What is Hive: Return Code 2 from org.apache.hadoop.hive.ql.exec.MapRedTask

That's not the real error, here's how to find it:

Go to the hadoop jobtracker web-dashboard, find the hive mapreduce jobs that failed and look at the logs of the failed tasks. That will show you the real error.

The console output errors are useless, largely beause it doesn't have a view of the individual jobs/tasks to pull the real errors (there could be errors in multiple tasks)

Hope that helps.

I know I am 3 years late on this thread, however still providing my 2 cents for similar cases in future.

I recently faced the same issue/error in my cluster. The JOB would always get to some 80%+ reduction and fail with the same error, with nothing to go on in the execution logs either. Upon multiple iterations and research I found that among the plethora of files getting loaded some were non-compliant with the structure provided for the base table(table being used to insert data into partitioned table).

Point to be noted here is whenever I executed a select query for a particular value in the partitioning column or created a static partition it worked fine as in that case error records were being skipped.

TL;DR: Check the incoming data/files for inconsistency in the structuring as HIVE follows Schema-On-Read philosophy.

Adding some information here, as it took me awhile to find the hadoop jobtracker web-dashboard in HDInsight (Azure's Hadoop), and a colleague finally showed me where it was. There is a shortcut on the head node called "Hadoop Yarn Status" which is just a link to a local http page (http://headnodehost:9014/cluster in my case). When opened the dashboard looked like this:

In that dashboard you can find your failed application, and then after clicking into it you can look at the logs of the individual map and reduce jobs.

In my case it seemed to still be running out of memory in the reducers, even though I had cranked the memory in the configuration already. For some reason it was not surfacing the "java outofmemory" errors I got earlier though.

I removed the _SUCCESS file from the EMR output path in S3 and it worked fine.

The top answer is right, that the error code doesn't give you much info. One of the common causes that we saw in our team for this error code was when the query was not optimized well. A known reason was when we do an inner join with the left side table magnitudes bigger than the table on right side. Swapping these tables would usually do the trick in such cases.