extend

This page explains how to use the extend operator in APL.

The extend operator in APL allows you to create new calculated fields in your result set based on existing data. You can define expressions or functions to compute new values for each row, making extend particularly useful when you need to enrich your data without altering the original dataset. You typically use extend when you want to add additional fields to analyze trends, compare metrics, or generate new insights from your data.

For users of other query languages

If you come from other query languages, this section explains how to adjust your existing queries to achieve the same results in APL.

Splunk SPL users

In Splunk, the eval command is used to create new fields or modify existing ones. In APL, you can achieve this using the extend operator.

Splunk example

index=myindex
| eval newField = duration * 1000

ANSI SQL users

In ANSI SQL, you typically use the SELECT clause with expressions to create new fields. In APL, extend is used instead to define these new computed fields.

SQL example

SELECT id, req_duration_ms, req_duration_ms * 1000 AS newField FROM logs;

Usage

Syntax

| extend NewField = Expression

Parameters

• NewField: The name of the new field to be created.
• Expression: The expression used to compute values for the new field. This can include mathematical operations, string manipulations, or functions.

Returns

The operator returns a copy of the original dataset with the following changes:

• Field names noted by extend that already exist in the input are removed and appended as their new calculated values.
• Field names noted by extend that do not exist in the input are appended as their new calculated values.

Use case examples

Log analysis

In log analysis, you can use extend to compute the duration of each request in seconds from a millisecond value.

Query

['sample-http-logs'] 
| extend duration_sec = req_duration_ms / 1000

Run in Playground

Output

_time	req_duration_ms	id	status	uri	method	geo.city	geo.country	duration_sec
2024-10-17 09:00:01	300	1234	200	/home	GET	London	UK	0.3

This query calculates the duration of HTTP requests in seconds by dividing the req_duration_ms field by 1000.

In log analysis, you can use extend to compute the duration of each request in seconds from a millisecond value.

Query

['sample-http-logs'] 
| extend duration_sec = req_duration_ms / 1000

Run in Playground

Output

_time	req_duration_ms	id	status	uri	method	geo.city	geo.country	duration_sec
2024-10-17 09:00:01	300	1234	200	/home	GET	London	UK	0.3

This query calculates the duration of HTTP requests in seconds by dividing the req_duration_ms field by 1000.

You can use extend to create a new field that categorizes the service type based on the service’s name.

Query

['otel-demo-traces'] 
| extend service_type = iff(['service.name'] in ('frontend', 'frontendproxy'), 'Web', 'Backend')

Run in Playground

Output

_time	span_id	trace_id	service.name	kind	status_code	service_type
2024-10-17 09:00:01	abc123	xyz789	frontend	client	200	Web
2024-10-17 09:00:01	def456	uvw123	checkoutservice	server	500	Backend

This query adds a new field service_type that categorizes the service into either Web or Backend based on the service.name field.

For security logs, you can use extend to categorize HTTP statuses as success or failure.

Query

['sample-http-logs'] 
| extend status_category = iff(status == '200', 'Success', 'Failure')

Run in Playground

Output

_time	id	status	uri	status_category
2024-10-17 09:00:01	1234	200	/home	Success

This query creates a new field status_category that labels each HTTP request as either a Success or Failure based on the status code.

List of related operators

• project: Use project to select specific fields or rename them. Unlike extend, it does not add new fields.
• summarize: Use summarize to aggregate data, which differs from extend that only adds new calculated fields without aggregation.