RDD.persist() can be useful here.

On 11 December 2014 at 14:34, ankits [via Apache Spark User List] <
ml-node+s1001560n20613...@n3.nabble.com> wrote:
>
> I'm using spark 1.1.0 and am seeing persisted RDDs being cleaned up too
> fast. How can i inspect the size of RDD in memory and get more information
> about why it was cleaned up. There should be more than enough memory
> available on the cluster to store them, and by default, the
> spark.cleaner.ttl is infinite, so I want more information about why this is
> happening and how to prevent it.
>
> Spark just logs this when removing RDDs:
>
> [2014-12-11 01:19:34,006] INFO  spark.storage.BlockManager [] [] -
> Removing RDD 33
> [2014-12-11 01:19:34,010] INFO  pache.spark.ContextCleaner []
> [akka://JobServer/user/context-supervisor/job-context1] - Cleaned RDD 33
> [2014-12-11 01:19:34,012] INFO  spark.storage.BlockManager [] [] -
> Removing RDD 33
> [2014-12-11 01:19:34,016] INFO  pache.spark.ContextCleaner []
> [akka://JobServer/user/context-supervisor/job-context1] - Cleaned RDD 33
>
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-- 
Regards,
Harihar Nahak
BigData Developer
Wynyard
Email:hna...@wynyardgroup.com | Extn: 8019




-----
--Harihar
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