Axiom
Overview

make-series

Introduction#

You find this operator useful when you want to:

  • Turn event data into array-encoded time series for further analysis.
  • Apply series_* functions (for example, series_fir, series_stats) to aggregated data.
  • Postprocess and then expand arrays back into rows with mv-expand for visualization or downstream queries.

Unlike summarize, which produces row-based aggregations, make-series is designed specifically for creating and manipulating array-based time series.

Usage#

Syntax#

make-series [Aggregation [, ...]]
    [default = DefaultValue]
    on TimeField
    [in Range]
    step StepSize
    [by GroupingField [, ...]]

Parameters#

Parameter Description
Aggregation One or more aggregation functions (for example, avg(), count(), sum()) applied to each time bin, producing arrays of values.
default A value to use when no records exist in a time bin.
TimeField The field containing timestamps used for binning.
Range An optional range expression specifying the start and end of the series (for example, from ago(1h) to now()).
StepSize The size of each time bin (for example, 1m, 5m, 1h).
GroupingField Optional fields to split the series by, producing parallel arrays for each group.

Returns#

The operator returns a table where each aggregation produces an array of values aligned with an array of time bins. Each row represents a group (if specified), with arrays that encode the entire time series.

Use case examples#

You want to create an array-based time series of request counts, then compute a rolling average using a series_* function, and finally expand back into rows for visualization.

Query

['sample-http-logs']
| make-series count() on _time from now()-24h to now() step 5m
| extend moving_avg_count=series_fir(count_, dynamic([1, 1, 1, 1, 1]))
| mv-expand moving_avg_count to typeof(long), count_ to typeof(long), time to typeof(datetime)
| project-rename _time=time
| summarize avg(moving_avg_count), avg(count_) by bin(_time, 5m)

Run in Playground

Output

_time count_ moving_avg_count
2025-09-29T10:00:00 120 118
2025-09-29T10:05:00 130 122
2025-09-29T10:10:00 110 121

The query turns request counts into arrays, applies a smoothing function, and then expands the arrays back into rows for analysis.

You want to analyze span durations per service, storing them as arrays for later manipulation.

Query

['otel-demo-traces']
| make-series avg(duration) on _time from ago(2h) to now() step 10m by ['service.name']

Run in Playground

Output

service.name avg_duration time
frontend [20ms, 18ms, 22ms, 19ms, ...] [2025-09-29T08:00, ...]
checkout [35ms, 40ms, 33ms, 37ms, ...] [2025-09-29T08:00, ...]

The query produces array-encoded time series per service, which you can further process with series_* functions.

You want to analyze the rate of HTTP 500 errors in your logs per minute.

Query

['sample-http-logs']
| where status == '500'
| make-series count() default=0 on _time from ago(30m) to now() step 1m

Run in Playground

Output

count_ _time
[1489, 1428, 1517, 1462, 1509, ...] ["2025-09-30T09:08:14.921301725Z", "2025-09-30T09:09:14.921301725Z", ...]

The query generates a time series of HTTP 500 error counts as an array-based time series for further analysis with series_* functions.

  • extend: Creates new calculated fields, often as preparation before make-series. Use extend when you want to preprocess data for time series analysis.
  • mv-expand: Expands arrays into multiple rows. Use mv-expand to work with the arrays returned by make-series.
  • summarize: Aggregates rows into groups but doesn't generate continuous time bins. Use summarize when you want flexible grouping without forcing evenly spaced intervals.
  • top: Returns the top rows by a specified expression, not time series. Use top when you want to focus on the most significant values instead of trends over time.

Other query languages#

Splunk SPL users

In Splunk SPL, the timechart command creates row-based time series, with one row per time bucket. In APL, the make-series operator instead encodes the series into arrays, which you can later manipulate or expand. This is a key difference from SPL’s row-based approach.

Splunk example

index=sample-http-logs
| timechart span=1m avg(req_duration_ms)

APL equivalent

['sample-http-logs']
| make-series avg(req_duration_ms) default=0 on _time from ago(1h) to now() step 1m
ANSI SQL users

In ANSI SQL, you typically use GROUP BY with a generated series or calendar table to create row-based time buckets. In APL, make-series creates arrays of values and timestamps in a single row. This lets you perform array-based computations on the time series before optionally expanding back into rows.

SQL example

SELECT
  time_bucket('1 minute', _time) AS minute,
  AVG(req_duration_ms) AS avg_duration
FROM sample_http_logs
WHERE _time > NOW() - interval '1 hour'
GROUP BY minute
ORDER BY minute

APL equivalent

['sample-http-logs']
| make-series avg(req_duration_ms) default=0 on _time from ago(1h) to now() step 1m

Updated

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