified percent of coverage.
  • FIG. 11B illustrates an example graphical user interface screen 1105 displaying the reporting application’s“Report Editor” page.
  • the screen may display interactive elements for defining various elements of a report.
  • the page includes a“Filters” element 1106, a“Split Rows” element 1107, a“Split Columns” element 1108, and a“Column Values” element 1109.
  • the page may include a list of search results 1111.
  • the Split Rows element 1107 is expanded, revealing a listing of fields 1110 that can be used to define additional criteria (e.g., reporting criteria).
  • the listing of fields 1110 may correspond to the selected fields.
  • the listing of fields 1110 may list only the fields previously selected, either automatically and/or manually by a user.
  • FIG. 11C illustrates a formatting dialogue 1112 that may be displayed upon selecting a field from the listing of fields 1110.
  • the dialogue can be used to format the display of the results of the selection (e.g., label the column for the selected field to be displayed as“component”).
  • FIG. 11D illustrates an example graphical user interface screen 1105 including a table of results 1113 based on the selected criteria including splitting the rows by the“component” field.
  • a column 1114 having an associated count for each component listed in the table may be displayed that indicates an aggregate count of the number of times that the particular field-value pair (e.g., the value in a row for a particular field, such as the value“BucketMover” for the field“component”) occurs in the set of events responsive to the initial search query.
  • 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
  • FIG. 12 illustrates an example graphical user interface screen 1200 that allows the user to filter search results and to perform statistical analysis on values extracted from specific fields in the set of events.
  • the top ten product names ranked by price are selected as a filter 1201 that causes the display of the ten most popular products sorted by price.
  • Each row is displayed by product name and price 1202. This results in each product displayed in a column labeled“product name” along with an associated price in a column labeled“price” 1206.
  • Statistical analysis of other fields in the events associated with the ten most popular products have been specified as column values 1203.
  • a count of the number of successful purchases for each product is displayed in column 1204.
  • These statistics may be produced by filtering the search results by the product name, finding all occurrences of a successful purchase in a field within the events and generating a total of the number of occurrences.
  • a sum of the total sales is displayed in column 1205, which is a result of the multiplication of the price and the number of successful purchases for each product.
  • the reporting application allows the user to create graphical visualizations of the statistics generated for a report.
  • FIG. 13 illustrates an example graphical user interface 1300 that displays a set of components and associated statistics 1301.
  • the reporting application allows the user to select a visualization of the statistics in a graph (e.g., bar chart, scatter plot, area chart, line chart, pie chart, radial gauge, marker gauge, filler gauge, etc.), where the format of the graph may be selected using the user interface controls 1302 along the left panel of the user interface 1300.
  • FIG. 14 illustrates an example of a bar chart visualization 1400 of an aspect of the statistical data 1301.
  • FIG 15 illustrates a scatter plot visualization 1500 of an aspect of the statistical data 1301.
  • 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.
  • 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.
  • the data intake and query system also employs a number of unique acceleration techniques that have been developed to speed up analysis operations performed at search time.
  • nodes can perform any one or any combination of the search functions described herein. In some cases, the nodes perform the search functions based on instructions received from a query coordinator.
  • FIG. 16 is an example search query received from a client and executed by search peers, in accordance with example embodiments.
  • FIG. 16 illustrates how a search query 1602 received from a client at a search head 210 can split into two phases, including: (1) subtasks 1604 (e.g., data retrieval or simple filtering) that may be performed in parallel by indexers 206 for execution, and (2) a search results aggregation operation 1606 to be executed by the search head when the results are ultimately collected from the indexers.
  • subtasks 1604 e.g., data retrieval or simple filtering
  • a search head 210 determines that a portion of the operations involved with the search query may be performed locally by the search head.
  • the search head modifies search query 1602 by substituting“stats” (create aggregate statistics over results sets received from the indexers at the search head) with“prestats” (create statistics by the indexer from local results set) to produce search query 1604, and then distributes search query 1604 to distributed indexers, which are also referred to as“search peers” or“peer indexers.”
  • 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.
  • the search head may distribute the full search query to the search peers as illustrated in FIG. 6A, or may alternatively distribute a modified version (e.g., a more restricted version) of the search query to the search peers.
  • the indexers are responsible for producing the results and sending them to the search head. After the indexers return the results to the search head, the search head aggregates the received results 1606 to form a single search result set. By executing the query in this manner, the system effectively distributes the computational operations across the indexers while minimizing data transfers.
  • the data is stored in one or more dataset sources, such as, but not limited to an indexer (or data store controlled by an indexer), common storage, external data source, ingested data buffer, query acceleration data store, etc.
  • dataset sources such as, but not limited to an indexer (or data store controlled by an indexer), common storage, external data source, ingested data buffer, query acceleration data store, etc.
  • a query coordinator can aggregate results from multiple indexers or nodes, perform an aggregation operation 1606, determine what, if any, portion of the operations of the search query are to be performed locally by the query coordinator, modify or translate a search query for an indexer or other dataset source, distribute the query to indexers, peers, or nodes, etc.
  • data intake and query system 108 can construct and maintain one or more keyword indices to quickly identify events containing specific keywords. This technique can greatly speed up the processing of queries involving specific keywords.
  • an indexer first identifies a set of keywords. Then, the indexer includes the identified keywords in an index, which associates each stored keyword with references to events containing that keyword, or to locations within events where that keyword is located. When an indexer subsequently receives a keyword-based query, the indexer can access the keyword index to quickly identify events containing the keyword.
  • a node or other components of the system that performs search operations can use the keyword index to identify events, etc.
  • system 108 creates 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.
  • 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.
  • 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.
  • the system can maintain a separate summarization table for each indexer.
  • the indexer- specific summarization table includes entries for the events in a data store that are managed by the specific indexer. Indexer-specific summarization tables may also be bucket-specific.
  • the summarization table can be populated by running a periodic query that scans a set of events to find instances of a specific field-value combination, or alternatively instances of all field-value combinations for a specific field.
  • a periodic query can be initiated by a user, or can be scheduled to occur automatically at specific time intervals.
  • a periodic query can also be automatically launched in response to a query that asks for a specific field-value combination.
  • 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. Patent No. 8,682,925, entitled“DISTRIBUTED HIGH PERFORMANCE ANALYTICS STORE”, issued on 25 March 2014, U.S. Patent No.
  • system 108 creates 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.
  • a“summarization table” also referred to as a“lexicon” or“inverted index”
  • the search engine will employ the inverted index separate from the raw record data store to generate responses to the received queries.
  • the term“summarization table” or“inverted index” as used herein is a data structure that may be generated by an indexer that includes at least field names and field values that have been extracted and/or indexed from event records.
  • An inverted index may also include reference values that point to the location(s) in the field searchable data store where the event records that include the field may be found.
  • an inverted index may be stored using well-known compression techniques to reduce its storage size.
  • 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.
  • 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.
  • the reference values may be ordered based on each event record's timestamp.
  • 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.
  • an inverted index is generated in response to a user-initiated collection query.
  • selection query 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.
  • 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.
  • a query can comprise an initial step that calls up a pre -generated inverted index on which further filtering and processing can be performed. For example, referring back to FIG. 13, a set of events generated at block 1320 by either using a“collection” query to create a new inverted index or by calling up a pre-generated inverted index. A query with several pipelined steps will start with a pre-generated index to accelerate the query.
  • FIG. 7C illustrates the manner in which an inverted index is created and used in accordance with the disclosed embodiments.
  • an inverted index 722 can be created in response to a user-initiated collection query using the event data 723 stored in the raw record data store.
  • Each entry in the inverted index 722 includes an event reference value that references the location of a source record in the field searchable data store. The reference value may be used to access the original event record directly from the field searchable data store.
  • the responsive indexers may generate summarization information based on the fields of the event records located in the field searchable data store.
  • 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.
  • a collection query may include an explicit list of fields to summarize.
  • a collection query may include terms or expressions that explicitly define the fields, e.g., using regex rules.
  • 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.
  • 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.
  • collection queries may be saved and scheduled to run periodically. These scheduled collection queries may periodically update the summarization information corresponding to the query. For example, if the collection query that generates inverted index 722 is scheduled to run periodically, one or more indexers would periodically search through the relevant buckets to update inverted index 722 with event data for any new events with the“clientip” value of“127.0.0.1.”
  • the inverted indexes that include fields, values, and reference value
  • inverted index 722 for event records may be included in the summarization information provided to the user.
  • 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. 7C rather than viewing the fields within inverted index 722, a user may want to generate a count of all client requests from IP address“127.0.0.1.” In this case, the search engine would simply return a result of“4” rather than including details about the inverted index 722 in the information provided to the user.
  • the pipelined search language e.g., SPL of the SPLUNK® ENTERPRISE system can be used to pipe the contents of an inverted index to a statistical query using the“stats” command for example.
  • a “stats” query refers to queries that generate result sets that may produce aggregate and statistical results from event records, e.g., average, mean, max, min, rms, etc. Where sufficient information is available in an inverted index, a“stats” query may generate their result sets rapidly from the summarization information available in the inverted index rather than directly scanning event records.
  • inverted index 722 can be pipelined to a stats query, e.g., a“count” function that counts the number of entries in the inverted index and returns a value of“4.”
  • a“count” function that counts the number of entries in the inverted index and returns a value of“4.”
  • inverted indexes may enable various stats queries to be performed absent scanning or search the event records. Accordingly, this optimization technique enables the system to quickly process queries that seek to determine how many events have a particular value for a particular field. To this end, the system can examine the entry in the inverted index 722 to count instances of the specific value in the field without having to go through the individual events or perform data extractions at search time.
  • 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.
  • the system can maintain a separate inverted index for each indexer.
  • the indexer-specific inverted index includes entries for the events in a data store that are managed by the specific indexer. Indexer-specific inverted indexes may also be bucket-specific.
  • each indexer may generate a partial result set from previously generated summarization information. The partial result sets may be returned to the search head that received the query and combined into a single result set for the query
  • 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.
  • summarization information is absent from an indexer that includes responsive event records, further actions may be taken, such as, the summarization information may generated on the fly, warnings may be provided the user, the collection query operation may be halted, the absence of summarization information may be ignored, or the like, or combination thereof.
  • an inverted index may be set up to update continually.
  • the query may ask for the inverted index to update its result periodically, e.g., every hour.
  • the inverted index may be a dynamic data structure that is regularly updated to include information regarding incoming events.
  • the system can use the inverted index to obtain partial results for the events that are covered by inverted index, but may also have to search through other events that are not covered by the inverted index to produce additional results on the fly.
  • an indexer would need to search through event data on the data store to supplement the partial results.
  • additional results can then be combined with the partial results to produce a final set of results for the query. Note that in typical instances where an inverted index is not completely up to date, the number of events that an indexer would need to search through to supplement the results from the inverted index would be relatively small.
  • the inverted indexes can be made available, as part of a common storage, to nodes or other components of the system that perform search operations.
  • 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.
  • 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.
  • a user would include syntax in the query that explicitly directs the initial searching or filtering step to the inverted index.
  • the user can generate a query that explicitly directs or pipes the contents of the already generated inverted index 722 to another filtering step requesting the user ids for the entries in inverted index 722 where the server response time is greater than“0.0900” microseconds.
  • the search engine would use the reference values stored in inverted index 722 to retrieve the event data from the field searchable data store, filter the results based on the“response time” field values and, further, extract the user id field from the resulting event data to return to the user.
  • the user ids“frank” and“matt” would be returned to the user from the generated results table 725.
  • the same methodology can be used to pipe the contents of the inverted index to a processing step.
  • the user is able to use the inverted index 722 to efficiently and quickly perform aggregate functions on field values that were not part of the initially generated inverted index.
  • 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.”
  • the search engine would again use the reference values stored in inverted index 722 to retrieve the event data from the field searchable data store and, further, extract the object size field values from the associated events 731, 732, 733 and 734.
  • the SPLUNK® ENTERPRISE system can be configured to automatically determine if any prior-generated inverted index can be used to expedite a user query.
  • the user’s query may request the average object size (size of the requested gif) requested by clients from IP address“127.0.0.1.” without any reference to or use of inverted index 722.
  • the search engine in this case, would automatically determine that an inverted index 722 already exists in the system that could expedite this query.
  • a search engine may search though all the existing inverted indexes to determine if a pre generated inverted index could be used to expedite the search comprising the field-value pair. Accordingly, the search engine would automatically use the pre-generated inverted index, e.g., inverted index 722 to generate the results 725 without any user-involvement that directs the use of the inverted index.
  • the data intake and query system includes one or more forwarders that receive raw machine data from a variety of input data sources, and one or more indexers that process and store the data in one or more data stores. By distributing events among the indexers and data stores, the indexers can analyze events for a query in parallel.
  • a multiple indexer implementation of the search system would maintain a separate and respective inverted index for each of the above -described time-specific buckets that stores events for a specific time range.
  • a bucket-specific inverted index includes entries for specific field- value combinations that occur in events in the specific bucket.
  • a search head would be able to correlate and synthesize data from across the various buckets and indexers.
  • This feature advantageously expedites searches because instead of performing a computationally intensive search in a centrally located inverted index that catalogues all the relevant events, an indexer is able to directly search an inverted index stored in a bucket associated with the time -range specified in the query. This allows the search to be performed in parallel across the various indexers. Further, if the query requests further filtering or processing to be conducted on the event data referenced by the locally stored bucket-specific inverted index, the indexer is able to simply access the event records stored in the associated bucket for further filtering and processing instead of needing to access a central repository of event records, which would dramatically add to the computational overhead.
  • the system can use the bucket-specific inverted index to obtain partial results for the events that are covered by bucket-specific inverted index, but may also have to search through the event data in the bucket associated with the bucket-specific inverted index to produce additional results on the fly.
  • an indexer would need to search through event data stored in the bucket (that was not yet processed by the indexer for the corresponding inverted index) to supplement the partial results from the bucket-specific inverted index.
  • FIG. 7D presents a flowchart illustrating how an inverted index in a pipelined search query can be used to determine a set of event data that can be further limited by filtering or processing in accordance with the disclosed embodiments.
  • a query is received by a data intake and query system.
  • the query can be receive as a user generated query entered into search bar of a graphical user search interface.
  • the search interface also includes a time range control element that enables specification of a time range for the query.
  • an inverted index is retrieved.
  • the inverted index can be retrieved in response to an explicit user search command inputted as part of the user generated query.
  • the search engine can be configured to automatically use an inverted index if it determines that using the inverted index would expedite the servicing of the user generated query.
  • Each of the entries in an inverted index keeps track of instances of a specific value in a specific field in the event data and includes references to events containing the specific value in the specific field.
  • the search engine will employ the inverted index separate from the raw record data store to generate responses to the received queries.
  • the query engine determines if the query contains further filtering and processing steps. If the query contains no further commands, then, in one embodiment, summarization information can be provided to the user at block 754.
  • the query engine determines if the commands relate to further filtering or processing of the data extracted as part of the inverted index or whether the commands are directed to using the inverted index as an initial filtering step to further filter and process event data referenced by the entries in the inverted index. If the query can be completed using data already in the generated inverted index, then the further filtering or processing steps, e.g., a“count” number of records function,“average” number of records per hour etc. are performed and the results are provided to the user at block 752.
  • the further filtering or processing steps e.g., a“count” number of records function,“average” number of records per hour etc. are performed and the results are provided to the user at block 752.
  • the indexers will access event data pointed to by the reference values in the inverted index to retrieve any further information required at block 756. Subsequently, any further filtering or processing steps are performed on the fields extracted directly from the event data and the results are provided to the user at step 758.
  • these functions can be performed by another component of the system, such as a query coordinator or node. For example, nodes can use inverted indexes to identify relevant data, etc. The inverted indexes can be stored with buckets in a common storage, etc.
  • a data server system such as the data intake and query system can accelerate the process of periodically generating updated reports based on query results.
  • a summarization engine automatically examines the query to determine whether generation of updated reports can be accelerated by creating intermediate summaries. If reports can be accelerated, the summarization engine periodically generates a summary covering data obtained during a latest non overlapping time period. For example, where the query seeks events meeting a specified criteria, a summary for the time period includes only events within the time period that meet the specified criteria. Similarly, if the query seeks statistics calculated from the events, such as the number of events that match the specified criteria, then the summary for the time period includes the number of events in the period that match the specified criteria.
  • the summarization engine schedules the periodic updating of the report associated with the query. During each scheduled report update, the query engine determines whether intermediate summaries have been generated covering portions of the time period covered by the report update. If so, then the report is generated based on the information contained in the summaries. Also, if additional event data has been received and has not yet been summarized, and is required to generate the complete report, the query can be run on these additional events. Then, the results returned by this query on the additional events, along with the partial results obtained from the intermediate summaries, can be combined to generate the updated report. This process is repeated each time the report is updated.
  • the data intake and query system provides various schemas, dashboards, and visualizations that simplify developers’ tasks to create applications with additional capabilities.
  • One such application is the an enterprise security application, such as SPLUNK® ENTERPRISE SECURITY, which performs monitoring and alerting operations and includes analytics to facilitate identifying both known and unknown security threats based on large volumes of data stored by the data intake and query system.
  • the enterprise security application provides the security practitioner with visibility into security-relevant threats found in the enterprise infrastructure by capturing, monitoring, and reporting on data from enterprise security devices, systems, and applications.
  • the enterprise security application provides a top-down and bottom-up view of an organization's security posture.
  • the enterprise security application leverages the data intake and query system search-time normalization techniques, saved searches, and correlation searches to provide visibility into security-relevant threats and activity and generate notable events for tracking.
  • the enterprise security application enables the security practitioner to investigate and explore the data to find new or unknown threats that do not follow signature-based patterns.
  • SIEM Security Information and Event Management
  • 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.
  • 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.
  • this security-related information can include any information that can be used to identify security threats.
  • 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.
  • IP addresses IP addresses
  • domain names domain names
  • asset identifiers network traffic volume
  • uniform resource locator strings uniform resource locator strings
  • 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.
  • 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.
  • 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.
  • FIG. 17A illustrates an example key indicators view 1700 that comprises a dashboard, which can display a value 1701, for various security-related metrics, such as malware infections 1702. It can also display a change in a metric value 1703, which indicates that the number of malware infections increased by 63 during the preceding interval.
  • Key indicators view 1700 additionally displays a histogram panel 1704 that displays a histogram of notable events organized by urgency values, and a histogram of notable events organized by time intervals.
  • This key indicators view is described in further detail in pending U.S. Patent Application No. 13/956,338, entitled“KEY INDICATORS VIEW”, filed on 31 July 2013, and which is hereby incorporated by reference in its entirety for all purposes.
  • FIG. 17B illustrates an example incident review dashboard 1710 that includes a set of incident attribute fields 1711 that, for example, enables a user to specify a time range field 1712 for the displayed events. It also includes a timeline 1713 that graphically illustrates the number of incidents that occurred in time intervals over the selected time range.
  • 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.
  • the data intake and query platform provides various features that simplify the developers’ 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.
  • Machine-generated data such as performance information and log data obtained from the data center.
  • 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.
  • the rest of the data is not saved and discarded during pre-processing.
  • 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.
  • this performance-related information can include values for performance metrics obtained through an application programming interface (API) provided as part of the vSphere HypervisorTM system distributed by VMware, Inc. of Palo Alto, California.
  • API application programming interface
  • 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.
  • 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.
  • 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. 17C, wherein nodes 1733 and 1734 are selectively expanded.
  • nodes 1731-1739 can be displayed using different patterns or colors to represent different performance states, such as a critical state, a warning state, a normal state or an unknown/offline state.
  • the ease of navigation provided by selective expansion in combination with the associated performance-state information enables a user to quickly diagnose the root cause of a performance problem.
  • the proactive monitoring tree is described in further detail in U.S. Patent No. 9,185,007, entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATE SORTING”, issued on 10 November 2015, and U.S. Patent No. 9,426,045, also entitled“PROACTIVE MONITORING TREE WITH SEVERITY STATE SORTING”, issued on 23 August 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.
  • a user For example, the screen illustrated in FIG. 17D 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.
  • a user is able to operate pull-down menus 1742 to selectively display different performance metric graphs for the selected time range. This enables the user to correlate trends in the performance-metric graph with corresponding event and log data to quickly determine the root cause of a performance problem.
  • This user interface is described in more detail in U.S.
  • 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.
  • An IT monitoring application such as SPLUNK® IT SERVICE INTELLIGENCETM, which performs monitoring and alerting operations.
  • the IT monitoring application also includes analytics to help an analyst diagnose the root cause of performance problems based on large volumes of data stored by the data intake and query system as correlated to the various services an IT organization provides (a service-centric view). This differs significantly from conventional IT monitoring systems that lack the infrastructure to effectively store and analyze large volumes of service -related events.
  • Traditional service monitoring systems typically use fixed schemas to extract data from pre -defined fields at data ingestion time, wherein the extracted data is typically stored in a relational database. This data extraction process and associated reduction in data content that occurs at data ingestion time inevitably hampers future investigations, when all of the original data may be needed to determine the root cause of or contributing factors to a service issue.
  • 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.
  • the IT monitoring application enables a user to define an IT operations infrastructure from the perspective of the services it provides.
  • 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.
  • KPI Key Performance Indicators
  • Each KPI measures an aspect of service performance at a point in time or over a period of time (aspect KPI’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's of the service to indicate an overall health score for the service.
  • the IT monitoring application facilitates the production of meaningful aggregate KPI'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.
  • the IT monitoring application implements a translation of individual KPI values to a common domain of“state” values.
  • 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.
  • KPI values 95-100 may be set to correspond to‘critical’ in the state domain.
  • KPI values from disparate KPI'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'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'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.
  • 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, California.
  • ITSM IT service management
  • SPLUNK® IT SERVICE INTELLIGENCETM 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).
  • NOC network operations center
  • the interface may prominently display a services health section with tiles for the aggregate KPI’s indicating overall health for defined services and a general KPI section with tiles for KPI'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'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.
  • 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.
  • the capabilities of a data intake and query system are typically limited to resources contained within that system.
  • the data intake and query system has search and analytics capabilities that are limited in scope to the indexers responsible for storing and searching a subset of events contained in their corresponding internal data stores.
  • the data intake and query system typically has limited capabilities to process the combination of partial search results from the indexers and external data sources to produce comprehensive search results.
  • the search head of a data intake and query system may retrieve partial search results from external data systems over a network.
  • the search head may also retrieve partial results from its indexers, and combine those partial search results with the partial results of the external data sources to produce final results for a query.
  • the search head can implement map-reduce techniques, where each data source returns partial search results and the search head can combine the partial search results to produce the final results of a query.
  • Flowever obtaining results in this manner from distributed data systems including internal data stores and external data stores has limited value because the search head can act as a bottleneck for processing complex search queries on distributed data systems.
  • the bottleneck effect at the search head worsens as the number of distributed data systems increases.
  • the search head 210 and the indexers 206 can act as bottlenecks due to the number of queries received by the data intake and query system 108 and the amount of processing done by the indexers during data ingestion, indexing, and search.
  • Embodiments of the disclosed data fabric service (DFS) system overcome the aforementioned drawbacks by expanding on the capabilities of a data intake and query system to enable application of a query across distributed data systems, which may also be referred to as dataset sources, including internal data stores coupled to indexers (illustrated in FIG. 33), external data stores coupled to the data intake and query system over a network (illustrated in FIGS. 33, 46, 48), common storage (illustrated in FIGS. 46, 48), query acceleration data stores (e.g., query acceleration data store 3308 illustrated in FIGS. 33, 46, 48), ingested data buffers (illustrated in FIG. 48) that include ingested streaming data.
  • the disclosed embodiments are scalable to accommodate application of a query on a growing number of diverse data systems.
  • the disclosed DFS system extends the capabilities of the data intake and query system and mitigates the bottleneck effect at the search head by including one or more query coordinators communicatively coupled to worker nodes distributed in a big data ecosystem.
  • the worker nodes can be communicatively coupled to the various dataset sources (e.g., indexers, common storage, external data systems that contain external data stores, ingested data buffers, query acceleration data stores, etc.)
  • the data intake and query system can receive a query input by a user at a client device via a search head.
  • the search head can coordinate with a search process master and/or one or more query coordinators (the search process master and query coordinators can collectively referred to as a search process service) to execute a search scheme applied to one or more dataset sources (e.g., indexers, common storage, ingested data buffer, query acceleration data store, external data stores, etc.).
  • the worker nodes can collect, process, and aggregate the partial results from the dataset sources, and transfer the aggregate results to a query coordinator.
  • the query coordinator can operate on the aggregate results, and send finalized results to the search head, which can render the results of the query on a display device.
  • the search head in conjunction with the search process master and query coordinator(s) can apply a query to any one or more of the distributed dataset sources.
  • the worker nodes can act in accordance with the instructions received by a query coordinator to obtain relevant datasets from the different dataset sources, process the datasets, aggregate the partial results of processing the different datasets, and communicate the aggregated results to the query coordinator, or elsewhere.
  • the search head of the data intake and query system can offload at least some query processing to the query coordinator and worker nodes, to both obtain the datasets from the dataset sources and aggregate the results of processing the different datasets. This system is scalable to accommodate any number of worker nodes communicatively coupled to any number and types of data sources.
