ES|QL commands

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Source commands

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An ES|QL source command produces a table, typically with data from Elasticsearch. An ES|QL query must start with a source command.

A source command producing a table from Elasticsearch

ES|QL supports these source commands:

Processing commands

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ES|QL processing commands change an input table by adding, removing, or changing rows and columns.

A processing command changing an input table

ES|QL supports these processing commands:

  • DISSECT
  • DROP
  • ENRICH
  • EVAL
  • GROK
  • [preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. INLINESTATS ... BY
  • KEEP
  • LIMIT
  • [preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. LOOKUP
  • [preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. MV_EXPAND
  • RENAME
  • SORT
  • STATS ... BY
  • WHERE

FROM

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The FROM source command returns a table with data from a data stream, index, or alias.

Syntax

FROM index_pattern [METADATA fields]

Parameters

index_pattern
A list of indices, data streams or aliases. Supports wildcards and date math.
fields
A comma-separated list of metadata fields to retrieve.

Description

The FROM source command returns a table with data from a data stream, index, or alias. Each row in the resulting table represents a document. Each column corresponds to a field, and can be accessed by the name of that field.

By default, an ES|QL query without an explicit LIMIT uses an implicit limit of 1000. This applies to FROM too. A FROM command without LIMIT:

FROM employees

is executed as:

FROM employees
| LIMIT 1000

Examples

FROM employees

You can use date math to refer to indices, aliases and data streams. This can be useful for time series data, for example to access today’s index:

FROM <logs-{now/d}>

Use comma-separated lists or wildcards to query multiple data streams, indices, or aliases:

FROM employees-00001,other-employees-*

Use the format <remote_cluster_name>:<target> to query data streams and indices on remote clusters:

FROM cluster_one:employees-00001,cluster_two:other-employees-*

Use the optional METADATA directive to enable metadata fields:

FROM employees METADATA _id

Use enclosing double quotes (") or three enclosing double quotes (""") to escape index names that contain special characters:

FROM "this=that", """this[that"""

ROW

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The ROW source command produces a row with one or more columns with values that you specify. This can be useful for testing.

Syntax

ROW column1 = value1[, ..., columnN = valueN]

Parameters

columnX
The column name. In case of duplicate column names, only the rightmost duplicate creates a column.
valueX
The value for the column. Can be a literal, an expression, or a function.

Examples

ROW a = 1, b = "two", c = null
a:integer b:keyword c:null

1

"two"

null

Use square brackets to create multi-value columns:

ROW a = [2, 1]

ROW supports the use of functions:

ROW a = ROUND(1.23, 0)

SHOW

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The SHOW source command returns information about the deployment and its capabilities.

Syntax

SHOW item

Parameters

item
Can only be INFO.

Examples

Use SHOW INFO to return the deployment’s version, build date and hash.

SHOW INFO
version date hash

8.13.0

2024-02-23T10:04:18.123117961Z

04ba8c8db2507501c88f215e475de7b0798cb3b3

DISSECT

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DISSECT enables you to extract structured data out of a string.

Syntax

DISSECT input "pattern" [APPEND_SEPARATOR="<separator>"]

Parameters

input
The column that contains the string you want to structure. If the column has multiple values, DISSECT will process each value.
pattern
A dissect pattern. If a field name conflicts with an existing column, the existing column is dropped. If a field name is used more than once, only the rightmost duplicate creates a column.
<separator>
A string used as the separator between appended values, when using the append modifier.

Description

DISSECT enables you to extract structured data out of a string. DISSECT matches the string against a delimiter-based pattern, and extracts the specified keys as columns.

Refer to Process data with DISSECT for the syntax of dissect patterns.

Examples

The following example parses a string that contains a timestamp, some text, and an IP address:

ROW a = "2023-01-23T12:15:00.000Z - some text - 127.0.0.1"
| DISSECT a """%{date} - %{msg} - %{ip}"""
| KEEP date, msg, ip
date:keyword msg:keyword ip:keyword

2023-01-23T12:15:00.000Z

some text

127.0.0.1

By default, DISSECT outputs keyword string columns. To convert to another type, use Type conversion functions:

ROW a = "2023-01-23T12:15:00.000Z - some text - 127.0.0.1"
| DISSECT a """%{date} - %{msg} - %{ip}"""
| KEEP date, msg, ip
| EVAL date = TO_DATETIME(date)
msg:keyword ip:keyword date:date

some text

127.0.0.1

2023-01-23T12:15:00.000Z

DROP

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The DROP processing command removes one or more columns.

