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title description menu weight flux/v0/tags introduced
aggregateWindow() function `aggregateWindow()` downsamples data by grouping data into fixed windows of time and applying an aggregate or selector function to each window.
flux_v0_ref
name parent identifier
aggregateWindow universe universe/aggregateWindow
101
transformations
aggregates
selectors
0.7.0

aggregateWindow() downsamples data by grouping data into fixed windows of time and applying an aggregate or selector function to each window.

All columns not in the group key other than the specified column are dropped from output tables. This includes _time. aggregateWindow() uses the timeSrc and timeDst parameters to assign a time to the aggregate value.

aggregateWindow() requires _start and _stop columns in input data. Use range() to assign _start and _stop values.

This function is intended to be used when timeColumn (_time by default) is not in the group key. If timeColumn is in the group key, resulting output is confusing and generally not useful.

Downsample by calendar months and years

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

Downsample by week

When windowing by week (1w), weeks are determined using the Unix epoch (1970-01-01T00:00:00Z UTC). The Unix epoch was on a Thursday, so all calculated weeks begin on Thursday.

Function type signature
(
    <-tables: stream[D],
    every: duration,
    fn: (<-: stream[B], column: A) => stream[C],
    ?column: A,
    ?createEmpty: bool,
    ?location: {zone: string, offset: duration},
    ?offset: duration,
    ?period: duration,
    ?timeDst: string,
    ?timeSrc: string,
) => stream[E] where B: Record, C: Record, D: Record, E: Record

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

Parameters

every

({{< req >}}) Duration of time between windows.

period

Duration of windows. Default is the every value.

period 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.

fn

({{< req >}}) Aggregate or selector function to apply to each time window.

location

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

column

Column to operate on.

timeSrc

Column to use as the source of the new time value for aggregate values. Default is _stop.

timeDst

Column to store time values for aggregate values in. Default is _time.

createEmpty

Create empty tables for empty window. Default is true.

Note: When using createEmpty: true, aggregate functions return empty tables, but selector functions do not. By design, selectors drop empty tables.

tables

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

Examples

Use an aggregate function with default parameters

data
    |> aggregateWindow(every: 20s, fn: mean)

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

Input data

*_start *_stop _time *tag _value
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:00Z t1 -2.18
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:10Z t1 10.92
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:20Z t1 7.35
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:30Z t1 17.53
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:40Z t1 15.23
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:50Z t1 4.43
*_start *_stop _time *tag _value
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:00Z t2 19.85
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:10Z t2 4.97
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:20Z t2 -3.75
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:30Z t2 19.77
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:40Z t2 13.86
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:50Z t2 1.86

Output data

_time *_start *_stop *tag _value
2021-01-01T00:00:20Z 2021-01-01T00:00:00Z 2021-01-01T00:01:00Z t1 4.37
2021-01-01T00:00:40Z 2021-01-01T00:00:00Z 2021-01-01T00:01:00Z t1 12.440000000000001
2021-01-01T00:01:00Z 2021-01-01T00:00:00Z 2021-01-01T00:01:00Z t1 9.83
_time *_start *_stop *tag _value
2021-01-01T00:00:20Z 2021-01-01T00:00:00Z 2021-01-01T00:01:00Z t2 12.41
2021-01-01T00:00:40Z 2021-01-01T00:00:00Z 2021-01-01T00:01:00Z t2 8.01
2021-01-01T00:01:00Z 2021-01-01T00:00:00Z 2021-01-01T00:01:00Z t2 7.859999999999999

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

Specify parameters of the aggregate function

To use functions that don’t provide defaults for required parameters with aggregateWindow(), define an anonymous function with column and tables parameters that pipes-forward tables into the aggregate or selector function with all required parameters defined:

data
    |> aggregateWindow(
        column: "_value",
        every: 20s,
        fn: (column, tables=<-) => tables |> quantile(q: 0.99, column: column),
    )

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

Input data

*_start *_stop _time *tag _value
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:00Z t1 -2.18
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:10Z t1 10.92
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:20Z t1 7.35
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:30Z t1 17.53
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:40Z t1 15.23
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:50Z t1 4.43
*_start *_stop _time *tag _value
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:00Z t2 19.85
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:10Z t2 4.97
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:20Z t2 -3.75
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:30Z t2 19.77
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:40Z t2 13.86
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:50Z t2 1.86

