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
