Re: Shuffle produces one huge partition and many tiny partitions
Sorry Du, Repartition means coalesce(shuffle = true) as per [1]. They are the same operation. Coalescing with shuffle = false means you are specifying the max amount of partitions after the coalesce (if there are less partitions you will end up with the lesser amount. [1] https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/rdd/RDD.scala#L341 On Thu, Jun 18, 2015 at 7:55 PM, Du Li l...@yahoo-inc.com.invalid wrote: repartition() means coalesce(shuffle=false) On Thursday, June 18, 2015 4:07 PM, Corey Nolet cjno...@gmail.com wrote: Doesn't repartition call coalesce(shuffle=true)? On Jun 18, 2015 6:53 PM, Du Li l...@yahoo-inc.com.invalid wrote: I got the same problem with rdd,repartition() in my streaming app, which generated a few huge partitions and many tiny partitions. The resulting high data skew makes the processing time of a batch unpredictable and often exceeding the batch interval. I eventually solved the problem by using rdd.coalesce() instead, which however is expensive as it yields a lot of shuffle traffic and also takes a long time. Du On Thursday, June 18, 2015 1:00 AM, Al M alasdair.mcbr...@gmail.com wrote: Thanks for the suggestion. Repartition didn't help us unfortunately. It still puts everything into the same partition. We did manage to improve the situation by making a new partitioner that extends HashPartitioner. It treats certain exception keys differently. These keys that are known to appear very often are assigned random partitions instead of using the existing partitioning mechanism. -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Shuffle-produces-one-huge-partition-and-many-tiny-partitions-tp23358p23387.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: Shuffle produces one huge partition and many tiny partitions
I got the same problem with rdd,repartition() in my streaming app, which generated a few huge partitions and many tiny partitions. The resulting high data skew makes the processing time of a batch unpredictable and often exceeding the batch interval. I eventually solved the problem by using rdd.coalesce() instead, which however is expensive as it yields a lot of shuffle traffic and also takes a long time. Du On Thursday, June 18, 2015 1:00 AM, Al M alasdair.mcbr...@gmail.com wrote: Thanks for the suggestion. Repartition didn't help us unfortunately. It still puts everything into the same partition. We did manage to improve the situation by making a new partitioner that extends HashPartitioner. It treats certain exception keys differently. These keys that are known to appear very often are assigned random partitions instead of using the existing partitioning mechanism. -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Shuffle-produces-one-huge-partition-and-many-tiny-partitions-tp23358p23387.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: Shuffle produces one huge partition and many tiny partitions
Doesn't repartition call coalesce(shuffle=true)? On Jun 18, 2015 6:53 PM, Du Li l...@yahoo-inc.com.invalid wrote: I got the same problem with rdd,repartition() in my streaming app, which generated a few huge partitions and many tiny partitions. The resulting high data skew makes the processing time of a batch unpredictable and often exceeding the batch interval. I eventually solved the problem by using rdd.coalesce() instead, which however is expensive as it yields a lot of shuffle traffic and also takes a long time. Du On Thursday, June 18, 2015 1:00 AM, Al M alasdair.mcbr...@gmail.com wrote: Thanks for the suggestion. Repartition didn't help us unfortunately. It still puts everything into the same partition. We did manage to improve the situation by making a new partitioner that extends HashPartitioner. It treats certain exception keys differently. These keys that are known to appear very often are assigned random partitions instead of using the existing partitioning mechanism. -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Shuffle-produces-one-huge-partition-and-many-tiny-partitions-tp23358p23387.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: Shuffle produces one huge partition and many tiny partitions
repartition() means coalesce(shuffle=false) On Thursday, June 18, 2015 4:07 PM, Corey Nolet cjno...@gmail.com wrote: Doesn't repartition call coalesce(shuffle=true)?On Jun 18, 2015 6:53 PM, Du Li l...@yahoo-inc.com.invalid wrote: I got the same problem with rdd,repartition() in my streaming app, which generated a few huge partitions and many tiny partitions. The resulting high data skew makes the processing time of a batch unpredictable and often exceeding the batch interval. I eventually solved the problem by using rdd.coalesce() instead, which however is expensive as it yields a lot of shuffle traffic and also takes a long time. Du On Thursday, June 18, 2015 1:00 AM, Al M alasdair.mcbr...@gmail.com wrote: Thanks for the suggestion. Repartition didn't help us unfortunately. It still puts everything into the same partition. We did manage to improve the situation by making a new partitioner that extends HashPartitioner. It treats certain exception keys differently. These keys that are known to appear very often are assigned random partitions instead of using the existing partitioning mechanism. -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Shuffle-produces-one-huge-partition-and-many-tiny-partitions-tp23358p23387.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: Shuffle produces one huge partition and many tiny partitions
Thanks for the suggestion. Repartition didn't help us unfortunately. It still puts everything into the same partition. We did manage to improve the situation by making a new partitioner that extends HashPartitioner. It treats certain exception keys differently. These keys that are known to appear very often are assigned random partitions instead of using the existing partitioning mechanism. -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Shuffle-produces-one-huge-partition-and-many-tiny-partitions-tp23358p23387.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
