series_exp
You can use series_exp when you want to apply exponential transformations to your data, such as modeling exponential growth patterns, converting logarithmic data back to linear scale, or applying mathematical transformations for machine learning preprocessing. Typical applications include financial modeling, population growth analysis, and signal processing.
Usage#
Syntax#
series_exp(array)Parameters#
| Parameter | Type | Description |
|---|---|---|
array |
dynamic | A dynamic array of numeric values. |
Returns#
A dynamic array where each element is the exponential (e^x) of the corresponding input element.
Use case examples#
In log analysis, you can use series_exp to transform logarithmic request durations back to linear scale or model exponential growth patterns in user activity.
Query
['sample-http-logs']
| summarize log_durations = make_list(log(req_duration_ms)) by id
| extend exp_durations = series_exp(log_durations)Output
| id | log_durations | exp_durations |
|---|---|---|
| u123 | [4.6, 5.0, 5.3] | [99.5, 148.4, 200.3] |
| u456 | [4.2, 4.8, 5.1] | [66.7, 121.5, 164.0] |
This query transforms logarithmic request durations back to their original linear scale, useful for analyzing actual performance metrics.
In OpenTelemetry traces, you can use series_exp to model exponential growth patterns in span durations or transform logarithmic latency data for analysis.
Query
['otel-demo-traces']
| summarize log_durations = make_list(log(toint(duration))) by ['service.name']
| extend exp_durations = series_exp(log_durations)Output
| service.name | log_durations | exp_durations |
|---|---|---|
| frontend | [6.2, 6.5, 6.8] | [492.7, 665.1, 897.9] |
| productcatalogservice | [5.8, 6.1, 6.4] | [330.3, 445.9, 601.8] |
This query transforms logarithmic span durations back to linear scale, useful for analyzing actual latency patterns across services.
In security logs, you can use series_exp to analyze exponential patterns in request frequencies or transform logarithmic attack intensity data.
Query
['sample-http-logs']
| summarize log_durations = make_list(log(req_duration_ms)) by status
| extend exp_durations = series_exp(log_durations)Output
| status | log_durations | exp_durations |
|---|---|---|
| 200 | [4.5, 5.0, 5.5] | [90.0, 148.4, 245.0] |
| 500 | [5.2, 5.7, 6.2] | [181.3, 298.9, 492.7] |
This query transforms logarithmic request durations back to linear scale grouped by status code, useful for analyzing actual performance patterns in different response types.
List of related functions#
- series_abs: Returns the absolute value of each element in an array. Use when you need to normalize values before applying exponential transformations.
- series_cos: Returns the cosine of each element in an array. Use for trigonometric transformations instead of exponential.
- series_sin: Returns the sine of each element in an array. Use for periodic transformations instead of exponential growth.
- series_tan: Returns the tangent of each element in an array. Use for trigonometric transformations with different periodicity.
- series_floor: Returns the floor of each element in an array. Use for rounding down instead of exponential transformation.
Other query languages#
Splunk SPL users
In Splunk SPL, exponential calculations are typically done with the eval function and the exp() expression. To compute exponentials across multiple values, you usually need to expand arrays and apply the transformation row by row. In APL, series_exp works directly on dynamic arrays, making it efficient for series-wide exponential transformations.
Splunk example
... | eval exp_val=exp(log_value)APL equivalent
datatable(log_values: dynamic)
[
dynamic([0, 1, 2, 3])
]
| extend exp_values = series_exp(log_values)ANSI SQL users
In SQL, exponential calculations use the EXP() function, but this only works on single values, not arrays. To compute exponentials for array elements, you typically need to unnest arrays and apply EXP() row by row. In APL, series_exp eliminates this complexity by directly applying exponential transformation to each element in an array.
SQL example
SELECT EXP(log_value) AS exp_val
FROM measurements;APL equivalent
datatable(log_values: dynamic)
[
dynamic([0, 1, 2, 3])
]
| extend exp_values = series_exp(log_values)