I give the executor 14gb and would like to cut it.
I expect the critical operations to run hundreds of millions of times which is why we run on a cluster. I will try DISK_ONLY_SER
Thanks

Steven Lewis sent from my phone

On May 7, 2015 10:59 AM, "ayan guha" <guha.ayan@gmail.com> wrote:
2*2 cents

1. You can try repartition and give a large number to achieve smaller partitions.
2. OOM errors can be avoided by increasing executor memory or using off heap storageĀ 
3. How are you persisting? You can try using persist using DISK_ONLY_SER storage level
4. You may take a look in the algorithm once more. "Tasks typically preform both operations several hundred thousand times." why it can not be done distributed way?

On Thu, May 7, 2015 at 3:16 PM, Steve Lewis <lordjoe2000@gmail.com> wrote:
I am performing a job where I perform a number of steps in succession.
One step is a map on a JavaRDD which generates objects taking up significant memory.
The this is followed by a join and anĀ aggregateByKey.
The problem is that the system is running getting OutOfMemoryErrors -
Most tasks work but a few fail. Tasks typically preform both operations several hundred thousand times.
I am convinced things would work if the map ran to completion and shuffled results to disk before starting the aggregateByKey.
I tried calling persist and then count on the results of the map to force execution but this does not seem to help. Smaller partitions might also help if these could be forced.
Any ideas?



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Best Regards,
Ayan Guha