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:

FROM

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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 500. This applies to FROM too. A FROM command without LIMIT:

FROM employees

is executed as:

FROM employees
| LIMIT 500

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 METADATA directive to enable metadata fields:

FROM employees [METADATA _id]

ROW

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Syntax

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

Parameters

columnX
The column name.
valueX
The value for the column. Can be a literal, an expression, or a function.

Description

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

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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Syntax

SHOW item

Parameters

item
Can be INFO or FUNCTIONS.

Description

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

  • Use SHOW INFO to return the deployment’s version, build date and hash.
  • Use SHOW FUNCTIONS to return a list of all supported functions and a synopsis of each function.

Examples

SHOW functions
| WHERE STARTS_WITH(name, "is_")
name:keyword synopsis:keyword argNames:keyword argTypes:keyword argDescriptions:keyword returnType:keyword description:keyword optionalArgs:boolean variadic:boolean

is_finite

? is_finite(arg1:?)

arg1

?

""

?

""

false

false

is_infinite

? is_infinite(arg1:?)

arg1

?

""

?

""

false

false

is_nan

? is_nan(arg1:?)

arg1

?

""

?

""

false

false

DISSECT

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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.
<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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Syntax

DROP columns

Parameters

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

Description

The DROP processing command removes one or more columns.

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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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.
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
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.

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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Syntax

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

Parameters

columnX
The column name.
valueX
The value for the column. Can be a literal, an expression, or a function.

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

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

GROK

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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.

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

KEEP

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Syntax

KEEP columns

Parameters columns:: A comma-separated list of columns to keep. Supports wildcards.

Description

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

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*

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*, *

LIMIT

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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

MV_EXPAND

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Syntax

MV_EXPAND column

Parameters

column
The multivalued column to expand.

Description

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

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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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.

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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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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Syntax

STATS [column1 =] expression1[, ..., [columnN =] expressionN] [BY grouping_column1[, ..., grouping_columnN]]

Parameters

columnX
The name by which the aggregated value is returned. If omitted, the name is equal to the corresponding expression (expressionX).
expressionX
An expression that computes an aggregated value.
grouping_columnX
The column containing the values to group by.

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:

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

Grouping on a single column is currently much more optimized than grouping on many columns. 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)

It’s also possible to group by multiple values (only supported for long and keyword family fields):

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

WHERE

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Syntax

WHERE expression

Parameters

expression
A boolean expression.

Description

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

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

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 and operators.

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

Ebbe

Callaway

Eberhardt

Terkki

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.

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

Alejandro

McAlpine

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.