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From Anton Brazhnyk <anton.brazh...@genesys.com>
Subject RE: how to setup steady state stream partitions
Date Wed, 10 Sep 2014 21:42:17 GMT
Just a guess.
updateStateByKey has overloaded variants with partitioner as parameter. Can it help?

-----Original Message-----
From: qihong [mailto:qchen@pivotal.io] 
Sent: Tuesday, September 09, 2014 9:13 PM
To: user@spark.incubator.apache.org
Subject: Re: how to setup steady state stream partitions

Thanks for your response. I do have something like:

val inputDStream = ...
val keyedDStream = inputDStream.map(...)  // use sensorId as key val partitionedDStream =
keyedDstream.transform(rdd => rdd.partitionBy(new
MyPartitioner(...)))
val stateDStream = partitionedDStream.updateStateByKey[...](udpateFunction)

The partitionedDStream does have steady partitions, but stateDStream does not have steady
partitions, i.e., in the partition 0 of partitionedDStream, there's only data for sensors
0 to 999, but the partition 0 of stateDStream contains data for some sensors from 0 to 999
range, and lot of sensor from other partitions of partitionedDStream. 

I wish the partition 0 of stateDStream only contains the data from the partition 0 of partitionedDStream,
partiton 1 of stateDStream only from partition 1 of partitionedDStream, and so on. Anyone
knows how to implement that?

Thanks!



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