  • embodiments of the DFS system can extend the capabilities of a data intake and query system by leveraging computing assets from anywhere in a big data ecosystem to collectively execute queries on diverse data systems regardless of whether data stores are internal of the data intake and query system and/or external data stores that are communicatively coupled to the data intake and query system over a network.
  • FIG. 18 is a system diagram illustrating a DFS system architecture in which an embodiment may be implemented.
  • the DFS system 200 includes a data intake and query system 202 communicatively coupled to a network of distributed components that collectively form a big data ecosystem.
  • the data intake and query system 202 may include the components of data intake and query systems discussed above including any combination of forwarders, indexers, data stores, and a search head.
  • the data intake and query system 202 is illustrated with fewer components to aid in understanding how the disclosed embodiments extend the capabilities of data intake and query systems to apply search queries and analytics operations on distributed data systems including internal data systems (e.g., indexers with associated data stores) and/or external data systems in a big data ecosystem.
  • the data intake and query system 202 includes a search head 210 communicatively coupled to multiple peer indexers 206 (also referred to individually as indexer 206). Each indexer 206 is responsible for storing and searching a subset of events contained in a corresponding data store (not shown).
  • the peer indexers 206 can analyze events for a search query in parallel. For example, each indexer 206 can return partial results in response to a search query as applied by the search head 210.
  • the disclosed technique expands the capabilities of the data intake and query system 202 to obtain and harmonize search results from external data sources 209, alone or in combination with the partial search results of the indexers 206. More specifically, the data intake and query system 202 runs various processes to apply a search query to the indexers 206 as well as external data sources 209.
  • a daemon 211 of the data intake and query system 202 can operate as a background process that coordinates the application of a search query on the indexers and/or the external data stores.
  • the daemon 211 includes software components for the search head 210 and indexers 206 to interface with a DFS master 212 and a distributed network of worker nodes 214.
  • the worker nodes 214 may be considered external to the data intake and query system 202. In certain embodiments, the worker nodes 214 may be considered part of the data intake and query system 202.
  • the DFS master 212 is communicatively coupled to the search head 210 via the daemon 211-
  • the DFS master 212 can include software components running on a device of any system, including the data intake and query system 202. As such, the DFS master 212 can include software and underlying logic for establishing a logical connection to the search head 210 when external data systems need to be searched.
  • the DFS master 212 is part of the DFS search service (“search service”) that includes a search service provider 216 (also referred to as a query coordinator), which interfaces with the worker nodes 214.
  • the search head 210 can interact with the search service residing on the same machine or on different machines.
  • the search head 210 can dispatch requests for search queries to the DFS master 212, which can spawn search service providers 216 of the search service for each search query.
  • Other functions of the search service provider 216 can include providing data isolation across different searches based on role/access control, as well as fault tolerance (e.g., localized to a search head). For example, if a search operation fails, then its spawned search service provider may fail but other search service providers for other searches can continue to operate.
  • the search head 210 can analyze a query and determine that the DFS system 200 can execute the query. Accordingly, the search head 210 can send the query to the query master 212, which can send it to, or spawn, a search service provider 216.
  • the search service provider can define a search scheme in response to a received search query that requires searching both the indexers 206 and the external data sources 209. A portion of the search scheme can be applied 2l0to the indexers 206 and another portion of the search scheme can be communicated to the worker nodes 214 for application to the external data sources 209.
  • the search service provider 216 can collect an aggregate of partial search results of the indexers 206 and of the external data sources 209 from the worker nodes 214, and communicate the aggregate partial search results to the search head 210.
  • the DFS master 212, search head 210, or the worker nodes 214 can produce the final search results, which the search head 210 can cause to be presented on a user interface of a display device.
  • the worker nodes 214 can act as agents of the DFS master 212 via the search service provider 216, which can act on behalf of the search head 210 to apply a search query to distributed data systems.
  • the DFS master 212 can manage different search operations and balance workloads in the DFS system 200 by keeping track of resource utilization while the search service provider 216 is responsible for executing search operations and obtaining the search results.
  • the search service provider 216 can cause the worker nodes 214 to apply a search query to the external data sources 209.
  • the search service provider 216 can also cause the worker nodes 214 to collect the partial search results from the indexers 206 and/or the external data sources 209 over the computer network.
  • the search service provider 216 can cause the worker nodes 214 to aggregate the partial search results collected from the indexers 206 and/or the external data sources 209.
  • the search head 210 can offload at least some processing to the worker nodes 214 because the distributed worker nodes 214 can extract partial search results from the external data sources 209, and collect the partial search results of the indexers 206 and the external data sources 209. Moreover, the worker nodes 214 can aggregate the partial search results collected from the diverse data systems and transfer them to the search service, which can finalize the search results and send them to the search head 210. Aggregating the partial search results of the diverse data systems can include combining partial search results, arranging the partial search results in an ordered manner, and/or performing operations derive other search results from the collected partial search results (e.g., transform the partial search results).
  • control and data flows can traverse the components of the DFS system 200.
  • the control flow can include instructions from the DFS master 212 to the worker nodes 214 to carry out the operations detailed further below.
  • the data flow can include aggregate partial search results transferred to the search service provider 216 from the worker nodes 214.
  • the partial search results of the indexers 206 can be transferred by peer indexers to the worker nodes 214 in accordance with a parallel export technique. A more detailed description of the control flow, data flow, and parallel export techniques are provided further below.
  • the DFS system 200 can use a redistribute operator of a data intake and query system.
  • the redistribute operator can distribute data in a sharded manner to the different worker nodes 214.
  • Use of the redistribute operator may be more efficient than the parallel exporting because it is closely coupled to the existing data intake and query system.
  • the parallel exporting techniques have capabilities to interoperate with open source systems other than the worker nodes 214. Hence, use of the redistribute operator can provide greater efficiency but less interoperability and flexibility compared to using parallel export techniques.
  • the worker nodes 214 can be communicatively coupled to each other, and to the external data sources 209.
  • Each worker node 214 can include one or more software components or modules 218 (“modules”) operable to carry out the functions of the DFS system 200 by communicating with the search service provider 216, the indexers 206, and the external data sources 209.
  • the modules 218 can run on a programming interface of the worker nodes 214.
  • An example of such an interface is APACHE SPARK, which is an open source computing framework that can be used to execute the worker nodes 214 with implicit parallelism and fault-tolerance.
  • SPARK includes an application programming interface (API) centered on a data structure called a resilient distributed dataset (RDD), which is a read-only multiset of data items distributed over a cluster of machines (e.g., the devices running the worker nodes 214).
  • RDD resilient distributed dataset
  • the RDDs function as a working set for distributed programs that offer a form of distributed shared memory.
  • the search service provider 216 can act as a manager of the worker nodes 214, including their distributed data storage systems, to extract, collect, and store partial search results via their modules 218 running on a computing framework such as SPARK.
  • a computing framework such as SPARK.
  • the embodiments disclosed herein are not limited to an implementation that uses SPARK. Instead, any open source or proprietary computing framework running on a computing device that facilitates iterative, interactive, and/or exploratory data analysis coordinated with other computing devices can be employed to run the modules 218 for the DFS master 212 to apply search queries to the distributed data systems.
  • the worker nodes 214 can harmonize the partial search results of a distributed network of data storage systems, and provide those aggregated partial search results to the search service provider 216.
  • the search service provider 216 or DFS master 212 can further operate on the aggregated partial search results to obtain final results that are communicated to the search head 210, which can output the search results as reports or visualizations on a display device.
  • the DFS system 200 is scalable to accommodate any number of worker nodes 214.
  • the DFS system can scale to accommodate any number of distributed data systems upon which a search query can be applied and the search results can be returned to the search head and presented in a concise or comprehensive way for an analyst to obtain insights into big data that is greater in scope and provides deeper insights compared to existing systems.
  • FIG. 19 is an operation flow diagram illustrating an example of an operation flow of the DFS system 200.
  • the operation flow 2100 includes control flows and data flows of the data intake and query system 202, the DFS master 212 and/or the search service provider 216 (the DFS master 212 and search service provider 216 collectively the“search service 220”), one or more worker nodes 214, and/or one or more external data sources 209.
  • a combination of the search service 220 and the worker nodes 214 collectively enable the data fabric services that can be implemented on the distributed data systems including, for example, the data intake and query system 202 and the external data sources 209.
  • the search head 210 of the data intake and query system 202 receives a search query.
  • a search query For example, an analyst may submit a search query to the search head 210 over a network from an application (e.g., web browser) running on a client device, through a network portal (e.g., website) administered by the data intake and query system 202.
  • the search head 210 may receive the search query in accordance with a schedule of search queries.
  • the search query can be expressed in a variety of languages such as a pipeline search language, a structured query language, etc.
  • the search head 210 processes the search query to determine whether the DFS system 200 is to handle the search query. In some embodiments, if the search query only requires searching the indexers 206, the search head 210 can conduct the search on the indexers 206 by using, for example, map- reduce techniques without invoking or engaging the DFS system. In some embodiments, however, the search head 210 can invoke or engage the DFS system to utilize the worker nodes 214 to search the indexers 206 alone, search the external data sources 209 alone, or search both and harmonize the partial search results of the indexers 206 alone, and return the search results to the search head 210 via the search service 220.
  • search head 210 determines that the DFS system 200 is to handle the search query, then the search head 210 can invoke and engage the DFS system 200. Accordingly, in some embodiments, the search head 210 can engage the search service 220 when a search query is to be applied to at least one external data system, such as a combination of the indexers 206 and at least one of the external data sources 209, or is otherwise to be handled by the DFS system 200. 2lOThe search head 210 can pass search query to the DFS master 212, which can create (e.g., spawn) a search service provider (e.g., search service provider 216) to conduct the search.
  • a search service provider e.g., search service provider 216
  • the DFS system 200 can be launched by using a modular input, which refers to a platform add-on of the data intake and query system 202 that can be accessed in a variety of ways such as, for example, over the Internet on a network portal.
  • a modular input can be used to launch the search service 220 and worker nodes 214 of the DFS system 200.
  • a modular input can be used to launch a monitor function used to monitor nodes of the DFS system.
  • the monitor In the event that a launched service or node fails, the monitor allows the search head to detect the failed service or node, and re-launch the failed service or node or launch or reuse another launched service or node to provide the functions of the failed service or node.
  • the monitor function for monitoring nodes can be launched and controlled by the search service provider 216.
  • the search head 210 executes a search phase generation process to define a search scheme based on the scope of the search query.
  • the search phase generation process involves an evaluation of the scope of the search query to define one or more phases to be executed by the data intake and query system 202 and/or the DFS system, to obtain search results that would satisfy the search query.
  • the search phases, or layers may include a combination of phases for initiating search operations, searching the indexers 206, searching the external data sources 209, and/or finalizing search results for return back to the search head 210.
  • the combination of search phases can include phases for operating on the partial search results retrieved from the indexers 206 and/or the external data sources 209.
  • a search phase may require correlating or combining partial search results of the indexers 206 and/or the external data sources 209.
  • a combination of phases may be ordered as a sequence that requires an earlier phase to be completed before a subsequent phase can begin.
  • the disclosure is not limited to any combination or order of search phases.
  • a search scheme can include any number of search phases arranged in any order that could be different from another search scheme applied to the same or another arrangement or subset of data systems.
  • a first search phase may be executed by the search head 210 to extract partial search results from the indexers 206.
  • a second search phase may be executed by the worker nodes 214 to extract and collect partial search results from the external data sources 209.
  • a third search phase may be executed by the indexers 206 and worker nodes 214 to export partial search results in parallel to the worker nodes 214 from the (peer) indexers 206. As such, the third phase involves collecting the partial search results from the indexers 206 by the worker nodes 214.
  • a fourth search phase may be executed by the worker nodes 214 to aggregate (e.g., combine and/or operate on) the partial search results of the indexers 206 and/or the worker nodes 214.
  • a sixth and seventh phase may involve transmitting the aggregate partial search results to the search service220, and operating on the aggregate partial search results to produce final search results, respectively.
  • the search results can then be transmitted to the search head 210.
  • an eighth search phase may involve further operating on the search results by the search head 210 to obtain final search results that can be, for example, rendered on a user interface of a display device.
  • the search head 210 initiates a communications search protocol that establishes a logical connection with the worker nodes 214 via the search service 220.
  • the search head 210 may communicate information to the search service 220 including a portion of the search scheme to be performed by the worker nodes 214.
  • a portion of the search scheme transmitted to the DFS master 212 may include search phase(s) to be performed by the DFS master 212 and the worker nodes 214.
  • the information may also include specific control information enabling the worker nodes 214 to access the indexers 206 as well as the external data sources 209 subject to the search query.
  • the search service 220 can define an executable search process performed by the search service 220
  • the DFS master 212 or the search service provider 216 can define a search process as a logical directed acyclic graph (DAG) based on the search phases included in the portion of the search scheme received from the search head 210.
  • DAG logical directed acyclic graph
  • the DAG includes a finite number of vertices and edges, with each edge directed from one vertex to another, such that there is no way to start at any vertex and follow a consistently-directed sequence of edges that eventually loops back to the same vertex.
  • the DAG can be a directed graph that defines a topological ordering of the search phases performed by the DFS system.
  • a sequence of the vertices represents a sequence of search phases such that every edge is directed from earlier to later in the sequence of search phases.
  • the DAG may be defined based on a search string for each phase or metadata associated with a search string.
  • the metadata may be indicative of an ordering of the search phases such as, for example, whether results of any search string depend on results of another search string such that the later search string must follow the former search string sequentially in the DAG.
  • step 2110 the search head 210 starts executing local search phases that operate on the indexers 206 if the search query requires doing so. If the scope of the search query requires searching at least one external data system, then, in step 2112, the search head 210 sends information to the DFS master 212 triggering execution of the executable search process defined in step 2108.
  • step 2114 the search service 220 starts executing the search phases that cause the worker nodes 214 to extract partial search results from the external data stores 209 and collect the extracted partial search results at the worker nodes 214, respectively.
  • the search service 220 can start executing the search phases of the DAG that cause the worker nodes 214 to search the external data sources 209.
  • step 2116 the worker nodes 214 collect the partial search results extracted from the external data sources 209.
  • the search phases executed by the DFS system can also cause the worker nodes 214 to communicate with the indexers 206.
  • the search head 210 can commence a search phase that triggers a remote pipeline executed on the indexers 206 to export their partial search results to the worker nodes 214.
  • the worker nodes 214 can collect the partial search results of the indexers 206.
  • the search head 210 may bypass triggering the pipeline of partial search results from the indexers 206.
  • the worker nodes 214 can aggregate the partial search results and send them to the search service 220.
  • the search service provider 216 can begin collecting the aggregated search results from the worker nodes 214.
  • the aggregation of the partial search results may include combining the partial search results of indexers 206, the external data stores 209, or both.
  • the aggregated partial search results can be time-ordered or unordered depending on the requirements of the type of search query.
  • aggregation of the partial search results may involve performing one or more operations on a combination of partial search results.
  • the worker nodes 214 may operate on a combination of partial search results with an operator to output a value derived from the combination of partial search results.
  • This transformation may be required by the search query.
  • the search query may be an average or count of data events that include specific keywords.
  • the transformation may involve determining a correlation among data from different data sources that have a common keyword.
  • transforming the search results may involve creating new data derived from the partial search results obtained from the indexers 206 and/or external data sources 209.
  • step 2124 a data pipeline is formed to the search head 210 through the search service 220 once the worker nodes 214 have received the partial search results from the indexers 206 and the external data stores 209, and aggregated the partial search results (e.g., and transformed the partial search results).
  • the aggregate search received by the search service 220 may optionally be operated on to produce final search results.
  • the aggregate search results may include different statistical values of partial search results collected from different worker nodes 214.
  • the search service 220 may operate on those statistical values to produce search results that reflect statistical values of the statistical values obtained from the all the worker nodes 214.
  • the produced search results can be transferred in a big data pipeline to the search head 210.
  • the big data pipeline is essentially a pipeline of the data intake and query system 202 extended into the big data ecosystem.
  • the search results are transmitting to the search head 210 where the search query was received by a user.
  • the search head 210 can render the search results or data indicative of the search results on a display device.
  • the search head 210 can make the search results available for visualizing on a user interface rendered via a computer portal.
  • some operations can be performed by different components of the system.
  • some of the tasks described as being performed by the search head 210 can be performed by the search service 220, such as the search service provider 216.
  • step 2104 can be omitted and steps 2110, 2112, and 2118 can be performed by the search service provider 216.
  • the search head 210 upon receiving the search query at step 2102, the search head 210 can determine that the DFS system 200 will handle the query. Accordingly, at 2106, the search head can communicate the search query to the search service 220 to initiate the search.
  • the search service provider 216 can define the search scheme 2104 and search process 2108.
  • the search service provider 216 can determine whether any indexers 206 or external data sources 209 will be accessed. Once the scheme and process are defined, the search service provider 216 can trigger a search of the indexers (2110) and an external search of the external data sources (2112). The partial search results from both can be communicated to the worker nodes 214 for processing (2116, 2118), which can aggregate them together (2122). The results can then be provided to the search service 220 (2124), further processed (2126), and then communicated to the search head 210 for rendering for the client device (2128). In some cases, the further processing 2126 performed by the search service 220 can include additional transforms on the results received from the worker nodes 214 based on the query. Accordingly, in such an embodiment, the system can delegate some of the search head 210 processing to the search service 220, thereby freeing up the search head 210 to handle additional queries.
  • the disclosed embodiments include techniques for exporting partial search results in parallel from peer indexers of a data intake and query system to the worker nodes.
  • partial search results e.g., time-indexed events
  • exporting the partial search results from the peer indexers in parallel can improve the rate at which the partial search results are transferred to the worker nodes for subsequent combination with partial search results of the external data systems.
  • the rate at which the search results of a search query can be obtained from the distributed data system can be improved by implementing parallel export techniques.
  • FIG. 20 is an operation flow diagram illustrating an example of a parallel export operation performed in a DFS system according to some embodiments of the present disclosure.
  • the operation 2200 for parallel exporting of partial search results from peer indexers 206 begins by processing a search query that requires transferring of partial search results from the peer indexers 206 to the worker nodes 214.
  • the search head 210 receives a search query as, for example, input by a user of a client device.
  • the search head 210 processes the search query to determine whether internal data stores 222 of peer indexers 206 must be searched for partial search results. If so, in step 2206, the search head 210 executes a process to search the peer indexers 206 and retrieve the partial search results.
  • each peer indexer 206 can return its partial search results retrieved from respective internal data stores 222.
  • the partial search results (e.g., time -indexed events) obtained by the peer indexers 206 can be sharded into chunks of events (“event chunks”).
  • Sharding involves partitioning large data sets into smaller, faster, more easily managed parts called data shards.
  • the sharded partitions can be determined from policies, which can be based on hash values by default.
  • the retrieved events can be grouped into chunks (e.g., micro-batches) based on a value associated with a search query and/or the corresponding retrieved events.
  • the retrieved events can be sharded in chunks based on the field names passed as part of a search query process of the data intake and query system.
  • the parallel exporting technique can include a mechanism to reconstruct the ordering of event chunks at the worker nodes 214.
  • the order from which the event chunks flowed from peer indexers 206 can be tracked to enable collating the chunks in time order at the worker nodes 214.
  • metadata of event chunks can be preserved when parallel exporting such that the chunks can be collated by the worker nodes 214 that receive the event chunks.
  • Metadata examples include SearchResultsInfo (SRI) (a data structure of SPLUNK® which carries control and meta information for the search operations) or timestamps indicative of, for example, the times when respective events or event chunks started flowing out from the peer indexers 206. If time ordering is not required, preserving the time ordering of chunks by using timestamps may be unnecessary.
  • SRI SearchResultsInfo
  • SPLUNK® which carries control and meta information for the search operations
  • timestamps indicative of, for example, the times when respective events or event chunks started flowing out from the peer indexers 206. If time ordering is not required, preserving the time ordering of chunks by using timestamps may be unnecessary.
  • the parallel exporting technique can be modified in a variety of ways to improve performance of the DFS system.
  • the event chunks can be load balanced across the peer indexers 206 and/or receiving worker nodes 214 to improve network efficiency and utilization of network resources.
  • a dynamic list of receivers e.g., worker nodes 2114
  • the list may indicate a current availability of worker nodes to receive chunks from export processors of the peer indexers 206.
  • the list can be updated dynamically to reflect the availability of the worker nodes 214.
  • parameters on the list indicative of the availability of the worker nodes 214 can be passed to the export processers periodically or upon the occurrence of an event (e.g., a worker node 214 becomes available).
  • the export processers can then perform a load balancing operation on the event chunks over the receiving worker nodes 214.
  • the worker nodes 214 may include driver programs that consume the events and event chunks.
  • the worker nodes 214 can include a software development kit (SDK) that allows third party developers to control the consumption of events from the peer indexers 206 by the worker nodes 214.
  • SDK software development kit
  • third party developers can control the drivers causing the consumption of events and event chunks from the peer indexers 206 by the worker nodes 214.
  • the event chunks are exported from the peer indexers 206 in parallel to the worker nodes 214.
  • the rate of exporting events or event chunks in parallel by the peer indexers 206 can be based on an amount of shared memory available to the worker nodes 214. Accordingly, techniques can be employed to reduce the amount of memory required to store transferred events. For example, when the worker nodes 214 are not local (e.g., remote from the peer indexers 206), compressed payloads of the event chunks can be transferred to improve performance.
  • the disclosed DFS system can provide a big data pipeline and native processor as a mechanism to execute infrastructure, analytics, and domain-based processors based on data from one or more external data sources over different compute engines.
  • the mechanism can execute parallelized queries to extract results from external systems.
  • some operations can be performed by different components of the system.
  • some of the tasks described as being performed by the search head 210 can be performed by the search service 220, such as the search service provider 216.
  • the search head 210 can process the search query to determine whether the search query is to be handled by the DFS system 202. For example, in some embodiments, the search head 210 can handle queries for the indexers 206 and in other embodiments, the search service 220 can handle queries for the indexers 206. Based on a determination that the search process is to handle the search query, the search head 210 can forward the query to the search service 220. The search service provider 216 can further process the query (2210) and determine that the search includes searching the indexer 206.
  • the search service provider can execute a process to search the peer indexers 206 and provide the partial search results to the worker nodes 214, or instruct the worker nodes 214 to instruct the indexers 206 to execute the search. Steps 2210, 2212, 2214, 2216, and 2218 can then perform as illustrated such that the partial search results are exported to the worker nodes 214 for further processing.
  • the disclosed embodiments include techniques to process search queries in different ways by the DFS system depending on the type of search results sought in response to a search query.
  • a data intake and query system can receive search queries that cause the DFS system to process the search queries differently based on the search results sought in accordance with the search queries. For example, some search queries may require ordered search results, and an order of the search results may be unimportant for other search queries.
  • a search query executed on internal data sources (e.g., indexers) and/or external data sources may require sorting and organizing timestamped partial search results across the multiple diverse data sources.
  • the multiple internal or external data sources may not store timestamped data. That is, some data sources may store time -ordered data while other data sources may not store time -ordered data, which prevents returning time-ordered search results for a search query.
  • the disclosed embodiments provide techniques for harmonizing time-ordered and unordered data from across multiple internal or external data sources to provide time -ordered search results.
  • a search query may require search results that involve performing a transformation of data collected from multiple internal and/or external data sources.
  • the transformed data can be provided as the search results in response to the search query.
  • the search query may be agnostic to the ordering of the search results.
  • the search results of a search query may require counts of different types of events generated over the same period of time.
  • search results that satisfy the search query could be ordered or unordered counts.
  • the techniques described below provide mechanisms to obtain search result from the big data ecosystem that are transformed, time -ordered, unordered, or any combinations of these types of search results.
  • the disclosed embodiments include techniques to obtain ordered search results based on partial search results from across multiple diverse internal and/or external data sources.
  • the ordering of the search results may be with respect to a parameter associated with the partial search results.
  • An example of a parameter includes time.
  • the disclosed technique can provide a time -ordered search result based on partial search results obtained from across multiple internal and/or external data sources.
  • the disclosed technique can provide time-ordered search results regardless of whether the partial search results obtained from the diverse data sources are timestamped.
  • An ordered search (e.g., ordered data execution) can be referred to as“cursored” mode of data access.
  • the DFS system can execute time-ordered searches or retrieve events from multiple data sources and presents the events in a time ordered manner.
  • the DFS system can implement a micro-batching mechanism based on the event time across worker nodes.
  • the DFS system can ensure that per peer ordering is enforced across the worker nodes and final collation is performed at a local search head or search service provider.
  • the DFS system can maintain per source ordering prior to ordered collation in the local search head or search service provider.
  • FIG. 21 is a flowchart illustrating a method 2300 performed the DFS system to obtain time- ordered search results in response to a cursored search query according to some embodiments of the present disclosure.
  • the method 2300 for processing cursored search queries can involve a micro batching process executed by worker nodes to ensure time orderliness of partial search results obtained from data sources.
  • one or more worker nodes collect partial search results from the internal and/or external data sources.
  • the worker node may collect partial search results corresponding to data having a data structure as specified by the search query.
