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title description menu weight flux/v0/tags introduced
experimental.window() function `experimental.window()` groups records based on time.
flux_v0_ref
name parent identifier
experimental.window experimental experimental/window
101
transformations
date/time
0.106.0

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}]

{{% caption %}} For more information, see Function type signatures. {{% /caption %}}

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)

{{< expand-wrapper >}} {{% expand "View example input and output" %}}

Input data

*_start *_stop _time _value *tag
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:00Z -2 t1
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:10Z 10 t1
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:20Z 7 t1
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:30Z 17 t1
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:40Z 15 t1
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:50Z 4 t1
*_start *_stop _time _value *tag
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:00Z 19 t2
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:10Z 4 t2
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:20Z -3 t2
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:30Z 19 t2
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:40Z 13 t2
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:50Z 1 t2

Output data

*_start *_stop _time _value *tag
2021-01-01T00:00:00Z 2021-01-01T00:00:30Z 2021-01-01T00:00:00Z -2 t1
2021-01-01T00:00:00Z 2021-01-01T00:00:30Z 2021-01-01T00:00:10Z 10 t1
2021-01-01T00:00:00Z 2021-01-01T00:00:30Z 2021-01-01T00:00:20Z 7 t1
*_start *_stop _time _value *tag
2021-01-01T00:00:00Z 2021-01-01T00:00:30Z 2021-01-01T00:00:00Z 19 t2
2021-01-01T00:00:00Z 2021-01-01T00:00:30Z 2021-01-01T00:00:10Z 4 t2
2021-01-01T00:00:00Z 2021-01-01T00:00:30Z 2021-01-01T00:00:20Z -3 t2
*_start *_stop _time _value *tag
2021-01-01T00:00:30Z 2021-01-01T00:01:00Z 2021-01-01T00:00:30Z 17 t1
2021-01-01T00:00:30Z 2021-01-01T00:01:00Z 2021-01-01T00:00:40Z 15 t1
2021-01-01T00:00:30Z 2021-01-01T00:01:00Z 2021-01-01T00:00:50Z 4 t1
*_start *_stop _time _value *tag
2021-01-01T00:00:30Z 2021-01-01T00:01:00Z 2021-01-01T00:00:30Z 19 t2
2021-01-01T00:00:30Z 2021-01-01T00:01:00Z 2021-01-01T00:00:40Z 13 t2
2021-01-01T00:00:30Z 2021-01-01T00:01:00Z 2021-01-01T00:00:50Z 1 t2

{{% /expand %}} {{< /expand-wrapper >}}

Window by calendar month

import "experimental"

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

{{< expand-wrapper >}} {{% expand "View example input and output" %}}

Input data

*_start *_stop _time _value
2021-01-01T00:00:00Z 2021-03-01T00:00:00Z 2021-01-01T00:00:00Z 32.1
2021-01-01T00:00:00Z 2021-03-01T00:00:00Z 2021-01-02T00:00:00Z 32.9
2021-01-01T00:00:00Z 2021-03-01T00:00:00Z 2021-01-03T00:00:00Z 33.2
2021-01-01T00:00:00Z 2021-03-01T00:00:00Z 2021-02-01T00:00:00Z 38.3
2021-01-01T00:00:00Z 2021-03-01T00:00:00Z 2021-02-02T00:00:00Z 38.4
2021-01-01T00:00:00Z 2021-03-01T00:00:00Z 2021-02-03T00:00:00Z 37.8

Output data

*_start *_stop _time _value
2021-01-01T00:00:00Z 2021-02-01T00:00:00Z 2021-01-01T00:00:00Z 32.1
2021-01-01T00:00:00Z 2021-02-01T00:00:00Z 2021-01-02T00:00:00Z 32.9
2021-01-01T00:00:00Z 2021-02-01T00:00:00Z 2021-01-03T00:00:00Z 33.2
*_start *_stop _time _value
2021-02-01T00:00:00Z 2021-03-01T00:00:00Z 2021-02-01T00:00:00Z 38.3
2021-02-01T00:00:00Z 2021-03-01T00:00:00Z 2021-02-02T00:00:00Z 38.4
2021-02-01T00:00:00Z 2021-03-01T00:00:00Z 2021-02-03T00:00:00Z 37.8

{{% /expand %}} {{< /expand-wrapper >}}