Hi, Tyson,
Could you open a new JIRA with correctness label? SPARK-23207 might not
cover all the scenarios, especially when you using cache.
Cheers,
Xiao
On Fri, Aug 9, 2019 at 9:26 AM <tcon...@gmail.com> wrote:
> Hi Sean,
>
> To finish the job, I did need to set spark.stage.maxConsecutiveAttempts to
> a large number e.g., 100; a suggestion from Jiang Xingbo.
>
> I haven't seen any recent movement/PRs on this issue, but I'll see if we
> can repro with a more recent version of Spark.
>
> Best regards,
> Tyson
>
> -----Original Message-----
> From: Sean Owen <sro...@gmail.com>
> Sent: Friday, August 9, 2019 7:49 AM
> To: tcon...@gmail.com
> Cc: dev <dev@spark.apache.org>
> Subject: Re: [SPARK-23207] Repro
>
> Interesting but I'd put this on the JIRA, and also test vs master first.
> It's entirely possible this is something else that was subsequently fixed,
> and maybe even backported for 2.4.4.
> (I can't quite reproduce it - just makes the second job fail, which is
> also puzzling)
>
> On Fri, Aug 9, 2019 at 8:11 AM <tcon...@gmail.com> wrote:
> >
> > Hi,
> >
> >
> >
> > We are able to reproduce this bug in Spark 2.4 using the following
> program:
> >
> >
> >
> > import scala.sys.process._
> >
> > import org.apache.spark.TaskContext
> >
> >
> >
> > val res = spark.range(0, 10000 * 10000, 1).map{ x => (x % 1000,
> > x)}.repartition(20)
> >
> > res.distinct.count
> >
> >
> >
> > // kill an executor in the stage that performs repartition(239)
> >
> > val df = res.repartition(113).cache.repartition(239).map { x =>
> >
> > if (TaskContext.get.attemptNumber == 0 &&
> > TaskContext.get.partitionId < 1) {
> >
> > throw new Exception("pkill -f java".!!)
> >
> > }
> >
> > x
> >
> > }
> >
> > df.distinct.count()
> >
> >
> >
> > The first df.distinct.count correctly produces 100000000
> >
> > The second df.distinct.count incorrect produces 99999769
> >
> >
> >
> > If the cache step is removed then the bug does not reproduce.
> >
> >
> >
> > Best regards,
> >
> > Tyson
> >
> >
>
>
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