  • the worker nodes may query an external data source for partial search results based on specific keywords specified by a cursored search query, and collect the partial search results.
  • the worker nodes may also collect partial search results from indexers, which were returned in response to application of the search query by the search head (or search service provider) to the indexers.
  • the partial search results may be communicated from each data source to the worker node in chunks (e.g., micro-batches).
  • step 2304 the worker nodes perform deserialization of the partial search results obtained from the data sources.
  • partial search results transmitted by the data sources could been serialized such that data objects were converted into a stream of bytes in order to transmit the object, or store the object in memory.
  • the serialization process allows for saving the state of an object in order to reconstruct it at the worker node by using reverse process of deserialization.
  • the worker nodes receive the partial search results collected from the data sources and transform them into a specified format.
  • partial search results in diverse formats can be transformed into a common specified format.
  • the specified format may be specified to facilitate processing by the worker nodes.
  • diverse data types obtained from diverse data sources can be transformed into a common format to facilitate subsequent aggregation across all the partial search results obtained in response to the search query.
  • the partial search results obtained by the worker nodes can be transformed into, for example, data events having structures that are compatible to the data intake and query system.
  • the worker nodes may determine whether the partial search results are associated with respective time values. For example, the worker nodes may determine that events or event chunks from an internal data source are timestamped as shown in FIG. 2, but events or event chunks from an external data source may not be timestamped.
  • the timestamped events may also be marked with an “OriginType” (e.g., mysql-origin, cloud-aws-s),“SourceType” (e.g., cvs, json, sql), and“Host ⁇ >” (e.g., IP address where the event originated), or other data useful for ordering the partial search. If all the partial search results from across the diverse data systems are adequately marked, then harmonizing the partial search results may not require different types of processing. However, typically at least some partial search results from across the diverse distributed data systems are not adequately marked to facilitate harmonization.
  • the worker nodes can implement bifurcate processing of the partial search results depending on whether or not the partial search results are adequately marked. Specifically, the partial search results that are timestamped can be processed one way, and the partial search results that are not timestamped can be processed a different way.
  • the worker nodes can execute the different types of processing interchangeably, or execute one type of processing after the other type of processing has completed.
  • step 2310 for time -ordered partial search results, respective worker nodes can be assigned
  • time-ordered partial search results e.g., events or event chunks
  • Assigning a worker node to obtain time- ordered partial search results of the same data source avoids the need for additional processing among multiple nodes otherwise required if they each received different time-ordered chunks from the same data source.
  • setting a worker node to collect all the time-ordered partial search results from its source avoids the added need to distribute the time-ordered partial search results between worker nodes to reconstruct the overall time orderliness of the partial search results.
  • a worker node can respond to timestamped partial search results it receives by setting itself (or another worker node) to receive all of the partial search results from the source of the time- stamped partial search results.
  • the worker node can be set by broadcasting the assignment to other worker nodes, which collectively maintain a list of assigned worker nodes and data sources.
  • a worker node that receives timestamped partial search results can communicate an indication about the timestamped partial search results to the DFS master or search service provider. Then the DFS master or search service provider can set a specific set of worker nodes to receive all the timestamped data from the specific source.
  • the worker nodes read the collected partial search results (e.g., events or event chunks) and arrange the partial search results in time order.
  • each collected event or event chunk may be associated with any combination of a start time, an end time, a creation time, or some other time value.
  • the worker node can use the time values (e.g., timestamps) associated with the events or event chunks to arrange the events and/or the event chunks in a time-order.
  • the worker nodes may stream the time -ordered partial search results in parallel as time-ordered chunks via the search service (e.g., to the DFS master or search service provider of the DFS system).
  • the worker nodes can respond differently to partials search results that are not associated with timestamps (e.g., lack an associated time value that facilitates time ordering).
  • the worker nodes can associate events or chunks with a time value indicative of the time of ingestion of the events or event chunks by the respective worker nodes (e.g., an ingestion timestamp).
  • the worker nodes can associate the partial search results with any time value that can be measured relative to a reference time value (e.g., not limited to an ingestion timestamp).
  • the partial search results timestamped by the worker nodes can also be marked with a flag to distinguish those partial search results from the partial search results that were timestamped before being collected by the worker nodes.
  • the worker nodes sort the newly timestamped partial search results and create chunks (e.g., micro-batches) upon completion of collecting all of the partial search results from the data sources.
  • the chunks may be created to contain a default minimum or maximum number of partial search results (e.g., a default chunk size).
  • the worker nodes can create time- ordered partial search results obtained from data sources that did not provide time-ordered partial search results.
  • the worker nodes can apply spillover techniques to disk as needed.
  • the worker nodes can provide an extensive FIB/status update mechanism to notify the DFS master of its current blocked state.
  • the worker nodes can ensure a keep-alive to override timeout and provide notifications.
  • the worker nodes may stream the time- ordered partial search results in parallel as time-ordered chunks via the search service (e.g., to the DFS master or search service provider of the DFS system).
  • time-ordered partial search results can be created from a combination of time- ordered and non-time-ordered partial search collected from diverse data sources.
  • the time -ordered partial search results can be streamed in parallel from multiple worker nodes to the service provider, which can stream each search stream to the search head of the data intake and query system.
  • time-ordered search results can be produced from diverse data types of diverse data systems when the scope of a search query requires doing so.
  • FIG. 22 is a flowchart illustrating a method 2400 performed by a data intake and query system of a DFS system in response to a cursored search query according to some embodiments of the present disclosure.
  • the method 2400 can be performed by the data intake and query system to collate the time-ordered partial search results obtained by querying internal and/or external data sources.
  • the search head, search service provider, or one or more worker nodes receive one or more streams of time -ordered partial search results (e.g., event chunks) from a data source.
  • the search head or search service provider creates multiple search collectors to collect the time ordered event chunks.
  • the search head or search service provider can add a class of collectors to collate search results from the worker nodes.
  • the search head or search service provider can create multiple collectors; such as a collector for each indexer, as well as a single collector for each external data source or other data source.
  • the search head or search service provider may create a collector for each stream, which could include time-ordered chunks from a single worker node or a single data source. Hence, each collector receives time -ordered chunks.
  • the collectors perform a deserialization process on the received chunks and their contents, which had been serialized for transmission from the search service.
  • each collector adds the de-serialized partial search or their chunks to a collector queue.
  • the search head or search service provider may include any number of collector queues.
  • the search head or search service provider may include a collector queue for each collector or for each data source that provided partial search results.
  • the search head, search service provider, or designated worker node(s) can collate the time -ordered partial search results obtained from the data sources as time-ordered search results of the presented search query.
  • the search head, search service provider, or designated worker node(s) may apply a collation operation based on the time -order of events contained in the chunks from the queues of different collectors to provide time-ordered search results.
  • the time-ordered search results could be provided to an analyst on a variety of mediums and in a variety of formats.
  • the time -ordered search results may be rendered as a timeline visualization on a user interface on a display device.
  • the raw search results e.g., entire raw events
  • the visualization can allow the analyst to investigate the search results.
  • the time-ordered results may be provided to an analyst automatically on printed reports, or transmitted in a message sent over a network to a device to alert the analyst of a condition based on the search results.
  • FIGS. 21 and 22 include a combination of steps to obtain time-ordered search results from across diverse data sources that may or may not provide timestamped data
  • the disclosed embodiments are not so limited. Instead, any portion of the combination of steps illustrated in FIGS. 21 and 22 could be performed depending on the scope of the search query. For example, only a subset of steps may be performed when the search results for a search query are obtained exclusively from a single external data source that stores timestamped data.
  • the disclosed embodiments include a technique to obtain search results from the application of transformation operations on partial search results obtained from across internal and/or external data sources.
  • transformation operations include arithmetic operations such as an average, mean, count, or the like.
  • reporting transformations include join operations, statistics, sort, top head.
  • the search results of a search query can be derived from partial search results rather than include the actual partial search results.
  • the ordering of the search results may be nonessential.
  • An example of a search query that requires a transformation operation is a“batch” or“reporting” search query.
  • the related disclosed techniques involve obtaining data stored in the big data ecosystem, and returning that data or data derived from that data.
  • the DFS system executes blocking transforming searches, for example, to join across one or multiple available data sources. Since ordering is not needed, the DFS system can implement sharding of the data from the various data sources and execute aggregation (e.g., reduction of map-reduction) in parallel.
  • the DFS architecture can also execute multiple DFS operations in parallel to receive sharded data from the different sources.
  • FIG. 23 is a flowchart illustrating a method 2500 performed by nodes of a DFS system to obtain search results in response to a batch or reporting search query according to some embodiments of the present disclosure.
  • the method 2500 for processing batch or reporting search queries can involve steps performed by the DFS master, the service provider, and/or worker nodes to transform partial search results into search results into batch or reporting search results.
  • the disclosed techniques also support both streaming and non-streaming for multiple data sources.
  • the transformation operations generally occur at the worker nodes.
  • an operation may include a statistical count of events having a particular IP address.
  • the DFS can shard the data in certain partitions, and then each worker node can apply the transformation to that particular partition.
  • the transformed results are collated at the search service provider, and then transmitted to the search head.
  • another reshuffle of the partial search results can be executed among the worker nodes to put the different partitions on the same worker node, and then transforms can be applied. If this is the last reporting search, then results are sent back to the service provide node and then to the search head. This process continues as dictated by the DAG generated from the phase desired by the search head.
  • the worker nodes collect partial search results from the internal and/or external data sources.
  • a worker node may collect partial search results including data having data structures specified by the search query.
  • the worker node may query an external data source for partial search results based on specific keywords included in a reporting search query, and collect the partial search results.
  • the worker node may also collect partial search results from indexers, which were returned in response to application of the reporting search query by the search head (or search service provider or nodes) to the indexers.
  • the partial search results may be communicated from each data source to the worker nodes individually or in chunks (e.g., micro-batches). The worker nodes thus ingest partial search results obtained from the data sources in response to a search query.
  • the worker nodes can perform deserialization of the partial search results obtained from the data sources.
  • the partial search results transmitted by the data sources can be serialized by converting objects into a stream of bytes, which allows for saving the state of an object for subsequent recreation of the object at the worker nodes by using the reverse process of deserialization.
  • the worker nodes transform the de-serialized partial search results into a specified format.
  • partial search results collected in diverse formats can be transformed into a common specified format.
  • the specified format may be specified to facilitate processing by a worker node.
  • diverse data types obtained from diverse data sources can be transformed into a common format to facilitate subsequent aggregation across all the partial search results obtained in response to the search query.
  • the partial search results obtained by worker nodes can be transformed into, for example, data events having structures that are compatible to the data intake and query system.
  • the time-order of partial search results is not necessarily considered when processing reporting queries.
  • the worker nodes can associate events or event chunks with a time value indicative of the time of ingestion of the events or chunks by the worker nodes (e.g., ingestion timestamp).
  • the worker nodes can associate the partial search results with any time value that can be measured relative to a reference time value. Associating time values with partial search results may facilitate tracking partial search results when processing reporting searches, or may be necessary when performing reporting searches that require time-ordered results (e.g., a hybrid of cursored and reporting searches).
  • the worker nodes determine whether the ingested partial search results were obtained by an internal data source or an external data source to bifurcate processing respectively.
  • the worker nodes process the ingested partial search results differently depending on whether they were obtained from an internal data source (e.g., indexers) or an external data source, if needed. That is, this can be the case only when reporting searches are run in the indexers; however, if all the processors in the indexers are streaming, then no processing unique to the indexer data is needed.
  • data from external data sources can be sanitized in terms of coding, timestamped, and throttles based on the timestamp.
  • the worker nodes read the partial search results obtained from indexers of a data intake and query system in a sharded way.
  • the worker nodes may use a list identifying indexers from which to pull the sharded partial search results.
  • sharding involves partitioning datasets into smaller, faster, and more manageable parts called data shards.
  • the sharded partitions can be determined from policies, which can be based on hash values by default.
  • the map step can be determined by the sharding and a predicate passed, which maps records matching the predicate to whatever is needed as the search result.
  • the reduce step involves the aggregation of the shards.
  • the results of a query are those items for which the predicate returns true.
  • the partial search results of the indexers are aggregated (e.g., combined and/or transformed) by the worker nodes.
  • the partial search results can be in a pre-streaming format (semi-reduced), and need to be aggregated (e.g., reduced or combined) prior to aggregation with partial search results of external data sources.
  • the aggregated partial search results of the indexers are aggregated (e.g., combined and/or transformed) with the partial search results obtained from external data sources.
  • the aggregated partial search results of internal and external data stores can be transmitted from the worker nodes in parallel to the search service (e.g., to the DFS master or search service provider of the DFS system).
  • step 2520 for external data sources, the worker nodes push predicates for the reporting search query to the external data sources.
  • a predicate is a function that takes an argument, and returns a Boolean value indicating of true or false.
  • the predicate can be passed as a query expression including candidate items, which can be evaluated to return a true or false value for each candidate item.
  • the network nodes can determine whether the external data sources may or may not be able to execute a sharded query.
  • the worker node reads the results in different shards. In some embodiments, the DFS master randomly chooses which worker nodes will execute the shards.
  • a worker node has the ability to spillover to disk, and redistribute to other worker nodes.
  • the worker nodes can apply an aggregation (e.g., (e.g., combine and/or transform) or stream processing to have the partial search results ready for further processing against results from partial search results from the internal sources.
  • the worker nodes aggregate the partial search results from all data sources in response in response to the search query.
  • the worker nodes can apply a process similar to a reduction step of a map-reduce operation across all the partial search results obtained from diverse data sources.
  • the aggregate partial search results can be transmitted from the worker nodes in parallel to the search service provider 216.
  • the search service provider can collect all the finalized searches results from the worker nodes, and return the results to the search head.
  • FIG. 24 is a flowchart illustrating a method performed by a data intake and query system of a
  • DFS system in response to a batch or reporting search query according to some embodiments of the present disclosure.
  • the method 2600 is performed by the data intake and query system to provide the batch or reporting search results obtained by querying internal and/or external data sources.
  • a search head, search service provider, or designated worker node(s) of receives the aggregate partial search results via a hybrid collector.
  • the number and function of the hybrid collectors is defined depending on the type of search executed. For example, for the transforming search, the search head or search service provider can create only one collector to receive the final results from the worker nodes and after serialization directly pushes into the search result queue.
  • the search head or search service provider uses an existing job pool to de-serialize search results, and can push the search results out. In such an operation, collation is not needed.
  • the transformed search results could be provided to an analyst on a variety of mediums and in a variety of formats.
  • the time -ordered search results may be rendered as a timeline visualization on a user interface on a display device.
  • the visualization can allow the analyst to investigate the search results.
  • the time -ordered results may be provided to an analyst automatically on printed reports, or transmitted in a message sent over a network to a device to alert the analyst of a condition based on the search results.
  • FIGS. 23 through 26 include a combination of steps to obtain time ordered, unordered, or transformed search results from across multiple data sources that may or may not store timestamped data
  • the disclosed embodiments are not so limited. Instead, a portion of a combination of steps illustrated in any of these figures could be performed depending on the scope of the search query. For example, only a subset of steps may be performed when the partial search results for a search query is obtained exclusively from an external data source.
  • FIG. 25 is a system diagram illustrating a co-located deployment of a DFS system with the data intake and query system in which an embodiment may be implemented.
  • the system 224 shows only some components of a data intake and query system but can include other components (e.g., forwarders, internal data stores) that have been omitted for brevity.
  • the system 224 includes search heads 226-1 and 226-2 (referred to collectively as search heads 226).
  • the search heads 226 collectively form a search head cluster 228.
  • the cluster 228 can include any number of search heads.
  • an embodiment of the co-located deployment can include a single search head rather than the cluster 228.
  • the search heads 226 can operate alone or collectively to carry out search operations in the context of the co-located deployment.
  • a search head of the cluster 228 can operate as a leader that orchestrates search.
  • the search head 226-1 is a leader of the cluster 228.
  • Any of the search heads 226 can receive search queries that are processed collectively by the cluster 228.
  • a particular search head can be designated to receive a search query and coordinate the operations of some or all of the search heads of a cluster 228.
  • a search head of the cluster 228 can support failover operations in the event that another search head of the cluster 228 fails.
  • the cluster 228 is coupled to N peer indexers 230.
  • the search head 226-1 can be a leader of the cluster 228 that is coupled to each of the N peer indexers 230.
  • the system 224 can run one or more daemons 232 that can carry out the DFS operations of the co-located deployment.
  • the daemon 232-1 of the search head 226-1 is communicatively coupled to a DFS master 234, which coordinates control of DFS operations.
  • each of the N peer indexers 230 run daemons 232 communicatively coupled to respective worker nodes 236.
  • the worker nodes 236 are coupled to one or more data sources from which data can be collected as the partial search results of a search query.
  • the worker nodes 236 can collect partial search results of the indexers from internal data sources (not shown) and one or more of external data sources 240.
  • the worker nodes 236 are communicatively coupled to the DFS master 234 or a search service provider to form the DFS architecture of the illustrated co-located embodiment.
  • FIG. 26 is an operation flow diagram illustrating an example of an operation flow of a co located deployment of a DFS system with a data intake and query system according to some embodiments of the present disclosure.
  • the operational flow 2800 shows the processes for establishing the co-located DFS system and search operations carried out in the context of the co-located deployment.
  • a search head of the cluster 228 can launch the DFS master 234 and/or launch a connection to the DFS master 234.
  • a search head can use a modular input to launch an open source DFS master 234.
  • the search head can use the modular input to launch a monitor of the DFS master 234.
  • the modular input can be a platform add-on of the data intake and query system that can be accessed in a variety of ways such as, for example, over the Internet on a network portal.
  • the peer indexers 230 can launch worker nodes 236.
  • each peer indexer 230 can use a modular input to launch an open source worker node.
  • only some of the peer indexers 230 launch worker nodes, which results in a topology where not all of the peer indexers 230 have an associated worker node.
  • the peer indexers 206 can use the modular input to launch a monitor of the worker nodes 236.
  • the cluster 228 can launch one or more instances of a DFS service.
  • any or each of the search heads of the cluster 228 can launch or communicate with an instance of the DFS service.
  • the co-located deployment can launch and use multiple instances of a DFS service but need only launch and use a single DFS master 234.
  • the lead search head using the monitoring modular input can restart the failed DFS master.
  • another search head can be designated as the cluster 228’s leader and can re launch the DFS master.
  • a search head of the cluster 228 can receive a search query.
  • a search query may be input by a user on a user interface of a display device.
  • the search query can be input to the search head in accordance with a scheduled search.
  • a search head of the cluster 228 can initiate a DFS search session with the local
  • any of the member search heads of the cluster 228 can receive a search query and, in response to the search query, a search head can initiate a DFS search session using an instance of the DFS service.
  • a search head of the cluster 228 (or a search service provider) triggers a distributed search on the peer indexers 230 if the search query requires doing so.
  • the search query is applied on the peer indexers 230 to collect partial search results from internal data stores (not shown).
  • step 2814 the distributed search operations continue with the peer indexers 230 retrieving partial search results from internal data stores, and transporting those partial search results to the worker nodes 236.
  • the internal partial search results are partially reduced (e.g., combined), and transported by the peer indexers 230 to their respective worker nodes 236 in accordance with parallel exporting techniques.
  • the peer indexer can transfer its partial search results to the nearest worker node in the topology of worker nodes.
  • the worker nodes 236 collect the partial search results extracted from the external data sources 240.
  • the worker nodes 236 can aggregate (e.g., merge and reduce) the partial search results from the internal data sources and the external data sources 240.
  • the aggregation of the partial search results may include combining the partial search results of indexers 230 and/or the external data stores 240.
  • the worker nodes 236 can aggregate the collective partial search results at scale based on DFS native processors residing at the worker nodes 236.
  • the aggregated partial search results can be stored in memory at worker nodes before being transferred between other worker nodes to execute a multi-staged parallel aggregation operation.
  • the aggregated partial search results can be read by the DFS service running locally to the cluster 228. For example, the DFS service can commence reading the aggregated search results as event chunks.
  • step 2820 the aggregate partial search results read by the DFS service are transferred to the DFS master 234 or search service provider. Then, in step 2822, the DFS master 234 can transfer the final search results to the cluster 228.
  • the aggregated partial search results can be transferred by the worker nodes 236 as event chunks at scale to the DFS master 234, which can transfer search results (e.g., those received or derived therefrom) to the lead search head orchestrating the DFS session.
  • a search head can cause the search results or data indicative of the search results to be rendered on user interface of a display device.
  • the search head member can make the search results available for visualizing on a user interface rendered on the display device.
  • step 2806 can be omitted.
  • the cluster 228 can communicate the query to the search service.
  • the search service can trigger the distributed search, etc.
  • FIG. 27 is a cloud-based system diagram illustrating a cloud deployment of a DFS system in which an embodiment may be implemented.
  • a cloud computing platform can share processing resources and data in a multi tenant network.
  • the platform’ s computing services can be used on demand in a cloud deployment of a DFS system.
  • the platform’s ubiquitous, on-demand access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services), which can be rapidly provisioned and released with minimal effort, can be used to improve the performance and flexibility of a data intake and query system extended by a DFS system.
  • a cloud-based system 242 includes components of a data intake and query system extended by the DFS system implemented on a cloud computing platform.
  • the cloud-based system 242 is shown with only some components of a data intake and query system in a cloud deployment but can include other components (e.g., forwarders) that have been omitted for brevity.
  • the components of the cloud-based system 242 can be understood by analogy to other embodiments described elsewhere in this disclosure.
  • An example of a suitable cloud computing platform include Amazon web services (AWS), which includes elastic MapReduce (EMR) web services.
  • AWS Amazon web services
  • EMR elastic MapReduce
  • the disclosed embodiments are not so limited.
  • the cloud-based system 242 could include any cloud computing platform that uses EMR-like clusters (“EMR clusters”).
  • the cloud-based system 242 includes a search head 244 as a tenant of a cloud computing platform. Although shown with only the search head 244, the cloud-based system 242 can include any number of search heads that act independently or collectively in a cluster. The search head 244 and other components of the cloud-based system 242 can be configured on the cloud computing platform.
  • the cloud-based system 242 also includes any number of worker nodes 246 as cloud instances (“cloud worker nodes 246”).
  • the cloud worker nodes 246 can include software modules 248 running on hardware devices of a cloud computing platform.
  • the software modules 248 of the cloud worker nodes 246 are communicatively coupled to a search service (e.g., including a DFS master 250 or search service provider), which is communicatively coupled to a daemon 252 of the search head 244 to collectively carry out operations of the cloud-based system 242.
  • a search service e.g., including a DFS master 250 or search service provider
  • the cloud-based system 242 includes index cache components 254.
  • the index cache components 254 are communicatively coupled to cloud storage 256, which can form a global index 258.
  • the index cache components 254 are analogous to indexers, and the cloud storage 256 is analogous to internal data stores described elsewhere in this disclosure.
  • the index cache components 254 are communicatively coupled to the cloud worker nodes 246, which can collect partial search results from the cloud storage 256 by applying a search query to the index cache components 254.
  • the cloud worker nodes 246 can be communicatively coupled to one or more external data sources 260.
  • the cloud worker nodes 246 are coupled to the external data sources 260 while others are only coupled to the index cache components 254.
  • the cloud worker nodes 246-1 and 246-3 are coupled to both the external data sources 260 and the index cache component 254, while the cloud worker node 246-2 is coupled to the index cache component 254-1 but not the external data sources 260.
  • the scale of the cloud-based system 242 can be changed dynamically as needed based on any number of metrics.
  • the scale can change based on pricing constraints.
  • the scale of the EMR cluster of nodes can be configured to improve the performance of search operations.
  • the cloud-based system 242 can scale the EMR cluster depending on the scope of a search query to improve the efficiency and performance of search processing.
  • the EMR clusters can have access to flexible data stores such as a
  • the cloud-based system 242 can allow for a sharded query of data within these flexible data stores in a manner which makes scaling and aggregating partial search results (e.g., merging) most efficient while in place (e.g., reduces shuffling of partial search results between cloud worker nodes).
  • HDFS Hadoop distributed file system
  • S3 Amazon simple storage services
  • NoSQL NoSQL
  • SQL NoSQL
  • custom SQL custom SQL.
  • the cloud-based system 242 can allow for a sharded query of data within these flexible data stores in a manner which makes scaling and aggregating partial search results (e.g., merging) most efficient while in place (e.g., reduces shuffling of partial search results between cloud worker nodes).
  • FIG. 28 is a flow diagram illustrating an example of a method 3000 performed in a cloud- based DFS system (“cloud-based system”) according to some embodiments of the present disclosure.
  • cloud-based system The operations of the cloud-based system are analogous to those described elsewhere in this disclosure with reference to other embodiments and, as such, a person skilled in the art would understand those operations in the context of a cloud deployment. Accordingly, a description of the flow diagram highlights some distinctions of the cloud deployment over other embodiments described herein.
  • the search head of the cloud-based system receives a search query.
  • the cloud-based system determines the type of EMR cluster to use based on the scope of the received search query.
