You can decouple the batch interval and the window sizes. If during processing 
you’re aggregating data and your operations benefit of an inverse function, 
then you can optimally process windows of data.

E.g. You could set a global batch interval of 10 seconds. You can process the 
incoming data from Kafka, aggregating the input.
Then you can create a window of 3 minutes (both length and slide) over the 
partial results. In this case the inverse function is not helpful as all the 
data is new in every window.

You can even coalesce the final Dstream to avoid writing many small files. For 
example you could be writing LESS files MORE OFTEN and achieve a similar effect.

All of this is of course hypothetical since I don’t know what processing you 
are applying to the data coming from Kafka. More like food for thought.

-adrian





On 10/29/15, 2:50 PM, "Afshartous, Nick" <nafshart...@turbine.com> wrote:

>< Does it work as expected with smaller batch or smaller load? Could it be 
>that it's accumulating too many events over 3 minutes?
>
>Thanks for you input.  The 3 minute window was chosen because we write the 
>output of each batch into S3.  And with smaller batch time intervals there 
>were many small files being written to S3, something to avoid.  That was the 
>explanation of the developer who made this decision (who's no longer on the 
>team).   We're in the process of re-evaluating.
>--
>     Nick
>
>-----Original Message-----
>From: Adrian Tanase [mailto:atan...@adobe.com]
>Sent: Wednesday, October 28, 2015 4:53 PM
>To: Afshartous, Nick <nafshart...@turbine.com>
>Cc: user@spark.apache.org
>Subject: Re: Spark/Kafka Streaming Job Gets Stuck
>
>Does it work as expected with smaller batch or smaller load? Could it be that 
>it's accumulating too many events over 3 minutes?
>
>You could also try increasing the parallelism via repartition to ensure 
>smaller tasks that can safely fit in working memory.
>
>Sent from my iPhone
>
>> On 28 Oct 2015, at 17:45, Afshartous, Nick <nafshart...@turbine.com> wrote:
>>
>>
>> Hi, we are load testing our Spark 1.3 streaming (reading from Kafka)  job 
>> and seeing a problem.  This is running in AWS/Yarn and the streaming batch 
>> interval is set to 3 minutes and this is a ten node cluster.
>>
>> Testing at 30,000 events per second we are seeing the streaming job get 
>> stuck (stack trace below) for over an hour.
>>
>> Thanks on any insights or suggestions.
>> --
>>      Nick
>>
>> org.apache.spark.streaming.api.java.AbstractJavaDStreamLike.mapPartiti
>> onsToPair(JavaDStreamLike.scala:43)
>> com.wb.analytics.spark.services.streaming.drivers.StreamingKafkaConsum
>> erDriver.runStream(StreamingKafkaConsumerDriver.java:125)
>> com.wb.analytics.spark.services.streaming.drivers.StreamingKafkaConsum
>> erDriver.main(StreamingKafkaConsumerDriver.java:71)
>> sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
>> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.j
>> ava:57)
>> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccess
>> orImpl.java:43)
>> java.lang.reflect.Method.invoke(Method.java:606)
>> org.apache.spark.deploy.yarn.ApplicationMaster$$anon$2.run(Application
>> Master.scala:480)
>>
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>Notice: This communication is for the intended recipient(s) only and may 
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