Axiom
Overview

series_fill_const

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.

Usage#

Syntax#

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#

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

['sample-http-logs']
| summarize durations = make_list(req_duration_ms) by id
| extend filled_durations = series_fill_const(durations, 0)

Run in Playground

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.

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

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

Run in Playground

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.

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

['sample-http-logs']
| summarize durations = make_list(req_duration_ms) by status
| extend filled_durations = series_fill_const(durations, 0)

Run in Playground

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.

  • 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: 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: Fills missing values using linear interpolation. Use when you want smooth transitions between known values.
  • series_equals: Compares each element to a specified value. Use for identifying specific values after filling operations.
  • series_greater: Returns elements greater than a specified value. Use for threshold analysis after filling missing data.

Other query languages#

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.

Splunk example

... | fillnull value=0 field_name

APL equivalent

datatable(values: dynamic)
[
  dynamic([null, 100, null, 200])
]
| extend filled_values = series_fill_const(values, 0)
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.

SQL example

SELECT COALESCE(value, 0) AS filled_value
FROM measurements;

APL equivalent

datatable(values: dynamic)
[
  dynamic([null, 100, null, 200])
]
| extend filled_values = series_fill_const(values, 0)

Updated

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