[discrete]
[[esql-stats-by]]
=== `STATS ... BY`

The `STATS ... BY` processing command groups rows according to a common value
and calculates one or more aggregated values over the grouped rows.

**Syntax**

[source,esql]
----
STATS [column1 =] expression1[, ..., [columnN =] expressionN]
[BY grouping_expression1[, ..., grouping_expressionN]]
----

*Parameters*

`columnX`::
The name by which the aggregated value is returned. If omitted, the name is
equal to the corresponding expression (`expressionX`).
If multiple columns have the same name, all but the rightmost column with this
name will be ignored.

`expressionX`::
An expression that computes an aggregated value.

`grouping_expressionX`::
An expression that outputs the values to group by.
If its name coincides with one of the computed columns, that column will be ignored.

NOTE: Individual `null` values are skipped when computing aggregations.

*Description*

The `STATS ... BY` processing command groups rows according to a common value
and calculate one or more aggregated values over the grouped rows. If `BY` is
omitted, the output table contains exactly one row with the aggregations applied
over the entire dataset.

The following <<esql-agg-functions,aggregation functions>> are supported:

include::../functions/aggregation-functions.asciidoc[tag=agg_list]

The following <<esql-group-functions,grouping functions>> are supported:

include::../functions/grouping-functions.asciidoc[tag=group_list]

NOTE: `STATS` without any groups is much much faster than adding a group.

NOTE: Grouping on a single expression is currently much more optimized than grouping
      on many expressions. In some tests we have seen grouping on a single `keyword`
      column to be five times faster than grouping on two `keyword` columns. Do
      not try to work around this by combining the two columns together with
      something like <<esql-concat>> and then grouping - that is not going to be
      faster.

*Examples*

Calculating a statistic and grouping by the values of another column:

[source.merge.styled,esql]
----
include::{esql-specs}/stats.csv-spec[tag=stats]
----
[%header.monospaced.styled,format=dsv,separator=|]
|===
include::{esql-specs}/stats.csv-spec[tag=stats-result]
|===

Omitting `BY` returns one row with the aggregations applied over the entire
dataset:

[source.merge.styled,esql]
----
include::{esql-specs}/stats.csv-spec[tag=statsWithoutBy]
----
[%header.monospaced.styled,format=dsv,separator=|]
|===
include::{esql-specs}/stats.csv-spec[tag=statsWithoutBy-result]
|===

It's possible to calculate multiple values:

[source.merge.styled,esql]
----
include::{esql-specs}/stats.csv-spec[tag=statsCalcMultipleValues]
----
[%header.monospaced.styled,format=dsv,separator=|]
|===
include::{esql-specs}/stats.csv-spec[tag=statsCalcMultipleValues-result]
|===

[[esql-stats-mv-group]]
If the grouping key is multivalued then the input row is in all groups:

[source.merge.styled,esql]
----
include::{esql-specs}/stats.csv-spec[tag=mv-group]
----
[%header.monospaced.styled,format=dsv,separator=|]
|===
include::{esql-specs}/stats.csv-spec[tag=mv-group-result]
|===

It's also possible to group by multiple values:

[source,esql]
----
include::{esql-specs}/stats.csv-spec[tag=statsGroupByMultipleValues]
----

If the all grouping keys are multivalued then the input row is in all groups:

[source.merge.styled,esql]
----
include::{esql-specs}/stats.csv-spec[tag=multi-mv-group]
----
[%header.monospaced.styled,format=dsv,separator=|]
|===
include::{esql-specs}/stats.csv-spec[tag=multi-mv-group-result]
|===

Both the aggregating functions and the grouping expressions accept other
functions. This is useful for using `STATS...BY` on multivalue columns.
For example, to calculate the average salary change, you can use `MV_AVG` to
first average the multiple values per employee, and use the result with the
`AVG` function:

[source.merge.styled,esql]
----
include::{esql-specs}/stats.csv-spec[tag=docsStatsAvgNestedExpression]
----
[%header.monospaced.styled,format=dsv,separator=|]
|===
include::{esql-specs}/stats.csv-spec[tag=docsStatsAvgNestedExpression-result]
|===

An example of grouping by an expression is grouping employees on the first
letter of their last name:

[source.merge.styled,esql]
----
include::{esql-specs}/stats.csv-spec[tag=docsStatsByExpression]
----
[%header.monospaced.styled,format=dsv,separator=|]
|===
include::{esql-specs}/stats.csv-spec[tag=docsStatsByExpression-result]
|===

Specifying the output column name is optional. If not specified, the new column
name is equal to the expression. The following query returns a column named
`AVG(salary)`:

[source.merge.styled,esql]
----
include::{esql-specs}/stats.csv-spec[tag=statsUnnamedColumn]
----
[%header.monospaced.styled,format=dsv,separator=|]
|===
include::{esql-specs}/stats.csv-spec[tag=statsUnnamedColumn-result]
|===

Because this name contains special characters, <<esql-identifiers,it needs to be
quoted>> with backticks (+{backtick}+) when using it in subsequent commands:

[source.merge.styled,esql]
----
include::{esql-specs}/stats.csv-spec[tag=statsUnnamedColumnEval]
----
[%header.monospaced.styled,format=dsv,separator=|]
|===
include::{esql-specs}/stats.csv-spec[tag=statsUnnamedColumnEval-result]
|===