project

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

project operator

The project operator in Axiom Processing Language (APL) is used to select specific fields from a dataset, potentially renaming them or applying calculations on the fly. With project, you can control which fields are returned by the query, allowing you to focus on only the data you need.

This operator is useful when you want to refine your query results by reducing the number of fields, renaming them, or deriving new fields based on existing data. It’s a powerful tool for filtering out unnecessary fields and performing light transformations on your dataset.

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 SPL, the equivalent of the project operator is typically the table or fields command. While SPL’s table focuses on selecting fields, fields controls both selection and exclusion, similar to project in APL.

Splunk example

| table _time, status, uri

ANSI SQL users

In ANSI SQL, the SELECT statement serves a similar role to the project operator in APL. SQL users will recognize that project behaves like selecting fields from a table, with the ability to rename or transform fields inline.

SQL example

SELECT _time, status, uri FROM sample_http_logs;

Usage

Syntax

| project FieldName [= Expression] [, ...]

Or

| project FieldName, FieldName, FieldName, ...

Or

| project [FieldName, FieldName[,] = Expression [, ...]

Parameters

• FieldName: The names of the fields in the order you want them to appear in the result set. If there is no Expression, then FieldName is compulsory and a field of that name must appear in the input.
• Expression: Optional scalar expression referencing the input fields.

Returns

The project operator returns a dataset containing only the specified fields.

Use case examples

Log analysis

In this example, you’ll extract the timestamp, HTTP status code, and request URI from the sample HTTP logs.

Query

['sample-http-logs']
| project _time, status, uri

Run in Playground

Output

_time	status	uri
2024-10-17 12:00:00	200	/api/v1/getData
2024-10-17 12:01:00	404	/api/v1/getUser

The query returns only the timestamp, HTTP status code, and request URI, reducing unnecessary fields from the dataset.

In this example, you’ll extract the timestamp, HTTP status code, and request URI from the sample HTTP logs.

Query

['sample-http-logs']
| project _time, status, uri

Run in Playground

Output

_time	status	uri
2024-10-17 12:00:00	200	/api/v1/getData
2024-10-17 12:01:00	404	/api/v1/getUser

The query returns only the timestamp, HTTP status code, and request URI, reducing unnecessary fields from the dataset.

In this example, you’ll extract trace information such as the service name, span ID, and duration from OpenTelemetry traces.

Query

['otel-demo-traces']
| project ['service.name'], span_id, duration

Run in Playground

Output

service.name	span_id	duration
frontend	span-1234abcd	00:00:02
cartservice	span-5678efgh	00:00:05

The query isolates relevant tracing data, such as the service name, span ID, and duration of spans.

In this example, you’ll focus on security log entries by projecting only the timestamp, user ID, and HTTP status from the sample HTTP logs.

Query

['sample-http-logs']
| project _time, id, status

Run in Playground

Output

_time	id	status
2024-10-17 12:00:00	user1	200
2024-10-17 12:01:00	user2	403

The query extracts only the timestamp, user ID, and HTTP status for analysis of access control in security logs.

List of related operators

• extend: Use extend to add new fields or calculate values without removing any existing fields.
• summarize: Use summarize to aggregate data across groups of rows, which is useful when you’re calculating totals or averages.
• where: Use where to filter rows based on conditions, often paired with project to refine your dataset further.