Identifying events derived from machine data that match a particular portion of machine data

Patent metadata

Field	Value
Publication number	US-9317582-B2
Application number	US-201514691195-A
Country	US
Kind code	B2
Filing date	Apr 20, 2015
Priority date	Jul 25, 2005
Publication date	Apr 19, 2016
Grant date	Apr 19, 2016

Abstract

Methods and apparatus consistent with the invention provide the ability to organize and build understandings of machine data generated by a variety of information-processing environments. Machine data is a product of information-processing systems (e.g., activity logs, configuration files, messages, database records) and represents the evidence of particular events that have taken place and been recorded in raw data format. In one embodiment, machine data is turned into a machine data web by organizing machine data into events and then linking events together.

First claim

The invention claimed is:

1. A method, comprising:
analyzing machine data stored in at least one storage device in order to segment the machine data into a plurality of events by determining beginning and ending of each event in the plurality of events in the machine data, each event in the plurality of events including some machine data from the stored machine data segmented for that event, the plurality of events including both events produced from a first data resource and events produced from a second data resource that is different from the first data resource, the machine data in one or more events produced from the first data resource having a different data format than the machine data in one or more events produced from the second data resource;
identifying, in the plurality of events, one or more events that include a particular portion of machine data;
wherein the method is performed by one or more computing devices.

2. The method as recited in claim 1 , wherein the particular portion of machine data is identified in events derived from at least two machine data sources.

3. The method as recited in claim 1 , wherein the particular portion of machine data includes one or more tokens.

4. The method as recited in claim 1 , wherein the particular portion of machine data includes one or more keywords.

5. The method as recited in claim 1 , wherein the particular portion of machine data includes one or more segment values.

6. The method as recited in claim 1 , wherein the particular portion of machine data includes one or more extracted entities.

7. The method as recited in claim 1 , wherein the particular portion of machine data includes a particular value for an extracted entity.

8. The method as recited in claim 1 , wherein the particular portion of machine data includes one or more semantic entities.

9. The method as recited in claim 1 , wherein the particular portion of machine data includes a particular value for a semantic entity.

10. The method as recited in claim 1 , wherein the particular portion of machine data includes a particular punctuation structure.

11. The method as recited in claim 1 , wherein the particular portion of machine data is associated with an event type.

12. The method as recited in claim 1 , further comprising:
wherein the particular portion of machine data is associated with an event type; and
generating statistical information for the event type.

13. The method as recited in claim 1 , further comprising:
wherein the particular portion of machine data is associated with an event type;
generating statistical information for the event type; and
wherein the statistical information is accessible via an application programming interface.

14. The method as recited in claim 1 , further comprising:
wherein the particular portion of machine data is associated with an event type;
generating a count of events associated with the event type.

15. The method as recited in claim 1 , further comprising:
wherein the particular portion of machine data is associated with an event type;
generating a count of events associated with the event type; and
causing display of the count.

16. The method as recited in claim 1 , further comprising:
identifying a machine data source for at least a portion of the machine data.

17. The method as recited in claim 1 , further comprising:
identifying a machine data source using at least a portion of the machine data.

18. The method as recited in claim 1 , further comprising:
constructing links between events in the plurality of events;
wherein the links represent relationships between events in the plurality of events.

19. The method as recited in claim 1 , further comprising:
constructing links between events in the plurality of events;
wherein the links represent relationships between events in the plurality of events;
constructing a path by chaining event links together;
generating statistical information based on occurrences of one or more paths.

20. The method as recited in claim 1 , further comprising associating a time stamp with each event in the plurality of events.

21. One or more non-transitory computer-readable storage media, storing one or more sequences of instructions, which when executed by one or more processors cause performance of:
analyzing machine data stored in at least one storage device in order to segment the machine data into a plurality of events by determining beginning and ending of each event in the plurality of events in the machine data, each event in the plurality of events including some machine data from the stored machine data segmented for that event, the plurality of events including both events produced from a first data resource and events produced from a second data resource that is different from the first data resource, the machine data in one or more events produced from the first data resource having a different data format than the machine data in one or more events produced from the second data resource;
identifying, in the plurality of events, one or more events that include a particular portion of machine data.

22. The one or more non-transitory computer-readable storage media as recited in claim 21 , wherein the particular portion of machine data is identified in events derived from at least two machine data sources.

23. The one or more non-transitory computer-readable storage media as recited in claim 21 , wherein the particular portion of machine data includes one or more tokens.

24. The one or more non-transitory computer-readable storage media as recited in claim 21 , wherein the particular portion of machine data includes one or more keywords.

25. The one or more non-transitory computer-readable storage media as recited in claim 21 , wherein the particular portion of machine data includes one or more segment values.

26. An apparatus, comprising:
a subsystem, implemented at least partially in hardware, that analyzes machine data stored in at least one storage device in order to segment the machine data into a plurality of events by determining beginning and ending of each event in the plurality of events in the machine data, each event in the plurality of events including some machine data from the stored machine data segmented for that event, the plurality of events including both events produced from a first data resource and events produced from a second data resource that is different from the first data resource, the machine data in one or more events produced from the first data resource having a different data format than the machine data in one or more events produced from the second data resource;
a subsystem, implemented at least partially in hardware, that identifies, in the plurality of events, one or more events that include a particular portion of machine data.

27. The apparatus as recited in claim 26 , wherein the particular portion of machine data is identified in events derived from at least two machine data sources.

28. The apparatus as recited in claim 26 , wherein the particular portion of machine data includes one or more tokens.

29. The apparatus as recited in claim 26 , wherein the particular portion of machine data includes one or more keywords.

30. The apparatus as recited in claim 26 , wherein the particular portion of machine data includes one or more

Assignees

• Splunk Inc

Inventors

• Baum Michael Joseph
• Carasso R David
• Das Robin Kumar
• Hall Bradley
• Murphy Brian Philip
• Sorkin Stephen Phillip
• Stechert Andre David
• Swan Erik M
• Greene Rory
• Mealy Nicholas Christian
• Noren Christina Frances Regina

Classifications

• G06F16/285 Primary

  Clustering or classification · CPC title

• G06F9/542

  Event management; Broadcasting; Multicasting; Notifications · CPC title

• G06F16/24573

  using data annotations, e.g. user-defined metadata · CPC title

• G06F16/3331

  Query processing · CPC title

• G06F16/316

  Indexing structures · CPC title