Thanks for the suggestion Arun, I hadn't seen these params before. No way to do it for a job in flight though I guess?
Cheers, Mat On 20 November 2011 16:43, Arun C Murthy <a...@hortonworks.com> wrote: > Mat, > > Take a look at mapred.max.(map|reduce).failures.percent. > > See: > http://hadoop.apache.org/common/docs/r0.20.205.0/api/org/apache/hadoop/mapred/JobConf.html#setMaxMapTaskFailuresPercent(int) > http://hadoop.apache.org/common/docs/r0.20.205.0/api/org/apache/hadoop/mapred/JobConf.html#setMaxReduceTaskFailuresPercent(int) > > hth, > Arun > > On Nov 20, 2011, at 1:31 PM, Mat Kelcey wrote: > >> Hi, >> >> I have a largish job running that, due to the quirks of the third >> party input format I'm using, has 280,000 map tasks. ( I know this is >> far from ideal but it's it'll do for me ) >> >> I'm passing this data (the common crawl web crawl dataset) through a >> visible-text-from-html extraction library (boilerpipe) which is >> struggling with _1_ particular task. It's hits a sequence of records >> that are _insanely_ slow to parse for some reason. Rather than a few >> minutes per split it's took 7+ hrs before I started explicitly trying >> to fail the task (hadoop job -fail-task). Since I'm running with bad >> record skipping I was hoping I could issue -fail-task a few times and >> ride over the bad records but it looks like there's quite a few there. >> Since it's only 1 of the 280,000 I'm actually happy to just give up on >> the entire split. >> >> Now if I was running a map only job I'd just kill the job since I'd >> have the output of the other 279,999. This job has a no-op reduce step >> though since I wanted to take the chance to compact the output into a >> much smaller number of sequence files ( I regret that decision now) As >> such I can't just kill the job since I'd lose the rest of the >> processed data (if I understand correctly?) >> >> So does anyone know a way to just abandon the entire split? >> >> Cheers, >> Mat > >
