Documentation

experimental.window() function

experimental.window() is subject to change at any time.

experimental.window() groups records based on time.

_start and _stop columns are updated to reflect the bounds of the window the row’s time value is in. Input tables must have _start, _stop, and _time columns.

A single input record can be placed into zero or more output tables depending on the specific windowing function.

By default the start boundary of a window will align with the Unix epoch modified by the offset of the location option.

Calendar months and years

every, period, and offset support all valid duration units, including calendar months (1mo) and years (1y).

Function type signature
(
    <-tables: stream[{A with _time: time, _stop: time, _start: time}],
    ?createEmpty: bool,
    ?every: duration,
    ?location: {zone: string, offset: duration},
    ?offset: duration,
    ?period: duration,
) => stream[{A with _time: time, _stop: time, _start: time}]

For more information, see Function type signatures.

Parameters

every

Duration of time between windows. Default is the 0s.

period

Duration of the window. Default is 0s.

Period is the length of each interval. It can be negative, indicating the start and stop boundaries are reversed.

offset

Duration to shift the window boundaries by. Default is 0s.

offset can be negative, indicating that the offset goes backwards in time.

location

Location used to determine timezone. Default is the location option.

createEmpty

Create empty tables for empty windows. Default is false.

tables

Input data. Default is piped-forward data (<-).

Examples

Window data into thirty second intervals

import "experimental"

data
    |> experimental.window(every: 30s)

View example input and output

Window by calendar month

import "experimental"

data
    |> experimental.window(every: 1mo)

View example input and output


Was this page helpful?

Thank you for your feedback!


New in InfluxDB 3.5

Key enhancements in InfluxDB 3.5 and the InfluxDB 3 Explorer 1.3.

See the Blog Post

InfluxDB 3.5 is now available for both Core and Enterprise, introducing custom plugin repository support, enhanced operational visibility with queryable CLI parameters and manual node management, stronger security controls, and general performance improvements.

InfluxDB 3 Explorer 1.3 brings powerful new capabilities including Dashboards (beta) for saving and organizing your favorite queries, and cache querying for instant access to Last Value and Distinct Value caches—making Explorer a more comprehensive workspace for time series monitoring and analysis.

For more information, check out:

InfluxDB Docker latest tag changing to InfluxDB 3 Core

On November 3, 2025, the latest tag for InfluxDB Docker images will point to InfluxDB 3 Core. To avoid unexpected upgrades, use specific version tags in your Docker deployments.

If using Docker to install and run InfluxDB, the latest tag will point to InfluxDB 3 Core. To avoid unexpected upgrades, use specific version tags in your Docker deployments. For example, if using Docker to run InfluxDB v2, replace the latest version tag with a specific version tag in your Docker pull command–for example:

docker pull influxdb:2