ES|QL commands

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

ES|QL supports these source commands:

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

ES|QL supports these processing commands:

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

Syntax

FROM index_pattern [METADATA fields]

Parameters

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.

FROM employees

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"""

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

Examples

ROW a = 1, b = "two", c = 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)

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

Syntax

SHOW item

Parameters

Examples

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

SHOW INFO

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

Syntax

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

Parameters

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

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)

The DROP processing command removes one or more columns.

Syntax

DROP columns

Parameters

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 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

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.

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

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

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

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

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

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

Syntax

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

Parameters

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

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

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

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`)

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

Syntax

GROK input "pattern"

Parameters

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 [email protected] 42"
| GROK a """%{TIMESTAMP_ISO8601:date} %{IP:ip} %{EMAILADDRESS:email} %{NUMBER:num}"""
| KEEP date, ip, email, num

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 [email protected] 42"
| GROK a """%{TIMESTAMP_ISO8601:date} %{IP:ip} %{EMAILADDRESS:email} %{NUMBER:num:int}"""
| KEEP date, ip, email, num

For other type conversions, use Type conversion functions:

ROW a = "2023-01-23T12:15:00.000Z 127.0.0.1 [email protected] 42"
| GROK a """%{TIMESTAMP_ISO8601:date} %{IP:ip} %{EMAILADDRESS:email} %{NUMBER:num:int}"""
| KEEP date, ip, email, num
| EVAL date = TO_DATETIME(date)

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}"""

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

Syntax

KEEP columns

Parameters

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):

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

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*, *

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*

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

FROM employees
| KEEP first_name*, last_name, first_na*

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

FROM employees
| KEEP *, first_name

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

Syntax

LIMIT max_number_of_rows

Parameters

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:

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

Example

FROM employees
| SORT emp_no ASC
| LIMIT 5

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

Syntax

MV_EXPAND column

Parameters

Example

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

The RENAME processing command renames one or more columns.

Syntax

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

Parameters

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

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

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

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

Syntax

STATS [column1 =] expression1 [WHERE boolean_expression1][,
      ...,
      [columnN =] expressionN [WHERE boolean_expressionN]]
      [BY grouping_expression1[, ..., grouping_expressionN]]

Parameters

Description

The STATS processing command groups rows according to a common value and calculates one or more aggregated values over the grouped rows. For the calculation of each aggregated value, the rows in a group can be filtered with WHERE. 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:

The following grouping functions are supported:

Examples

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

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

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

FROM employees
| STATS avg_lang = AVG(languages)

It’s possible to calculate multiple values:

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

To filter the rows that go into an aggregation, use the WHERE clause:

FROM employees
| STATS avg50s = AVG(salary)::LONG WHERE birth_date < "1960-01-01",
        avg60s = AVG(salary)::LONG WHERE birth_date >= "1960-01-01"
        BY gender
| SORT gender

The aggregations can be mixed, with and without a filter and grouping is optional as well:

FROM employees
| EVAL Ks = salary / 1000 // thousands
| STATS under_40K = COUNT(*) WHERE Ks < 40,
        inbetween = COUNT(*) WHERE 40 <= Ks AND Ks < 60,
        over_60K  = COUNT(*) WHERE 60 <= Ks,
        total     = COUNT(*)

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

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 all the 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

Both the aggregating functions and the grouping expressions accept other functions. This is useful for using STATS 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)

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)`

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)

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)`)

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

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

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

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:

Supported types

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

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

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

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)

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