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Use the tstats command to perform statistical queries on indexed fields in tsidx files. The indexed fields can be from normal index data, tscollect data, or accelerated data models.


| tstats [prestats=<bool>] [local=<bool>] [append=<bool>] [summariesonly=<bool>] [allow_old_summaries=<bool>] [chunk_size=<unsigned int>] <stats-func>... [ FROM ( <namespace> | sid=<tscollect-job-id> | datamodel=<data_model-name> )] [WHERE <search-query>] [BY <field-list> [span=<timespan>] ]

Required arguments

Syntax: count(<field>) | <function>(<field>) [AS <string>]
Description: Either perform a basic count of a field or perform a function on a field. For a list of the supported functions for the tstats command, refer to the table below. You can specify one or more functions. You can also rename the result using the AS keyword, unless you are in prestats mode. You cannot use wildcards to specify field names. You cannot use wildcards in the BY clause with the tstats command. See Usage.
The following table lists the supported functions by type of function. For descriptions and examples, see Statistical and charting functions.
Type of function Supported functions and syntax
Aggregate functions avg()








Event order functions earliest()
Multivalue stats and chart functions list(X)
Syntax: <string>
Description: Define a location for the tsidx file with $SPLUNK_DB/tsidxstats. If you have Splunk Enterprise, you can configure this location by editing indexes.conf and setting the tsidxStatsHomePath attribute.
Syntax: sid=<tscollect-job-id>
Description: The job ID string of a tscollect search (that generated tsidx files).
Syntax: datamodel=<data_model-name>
Description: The name of an accelerated data model.

Optional arguments

Syntax: append=<bool>
Description: When in prestats mode (prestats=t), enables append=t where the prestats results append to existing results, instead of generating them.
Default: false
Syntax: allow_old_summaries=true | false
Description: Only applies when selecting from an accelerated data model. To return results from summary directories only when those directories are up-to-date, set this parameter to false. If the data model definition has changed, summary directories that are older than the new definition are not used when producing output from tstats. This default ensures that the output from tstats will always reflect your current configuration. When set to true, tstats will use both current summary data and summary data that was generated prior to the definition change. Essentially this is an advanced performance feature for cases where you know that the old summaries are "good enough".
Default: false
Syntax: chunk_size=<unsigned_int>
Description: Advanced option. This argument controls how many events are retrieved at a time within a single TSIDX file when answering queries. Only consider supplying a lower value for this if you find a particular query is using too much memory. The case that could cause this would be an excessively high cardinality split-by, such as grouping by several fields that have a very large amount of distinct values. Setting this value too low can negatively impact the overall run time of your query.
Default: 10000000
Syntax: local=true | false
Description: If true, forces the processor to be run only on the search head.
Default: false
Syntax: prestats=true | false
Description: Use this to output the answer in prestats format, which enables you to pipe the results to a different type of processor, such as chart or timechart, that takes prestats output. This is very useful for creating graph visualizations.
Default: false
Syntax: summariesonly=<bool>
Description: Only applies when selecting from an accelerated data model. When false, generates results from both summarized data and data that is not summarized. For data not summarized as TSIDX data, the full search behavior will be used against the original index data. If set to true, 'tstats' will only generate results from the TSIDX data that has been automatically generated by the acceleration and non-summarized data will not be provided.
Default: false
Syntax: <field>, ...
Description: Specify one or more fields to group results.


The tstats command is a generating command. Generating commands use a leading pipe character. The tstats command must be the first command in a search pipeline, except when (append=true).

Wildcard characters

The tstats command does not support wildcard characters in field values in aggregate functions or BY clauses.

For example, you cannot specify | tstats avg(foo*) or | tstats count WHERE host=x BY source*.

Samples of aggregate functions include avg(), count(), max(), min(), and sum().

Any results returned where the aggregate function or BY clause includes a wildcard character are only the most recent few minutes of data that has not been summarized. Include the summariesonly=t argument with your tstats command to return only summarized data.

Selecting data

Use the tstats command to perform statistical queries on indexed fields in tsidx files. You can select the data for the indexed fields in several ways.

