Hi, I already posted this question on the users mailing list (http://apache-spark-user-list.1001560.n3.nabble.com/Using-groupByKey-with-many-values-per-key-td24538.html) but did not get a reply. Maybe this is the correct forum to ask.
My problem is, that doing groupByKey().mapToPair() loads all values for a key into memory which is a problem when the values don't fit into memory. This was not a problem with Hadoop map/reduce, as the Iterable passed to the reducer read from disk. In Spark, the Iterable passed to mapToPair() is backed by a CompactBuffer containing all values. Is it possible to change this behavior without modifying Spark, or is there a plan to change this? Thank you very much for your help! Christoph. -- View this message in context: http://apache-spark-developers-list.1001551.n3.nabble.com/groupByKey-and-keys-with-many-values-tp13985.html Sent from the Apache Spark Developers List mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
