aggregate.rate() function
The aggregate.rate() function is experimental and subject to change at any time.
By using this function, you accept the risks of experimental functions.
The aggregate.rate() function calculates the rate of change per windows of time
for each input table.
import "experimental/aggregate"
aggregate.rate(
every: 1m,
groupColumns: ["column1", "column2"],
unit: 1s
)
aggregate.rate() 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.
Parameters
every
(Required) Duration of time windows.
groupColumns
List of columns to group by. Default is [].
unit
The time duration to use when calculating the rate. Default is 1s.
tables
Input data.
Default is piped-forward data (<-).
Examples
Calculate the average rate of change in sample data
The following example uses data provided by the sampledata package
to show how aggregate.rate() transforms data.
import "experimental/aggregate"
import "sampledata"
data = sampledata.int()
|> range(start: sampledata.start, stop: sampledata.stop)
data
|> aggregate.rate(
every: 30s,
unit: 1s,
groupColumns: ["tag"]
)
Function definition
package aggregate
import "experimental"
rate = (tables=<-, every, groupColumns=[], unit=1s) =>
tables
|> derivative(nonNegative:true, unit:unit)
|> aggregateWindow(every: every, fn : (tables=<-, column) =>
tables
|> mean(column: column)
|> group(columns: groupColumns)
|> experimental.group(columns: ["_start", "_stop"], mode:"extend")
|> sum()
)
Used functions:
aggregateWindow()
derivative()
experimental.group()
group()
mean()
sum()
Support and feedback
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