Axiom
Overview

series_tan

This function is useful when you want to:

  • Apply trigonometric transformations to time series data.
  • Prepare data for advanced mathematical or statistical analysis.
  • Detect periodic or angular patterns in logs, traces, or security events.

Usage#

Syntax#

series_tan(array)

Parameters#

Parameter Type Description
array dynamic (array of numeric values) The array of numeric values to apply the tangent function to.

Returns#

A dynamic array where each element is the tangent of the corresponding input element.

Use case examples#

You want to analyze request durations and apply a trigonometric transformation to highlight periodic anomalies.

Query

['sample-http-logs']
| summarize durations = make_list(req_duration_ms) by id
| extend tan_series = series_tan(durations)

Run in Playground

Output

id durations tan_series
A123 [100, 200, 300] [1.56, -2.19, -0.14]
B456 [150, 250, 350] [14.10, -0.75, 0.51]

This query groups request durations by user ID, transforms them into arrays, and applies the tangent function element-wise.

You want to transform span durations for mathematical modeling of system behavior.

Query

['otel-demo-traces']
| summarize spans = make_list(duration) by ['service.name']
| extend tan_series = series_tan(spans)

Run in Playground

Output

service.name spans tan_series
frontend [50ms, 100ms, 150ms] [0.05, 0.10, 0.15]
cartservice [75ms, 125ms, 175ms] [0.07, 0.13, 0.18]

This query collects span durations for each service, builds arrays, and applies tangent transformation for further modeling.

You want to detect anomalies in request durations for failed HTTP requests by applying the tangent transformation.

Query

['sample-http-logs']
| where status != '200'
| summarize durations = make_list(req_duration_ms) by geo.country
| extend tan_series = series_tan(durations)

Run in Playground

Output

geo.country durations tan_series
US [120, 220, 320] [2.57, -1.37, 0.73]
UK [140, 240, 340] [9.96, -0.46, 0.28]

This query filters failed requests, groups their durations by country, and applies tangent transformation to detect anomalies.

  • series_abs: Returns the absolute value of each element in an array. Use it to normalize negative values in arrays.
  • series_acos: Computes the arccosine of each element in an array. Use when you want the inverse cosine.
  • series_atan: Computes the arctangent of each element in an array. Use when you want the inverse tangent.
  • series_cos: Returns the cosine of each element in an array. Use it when analyzing cyclical data with a phase shift.
  • series_sin: Returns the sine of each element in an array. Use it when analyzing cyclical data with a phase shift.

Other query languages#

Splunk SPL users

Splunk SPL doesn’t include a direct tan function for arrays. In SPL, you often use eval with tan() on scalar values. In APL, series_tan applies the tangent function element-wise to an entire array, which makes it better suited for time series or dynamic arrays.

Splunk example

... | eval tan_val=tan(duration)

APL equivalent

['sample-http-logs']
| extend series = pack_array(req_duration_ms, req_duration_ms*2, req_duration_ms*3)
| extend tan_series = series_tan(series)
ANSI SQL users

In ANSI SQL, you use the TAN() function on scalar values, but there is no native array type or array-wide trigonometric function. In APL, series_tan applies tan to each array element, which makes it easy to work with time series data.

SQL example

SELECT TAN(duration) AS tan_val
FROM otel_demo_traces;

APL equivalent

['otel-demo-traces']
| extend series = pack_array(duration, duration*2, duration*3)
| extend tan_series = series_tan(series)

Updated

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