Re: Larger heap leads to perf degradation due to GC

l. This seems to be general issues in JVM with very large heaps. >>> I agree that the best workaround would be to keep the heap size below 32GB. >>> Thanks guys! >>> >>> Mingyu >>> >>> From: Arun Ahuja >>> Date: Monday, October 6, 2014 at 7

Re: Larger heap leads to perf degradation due to GC

eap size below 32GB. >> Thanks guys! >> >> Mingyu >> >> From: Arun Ahuja >> Date: Monday, October 6, 2014 at 7:50 AM >> To: Andrew Ash >> Cc: Mingyu Kim , "user@spark.apache.org" < >> user@spark.apache.org>, Dennis Lawler

Re: Larger heap leads to perf degradation due to GC

gt; From: Arun Ahuja > Date: Monday, October 6, 2014 at 7:50 AM > To: Andrew Ash > Cc: Mingyu Kim , "user@spark.apache.org" < > user@spark.apache.org>, Dennis Lawler > Subject: Re: Larger heap leads to perf degradation due to GC > > We have used the strategy

Re: Larger heap leads to perf degradation due to GC

; , Dennis Lawler Subject: Re: Larger heap leads to perf degradation due to GC We have used the strategy that you suggested, Andrew - using many workers per machine and keeping the heaps small (< 20gb). Using a large heap resulted in workers hanging or not responding (leading to timeout

Re: Larger heap leads to perf degradation due to GC

We have used the strategy that you suggested, Andrew - using many workers per machine and keeping the heaps small (< 20gb). Using a large heap resulted in workers hanging or not responding (leading to timeouts). The same dataset/job for us will fail (most often due to akka disassociated or fetch

Re: Larger heap leads to perf degradation due to GC

Hi Mingyu, Maybe we should be limiting our heaps to 32GB max and running multiple workers per machine to avoid large GC issues. For a 128GB memory, 32 core machine, this could look like: SPARK_WORKER_INSTANCES=4 SPARK_WORKER_MEMORY=32 SPARK_WORKER_CORES=8 Are people running with large (32GB+) e