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From Pavel Plotnikov <pavel.plotni...@team.wrike.com>
Subject Re: physical memory usage keep increasing for spark app on Yarn
Date Fri, 20 Jan 2017 10:23:33 GMT
Hi Yang,
i have faced with the same problem on Mesos and to circumvent this issue i
am usually increase partition number. On last step in your code you reduce
number of partitions to 1, try to set bigger value, may be it solve this
problem.

Cheers,
Pavel

On Fri, Jan 20, 2017 at 12:35 PM Yang Cao <cybeater@gmail.com> wrote:

> Hi all,
>
> I am running a spark application on YARN-client mode with 6 executors
> (each 4 cores and executor memory = 6G and Overhead = 4G, spark version:
> 1.6.3 / 2.1.0). I find that my executor memory keeps increasing until get
> killed by node manager; and give out the info that tells me to boost spark.yarn.excutor.memoryOverhead.
> I know that this param mainly control the size of memory allocated
> off-heap. But I don’t know when and how the spark engine will use this part
> of memory. Also increase that part of memory not always solve my
> problem. sometimes works sometimes not. It trends to be useless when the
> input data is large.
>
> FYI, my app’s logic is quite simple. It means to combine the small files
> generated in one single day (one directory one day) into a single one and
> write back to hdfs. Here is the core code:
>
> val df = spark.read.parquet(originpath).filter(s"m = ${ts.month} AND d = ${ts.day}").coalesce(400)
>
> val dropDF = df.drop("hh").drop("mm").drop("mode").drop("y").drop("m").drop("d")
>
> dropDF.repartition(1).write.mode(SaveMode.ErrorIfExists).parquet(targetpath)
>
> The source file may have hundreds to thousands level’s partition. And the
> total parquet file is around 1to 5 gigs. Also I find that in the step that
> shuffle reading data from different machines, The size of shuffle read is
> about 4 times larger than the input size, Which is wired or some principle
> I don’t know.
>
> Anyway, I have done some search myself for this problem. Some article said
> that it’s on the direct buffer memory (I don’t set myself). Some article
> said that people solve it with more frequent full GC. Also I find one
> people on SO with very similar situation:
> http://stackoverflow.com/questions/31646679/ever-increasing-physical-memory-for-a-spark-application-in-yarn
> This guy claimed that it’s a bug with parquet but comment questioned him.
> People in this mail list may also receive an email hours ago from
> blondowski who described this problem while writing json:
> http://apache-spark-user-list.1001560.n3.nabble.com/Executors-running-out-of-memory-tt28325.html#none
>
> So it looks like to be common question for different output format. I hope
> someone with experience about this problem could make an explanation about
> this issue. Why this happen and what is a reliable way to solve this
> problem.
>
> Best,
>
>
>

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