Splunk Inc.

Murugan Kandaswamy

Vishal Patel

Amrittpal Singh Bath

Pratiksha Shah

Jindrich Dinga

Kieran Nicholas Cairney

Steve Yu Zhang

Christopher Madden Pride

Ledion Bitincka

Alexander Douglas James

David Marquardt

David Everett Simmen

Igor Stojanovski

Thomas Allan Haggie

Clint Sharp

Hailun Yan

Marc Andre Chene

Esguerra Ma Kharisma

A log-to-metrics transformation system includes a log-to-metrics application executing on a processor. The log-to-metrics transformation system receives a format associated with machine data, and further receives, via a first graphical control, a first set of metric identifiers corresponding to a first set of metrics associated with the machine data. The log-to-metrics transformation system generates a first set of mappings between the first set of metric identifiers and a first set of field values included in the machine data. The log-to-metrics transformation system stores the first set of mappings and an association with the format of the machine data. The log-to-metrics transformation system, based on the first set of mappings, causes the first set of field values to be extracted from the machine data. Further, a first metric included in the first set of metrics is determined based on at least a portion of the first set of field values.

Automated generation of metrics from log data

The disclosed technique can be performed by a data intake and query system. The technique includes ingesting data including log data obtained over a network from systems, and receiving user input indicating a scope for retrieving data and a criterion expressed in a structured language. The technique further includes retrieving data based on the scope indicated by the user input and extracting a first field value and a second field value from the retrieved data based on the criterion and the scope. The first field value includes a first numerical value indicative of a measured characteristic of a computing device and the second field value includes a first dimension. The technique further includes storing a first structured metric and the first dimension in a time-series metrics store. The first structured metric includes the first numerical value. The first dimension is associated with the first numerical value.

Generating structured metrics from log data

The disclosed embodiments include a method performed by a data intake and query system. The method includes ingesting each metric including at least one key value and a measured value taken of a computing resource, and storing each metric in an index of a metrics store, where the index defines at least one dimension populated with the at least one key value and a measure populated with the measured value. The method further includes cataloging metadata in a metrics catalog, where the metadata is related to the metrics stored in the metrics store, performing an analysis of metrics data included in the metrics store and/or the metrics catalog to obtain results, and causing display of the results or an indication of the results on a display device.

Metrics store system

The disclosed embodiments include a method performed by a data intake and query system. The method includes ingesting data including raw data obtained over a computer network from a plurality of remote computer systems, and generating events, where each event includes a segment of the raw data and a respective timestamp. The method further includes extracting field values from at least a portion of the raw data of the events, where the field values each include a numerical value, and each numerical value is indicative of a measured characteristic of a computing device. The method further includes generating structured metrics, where each structured metric has a respective numerical value, and indexing the plurality of structured metrics.

Techniques for generating structured metrics from ingested events

The disclosed embodiments include a method performed by a data intake and query system. The method includes ingesting collected data including metrics data including key values and numerical values, where each numerical value is indicative of a measured characteristic of a computing resource (e.g., device), and populating a first portion of a metric-series index (msidx) file with the key values and a second portion of the msidx file with the numerical values. The first portion is distinct from the second portion. The method further includes generating metrics from the metrics data, where each metric has dimensions populated with at least some of the key values and has one of the numerical values. The method further includes indexing the metrics by at least one of the dimensions.

Techniques for ingesting metrics data

Growing community of inventors

Sunnyvale, CA, United States of America

Murugan Kandaswamy