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https://issues.apache.org/jira/browse/PIG-2852?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Cheolsoo Park updated PIG-2852:
-------------------------------
Status: Patch Available (was: Open)
> Old Hadoop API being used sometimes when running parellel in local mode
> -----------------------------------------------------------------------
>
> Key: PIG-2852
> URL: https://issues.apache.org/jira/browse/PIG-2852
> Project: Pig
> Issue Type: Bug
> Affects Versions: 0.10.0, 0.9.2
> Environment: Hadoop 0.20.205.0
> Reporter: Brian Tan
> Assignee: Cheolsoo Park
> Attachments: 1.pig, 1.py, generate_1_txt.py, PIG-2852.patch
>
>
> When I run parallel jobs in local mode, I sometimes/randomly see the
> following error:
> java.lang.ClassCastException:
> org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.PigSplit cannot
> be cast to org.apache.hadoop.mapred.InputSplit
> at org.apache.hadoop.mapred.MapTask.runOldMapper(MapTask.java:413)
> at org.apache.hadoop.mapred.MapTask.run(MapTask.java:373)
> at
> org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:212)
> Note the method runOldMapper. Pig sets mapred.mapper.new-api to true in
> JobControlCompiler.java, so I'm not sure why this can happen (sometimes)
> Steps to reproduce:
> 1) Create a 10 million line large file '1.txt'
> fout = open('1.txt', 'w')
> for i in range(1000 * 1000 * 10):
> fout.write('%s\n' % ('\t'.join(str(x) for x in range(10))))
> fout.close()
> 2) Create pig source '1.pig'
> data = load '1.txt' as (a1:int, a2:int, a3:int, a4:int, a5:int, a6:int,
> a7:int, a8:int, a9:int, a10:int);
> grouped = group data all;
> stats = foreach grouped generate '$METRIC' as metric, MIN(data.$METRIC) as
> min_value, MAX(data.$METRIC) as max_value;
> dump stats;
> 3) Create python controller '1.py'
> from org.apache.pig.scripting import Pig
> def runMultiple():
> compiled = Pig.compileFromFile('1.pig')
> params = [{'METRIC' : metric} for metric in ['a3', 'a5', 'a7']]
> bound = compiled.bind(params)
> results = bound.run()
> for result in results:
> if result.isSuccessful():
> itr = result.result('stats').iterator()
> print itr.next().getAll()
> else:
> print 'Failed'
> if __name__ == "__main__":
> runMultiple()
> 4) pig -x local 1.py
> You may need to run it a few times to get the error (it's only in stderr
> output, not even in pig logs). The occurrence seems to be random (sometimes
> the parallel jobs can succeed). Maybe caused by multi-threading complexities?
> I tried pseudo distributed mode but haven't seen this error in that mode.
> This has never happened in non-parallel job execution.
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