I believe spark.shuffle.memoryFraction is the one you are looking for. spark.shuffle.memoryFraction : Fraction of Java heap to use for aggregation and cogroups during shuffles, if spark.shuffle.spill is true. At any given time, the collective size of all in-memory maps used for shuffles is bounded by this limit, beyond which the contents will begin to spill to disk. If spills are often, consider increasing this value at the expense of spark.storage.memoryFraction.
You can give it a try. Thanks Best Regards On Mon, Aug 18, 2014 at 12:21 PM, Ghousia <ghousia.ath...@gmail.com> wrote: > Thanks for the answer Akhil. We are right now getting rid of this issue by > increasing the number of partitions. And we are persisting RDDs to > DISK_ONLY. But the issue is with heavy computations within an RDD. It would > be better if we have the option of spilling the intermediate transformation > results to local disk (only in case if memory consumption is high) . Do we > have any such option available with Spark? If increasing the partitions is > the only the way, then one might end up with OutOfMemory Errors, when > working with certain algorithms where intermediate result is huge. > > > On Mon, Aug 18, 2014 at 12:02 PM, Akhil Das <ak...@sigmoidanalytics.com> > wrote: > >> Hi Ghousia, >> >> You can try the following: >> >> 1. Increase the heap size >> <https://spark.apache.org/docs/0.9.0/configuration.html> >> 2. Increase the number of partitions >> <http://stackoverflow.com/questions/21698443/spark-best-practice-for-retrieving-big-data-from-rdd-to-local-machine> >> 3. You could try persisting the RDD to use DISK_ONLY >> <http://spark.apache.org/docs/latest/programming-guide.html#rdd-persistence> >> >> >> >> Thanks >> Best Regards >> >> >> On Mon, Aug 18, 2014 at 10:40 AM, Ghousia Taj <ghousia.ath...@gmail.com> >> wrote: >> >>> 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 >>> >>> >> >
