Hi, I am trying to implement machine learning algorithms on Spark. I am working on a 3 node cluster, with each node having 5GB of memory. Whenever I am working with slightly more number of records, I end up with OutOfMemory Error. Problem is, even if number of records is slightly high, the intermediate result from a transformation is huge and this results in OutOfMemory Error. To overcome this, we are partitioning the data such that each partition has only a few records.
Is there any better way to fix this issue. Some thing like spilling the intermediate data to local disk? Thanks, Ghousia. -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/OutOfMemory-Error-tp12275.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
