Aggregate Data groups time-series data from the analytics and reporting store into automatically sized buckets over a specified time range. The platform selects a sensible interval — minutes, hours, days, or months — based on the window you provide, so the granularity of the output adapts to the query rather than remaining fixed.
Overview
Use Aggregate Data when you need a summarized, time-bucketed view of reporting data rather than raw rows. Supply a query or data source and a time range; the platform auto-selects the bucket interval that gives a meaningful histogram for that window — a one-hour window yields minute-level buckets, while a one-year window yields monthly buckets.
This action is well-suited to dashboard-feeding automations, trend detection, and any scenario where you need to answer "how did this metric change over time" rather than "what are the individual records." The auto-bucketing means you do not need to calculate or hardcode an interval — the platform infers it from your time range.
Bucket size adapts to the window: The aggregation interval is chosen automatically based on your time range. Expect minute-level buckets for short windows and day- or month-level buckets for longer ones. The interval is returned alongside the data so downstream nodes know the resolution.
Input
Field | Description | Required |
|---|---|---|
Query / Data Source | The SQL query or data source reference defining the time-series data to aggregate. The data must include a timestamp column the platform can use for bucketing. | * |
Time Range Start | The start of the aggregation window. Buckets begin at or after this point. | * |
Time Range End | The end of the aggregation window. Buckets end at or before this point. | * |
Aggregation Function | The function to apply within each bucket — for example, sum, count, avg, min, or max. When omitted, the platform applies a default aggregation suitable for the data type. |
Aggregate Data Input tab with Query or Data Source, Time Range Start and End, and Aggregation Function configured
Output
The action returns a time-bucketed result set:
An array of bucket objects, each containing a timestamp marking the bucket's start, the aggregated value for that bucket, and the bucket interval used.
The auto-selected interval (for example, 1h, 1d, 1mo), so downstream nodes know the resolution of the data.
Aggregate Data Output tab showing time-bucketed result array with bucket timestamps, aggregated values, and auto-selected interval
Notes
Keep the following in mind when using Aggregate Data.
The bucket interval is chosen automatically by the platform based on the time range. You cannot force a specific interval — design your downstream logic to consume whatever interval the platform selects.
Bucket size will differ between runs if the time range changes — a query run today over the last 7 days will produce different granularity than the same query run over the last 365 days.
The aggregation function applies within each bucket. If no function is specified, a platform default is used — confirm the default matches your intent before deploying.
This action runs against the analytics and reporting store — not the workflow-log store. For aggregating log data over time, use LogQL actions.
For large time ranges, the number of buckets returned remains manageable because the interval widens — you will not receive thousands of minute-level buckets for a year-long query.
If your downstream visualization or logic depends on a fixed interval, test the aggregation output across the full range of time windows your automation will encounter. The interval may change between runs as the window shifts.