Syntax

DROP columns

Parameters

columns
A comma-separated list of columns to remove. Supports wildcards.

Examples

FROM employees
| DROP height

Rather than specify each column by name, you can use wildcards to drop all columns with a name that matches a pattern:

FROM employees
| DROP height*

ENRICH

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ENRICH enables you to add data from existing indices as new columns using an enrich policy.

Syntax

ENRICH policy [ON match_field] [WITH [new_name1 = ]field1, [new_name2 = ]field2, ...]

Parameters

policy
The name of the enrich policy. You need to create and execute the enrich policy first.
mode
The mode of the enrich command in cross cluster ES|QL. See enrich across clusters.
match_field
The match field. ENRICH uses its value to look for records in the enrich index. If not specified, the match will be performed on the column with the same name as the match_field defined in the enrich policy.
fieldX
The enrich fields from the enrich index that are added to the result as new columns. If a column with the same name as the enrich field already exists, the existing column will be replaced by the new column. If not specified, each of the enrich fields defined in the policy is added. A column with the same name as the enrich field will be dropped unless the enrich field is renamed.
new_nameX
Enables you to change the name of the column that’s added for each of the enrich fields. Defaults to the enrich field name. If a column has the same name as the new name, it will be discarded. If a name (new or original) occurs more than once, only the rightmost duplicate creates a new column.

Description

ENRICH enables you to add data from existing indices as new columns using an enrich policy. Refer to Data enrichment for information about setting up a policy.

esql enrich

Before you can use ENRICH, you need to create and execute an enrich policy.

Examples

The following example uses the languages_policy enrich policy to add a new column for each enrich field defined in the policy. The match is performed using the match_field defined in the enrich policy and requires that the input table has a column with the same name (language_code in this example). ENRICH will look for records in the enrich index based on the match field value.

ROW language_code = "1"
| ENRICH languages_policy
language_code:keyword language_name:keyword

1

English

To use a column with a different name than the match_field defined in the policy as the match field, use ON <column-name>:

ROW a = "1"
| ENRICH languages_policy ON a
a:keyword language_name:keyword

1

English

By default, each of the enrich fields defined in the policy is added as a column. To explicitly select the enrich fields that are added, use WITH <field1>, <field2>, ...:

ROW a = "1"
| ENRICH languages_policy ON a WITH language_name
a:keyword language_name:keyword

1

English

You can rename the columns that are added using WITH new_name=<field1>:

ROW a = "1"
| ENRICH languages_policy ON a WITH name = language_name
a:keyword name:keyword

1

English

In case of name collisions, the newly created columns will override existing columns.

EVAL

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The EVAL processing command enables you to append new columns with calculated values.

Syntax

EVAL [column1 =] value1[, ..., [columnN =] valueN]

Parameters

columnX
The column name. If a column with the same name already exists, the existing column is dropped. If a column name is used more than once, only the rightmost duplicate creates a column.
valueX
The value for the column. Can be a literal, an expression, or a function. Can use columns defined left of this one.

Description

The EVAL processing command enables you to append new columns with calculated values. EVAL supports various functions for calculating values. Refer to Functions for more information.

Examples

FROM employees
| SORT emp_no
| KEEP first_name, last_name, height
| EVAL height_feet = height * 3.281, height_cm = height * 100
first_name:keyword last_name:keyword height:double height_feet:double height_cm:double

Georgi

Facello

2.03

6.66043

202.99999999999997

Bezalel

Simmel

2.08

6.82448

208.0

Parto

Bamford

1.83

6.004230000000001

183.0

If the specified column already exists, the existing column will be dropped, and the new column will be appended to the table:

FROM employees
| SORT emp_no
| KEEP first_name, last_name, height
| EVAL height = height * 3.281
first_name:keyword last_name:keyword height:double

Georgi

Facello

6.66043

Bezalel

Simmel

6.82448

Parto

Bamford

6.004230000000001

Specifying the output column name is optional. If not specified, the new column name is equal to the expression. The following query adds a column named height*3.281:

FROM employees
| SORT emp_no
| KEEP first_name, last_name, height
| EVAL height * 3.281
first_name:keyword last_name:keyword height:double height * 3.281:double

Georgi

Facello

2.03

6.66043

Bezalel

Simmel

2.08

6.82448

Parto

Bamford

1.83

6.004230000000001

Because this name contains special characters, it needs to be quoted with backticks (`) when using it in subsequent commands:

FROM employees
| EVAL height * 3.281
| STATS avg_height_feet = AVG(`height * 3.281`)
avg_height_feet:double

5.801464200000001

GROK

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GROK enables you to extract structured data out of a string.

