which returns a tub with may measurements for each batch
by design/built-in assumptions for the data:
(1) each batch of measurements starts and stops randomly
(2) only one batch can be actively collecting measurements at a time
(3) after one batch stops measuring and another batch starts measuring you cannot go back to the first batch. therefore batches cannot have overlapping windows
the question:
How can I get the average measurement for each batch? I’d like to essentially “window” the data where each window is a single batch. each batch’s start and end time is each table’s _start and _stop
Hello @Addison_Williams,
First it’s good to know that you can pass in your own function into the aggregateWindow() function. There might be something there for you to write a custom aggregation.
This is explained in this blog as well:
Depending on your data this might be the best solution.
To achieve this special windowing with real time data I can think of the following approach:
Use a task with a map() and conditional query logic to write a unique tag to your data for the duration of the batch.
For example you could generate a unique tag by simply making the tag the start time of each batch i.e. converting now() to a string
Then you could group by unique tag for each batch and calculate the mean.
I hope this helps. Let me know what all I can clarify.
