If you make your driver memory too low it is likely you are going to hit
You have not mentioned with Spark mode you are using (Local, Standalone,
Dr Mich Talebzadeh
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On 19 September 2016 at 23:48, Anand Viswanathan <
> Thank you so much, Kevin.
> My data size is around 4GB.
> I am not using collect(), take() or takeSample()
> At the final job, number of tasks grows up to 200,000
> Still the driver crashes with OOM with default —driver-memory 1g but Job
> succeeds if i specify 2g.
> Thanks and regards,
> Anand Viswanathan
> On Sep 19, 2016, at 4:00 PM, Kevin Mellott <kevin.r.mell...@gmail.com>
> Hi Anand,
> Unfortunately, there is not really a "one size fits all" answer to this
> question; however, here are some things that you may want to consider when
> trying different sizes.
> - What is the size of the data you are processing?
> - Whenever you invoke an action that requires ALL of the data to be
> sent to the driver (such as collect), you'll need to ensure that your
> memory setting can handle it.
> - What level of parallelization does your code support? The more
> processing you can do on the worker nodes, the less your driver will need
> to do.
> Related to these comments, keep in mind that the --executor-memory,
> --num-executors, and --executor-cores configurations can be useful when
> tuning the worker nodes. There is some great information in the Spark
> Tuning Guide (linked below) that you may find useful as well.
> Hope that helps!
> On Mon, Sep 19, 2016 at 9:32 AM, Anand Viswanathan <
> anand_v...@ymail.com.invalid> wrote:
>> Spark version :spark-1.5.2-bin-hadoop2.6 ,using pyspark.
>> I am running a machine learning program, which runs perfectly by
>> specifying 2G for —driver-memory.
>> However the program cannot be run with default 1G, driver crashes with
>> OOM error.
>> What is the recommended configuration for —driver-memory…? Please suggest.
>> Thanks and regards,