MapReduce reducer ramp down is suboptimal with potential job-hanging issues

Today if we are setting "yarn.app.mapreduce.am.job.reduce.rampup.limit" and "mapreduce.job.reduce.slowstart.completedmaps" then reducers are launched more aggressively. However the calculation to either Ramp up or Ramp down reducer is not done 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 then it 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 it should preempt the reducer one by one with some interval and should ramp up slowly...
  • Preemption of reducers 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.