  • the cloud-based system can support two different types of EMR clusters. In a first type scenario, a single large EMR cluster could be used for all search operations. In a second type scenario, subsets of smaller EMR clusters can be used for each type of search load. That is, a smaller subset of an EMR cluster can be used for a less complex aggregation processing of partial search results from different data sources.
  • the scale of an EMR cluster for the first or second type can be set for each search load by a user or based on a role quota.
  • the scale of the EMR cluster can depend on the user submitting the search query and/or the user’s designated role in the cloud-based system.
  • the cloud-based system is dynamically scaled based on the needs determined from the received search query.
  • the search heads or cloud worker nodes can be scaled under the control of a search service to grow or shrink as needed based on the scale of the EMR cluster used to process search operations.
  • the cloud worker nodes can collect the partial search results from various data sources. Then, in step 3010, the cloud worker nodes can aggregate the partial search results collected from the various data sources . Since the cloud worker nodes can scale dynamically, this allows for aggregating (e.g., merging) partial search results in an EMR cluster of any scale.
  • the resulting aggregated search results can be computed and reported at scale to the search head or search service provider.
  • the cloud-based system can ensure that data (e.g., partial search results) from diverse data sources (e.g., including time-indexed events with raw data or other type of data) are reduced (e.g., combined) at scale on each EMR node of the EMR cluster before sending the aggregated search results to the search head or search service provider.
  • the cloud-based system may include various other features that improve on the data intake and query system extended by the DFS system.
  • the cloud-based system can collect metrics which can allow for a heuristic determination of spikes in DFS search requirements. The determination can also be accelerated through auto-scaling of the EMR clusters.
  • the cloud-based system can allow DFS apps of the data intake and query system to be bundled and replicated over an EMR cluster to ensure that they are executed at scale.
  • the cloud-based system can include mechanisms that allow user- or role-quota-honoring based on a live synchronization between the data intake and query system user management features and a cloud access control features.
  • the disclosed embodiments include techniques for organizing and presenting search results obtained from within a big data ecosystem via a data intake and query system.
  • a data intake and query system may cause output of the search results or data indicative of the search results on a display device.
  • An example of a display device is the client device 22 shown in FIG. 1A connected to the data intake and query system 16 over the network 33.
  • the data intake and query system 16 can receive a search query input by a user at the client device 22.
  • the data intake and query system 16 can run the query on distributed data systems to obtain search results.
  • the search results are then communicated to the client device 22 over the network 33.
  • the search results can be rendered in a visual way on the display of the client device 22 using items such as windows, icons, menus, and other graphics or controls.
  • a client device can run a web browser that renders a website, which can grant a user access to the data intake and query system 16.
  • the client device can run a dedicated application that grants a user access to the data intake and query system 16.
  • the client device can render a graphical user interface (GUI), which includes components that facilitate submitting search queries, and facilitate interacting with and interpreting search results obtained by applying the submitted search queries on distributed data systems of a big data ecosystem.
  • GUI graphical user interface
  • the disclosed embodiments include a timeline tool for visualizing the search results obtained by applying a search query to a combination of internal data systems and/or external data systems.
  • the timeline tool includes a mechanism that supports visualizing the search results by organizing the search results in a time -ordered manner. For example, the search results can be organized into graphical time bins.
  • the timeline tool can present the time bins and the search results contained in one or more time bins. Hence, the timeline tool can be used by an analyst to visually investigate structured or raw data events which can be of interest to the analyst.
  • the timeline mechanism supports combining timestamped and non-timestamped search results obtained from diverse data systems to present a visualization of the combined search results. For example, a search query may be applied to the external data systems that each use different compute resources and run different execution engines.
  • the timeline mechanism can harmonize the search results from these data systems, and a GUI rendered on a display device can present the harmonized results in a time-ordered visualization.
  • FIG. 29 is a flowchart of a method 3100 for illustrating a timeline mechanism that supports rendering search results in a time -ordered visualization according to some embodiments of the present disclosure.
  • the search head can dictate to the DFS master whether a cursored or reporting search should be executed, or a search service provider can make this determination.
  • the search service provider can define a search scheme and/or search process and create a DAG.
  • the DAG can orchestrate the search operations performed by the worker nodes for the cursored or reporting search.
  • the search service receives an indication that a request for a timeline visualization was received by the data intake and query system. For example, a user may input a request for a timeline visualization before, after, or when a search query is input at a client device. In another example, the data intake and query system automatically processes time-ordered requests to visualize in a timeline
  • the search service determines whether the requested visualization is for the search results of a cursored search or a time -ordered reporting search.
  • a cursored search may query indexers of the data intake and query system as well as external data stores for a combination of time ordered partial search results.
  • a time -ordered reporting search may require querying the indexers and external data stores for a time -ordered statistic based on the combination of time ordered partial search results.
  • the search results for the timeline tool can be obtained in accordance with a“Fast,”“Smart,” or“Verbose” search mode depending on whether a cursored search or a reporting search was received.
  • a cursored search supports all three modes whereas a reporting search may only support the Verbose mode.
  • the Fast mode prioritizes performance of the search and does not return nonessential search results. This means that the search returns what is essential and required.
  • the Verbose mode returns all of the field and event data it possibly can, even if the search takes longer to complete, and even if the search includes reporting commands.
  • the default Smart mode switches between the Fast and Verbose modes depending on the type of search being run (e.g., cursored or reporting).
  • step 3106 if the search is a cursored search, the search service creates buckets for the search results obtained from distributed data systems.
  • the buckets are created based on a timespan value.
  • the timespan value may be a default value or a value selected by a user. For example, a timespan value may be 24 hours.
  • the buckets may each represent a distinct portion of the timespan. For example, each bucket may represent a distinct hour over a time-span of 24 hours.
  • the number of buckets that are created may be a default value depending on the timespan, or depending on the number of data systems from which search results were collected. For example, a default number of buckets (e.g., 1,000 buckets) may be created to span a default or selected timespan. In another example, distinct and unique buckets are created for portions of the timespan. In another example, a unique bucket is created per data system. In yet another example, buckets are created for the same portion of the timespan but for different data systems.
  • a default number of buckets e.g., 1,000 buckets
  • distinct and unique buckets are created for portions of the timespan.
  • a unique bucket is created per data system.
  • buckets are created for the same portion of the timespan but for different data systems.
  • search results obtained by application of the search query to the different data systems are collected into the search buckets.
  • each bucket can collect the partial search results from different data systems that are timestamped with values within the range of the bucket.
  • the buckets support the timeline visualization by organizing the search results.
  • the search service transfers a number of search results contained in the buckets to the search head.
  • the search service may need to collect all the search results from across the data systems into the buckets before transferring the search results to the search head to ensure that the timeline visualization is rendered accurately.
  • the search results of the bucket may be transferred from the buckets in chronological order. For example, the contents of the buckets representing beginning of the timespan are transferred first, and the contents of the next buckets in time are transferred next, and so on.
  • the number of search results transferred to the search head from the buckets may be a default or maximum value. For example, the first 1,000 search results from the buckets at the beginning of the timespan may be first transferred to the search head first.
  • the search service transfer a maximum number of search results per bin to the search head. In other words, the number of search results transferred to the search head corresponds to the maximum number that can be contained in one or more bin of the timeline visualization.
  • the search results of the reporting search received by the search head from the buckets are rendered in a timeline visualization.
  • step 3114 if the search is a time -ordered reporting search, the search service creates buckets for the search results obtained from distributed data systems.
  • the buckets can be created based on the number of shards or partitions from which the search results are collected.
  • the search results are collected from across the partitions.
  • partial search results e.g., treated as raw events
  • the search service may need to collect partial search results across the different data sources before sending search results to the search head.
  • the search service transfers a number of search results contained in the buckets to the search head.
  • the search service may need to collect all the search results from across the data systems into the buckets before transferring the search results to the search head to ensure that the timeline visualization is rendered accurately.
  • the search results of the bucket may be transferred from the buckets in chronological order. For example, the contents of the buckets representing beginning of the timespan are transferred first, and the contents of the next buckets in time are next, and so on.
  • the number of search results transferred to the search head from the buckets may be a default or maximum value. For example, the first 1,000 search results from the buckets at the beginning of the timespan may be first transferred to the search head first.
  • the search service transfers a maximum number of search results per bin to the search head. In other words, the number of search results transferred to the search head corresponds to the maximum number that can be contained in one or more bin of the timeline visualization.
  • the search results of the reporting search received by the search head from the buckets are rendered in a timeline visualization.
  • FIG. 30 illustrates a timeline visualization rendered on a user interface 62 in which an embodiment may be implemented.
  • the timeline visualization presents event data obtained in accordance with a search query submitted to a data intake and query system.
  • the search query is input to search field 64 using SPL, in which a set of inputs is operated on by a first command line, and then a subsequent command following the pipe symbol“I” operates on the results produced by the first command, and so on for additional commands.
  • a command on the left of the pipe symbol can set the scope of the search, which could include external data systems.
  • Other commands on the right of the pipe symbol (and subsequent pipe symbols) can specify a field name and/or statistical operation to perform on the data sources.
  • the search head or search service provider can implement specific mechanism to parse the SPL.
  • the search head or search service provider can determine that some portion of the search query is to be executed on the worker nodes base on the scope of the search query.
  • the search query can include a specific search command that triggers the search head to realize which portion of the search query should be executed by the DFS system.
  • the phase generator can define the search phases, and where each of those phases will be executed.
  • the search head or search service provider can optimize to push as much of the search operation as possible, for example, first to the external data source and then to the DFS system. In some embodiments, only the commands not included in the DFS command set will be executed back on the search head or search service provider once the results are retrieved to the search head or search service provider.
  • the timeline visualization presents multiple dimensions of data in a compact view, which reduced the cognitive burden on analysts viewing a complex collection of data from internal and/or external data systems. That is, the timeline visualization provides a single unified view to facilitate analysis of events stored across the big data ecosystem. Moreover, the timeline visualization includes selectable components to manipulate the view in a manner suitable for the needs of an analyst.
  • the timeline visualization includes a graphic 66 that depicts a summary of the search results in a timeline lane (e.g., in the form of raw events), as well as a list of the specific search results 68.
  • the timeline summary of the search results are presented as rectangular bins that are chronologically ordered and span a period of time (e.g., September 5, 2016 5:00 PM through September 6, 2016 3:00 PM).
  • the height of a bin represents the magnitude of the quantity of events in that group relative to another group arranged along the timeline. As such, the height of each bin indicates a count of events for a subset of the period of events relative to other counts for other bins within the period of time.
  • the events in a group represented by a bin may have a timestamp value included in the range of time values of the corresponding bin.
  • Below the timeline summary is a listing of events of the search results presented in chronological order.
  • FIG. 31 illustrates a selected bin 70 of the timeline visualization and the contents of the selected bin 70 according to some embodiments of the present disclosure.
  • the timeline visualization may include graphic components that enable an analyst to investigate additional dimensions of the search results summarized in the timeline.
  • each bin representing a group of events may be selectable by an analyst. Selecting a bin may cause the GUI to display the specific group of events associated with the bin in the list below the timeline summary. Specifically, selecting a bin may cause the GUI to display the events of the search results that are timestamped within a range of the corresponding group.
  • the timeline visualization is customizable and adaptable to present search results in various convenient manners. For example, a user can change the ordering of groups of events to obtain a different visualization of the same groups. In another example, a user can change the range of the timeline to obtain a filtered visualization of the search results. In yet another example, a user can hide some events to obtain a sorted visualization of a subset of the search results.
  • the activity for each data system may appear in a separate timeline lane. If an activity start-time and duration are available for a particular data system, the respective timeline may show a duration interval as a horizontal bar in the lane. If a start time is available, the timeline visualization may render an icon of that time on the visualization. As such, the timeline visualization can be customized and provide interactive features to visualize search results, and communicate the results in dashboards and reports.
  • the timeline visualization can support a timeline visualization of external data systems, where each external data system may operate using different compute resources and engines.
  • the timeline visualization can depict search results obtained from one or more external data systems, collated and presented in a single and seamless visualization.
  • the timeline visualization is a tool of underlying logic that facilitates investigating events obtained from any of the external data systems, internal data systems (e.g., indexers), or a combination of both.
  • the underlying logic can manage and control the timeline visualization rendered on the GUI in response to data input and search results obtained from within the big data ecosystem.
  • the underlying logic is under the control and management of the data intake and query system.
  • an analyst can interface with the data intake and query system to use the timeline visualization.
  • the timeline logic can cause the timeline visualization to render activity time intervals and discrete data events obtained from various data system resources in internal and/or external data systems.
  • the underlying logic includes the search service. Since the bins may include events data from multiple data systems, each bin can represent an overlapping bin across multiple data systems. Accordingly, the search service can collect the data events across the different data systems before sending them to the search head. To finalize a search operation, the search service may transmit the maximum number of events per bin or the maximum size per bin to the search head.
  • the underlying logic uses the search head of the data intake and query system to collect data events from the various data systems that are presented on the timeline visualization.
  • the events are collected in accordance with any of the methods detailed above, and the timeline visualization is a portal for viewing the search results obtained by implementing those methods.
  • the collected events can have timestamps indicative of, for example, times when the event was generated.
  • the timestamps can be used by the underlying logic to sort the events into the bins associated with any parameter such as a time range.
  • the underlying logic may include numerous bins delineated by respective chronological time ranges over a total period of time that includes ah the bins. In some embodiments, a maximum amount of events transferred into the time bins could be set.
  • the underlying logic of the timeline visualization can automatically create bins for a default timespan in response to cursored searches of ordered data. For example, an analyst may submit a cursored search, and the underlying logic may cause the timeline visualization to render a display for events within a default timespan.
  • the amount and rate at which the events are transferred to the search head for subsequent display on the timeline visualization could vary under the control of the underlying logic. For example, a maximum number of events could be transferred on a per bin basis by the worker nodes to the search head. As such, the DFS system could balance the load on the network.
  • the underlying logic of the timeline visualization can utilize the sharding mechanism detailed above for reporting searches of ordered data from external data systems. Specifically, the data could be sharded across partitions in response to a reporting search, where executors have overlapping partitions. Further, the underlying logic may control the search head or search service provider to collect the events data across the shards/partitions in time order for rendering on the timeline visualization. Under either the cursored search or reporting search, the underlying logic may impose the maximum size of total events transferred into bins.
  • the disclosed embodiments also include monitoring and metering services of the DFS system.
  • these services can include techniques for monitoring and metering metrics of the DFS system.
  • the metrics are standards for measuring use or misuse of the DFS system.
  • Examples of the metrics include data or components of the DFS system.
  • a metric can include data stored or communicated by the DFS system or components of the DFS system that are used or reserved for exclusive use by customers.
  • the metrics can be measured with respect to time or computing resources (e.g., CPU utilization, memory usage) of the DFS system.
  • a DFS service can include metering the usage of particular worker nodes by a customer over a threshold period of time.
  • a DFS service can meter the amount hours that a worker node spends running one or more tasks (e.g., a search requests) for a customer.
  • a DFS service can meter the amount of resources used to run one or more tasks rather than, or in combination with, the amount of time taken to complete the task(s).
  • the licensing approaches include the total DFS hours used per month billed on a per hour basis; the maximum capacity that can be run at any one time, e.g. the total number of workers with a cap on the amount of size of each worker defined by CPU and RAM available to that worker; and finally a data volume based approach where the customer is charged by the amount of data brought into the DFS for processing.
  • FIG. 32 is a flow diagram illustrating monitoring and metering services of the DFS system according to some embodiments of the present disclosure.
  • the DFS services can monitor one or more metrics of a DFS system.
  • the DFS services can monitor the DFS system for a variety of reasons.
  • a DFS service can track metrics and/or display monitored metrics or data indicative of the monitored metrics.
  • the metrics can be preselected by, for example, a system operator or administrator seeking to analyze system stabilities, instabilities, or vulnerabilities.
  • the DFS services can meter use of the DFS system as a mechanism for billing customers.
  • the DFS services can monitor specific metrics for specific customers that use the DFS system.
  • the metering services can differ depending on whether the customer has a subscription to use the DFS system or is using the DFS system on an on-demand basis.
  • a DFS service can run a value-based licensing agreement that allows customers to have a fair exchange of value for their use of the DFS service.
  • step 3208 a determination is made about whether a customer has a subscription to use the
  • the subscription can define the scope of a license granted to a customer to access or use the DFS system.
  • the scope can define an amount of functionality available to the customer.
  • the functionality can include, for example, the number or types of searches that can be performed on the DFS system.
  • the scope granted to a user can vary in proportion to cost. For example, customers can purchase subscriptions of different scope for different prices, depending on the needs of the customers.
  • a DFS service can run a value-based licensing agreement that allows customers to have a fair exchange of value for their use of the DFS service.
  • the DFS service can meter metrics based on a subscription purchased by the customer. For example, a subscription to a DFS service may limit the amount of searches that a customer can submit to the DFS system. As such, the DFS service will meter the number of searches that are submitted by the customer. In another example, a subscription to the DFS service may limit the time a user can actively access a DFS service. As such, the DFS service will meter the amount of time that a user spends actively using the DFS service.
  • a DFS service determines whether the customer’s use of the DFS system exceeded a threshold amount granted by the subscription. For example, a customer may exceed the scope of a paid subscription by using functionality not included in the paid subscription or using more functionality than that granted by the subscription. In some embodiments, the excess use can be measured with respect to a metric such as time or use of computing resources.
  • a DFS service determines whether a customer exceeded the scope of the customer’s subscription. In step 3214, if the customer did not exceed the subscription, no action is taken (e.g., the customer is not charged additional fees). Referring back to step 3212, a variety of actions can be taken if the customer has exceed the subscription.
  • the DFS service can charge the customer for the excess amount of the metered metric. For example, the DFS service may begin metering the amount of time a customer spends using the DFS system after a threshold amount of time has been exceeded.
  • the DFS service can alternatively or additionally prevent the customer from accessing the DFS system if the customer exceeds the subscription or has not paid the additional charges of step 3216.
  • customer may still access the DFS system through a variety of other techniques.
  • a DFS service may provide limited or temporary access to the DFS system to a non-subscribed customer.
  • a DFS service may provide access to the DFS service on-demand.
  • a DFS service meters metrics on a non-subscription basis. For example, in step 3222, the customer can pay for each instance the customer uses the DFS system. In another example, in step 3224, a DFS service can start charging a non-subscribed customer for using the DFS system once the metrics of the service exceed a threshold amount. For example, a DFS service may provide free limited access or temporary full access to the DFS system. When the measuring metrics exceed the free limited access, the customer may be charged for access that exceeds the free amount. In either case, in step 3218, the DFS service can prevent the customer from accessing the DFS system if the measuring metrics exceed the threshold amount or the customer has not paid the charges of step 3222 or 3224. In some embodiments, a DFS server can allow the customer to complete an active search that exceeded a measuring metric but deny the customer from using the DFS system any further until additional payment authorized.
  • FIG. 33 is a system diagram illustrating an environment 3300 for ingesting and indexing data, and performing queries on one or more datasets from one or more dataset sources.
  • the environment 3300 includes data sources 201, client devices 404, described in greater detail above with reference to FIG. 4, and external data sources 3318 communicatively coupled to a data intake and query system 3301.
  • the external data sources 3318 can be similar to the external data systems 12-1, 12-2 described above with reference to FIG. 1A or the external data sources described above with reference to FIG. 4
  • the data intake and query system 3301 includes any combination of forwarders 204, indexers 206, data stores 208, and a search head 210, as discussed in greater detail above with reference to FIGS. 2-4.
  • the forwarders 204 can forward data from the data sources 203 to the indexers 206
  • the indexers 206 can ingest, parse, index, and store the data in the data stores 208
  • the search head 210 can receive queries from, and provide the results of the queries to, client devices 404 on behalf of the system 3301.
  • the system 3301 further includes a search process master 3302 (in some embodiments also referred to as DFS master), one or more query coordinators 3304 (in some embodiments also referred to as search service providers), worker nodes 3306, and a query acceleration data store 3308.
  • a workload advisor 3310, workload catalog 3312, node monitor 3314, and dataset compensation module 3316 can be included in the search process master 3302.
  • any one or any combination of the workload advisor 3310, workload catalog 3312, node monitor 3314, and dataset compensation module 3316 can be included elsewhere in the system 3301, such as in as a separate device or as part of a query coordinator 3304.
  • the functionality of the search head 210 and the indexers 206 in the illustrated embodiment of FIG. 33 can differ in some respects from the functionality described previously with respect to other embodiments.
  • the search head 210 can perform some processing on the query and then communicate the query to the search process master 3302 and coordinator(s) 3304 for further processing and execution.
  • the search head 210 can authenticate the client device or user that sent the query, check the syntax and/or semantics of the query, or otherwise determine that the search request is valid.
  • a daemon running on the search head 210 can receive a query.
  • the search head 210 can spawn a search process to further handle the query, including communicating the query to the search process master 3302 or query coordinator 3304.
  • the search head 210 can receive the results of the query from the search process master 3302 or query coordinator 3304 and serve the results to the client device 404.
  • the search head 210 may not perform any additional processing on the results received from the search process master 3302 or query coordinator 3304.
  • the search head 210 can terminate the search process upon receiving and communicating the results.
  • the indexers 206 in the illustrated embodiment of FIG. 33 can receive the relevant subqueries from the query coordinator 3304 rather than the search head 210, search the corresponding data stores 208 for relevant events, and provide their individual results of the search to the worker nodes 3306 instead of the search head 210 for further processing.
  • the indexers 206 can analyze events for a query in parallel. For example, each indexer 206 can search its corresponding data stores 208 in parallel and communicate its partial results to the worker nodes 3306.
  • the search head 210, search process master 3302, and query coordinator 3304 can be implemented using separate computer systems, processors, or virtual machines, or may alternatively comprise separate processes executing on one or more computer systems, processors, or virtual machines. In some embodiments, running the search head 210, search process master 3302, and/or query coordinator 3304 on the same machine can increase performance of the system 3301 by reducing communications over networks. In either case, the search process master 3302 and query coordinator 3304 can be communicatively coupled to the search head 210.
  • the search process master 3302 and query coordinator 3304 can be used to reduce the processing demands on the search head 210. Specifically, the search process master 3302 and coordinator 3304 can perform some of the preliminary query processing to reduce the amount of processing done by the search head 210 upon receipt of a query. In addition, the search process master 3302 and coordinator 3304 can perform some of the processing on the results of the query to reduce the amount of processing done by the search head 210 prior to communicating the results to a client device. For example, upon receipt of a query, the search head 210 can determine that the query can be processed by the search process master 3302. In turn, the search process master 3302 can identify a query coordinator 3304 that can process the query. In some cases, if there is not a query coordinator 3304 that can handle the incoming query, the search process master 3302 can spawn an additional query coordinator 3304 to handle the query.
  • the query coordinator(s) 3304 can coordinate the various tasks to execute queries assigned to them and return the results to the search head 210. For example, as will be described in greater detail below, the query coordinator 3304 can determine the amount of resources available for a query, allocate resources for the query, determine how the query is to be broken up between dataset sources, generate commands for the dataset sources to execute, determine what tasks are to be handled by the worker nodes 3306, spawn the worker nodes 3306 for the different tasks, instruct different worker nodes 3306 to perform the different tasks and where to route the results of each task, monitor the worker nodes 3306 during the query, control the flow of data between the worker nodes 3306, process the aggregate results from the worker nodes 3306, and send the finalized results to the search head 210 or to another dataset destination.
  • the query coordinators 3304 can provide data isolation across different searches based on role/access control, as well as fault tolerance (e.g., localized to a search head). For example, if a search operation fails, then its spawned query coordinator 3304 may fail but other query coordinators 3304 for other queries can continue to operate. In addition, queries that are to be isolated from one another can use different query coordinators 3304.
  • the worker nodes 3306 can perform the various tasks assigned to them by a query coordinator 3304.
  • the worker nodes 3306 can intake data from the various dataset sources, process the data according to the query, collect results from the processing, combine results from various operations, route the results to various destinations, etc.
  • the worker nodes 3306 and indexers 206 can be implemented using separate computer systems, processors, or virtual machines, or may alternatively comprise separate processes executing on one or more computer systems, processors, or virtual machines.
  • the worker nodes 3306 can be similar to or perform functions similar to worker nodes 214 described herein.
  • the query acceleration data store 3308 can be used to store datasets for accelerated access.
  • the worker nodes 3306 can obtain data from the indexers 206, external data sources 3318, or other location (e.g., common storage, ingested data buffer, etc.) and store the data in the query acceleration data store 3308.
  • the worker nodes 3306 can access the data in the query acceleration data store 3308 and process the data according to the query.
  • the worker nodes 3306 can begin working on the dataset obtained from the query acceleration data store 3308, while also obtaining the other dataset(s) from the other dataset source(s). In this way, a client device 4l4a-404n can rapidly receive a response to a provided query, while the worker nodes 3306 obtain datasets from the other dataset sources.
  • the query acceleration data store 3308 can be, for example, a distributed in-memory database system, storage subsystem, and so on, which can maintain (e.g., store) datasets in both low-latency memory (e.g., random access memory, such as volatile or non-volatile memory) and longer-latency memory (e.g., solid state storage, disk drives, and so on).
  • low-latency memory e.g., random access memory, such as volatile or non-volatile memory
  • longer-latency memory e.g., solid state storage, disk drives, and so on.
