Job Tracker can starve reduces with very large input.
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Key: MAPREDUCE-2684
URL: https://issues.apache.org/jira/browse/MAPREDUCE-2684
Project: Hadoop Map/Reduce
Issue Type: Bug
Components: jobtracker
Affects Versions: 0.20.204.0
Reporter: Robert Joseph Evans
Assignee: Robert Joseph Evans
If mapreduce.reduce.input.limit is mis-configured or if a cluster is just
running low on disk space in general then reduces with large a input may never
get scheduled causing the Job to never fail and never succeed, just starve
until the job is killed.
The JobInProgess tries to guess at the size of the input to all reducers in a
job. If the size is over mapreduce.reduce.input.limit then the job is killed.
If it is not then findNewReduceTask() checks to see if the estimated size is
too big to fit on the node currently looking for work. If it is not then it
will let some other task have a chance at the slot.
The idea is to keep track of how often it happens that a Reduce Slot is
rejected because of the lack of space vs how often it succeeds and then guess
if the reduce tasks will ever be scheduled.
So I would like some feedback on this.
1) How should we guess. Someone who found the bug here suggested P1 + (P2 *
S), where S is the number of successful assignments. Possibly P1 = 20 and P2 =
2.0. I am not really sure.
2) What should we do when we guess that it will never get a slot? Should we
fail the job or do we say, even though it might fail, well lets just schedule
the it and see if it really will fail.
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