Commands

The dedup (data deduplication) command removes duplicate documents defined by a field from the search result.

Example 1: Dedup by one field

To remove duplicate documents with the same gender:

search source=accounts | dedup gender | fields account_number, gender;

Example 2: Keep two duplicate documents

To keep two duplicate documents with the same gender:

search source=accounts | dedup 2 gender | fields account_number, gender;

Example 3: Keep or ignore an empty field by default

To keep two duplicate documents with a null field value:

search source=accounts | dedup email keepempty=true | fields account_number, email;

To remove duplicate documents with the null field value:

search source=accounts | dedup email | fields account_number, email;

Example 4: Dedup of consecutive documents

To remove duplicates of consecutive documents:

search source=accounts | dedup gender consecutive=true | fields account_number, gender;

Limitations

The dedup command is not rewritten to OpenSearch DSL, it is only executed on the coordination node.

eval

The eval command evaluates an expression and appends its result to the search result.

Syntax

eval <field>=<expression> ["," <field>=<expression> ]...

Example 1: Create a new field

To create a new doubleAge field for each document. doubleAge is the result of age multiplied by 2:

search source=accounts | eval doubleAge = age * 2 | fields age, doubleAge;

Example 2: Overwrite the existing field

To overwrite the age field with age plus 1:

search source=accounts | eval age = age + 1 | fields age;

Example 3: Create a new field with a field defined with the eval command

To create a new field ddAge. ddAge is the result of doubleAge multiplied by 2, where doubleAge is defined in the eval command:

search source=accounts | eval doubleAge = age * 2, ddAge = doubleAge * 2 | fields age, doubleAge, ddAge;

Limitation

The eval command is not rewritten to OpenSearch DSL, it is only executed on the coordination node.

fields

Use the fields command to keep or remove fields from a search result.

Syntax

fields [+|-] <field-list>

Example 1: Select specified fields from result

To get account_number, firstname, and lastname fields from a search result:

search source=accounts | fields account_number, firstname, lastname;

Example 2: Remove specified fields from a search result

To remove the account_number field from the search results:

search source=accounts | fields account_number, firstname, lastname | fields - account_number;

parse

Use the parse command to parse a text field using regular expression and append the result to the search result.

Syntax

parse <field> <regular-expression>

The regular expression is used to match the whole text field of each document with Java regex engine. Each named capture group in the expression will become a new field.

Example 1: Create new field

The example shows how to create new field host for each document. host will be the hostname after @ in email field. Parsing a null field will return an empty string.

os> source=accounts | parse email '.+@(?<host>.+)' | fields email, host ;
fetched rows / total rows = 4/4

Example 2: Override the existing field

The example shows how to override the existing address field with street number removed.

os> source=accounts | parse address '\d+ (?<address>.+)' | fields address ;
fetched rows / total rows = 4/4

Example 3: Filter and sort be casted parsed field

Limitations

A few limitations exist when using the parse command:

• Fields defined by parse cannot be parsed again. For example, source=accounts | parse address '\d+ (?<street>.+)' | parse street '\w+ (?<road>\w+)' ; will fail to return any expressions.
• Fields defined by parse cannot be overridden with other commands. For example, when entering source=accounts | parse address '\d+ (?<street>.+)' | eval street='1' | where street='1' ; where will not match any documents since street cannot be overridden.
• The text field used by parse cannot be overridden. For example, when entering source=accounts | parse address '\d+ (?<street>.+)' | eval address='1' ; street will not be parse since address is overridden.
• Fields defined by parse cannot be filtered/sorted after using them in the stats command. For example, source=accounts | parse email '.+@(?<host>.+)' | stats avg(age) by host | where host=pyrami.com ; where will not parse the domain listed.

Use the rename command to rename one or more fields in the search result.

rename <source-field> AS <target-field>["," <source-field> AS <target-field>]...

Example 1: Rename one field

Rename the account_number field as an:

search source=accounts | rename account_number as an | fields an;

Example 2: Rename multiple fields

Rename the account_number field as an and employer as emp:

Limitations

The rename command is not rewritten to OpenSearch DSL, it is only executed on the coordination node.

sort

Use the sort command to sort search results by a specified field.

Syntax

sort [count] <[+|-] sort-field>...