Syntax

GROK input "pattern"

Parameters

input
The column that contains the string you want to structure. If the column has multiple values, GROK will process each value.
pattern
A grok pattern. If a field name conflicts with an existing column, the existing column is discarded. If a field name is used more than once, a multi-valued column will be created with one value per each occurrence of the field name.

Description

GROK enables you to extract structured data out of a string. GROK matches the string against patterns, based on regular expressions, and extracts the specified patterns as columns.

Refer to Process data with GROK for the syntax of grok patterns.

Examples

The following example parses a string that contains a timestamp, an IP address, an email address, and a number:

ROW a = "2023-01-23T12:15:00.000Z 127.0.0.1 some.email@foo.com 42"
| GROK a """%{TIMESTAMP_ISO8601:date} %{IP:ip} %{EMAILADDRESS:email} %{NUMBER:num}"""
| KEEP date, ip, email, num
date:keyword ip:keyword email:keyword num:keyword

2023-01-23T12:15:00.000Z

127.0.0.1

some.email@foo.com

42

By default, GROK outputs keyword string columns. int and float types can be converted by appending :type to the semantics in the pattern. For example {NUMBER:num:int}:

ROW a = "2023-01-23T12:15:00.000Z 127.0.0.1 some.email@foo.com 42"
| GROK a """%{TIMESTAMP_ISO8601:date} %{IP:ip} %{EMAILADDRESS:email} %{NUMBER:num:int}"""
| KEEP date, ip, email, num
date:keyword ip:keyword email:keyword num:integer

2023-01-23T12:15:00.000Z

127.0.0.1

some.email@foo.com

42

For other type conversions, use Type conversion functions:

ROW a = "2023-01-23T12:15:00.000Z 127.0.0.1 some.email@foo.com 42"
| GROK a """%{TIMESTAMP_ISO8601:date} %{IP:ip} %{EMAILADDRESS:email} %{NUMBER:num:int}"""
| KEEP date, ip, email, num
| EVAL date = TO_DATETIME(date)
ip:keyword email:keyword num:integer date:date

127.0.0.1

some.email@foo.com

42

2023-01-23T12:15:00.000Z

If a field name is used more than once, GROK creates a multi-valued column:

FROM addresses
| KEEP city.name, zip_code
| GROK zip_code """%{WORD:zip_parts} %{WORD:zip_parts}"""
city.name:keyword zip_code:keyword zip_parts:keyword

Amsterdam

1016 ED

["1016", "ED"]

San Francisco

CA 94108

["CA", "94108"]

Tokyo

100-7014

null

INLINESTATS ... BY

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INLINESTATS is highly experimental and only available in SNAPSHOT versions.

The INLINESTATS command calculates an aggregate result and adds new columns with the result to the stream of input data.

Syntax

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

Individual null values are skipped when computing aggregations.

Description

The INLINESTATS command calculates an aggregate result and merges that result back into the stream of input data. Without the optional BY clause this will produce a single result which is appended to each row. With a BY clause this will produce one result per grouping and merge the result into the stream based on matching group keys.

All of the aggregation functions are supported.

Examples

Find the employees that speak the most languages (it’s a tie!):

FROM employees
| KEEP emp_no, languages
| INLINESTATS max_lang = MAX(languages)
| WHERE max_lang == languages
| SORT emp_no ASC
| LIMIT 5
emp_no:integer languages:integer max_lang:integer

10002

5

5

10004

5

5

10011

5

5

10012

5

5

10014

5

5

Find the longest tenured employee who’s last name starts with each letter of the alphabet:

FROM employees
| KEEP emp_no, avg_worked_seconds, last_name
| INLINESTATS max_avg_worked_seconds = MAX(avg_worked_seconds) BY SUBSTRING(last_name, 0, 1)
| WHERE max_avg_worked_seconds == avg_worked_seconds
| SORT last_name ASC
| LIMIT 5
emp_no:integer avg_worked_seconds:long last_name:keyword SUBSTRING(last_name, 0, 1):keyword max_avg_worked_seconds:long

