summarize

This page explains how to use the summarize operator function in APL.

Introduction

The summarize operator in APL enables you to perform data aggregation and create summary tables from large datasets. You can use it to group data by specified fields and apply aggregation functions such as count(), sum(), avg(), min(), max(), and many others. This is particularly useful when analyzing logs, tracing OpenTelemetry data, or reviewing security events. The summarize operator is helpful when you want to reduce the granularity of a dataset to extract insights or trends.

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 stats command performs a similar function to APL’s summarize operator. Both operators are used to group data and apply aggregation functions. In APL, summarize is more explicit about the fields to group by and the aggregation functions to apply.

Splunk example

index="sample-http-logs" | stats count by method

ANSI SQL users

The summarize operator in APL is conceptually similar to SQL’s GROUP BY clause with aggregation functions. In APL, you explicitly specify the aggregation function (like count(), sum()) and the fields to group by.

SQL example

SELECT method, COUNT(*) 
FROM sample_http_logs 
GROUP BY method

Usage

Syntax

| summarize [[Field1 =] AggregationFunction [, ...]] [by [Field2 =] GroupExpression [, ...]]

Parameters

• Field1: A field name.
• AggregationFunction: The aggregation function to apply. Examples include count(), sum(), avg(), min(), and max().
• GroupExpression: A scalar expression that can reference the dataset.

Returns

The summarize operator returns a table where:

• The input rows are arranged into groups having the same values of the by expressions.
• The specified aggregation functions are computed over each group, producing a row for each group.
• The result contains the by fields and also at least one field for each computed aggregate. Some aggregation functions return multiple fields.

Use case examples

Log analysis

In log analysis, you can use summarize to count the number of HTTP requests grouped by method, or to compute the average request duration.

Query

['sample-http-logs']
| summarize count() by method

Run in Playground

Output

method	count_
GET	1000
POST	450

This query groups the HTTP requests by the method field and counts how many times each method is used.

Other examples

['sample-http-logs']
| summarize topk(content_type, 20)

Run in Playground

['github-push-event']
| summarize topk(repo, 20) by bin(_time, 24h)

Run in Playground

Returns a table that shows the heatmap in each interval [0, 30], [30, 20, 10], and so on. This example has a cell for HISTOGRAM(req_duration_ms).

['sample-http-logs']
| summarize histogram(req_duration_ms, 30)

Run in Playground

['github-push-event']
| where _time > ago(7d)
| where repo contains "axiom"
| summarize count(), numCommits=sum(size) by _time=bin(_time, 3h), repo
| take 100

Run in Playground

List of related operators

• count: Use when you only need to count rows without grouping by specific fields.
• extend: Use to add new calculated fields to a dataset.
• project: Use to select specific fields or create new calculated fields, often in combination with summarize.