dcount

This page explains how to use the dcount aggregation function in APL.

The dcount aggregation function in Axiom Processing Language (APL) counts the distinct values in a column. This function is essential when you need to know the number of unique values, such as counting distinct users, unique requests, or distinct error codes in log files.

Use dcount for analyzing datasets where it’s important to identify the number of distinct occurrences, such as unique IP addresses in security logs, unique user IDs in application logs, or unique trace IDs in OpenTelemetry traces.

The dcount aggregation in APL is a statistical aggregation that returns estimated results. The estimation comes with the benefit of speed at the expense of accuracy. This means that dcount is fast and light on resources even on a large or high-cardinality dataset, but it doesn’t provide precise results.

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, you can count distinct values using the dc function within the stats command. In APL, the dcount function offers similar functionality.

Splunk example

| stats dc(user_id) AS distinct_users

ANSI SQL users

In ANSI SQL, distinct counting is typically done using COUNT with the DISTINCT keyword. In APL, dcount provides a direct and efficient way to count distinct values.

SQL example

SELECT COUNT(DISTINCT user_id) AS distinct_users
FROM sample_http_logs

Usage

Syntax

dcount(column_name)

Parameters

• column_name: The name of the column for which you want to count distinct values.

Returns

The function returns the count of distinct values found in the specified column.

Use case examples

Log analysis

In log analysis, you can count how many distinct users accessed the service.

Query

['sample-http-logs']
| summarize dcount(id)

Run in Playground

Output

distinct_users
45

This query counts the distinct values in the id field, representing the number of unique users who accessed the system.

In log analysis, you can count how many distinct users accessed the service.

Query

['sample-http-logs']
| summarize dcount(id)

Run in Playground

Output

distinct_users
45

This query counts the distinct values in the id field, representing the number of unique users who accessed the system.

In OpenTelemetry traces, you can count how many unique trace IDs are recorded.

Query

['otel-demo-traces']
| summarize dcount(trace_id)

Run in Playground

Output

distinct_traces
321

This query counts the distinct trace IDs in the dataset, helping you determine how many unique traces are being captured.

In security logs, you can count how many distinct IP addresses were logged.

Query

['sample-http-logs']
| summarize dcount(['geo.city'])

Run in Playground

Output

distinct_cities
35

This query counts the number of distinct cities recorded in the logs, which helps analyze the geographic distribution of traffic.

List of related aggregations

• count: Counts the total number of records in the dataset, including duplicates. Use it when you need to know the overall number of records.
• countif: Counts records that match a specific condition. Use countif when you want to count records based on a filter or condition.
• dcountif: Counts the distinct values in a column but only for records that meet a condition. It’s useful when you need a filtered distinct count.
• sum: Sums the values in a column. Use this when you need to add up values rather than counting distinct occurrences.
• avg: Calculates the average value for a column. Use this when you want to find the average of a specific numeric field.