Re: Imbalanced shuffle read
Adding a call to rdd.repartition() after randomizing the keys has no effect
either. code -
//partitioning is done like partitionIdx = f(key) % numPartitions
//we use random keys to get even partitioning
val uniform = other_stream.transform(rdd = {
rdd.map({ kv =
val k = kv._1
val v = kv._2
(UUID.randomUUID().toString, v)
})
rdd.repartition(20)
})
uniform.foreachRDD(rdd = {
rdd.forEachPartition(partition = {
--
View this message in context:
http://apache-spark-user-list.1001560.n3.nabble.com/Imbalanced-shuffle-read-tp18648p18791.html
Sent from the Apache Spark User List mailing list archive at Nabble.com.
-
To unsubscribe, e-mail: user-unsubscr...@spark.apache.org
For additional commands, e-mail: user-h...@spark.apache.org
Re: Imbalanced shuffle read
I have made some progress - the partitioning is very uneven, and everything
goes to one partition. I see that spark partitions by key, so I tried this:
//partitioning is done like partitionIdx = f(key) % numPartitions
//we use random keys to get even partitioning
val uniform = other_stream.transform(rdd = {
rdd.map({ kv =
val k = kv._1
val v = kv._2
(UUID.randomUUID().toString, v)
})
})
uniform.foreachRDD(rdd = {
rdd.forEachPartition(partition = {
...
As you can see, I'm using random keys. Even in this case, when running with
2 nodes, i verified that one partition is completely empty, and the other
contains all the records.
What is going wrong with the partitioning here?
--
View this message in context:
http://apache-spark-user-list.1001560.n3.nabble.com/Imbalanced-shuffle-read-tp18648p18790.html
Sent from the Apache Spark User List mailing list archive at Nabble.com.
-
To unsubscribe, e-mail: user-unsubscr...@spark.apache.org
For additional commands, e-mail: user-h...@spark.apache.org
Imbalanced shuffle read
Im running a job that uses groupByKey(), so it generates a lot of shuffle data. Then it processes this and writes files to HDFS in a forEachPartition block. Looking at the forEachPartition stage details in the web console, all but one executor is idle (SUCCESS in 50-60ms), and one is RUNNING with a huge shuffle read and takes a long time to finish. Can someone explain why the read is all on one node and how to parallelize this better? -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Imbalanced-shuffle-read-tp18648.html Sent from the Apache Spark User List mailing list archive at Nabble.com. - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
Re: Imbalanced shuffle read
When you calls the groupByKey() try providing the number of partitions like groupByKey(100) depending on your data/cluster size. Thanks Best Regards On Wed, Nov 12, 2014 at 6:45 AM, ankits ankitso...@gmail.com wrote: Im running a job that uses groupByKey(), so it generates a lot of shuffle data. Then it processes this and writes files to HDFS in a forEachPartition block. Looking at the forEachPartition stage details in the web console, all but one executor is idle (SUCCESS in 50-60ms), and one is RUNNING with a huge shuffle read and takes a long time to finish. Can someone explain why the read is all on one node and how to parallelize this better? -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Imbalanced-shuffle-read-tp18648.html Sent from the Apache Spark User List mailing list archive at Nabble.com. - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
