A service monitoring system receives a selection of key performance indicators (KPIs) that each indicate a different aspect of how a service provided by one or more entities is performing. Each entity of the one or more entities produces machine data or wherein each entity has its operation reflected in machine data not produced by the entity. Each KPI is defined by a different search query that derives one or more values from the machine data pertaining to the one or more entities providing the service, where each of the one or more values is associated with a point in time and representing the aspect of how the service is performing at the associated point in time. For each of the selected KPIs, the service monitoring system derives the one or more values and causes display of a graphical visualization of the derived one or more values for the KPI along a time-based graph lane. The graph lanes for the selected KPIs are parallel to each other and the graphical visualizations in the graph lanes are all calibrated to a same time scale.

Defining a graphical visualization along a time-based graph lane using key performance indicators derived from machine data

Systems and methods are disclosed for associating an entity with a risk score that may indicate a security threat associated with the entity's activity An exemplary method may involve monitoring the activity of a subset of the set of entities (eg, entities included in a watch list) by executing a search query against events indicating the activity of the subset of entities The events may be associated with timestamps and may include machine data Executing the search query may produce search results that pertain to activity of a particular entity from the subset The search results may be evaluated based on a triggering condition corresponding to the statistical baseline When the triggering condition is met, a risk score for the particular entity may be updated The updated risk score may be displayed to a user via a graphical user interface (GUI)

User activity monitoring

Fields may be discovered in events that are returned in response to an initial search. The events may comprise portions of raw data. Furthermore, the fields may be defined by extraction rules for extracting values from corresponding portions of raw data. The displaying of a graphical user interface (GUI) may be caused where the GUI enables a user to select or enter criteria for a subset of the discovered fields without entering a search query in a search bar. At least one criterion for at least one field from the subset of the discovered fields may be received through a portion of the GUI that does not include a search bar for entering a search query. The events returned in response to the initial search query may be caused to be filtered based on the received criterion.

Discovering fields to filter data returned in response to a search

A processing device performs a search query to produce a search result set having entries having data items. Each data item has an ordinal position. A table, having rows and columns, is displayed in a graphical user interface. Each data item of a particular entry appears in a respective column of the same row of the table. Each column corresponds to the ordinal position of its respective data item. User input is received designating, for each respective column, a field name and an entity definition component type to which the respective column pertains, and stores for each data item of the particular entry an element value of an entity definition. The element has the element name designated for the respective column in which the data item appeared, and is associated with an entity definition component having the type designated for the respective column in which the data item appeared.

Creating an entity definition from a search result set

Processing devices receive a file having entries having data items separated by delimiters. Each data item has an ordinal position. The processing device(s) cause display of a table, having rows and columns, in a graphical user interface. Each data items of a particular entry appears in a respective column of the same row. Each column corresponds to the ordinal position of its respective data item. User input is received designating, for each respective column, a field name and an entity definition component type to which the respective column pertains, and stores for each of the data items of the particular entry a value of an element of an entity definition. The element has the element name designated for the respective column in which the data item appeared, and is associated with an entity definition component having the type designated for the respective column in which the data item appeared.

Creating an entity definition from a file

Embodiments are directed are towards the transparent summarization of events. Queries directed towards summarizing and reporting on event records may be received at a search head. Search heads may be associated with one more indexers containing event records. The search head may forward the query to the indexers the can resolve the query for concurrent execution. If a query is a collection query, indexers may generate summarization information based on event records located on the indexers. Event record fields included in the summarization information may be determined based on terms included in the collection query. If a query is a stats query, each indexer may generate a partial result set from previously generated summarization information, returning the partial result sets to the search head. Collection queries may be saved and scheduled to run and periodically update the summarization information.

Distributed high performance analytics store

Embodiments are directed towards receiving and processing search queries directed towards relatively large sets of data. The data is stored in a record based datastore. From the stored data, field names, corresponding field values, and posting values may be determined. Posting values may be employed to locate records in the datastore that include the field names and field values. The field names, field values, and posting values may be employed to generate a lexicon. If queries are received, a lexicon query processor may employ the lexicon separate from the datastore to generate responses to the received queries. Queries may include clauses that may be processed using the lexicon separate from the datastore, such as, where clause expressions, group-by clause expressions, aggregation functions, or the like. A time values array may be used to enable queries to process group-by-time expressions that may return results grouped into sub-sets based on time ranges.

Flexible schema column store

Supplementing a high performance analytics store with evaluation of individual events to respond to an event query

Embodiments are directed towards managing and tracking item identification of a plurality of items to determine if an item is a new or existing item, where an existing item has been previously processed. In some embodiments, two or more item identifiers may be generated. In one embodiment, generating the two or more item identifiers may include analyzing the item using a small item size characteristic, a compressed item, or for an identifier collision. The two or more item identifiers may be employed to determine if the item is a new or existing item. In one embodiment, the two or more item identifiers may be compared to a record about an existing item to determine if the item is a new or existing item. If the item is an existing item, then the item may be further processed to determine if the existing item has actually changed.

File identification management and tracking

Embodiments are directed towards previewing results generated from indexing data raw data before the corresponding index data is added to an index store. Raw data may be received from a preview data source. After an initial set of configuration information may be established, the preview data may be submitted to an index processing pipeline. A previewing application may generate preview results based on the preview index data and the configuration information. The preview results may enable previewing how the data is being processed by the indexing application. If the preview results are not acceptable, the configuration information may be modified. The preview application enables modification of the configuration information until the generated preview results may be acceptable. If the configuration information is acceptable, the preview data may be processed and indexed in one or more index stores.

Stephen Phillip Sorkin

Papers

Distributed high performance analytics store

Flexible schema column store

Supplementing a high performance analytics store with evaluation of individual events to respond to an event query

File identification management and tracking

Interactive selection and display of a raw data parsing rule