---
title: approx_count_distinct
description: |
  Returns an approximate count of unique, non-null values for one or more expressions.

  * Accepts any number of expressions, of any type.
  * Given a single expression, counts its unique, non-null values.
  * Given several expressions, counts the unique combinations of their values. A document is counted if at least one of the expressions is non-null.
  * The result is approximate, optimized for performance rather than precision.

  !!! note
      `approx_count_distinct` is an aggregation function and must be used with a grouping keyword such as `groupby`.
url: https://coralogix.com/docs/dataprime/language-reference/functions-reference/aggregation/approx_count_distinct/
product: dataengine
feature: DataPrime Language
feature_id: dataprime-language
feature_identifier: dataengine/dataprime-language
last_updated: '2026-10-21'
---

# `approx_count_distinct`

## Description

Returns an approximate count of unique, non-null values for one or more expressions.

* Accepts any number of expressions, of any type.
* Given a single expression, counts its unique, non-null values.
* Given several expressions, counts the unique combinations of their values. A document is counted if at least one of the expressions is non-null.
* The result is approximate, optimized for performance rather than precision.

Note

`approx_count_distinct` is an aggregation function and must be used with a grouping keyword such as `groupby`.

## Syntax

```
approx_count_distinct(expression: any, ...values: any): number
```

## Arguments

| Name         | Type  | Required  | Description                                                                                    |
| ------------ | ----- | --------- | ---------------------------------------------------------------------------------------------- |
| `expression` | `any` | **true**  | Expression whose unique, non-null values are counted approximately                             |
| `...values`  | `any` | **false** | Additional expressions, of any type, counted together with `expression` as unique combinations |

## Example 1

**Use case: Estimate the number of active users per application**

Suppose you want to know how many unique users have been active in a given timeframe. Since you only care whether the number is above a threshold, an approximate count is sufficient.

### Example data

```
{ "applicationname": "checkout-service", "username": "alice" },

{ "applicationname": "checkout-service", "username": "bob" },

{ "applicationname": "checkout-service", "username": "charlie" },

{ "applicationname": "checkout-service", "username": "alice" }
```

### Example query

```
groupby $l.applicationname aggregate approx_count_distinct($d.username) as active_users
```

### Example output

| applicationname  | active\_users |
| ---------------- | ------------- |
| checkout-service | 3             |

## Example 2

**Use case: Estimate the number of distinct endpoint and status code pairs**

Pass several expressions to count unique combinations of their values rather than each field separately. The expressions don't have to share a type.

### Example data

```
{ "applicationname": "checkout-service", "endpoint": "/cart", "status": 200 },

{ "applicationname": "checkout-service", "endpoint": "/cart", "status": 500 },

{ "applicationname": "checkout-service", "endpoint": "/pay", "status": 200 },

{ "applicationname": "checkout-service", "endpoint": "/cart", "status": 200 }
```

### Example query

```
groupby $l.applicationname aggregate approx_count_distinct($d.endpoint, $d.status) as unique_pairs
```

### Example output

| applicationname  | unique\_pairs |
| ---------------- | ------------- |
| checkout-service | 3             |