10065

372660279

Awdeh

A

372660279

10074

382397583

Bernatsky

B

382397583

10044

387408356

Casley

C

387408356

10030

394597613

Demeyer

D

394597613

10087

305782871

Eugenio

E

305782871

Find the northern and southern most airports:

FROM airports
| MV_EXPAND type
| EVAL lat = ST_Y(location)
| INLINESTATS most_northern=MAX(lat), most_southern=MIN(lat) BY type
| WHERE lat == most_northern OR lat == most_southern
| SORT lat DESC
| KEEP type, name, location
type:keyword name:text location:geo_point

mid

Svalbard Longyear

POINT (15.495229 78.246717)

major

Tromsø Langnes

POINT (18.9072624292132 69.6796790473478)

military

Severomorsk-3 (Murmansk N.E.)

POINT (33.2903527616285 69.0168711826804)

spaceport

Baikonur Cosmodrome

POINT (63.307354423875 45.9635739403124)

small

Dhamial

POINT (73.0320498392002 33.5614146278861)

small

Sahnewal

POINT (75.9570722403652 30.8503598561702)

spaceport

Centre Spatial Guyanais

POINT (-52.7684296893452 5.23941001258035)

military

Santos Air Force Base

POINT (-46.3052704931003 -23.9237590410637)

major

Christchurch Int’l

POINT (172.538675565223 -43.4885486784104)

mid

Hermes Quijada Int’l

POINT (-67.7530268462675 -53.7814746058316)

Our test data doesn’t have many "small" airports.

If a BY field is multivalued then INLINESTATS will put the row in each bucket like STATS ... BY:

FROM airports
| INLINESTATS min_scalerank=MIN(scalerank) BY type
| EVAL type=MV_SORT(type), min_scalerank=MV_SORT(min_scalerank)
| KEEP abbrev, type, scalerank, min_scalerank
| WHERE abbrev == "GWL"
abbrev:keyword type:keyword scalerank:integer min_scalerank:integer

GWL

[mid, military]

9

[2, 4]

To treat each group key as its own row use MV_EXPAND before INLINESTATS:

FROM airports
| KEEP abbrev, type, scalerank
| MV_EXPAND type
| INLINESTATS min_scalerank=MIN(scalerank) BY type
| SORT min_scalerank ASC
| WHERE abbrev == "GWL"
abbrev:keyword type:keyword scalerank:integer min_scalerank:integer

GWL

mid

9

2

GWL

military

9

4

KEEP

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The KEEP processing command enables you to specify what columns are returned and the order in which they are returned.

Syntax

KEEP columns

Parameters

columns
A comma-separated list of columns to keep. Supports wildcards. See below for the behavior in case an existing column matches multiple given wildcards or column names.

Description

The KEEP processing command enables you to specify what columns are returned and the order in which they are returned.

Precedence rules are applied when a field name matches multiple expressions. Fields are added in the order they appear. If one field matches multiple expressions, the following precedence rules apply (from highest to lowest priority):

  1. Complete field name (no wildcards)
  2. Partial wildcard expressions (for example: fieldNam*)
  3. Wildcard only (*)

If a field matches two expressions with the same precedence, the rightmost expression wins.

Refer to the examples for illustrations of these precedence rules.

Examples

The columns are returned in the specified order:

FROM employees
| KEEP emp_no, first_name, last_name, height
emp_no:integer first_name:keyword last_name:keyword height:double

10001

Georgi

Facello

2.03

10002

Bezalel

Simmel

2.08

10003

Parto

Bamford

1.83

10004

Chirstian

Koblick

1.78

10005

Kyoichi

Maliniak

2.05

Rather than specify each column by name, you can use wildcards to return all columns with a name that matches a pattern:

FROM employees
| KEEP h*
height:double height.float:double height.half_float:double height.scaled_float:double hire_date:date

The asterisk wildcard (*) by itself translates to all columns that do not match the other arguments.

This query will first return all columns with a name that starts with h, followed by all other columns:

FROM employees
| KEEP h*, *
height:double height.float:double height.half_float:double height.scaled_float:double hire_date:date avg_worked_seconds:long birth_date:date emp_no:integer first_name:keyword gender:keyword is_rehired:boolean job_positions:keyword languages:integer languages.byte:integer languages.long:long languages.short:integer last_name:keyword salary:integer salary_change:double salary_change.int:integer salary_change.keyword:keyword salary_change.long:long still_hired:boolean

The following examples show how precedence rules work when a field name matches multiple expressions.