Output data

*_start *_stop *tag _value _time
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z t1 10.92 2021-01-01T00:00:20Z
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z t1 17.53 2021-01-01T00:00:40Z
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z t1 15.23 2021-01-01T00:01:00Z
*_start *_stop *tag _value _time
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z t2 19.85 2021-01-01T00:00:20Z
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z t2 19.77 2021-01-01T00:00:40Z
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z t2 13.86 2021-01-01T00:01:00Z

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

Downsample by calendar month

data
    |> aggregateWindow(every: 1mo, fn: mean)

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

Input data

*_start *_stop _time *tag _value
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:00Z t1 -2.18
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:10Z t1 10.92
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:20Z t1 7.35
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:30Z t1 17.53
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:40Z t1 15.23
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:50Z t1 4.43
*_start *_stop _time *tag _value
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:00Z t2 19.85
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:10Z t2 4.97
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:20Z t2 -3.75
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:30Z t2 19.77
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:40Z t2 13.86
2021-01-01T00:00:00Z 2021-01-01T00:01:00Z 2021-01-01T00:00:50Z t2 1.86

Output data

_time *_start *_stop *tag _value
2021-01-01T00:01:00Z 2021-01-01T00:00:00Z 2021-01-01T00:01:00Z t1 8.88
_time *_start *_stop *tag _value
2021-01-01T00:01:00Z 2021-01-01T00:00:00Z 2021-01-01T00:01:00Z t2 9.426666666666668

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

Downsample by calendar week starting on Monday

Flux increments weeks from the Unix epoch, which was a Thursday. Because of this, by default, all 1w windows begin on Thursday. Use the offset parameter to shift the start of weekly windows to the desired day of the week.

Week start Offset
Monday -3d
Tuesday -2d
Wednesday -1d
Thursday 0d
Friday 1d
Saturday 2d
Sunday 3d 
data
    |> aggregateWindow(every: 1w, offset: -3d, fn: mean)

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

Input data

_start _stop _time *tag _value
2022-01-01T00:00:00Z 2022-01-31T23:59:59Z 2022-01-01T00:00:00Z t1 2
2022-01-01T00:00:00Z 2022-01-31T23:59:59Z 2022-01-03T00:00:00Z t1 2.2
2022-01-01T00:00:00Z 2022-01-31T23:59:59Z 2022-01-06T00:00:00Z t1 4.1
2022-01-01T00:00:00Z 2022-01-31T23:59:59Z 2022-01-09T00:00:00Z t1 3.8
2022-01-01T00:00:00Z 2022-01-31T23:59:59Z 2022-01-11T00:00:00Z t1 1.7
2022-01-01T00:00:00Z 2022-01-31T23:59:59Z 2022-01-12T00:00:00Z t1 2.1
2022-01-01T00:00:00Z 2022-01-31T23:59:59Z 2022-01-15T00:00:00Z t1 3.8
2022-01-01T00:00:00Z 2022-01-31T23:59:59Z 2022-01-16T00:00:00Z t1 4.2
2022-01-01T00:00:00Z 2022-01-31T23:59:59Z 2022-01-20T00:00:00Z t1 5
2022-01-01T00:00:00Z 2022-01-31T23:59:59Z 2022-01-24T00:00:00Z t1 5.8
2022-01-01T00:00:00Z 2022-01-31T23:59:59Z 2022-01-28T00:00:00Z t1 3.9

Output data

_time *tag *_start *_stop _value
2022-01-03T00:00:00Z t1 2022-01-01T00:00:00Z 2022-01-31T23:59:59Z 2
2022-01-10T00:00:00Z t1 2022-01-01T00:00:00Z 2022-01-31T23:59:59Z 3.3666666666666667
2022-01-17T00:00:00Z t1 2022-01-01T00:00:00Z 2022-01-31T23:59:59Z 2.95
2022-01-24T00:00:00Z t1 2022-01-01T00:00:00Z 2022-01-31T23:59:59Z 5
2022-01-31T00:00:00Z t1 2022-01-01T00:00:00Z 2022-01-31T23:59:59Z 4.85
2022-01-31T23:59:59Z t1 2022-01-01T00:00:00Z 2022-01-31T23:59:59Z

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