  • the accelerated data store 3308 can maintain particular datasets in the low-latency memory, and other datasets in the longer- latency memory.
  • 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.).
  • the query acceleration data store 3308 can be used to serve interactive or iterative searches.
  • datasets which are determined to be frequently accessed by a user can be stored in the lower-latency memory.
  • datasets of less than a threshold size can be stored in the lower-latency memory.
  • a user can indicate in a query that particular datasets are to be stored in the query acceleration data store 3308.
  • the query can then indicate operations to be performed on the particular datasets.
  • the worker nodes 3306 can obtain information directly from the query acceleration data store 3308.
  • the query acceleration data store 3308 can implement access controls (e.g., an access control list) with respect to the stored datasets.
  • the stored datasets can optionally be accessible only to users associated with requests for the datasets.
  • 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.
  • the worker nodes 3306 can store data from any dataset source, including data from a dataset source that has not been transformed by the nodes 3306, processed data (e.g., data that has been transformed by the nodes 3306), partial results, or aggregated results from a query in the query acceleration data store 3308.
  • the results stored in the query acceleration data store 3308 can be served at a later time to the search head 210, combined with additional results obtained from a later query, transformed or further processed by the worker nodes 3306, etc.
  • system 3301 can include fewer or more components as desired.
  • the system 3301 does not include a search head 210.
  • the search process master 3302 can receive query requests from clients 404 and return results of the query to the client devices 404.
  • the functionality described herein for one component can be performed by another component.
  • the workload advisor 3310 and dataset compensation module 3316 are described as being implemented in the search process master 3302, it will be understood that these components and their functionality can be implemented in the query coordinator 3304.
  • the nodes 3306 can be used to index data and store it in one or more data stores, such as the common storage or ingested data buffer, described in greater detail below. 11.1.
  • FIG. 34 is a block diagram illustrating an embodiment of multiple machines 3402, each having multiple nodes 3306-1, 3306-n (individually and collectively referred to as node 3306 or nodes 3306) residing thereon.
  • the worker nodes 3306 across the various machines 3402 can be communicatively coupled to each other, to the various components of the system 3301, such as the indexers 206, query coordinator 3304, search head 210, common storage, ingested data buffer, etc., and to the external data sources 3318.
  • the machines 3402 can be implemented using multi-core servers or computing systems and can include an operating system layer 3404 with which the nodes 3306 interact.
  • each machine 3402 can include 32, 48, 64, or more processor cores, multiple terabytes of memory, etc.
  • each node 3306 includes four processors 3406, memory 3408, a monitoring module 3410, and a serialization/deserialization module 3412. It will be understood that each node 3306 can include fewer or more components as desired. Furthermore, it will be understood that the nodes 3306 can include different components and resources from each other. For example, node 3306-1 can include fewer or more processors 3406 or memory 3408 than the node 3306-n.
  • the processors 3406 and memory 3408 can be used by the nodes 3306 to perform the tasks assigned to it by the query coordinator 3304 and can correspond to a subset of the memory and processors of the machine 3402.
  • reference to a worker node 3306 can also be understood to be a reference to one or more processors 3406 of a worker node 3306 and vice versa (e.g., allocating, assigning, or selecting a worker node 3306 can refer to allocating, assigning, or selecting one or more processors 3406 of a worker node 3306).
  • the serialization/deserialization module 3412 can be used to serialize/deserialize data for communication between components of the system 3301, as will be described in greater detail below.
  • the monitoring module 3410 can be used to monitor the state and utilization rate of the node 3306 or processors 3406 and report the information to the search process master 3302 or query coordinator 3304.
  • the monitoring module 3410 can indicate the number of processors in use by the node 3306, the utilization rate of each processor, whether a processor is unavailable or not functioning, the amount of memory used by the processors 3406 or node 3306, etc.
  • each worker node 3306 can include one or more software components or modules
  • modules operable to carry out the functions of the system 3301 by communicating with the query coordinator 3304, the indexers 206, and the dataset sources.
  • the modules can run on a programming interface of the worker nodes 3306.
  • An example of such an interface is APACF1E SPARK, which is an open source computing framework that can be used to execute the worker nodes 3306 with implicit parallelism and fault- tolerance.
  • SPARK includes an application programming interface (API) centered on a data structure called a resilient distributed dataset (RDD), which is a read-only multiset of data items distributed over a cluster of machines (e.g., the devices running the worker nodes 3306).
  • RDD resilient distributed dataset
  • the RDDs function as a working set for distributed programs that offer a form of distributed shared memory.
  • the worker nodes 3306 can collect and process data or partial search results of a distributed network of data storage systems, and provide aggregated partial search results or finalized search results to the query coordinator 3304 or other destination. Accordingly, the query coordinator 3304 can act as a manager of the worker nodes 3306, including their distributed data storage systems, to extract, collect, and store partial search results via their modules running on a computing framework such as SPARK.
  • a computing framework such as SPARK.
  • the embodiments disclosed herein are not limited to an implementation that uses SPARK. Instead, any open source or proprietary computing framework running on a computing device that facilitates iterative, interactive, and/or exploratory data analysis coordinated with other computing devices can be employed to run the modules 218 for the query coordinator 3304 to apply search queries to the distributed data systems.
  • a node 3306 can receive instructions from a query coordinator 3304 to perform one or more tasks.
  • the node 3306 can be instructed to intake data from a particular dataset source, parse received data from a dataset source to identify relevant data in the dataset, collect partial results from the parsing, join results from multiple datasets, or communicate partial or completed results to a destination, etc.
  • the instructions to perform a task can come in the form of a DAG.
  • the node 3306 can determine what task it is to perform in the DAG, and execute it.
  • the node 3306 can determine how many processors 3406 to allocate to the different tasks. In some embodiments the node can determine that all processors 3406 are to be used for a particular task or only a subset of the processors 3406. In certain embodiments, each processor 3406 of the node 3306 can be used in association with one or more a partitions to intake, process, or collect data according to a task. Upon completion of the task, the node 3306 can inform the query coordinator 3304 that the task has been completed.
  • partition can refer to different things.
  • a partition can refer to a set of data in one or more data stores, such as an index, or a stream of data.
  • a partition can refer to smaller sets of data, such as when data is partitioned (or split up) into smaller parts.
  • one or more partitions can be assigned to a processor 3406 or a worker node 3306, and reference to a partition performing an action can refer to a processor 3406 performing the action on one or more groups of data or data entries assigned thereto.
  • reference to assigning a job or action to a partition can refer to the assignment of a processor 3406 or worker node 3306 to perform that job or action.
  • the assignment of a partition to receive data from an external data source can refer to a processor 3406 receiving data from the external data source and grouping the data into one or more groups or partitions of data.
  • a partition can refer to an index, a task, a set or group of data, data entries, events, or records, or can refer to a processor 3406 that performs a particular action on one or more groups or sets of data, data entries, or records.
  • a partition can refer to a group of data, data entries, events, or records and computer-executable instructions that indicate how the group of data is to be processed by a processor 3406 or worker node 3306.
  • the processors 3406 of the node 3306 can be used to communicate with a dataset source (non-limiting examples: external data sources 3318, indexers 206, common storage, query acceleration data store 3308, ingested data buffer, etc.). Once the node 3306 is in communication with the dataset source it can intake the data from the dataset source. As described in greater detail below, in some embodiments, multiple processors of a node (or different nodes) can be assigned to intake data from a particular source as one or more partitions.
  • the processors 3406 of the node 3306 can be used to review the data and identify portions of the data that are relevant to the query. For example, if a query includes a request for events with certain errors or error types, the processors 3406 of the node 3306 can parse the incoming data to identify different events, parse the different events to identify error fields or error keywords in the events, and determine the error type of the error. In some cases, this processing can be similar to the processing described in greater detail above with reference to the indexers 206 processing data to identify relevant results in the data stores 208.
  • the processors 3406 of the node 3306 can be used to receive data from dataset sources or processing nodes.
  • a collector partition or processor 3406 can collect all of the errors of a certain type from one or more parsing partitions or processors 3406. For example, if there are seven possible types of errors coming from a particular dataset source, a collector partition could collect all type 1 errors (or events with a type 1 error), while another collector partition could collect all type 2 errors (or events with a type 2 error), etc.
  • the processors 3406 of the node 3306 can be used to receive data corresponding to two different datasets and combine or further process them. For example, if data is being retrieved from an external data source and a data store 208 of the indexers 206, join partitions could be used to compare and collate data from the different data stores in order to aggregate the results.
  • the processors 3406 of the node 3306 can be used to prepare the data for communication to the destination and then communicate the data to the destination. For example, in communicating the data to a particular destination, the node 3306 can communicate with the particular destination to ensure the data will be received. Once communication with the destination has been established, the partition, or processor associated with the partition, can begin sending the data to the destination. As described in greater detail below, in some embodiments, multiple partitions of a node (or different nodes) can be assigned to communicate data to a particular destination. Furthermore, the nodes 3306 can be instructed to transform the data so that the destination can properly understand and store the data. Furthermore, the nodes can communicate the data to multiple destinations. For example, one copy of the data may be communicated to the query coordinator 3304 and another copy can be communicated to the query acceleration data store 3308.
  • the system 3301 is scalable to accommodate any number of worker nodes 3306. As such, the system 3301 can scale to accommodate any number of distributed data systems upon which a search query can be applied and the search results can be returned to the search head and presented in a concise or comprehensive way for an analyst to obtain insights into big data that is greater in scope and provides deeper insights compared to existing systems.
  • the serialization/deserialization module 3412 can generate and transmit serialized event groups.
  • An event group can include the following information: number of events in the group, header information, event information, and changes to the cache or cache deltas.
  • the serialization/deserialization module 3412 can identify the differences between the pieces of information using a type code or token.
  • the type code can be in the form of a type byte.
  • the serialization/deserialization module 3412 can include a header type code indicating that header information is to follow.
  • type codes can be used to identify event data or cache deltas.
  • the header information can indicate the number and order of fields in the events, as well as the name of each field.
  • the event information for each event can include the number of fields in the event, as well as the value for that field.
  • the cache deltas can identify changes to make to the cache relied upon to serialize/deserialize the data.
  • the serialization/deserialization module 3412 can determine the number of events to group, determine the order and field names for the fields in the events of the group, parse the events, determine the number of fields for each event, identify and serialize serializable field values in the event fields, and identify cache deltas. In some cases, the serialization/deserialization module 3412 performs the various tasks in a single pass of the data, meaning that it performs the identification, parsing, and serializing during a single review of the data. In this manner, the serialization/deserialization module 3412 can operate on streaming data and avoid adding delay to the serialization/deserialization process.
  • an event group includes an identifier indicating the number of events in the group followed by a header type code and a number of fields indicating the number of fields in the events.
  • the event group can include a type code indicating whether the field name is already stored in cache or a type code indicating that the field name is included.
  • the event group can include an identifier or the field name. For example, if the type code indicates the field name is stored in cache (e.g., a cache code), an identifier can be included to enable a receiving component to lookup the field name using the cache. If the type code indicates the field name is not stored in cache (e.g., a data code), the name of the field name can be included.
  • the event group can include number of fields in the event.
  • the event group can include a type code indicating whether the field name is already stored in cache or a type code indicating that the field name is included.
  • the event group can also include cache delta information.
  • the cache delta information can include a cache delta type code indicating that the cache is to be changed, a number of new entries, and a number of dropped entries.
  • the cache delta information can include the data or string being cached, and an identifier for the data.
  • the cache delta information can include the identifier of the cache entry to be dropped.
  • the serialization/deserialization module 3412 can determine that the field names for the events are source, sourcetype, salejype, company name, and price and that this information is not in cache. The serialization/deserialization module 3412 can then generate the following event group:
  • the serialization/deserialization module 3412 can reduce the amount of data communicated for each group. For example, instead of transmitting the string“ronnie.sv.splunk.com” each time, the serialization/deserialization module 3412 serializes it and then communicates the cache ID thereafter.
  • Entries can be added or dropped using a variety of techniques. In some cases, every new field value is cached. In certain cases, a field value is cached after it has been identified a threshold number of times. Similarly, an entry can be dropped after a threshold number of events or event groups have been processed without the particular value being identified.
  • the serialization/deserialization module 3412 can track X values at a time in a cache C and track up to Y values at a time that are not cached and how many time those values have been identified in a candidate set D. When a value is received, if it is in the cache C, then the identifier can be returned. If the value is not in the cache C, then it can be added to D.
  • the cache is built as the data is processed, and changes are transmitted as they occur.
  • the receiver can start with an empty cache, and apply each delta as it comes along. As mentioned above, each delta can have two sections: new entries, and dropped entries.
  • the receiver or deserializer does not drop cache entries until told to do so, otherwise, it may not be able interpret identifiers received from the serializer.
  • the serializer performs cache maintenance by informing the deserializer when to drop entries. Upon receipt of such a command, the deserializer can remove the identified entries.
  • the search process master 3302 can perform various functions to reduce the workload of the search head 210. For example, the search process master 3302 can parse an incoming query and allocate the query to a particular query coordinator 3304 for execution or spawn an additional query coordinator 3304 to execute the query. In addition, the search process master 3302 can track and store information regarding the system 3301, queries, external data stores, etc., to aid the query coordinator 3304 in processing and executing a particular query. In some embodiments, the search process master 3302.
  • the search process master 3302 can determine whether a query coordinator
  • the search process master 3302 can spawn query coordinators 3304 for different users.
  • the search process master 3302 can spawn query coordinators 3304 if it determines that a query coordinator 3304 is over utilized.
  • the search process master 3302 can include a workload advisor 3310, workload catalog 3312, node monitor 3314, and dataset compensation module 3316. Although illustrated as being a part of the search process master 3302, it will be understood that any one or any combination of these components can be implemented separately or included in one or more query coordinators 3304. Furthermore, although illustrated as individual components, it will be understood that any one or any combination of the workload advisor 3310, workload catalog 3312, node monitor 3314, and dataset compensation module 3316 can be implemented by the same machine, processor, or computing device.
  • the workload advisor 3310 can be used to provide resource allocation recommendations to a query coordinator 3304 for processing queries
  • the workload catalog 3312 can store data related to previous queries
  • the node monitor 3314 can receive information from the worker nodes 3306 regarding a current status and/or utilization rate of the nodes 3306
  • the dataset compensation module 3316 can be used by the query coordinator 3304 to enhance interactions with external data sources.
  • the workload catalog 3312 can store relevant information to aid the workload advisor 3310 in providing a resource allocation recommendation to a query coordinator 3304. As queries are received and processed by the system 3301, the workload catalog 3312 can store relevant information about the queries to improve the workload advisor’s 3310 ability to recommend the appropriate amount of resources for each query.
  • the system 3301 can track any one or any combination of the following data points about a query: which dataset sources were accessed, what was accessed in each dataset source (particular tables, buckets, etc.), the amount of data retrieved from the dataset sources (individually and collectively), the time taken to obtain the data from the dataset sources, the number of nodes 3306 used to obtain the data from each dataset source, the utilization rate of the nodes 3306 while obtaining the data from the dataset source, the number of transformations or phases (processing, collecting, reducing, joining, branching, etc.) performed on the data obtained from the dataset sources, the time to complete each transformation, the number of nodes 3306 assigned to each phase, the utilization rate of each node 3306 assigned to the particular phase, the processing performed by the query coordinator 3304 on results (individual or aggregatee), time to store or deliver results to a particular destination, resources used to store/deliver results, total time to complete query, time of day of query request, etc.
  • the workload catalog can include identifying information corresponding to the datasets with which the system interacts (e.g., indexers, common storage, ingested data buffer, external data sources, query acceleration data store, etc.).
  • This information can include, but is not limited to, relationships between datasets, size of dataset, rate of growth of dataset, type of data, selectivity of dataset, provider of dataset, indicator for private information (e.g., personal health information, etc.), trustworthiness of a dataset, dataset preferences, etc.
  • the workload catalog 3312 can collect the data from the various components of the system 3301, such as the query coordinator 3304, worker nodes 3306, indexers 206, etc. For example, for each task performed by each node 3306, the node 3306 can report relevant timing and resource utilization information to the query coordinator 3304 or directly to the workload catalog 3312. Similarly, the query coordinator 3304 can report relevant timing, usage, and data information for each phase of a search, each transformation of data, or for a total query.
  • the workload advisor 3310 can estimate the compute cost to perform a particular data transformation or query, or to access a particular dataset. Further, the workload advisor can determine the amount of resources (nodes, memory, processors, partitions, etc.) to recommend for a query in order to provide the results within a particular amount of time.
  • the node monitor 3314 can also store relevant information to aid the workload advisor 3310 in providing a resource allocation recommendation.
  • the node monitor 3314 can track and store information regarding any one or any combination of: total number of processors or nodes in the system 3301, number of processors or nodes that are not available or not functioning, number of available processors or nodes, utilization rate of the processors or nodes, number of worker nodes, current tasks being completed by the worker nodes 3306 or processors, estimated time to complete a task by the nodes 3306 or processors, amount of available memory, total memory in the system 3301, tasks awaiting execution by the nodes 3306 or processors, etc.
  • the node monitor 3314 can collect the relevant information by communicating with the monitoring module 3410 of each node 3306 of the system 3301. As described above, the monitoring modules 3410 of each node 3306 can report relevant information about the node state and utilization rate. Using the information from the node monitor 3314, the workload advisor 3310 can ascertain the general state of any particular processor, node, or the system 3301, and determine the number of nodes 3306 or processors 3406 available for a particular task or query.
  • the external data sources 3318 with which the system 3301 can interact vary significantly.
  • some external data source may have processing capabilities that can be used to perform some processing on the data that resides there prior to communicating the data to the nodes 3306.
  • the external data sources 3318 may support parallel reads from multiple partitions.
  • other external data sources 3318 may not be able to perform much, if any, processing on the data contained therein and/or may only be able to provide serial reads from a single partition.
  • each external data source 3318 may have particular requirements for interacting with it, such as a particular API, throttling requirements, etc.
  • the type and amount of data stored in each external data source 3318 can vary significantly. As such, the system’s 3301 interaction with the different external data sources 3318 can vary significantly.
  • the dataset compensation model 3316 can include relevant information related to each external data source 3318 with which the system 3301 can interact.
  • the dataset compensation model 3316 can include any one or any combination of: the amount of data stored in an external data source 3318, the type of data stored in an external data source, query commands supported by an external data source (e.g., aggregation, filtering ordering), query translator to translate a query into tasks supported by an external data source, the file system type and hierarchy of the external data source 3318, number of partitions supported by an external data source 3318, endpoint locations (e.g., location of processing nodes or processors), throttling requirements (e.g., number and rate at which requests can be sent to the external data source), etc.
  • each external data source 3318 can be collected in a variety of ways.
  • some of the information about the external data source 3318 can be received when a customer sets up the external data source 3318 for use with the system 3301.
  • a customer can indicate the type of external data source 3318 e.g., MySQL, PostgreSQL, and Oracle databases; NoSQL data stores like Cassandra, Mongo DB, cloud storage like Amazon S3 HDFS, etc.
  • the system 3301 can determine certain characteristics about the external data store 3318, such as whether it supports multiple partitions.
  • dataset sources have different capabilities. For example, not only can different datasets sources support a different number of partitions, but the dataset sources can support different functions. For example, some dataset sources may be capable of data aggregation, filtering, or ordering, etc., while others may not be.
  • the dataset compensation module 3316 can store the capabilities of the different dataset sources to aid in providing a seamless experience to users.
  • the system 3301 can collect relevant information about an external data source by communicating with it.
  • the query coordinator 3304 or a worker node 3306 can interact with the external data source 3318 to determine the number of partitions available for accessing data. In some cases, the number of available partitions may change as computing resources on the external data source 3318 become available or unavailable, etc.
  • the system 3301 accesses the external data source 3318 as part of a query it can track relevant information, such as the tables or amount of data accessed, tasks that the external data source was able to perform, etc.
  • the system 3301 can interact with an external data source 3318 to identify the endpoint that will handle any subqueries and its location. The endpoint and endpoint location may change depending on the subquery that is to be run on the external data source. Accordingly, in some embodiments, the system 3301 can request endpoint information with each query that is to access the particular external data source.
  • a query coordinator 3304 can determine how to interact with it and how to process data obtained from the external data source 3318. For example, if an external data source 3318 supports parallel reads, the query coordinator 3304 can allocate multiple worker nodes 3306 to read the data from the external data source 3318 in parallel. In some embodiments, the query coordinator 3304 can allocate sufficient worker nodes 3306 or processors 3406 to establish a 1:1 relationship with the available partitions at the external data source 3318.
  • the query coordinator 3304 can use the information from the dataset compensation module 3316 to translate the query into commands understood by the external data source 3318 and push some processing to the external data source 3318, thereby reducing the amount of system 3301 resources (e.g., nodes 3306) used to process the query.
  • system 3301 resources e.g., nodes 3306
  • the query coordinator can determine the amount of data in the different external data sources that will be accessed by a particular query. Using that information, the query coordinator 3304 can intelligently interact with the external data sources 3318. For example, if the query coordinator 3304 determines that data with similar characteristics in two external data sources are to be accessed and the data from each will eventually be combined, the query coordinator 3304 can first interact with or query the external data source 3318 that includes less data and then using information gleaned from that data prepare a more narrowly tailored query for the external data source 3318 with more data.
  • the query coordinator 3304 determines that a union operation is to be performed on the data from the HDFS data source and the Oracle data source based on the source of the errors.
  • the query coordinator 3304 can instruct the nodes 3306 to first intake and process the data from the HDFS data source.
  • the nodes 3306 determine that the HDFS data source only includes fifty types of errors in the specified timeframe from ten sources. Accordingly, using that information, the query coordinator 3304 can instruct the nodes 3306 to limit the intake of data from the Oracle data store based on the error type and/or the source based on the error types and sources identified by first analyzing the HDFS data source.
  • the query coordinator 3304 can reduce the amount of data requested by the Oracle data store and the amount of processing needed to obtain the relevant result. For example, if the Oracle data store included two hundred error types from one hundred sources, the query coordinator 3304 avoided having to intake and process the data from all one hundred sources. Instead only the data from sources that matched the ten sources from the HDFS data source were requested and processed by the nodes 3306.
  • the query coordinator(s) 3304 can act as the primary coordinator or controller for queries that are assigned to it by the search head 210 or search process master 3302. As such, the query coordinator can process a query, identify the resources to be used to execute the query, control and monitor the nodes to execute the query, process aggregate results of the query, and provide finalized results to the search head 210 or search process master 3302 for delivery to a client device 404.
  • the query coordinator 3304 can analyze the query. In some cases analyzing the query can include verifying that the query is semantically correct or performing other checks on the query to determine whether it is executable by the system. In addition, the query coordinator 3304 can analyze the query to identify the dataset sources that are to be accessed and to define an executable search process. For example, the query coordinator 3304 can determine whether data from the indexers 206, external data sources 3318, query acceleration data store 3308, or other dataset sources (e.g., common storage, ingested data buffers, etc.) are to be accessed to obtain the relevant datasets.
  • dataset sources e.g., common storage, ingested data buffers, etc.
  • the query coordinator 3304 can identify the different entities that can perform some processing on the datasets. For example, the query coordinator 3304 can determine what portion(s) of the query can be delegated to the indexers 206, nodes 3306, and external data sources 3318, and what portions of the query can be executed by the query coordinator 3304, search process master 3302, or search head 210. For tasks that can be completed by the indexers 206, the query coordinator 3304 can generate task instructions for the indexers 206 to complete, as well as instructions to route all results from the indexers 206 to the nodes 3306. For tasks that can be completed by the external data sources 3318, the query coordinator 3304 can use the dataset compensation module 3316 to generate task instructions for the external data sources 3318 and to determine how to set up the nodes 3306 to receive data from the external data sources 3318.
  • the query coordinator 3304 can generate a logical directed acyclic graph (DAG) based on the query.
  • DAG logical directed acyclic graph
  • FIG. 35 is a diagram illustrating an embodiment of a DAG 2000 generated as part of a search process.
  • the DAG 2000 includes seven vertices and six edges, with each edge directed from one vertex to another, such that by starting at any particular vertex and following a consistently-directed sequence of edges the DAG 2000 will not return to the same vertex.
  • the DAG 2000 can correspond to a topological ordering of search phases, or layers, performed by the nodes 3306.
  • a sequence of the vertices can represent a sequence of search phases such that each edge is directed from earlier to later in the sequence of search phases.
  • the DAG 2000 may be defined based on a search string for each phase or metadata associated with a search string.
  • the metadata may be indicative of an ordering of the search phases such as, for example, whether results of any search string depend on results of another search string such that the later search string must follow the former search string sequentially in the DAG 2000.
  • the DAG 2000 can correspond to a query that identifies data from two dataset sources that are to be combined and then communicated to different locations. Accordingly, the DAG 2000 includes intake vertices 3502, 3508, a process vertex 3504, collect vertices 3506, 3510, a join vertex 3512, and a branch vertex 3514.
  • Each vertex 3502, 3504, 3506, 3508, 3510, 3512, 3514 can correspond to a search phase performed by one or more processors 3406 of one or more nodes 3306 on a particular set of data or partitions.
