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From "Mario Briggs (JIRA)" <>
Subject [jira] [Commented] (SPARK-14597) Streaming Listener timing metrics should include time spent in JobGenerator's graph.generateJobs
Date Thu, 14 Apr 2016 15:30:26 GMT


Mario Briggs commented on SPARK-14597:

I think there is an opportunity to merge both your approaches above. Let me explain, taking
how the onOutputOperationStarted/onOutputOperationCompleted is already implemented. 

So rather than providing a single time metric and a single start/complete event that encompasses
the generateJob for all OutputStreams, you could provide a start/complete event for each individual
outputstream generateJob and onBatchComplete provide the metric for generateJob of all OutputStreams.
This way a user can also figure out if a individual outputstream is the culprit. 

The above would require 2 additional things - pass an eventLoop to DStreamGraph.generateJobs()
method. This eventLoop should not be the existing eventLoop instance in JobGenerator, but
rather another new eventLoop instance (say genJobEventLoop) in JobGenerator. This is because
the existing JobGenerator.eventLoop instance's thread is used to actually drive the Job Generation
and making that thread do additional tasks will increase latency in Streaming. This new 'genJobEventLoop'
will handle a GenJobStarted and GenJobCompleted event and use those events to fire corresponding
events to the ListenerBus and gather the generateJob metric for all outputStreams and set
it in the JobSet 

> Streaming Listener timing metrics should include time spent in JobGenerator's graph.generateJobs
> ------------------------------------------------------------------------------------------------
>                 Key: SPARK-14597
>                 URL:
>             Project: Spark
>          Issue Type: Improvement
>          Components: Spark Core, Streaming
>    Affects Versions: 1.6.1, 2.0.0
>            Reporter: Sachin Aggarwal
>            Priority: Minor
> While looking to tune our streaming application, the piece of info we were looking for
was actual processing time per batch. The StreamingListener.onBatchCompleted event provides
a BatchInfo object that provided this information. It provides the following data
>  - processingDelay
>  - schedulingDelay
>  - totalDelay
>  - Submission Time
>  The above are essentially calculated from the streaming JobScheduler clocking the processingStartTime
and processingEndTime for each JobSet. Another metric available is submissionTime which is
when a Jobset was put on the Streaming Scheduler's Queue. 
> So we took processing delay as our actual processing time per batch. However to maintain
a stable streaming application, we found that the our batch interval had to be a little less
than DOUBLE of the processingDelay metric reported. (We are using a DirectKafkaInputStream).
On digging further, we found that processingDelay is only clocking time spent in the ForEachRDD
closure of the Streaming application and that JobGenerator's graph.generateJobs (
method takes a significant more amount of time.
>  Thus a true reflection of processing time is
>  a - Time spent in JobGenerator's Job Queue (JobGeneratorQueueDelay)
>  b - Time spent in JobGenerator's graph.generateJobs (JobSetCreationDelay)
>  c - Time spent in JobScheduler Queue for a Jobset (existing schedulingDelay metric)
>  d - Time spent in Jobset's job run (existing processingDelay metric)
>  Additionally a JobGeneratorQueue delay (#a) could be due to either graph.generateJobs
taking longer than batchInterval or other JobGenerator events like checkpointing adding up
time. Thus it would be beneficial to report time taken by the checkpointing Job as well

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