Normal index data
Use a FROM clause to specify a namespace, search job ID, or data model. If you do not specify a FROM clause, the Splunk software selects from index data in the same way as the search command. You are restricted to selecting data from your allowed indexes by user role. You control exactly which indexes you select data from by using the WHERE clause. If no indexes are mentioned in the WHERE clause, the Splunk software uses the default indexes. By default, role-based search filters are applied, but can be turned off in the limits.conf file.
Data manually collected with the tscollect command
You can select data from your namespace by specifying FROM <namespace>. If you did not specify a namespace with the tscollect command, the data is collected into the dispatch directory of that job. If the data is in the dispatch directory, you select the data by specifying FROM sid=<tscollect-job-id>.
An accelerated data model
You can select data from a high-performance analytics store, which is a collection of .tsidx data summaries, for an accelerated data model. You can select data from this accelerated data model by using FROM datamodel=<data_model_name>.

Search filters cannot be applied to accelerated data models. This includes both role-based and user-based search filters.

An accelerated data model object
When you select data within an accelerated data model, you can further constrain your search by indicating an object within that data model that you want to select data from. You do this by using a where clause to indicate the nodename of the data model object. The nodename value indicates where the object is in a data model hierarchy.
When you use nodename in a search, you always use the following construction: FROM datamodel=<data_model_name> where nodename=<root_object_name>.<parent_object_name>.<...>.<target_object_name>.
For example, say you want to search on an object named scheduled_reports in your internal_server data model. In that data model, the scheduled_reports object is a child of the scheduler object, which in turn is a child of the server root event object. This means that you should represent the scheduled_report object in your search as nodename=server.scheduler.scheduled_reports.
If you run that search and decide you want to search on the contents of the scheduler data model object instead, you would use nodename=server.scheduler in your new search.

Search filters cannot be applied to accelerated data model objects. This includes both role-based and user-based search filters.

You might see a count mismatch in the events retrieved when searching tsidx files. It is not possible to distinguish between indexed field tokens and raw tokens in tsidx files. On the other hand, it is more explicit to run the tstats on accelerated data models or from a tscollect, where only the fields and values are stored and not the raw tokens.

Filtering with where

You can provide any number of aggregates (aggregate-opt) to perform and also have the option of providing a filtering query using the WHERE keyword. This query looks like a normal query you would use in the search processor. This supports all the same time arguments as search, such as earliest=-1y.

Grouping by _time

You can provide any number of GROUPBY fields. If you are grouping by _time, supply a timespan with span for grouping the time buckets, for example span='1hr' or '3d'. This parameter also supports 'auto'.


Example 1: Gets the count of all events in the mydata namespace.

| tstats count FROM mydata

Example 2: Returns the average of the field foo in mydata, specifically where bar is value2 and the value of baz is greater than 5.

| tstats avg(foo) FROM mydata WHERE bar=value2 baz>5

Example 3: Gives the count by source for events with host=x.

| tstats count WHERE host=x BY source

Example 4: Gives a timechart of all the data in your default indexes with a day granularity.

| tstats prestats=t count BY _time span=1d | timechart span=1d count

Example 5: Use prestats mode in conjunction with append to compute the median values of foo and bar, which are in different namespaces.

| tstats prestats=t median(foo) FROM mydata | tstats prestats=t append=t median(bar) FROM otherdata | stats median(foo) median(bar)

Example 6: Uses the summariesonly argument to get the time range of the summary for an accelerated data model named mydm.

| tstats summariesonly=t min(_time) AS min, max(_time) AS max FROM datamodel=mydm | eval prettymin=strftime(min, "%c") | eval prettymax=strftime(max, "%c")

Example 7: Uses summariesonly in conjunction with timechart to reveal what data has been summarized over the past hour for an accelerated data model titled mydm.

| tstats summariesonly=t prestats=t count FROM datamodel=mydm BY _time span=1h | timechart span=1h count

See also

stats, tscollect


Have questions? Visit Splunk Answers and see what questions and answers the Splunk community has using the tstats command.

Last modified on 14 February, 2018

This documentation applies to the following versions of Splunk® Enterprise: 6.3.0, 6.3.1, 6.3.2, 6.3.3, 6.3.4, 6.3.5, 6.3.6, 6.3.7, 6.3.8, 6.3.9, 6.3.10, 6.3.11, 6.3.12, 6.3.13, 6.3.14

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