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From Arun C Murthy <...@hortonworks.com>
Subject Re: is there a way to just abandon a map task?
Date Mon, 21 Nov 2011 01:20:31 GMT

On Nov 20, 2011, at 5:18 PM, Mat Kelcey wrote:

> 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?

Unfortunately, no. You'll need to re-run the job.

Also, you want to use 'bin/mapred job -fail-task <taskattemptid>' 4 times to abandon
the task. If you use '-kill-task' it will continue to be re-run.


> Cheers,
> Mat
> On 20 November 2011 16:43, Arun C Murthy <acm@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

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