> ## Documentation Index
> Fetch the complete documentation index at: https://axiom.co/docs/llms.txt
> Use this file to discover all available pages before exploring further.

<AgentInstructions>

## Submitting Feedback

If you encounter incorrect, outdated, or confusing documentation on this page, submit feedback:

POST https://axiom.co/docs/feedback

```json
{
  "path": "/apl/scalar-functions/mathematical-functions/range",
  "feedback": "Description of the issue"
}
```

Only submit feedback when you have something specific and actionable to report.

</AgentInstructions>

# range

> This page explains how to use the range function in APL.

Use the `range` function in APL to create a dynamic array of evenly spaced values. You can generate numeric, datetime, or timespan sequences that increase by a constant step, which defaults to 1 for numbers and 1 hour for time-based types. The function stops once the value exceeds the specified endpoint or the maximum result size.

`range` is useful when you want to produce test values, synthetic sequences, time intervals, or loop-like constructs without relying on input data. It helps you populate arrays that can be expanded or joined with real data for further analysis.

## 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.

<AccordionGroup>
  <Accordion title="Splunk SPL users">
    In SPL, generating sequences often involves `makeresults` combined with `streamstats` or manual iteration logic. APL’s `range` function simplifies this by producing arrays of equally spaced values directly.

    <CodeGroup>
      ```sql Splunk example theme={null}
      | makeresults count=10
      | streamstats count as x
      ```

      ```kusto APL equivalent theme={null}
      print r = range(1, 10, 1)
      ```
    </CodeGroup>
  </Accordion>

  <Accordion title="ANSI SQL users">
    In ANSI SQL, generating a series of numbers usually involves recursive CTEs. APL’s `range` is more concise and efficient for creating sequences without writing complex recursion logic.

    <CodeGroup>
      ```sql SQL example theme={null}
      WITH RECURSIVE seq AS (
        SELECT 1 AS x
        UNION ALL
        SELECT x + 1 FROM seq WHERE x < 10
      )
      SELECT * FROM seq;
      ```

      ```kusto APL equivalent theme={null}
      print r = range(1, 10, 1)
      ```
    </CodeGroup>
  </Accordion>
</AccordionGroup>

## Usage

### Syntax

```kusto theme={null}
range(start, stop [, step])
```

### Parameters

| Name  | Type                                | Required | Description                                                               |
| ----- | ----------------------------------- | -------- | ------------------------------------------------------------------------- |
| start | scalar (number, datetime, timespan) | ✓        | First value in the array.                                                 |
| stop  | scalar (same type as `start`)       | ✓        | Upper bound of the array. The last value is less than or equal to `stop`. |
| step  | scalar (same type as `start`)       |          | Difference between values. Defaults to 1 (numeric) or 1h (time).          |

### Returns

A dynamic array that includes values starting at `start`, incremented by `step`, up to and including `stop` (if it aligns exactly with a step). The array truncates if it reaches the system limit of 1,048,576 elements.

## Use case examples

<Tabs>
  <Tab title="Log analysis">
    Generate an array of durations to help classify HTTP request latencies.

    **Query**

    ```kusto theme={null}
    print r = range(100, 500, 100)
    | project r
    ```

    [Run in Playground](https://play.axiom.co/axiom-play-qf1k/query?initForm=%7B%22apl%22%3A%22print%20r%20%3D%20range\(100%2C%20500%2C%20100\)%20%7C%20project%20r%22%7D)

    **Output**

    ```json theme={null}
    [
      100,
      200,
      300,
      400,
      500
    ]
    ```

    This creates an array of thresholds that you can use to bucket or filter request durations in `['sample-http-logs']`.
  </Tab>

  <Tab title="OpenTelemetry traces">
    Build a set of time intervals to align span activity over a fixed period.

    **Query**

    ```kusto theme={null}
    print intervals = range(datetime(2025-07-29T12:00:00Z), datetime(2025-07-29T13:00:00Z), 15m)
    | project intervals
    ```

    [Run in Playground](https://play.axiom.co/axiom-play-qf1k/query?initForm=%7B%22apl%22%3A%22print%20intervals%20%3D%20range\(datetime\(2025-07-29T12%3A00%3A00Z\)%2C%20datetime\(2025-07-29T13%3A00%3A00Z\)%2C%2015m\)%20%7C%20project%20intervals%22%7D)

    **Output**

    ```json theme={null}
    [
      "2025-07-29T12:00:00Z",
      "2025-07-29T12:15:00Z",
      "2025-07-29T12:30:00Z",
      "2025-07-29T12:45:00Z",
      "2025-07-29T13:00:00Z"
    ]
    ```

    This array helps divide the hour into 15-minute blocks for analyzing activity in `['otel-demo-traces']`.
  </Tab>

  <Tab title="Security logs">
    Create a list of expected hourly intervals to detect missing logs.

    **Query**

    ```kusto theme={null}
    print expected = range(datetime(2025-07-29T00:00:00Z), datetime(2025-07-29T05:00:00Z), 1h)
    | project expected
    ```

    [Run in Playground](https://play.axiom.co/axiom-play-qf1k/query?initForm=%7B%22apl%22%3A%22print%20expected%20%3D%20range\(datetime\(2025-07-29T00%3A00%3A00Z\)%2C%20datetime\(2025-07-29T05%3A00%3A00Z\)%2C%201h\)%20%7C%20project%20expected%22%7D)

    **Output**

    ```json theme={null}
    [
      "2025-07-29T00:00:00Z",
      "2025-07-29T01:00:00Z",
      "2025-07-29T02:00:00Z",
      "2025-07-29T03:00:00Z",
      "2025-07-29T04:00:00Z",
      "2025-07-29T05:00:00Z"
    ]
    ```

    This produces an array of expected log collection times for the `['sample-http-logs']` dataset. You can join this with actual data to detect missing records.
  </Tab>
</Tabs>