series_add
You can use series_add when you want to combine metrics from different sources, calculate cumulative values, or perform mathematical transformations on time-series data. Common applications include merging performance metrics, calculating total resource usage, and combining error rates from multiple services.
Usage#
Syntax#
series_add(array1, array2)Parameters#
| Parameter | Type | Description |
|---|---|---|
array1 |
dynamic | The first dynamic array of numeric values. |
array2 |
dynamic | The second dynamic array of numeric values. |
Returns#
A dynamic array where each element is the sum of the corresponding elements from array1 and array2. If the arrays have different lengths, the result array has the length of the shorter array.
Use case examples#
In log analysis, you can use series_add to combine request durations from different processing stages to calculate total processing time.
Query
['sample-http-logs']
| summarize stage1_durations = make_list(req_duration_ms), stage2_durations = make_list(req_duration_ms * 0.3) by id
| extend total_durations = series_add(stage1_durations, stage2_durations)Output
| id | stage1_durations | stage2_durations | total_durations |
|---|---|---|---|
| u123 | [100, 200, 150] | [30, 60, 45] | [130, 260, 195] |
| u456 | [80, 120] | [24, 36] | [104, 156] |
This query combines processing durations from two stages to calculate the total processing time for each user's requests.
In OpenTelemetry traces, you can use series_add to combine span durations from different services to analyze total request processing time.
Query
['otel-demo-traces']
| summarize frontend_durations = make_list(iff(['service.name'] == 'frontend', duration, 0ms)), backend_durations = make_list(iff(['service.name'] == 'cart', duration, 0ms)) by trace_id
| extend total_durations = series_add(frontend_durations, backend_durations)Output
| trace_id | frontend_durations | backend_durations | total_durations |
|---|---|---|---|
| t123 | [00:00:01, 00:00:00.5] | [00:00:00.2, 00:00:00.3] | [00:00:01.2, 00:00:00.8] |
| t456 | [00:00:00.8] | [00:00:00.4] | [00:00:01.2] |
This query adds frontend and backend service durations to calculate the combined processing time per trace.
In security logs, you can use series_add to combine request durations from different security checks to analyze total security processing overhead.
Query
['sample-http-logs']
| summarize auth_durations = make_list(req_duration_ms * 0.1), validation_durations = make_list(req_duration_ms * 0.05) by status
| extend total_security_durations = series_add(auth_durations, validation_durations)Output
| status | auth_durations | validation_durations | total_security_durations |
|---|---|---|---|
| 200 | [10, 20, 15] | [5, 10, 7.5] | [15, 30, 22.5] |
| 401 | [25, 30] | [12.5, 15] | [37.5, 45] |
This query combines authentication and validation processing times to calculate total security overhead by HTTP status code.
List of related functions#
- series_abs: Returns the absolute value of each element in an array. Use when you need to remove negative signs without rounding.
- series_cosine_similarity: Calculates cosine similarity between two arrays. Use when you need normalized similarity measures rather than raw dot products.
- series_divide: Performs element-wise division between two arrays. Use when you need to calculate ratios or normalize values.
- series_dot_product: Calculates the dot product between two arrays. Use when you need the raw dot product value rather than normalized similarity.
- series_sum: Calculates the sum of all elements in a single array. Use when you need to sum elements within one array rather than computing dot products.
Other query languages#
Splunk SPL users
In Splunk SPL, you typically use the eval command with mathematical operators to add values. However, adding arrays element-wise requires more complex operations. In APL, series_add directly performs element-wise addition on dynamic arrays.
Splunk example
... | eval combined_value = field1 + field2APL equivalent
datatable(x: dynamic, y: dynamic)
[
dynamic([1, 2, 3]), dynamic([4, 5, 6])
]
| extend combined_series = series_add(x, y)ANSI SQL users
In SQL, you add individual values using the + operator, but there's no built-in function for element-wise array addition. You would need to unnest arrays and perform complex joins. In APL, series_add handles this operation directly on dynamic arrays.
SQL example
SELECT value1 + value2 AS sum_value
FROM my_table;APL equivalent
datatable(x: dynamic, y: dynamic)
[
dynamic([1, 2, 3]), dynamic([4, 5, 6])
]
| extend sum_series = series_add(x, y)