What is the partition count of the RDD, its possible that you dont have enough memory to store the whole RDD on a single machine. Can you try forcibly repartitioning the RDD & then cacheing.  
Regards
Mayur

On Tue Oct 28 2014 at 1:19:09 AM shahab <shahab.mokari@gmail.com> wrote:
I used Cache followed by a "count" on RDD to ensure that caching is performed.

val rdd = srdd.flatMap(mapProfile_To_Sessions).cache

val count = rdd.count

//so at this point RDD should be cahed ? right? 


On Tue, Oct 28, 2014 at 8:35 AM, Sean Owen <sowen@cloudera.com> wrote:

Did you just call cache()? By itself it does nothing but once an action requires it to be computed it should become cached.

On Oct 28, 2014 8:19 AM, "shahab" <shahab.mokari@gmail.com> wrote:
Hi,

I have a standalone spark , where the executor is set to have 6.3 G memory , as I am using two workers so in total there 12.6 G memory and 4 cores.

I am trying to cache a RDD with approximate size of 3.2 G, but apparently it is not cached as neither I can see  "  BlockManagerMasterActor: Added rdd_XX in memory " nor  the performance of running the tasks is improved

But, why it is not cached when there is enough memory storage?
I tried with smaller RDDs. 1 or 2 G and it works, at least I could see "BlockManagerMasterActor: Added rdd_0_1 in memory" and improvement in results.

Any idea what I am missing in my settings, or... ?

thanks,
/Shahab