Stream Analytics: How can I start and stop a TUMBLINGWINDOW aggregation job inorder to reduce costs while still getting the same aggregation results?

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Context

I have created a streaming job using Azure portal which aggregates data using a day wise TUMBLINGWINDOW. Have attached a code snippet below, modified from the docs, which shows similar logic.

SELECT
    DATEADD(day, -1, System.Timestamp()) AS WindowStart
    System.Timestamp() AS WindowEnd, 
    TollId, 
    COUNT(*)
FROM Input TIMESTAMP BY EntryTime  
GROUP BY TumblingWindow(day, 1), TollId

Now that the job has been running and can see it producing output I want to be able to reduce the costs ideally by setting some sort of time scheduling so that the job can run and still produce the same output without being on all the time.

The only real constraint being that the aggregated output at the end of each TUMBLINGWINDOW has to remain the same as if it were running all the time (no impact of stop-starting on output).

This then brings me to my question.

Update: 2021-02-28

Before going into the question another thing that drove me was that through Azure portal you can manually start and stop a job. When you start/restart a job you can set a custom start time for the job/query. With this level of control say I start a job (or have a job running) and then decide to stop it for majority of the day and then turn it on at say 11:30pm each day with a custom start time of midnight of the current day then it would be able to be on for approx 30min before it would output the results (yet still to my understanding produce the same aggregation results/effect compared to if it was on the whole day up until that point). This job could then be paused again at 00:30am ( the next day for which it stays paused for the majority of the day (1380min total until 11:30pm again) upon which the same above logic is applied. This way it remains off the majority of the day yet still can produce the same output for each day wise window (correct me if I am wrong in my thinking). The only issue with this to me seems to be the fact someone would have to manually perform this. Thus I was driven to the docs looking for a way to automate this.

Question

How can I start and stop a job in an automated fashion such that the required output would still remain intact but so that the job doesn't have to remain on all the time (like it currently is)?

Does the documentation linked above suffice given the context above, if so what are some possible arrangements for the N minutes (on) and M minutes (off) time variables for this to work?

Is this possible given the scenario that I want to aggregate on a one day TUMBLINGWINDOW window (whereby I want each window to start and end at midnight of each day, as per its default behaviour.)?

Eg

Window start: 2022-02-20 00:00:00 Window end: 2022-02-21 00:00:00 (aggregation performed),

Window start: 2022-02-21 00:00:00 Window end: 2022-02-22 00:00:00 (aggregation performed),

Window start: 2022-02-22 00:00:00 Window end: 2022-02-23 00:00:00 (aggregation performed),

....so on

Thoughts

I found this documentation from Microsoft regarding auto-pausing jobs using a few methods

However came across a paragraph (quoted below) which made me doubtful whether it seems reasonable in my particular use case (TUMBLING 1 day window as described in my question section).

Note

There are downsides to auto-pausing a job. The main ones being the loss of the low latency /real time capabilities, and the potential risks from allowing the input event backlog to grow unsupervised while a job is paused. Auto-pausing should not be considered for most production scenarios running at scale.

Could this method

azure-stream-analytics azure tsql
1 Answers

There are 3 ways to lower costs:

  • downscale your job, you will have higher latency but for a lower cost, up to a point where your job crashes because it runs out of memory over time and/or can't catch up with its backlog. Here you need to keep an eye on your metrics to make sure you can react before it's too late
  • going further, you can regroup multiple queries into a single job. This job most likely won't be aligned in partitions, so it won't be able to scale linearly (adding SUs is not guaranteed to give you better performance). Same comment as above, plus you need to remember that when you need to scale back up, you probably will have to break down that job into multiple jobs to again be able to scale in a linear fashion
  • finally you can auto-pause a job, one way to implement that being explained in the doc you linked. I wrote that doc, and what I meant by that comment is that here again you are taking the risk of overloading the job if it can't run long enough to process the backlog of events. This is a risky proposition for most production scenarios

But if you know what you are doing, and are monitoring closely the appropriate metrics (as explained in the doc), this is definitely something you should explore.

Finally, all of these approaches, including the auto-pause one, will deal with tumbling windows transparently for you.

Update: 2022-03-03 following comments here

Update: 2022-03-04 following comments there

There are 3 time dimensions here:

  • When the job is running or not: the wall clock
  • When the time window is expected to output results: Tumbling(day,1) -> 00:00AM every day, this is absolute (on the day, on the hour, on the minute...) and independent of the job start time below
  • What output you want produced from the job, via the job start time

Let's say you have the job running 24/7 for multiple months, and decide to stop it at noon (12:00PM) on the 1st day of March.

It already has generated an output for the last day of February, at 00:00AM Mar1.

You won't see a difference in output until the following day, 00:00AM Mar2, when you expect to see the daily window of Mar1, but it's not output because the job is stopped.

Let's start the job at 01:00AM Mar2 wall clock time. If you want the missing time window, you should either pick a start time at 'when last stopped' (noon the day before), or a custom time any time before 23:59PM Mar1. What you are driving is the output window you want. Here you are telling ASA you want all the windows from that point onward.

ASA will then reload all the data it needs to generate that window (make sure the event hub has enough retention for that, we don't cache data between restarts in the job): Azure Stream Analytics will automatically look back at the data in the input source. For instance, if you start a job “Now” and if your query uses a 5-minutes Tumbling Window, Azure Stream Analytics will seek data from 5 minutes ago in the input. The first possible output event would have a timestamp equal to or greater than the current time, and ASA guarantees that all input events that may logically contribute to the output has been accounted for.

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