Splunk Inc.

Cisco Technology, Inc.

Netapp, Inc.

Alexandros Batsakis

Ashish Mathew

Sourav Pal

Christopher Madden Pride

Igor Stojanovski

Bharath Kishore Reddy Aleti

James Monschke

Arindam Bhattacharjee

Sai Krishna Sajja

Paul J Lucas

Tameem Anwar

Eric Timothy Woo

Ledion Bitincka

Anish Shrigondekar

Nicholas Robert Romito

Mehul Goyal

Balaji Rao

Tianyi Gou

Douglas Rapp

Alexander Douglas James

Ruochen Zhang

Nitilaksha Halakatti

Steve Wong

Steve Yu Zhang

Xiaowei Wang

Zhenghui Xie

Rama Gopalan

Mitchell Neuman Blank

Scott Calvert

Sophia Rui Zhu

Bei Li

Hongxun Liu

Ningxuan He

Sogol Moshtaghi

Manuel Martinez

Jianming Zhang

Jindrich Dinga

Jiahan Wang

Manu Jose

Hailun Yan

Ankit Jain

Bharath Aleti

Abhinav Prasad Nekkanti

Kieran Nicholas Cairney

Jonah Pan

Prem Kumar Jayaraj

Vishal Patel

Eric Sammer

Joseph Gabriel Echeverria

James F Lentini

Hasan Alayli

Karthikeyan Sabhanatarajan

Thomas M Talpey

Arkady Kanevsky

Nir Frenkel

Brad Lovering

John Nguyen

Sai Krishna

Clifton Gordon

Nitilaksha Satyaveera Halakatti

Manuel Gregorio Martinez

A data intake and query system can manage the search of large amounts of data using one or more processing nodes. When a new processing node is added or becomes available, the node coordinator can reassign duties from one or more processing nodes to the new processing node. The node coordinator can initially assign the new processing node one or more groups of data for backup purposes. At a later time, the node coordinator can reassign the new processing node to the one or more groups of data for searching purposes.

Reassignment of processing nodes

A computing device can receive a query that identifies a set of data to be processed and determine that a portion of the set of data resides in an external data system. The query system can request data identifiers associated with data objects of the set of data from the external data system and communicate the data identifiers to a data queue. The computing device can instruct one or more search nodes to retrieve the identifiers from the data queue. The search nodes can use the data identifiers to retrieve data objects from the external data system and process the data objects according to instructions received from the computing device. The search nodes can provide results of the processing to the computing device.

Retrieving data identifiers from queue for search of external data system

Systems and methods are disclosed for processing and executing queries in a data intake and query system. The data intake and query system receives raw machine data at an indexing system, and stores at least a portion of the raw machine data in buckets using containerized indexing nodes instantiated in a containerized environment. The data intake and query system stores the buckets in a shared storage system.

Processing data using nodes in a scalable environment

Embodiments are disclosed for performing cache aware searching. In response to a search query, a first bucket and a second bucket in remote storage for processing the search query. A determination is made that a first file in the first bucket is present in a cache when the search query is received. In response to the search query, a search is performed using the first file based on the determination that the first file is present in the cache when the search query is received, and the search is performed using a second file from the second bucket once the second file is stored in the cache.

Cache aware searching based on files of buckets

A data intake and query system can generate local data enrichment objects and receive federated data enrichment objects from another data intake and query system. In response to receiving a query, the data intake and query system can determine whether the query is subquery of a federated query. If the query is a subquery, the data intake and query system can use the federated data enrichment objects to execute the query.

Federated data enrichment objects

Reassigning a processing node from downloading to searching a data group

Processing data using containerized nodes in a containerized scalable environment

Systems and methods are described for establishing and managing components of a distributed computing framework implemented in a data intake and query system. The distributed computing framework may include a master and a plurality of worker nodes. The master may selectively operate on a search head captain that is chosen from the search heads of the data intake and query system. The search head captain may distribute configuration information for the master and the distributed computing framework to the other search heads, which in turn, may distribute that configuration information to indexers of the data intake and query system. Worker nodes may be selectively activated for operation on the indexers based on the configuration information, and the worker nodes may additionally use the configuration information to contact the master and join the distributed computing framework. This approach may provide numerous benefits, including improved security, flexibility in the selection of worker nodes, and redundancy for failures of physical components of the data intake and query system.

Management of distributed computing framework components

Embodiments described herein facilitate enhancement of data model acceleration, including generating data model summaries and performing searches in an accelerated manner. In one implementation, obtaining a search query from a user device. A determination may be made to execute a search, in association with the search query, via an external computing service. As such, the search query, or a variant thereof, can be provided to the external computing service, wherein the external computing service executes the search using data model summaries stored in a remote data store that is separate from a set of events from which the data model summaries were generated. A set of search results are received from the external computing service, and such search results are provided to the user device.

Enhanced search performance using data model summaries stored in a remote data store

Systems and methods are described for monitoring indexing nodes, populating and maintaining a resource catalog with relevant information, receiving requests for indexing node availability or assignments, identifying indexing nodes that are available to process data, and/or communicating information relating to available indexing nodes. The system can maintain the resource catalog based on communications with each of the containerized indexing nodes. The system can receive, from a partition manager of a data intake and query system, a request for a containerized indexing node that the partition manager can assign to process data received by the partition manager. The system can identify an available containerized indexing node to process the data. The system can communicate, to the partition manager, an indexing node identifier associated with the available containerized indexing node.

Identifying an indexing node to process data using a resource catalog

Systems and methods are disclosed for processing and executing queries in a data intake and query system. The data intake and query system receives a query identifying a set of data to be processed and a manner of processing the set of data. The data intake and query system identifies buckets that are to be searched and search nodes to execute the query. The data intake and query system maps the identified buckets to the search nodes and executes the query using the identified bucket and search nodes.

Managing efficient query execution including mapping of buckets to search nodes

Systems and methods are disclosed for processing and executing queries in a data intake and query system. The data intake and query system receives a query identifying a set of data to be processed and a manner of processing the set of data. The data intake and query system uses a search node catalog to identify search nodes that are available to execute the query and uses a bucket catalog to identify buckets to be searched. The data intake and query system executes the query using the identified bucket and search nodes.

Identifying buckets for query execution using a catalog of buckets

Embodiments described herein facilitate enhancement of data model acceleration, including generating data model summaries and performing searches in an accelerated manner. In one implementation, a set of events are indexed, each of the events having a corresponding index time representing a time at which the event was indexed in an indexer. Index time parameters including an index earliest time indicating a first index time at which to begin generating a data model summary and an index latest time indicating a second index time at which to complete generating the data model summary are obtained. Thereafter, a data model summary is generated. Such a data model summary summarizes events having corresponding index times between the index earliest time and the index latest time. The data model summary is provided to a remote data store that is separate from the indexer at which at least a portion of the events were indexed.

Facilitating generation of data model summaries

Systems and methods are described for improving data availability and/or resiliency of indexers of a data intake and query system. Due to a lag between the time at which data is received and the time at which the data is available for searching, the data intake and query system may receive a query indicating that received (but unavailable for search) data is to be included as part of the query. A cluster master can dynamically track what data is available for searching by different indexers and map the data to filter criteria using a bucket map identifier. When a search head receives a query, it can request a bucket map identifier from the cluster master and send the bucket map identifier to the indexers that will be executing the query. The indexers can use the bucket map identifier to request the individual buckets that they are assigned to search.

Mapping buckets and search peers to a bucket map identifier for searching

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San Francisco, CA, United States of America

Alexandros Batsakis