Task preemption is necessary in a multi-user Hadoop cluster for two reasons: users might submit long-running tasks by mistake (e.g. an infinite loop in a map program), or tasks may be long due to having to process large amounts of data. The Fair Scheduler (
HADOOP-3746) has a concept of guaranteed capacity for certain queues, as well as a goal of providing good performance for interactive jobs on average through fair sharing. Therefore, it will support preempting under two conditions:
1) A job isn't getting its guaranteed share of the cluster for at least T1 seconds.
2) A job is getting significantly less than its fair share for T2 seconds (e.g. less than half its share).
T1 will be chosen smaller than T2 (and will be configurable per queue) to meet guarantees quickly. T2 is meant as a last resort in case non-critical jobs in queues with no guaranteed capacity are being starved.
When deciding which tasks to kill to make room for the job, we will use the following heuristics:
- Look for tasks to kill only in jobs that have more than their fair share, ordering these by deficit (most overscheduled jobs first).
- For maps: kill tasks that have run for the least amount of time (limiting wasted time).
- For reduces: similar to maps, but give extra preference for reduces in the copy phase where there is not much map output per task (at Facebook, we have observed this to be the main time we need preemption - when a job has a long map phase and its reducers are mostly sitting idle and filling up slots).