Example 1: Sort by one field

To sort all documents by the age field in ascending order:

search source=accounts | sort age | fields account_number, age;

Example 2: Sort by one field and return all results

To sort all documents by the age field in ascending order and specify count as 0 to get back all results:

search source=accounts | sort 0 age | fields account_number, age;

Example 3: Sort by one field in descending order

To sort all documents by the age field in descending order:

search source=accounts | sort - age | fields account_number, age;

Example 4: Specify the number of sorted documents to return

To sort all documents by the age field in ascending order and specify count as 2 to get back two results:

search source=accounts | sort 2 age | fields account_number, age;

Example 5: Sort by multiple fields

To sort all documents by the gender field in ascending order and age field in descending order:

search source=accounts | sort + gender, - age | fields account_number, gender, age;

stats

Use the stats command to aggregate from search results.

The following table lists the aggregation functions and also indicates how each one handles null or missing values:

Syntax

stats <aggregation>... [by-clause]...

Example 1: Calculate the average value of a field

To calculate the average age of all documents:

search source=accounts | stats avg(age);

Example 2: Calculate the average value of a field by group

To calculate the average age grouped by gender:

search source=accounts | stats avg(age) by gender;

Example 3: Calculate the average and sum of a field by group

To calculate the average and sum of age grouped by gender:

search source=accounts | stats avg(age), sum(age) by gender;

Example 4: Calculate the maximum value of a field

To calculate the maximum age:

search source=accounts | stats max(age);

Example 5: Calculate the maximum and minimum value of a field by group

To calculate the maximum and minimum age values grouped by gender:

search source=accounts | stats max(age), min(age) by gender;

where

Use the where command with a bool expression to filter the search result. The where command only returns the result when the bool expression evaluates to true.

Syntax

To get all documents from the accounts index where account_number is 1 or gender is F:

search source=accounts | where account_number=1 or gender=\"F\" | fields account_number, gender;

Use the head command to return the first N number of results in a specified search order.

Syntax

head [N]

Example 1: Get the first 10 results

To get the first 10 results:

search source=accounts | fields firstname, age | head;

Example 2: Get the first N results

To get the first two results:

search source=accounts | fields firstname, age | head 2;

Limitations

The head command is not rewritten to OpenSearch DSL, it is only executed on the coordination node.

rare

Use the rare command to find the least common values of all fields in a field list. A maximum of 10 results are returned for each distinct set of values of the group-by fields.

rare <field-list> [by-clause]

Example 1: Find the least common values in a field

To find the least common values of gender:

search source=accounts | rare gender;

Example 2: Find the least common values grouped by gender

To find the least common age grouped by gender:

search source=accounts | rare age by gender;

Limitations

The rare command is not rewritten to OpenSearch DSL, it is only executed on the coordination node.

top

Use the top command to find the most common values of all fields in the field list.

Syntax

top [N] <field-list> [by-clause]

Example 1: Find the most common values in a field

To find the most common genders:

search source=accounts | top gender;

Example 2: Find the most common value in a field

To find the most common gender:

search source=accounts | top 1 gender;

Example 3: Find the most common values grouped by gender

To find the most common age grouped by gender:

search source=accounts | top 1 age by gender;

Limitations

The top command is not rewritten to OpenSearch DSL, it is only executed on the coordination node.

ad

The ad command applies the Random Cut Forest (RCF) algorithm in the ML Commons plugin on the search result returned by a PPL command. Based on the input, the plugin uses two types of RCF algorithms: fixed in time RCF for processing time-series data and batch RCF for processing non-time-series data.

Syntax: Fixed In Time RCF For Time-series Data Command

ad <shingle_size> <time_decay> <time_field>

Syntax: Batch RCF for Non-time-series Data Command

ad <shingle_size> <time_decay>

Example 1: Detecting events in New York City from taxi ridership data with time-series data

The example trains a RCF model and use the model to detect anomalies in the time-series ridership data.

PPL query:

os> source=nyc_taxi | fields value, timestamp | AD time_field='timestamp' | where value=10844.0

Example 2: Detecting events in New York City from taxi ridership data with non-time-series data

PPL query:

os> source=nyc_taxi | fields value | AD | where value=10844.0

The kmeans command applies the ML Commons plugin’s kmeans algorithm to the provided PPL command’s search results.

Syntax

For , enter the number of clusters you want to group your data points into.

Example: Group Iris data

The example shows how to classify three Iris species (Iris setosa, Iris virginica and Iris versicolor) based on the combination of four features measured from each sample: the length and the width of the sepals and petals.

os> source=iris_data | fields sepal_length_in_cm, sepal_width_in_cm, petal_length_in_cm, petal_width_in_cm | kmeans 3