Complete field name has precedence over wildcard expressions:

FROM employees
| KEEP first_name, last_name, first_name*
first_name:keyword last_name:keyword

Wildcard expressions have the same priority, but last one wins (despite being less specific):

FROM employees
| KEEP first_name*, last_name, first_na*
last_name:keyword first_name:keyword

A simple wildcard expression * has the lowest precedence. Output order is determined by the other arguments:

FROM employees
| KEEP *, first_name
avg_worked_seconds:long birth_date:date emp_no:integer gender:keyword height:double height.float:double height.half_float:double height.scaled_float:double hire_date:date is_rehired:boolean job_positions:keyword languages:integer languages.byte:integer languages.long:long languages.short:integer last_name:keyword salary:integer salary_change:double salary_change.int:integer salary_change.keyword:keyword salary_change.long:long still_hired:boolean first_name:keyword

LIMIT

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The LIMIT processing command enables you to limit the number of rows that are returned.

Syntax

LIMIT max_number_of_rows

Parameters

max_number_of_rows
The maximum number of rows to return.

Description

The LIMIT processing command enables you to limit the number of rows that are returned. Queries do not return more than 10,000 rows, regardless of the LIMIT command’s value.

This limit only applies to the number of rows that are retrieved by the query. Queries and aggregations run on the full data set.

To overcome this limitation:

  • Reduce the result set size by modifying the query to only return relevant data. Use WHERE to select a smaller subset of the data.
  • Shift any post-query processing to the query itself. You can use the ES|QL STATS ... BY command to aggregate data in the query.

The default and maximum limits can be changed using these dynamic cluster settings:

  • esql.query.result_truncation_default_size
  • esql.query.result_truncation_max_size

Example

FROM employees
| SORT emp_no ASC
| LIMIT 5

LOOKUP

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LOOKUP is highly experimental and only available in SNAPSHOT versions.

LOOKUP matches values from the input against a table provided in the request, adding the other fields from the table to the output.

Syntax

LOOKUP table ON match_field1[, match_field2, ...]

Parameters

table
The name of the table provided in the request to match. If the table’s column names conflict with existing columns, the existing columns will be dropped.
match_field
The fields in the input to match against the table.

Examples

resp = client.esql.query(
    format="txt",
    query="\n      FROM library\n    | SORT page_count DESC\n    | KEEP name, author\n    | LOOKUP era ON author\n    | LIMIT 5\n  ",
    tables={
        "era": {
            "author": {
                "keyword": [
                    "Frank Herbert",
                    "Peter F. Hamilton",
                    "Vernor Vinge",
                    "Alastair Reynolds",
                    "James S.A. Corey"
                ]
            },
            "era": {
                "keyword": [
                    "The New Wave",
                    "Diamond",
                    "Diamond",
                    "Diamond",
                    "Hadron"
                ]
            }
        }
    },
)
print(resp)
const response = await client.esql.query({
  format: "txt",
  query:
    "\n      FROM library\n    | SORT page_count DESC\n    | KEEP name, author\n    | LOOKUP era ON author\n    | LIMIT 5\n  ",
  tables: {
    era: {
      author: {
        keyword: [
          "Frank Herbert",
          "Peter F. Hamilton",
          "Vernor Vinge",
          "Alastair Reynolds",
          "James S.A. Corey",
        ],
      },
      era: {
        keyword: ["The New Wave", "Diamond", "Diamond", "Diamond", "Hadron"],
      },
    },
  },
});
console.log(response);
POST /_query?format=txt
{
  "query": """
      FROM library
    | SORT page_count DESC
    | KEEP name, author
    | LOOKUP era ON author
    | LIMIT 5
  """,
  "tables": {
    "era": {
      "author": {"keyword": ["Frank Herbert", "Peter F. Hamilton", "Vernor Vinge", "Alastair Reynolds", "James S.A. Corey"]},
      "era":    {"keyword": [ "The New Wave",           "Diamond",      "Diamond",           "Diamond",           "Hadron"]}
    }
  }
}

Which returns:

        name        |     author      |      era
--------------------+-----------------+---------------
Pandora's Star      |Peter F. Hamilton|Diamond
A Fire Upon the Deep|Vernor Vinge     |Diamond
Dune                |Frank Herbert    |The New Wave
Revelation Space    |Alastair Reynolds|Diamond
Leviathan Wakes     |James S.A. Corey |Hadron

MV_EXPAND

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This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features.

