Use the range function in APL to create a dynamic array of evenly spaced values. You can generate numeric, datetime, or timespan sequences that increase by a constant step, which defaults to 1 for numbers and 1 hour for time-based types. The function stops once the value exceeds the specified endpoint or the maximum result size.
range is useful when you want to produce test values, synthetic sequences, time intervals, or loop-like constructs without relying on input data. It helps you populate arrays that can be expanded or joined with real data for further analysis.
For users of other query languages
If you come from other query languages, this section explains how to adjust your existing queries to achieve the same results in APL.
In SPL, generating sequences often involves makeresults combined with streamstats or manual iteration logic. APL’s range function simplifies this by producing arrays of equally spaced values directly.
print r = range(1, 10, 1)In ANSI SQL, generating a series of numbers usually involves recursive CTEs. APL’s range is more concise and efficient for creating sequences without writing complex recursion logic.
print r = range(1, 10, 1)Usage
Syntax
range(start, stop [, step])Parameters
| Name | Type | Required | Description |
|---|---|---|---|
| start | scalar (number, datetime, timespan) | ✓ | First value in the array. |
| stop | scalar (same type as start) |
✓ | Upper bound of the array. The last value is less than or equal to stop. |
| step | scalar (same type as start) |
Difference between values. Defaults to 1 (numeric) or 1h (time). |
Returns
A dynamic array that includes values starting at start, incremented by step, up to and including stop (if it aligns exactly with a step). The array truncates if it reaches the system limit of 1,048,576 elements.
Use case examples
Generate an array of durations to help classify HTTP request latencies.
Query
print r = range(100, 500, 100)
| project rOutput
[
100,
200,
300,
400,
500
]This creates an array of thresholds that you can use to bucket or filter request durations in ['sample-http-logs'].
Build a set of time intervals to align span activity over a fixed period.
Query
print intervals = range(datetime(2025-07-29T12:00:00Z), datetime(2025-07-29T13:00:00Z), 15m)
| project intervalsOutput
[
"2025-07-29T12:00:00Z",
"2025-07-29T12:15:00Z",
"2025-07-29T12:30:00Z",
"2025-07-29T12:45:00Z",
"2025-07-29T13:00:00Z"
]This array helps divide the hour into 15-minute blocks for analyzing activity in ['otel-demo-traces'].
Create a list of expected hourly intervals to detect missing logs.
Query
print expected = range(datetime(2025-07-29T00:00:00Z), datetime(2025-07-29T05:00:00Z), 1h)
| project expectedOutput
[
"2025-07-29T00:00:00Z",
"2025-07-29T01:00:00Z",
"2025-07-29T02:00:00Z",
"2025-07-29T03:00:00Z",
"2025-07-29T04:00:00Z",
"2025-07-29T05:00:00Z"
]This produces an array of expected log collection times for the ['sample-http-logs'] dataset. You can join this with actual data to detect missing records.