Bumping this thread, so it gets attention. Grega
On Tue, Nov 26, 2013 at 12:26 PM, Grega Kešpret <[email protected]> wrote: > Also, is there a way to specify to Spark that it shouldn't resubmit failed > stages/tasks, but fail-fast in case any fetch failure occurs? > > Grega > -- > [image: Inline image 1] > *Grega Kešpret* > Analytics engineer > > Celtra — Rich Media Mobile Advertising > celtra.com <http://www.celtra.com/> | > @celtramobile<http://www.twitter.com/celtramobile> > > > On Mon, Nov 25, 2013 at 9:58 AM, Grega Kešpret <[email protected]> wrote: > >> Hi! >> >> We use Spark to process logs in batches and persist the end result in a >> db. Last week, we re-ran the job on the same data couple of times, only to >> find that one run had more results than the rest. Digging through the logs, >> we found out that a task has been lost and marked for resubmission. >> >> I marked the lines here: >> >> https://gist.github.com/gregakespret/7541805#file-spark-fetch-failure-L1432-L1509 >> >> Because of that, one block of data was processed two times and the final >> result was not correct. >> >> My question is how can we catch such occurrences in the code, so that we >> can do an effective rollback/discard the data that will get recomputed? >> >> Thanks, >> >> >> Grega >> -- >> [image: Inline image 1] >> *Grega Kešpret* >> Analytics engineer >> >> Celtra — Rich Media Mobile Advertising >> celtra.com <http://www.celtra.com/> | >> @celtramobile<http://www.twitter.com/celtramobile> >> > >
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