The MV_EXPAND processing command expands multivalued columns into one row per value, duplicating other columns.

Syntax

MV_EXPAND column

Parameters

column
The multivalued column to expand.

Example

ROW a=[1,2,3], b="b", j=["a","b"]
| MV_EXPAND a
a:integer b:keyword j:keyword

1

b

["a", "b"]

2

b

["a", "b"]

3

b

["a", "b"]

RENAME

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The RENAME processing command renames one or more columns.

Syntax

RENAME old_name1 AS new_name1[, ..., old_nameN AS new_nameN]

Parameters

old_nameX
The name of a column you want to rename.
new_nameX
The new name of the column. If it conflicts with an existing column name, the existing column is dropped. If multiple columns are renamed to the same name, all but the rightmost column with the same new name are dropped.

Description

The RENAME processing command renames one or more columns. If a column with the new name already exists, it will be replaced by the new column.

Examples

FROM employees
| KEEP first_name, last_name, still_hired
| RENAME  still_hired AS employed

Multiple columns can be renamed with a single RENAME command:

FROM employees
| KEEP first_name, last_name
| RENAME first_name AS fn, last_name AS ln

SORT

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The SORT processing command sorts a table on one or more columns.

Syntax

SORT column1 [ASC/DESC][NULLS FIRST/NULLS LAST][, ..., columnN [ASC/DESC][NULLS FIRST/NULLS LAST]]

Parameters

columnX
The column to sort on.

Description

The SORT processing command sorts a table on one or more columns.

The default sort order is ascending. Use ASC or DESC to specify an explicit sort order.

Two rows with the same sort key are considered equal. You can provide additional sort expressions to act as tie breakers.

Sorting on multivalued columns uses the lowest value when sorting ascending and the highest value when sorting descending.

By default, null values are treated as being larger than any other value. With an ascending sort order, null values are sorted last, and with a descending sort order, null values are sorted first. You can change that by providing NULLS FIRST or NULLS LAST.

Examples

FROM employees
| KEEP first_name, last_name, height
| SORT height

Explicitly sorting in ascending order with ASC:

FROM employees
| KEEP first_name, last_name, height
| SORT height DESC

Providing additional sort expressions to act as tie breakers:

FROM employees
| KEEP first_name, last_name, height
| SORT height DESC, first_name ASC

Sorting null values first using NULLS FIRST:

FROM employees
| KEEP first_name, last_name, height
| SORT first_name ASC NULLS FIRST

STATS ... BY

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

Syntax

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.

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 aggregation functions are supported:

  • AVG
  • COUNT
  • COUNT_DISTINCT
  • MAX
  • MEDIAN
  • MEDIAN_ABSOLUTE_DEVIATION
  • MIN
  • PERCENTILE
  • [preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. ST_CENTROID_AGG
  • SUM
  • TOP
  • VALUES
  • [preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. WEIGHTED_AVG

The following grouping functions are supported:

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

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 CONCAT and then grouping - that is not going to be faster.

Examples

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

FROM employees
| STATS count = COUNT(emp_no) BY languages
| SORT languages
count:long languages:integer

15

1

19

2

17

3

18

4

21

5

10

null

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

FROM employees
| STATS avg_lang = AVG(languages)
avg_lang:double

3.1222222222222222

It’s possible to calculate multiple values:

FROM employees
| STATS avg_lang = AVG(languages), max_lang = MAX(languages)
avg_lang:double max_lang:integer

3.1222222222222222

5

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

ROW i=1, a=["a", "b"] | STATS MIN(i) BY a | SORT a ASC
MIN(i):integer a:keyword

1

a

1

b

It’s also possible to group by multiple values:

FROM employees
| EVAL hired = DATE_FORMAT("yyyy", hire_date)
| STATS avg_salary = AVG(salary) BY hired, languages.long
| EVAL avg_salary = ROUND(avg_salary)
| SORT hired, languages.long

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

ROW i=1, a=["a", "b"], b=[2, 3] | STATS MIN(i) BY a, b | SORT a ASC, b ASC
MIN(i):integer a:keyword b:integer

