Hi Arkadiusz,
the partitionBy is not designed to have many distinct value the last time I
used it. If you search in the mailing list, I think there are couple of
people also face similar issues. For example, in my case, it won't work
over a million distinct user ids. It will require a lot of memory and very
long time to read the table back.
Best Regards,
Jerry
On Thu, Jan 14, 2016 at 2:31 PM, Arkadiusz Bicz <arkadiusz.b...@gmail.com>
wrote:
> Hi
>
> What is the proper configuration for saving parquet partition with
> large number of repeated keys?
>
> On bellow code I load 500 milion rows of data and partition it on
> column with not so many different values.
>
> Using spark-shell with 30g per executor and driver and 3 executor cores
>
>
> sqlContext.read.load("hdfs://notpartitioneddata").write.partitionBy("columnname").parquet("partitioneddata")
>
>
> Job failed because not enough memory in executor :
>
> WARN YarnSchedulerBackend$YarnSchedulerEndpoint: Container killed by
> YARN for exceeding memory limits. 43.5 GB of 43.5 GB physical memory
> used. Consider boosting spark.yarn.executor.memoryOverhead.
> 16/01/14 17:32:38 ERROR YarnScheduler: Lost executor 11 on
> datanode2.babar.poc: Container killed by YARN for exceeding memory
> limits. 43.5 GB of 43.5 GB physical memory used. Consider boosting
> spark.yarn.executor.memoryOverhead.
>
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