experimental.window() function
The experimental.window() function is subject to change at any time.
By using this function, you accept the risks of experimental functions.
The window() function groups records based on a time value.
New columns are added to uniquely identify each window.
Those columns are added to the group key of the output tables.
Input tables must have _start, _stop, and _time columns.
A single input record will 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 (zero time)
modified by the offset of the location option.
window(
every: 5m,
period: 5m,
offset: 12h,
createEmpty: false
)
Parameters
Calendar months and years
every, period, and offset support all valid duration units,
including calendar months (1mo) and years (1y).
every
(Required)
Duration of time between windows.
Defaults to period value.
period
Duration of the window.
Period is the length of each interval.
It can be negative, indicating the start and stop boundaries are reversed.
Defaults to every value.
offset
Offset is the duration by which to shift the window boundaries.
It can be negative, indicating that the offset goes backwards in time.
Defaults to 0, which will align window end boundaries with the every duration.
createEmpty
Specifies whether empty tables should be created.
Defaults to false.
tables
Input data.
Default is piped-forward data (<-).
Examples
Window data into 10 minute intervals
from(bucket:"example-bucket")
|> range(start: -12h)
|> window(every: 10m)
// ...
Window by calendar month
from(bucket:"example-bucket")
|> range(start: -1y)
|> window(every: 1mo)
// ...
Support and feedback
Thank you for being part of our community! We welcome and encourage your feedback and bug reports for Flux and this documentation. To find support, the following resources are available:
InfluxDB Cloud customers can contact InfluxData Support.