You has a cluster running with the Fail Scheduler enabled. There are currently no jobs running on
the cluster you submit a job A, so that only job A is running on the cluster. A while later, you
submit job B. Now job A and Job B are running on the cluster al the same time. How will the Fair’
Scheduler handle these two Jobs?
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A.
When job A gets submitted, it consumes all the task slot
B.
When job A gets submitted, it doesn’t consume all the task slot
C.
When job B gets submitted, Job A has to finish first, before job it can get scheduled.
D.
When job B gets submitted, it will get assigned tasks, while job A continues to run with fewer
tasks.
Explanation:
Fair scheduling is a method of assigning resources to jobs such that all jobs get, on
average, an equal share of resources over time. When there is a single job running, that job uses
the entire cluster. When other jobs are submitted, tasks slots that free up are assigned to the new
jobs, so that each job gets roughly the same amount of CPU time. Unlike the default Hadoop
scheduler, which forms a queue of jobs, this lets short jobs finish in reasonable time while not
starving long jobs. It is also a reasonable way to share a cluster between a number of users.
Finally, fair sharing can also work with job priorities – the priorities are used as weights to
determine the fraction of total compute time that each job should get.
Hadoop, Fair Scheduler Guide
A & D
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