Splunk Inc.

Splunkinc.

Brian John Bingham

Tristan Antonio Fletcher

Alok Anant Bhide

Brian C Reyes

Hemendra Singh Choudhary

John Robert Coates

Fang I Hsiao

Brent Stephen Boe

Leonid Alekseyev

Poorva Malviya

Cary Glen Noel

Techniques promote monitoring of hypervisor systems by presenting dynamic representations of hypervisor architectures that include performance indicators. A reviewer can interact with the representation to progressively view select lower-level performance indicators. Higher level performance indicators can be determined based on lower level state assessments. A reviewer can also view historical performance metrics and indicators, which can aid in understanding which configuration changes or system usages may have led to sub-optimal performance.

Interface for presenting performance data for hierarchical networked components represented in an expandable visualization of nodes

One or more processing devices cause display of a user interface that identifies a service definition representing a service, receive input identifying an entity, where the service is performed at least in part by the entity, and store the service definition representing the service in association with an entity definition representing the entity. The entity definition comprises information identifying data pertaining to the entity in a datastore of machine data that reflects activity in an information technology environment produced by a plurality of components of the information technology environment. The one or more processing devices receive input pertaining to a search definition representing a search producing a measure of the service, and store the search definition representing the search, where the search produces the measure of the service using at least a portion of the data pertaining to the entity.

Monitoring service-level performance using defined searches of machine data

A service monitoring system executing on one or more processors may have operations that are determined by control information. Control over the operation of the service monitoring system can be exerted through the use of a graphical interface. The graphical interface may present the control information of a new or existing correlation search definition for user interaction. The service monitoring system may maintain a data store of key performance indicator (KPI) data, where a KPI value in the data store is produced by a KPI-defining search query that derives the value from machine data associated with one or more entities that perform a monitored service. A correlation search definition of the service monitoring system determines how a search of the KPI data is conducted, how its data is evaluated to determine whether a triggering condition has been met, and, if so, determines what triggered action is to be initiated.

Performance monitoring using correlation search with triggering conditions

The disclosed system and method acquire and store performance measurements relating to performance of a component in an information technology (IT) environment and log data produced by the IT environment, in association with corresponding time stamps. The disclosed system and method correlate at least one of the performance measurements with at least one of the portions of log data.

Correlating data based on user-specified search criteria

A scheduler manages execution of a plurality of data-collection jobs, assigns individual jobs to specific forwarders in a set of forwarders, and generates and transmits tokens (e.g., pairs of data-collection tasks and target sources) to assigned forwarders. The forwarder uses the tokens, along with stored information applicable across jobs, to collect data from the target source and forward it onto an indexer for processing. For example, the indexer can then break a data stream into discrete events, extract a timestamp from each event and index (e.g., store) the event based on the timestamp. The scheduler can monitor forwarders' job performance, such that it can use the performance to influence subsequent job assignments. Thus, data-collection jobs can be efficiently assigned to and executed by a group of forwarders, where the group can potentially be diverse and dynamic in size.

Data intake and query system with distributed data acquisition, indexing and search

An example method of determining a state of a key performance indicator (KPI) comprises: receiving one or more entity definitions, wherein each entity definition associates an entity with machine data pertaining to the entity; receiving a service definition for a service provided by one or more entities, the service definition including a reference to a corresponding entity definition of the entity definitions, wherein the service definition includes a respective reference for each of the one or more entities; receiving definitions of one or more KPIs, each KPI defined by a respective search query that produces a value derived from particular machine data, wherein the particular machine data is identified by the service definition, wherein each value is indicative of performance of the service at a point in time or during a period of time; deriving, by performing on the machine data a search query associated with the KPI, one or more KPI values for the KPI; selecting, among a plurality of states of the KPI, a state satisfying a condition applied to the one or more KPI values; and causing display of a visual indicator of the state of the KPI.

Determining states of key performance indicators derived from machine data

A scheduler manages execution of a plurality of data-collection jobs, assigns individual jobs to specific forwarders in a set of forwarders, and generates and transmits tokens (e.g., pairs of data&#x2014;collection tasks and target sources) to assigned forwarders. The forwarder uses the tokens, along with stored information applicable across jobs, to collect data from the target source and forward it onto an indexer for processing. For example, the indexer can then break a data stream into discrete events, extract a timestamp from each event and index (e.g., store) the event based on the timestamp. The scheduler can monitor forwarders' job performance, such that it can use the performance to influence subsequent job assignments. Thus, data-collection jobs can be efficiently assigned to and executed by a group of forwarders, where the group can potentially be diverse and dynamic in size.

Distributed data acquisition, indexing and search system

One or more processing devices access a service definition for a service provided by one or more entities that each produce machine data or about which machine data is generated. The service definition identifies the entities that provide the service and, for each entity, definitional information includes information for identifying machine data pertaining to that entity. The processing devices access a key performance indicator (KPI) for the service that is defined by a search query that produces a value derived from the machine data pertaining to the entities identified in the service definition. The value indicates how the service is performing at a point in time or during a period of time and indicates a state of the KPI. A graphical interface is displayed and an indication of at least one threshold, which defines an end of a range of values representing a state of the KPI, for the KPI is received.

Threshold establishment for key performance indicators derived from machine data

Monitoring I.T. service-level performance using a machine data key performance indicator (KPI) correlation search

Processing of performance data and raw log data from an information technology environment using search criterion input via a graphical user interface

Determination of performance state of a user-selected parent component in a hierarchical computing environment based on performance states of related child components

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Denver, CO, United States of America

Brian John Bingham