  • the intake, process, and collect vertices 3502, 3504, 3506 can correspond to data search phases, or transformations, on data received from a first dataset source. More specifically, the intake phase or vertex 3502 can correspond to the processing of one or more partitions associated with data received from the first dataset source, the process phase 3504 can correspond to the processing of one or more partitions that resulted from the intake phase 3502, and the collect phase 3506 can correspond to one or more partitions that collect the results of the processing of the partitions in the process phase 3504.
  • the intake and collect vertices 3508, 3510 can correspond to data search phases performed using one or more partitions or by one or more processors 3406 on data received from a second dataset source.
  • the intake phase 3508 can correspond to one or more partitions that receive data from the second dataset source and the collect phase 3510 can correspond to one or more partitions that collect the results from the partitions in the intake phase 3508.
  • the join and branch phases 3512, 3514 can correspond to data search phases performed by one or more processors 3406 on partitions corresponding to data received from the different branches of the DAG 2000.
  • the join phase 3512 can correspond to one or more partitions used to combine the data received from the partitions in the collect phases 3506, 3510.
  • the branch phase 3514 can correspond to one or more partitions used to communicate results of the join phase 3512 to one or more destinations.
  • the partitions in the branch phase 3514, or processors assigned to the partitions in the branch phase 3514 can communicate results of the query to the query coordinator 3304, an external data source 3318, accelerated data source 3308, ingested data buffer, etc.
  • the number, order, and types of search phases in the DAG 2000 can be determined based on the query.
  • a query that indicates data is to be obtained from common storage and an Oracle database, collated, and the results sent to the query coordinator 3304 and an HDFS data store.
  • the query coordinator 3304 in response to determining that the common storage do not provide processing capabilities, the query coordinator 3304 can generate vertices 3502, 3504, 3506 indicating that an intake phase 3502, process phase 3504, and collect phase 3506 will be used to process the data from the common storage sufficiently to be combined with data from the Oracle database.
  • the query coordinator can generate vertices 3508, 3510 indicating that an intake phase 3508 and collect phase 3510 will be used to sufficiently process the data from the Oracle database for combination with the data from the common storage.
  • the query coordinator 3304 can further generate the join phase 3512 based on the query indicating that the data from the Oracle database and common storage is to be collated or otherwise combined (e.g., joined, unioned, etc.). In addition, based on the query indicating that the results of the combination are to be communicated to the query coordinator 3304 and the HDFS data store, the query coordinator 3304 can generate the branch phase 3514. As mentioned above, in each phase, the query coordinator 3304 can allocate one or more nodes 3306 or processors 3406 to perform the particular search phase on the partitions of the particular phase.
  • the DAG 2000 is a non-limiting example of the search phases that can be included as part of a search process.
  • the DAG 2000 can include fewer or more phases of any type.
  • the DAG 2000 can include fewer or more intake phases depending on the number of dataset sources.
  • the DAG 2000 can include multiple processing, collect, join, union, stats, or branch phases, in any order.
  • the query coordinator 3304 can calculate the relative cost of each phase of the search process, determine the amount of resources to allocate for each phase of the search process, generate tasks and instructions for particular nodes to be assigned to a particular search process, generate instructions for dataset sources, generate tasks for itself and/or the search head 210, etc.
  • the query coordinator 3304 can communicate with the workload advisor 3310, workload catalog 3312, and/or the node monitor 3314.
  • the workload advisor 3310 can use the data collected in the workload catalog 3312 to determine the cost of a query or an individual transformation or search phase of a search process and to provide a resource allocation recommendation.
  • the workload advisor 3310 can use the data from the node monitor module 3314 to determine the available resources in the system 3301. Using this information, the query coordinator 3304 can determine the cost for each search phase, the amount of resources available for allocation, and the amount of resources to allocate for each search phase.
  • the query coordinator 3304 can also generate the tasks and instructions for each node 3306.
  • the instructions can include computer executable instructions that when executed by the node 3306 cause the node 3306 to perform the task assigned to it by the query coordinator 3304.
  • the query coordinator 3304 can generate instructions on how to access a particular dataset source, what instructions are to be sent to the dataset source, what to do with the data received from the dataset source, where do send the received data, how to perform any load balancing or other tasks assigned to it, etc.
  • the query coordinator 3304 can generate instructions indicating how to parse the received data, relevant fields or keywords that are to be identified in the data, what to do with the identified field and keywords, where to send the results of the processing, etc.
  • the query coordinator 3304 can generate task instructions so that the nodes 3306 (which can also refer to one or more processors 3406 within a worker node 3306, execution environments within a worker node 3306 or processor 3406 of a worker node 3306, such as a virtualized computing device or software-based container, etc.) are able to perform the task assigned to that particular phase or partition.
  • the task instructions can tell the nodes 3306 what data or partitions they are to process, how they are to process the data, where they are to route the results of the processing of that phase, either between each other or to another destination.
  • the query coordinator 3304 can generate the tasks and instructions for all nodes 3306 and send the instructions to all of the allocated nodes 3306. Between them, the nodes 3306 can determine or assign which nodes 3306 will execute the different instructions and tasks.
  • the instructions sent to the nodes 3306 or processors 3406 can include additional parameters, such as a preference to use nodes 3306 or processors 3406on the same machine 3402 for subsequent tasks. Such instructions can help reduce the amount of data communicated over the network, etc.
  • Each node 3306 can assign specific processors 3406 and/or memory 3408 to execute particular tasks or partitions.
  • the query coordinator 3304 can use the dataset compensation module 3316.
  • the dataset compensation module 3316 can include relevant data about external data sources including, inter alia, processing abilities of the external dataset sources, number of partitions of the external dataset sources, instruction translators, etc. Using this information, the query coordinator 3304 can determine what processing to assign to the external data sources, and generate instructions that will be understood by the external data sources.
  • the query coordinator 3304 can have access to similar information about other dataset sources and/or communicate with the dataset sources to determine their processing capabilities and how to interact with them (non-limiting examples: number of partitions to use, processing that can be pushed to the dataset source, etc.).
  • the query coordinator 3304 can determine how to interact with the dataset destinations so that the datasets can be properly sent to the correct location in a manner that the destination can store them correctly.
  • the query coordinator 3304 can interact with one partition of the external dataset source using multiple nodes 3306 or processors 3406. For example, the query coordinator 3304 can allocate multiple nodes 3306 or processors 3406 to interact with a single partition of the external dataset source.
  • the query coordinator 3304 can break up a query or a subquery into multiple parts. Each part can be assigned to a different node 3306 or processor 3406, which can communicate the subqueries to the external dataset source. Thus, unbeknownst to the external dataset source, it can concurrently process data from a single query.
  • the query coordinator 3304 can determine the order for conducting the search process. As mentioned above, in some embodiments, the query coordinator 3304 can determine that processing data from one dataset source could speed up the search process as a whole (non-limiting example: using data from one dataset source to generate a more targeted search of another dataset source). Accordingly, the query coordinator 3304 can determine that one or more search phases are to be completed first and then based on information obtained from the search phase, additional search phases are to be initiated. Similarly, other optimizations can be determined by the query coordinator 3304.
  • Such optimizations can include, but are not limited to, pushing processing to the edges (e.g., to external data sources, etc.), identifying fields in a query that are key to the query and reducing processing based on the identified field (e.g., if a relevant field is identified in a final processing step, use the field to narrow the set of data that is searched for earlier in the search process), allocating the query to nodes that are physically close to each other or on the same machine, etc. 11.3.2. QUERY EXECUTION AND NODE CONTROL
  • the query coordinator 3304 can initiate the query execution. In some cases, in initiating the query, the query coordinator 3304 can communicate the generated task instructions to the various locations that will process the data. For example, the query coordinator 3304 can communicate task instructions to the indexers 206, based on a determination that the indexers 206 are to perform some amount of processing on the dataset. Similarly, the query coordinator 3304 can communicate task instructions to the nodes 3306, external data sources 3318, query acceleration data store 3308, common storage, and/or ingested data buffer, etc.
  • the query coordinator 3304 can generate task instructions for the nodes 3306 to interact with the dataset sources such that the dataset sources receive any task instructions from the nodes 3306 as opposed to the query coordinator 3304. For example, rather than communicating the task instructions directly to a dataset source, the query coordinator 3304 can assign one or more nodes 3306 to communicate task instructions to the external data sources 3318, indexers 206, or query acceleration data store 3308. In certain embodiments, the query coordinator 3304 can communicate the same search scheme or task instructions to the nodes 3306 or processors 3406 of the nodes 3306 that have been allocated for the query. The allocated nodes 3306 or processors 3406 of the nodes 3306 can then assign different nodes 3306 to perform different portions of the search scheme.
  • the dataset sources and nodes 3306 can begin operating in parallel. For example, if task instructions are sent to the indexers 206 and to the nodes 3306, both can begin executing the instructions in parallel.
  • the nodes 3306 can organize their processors 3406 according to task instructions. For example, some of the nodes 3306 can allocate one or more processors 3406 as part of an intake phase, another processor 3406 as part of a processing phase, etc. In some cases, all processors 3406 of a node 3306 can be allocated to the same task or to different tasks.
  • processors 3406 of a node 3306 can be allocated to tasks of the intake phase, and during a processing phase, all processors 3406 of a node 3306 can be allocated to tasks of the processing phase, etc.
  • FIG. 36 is a block diagram illustrating an embodiment of layers of partitions used to implement various search phases of a query.
  • the layers can correspond to search phases in a DAG, such as the DAG 2000 described in greater detail above.
  • various partitions are used to perform different search phases on data coming from a dataset source 3602.
  • the dataset source 3602 can correspond to indexers 206, external data sources 3318, the query acceleration data store 3308, common storage, an ingested data buffer, or other source of data from which the nodes 3306 can receive data.
  • the processors 3406 or worker nodes 3306 assigned to each layer can interact with the data or partitions based on task instructions received by the query coordinator 3304.
  • the partitions in the intake layer 3604 can correspond to data received from the dataset source 3602, which can be communicated or transformed to partitions in the processing layer 3606 by worker nodes 3306 in a load-balanced fashion.
  • the worker nodes 3306 can process the data of the partitions in the processing layer 3606 based on the task instructions, which are generated based on the query, and provide or transform the results to or into the partitions in the collector layer 3608.
  • the processors 3406 of the worker nodes 3306 associated with the partitions in the collector layer 3608 can communicate the results of their processing to the branch layer 3610.
  • the branch layer 3610 communicates the results received from the partitions in the collector layer 3608 to a first dataset destination 3614 and to partitions in a storage layer 3612 for storage in a second dataset destination 3616.
  • fewer or more layers can be included as desired, and can be based on the content of the particular query being executed.
  • the layers can correspond to different map-reduce procedures or commands.
  • the processing layer 3606 can correspond to a map procedure and the collector layer 3608 can correspond to a reduce procedure.
  • Flowever as described herein, it will be understood that various layers can correspond to map or reduce procedures.
  • partitions are included in the intake layer 3604, eight partitions are included in the processing layer 3606, five partitions are included in the collector layer 3608, one partition is included in the branch layer 3610, and three partitions are included in the storage layer 3612.
  • the number of partitions can correspond to the number of tasks or amount of data being processed in the layer. Thus, there is a larger amount of data to be processed in the processing layer 3606 than in the intake layer 3604 or collector layer 3608. Further, it will be understood that fewer or more partitions can be used in any layer as desired and fewer or additional layers can be included.
  • the query coordinator 3304 can allocate separate intake, processing, and collector layers 3604, 3606, 3608 for each dataset source 3602. Furthermore, based on the query commands, the query coordinator can allocate additional layers, such as a join layer to combine data received from multiple dataset sources, etc.
  • the query coordinator 3304 can use the workload advisor 3310 and/or dataset compensation module 3316.
  • the workload advisor 3310 can use historical data about executing individual search phases in queries to recommend an allocation scheme that provides sufficient resources to process the query in a reasonable amount of time.
  • the query coordinator 3304 can determine the number of partitions based on the amount of processors 3406 assigned to the query, the amount of memory available, the amount of data (or number of events) to be processed, and information about the events or query, such as the number of fields used in the query or part of the events.
  • the query coordinator 3304 can allocate partitions or processors 3406 for the intake layer 3604 and storage layer 3612 based on information about the number of partitions available for reading from the dataset source 3602 and writing data to the dataset destination 3616, respectively.
  • the query coordinator 3304 can obtain the information about the dataset source 3602 or dataset destination 3616 from a number of locations, including, but not limited to, the workload catalog 3312, the dataset compensation module 3316, or from the dataset source 3602 or dataset destination 3616 itself.
  • the information can inform the query coordinator 3304 as to the number of partitions available for reading from the dataset source 3602 and writing to the dataset destination 3616.
  • the query coordinator 3304 can allocate worker nodes 3306 or processors 3406 in the intake layer 3604 or the storage layer 3612 to have a one-to-one, one-to-many, or many-to-one correspondence with partitions supported by the dataset source 3602 or dataset destination 3616, respectively.
  • the correspondence between the worker nodes 3306 or processors 3406 in the intake or storage layer 3604, 3612 and the partitions supported by the dataset source or destination 3602, 3616, respectively, can be based on a threshold number of partitions, the type of the dataset source/destination, etc.
  • the query coordinator 3304 can allocate nodes 3306 or processors 3406 in the intake layer 3604 (or storage layer 3612) to have a one-to-one correspondence to partitions supported by the dataset source 3602 (or dataset destination 3616).
  • the number of partitions that satisfy the threshold number of partitions can be determined based on the number of nodes 3306 or processors 3406 in the system 3301, the number of available nodes 3306 in the system 3301, scheduled usage of nodes 3306, amount of memory available, etc. Accordingly, the threshold number of partitions can be dynamic depending on the status of the processors 3406, nodes 3306, or the system 3301. For example, if a large number of nodes 3306 are available, the threshold number of nodes can be larger, whereas, if only a relatively small number of nodes 3306 are available, the threshold number can be smaller.
  • the workload advisor 33010 can allocate fewer worker nodes 3306 or processors 3406 to an individual query. Alternatively, if the workload advisor 33010 does not expect many queries in the near term it can allocate a greater number of worker nodes 3306 or processors 3406 to an individual query.
  • the query coordinator 3304 can determine whether to match the number of partitions supported by the dataset source 3602 or dataset destination 3616 with corresponding worker nodes 3306 or processors 3406 in the intake layer 3604 or storage layer 3612, respectively, based on the type of the dataset source 3602 or dataset destination 3616.
  • the query coordinator 3304 can determine there should be a one-to-one correspondence of intake layer 3604 worker nodes 3306 or processors 3406 to dataset source 3602 supported partitions (or storage layer 3612 worker nodes 3306 or processors 3406 to dataset destination 3616 supported partitions) when the dataset source 3602 (or dataset destination 3616) is an external data source or ingested data buffer and that there should be a one-to-multiple correspondence when the dataset source 3602 (or dataset destination 3616) is indexers 206, common storage, query acceleration data store 3308, etc.
  • the query coordinator 3304 can allocate four worker nodes 3306 or processors 3406 to the intake layer 3604.
  • the allocated worker nodes 3306 or processors can intake the data as four or more partitions, as illustrated in FIG. 36.
  • the query coordinator 3304 can allocate three worker nodes 3306 or processors 3406 to the storage layer 3612, which can result in three or more partitions being worked on concurrently, as illustrated in FIG. 36.
  • the query coordinator 3304 can allocate four worker nodes 3306 or processors 3406 to the intake layer 3604 resulting in at least four partitions being worked on concurrently (or three worker nodes 3306 or processors 3406 to the storage layer 3612 resulting in at least three partitions being worked on concurrently), as illustrated in FIG. 36.
  • the query coordinator 3304 can allocate all worker nodes 3306 or all worker nodes 3306 assigned to its query to the intake layer 3604 for reading data from dataset source 3602 or sending data to dataset destination 3616.
  • the query coordinator 3304 can dynamically adjust the number of worker nodes 3306 or processors 3406 in the intake layer 3604. For example, if an additional partition of the dataset source 3602 becomes available or one of the partitions becomes unavailable, the query coordinator 3304 can dynamically increase or decrease the number of worker nodes 3306 or processors 3406 in the intake layer 3604. Similarly, if the query coordinator 3304 determines that the intake layer 3604 is taking too much time and additional resources are available, it can dynamically increase the number of worker nodes 3306 or processors 3406 in the intake layer 3604.
  • the query coordinator 3304 can dynamically increase or decrease the number of worker nodes 3306 or processors 3406 in the intake layer 3604. Similarly, the query coordinator can dynamically adjust the number of worker nodes 3306 or processors 3406 in the storage layer 3612. [00644] Similar to the intake layer 3604 and storage layer 3612, the query coordinator 3304 can estimate or determine a number of partitions for the different search layers 3606, 3608, 3610 based on information about the query and information in the workload catalog 3312 and allocate worker nodes 3306 or processors 3406 accordingly. For example, the query may include requests to process the data in a way that is resource intensive, resulting in a larger number of partitions.
  • the query coordinator 3304 can estimate that a larger number of partitions will be used in the processing layer and allocate additional worker nodes 3306 or processors 3406 to the processing layer 3606 or use multiple processing layers 3606 to process the data. In some cases, more partitions, worker nodes 3306, and/or processors 3406 can be allocated to the search layers for queries of larger datasets.
  • the query coordinator 3304 can monitor the partitions or processors 3406 in the search layers 3606, 3608, 3610 and dynamically adjust the number of partitions or processors 3406 in each depending on the status of the individual partitions, the status of the nodes 3306, the status of the query, etc. For example, if a partition becomes larger than a threshold size due to high cardinality or other reasons, a worker node 3306 can generate additional partitions and redistribute the data of the partition between the different partitions.
  • the worker nodes 3306 can redistribute partitions or tasks assigned to the worker node 3306 amongst themselves.
  • the query coordinator 3304 can determine that a significant number of results or partitions are being sent or assigned to a particular worker node 3306 in the collector layer 3608. As such, the query coordinator 3304 can allocate an additional worker node 3306 to the collector layer and/or instruct that the results from the partitions in the processing layer 3606 be distributed in a different manner to reduce the load on the particular worker node 3306 in the collector layer.
  • the query coordinator 3304 can allocate additional worker nodes 3306 or processors 3406 to the layer to increase parallelism and decrease the processing time. For example, the query coordinator 3304 can determine that a worker node 3306 assigned to the processing layer 3606 is not functioning or that there is significantly more data coming from the dataset source 3602 than was anticipated. Accordingly, the query coordinator 3304 can allocate additional worker nodes 3306 or processors 3406 to the intake layer 3604 or processing layer 3606.
  • the query coordinator 3304 determines that some of the worker nodes 3306 or processors 3406 are underutilized, then it can deallocate it from a particular layer and make it available for other queries, or assign it to a different layer, etc. Accordingly, the query coordinator 3304 can dynamically allocate and deallocate resources to intake and process the data from the dataset source 3602 in a time-efficient and performant manner.
  • the query coordinator 3304 can generate a DAG that includes the intake layer 3604, processing layer 3606, collector layer 3608, branch layer 3610, and storage layer 3612. Additionally, based on a determination that the external data source supports four partitions, the query coordinator 3304 allocates four worker nodes 3306 or processors 3406 to the intake layer 3604 to process the data from incoming partitions.
  • the query coordinator 3304 allocates eight partitions to the processing layer 3606, and five partitions to the collector layer 3608. Further, based on resource availability and the determination that the dataset destination is the query acceleration data store 3308, which can support more than a threshold number of partitions, the query coordinator 3304 allocates three worker nodes 3306 or processors to the storage layer 3612 to process partitions at that layer. The task instructions for each search layer can be sent to the nodes 3306, which assign processors 3406 to the various tasks and partitions.
  • the partitions in the intake layer 3604 communicate with the dataset source 3602 to receive the relevant data from the partitions of the dataset source 3602. The data is then communicated to the partitions in the processing layer 3606.
  • each worker node 3306 of the intake layer 3604 communicates data in a load- balanced fashion to partitions in the processing layer 3606.
  • the worker nodes 3306 or processors 3406 in the processing layer 3606 can parse the incoming data or partitions to identify events that include an error and identify the type of error.
  • the worker nodes 3306 or processors 3406 in the processing layer 3606 can communicate the results to partitions in the collector layer 3608.
  • one or more processors 3406 can apply a modulo five to the error type to each partition in the processing layer 3606 in order to attempt to equally separate the results between the partitions in the collector layer 3608.
  • a partition (or multiple related partitions) in the collector layer 3608 can include the total count of errors for that type.
  • the partitions in the collector layer 3608 can also include the event that included the particular error type.
  • the worker nodes 3306 or processors 3406 can send the results of processing the partitions in the collector 3608 to a partition in the branch layer 3610.
  • the worker nodes 3306 or processors 3406 can communicate the results in the partition of the branch layer 3610 to the query coordinator 3304, which can communicate the results to the search head or client device.
  • the branch layer 3610 can communicate the results to the partitions in the storage layer 3612, which communicate the results in parallel to the query acceleration data store 3308.
  • the query coordinator 3304 can monitor the worker nodes 3306 or processors 3406 processing partitions in the intake layer 3604, processing layer 3606, collector layer 3608, branch layer 3610, and storage layer 3612. If a worker node 3306 or processor 3406 becomes unavailable or becomes overloaded, the query coordinator 3304 can allocate additional resources or redistribute tasks or partitions. Similarly, if a worker node 3306 or processor 3406 is not being utilized, the query coordinator 3304 can deallocate it from a layer or redistribute the tasks or partitions. For example, if a partition on the external data source becomes unavailable, a corresponding worker node 3306 or processor 3406 in the intake layer 3604 may no longer receive any data.
  • the query coordinator 3304 can deallocate that worker node 3306 or processor 3406 from the intake layer 3604.
  • any change in state of a worker node 3306 or processor 3406 can be reported to the node monitor module 3314, which can be used by the query coordinator to allocate resources.
  • the nodes 3306 can communicate the results to the query coordinator 3304.
  • the query coordinator 3304 can perform any final processing. For example, in some cases, the query coordinator 3304 can collate the data from the nodes 3306.
  • the query coordinator 3304 can also send the results to the search head 210 or to a dataset destination. For example, based on a command (non-limiting example“into”), the query coordinator 210 can store results in the query acceleration data store 3308, an external data source 3318, an ingested data buffer, etc.
  • the query coordinator 3304 can communicate to the search process master 3302 that the query has been completed. In the event all queries assigned to the query coordinator 3304 have been completed, the query coordinator can shut down or enter a hibernation state and await additional queries assigned to it by the search process master 3302.
  • a query can indicate that information is to be stored (e.g., stored in non volatile or volatile memory) in the query acceleration data store 3308.
  • the query acceleration data store 3308 can store information (e.g., datasets) sourced from other dataset sources, such as, external data sources 3318, indexers 206, ingested data buffers, indexers, and so on.
  • information e.g., datasets
  • a user can indicate that particular information is to be stored in the query acceleration data store 3308 (e.g., cached).
  • the information can include the results of the query, partial results of the query, data (processed or unprocessed) received from another dataset source via the nodes 3306, etc.
  • the data intake and query system 3301 can cause queries directed to the particular information to utilize the query acceleration data store 3308. In this way, the stored information can be rapidly accessed and utilized.
  • the query can indicate that information is to be obtained from the external data sources 3318. Since the external data sources 3318 may have potentially high latency, response times to particular queries, the query can be constrained according to characteristics of the external data sources 3318. For example, particular external data sources 3318 may be limited in their processing speed, network bandwidth, and so on, such that the worker nodes 3306 are required to wait longer for information. As described herein, the query can therefore specify that particular information from the external data sources 3318 (or other dataset sources) be stored in the query acceleration data store 3308. Subsequent queries that utilize this particular information can then be executed more quickly. For example, in subsequent queries the worker nodes 3306 can obtain the particular information from the query acceleration data store 3308 rather than from the external data source 3318.
  • An example query can be of a particular form, such as:
  • the query indicates that information is to be obtained from a dataset source, such as an external data source 3318.
  • the query can indicate particular tables, documents, records, structured or unstructured information, and so on.
  • the data intake and query system 3301 can process the query and determine that the external data source is being referenced.
  • the next element of the query (e.g., a request parameter) includes logic to be applied to the data from the external data source, for example the logic can be implemented as structured query language (SQL), search processing language (SPL), and so on.
  • the worker nodes 3306 can obtain the requested data, and apply the logic to obtain information to be provided in response to the query.
  • an accelerated directive is included.
  • the accelerated directive can be a particular term (e.g.,“into query acceleration data store”), symbol, and so on, included in the query.
  • the accelerated directive can optionally be manually included in the query (e.g., a user can type the directive), or automatically.
  • a user can indicate in a user interface associated with entering queries that information is to be stored in the query acceleration data store 3308.
  • the user s client device or query coordinator 3304 can determine that information is to be stored in the data store 3308.
  • the query can be analyzed by the client device or query coordinator 3304, and based on a quantity of information being requested, the client device or query coordinator 3304can automatically include the accelerated directive (e.g., if greater than a threshold quantity is being requested, the directive can be included).
  • the data intake and query system 3301 can automatically store the requested information in the query acceleration data store 3308 without an accelerated directive in a received query.
