aggregateWindow() function

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

For more information, see Function type signatures.



(Required) Duration of time between windows.


Duration of windows. Default is the every value.

period can be negative, indicating the start and stop boundaries are reversed.


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

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


(Required) Aggregate or selector function to apply to each time window.


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


Column to operate on.


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


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


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.


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


Use an aggregate function with default parameters

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

View example input and output

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:

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

View example input and output

Downsample by calendar month

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

View example input and output

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
    |> aggregateWindow(every: 1w, offset: -3d, fn: mean)

View example input and output

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The future of Flux

Flux is going into maintenance mode. You can continue using it as you currently are without any changes to your code.

Flux is going into maintenance mode and will not be supported in InfluxDB 3.0. This was a decision based on the broad demand for SQL and the continued growth and adoption of InfluxQL. We are continuing to support Flux for users in 1.x and 2.x so you can continue using it with no changes to your code. If you are interested in transitioning to InfluxDB 3.0 and want to future-proof your code, we suggest using InfluxQL.

For information about the future of Flux, see the following: