series_subtract
This page explains how to use the series_subtract function in APL.
The series_subtract function performs element-wise subtraction between two numeric dynamic arrays (series). Each element in the first series is subtracted by the corresponding element at the same position in the second series.
You can use series_subtract when you need to compute differences between two time-series datasets. This is particularly useful for calculating deltas, deviations from baselines, changes over time, or comparing metrics between different groups or time periods.
Usage
Syntax
Parameters
| Parameter | Type | Description |
|---|---|---|
series1 | dynamic | A dynamic array of numeric values (minuend). |
series2 | dynamic | A dynamic array of numeric values (subtrahend). |
Returns
A dynamic array where each element is the result of subtracting the corresponding element of series2 from series1. If the arrays have different lengths, the shorter array is extended with null values.
Use case examples
In log analysis, you can use series_subtract to calculate the difference between current and baseline request durations, helping identify performance degradations.
Query
Output
| geo.city | current | baseline | delta |
|---|---|---|---|
| Seattle | [60, 65, 58, 62, 59] | [50, 55, 48, 52, 49] | [10, 10, 10, 10, 10] |
| Portland | [45, 50, 43, 47, 44] | [50, 55, 48, 52, 49] | [-5, -5, -5, -5, -5] |
This query calculates the difference between current request durations and baseline values, showing performance changes per city.
In OpenTelemetry traces, you can use series_subtract to compare span durations between different service versions or time periods.
Query
Output
| service.name | current | previous | improvement |
|---|---|---|---|
| frontend | [80, 95, 75, 90, 85] | [100, 120, 95, 110, 105] | [20, 25, 20, 20, 20] |
| checkout | [110, 125, 105, 120, 115] | [100, 120, 95, 110, 105] | [-10, -5, -10, -10, -10] |
This query compares current span durations with previous measurements, calculating performance improvements (positive values) or degradations (negative values).
In security logs, you can use series_subtract to detect anomalous behavior by comparing request patterns against expected baselines.
Query
Output
| status | observed | expected | anomaly_score |
|---|---|---|---|
| 200 | [46, 51, 49, 50, 48] | [45, 50, 48, 49, 47] | [1, 1, 1, 1, 1] |
| 500 | [145, 150, 148, 149, 147] | [45, 50, 48, 49, 47] | [100, 100, 100, 100, 100] |
This query calculates anomaly scores by comparing observed request durations against expected baselines, with large positive values indicating potential issues.
List of related functions
- series_multiply: Performs element-wise multiplication of two series. Use when you need to multiply rather than subtract.
- series_abs: Returns the absolute value of each element. Use after subtraction to get magnitude of differences.
- series_stats: Returns statistical summary of a series. Use to analyze the result of subtraction operations.
- series_sign: Returns the sign of each element. Use after subtraction to determine direction of changes.