  • the query system 3301 can automatically store data in the query acceleration data store 3308 based on a user ID (e.g., always store results for a particular user or based on recent use by the user), time of day (e.g., store results for queries made at the beginning or end of a work day, etc.), dataset source identity (e.g., store data from dataset source identified has having a slower response time, etc.), network topology (e.g., store data from sources on a particular network given the network bandwidth, etc.) etc.
  • a user ID e.g., always store results for a particular user or based on recent use by the user
  • time of day e.g., store results for queries made at the beginning or end of a work day, etc.
  • dataset source identity e.g., store data from dataset source identified has having a slower response time, etc.
  • network topology e.g., store data from sources on a particular network given the network bandwidth, etc.
  • the data intake and query system 3301 e.g., the query coordinator
  • the query acceleration data store 3308 can receive the processed result associated with the query (e.g., from the worker nodes 3306). The query acceleration data store 3308 can then provide the processed result to the query coordinator 3304 to be relayed to the requesting client. However, to increase response times, the worker nodes 3306 can provide processed information to the query acceleration data store 3308, and also to the query coordinator 3304. In this way, the query acceleration data store 3308 can store (e.g., in low latency memory, or longer latency memory such as solid state storage or disk storage) the received processed information, while the query coordinator 3304 can relay the received processed information to the requesting client.
  • the query acceleration data store 3308 can store (e.g., in low latency memory, or longer latency memory such as solid state storage or disk storage) the received processed information, while the query coordinator 3304 can relay the received processed information to the requesting client.
  • the processed result may be stored by the query acceleration data store 3308 in association with an identifier, such that the information can be easily referenced.
  • the query acceleration data store 3308 can generate a unique identifier upon receipt of information for storage by the worker nodes 3306.
  • the query coordinator 3304 can receive the identifier, such that the query coordinator 3304 can replace the initial portion with the unique identifier.
  • the query coordinator 3304 can generate the unique identifier.
  • the query coordinator can receive information from the query acceleration data store 3308 indicating that it stored information.
  • the query coordinator 3304 can maintain a mapping between generated unique identifiers and datasets, partitions, and so on, that are associated with information stored by the query acceleration data store 3308.
  • the query coordinator 3304 may optionally provide a unique identifier to the requesting client, such that a user of the requesting client can re-use the unique identifier.
  • the user’ s client can present a list of all such identifiers along with respective queries that are associated with the identifier. The user can select an identifier, and generate a new query that is based on an associated query.
  • the query acceleration data store can store additional information regarding the results.
  • the query acceleration data store can store information about the size of the dataset, the query that resulted in the dataset, the dataset source of the dataset, the time of the query that resulted in the dataset, the time range of data that was processed to produce the dataset, etc. This information can be used by the system 3301 to prompt a user as to what data is stored and can be used in the query acceleration data store, determine whether portions of an incoming query correspond to datasets in the accelerate data store, etc. This information can also be stored in the workload catalog 3312, or otherwise made available to the query coordinator 3304.
  • the query coordinator 3304 can cause the worker nodes 3306 to obtain the information from the query acceleration data store 3308.
  • a subsequent query can be
  • the query coordinator 3304 can determine that some portion of the data referenced in the query corresponds to data that is stored in the query acceleration data store 3308 (previously stored data) or was previously processed according to a prior query (e.g., the query represented above) and the results of the processing stored in the query acceleration data store 3308. For example, the query coordinator 3304 can compare the query to prior queries, and any portion of data that was referenced in a prior query. The query coordinator 3304 can then instruct the worker nodes 3306 to obtain the previously stored data or the results of processing the data from the query acceleration data store 3308. In some cases, the subsequent query can include an explicit command to obtain the data or results from the query acceleration data store 3308.
  • the worker nodes 3306 can avoid having to reprocess the data, and instead can utilize the prior processed result. Additionally, the worker nodes 3306 can more rapidly obtain information from the query acceleration data store 3308 than, for example, the external data sources 3318. As an example, the worker nodes 3306 may be in communication with the query acceleration data store 3308 via a direct connection (e.g., virtual networks, local area networks, wide area networks). In contrast, the worker nodes 3306 may be in communication with the external data sources 3318 via a global network (e.g., the internet).
  • a direct connection e.g., virtual networks, local area networks, wide area networks
  • a global network e.g., the internet
  • a first query can indicate that data from a dataset source is to be stored in the query acceleration data store 3308 with minimal processing by the nodes 3306 or without transforming the data from the dataset source.
  • a subsequent query can indicate that the data stored in the query acceleration data store 3308 is to be processed or transformed, or combined with other data or results to obtain a result.
  • the first query can indicate that data from the dataset source is to be transformed and the results stored in the query acceleration data store 3308.
  • the subsequent query can indicate that the results stored in the query acceleration data store 3308 are to be further processed, combined with data or results from another dataset source, or provided to a client device.
  • the worker nodes 3306 can perform any additional processing on the results obtained from the query acceleration data store 3308, while concurrently obtaining data from another dataset source and processing it to obtain additional results.
  • the results stored in the query acceleration data store 3308 can be communicated to a client device while the nodes concurrently obtain data from another dataset source and process it to obtain additional results.
  • the‘subsequent_logic’ can be applied by the worker nodes 3306 based on the processed result stored by the query acceleration data store 3308.
  • the result of the subsequent query can then be provided to the query coordinator 3304 to be relayed to the requesting client.
  • the query acceleration data store 3308 as described herein, can maintain information in low-latency memory (e.g., random access memory) or longer-latency memory. That is, the query acceleration data store 3308 can cause particular information to spill to disk when needed, ensuring that the data store 3308 can service large amounts of queries.
  • the query acceleration data store 3308 can determine which datasets are to be stored in the low-latency memory.
  • the query acceleration data store 3308 can be implemented as a distributed in-memory data store with spillover to disk capabilities.
  • the data in the query acceleration data store 3308 can be stored in low-latency volatile memory, and in the event, the capacity of the low-latency volatile memory is reached, the data can be stored to disk.
  • the query acceleration data store 3308 can utilize one or more storage policies to swap datasets between low-latency memory and longer-latency memory. Additionally, the query acceleration data store 3308 can flush particular datasets after determining that the datasets are no longer needed (e.g., the user can indicate that the datasets can be flushed, or a threshold amount of time can pass).
  • the query acceleration data store 3308 can store a portion of a dataset in low-latency memory while storing a remaining portion in longer-latency memory. In this way, the query acceleration data store 3308 can have faster access to at least a portion each user’s dataset. If a subsequent query is received by the data intake and query system 3301 that references a stored dataset, the query acceleration data store 3308 can access the portion of the stored dataset that is in low-latency memory. Since this access is, in general, with low-latency, the query acceleration data store 3308 can quickly provide this information to the worker nodes 3306 for processing.
  • the query acceleration data store 3308 can access the longer-latency memory and obtain a remaining portion of the stored dataset.
  • the worker nodes 3306 can then receive this remaining portion for processing. Therefore, the worker nodes 3306 can quickly respond to a request, based on the initially received portion from the low-latency memory.
  • the user can receive search results in a manner that appears to be in‘real-time’, that is, the search results can be provided in a less than a threshold amount of time (e.g., 1 second, 5 seconds, 10 seconds). Subsequent search results can then be provided upon the worker nodes 3306 processing the portion from the longer-latency memory.
  • the above-described storage policy may be based on a size of the dataset(s). For example, an example dataset may be less than a threshold, and the query acceleration data store 3308 may store the entirety of the dataset in low-latency memory. For an example dataset greater than the threshold, the data store 3308 may store a portion in low-latency memory. As the size of the dataset increases, the query acceleration data store 3308 can store an increasingly lesser sized portion in low-latency memory. In this way, the data store 3308 can ensure that large data sets do not consume the low-latency memory.
  • the data intake and query system 3301 can receive a first query that is a combination of the first query and second query described above.
  • an example initial query can be
  • the above example query indicates that the data intake and query system 3301 is to obtain information from an example dataset source (e.g., external data source 3318), process the information, and cause the query acceleration data store 3308 to store the processed information.
  • subsequent logic is to be applied to the processed information, and the result provided to the requesting client 404a-404n.
  • FIG. 36 illustrates a branch layer 3610, which for the example query described above, can be utilized to provide information both to the query acceleration data store 3308 and the data destination 3614 (e.g., the requesting client).
  • the worker nodes 3306 can provide the processed information for storage in the query acceleration data store 3308 while continuing to process the query (e.g., apply the subsequent logic). That is, the worker nodes 3306 can bifurcate the data (e.g., at branch layer 3610), such that the query acceleration data store 3308 can store partial results while the worker nodes 3306 service the query and provide the completed results to the query coordinator 3304.
  • another query may be received that references the partial results in the data store 3308, and one or more worker nodes 3306 may access the data store 3308 to service the other query.
  • the other query may be processed at a same time as the above-described example initial query.
  • Received queries can further indicate multiple datasets stored by the query acceleration data store 3308.
  • a first query can indicate that first information is to be obtained (e.g., from external data source 3318, indexers 206, common storage, and so on) and stored in the query acceleration data store 3308 as a first dataset.
  • a second query can indicate that second information is to obtained and stored in the data store 3308 as a second dataset.
  • Subsequent queries can then reference the stored first dataset and second dataset, such that logic can be applied to both the first and second dataset via rapid access to the query acceleration data store 3308.
  • queries can reference datasets stored by the query acceleration data store 3308, and also datasets to be obtained from another dataset source (e.g., from external data source 3318, indexers 206, ingested data buffer, and so on).
  • the data intake and query system 3301 may be able to provide results (e.g., search results) from the query acceleration data store 3308 while datasets is being obtained from another dataset source.
  • the system 3301 may be able to provide results from the data store 3308 while data obtained from another dataset source is being processed.
  • a first query can cause a dataset to be stored in the query acceleration data store 3308, with the dataset being from an external data source 3318 and representing records from a prior time period (e.g., one hour).
  • a second query can reference the stored dataset and further cause newer records to be obtained from the external data source (e.g., a subsequent hour).
  • particular logic indicated in the second query can enable the data intake and query system 3301 to provide results to a requesting client based on the stored dataset in the query acceleration data store 3308.
  • the second query can indicate that the system 3301 is to search for a particular name.
  • the worker nodes 3306 can obtain stored information from the query acceleration data store 3308, and identify instances of the particular name.
  • This access to the query acceleration data store 3308, as described above, can be low-latency.
  • the query acceleration data store 3308 may have a portion of the stored information in low- latency memory, such as RAM or volatile memory, and the worker nodes 3306 can quickly obtain the information and identify instances of the particular name. These identified instances can then be relayed to the requesting client.
  • the query acceleration data store 3308 may have a different portion of the stored information in longer-latency memory, and can similarly identify instances of the particular name to be provided to the requesting client.
  • the requesting client can view search results, for example search results based on the dataset stored by the query acceleration data store 3308, while subsequent search results are being determined (e.g., search results based on information from a different dataset source).
  • search results for example search results based on the dataset stored by the query acceleration data store 3308, while subsequent search results are being determined (e.g., search results based on information from a different dataset source).
  • the dataset being obtained from the other dataset source can be provided to the query acceleration data store 3308 for storage, for example, provided while the worker nodes 3306 apply logic to determine results from the obtained dataset.
  • each dataset can be associated with an access control list, and the query coordinator 3304 can provide an identification of a requesting user to the worker nodes 3306 and/or query acceleration data store 3308.
  • the identification can be an authorization or authentication token associated with the user.
  • the query acceleration data store 3308 can then ensure that only authorized users are allowed access to stored datasets.
  • a user who causes a dataset to be stored in the query acceleration data store 3308 e.g., based on a provided query
  • can be indicated as being authorized e.g., in an access control list associated with the dataset).
  • the user can indicate one or more other users as having access.
  • the data intake and query system 3301 can utilize role-based access controls to allow any user associated with a particular role to access particular datasets. In this way, the stored information can be secure while enabling the query acceleration data store 3308 to service multitudes of users.
  • FIG. 37 is a data flow diagram illustrating an embodiment of communications between various components within the environment 3300 to process and execute a query.
  • the search head 210 receives and processes a query.
  • the search head 210 communicates the query to the search process service 3702, which can refer to the search process master 3302 and/or query coordinator 3304.
  • the search process service processes the query.
  • the query coordinator 3304 can identify the dataset sources (e.g., external data sources 3318, indexers 206, query acceleration data store 3308, common storage, ingested data buffer, etc.) to be accessed, generate instructions for the dataset sources based on their processing capabilities or communication protocols, determine the size of the query, determine the amount of resources to allocate for the query, generate instructions for the nodes 3306 to execute the query, and generate tasks for itself to process results from the nodes 3306.
  • dataset sources e.g., external data sources 3318, indexers 206, query acceleration data store 3308, common storage, ingested data buffer, etc.
  • the query coordinator 3304 communicates the task instructions for the query to the worker nodes 3306 and/or the dataset sources 3704. As described above, in some embodiments, the query coordinator 3304 can communicate task instructions to the dataset sources 3704. In certain embodiments, the nodes 3306 communicate task instructions to the dataset sources 3704.
  • the nodes 3306 and/or dataset sources 3704 process the received instructions.
  • the instructions for the dataset sources 3704 can include instructions for performing certain transformations on the data prior to communicating the data to the nodes 3306, etc.
  • the instructions for the nodes 3306 can include instructions on how to access the relevant data, the number of search phases or layers to be generated, the number of partitions, worker nodes 3306, or processors 3406 to be allocated for each search phase or layer, the tasks for the partitions or processors 3406 in the different layer, data routing information to route data between the nodes 3306 and to the search process service 3702, etc.
  • the nodes 3306 can assign processors 3406 to different layers and partitions and begin executing the task instructions.
  • the nodes 3306 receive the data from the dataset source(s). As described in greater detail above, the nodes 3306 can receive the data from one or more dataset sources 3704 in parallel. In addition, the nodes 3306 can receive the data from a dataset source using one or more partitions or processors 3406. The data received from the dataset sources 3704 can be semi-processed data based on the processing capabilities of the dataset source 3704 or it can be unprocessed data from the dataset source 3704.
  • the nodes 3306 process the data based on the task instructions received from the query coordinator 3304. As described in greater detail above, the nodes 3306 can process the data using one or more layers, each having one or more partitions or processors 3406 assigned thereto. Although not illustrated in FIG. 37, it will be understood that the search process service 3702 can monitor the nodes 3306 and dynamically allocate resources based on the monitoring.
  • the nodes 3306 communicate the results of the processing to the query coordinator 3304 and/or to a dataset destination 3704.
  • the dataset destination 3704 can be the same as the dataset source.
  • the nodes 3306 can obtain data from the ingested data buffer and then return the results of the processing to a different section of the ingested data buffer, or obtain data from the query acceleration data store 3308 or an external data source 3318 and then return the results of the processing to the query acceleration data store 3308 or external data source 3318, respectively.
  • the dataset destination 3704 can be different from the dataset source 3704.
  • the nodes 3306 can obtain data from the ingested data buffer and then return the results of the processing to the query acceleration data store 3308 or an external data source 3318.
  • the search process service 3702 can perform additional processing, and at (10) the results can be communicated to the search head 210 for communication to the client device. In some cases, prior to communicating the results to the client device, the search head 210 can perform additional processing on the results.
  • the query data flow can include fewer or more steps.
  • the search process service 3702 does not perform any further processing on the results and can simply forward the results to the search head 210.
  • nodes 3306 receive data from multiple dataset sources 3704, etc.
  • FIG. 38 is a flow diagram illustrative of an embodiment of a routine 3800 implemented by the query coordinator 3304 to provide query results. Although described as being implemented by the query coordinator 3304, it will be understood that one or more elements outlined for routine 3800 can be implemented by one or more computing devices/components that are associated with the system 3301, such as the search head 210, search process master 3302, indexer 206, and/or worker nodes 3306. Thus, the following illustrative embodiment should not be construed as limiting.
  • the query coordinator 3304 receives a query.
  • the query coordinator 3304 can receive the query from the search head 210, search process master 3302, etc.
  • the query coordinator 3304 can receive the query from a client 404.
  • the query can be in a query language as described in greater detail above.
  • the query received by the query coordinator 3304 can correspond to a query received and reviewed by the search head 210.
  • the search head 210 can determine whether the query was submitted by an authenticated user and/or review the query to determine that it is in a proper format for the data intake and query system 3301, has correct semantics and syntax, etc.
  • the search head 210 can run a daemon to receive search queries, and in some cases, spawn a search process, to communicate the received query to and receive the results from the query coordinator 3304 or search process master 3302
  • processing the query can include any one or any combination of: identifying relevant dataset sources and destinations for the query, obtaining information about the dataset sources and destinations, determining processing tasks to execute the query, determining available resources for the query, and/or generating a query processing scheme to execute the query based on the information.
  • the query coordinator 3304 allocates multiple layers or search phases of partitions or processors 3406 to execute the query. Each level or phase can be given a different task in order to execute the query. For example, as described in greater detail above with reference to FIGS.
  • one level can be given the task of interacting with the dataset source and receiving data from the dataset source, another level can be tasked with processing the data received from the dataset source, a third level can be tasked with collecting results of processing the data, and additional levels can be tasked with communicating results to different destinations, storing the results in one or more dataset destinations, etc.
  • the query coordinator 3304 can allocate as many or as few levels of partitions or processors 3406 to execute the query.
  • the query coordinator 3304 distributes the query for execution.
  • Distributing the query for execution can include any one or any combination of: communicating the query processing scheme to the nodes 3306, monitoring the nodes 3306 during the processing of the query, or allocating/deallocating resources based on the status of the nodes and the query, and so forth, as described herein.
  • the query coordinator 3304 receives the results.
  • the query coordinator 3304 receives the results from the nodes 3306.
  • the nodes 3306 can communicate the results of the query to the query coordinator 3304.
  • the query coordinator 3304 receives the results from the query acceleration data store, or indexers 206, etc.
  • the query coordinator 3304 receives the results from one or more components of the data intake and query system 3301 depending on the dataset sources used in the query.
  • the query coordinator 3304 processes the results.
  • the results of a query cannot be finalized by the nodes 3306.
  • all of the data must be gathered before the results can be determined.
  • a result cannot be determined until all relevant data has been collected by the worker nodes.
  • the query coordinator 3304 can receive the results from the worker nodes 3306, and then collate the results.
  • the query coordinator 3304 communicates the results.
  • the query coordinator 3304 communicates the results to the search head 210, such as a search process generated by the search to handle the query.
  • the query coordinator 3304 communicates the results to the search process master 3302 or client device 404, etc.
  • routine 3800 can include monitoring nodes during execution of the query or query processing scheme, allocating or deallocating resources during the execution of the query, etc. Similarly, routine 3800 can include reporting completion of the query to a component, such as the search process master 3302, etc.
  • FIG. 38 can be implemented in a variety of orders.
  • the query coordinator 3304 can implement some blocks concurrently or change the order as desired.
  • the query coordinator 3304 can receive (3808), process (3810), and/or communicate results (3812) concurrently or in any order, as desired.
  • FIG. 39 is a flow diagram illustrative of an embodiment of a routine 3900 implemented by the query coordinator 3304 to process a query.
  • routine 3900 implemented by the query coordinator 3304 to process a query.
  • one or more elements outlined for routine 3900 can be implemented by one or more computing devices/components that are associated with the system 3301, such as the search head 210, search process master 3302, indexer 206, and/or worker nodes 3306.
  • the following illustrative embodiment should not be construed as limiting.
  • the query coordinator 3304 identifies dataset sources and/or destinations for the query.
  • the query explicitly identifies the dataset sources and destinations that are to be used in the query.
  • the query can include a command indicating that data is to be retrieved from the query acceleration data store 3308, ingested data buffer, common storage, indexers, or an external data source (inclusive of external data systems 12).
  • the query coordinator 3304 parses the query to identify the dataset sources and destinations that are to be used in the query.
  • the query may identify the name (or other identifier) or the location (e.g., my_index) of the relevant data and the query coordinator 3304 can use the name or identifier to determine whether that particular location is associated with the query acceleration data store 3308, ingested data buffer, common storage, indexers 206, or an external data source 3318.
  • the query coordinator 3304 can use the name or identifier to determine whether that particular location is associated with the query acceleration data store 3308, ingested data buffer, common storage, indexers 206, or an external data source 3318.
  • the query can include a reference or identifier that can be used to look up or otherwise identify the dataset source.
  • the query can include a reference to an external query configuration file that includes information about dataset sources, etc.
  • the external query configuration file can include details about the dataset source, such as, but not limited to, an identifier for the dataset source, search type to be performed on the dataset source (e.g., streaming, batch, reporting, etc.), maximum or estimate number (or size) of results expected from the dataset source, number IP address, port number, access credentials (e.g., account name/type, password, etc. to access the dataset source), etc.
  • the query coordinator identifies the dataset source based on timing requirements of the search. For example, in some cases, queries for data that satisfy a timing threshold or are within a time period are handled by indexers or correspond to data in an ingested data buffer, as described herein. In some embodiments, data that does not satisfy the timing threshold or is outside of the time period are stored in common storage, query acceleration data stores, external data sources, or by indexers. For example, as described in greater detail herein, in some cases, the indexers fill hot buckets with incoming data. Once a hot bucket is filled, it is stored. In some embodiments hot buckets are searchable and in other embodiments hot buckets are not.
  • a query that reflects a time period that includes hot buckets can indicate that the dataset source is the indexers, or hot buckets being processed by the indexers.
  • a query that reflects a time period that includes warm buckets can indicate that the dataset source is the indexers.
  • a query for data that satisfies the timing threshold or is within the time period can indicate that the ingested data buffer is the dataset source. Further, in embodiments, where warm buckets are stored in a common storage, a query for data that does not satisfy the timing threshold or is outside of the time period can indicate that the common storage is the dataset source.
  • the time period can be reflective of the time it takes for data to be processed by the data intake and query system 3301 and stored in a warm bucket. Thus, a query for data within the time period can indicate that the data has not yet been indexed and stored by the indexers 206 or that the data resides in hot buckets that are still being processed by the indexers 206.
  • the query coordinator 3304 identifies the dataset source based on the architecture of the system 3301. As described herein, in some architectures, real-time searches or searches for data that satisfy the timing threshold are handled by indexers. In other architectures, these same types of searches are handled by the nodes 3306 in combination with the ingested data buffer. Similarly, in certain architectures, historical searches, or searches for data that do not satisfy the timing threshold are handled by the indexers. In other architectures, these same types of searches are handled by the nodes 3306 in combination with the common storage.
  • the query coordinator 3304 obtains relevant information about the dataset sources/destinations.
  • the query coordinator 3304 can obtain the relevant information from a variety of sources, such as the workload advisor 3310, workload catalog 3312, dataset compensation module 3316, the dataset sources/destinations themselves, etc.
  • the query coordinator 3304 can obtain relevant information about the external dataset source 3318 from the dataset compensation module or by communicating with the external data source 3318.
  • the dataset source/destination is an indexer 206, common storage, query acceleration data store 3308, ingested data buffer, etc.
  • the query coordinator can obtain relevant information by communicating with the dataset source/destination and/or the workload advisor 3310 or workload catalog 3312.
  • the relevant information can include, but is not limited to, information to enable the query coordinator 3304 to generate a search scheme with sufficient information to interact with and obtain data from a dataset source or send data to a dataset destination.
  • the relevant information can include information related to the number of partitions supported by the dataset source/destination, location of compute nodes at the dataset source/destination, computing functionality of the dataset source/destination, commands supported by the dataset source/destination, physical location of the dataset source/destination, network speed and reliability in communicating with the dataset source/destination, amount of information stored by the dataset source/destination, computer language or protocols for communicating with the dataset source/destination, summaries or indexes of data stored by the dataset source/destination, data format of data stored by the dataset source/destination, etc.
  • the query coordinator 3304 determines processing requirement for the query.
  • the query coordinator 3304 parses the query.
  • the workload catalog 3312 can store information regarding the various transformations or commands that can be executed on data and the amount of processing to perform the transformation or command. In some cases, this information can be based on historical information from previous queries executed by the system 3301. For example, the query coordinator 3304 can determine that a“join” command will have significant computational requirements, whereas a“count by” command may not.
  • the query coordinator can determine the processing requirements of individual transformations on the data, as well as the processing requirements of the query.
  • the query coordinator 3304 determines available resources.
  • the nodes 3306 can include monitoring modules that monitor the performance and utilization of its processors. In some cases, a monitoring module can be assigned for each processor on a node. The information about the utilization rate and other scheduling information can be used by the query coordinator 3304 to determine the amount of resources available for the query.
  • the query coordinator 3304 generates a query processing scheme.
  • the query coordinator 3304 can use the information regarding the dataset sources/destinations, the processing requirements of the query and/or the available resources to generate the query processing scheme.
  • the query coordinator 3304 can generate instructions to be executed by the dataset sources/destinations, allocate partitions/processors for the query, generate instructions for the processors/nodes, generate instructions for itself, generate a DAG, etc.