1

a

2

1

a

3

1

b

2

1

b

3

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:

FROM employees
| STATS avg_salary_change = ROUND(AVG(MV_AVG(salary_change)), 10)
avg_salary_change:double

1.3904535865

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

FROM employees
| STATS my_count = COUNT() BY LEFT(last_name, 1)
| SORT `LEFT(last_name, 1)`
my_count:long LEFT(last_name, 1):keyword

2

A

11

B

5

C

5

D

2

E

4

F

4

G

6

H

2

J

3

K

5

L

12

M

4

N

1

O

7

P

5

R

13

S

4

T

2

W

3

Z

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):

FROM employees
| STATS AVG(salary)
AVG(salary):double

48248.55

Because this name contains special characters, it needs to be quoted with backticks (`) when using it in subsequent commands:

FROM employees
| STATS AVG(salary)
| EVAL avg_salary_rounded = ROUND(`AVG(salary)`)
AVG(salary):double avg_salary_rounded:double

48248.55

48249.0

WHERE

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The WHERE processing command produces a table that contains all the rows from the input table for which the provided condition evaluates to true.

Syntax

WHERE expression

Parameters

expression
A boolean expression.

Examples

FROM employees
| KEEP first_name, last_name, still_hired
| WHERE still_hired == true

Which, if still_hired is a boolean field, can be simplified to:

FROM employees
| KEEP first_name, last_name, still_hired
| WHERE still_hired

Use date math to retrieve data from a specific time range. For example, to retrieve the last hour of logs:

FROM sample_data
| WHERE @timestamp > NOW() - 1 hour

WHERE supports various functions. For example the LENGTH function:

FROM employees
| KEEP first_name, last_name, height
| WHERE LENGTH(first_name) < 4

For a complete list of all functions, refer to Functions overview.

For NULL comparison, use the IS NULL and IS NOT NULL predicates:

FROM employees
| WHERE birth_date IS NULL
| KEEP first_name, last_name
| SORT first_name
| LIMIT 3
first_name:keyword last_name:keyword

Basil

Tramer

Florian

Syrotiuk

Lucien

Rosenbaum

FROM employees
| WHERE is_rehired IS NOT NULL
| STATS COUNT(emp_no)
COUNT(emp_no):long

84

Use LIKE to filter data based on string patterns using wildcards. LIKE usually acts on a field placed on the left-hand side of the operator, but it can also act on a constant (literal) expression. The right-hand side of the operator represents the pattern.

The following wildcard characters are supported:

  • * matches zero or more characters.
  • ? matches one character.

Supported types

str pattern result

keyword

keyword

boolean

text

text

boolean

FROM employees
| WHERE first_name LIKE """?b*"""
| KEEP first_name, last_name
first_name:keyword last_name:keyword

Ebbe

Callaway

Eberhardt

Terkki

Matching the exact characters * and . will require escaping. The escape character is backslash \. Since also backslash is a special character in string literals, it will require further escaping.

ROW message = "foo * bar"
| WHERE message LIKE "foo \\* bar"

To reduce the overhead of escaping, we suggest using triple quotes strings """

ROW message = "foo * bar"
| WHERE message LIKE """foo \* bar"""

Use RLIKE to filter data based on string patterns using using regular expressions. RLIKE usually acts on a field placed on the left-hand side of the operator, but it can also act on a constant (literal) expression. The right-hand side of the operator represents the pattern.

Supported types

str pattern result

keyword

keyword

boolean

text

text

boolean

FROM employees
| WHERE first_name RLIKE """.leja.*"""
| KEEP first_name, last_name
first_name:keyword last_name:keyword

Alejandro

McAlpine

Matching special characters (eg. ., *, (…​) will require escaping. The escape character is backslash \. Since also backslash is a special character in string literals, it will require further escaping.

ROW message = "foo ( bar"
| WHERE message RLIKE "foo \\( bar"

To reduce the overhead of escaping, we suggest using triple quotes strings """

ROW message = "foo ( bar"
| WHERE message RLIKE """foo \( bar"""

The IN operator allows testing whether a field or expression equals an element in a list of literals, fields or expressions:

ROW a = 1, b = 4, c = 3
| WHERE c-a IN (3, b / 2, a)
a:integer b:integer c:integer

1

4

3

For a complete list of all operators, refer to Operators.