You may also be writing your algorithm in a way that it requires high peak
memory usage.  An example of this could be using .groupByKey() where
.reduceByKey() might suffice instead.  Maybe you can express the algorithm
in a different way that's more efficient?

On Thu, Oct 2, 2014 at 4:30 AM, Sean Owen <so...@cloudera.com> wrote:

> This looks like you are just running your own program. To run Spark
> programs, you use spark-submit. It has options that control the
> executor and driver memory. The settings below are not affecting
> Spark.
>
> On Wed, Oct 1, 2014 at 10:21 PM, 陈韵竹 <anny9...@gmail.com> wrote:
> > Thanks Sean. This is how I set this memory. I set it when I start to run
> the
> > job
> >
> > java -Xms64g -Xmx64g -cp
> >
> /root/spark/lib/spark-assembly-1.0.0-hadoop1.0.4.jar:/root/scala/lib/scala-library.jar:./target/MyProject.jar
> > MyClass
> >
> > Is there some problem with it?
> >
> >
> >
> > On Wed, Oct 1, 2014 at 2:03 PM, Sean Owen <so...@cloudera.com> wrote:
> >>
> >> How are you setting this memory? You may be configuring the wrong
> >> process's memory, like the driver and not the executors.
> >>
> >> On Oct 1, 2014 9:37 PM, "anny9699" <anny9...@gmail.com> wrote:
> >>>
> >>> Hi,
> >>>
> >>> After reading some previous posts about this issue, I have increased
> the
> >>> java heap space to "-Xms64g -Xmx64g", but still met the
> >>> "java.lang.OutOfMemoryError: GC overhead limit exceeded" error. Does
> >>> anyone
> >>> have other suggestions?
> >>>
> >>> I am reading a data of 200 GB and my total memory is 120 GB, so I use
> >>> "MEMORY_AND_DISK_SER" and kryo serialization.
> >>>
> >>> Thanks a lot!
> >>>
> >>>
> >>>
> >>> --
> >>> View this message in context:
> >>>
> http://apache-spark-user-list.1001560.n3.nabble.com/still-GC-overhead-limit-exceeded-after-increasing-heap-space-tp15540.html
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> Nabble.com.
> >>>
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