> ## 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.

# series_fill_const

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

The `series_fill_const` function fills missing values (nulls) in a numeric dynamic array (series) with a specified constant value. This function is useful for handling gaps in time series data where you want to replace missing data points with a known default value.

You can use `series_fill_const` when you have time series data with missing values and want to fill gaps with a specific constant value, such as zero, a default threshold, or a neutral value. This is particularly useful when missing values represent a specific state (like no activity, default configuration, or baseline values). Typical applications include financial data analysis, sensor data processing, and performance monitoring where a specific default value is meaningful.

## 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 Splunk SPL, filling missing values with constants is typically done with the `fillnull` command or `eval` expressions using conditional logic. To fill arrays with constants, you usually need to expand arrays and apply the transformation row by row. In APL, `series_fill_const` works directly on dynamic arrays, making it efficient for series-wide constant filling.

    <CodeGroup>
      ```sql Splunk example theme={null}
      ... | fillnull value=0 field_name
      ```

      ```kusto APL equivalent theme={null}
      datatable(values: dynamic)
      [
        dynamic([null, 100, null, 200])
      ]
      | extend filled_values = series_fill_const(values, 0)
      ```
    </CodeGroup>
  </Accordion>

  <Accordion title="ANSI SQL users">
    In SQL, filling missing values with constants is typically done with `COALESCE()` or `ISNULL()` functions, but these only work on single values, not arrays. To fill array elements with constants, you usually need to unnest arrays and apply the function row by row. In APL, `series_fill_const` eliminates this complexity by directly replacing null values with a constant in arrays.

    <CodeGroup>
      ```sql SQL example theme={null}
      SELECT COALESCE(value, 0) AS filled_value
      FROM measurements;
      ```

      ```kusto APL equivalent theme={null}
      datatable(values: dynamic)
      [
        dynamic([null, 100, null, 200])
      ]
      | extend filled_values = series_fill_const(values, 0)
      ```
    </CodeGroup>
  </Accordion>
</AccordionGroup>

## Usage

### Syntax

```kusto theme={null}
series_fill_const(array, constant_value)
```

### Parameters

| Parameter        | Type    | Description                                                     |
| ---------------- | ------- | --------------------------------------------------------------- |
| `array`          | dynamic | A dynamic array of numeric values that may contain null values. |
| `constant_value` | numeric | The constant value to use for filling null values.              |

### Returns

A dynamic array where null values are replaced with the specified constant value.

## Use case examples

<Tabs>
  <Tab title="Log analysis">
    In log analysis, you can use `series_fill_const` to fill missing request duration data with a default value like 0, which might represent no activity or baseline performance.

    **Query**

    ```kusto theme={null}
    ['sample-http-logs']
    | summarize durations = make_list(req_duration_ms) by id
    | extend filled_durations = series_fill_const(durations, 0)
    ```

    [Run in Playground](https://play.axiom.co/axiom-play-qf1k/query?initForm=%7B%22apl%22%3A%22%5B'sample-http-logs'%5D%20%7C%20summarize%20durations%20%3D%20make_list\(req_duration_ms\)%20by%20id%20%7C%20extend%20filled_durations%20%3D%20series_fill_const\(durations%2C%200\)%22%7D)

    **Output**

    | id   | durations               | filled\_durations |
    | ---- | ----------------------- | ----------------- |
    | u123 | \[null, 150, null, 200] | \[0, 150, 0, 200] |
    | u456 | \[100, null, null, 300] | \[100, 0, 0, 300] |

    This query fills missing request durations with 0, useful for representing periods of no activity or baseline performance.
  </Tab>

  <Tab title="OpenTelemetry traces">
    In OpenTelemetry traces, you can use `series_fill_const` to fill missing span duration data with a default value, such as 0 for spans that didn't execute or a baseline latency value.

    **Query**

    ```kusto theme={null}
    ['otel-demo-traces']
    | summarize durations = make_list(duration) by ['service.name']
    | extend filled_durations = series_fill_const(durations, 0)
    ```

    [Run in Playground](https://play.axiom.co/axiom-play-qf1k/query?initForm=%7B%22apl%22%3A%22%5B'otel-demo-traces'%5D%20%7C%20summarize%20durations%20%3D%20make_list\(duration\)%20by%20%5B'service.name'%5D%20%7C%20extend%20filled_durations%20%3D%20series_fill_const\(durations%2C%200\)%22%7D)

    **Output**

    | service.name          | durations                   | filled\_durations         |
    | --------------------- | --------------------------- | ------------------------- |
    | frontend              | \[null, 100ms, null, 200ms] | \[0ms, 100ms, 0ms, 200ms] |
    | productcatalogservice | \[50ms, null, null, 150ms]  | \[50ms, 0ms, 0ms, 150ms]  |

    This query fills missing span durations with 0ms, useful for representing spans that didn't execute or had no measurable duration.
  </Tab>

  <Tab title="Security logs">
    In security logs, you can use `series_fill_const` to fill missing request duration data with a default value, such as 0 for blocked requests or a baseline value for analysis.

    **Query**

    ```kusto theme={null}
    ['sample-http-logs']
    | summarize durations = make_list(req_duration_ms) by status
    | extend filled_durations = series_fill_const(durations, 0)
    ```

    [Run in Playground](https://play.axiom.co/axiom-play-qf1k/query?initForm=%7B%22apl%22%3A%22%5B'sample-http-logs'%5D%20%7C%20summarize%20durations%20%3D%20make_list\(req_duration_ms\)%20by%20status%20%7C%20extend%20filled_durations%20%3D%20series_fill_const\(durations%2C%200\)%22%7D)

    **Output**

    | status | durations               | filled\_durations |
    | ------ | ----------------------- | ----------------- |
    | 200    | \[null, 150, null, 250] | \[0, 150, 0, 250] |
    | 500    | \[100, null, null, 400] | \[100, 0, 0, 400] |

    This query fills missing request durations with 0 grouped by status code, useful for representing blocked or failed requests with no measurable duration.
  </Tab>
</Tabs>

## List of related functions

* [series\_fill\_forward](/apl/scalar-functions/time-series/series-fill-forward): Fills missing values by propagating the first known value forward. Use when you want to use the earliest available value to fill gaps.
* [series\_fill\_backward](/apl/scalar-functions/time-series/series-fill-backward): Fills missing values by propagating the last known value backward. Use when you want to use the most recent available value to fill gaps.
* [series\_fill\_linear](/apl/scalar-functions/time-series/series-fill-linear): Fills missing values using linear interpolation. Use when you want smooth transitions between known values.
* [series\_equals](/apl/scalar-functions/time-series/series-equals): Compares each element to a specified value. Use for identifying specific values after filling operations.
* [series\_greater](/apl/scalar-functions/time-series/series-greater): Returns elements greater than a specified value. Use for threshold analysis after filling missing data.