  • the query coordinator 3304 can use the information from the dataset compensation module 3316. This information can be used by the query coordinator 3304 to determine what processing can be done by an external data source, how to translate the commands or subqueries for execution to the external dataset source, the number of partitions, worker nodes 3306, or processors 3406 that can be used to read data from the external dataset source, etc. Similarly, the query coordinator 3304 can generate instructions for other dataset sources, such as the indexers, query acceleration data store, common storage, etc.
  • the query coordinator 3304 can generate instructions for the ingested data buffer to retain data until it receives an acknowledgment from the query coordinator that the data from the ingested data buffer has been received and processed. [00713]
  • the query coordinator 3304 can determine how to break up the processing requirements of the query into discrete or individual tasks, determine the number of partitions/processors to execute the task, etc. In some cases, to determine how to break up the processing requirements of the query into discrete or individual tasks, the query coordinator 3304 can parse the query into its different portions of the query and then determine the tasks to use to execute the different portions.
  • the query coordinator 3304 can then use this information to generate specific instructions for the nodes that enable the nodes to execute the individual tasks, route the results of each task to the next location, and route the results of the query to the proper destination.
  • the instructions for the nodes can further include instructions for interacting with the dataset sources/destinations.
  • instructions for the dataset sources can be embedded in the instructions for the nodes so that the nodes can communicate the instructions to the dataset sources/destinations.
  • the instructions generated by the query coordinator 3304 for the nodes can include all of the information in order to enable the nodes to handle the various tasks of the query and provide the query coordinator with the appropriate data so that the query coordinator 3304 can finalize the results and communicate them to the search head 210.
  • the query coordinator 3304 can use network topology information of the machines that will be executing the query to generate the instructions for the nodes. For example, the query coordinator 3304 can use the physical location of the processors that will execute the query to generate the instructions. As one example, the query coordinator 3304 can indicate that it is preferred that the processors assigned to execute the query be located on the same machine or close to each other.
  • the instructions for the nodes can be generated in the form of a DAG, as described in greater detail above.
  • the DAG can include the instructions for the nodes to carry out the processing tasks included in the DAG.
  • the DAG can include additional information, such as instructions on how to select processors 3406 for the different tasks or distribute partitions.
  • the DAG can indicate that it is preferable that a partition that will be receiving data from another partition be on the same machine, or nearby machine, in order to reduce network traffic.
  • the query coordinator 3304 can generate instructions for itself.
  • the instructions generated for itself can depend on the query that is being processed, the capabilities of the nodes 3306, and the results expected from the nodes.
  • the type of query requested may require the query coordinator 3304 to perform more or less processing.
  • a cursored search may require more processing by the query coordinator 3304 than a batch search. Accordingly, the query coordinator 3304 can generate tasks or instructions for itself based on the query requested.
  • the query coordinator 3304 can assign those tasks to itself and generate instructions for itself based on those tasks. Similarly, based on the form of the data that the query coordinator 3304 is expected to receive, it can generate instructions for itself in order to finalize the results for reporting.
  • the query coordinator 3304 can implement some blocks concurrently or change the order as desired. For example, the query coordinator 3304 can obtain information about the dataset sources/destinations (3904), determine processing requirements (3906), and determine available resources (3908) concurrently or in any order, as desired.
  • FIG. 40 is a flow diagram illustrative of an embodiment of a routine 4000 implemented by the system 3301 to generate a query processing scheme. It will be understood that one or more elements outlined for routine 4000 can be implemented by one or more computing devices/components that are associated with the system 3301, such as the search head 210, search process master 3302, query coordinator 3304, indexer 206, and/or worker nodes 3306. Thus, the following illustrative embodiment should not be construed as limiting.
  • the system 3301 tracks query-resource usage data.
  • the system 3301 can track detailed information related to queries that are executed by the system 3301, which in some embodiments can be stored in the workload catalog 3312, or otherwise stored to be accessible to the system 3301.
  • the system can track data indicating the resources used to execute the queries or timing information indicating the amount of time a query took to execute.
  • the system can track information on a per transformation level, indicating the resources used to perform a particular task or transformation on a set of data, the amount of data involved, the time it took to perform the transformation, etc. In some embodiments, this information and other information related to previous queries, datasets, and system components can be stored in the workload catalog 3312.
  • the system 3301 tracks resource utilization data.
  • the system 3301 can track detailed information related to utilization rates of system resources, which in some cases can be stored in the node monitoring module 3314.
  • the nodes 3306 can include monitoring modules 3410, which can monitor the utilization rates of processors, I/O, memory, and other components of the nodes 3306.
  • the information from the nodes 3306 of the system 3301 can be communicated to the node monitoring module 3314 for storage.
  • each node 3306 can include at least one monitoring module 3410.
  • each node 3306 can include at least one monitoring module for each processor 3406 of the node 3306.
  • the system 3301 receives a query, as described in greater detail above.
  • the system 3301 defines a query processing scheme, as described in greater detail above. In some cases the system 3301 can use the query-resource usage data and/or the resource utilization data to define the query processing scheme.
  • the system 3301 can use the query-resource usage data to determine the amount of time the query will take to complete compared to the amount of resources assigned to process the query.
  • the system can use this information to determine an amount of resources to allocate based on query. For example, the system can compare the datasets used for the received query with datasets used for previous queries, the types of transformations required by the received query compared to previous queries. Based on the comparison, the system 3301 can determine the effect of the amount of resources assigned to the query compared to the time to execute the query.
  • the system 3301 can further use the resource utilization data to define the query processing scheme. For example, the system 3301 can determine the amount of resources that are currently available for use to execute the query. Based on the amount of currently available resources, the system 3301 can determine how many resources should be allocated to the query. As an example, assume that based on the query-resource usage data, the system 3301 determines that thirty processors are preferred to process a query and that fewer than twenty processors would result in an undue delay. Based on the system 3301 determining that thirty processors are available, the system 3301 can allocate all thirty processors or at least twenty for the query.
  • the system 3301 can track usage over time to predict surges in queries or determine whether additional queries are expected in the near term. For example, the system 3301 may determine that there is a surge in queries around 9:00 AM when most users begin work. With continued reference to the example above, if the query is received at 8:55 AM and the thirty processors are available, the system 3301 may determine to allocate twenty processors rather than the preferred thirty because a large number of queries are expected at 9:00 AM.
  • the system executes the query.
  • the system communicates a query processing scheme to the nodes 3306.
  • the nodes obtain relevant data from the datasets, process the data, and return results to the query coordinator.
  • the query coordinator performs any additional processing based on the query processing scheme and communicates the results to the search head 210 for display on the client device 404.
  • the routine 4000 can further include, monitoring nodes during query execution, allocating/deallocating resources based on the query,
  • the various blocks described herein with reference to FIG. 40 can be implemented in a variety of orders.
  • the system 3301 can implement some blocks concurrently or change the order as desired.
  • the system 3301 can track query-resource usage data 4002, track resource utilization of nodes 4004, and receive a query 4006 concurrently or in any order, as desired.
  • the system 3301 can track resource utilization of nodes 4004 while executing the query 4010, etc. 16.0.
  • FIG. 41 is a flow diagram illustrative of an embodiment of a routine 4100 implemented by the query coordinator 3304 to execute a query on data from multiple dataset sources.
  • routine 4100 implemented by the query coordinator 3304 to execute a query on data from multiple dataset sources.
  • routine 4100 can be implemented by one or more computing devices/components that are associated with the system 3301, such as the search head 210, search process master 3302, indexer 206, and/or worker nodes 3306.
  • the following illustrative embodiment should not be construed as limiting.
  • the query coordinator 3304 receives a query, as described in greater detail above with reference to block 3802 of FIG. 38.
  • the query coordinator identifies the dataset sources, including the indexers 206 as one dataset source, as described in greater detail above with reference to block 3902 of FIG. 39.
  • the query coordinator 3304 can also identify a second dataset source, such as an external data source, a common storage, an ingested data buffer, query acceleration data store, etc.
  • the query coordinator 3304 generates a subquery for the indexers.
  • the subquery can be generated based on the processing capabilities of the indexers.
  • the subquery can indicate to the indexers that data to be processed by the indexers and the manner of processing the data by the indexers. Further, the subquery can instruct the indexers to provide the results (or partial results) of the subquery to the nodes 3306 for further processing. Accordingly, using the subquery, the indexers can identify the data to process, process the data, and communicate the results to the nodes 3306.
  • the subquery can be in any query language, as described herein.
  • the query coordinator 3304 allocates resources, such as partitions, worker nodes 3306, or processors 3406, for a second dataset.
  • the resource allocation can be based on the information about the dataset and/or the query requirements, as described in greater detail in blocks 3906, 3908, and 3910 of FIG. 39.
  • the query coordinator 3304 determines or allocates resources to combine the results (or partial results) from the two datasets. Similar to block 4108, the query coordinator 3304 can determine or allocate partitions, worker nodes 3306, or processors 3406 to combine the partial results from the different datasets based on the query requirements.
  • the query can include a command indicating that the results from different dataset sources are to be combined in some way.
  • the query coordinator 3304 executes the query as described in greater detail above with reference to block 4010 of FIG. 40.
  • the query coordinator 3304 can communicate the subquery to the indexers 206 or embed the subquery into the instructions to the nodes 3306 such that the nodes 3306 communicate the subquery to the indexers 206.
  • routine 4100 can further include, monitoring nodes during query execution, allocating/deallocating resources based on the query, etc.
  • the identification of the dataset sources, generation of a subquery and resource allocation can form part of a processing query block, similar to the process query block 3804 of FIG. 38.
  • the routine 4100 can include allocating resources to receive and process the partial results from the indexers 206 prior to combining the partial results from the different datasets.
  • the system 3301 can dynamically allocate resources based on the number of indexers 206 from which the nodes 3306 will receive data.
  • system 3301 can process and execute the query on any two or more dataset sources, and that the system 3301 can generate subqueries or instructions for the dataset sources or allocate resources for the dataset sources based on information about the dataset sources, as described in greater detail herein.
  • FIG. 41 can be implemented in a variety of orders.
  • the system 3301 can implement some blocks concurrently or change the order as desired.
  • the system 3301 can generate a subquery for the indexers 4106, allocate resources for the second dataset 4108, and allocate resources to combine partial results from the indexers and second dataset 4110 concurrently, or in any order, as desired.
  • FIG. 42 is a flow diagram illustrative of an embodiment of a routine 4200 implemented by the query coordinator 3304 to execute a query on data from an external data source.
  • routine 4200 implemented by the query coordinator 3304 to execute a query on data from an external data source.
  • routine 4200 can be implemented by one or more computing devices/components that are associated with the system 3301, such as the search head 210, search process master 3302, indexer 206, and/or worker nodes 3306.
  • the following illustrative embodiment should not be construed as limiting.
  • the query coordinator 3304 receives a query, as described in greater detail above with reference to block 3802 of FIG. 38.
  • the query coordinator identifies the external data sources, as described in greater detail above with reference to block 3902 of FIG. 39.
  • the query coordinator 3304 dynamically generates a subquery for the external data source.
  • the query coordinator 3304 can generate the subquery for the external data source based on information obtained about the external data source as described herein with reference to, inter alia, blocks 3904 and 3910 of FIG. 39.
  • the query coordinator 3304 obtains information about the external data source using an external query configuration file, as described herein at least with reference to FIGS. 50A, 50B, 51, 52, 54, and 61.
  • the information can indicate the type of external data source, APIs and languages to use to interface with the external data source, the type and amount of data stored in the external data source.
  • the information can indicate whether the external data source supports multiple partitions, and if so, how many. Further, the information can indicate the location of the processors of the external data source with which the nodes 3306 will interact. The information can also indicate the processing capabilities of the external data source, such as what commands or transformations the external data source can perform on the data stored therein. [00739]
  • the query coordinator 3304 can generate a subquery. In certain embodiments, the query coordinator 3304 generates a subquery that tasks the external data source with merely returning the data, performing some processing of the data, or processing the data as much as it can based on its capabilities. By pushing some processing of the data to the external data source, the query coordinator 3304 can reduce the processing load on the system 3301.
  • the query coordinator 3304 allocates resources, such as, but not limited to, partitions, worker nodes 3306, or processors 3406 to receive and process results from the external data source.
  • the query coordinator 3304 can allocate resources based on the query requirements and the data received from the external data source. For example, if the external data source can perform some processing on the data, then the query coordinator 3304 can allocate resources to receive the results of the processing. If the subquery indicated that the external data source was to return results without processing them, then the query coordinator 3304 can allocate resources to receive the unprocessed results from the external data source, and process them according to the query.
  • the query coordinator 3304 can allocate resources based on the number of partitions supported by the external data source. For example, if the external data source supports four partitions for reading data, then the query coordinator 3304 can allocate four worker nodes 3306 or processors 3406 to read from each of the partitions supported by the external data source. However, it will be understood that the query coordinator 3304 can allocate fewer or more worker nodes 3306 or processors 3406 as desired. Further, the number of worker nodes 3306 or processors 3406 allocated can be based on the resources available on the system 3301.
  • the query coordinator 3304 can allocate more worker nodes 3306 or processors 3406 than is supported by the external data source and/or submit multiple subqueries to the external data source. For example, if the external data source only supports a single partition, the query coordinator 3304 can allocate multiple worker nodes 3306 or processors 3406 to send different subqueries to the external data source and receive the results back. In this way, the query coordinator 3304 can increase the number of parallel reads from the external data source. As a non-limiting example, suppose an external data source only supports one partition and the query indicates that a data based on an age range of 20-49 is to be obtained from the external data source.
  • the query coordinator can break up the age range into three sets (20- 29, 30-39, 40-49) and send (or have nodes send) a subquery for each set to the external data source.
  • the external data source can process the requests concurrently and return results, and may not know that the requests are coming from the same system 3301. In this way, the system 3301 can receive results in parallel from an external data source that supports a single partition.
  • the query coordinator 3304 can similarly send multiple subqueries to one partition of a multi-partition-supporting external data source to increase the parallel reads from the external data source.
  • the query coordinator 3304 executes the query as described in greater detail above with reference to block 4010 of FIG. 40. It will be understood that fewer, more, or different blocks can be used as part of the routine 4200.
  • the routine 4200 can further include, monitoring nodes during query execution, allocating/deallocating resources based on the query, etc.
  • the identification of the external data source, generation of a subquery and resource allocation can form part of a processing query block, similar to the process query block 3804 of FIG. 38.
  • FIG. 42 can be implemented in a variety of orders.
  • the system 3301 can implement some blocks concurrently or change the order as desired.
  • the system 3301 can generate a subquery for the external data source 4206 and allocate resources concurrently 4208 or in any order, as desired.
  • FIG. 43 is a flow diagram illustrative of an embodiment of a routine 4300 implemented by the query coordinator 3304 to execute a query based on a dataset destination.
  • routine 4300 implemented by the query coordinator 3304 to execute a query based on a dataset destination.
  • routine 4300 can be implemented by one or more computing devices/components that are associated with the system 3301, such as the search head 210, search process master 3302, indexer 206, and/or worker nodes 3306.
  • the following illustrative embodiment should not be construed as limiting.
  • the query coordinator 3304 receives a query, as described in greater detail above with reference to block 3802 of FIG. 38.
  • the query coordinator identifies the dataset destination, as described in greater detail above with reference to block 3902 of FIG. 39.
  • the dataset destination can refer to the location where query results or partial query results are to be stored by the system 3301.
  • the nodes 3306 can process data from any dataset source and then store the data in a dataset destination, as well as provide the results to a client device 404.
  • the dataset destination can be the same as the dataset source. For example, data can be read from the ingested data buffer, processed, and then stored back in the ingested data buffer.
  • the dataset destination and dataset source are different.
  • data is read from the common storage, processed by the nodes, and the results stored in the query acceleration data store 3308, an external data source 3318, an ingested data buffer, etc.
  • the query coordinator 3304 determines the functionality of the dataset destination.
  • each dataset destination like dataset sources, can have different functionality and capabilities. This functionality can correspond to how to communicate with the dataset destination (e.g., the number of partitions supported by the dataset destination, the APIs, language, or communication protocols of the dataset destination), processing supported by the dataset destination (e.g., commands supported by the dataset destination), etc.
  • the query coordinator 3304 allocates or assigns resources, such as, but not limited to, worker nodes 3306 or processors 3406 to process and communicate results to the dataset destination. Similar to allocating resources to receive data from a dataset source, the query coordinator 3304 can allocate resources to process and communicate data to a dataset destination. For example, the query coordinator 3304 can allocate worker nodes 3306 or processors 3406 based on the partitions supported by the dataset destination, the processing capabilities of the dataset destination, etc. As part of allocating worker nodes 3306 or processors 3406, the query coordinator 3304 can instruct the worker nodes 3306 or processors 3406 on how to communicate the data to the dataset destination, include translated commands for the dataset destination, etc.
  • resources such as, but not limited to, worker nodes 3306 or processors 3406 to process and communicate results to the dataset destination. Similar to allocating resources to receive data from a dataset source, the query coordinator 3304 can allocate resources to process and communicate data to a dataset destination. For example, the query coordinator 3304 can allocate worker nodes 3306 or processors 3406
  • the query coordinator 3304 executes the query as described in greater detail above with reference to block 4010 of FIG. 40. It will be understood that fewer, more, or different blocks can be used as part of the routine 4300.
  • the routine 4300 can further include, monitoring nodes during query execution, allocating/deallocating resources based on the query, etc.
  • the identification of the dataset destination, determination of dataset destination functionality, and resource allocation can form part of a processing query block, similar to the process query block 3804 of FIG. 38.
  • FIG.s 43 can be implemented in a variety of orders.
  • the system 3301 can implement some blocks concurrently or change the order as desired.
  • the system 3301 can determine dataset destination functionality 4306 and allocate resources 4308 concurrently or in any order, as desired.
  • FIG. 44 is a flow diagram illustrative of an embodiment of a routine 4400 implemented by a serialization module, of a component of the data intake and query system 3301 to serialize data for communication to a destination, similar to the serialization/deserialization module 3412 of FIG. 34.
  • the destination can be another component of the data intake and query system 3301 or external to the data intake and query system 3301.
  • serialization module it will be understood that one or more elements outlined for routine 4300 can be implemented by one or more computing devices/components that are associated with the system 3301, such as the search head 210, search process master 3302, indexer 206, and/or worker nodes 3306.
  • the following illustrative embodiment should not be construed as limiting.
  • the serialization module identifies events for serialization. In some cases, as part of identifying the events for serialization, the serialization module groups the events. In some embodiments, the serialization module identifies the events for serialization based on a common source or sourcetype of the events, or other shared attribute, or based on a destination for the events. In certain embodiments, the serialization module identifies events for serialization based on timing information. For example, the serialization module can serialize events received within a certain time period, such as one second, ten second, one minute, etc. [00753] At block 4404, the serialization module determines header information for the events.
  • the header information can include the number of events in a group, the field names for the events in the group, etc.
  • the field names in the header can include all field names across all events. For example, if some events have different field names, both can be included in the header information.
  • the header information can also include mapping information for mapping field names to field positions (e.g., where a particular field name is located within an event, etc.).
  • the serialization module can serialize the header information. For example, if some field names are repetitive or have been identified before in previous groups, they can be replaced with an identifier indicating a cache entry that has that field name.
  • the identifier can be used by the receiving component to deserialize the data. Furthermore, the serialization module can update the cache based on the header information. For example, if some of the header information had not been seen before, the serialization module can update the cache so that an identifier can be used in place of the header information in the future.
  • the serialization module serializes the events. As part of serializing the events, the serialization module can identify field values in the events and determine whether the field values in each event are stored in cache. The field values that are stored in cache can be replaced with cache identifiers. In addition, the serialization module can identify data other data for removal. For example, in some embodiments, certain delimiters, such as or‘ ⁇ n’ can be removed from the events.
  • the serialization module can update the cache or generate update cache commands for the receiver. Updating the cache can include adding entries for data encountered in the events or removing entries that have not been used recently.
  • the cache can be updated with each event or each group and can be performed prior to, after, or concurrently with an event. For example, upon receiving a group of events, the receiver can update the cache and then process the events, update the cache while processing the events, or update the cache after the events are processed. In some cases, the receiver updates the cache following each event. In some cases, new entries are added to the cache prior to processing the events and entries are removed from the cache after processing the events in a group.
  • the serialization module communicates the serialized events to the destination.
  • the serialization module communicates the events in a streaming fashion.
  • the serialization module communicates the events once the serialization process for that event is completed.
  • the serialization module communicates the events as a group. In such embodiments, the serialization module waits until the group of events is serialized before transmitting the events as a group.
  • the serialization/deserialization module 3412 can determine the number of events to group, determine the order and field names for the fields in the events of the group, parse the events, determine the number of fields for each event, identify and serialize serializable field values in the event fields, and identify cache deltas. In some cases, the serialization/deserialization module 3412 performs the various tasks in a single pass of the data, meaning that it performs the identification, parsing, and serializing during a single review of the data. In this manner, the serialization/deserialization module 3412 can operate on streaming data and avoid adding delay to the serialization/deserialization process.
  • the routine 4400 can further include, building and updating the cache at the receiver, etc.
  • the various blocks described herein with reference to FIG. 44 can be implemented in a variety of orders.
  • the serialization module can implement some blocks concurrently or change the order as desired.
  • the serialization module can determine header information 4404 and serialize the events 4406 concurrently or in any order, as desired.
  • the data can be deserialized in a similar manner. That is, the receiver can determine the number of events in the group and the fields based on the header information and deserialize each event using the cache and data in the serialized group.
  • FIG. 45 is a flow diagram illustrative of an embodiment of a routine 4500 implemented by the query coordinator 3304 to execute a query utilizing a data store (e.g., query acceleration data store 3308).
  • a data store e.g., query acceleration data store 3308.
  • routine 4500 can be implemented by one or more computing devices/components that are associated with the system 3301, such as the search head 210, search process master 3302, indexer 206, and/or worker nodes 3306.
  • the following illustrative embodiment should not be construed as limiting.
  • the query coordinator 3304 receives a query, as described in greater detail above with reference to block 3802 of FIG. 38.
  • the query can reference a particular dataset stored by the query acceleration data store 3308, and reference information which is to be obtained from another dataset source (e.g., external data source 3318, ingested data buffer, common storage, indexers 206, etc.).
  • another dataset source e.g., external data source 3318, ingested data buffer, common storage, indexers 206, etc.
  • first partial results are identified.
  • a query can indicate datasets, including a particular dataset that is stored in the query acceleration data store 3308.
  • the query acceleration data store 3308 can store datasets that are indicated (e.g., by users, for example based on the users including a particular command) as benefiting from storage in the query acceleration data store 3308 (e.g., benefitting from caching).
  • the datasets stored in the query acceleration data store 3308 can correspond to results or partial results of queries previously processed by the system 3301.
  • the query coordinator 3304 can determine that the received query references one or more datasets stored by the query acceleration data store.

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Abstract

L'invention concerne des systèmes et des procédés destinés à recevoir, au niveau d'un premier système d'entrée et d'interrogation de données, une requête qui comprend une indication en vue de traiter des données gérées par un autre système d'entrée et d'interrogation de données. Le premier système d'entrée et d'interrogation de données identifie un second système d'entrée et d'interrogation de données qui gère les données à traiter et génère une sous-requête en vue de son exécution par le second système d'entrée et d'interrogation de données, génère des instructions pour qu'un ou plusieurs nœuds exécutants reçoivent et traitent des résultats de la sous-requête en provenance du second système d'entrée et d'interrogation de données, et donne comme consigne aux nœuds exécutants de fournir des résultats du traitement au premier système d'entrée et d'interrogation de données.
PCT/US2019/016108 2018-07-31 2019-01-31 Génération d'une sous-requête pour un système distinct d'entrée et d'interrogation de données WO2020027867A1 (fr)

Applications Claiming Priority (18)

Application Number Priority Date Filing Date Title
US16/051,223 US11243963B2 (en) 2016-09-26 2018-07-31 Distributing partial results to worker nodes from an external data system
US16/051,310 2018-07-31
US16/051,203 US11126632B2 (en) 2016-09-26 2018-07-31 Subquery generation based on search configuration data from an external data system
US16/051,215 US11615104B2 (en) 2016-09-26 2018-07-31 Subquery generation based on a data ingest estimate of an external data system
US16/051,223 2018-07-31
US16/051,304 2018-07-31
US16/051,300 2018-07-31
US16/051,310 US11314753B2 (en) 2016-09-26 2018-07-31 Execution of a query received from a data intake and query system
US16/051,215 2018-07-31
US16/051,197 US11663227B2 (en) 2016-09-26 2018-07-31 Generating a subquery for a distinct data intake and query system
US16/051,203 2018-07-31
US16/051,197 2018-07-31
US16/051,304 US11604795B2 (en) 2016-09-26 2018-07-31 Distributing partial results from an external data system between worker nodes
US16/051,300 US10977260B2 (en) 2016-09-26 2018-07-31 Task distribution in an execution node of a distributed execution environment
US16/147,165 US10956415B2 (en) 2016-09-26 2018-09-28 Generating a subquery for an external data system using a configuration file
US16/146,990 2018-09-28
US16/146,990 US11023463B2 (en) 2016-09-26 2018-09-28 Converting and modifying a subquery for an external data system
US16/147,165 2018-09-28

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