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
