aggregateWindow() function

The aggregateWindow() function applies an aggregate or selector function (any function with a column parameter) to fixed windows of time.

aggregateWindow(
  every: 1m,
  fn: mean,
  column: "_value",
  timeSrc: "_stop",
  timeDst: "_time",
  createEmpty: true
)

aggregateWindow() requires that input data have _start and _stop columns to calculate windows of time to operate on. Use range() to assign _start and _stop values.

As data is windowed into separate tables and processed, the _time column is dropped from each group key. This function copies the timestamp from a remaining column into the _time column.

aggregateWindow() restores the original _start and _stop values of input data and, by default, uses _stop to set the _time value for each aggregated window. Each row in the output of aggregateWindow represents an aggregated window ending at _time.

Parameters

every

Duration of windows.

fn

Aggregate or selector function used to operate on each window of time.

column

The column on which to operate. Defaults to "_value".

timeSrc

The time column from which time is copied for the aggregate record. Defaults to "_stop".

timeDst

The “time destination” column to which time is copied for the aggregate record. Defaults to "_time".

createEmpty

For windows without data, create a single-row table for each empty window (using table.fill()). Defaults to true.

tables

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

Examples

The following examples use data provided by the sampledata package to show how aggregateWindow() transforms data.

• Use an aggregate function with default parameters
• Specify parameters of the aggregate function
• Window and aggregate by calendar month

Use an aggregate function with default parameters

The following example uses the default parameters of the mean() function to aggregate time-based windows:

import "sampledata"

data = sampledata.float()
  |> range(start: sampledata.start, stop: sampledata.stop)

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

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:

import "sampledata"

data = sampledata.float()
  |> range(start: sampledata.start, stop: sampledata.stop)

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

Window and aggregate by calendar month

import "sampledata"

data = sampledata.float()
  |> range(start: sampledata.start, stop: sampledata.stop)

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