Splunk Inc.

Cisco Technology, Inc.

Manu Jose

Christopher Madden Pride

Alexander Douglas James

Arun Ramani

Ankit Jain

Sourav Pal

Nicholas Robert Romito

Andrew Peters

Scott Calvert

Igor Braylovskiy

Alexandros Batsakis

Hailun Yan

Jonah Pan

Vishal Patel

Jagmohan Singh

Hongxun Liu

Sanish N Mahadik

Michael Bach Soohoo

Joshua Weinstein

Artifact life tracking storage techniques include performing an artifact request of an artifact at an artifact storage node. A current time to live (TTL) value is identified. A determination is made whether to increment a TTL flag of the artifact. Responsive to determining that the TTL tag should be incremented, the TTL flag is incremented to a subsequent value in a TTL extender list. Responsive to incrementing the TTL tag, the TTL modified tag value is set to the current time value.

Artifact life tracking storage

A processing node selects a first task from a task list and sends, to a task assignment repository, a first write operation with a first task identifier of the first task to assign the first task to the processing node. The processing node detects failure of the first write operation based on the first task already being assigned and selects a second task from the task list. The processing node sends, to the task assignment repository, a second write operation with a second task identifier of the second task to assign the second task to the processing node. The processing node detects success of the second write operation and executes the second task.

Distributed task assignment in a cluster computing system

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

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 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 parses the query and uses a metadata catalog to dynamically identify configuration parameters of datasets and/or rules associated with the query. The identified configuration parameters are communicated to a query processing component of the data intake and query system for use in executing the query.

Identifying configuration parameters for a query using a metadata catalog

Systems and methods are disclosed for annotating a metadata catalog in a data intake and query system based on a query received by the data intake and query system. The metadata catalog can store information about datasets associated with the data intake and query system, including dataset configuration records of the datasets, which can be used to process queries for execution by the data intake and query system. The data intake and query system can receive 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 can parse the query to identify datasets and/or data fields associated with the query. Based on the identified datasets and/or fields, the data intake and query system can generate one or more annotations, and use the annotations to update the metadata catalog.

Revising catalog metadata based on parsing queries

Systems and methods are disclosed for annotating a metadata catalog in a data intake and query system based on a detected change to the metadata catalog. The metadata catalog can store information about datasets associated with the data intake and query system, including dataset configuration records of the datasets, which can be used to process queries for execution by the data intake and query system. Based on a detected change to the metadata catalog, such as a manual or system annotation, the data intake and query system can generate additional annotations for the metadata catalog and use the annotations to update the metadata catalog.

Automatically generating metadata for a metadata catalog based on detected changes to the metadata 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 first query identifying a set of data to be processed and a manner of processing the set of data. The data intake and query system parses the first query and uses a dataset association records of a metadata catalog to dynamically identify one or more datasets associated with the query and generate a second query. The data intake and query system executes the second query.

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Milpitas, CA, United States of America

Manu Jose