Omkar Vinit Joshi created MAPREDUCE-5507:
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Summary: MapReduce reducer preemption gets hanged
Key: MAPREDUCE-5507
URL: https://issues.apache.org/jira/browse/MAPREDUCE-5507
Project: Hadoop Map/Reduce
Issue Type: Bug
Reporter: Omkar Vinit Joshi
Today if we are setting "yarn.app.mapreduce.am.job.reduce.rampup.limit" and
"mapreduce.job.reduce.slowstart.completedmaps" then reducer are launched more
aggressively. However the calculation to either Ramp up or Ramp down reducer is
not down in most optimal way.
* If MR AM at any point sees situation something like
** scheduledMaps : 30
** scheduledReducers : 10
** assignedMaps : 0
** assignedReducers : 11
** finishedMaps : 120
** headroom : 756 ( when your map /reduce task needs only 512mb)
* then today it simply hangs because it thinks that there is sufficient room to
launch one more mapper and therefore there is no need to ramp down. However, if
this continues forever then this is not the correct way / optimal way.
* Ideally for MR AM when it sees that assignedMaps drops have dropped to 0 and
there are running reducers around should wait for certain time ( upper limited
by average map task completion time ... for heuristic sake)..but after that if
still it doesn't get new container for map task then should preempt the reducer
one by one with some interval and should ramp up slowly...
** Preemption of reducer can be done in little smarter way
*** preempt reducer on a node manager for which there is any pending map
request.
*** otherwise preempt any other reducer. MR AM will contribute to getting new
mapper by releasing such a reducer / container because it will reduce its
cluster consumption and thereby may become candidate for an allocation.
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