Splunk Inc.

Cisco Technology, Inc.

R David Carasso

Stephen Phillip Sorkin

Robin Kumar Das

Michael Joseph Baum

Andre David Stechert

Rory Greene

Bradley Hall

Nicholas Christian Mealy

Erik M Swan

Brian Philip Murphy

Micah James Delfino

Christina Frances Regina Noren

Johnvey Hwang

Mitchell Blank, Jr

Eric Timothy Woo

Leonid Budchenko

Brian Phillip Murphy

Embodiments are directed towards real time display of event records with an indication of previously provided extraction rules. A plurality of extraction rules may be provided to the system, such as automatically generated and/or user created extraction rules. These extraction rules may include regular expressions. A plurality of event records may be displayed to the user, such that text in a field defined by an extraction rule is emphasized in the display of the event record. The same emphasis may be provided for text in overlapping fields, or the emphasis may be somewhat different for different fields. The user interface may enable a user to select a portion of text of an event record, such as by rolling-over or clicking on an emphasized part of the event record. By selecting the portion of the event record, the interface may display each extraction rule associated with the selected portion.

Updating event records based on user edited extraction rule

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.

Segmenting machine data into events

Embodiments are directed towards real time display of event records and extracted values based on at least one extraction rule, such as a regular expression. A user interface may be employed to enable a user to have an extraction rule automatically generate and/or to manually enter an extraction rule. The user may be enabled to manually edit a previously provided extraction rule, which may result in real time display of updated extracted values. The extraction rule may be utilized to extract values from each of a plurality of records, including event records of unstructured machine data. Statistics may be determined for each unique extracted value, and may be displayed to the user in real time. The user interface may also enable the user to select at least one unique extracted value to display those event records that include an extracted value that matches the selected value.

Presenting filtered events having selected extracted values

Methods and apparatus consistent with the invention provide the ability to organize, index, search, and present time series data based on searches. Time series data are sequences of time stamped records occurring in one or more usually continuous streams, representing some type of activity. In one embodiment, time series data is stored as discrete events time stamps. A search is received and relevant event information is retrieved based in whole or in part on the time stamp, a keyword indexing mechanism, or statistical indices calculated at the time of the search.

Search phrase processing

Automated extraction rule modification based on rejected field values

Graphical user interface for extraction rules

Embodiments are directed towards generating a representative sampling as a subset from a larger dataset that includes unstructured data. A graphical user interface enables a user to provide various data selection parameters, including specifying a data source and one or more subset types desired, including one or more of latest records, earliest records, diverse records, outlier records, and/or random records. Diverse and/or outlier subset types may be obtained by generating clusters from an initial selection of records obtained from the larger dataset. An iteration analysis is performed to determine whether a sufficient number of clusters and/or cluster types have been generated that exceed at least one threshold and when not exceeded, additional clustering is performed on additional records. From the resultant clusters, and/or other subtype results, a subset of records is obtained as the representative sampling subset.

Selection of representative data subsets from groups of events

Embodiments are directed towards a graphical user interface identify locations within event records with splittable timestamp information. A display of event records is provided using any of a variety of formats. A splittable timestamp selector allows a user to select one or more locations within event records as having time related information that may be split across the one or more locations, including, information based on date, time of day, day of the week, or other time information. Any of a plurality of mechanisms is used to associate the selected locations with the split timestamp information, including tags, labels, or header information within the event records. In other embodiments, a separate table, list, index, or the like may be generated that associates the selected locations with the split timestamp information. The split timestamp information may be used within extraction rules for selecting subsets or the event records.

Using a timestamp selector to select a time information and a type of time information

Segmenting machine data into events to identify matching events

Segmenting machine data into events based on source signatures

Storing events derived from log data and performing a search on the events and data that is not log data

Filtering event records based on selected extracted value

Time series search phrase processing

Determining time stamps in machine data derived events

Methods and apparatus are disclosed to automatically timestamp events within streaming machine data. The streaming machine data is broken into a set of events using breaking rules. Each event can be analyzed by iterating over own time stamp format patterns from a list of known time stamp format patterns to determine whether a matching pattern exists in the event. When an individual event broken out from the streaming machine data includes time information according to at least one known time stamp format pattern of the list of known time stamp format patterns, a timestamp can be created for the event by extracting a time value from event ng the matching pattern determined to exist in the event.

Determining timestamps to be associated with events in machine data

Growing community of inventors

San Rafael, CA, United States of America

R David Carasso