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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