ccah-500 第57题 Which two best describes how FIFO Scheduler arbitrates the cluster resources for job

57.You have a cluster running with a FIFO scheduler enabled. You submit a large job A to the cluster, which you expect to run for one hour. Then, you submit job B to the cluster,
which you expect to run a couple of minutes only.You submit both jobs with the same priority.

Which two best describes how FIFO Scheduler arbitrates the cluster resources for job and its tasks?(Choose two)

A. Because there is a more than a single job on the cluster, the FIFO Scheduler will enforce a limit on the percentage of resources allocated to a particular job at any given
time

B. Tasks are scheduled on the order of their job submission

C. The order of execution of job may vary

D. Given job A and submitted in that order, all tasks from job A are guaranteed to finish before all tasks from job B

E. The FIFO Scheduler will give, on average, and equal share of the cluster resources over the job lifecycle

F. The FIFO Scheduler will pass an exception back to the client when Job B is submitted, since all slots on the cluster are use

Answer: A,D -->B D

 

reference:
http://stackoverflow.com/questions/7483624/running-jobs-parallely-in-hadoop http://www.aiotestking.com/cloudera/which-two-best-describes-how-fifo-scheduler-arbitrates-the-cluster-resources-for-job-and-its-tasks-2/
 

oreily:

 

Three schedulers are available in YARN: the FIFO, Capacity, and Fair Schedulers. The

FIFO Scheduler places applications in a queue and runs them in the order of submission

(first in, first out). Requests for the first application in the queue are allocated first; once

its requests have been satisfied, the next application in the queue is served, and so on.

 

The FIFO Scheduler has the merit of being simple to understand and not needing any

configuration, but it's not suitable for shared clusters. Large applications will use all the

resources in a cluster, so each application has to wait its turn. On a shared cluster it is

better to use the Capacity Scheduler or the Fair Scheduler. Both of these allow long-

running jobs to complete in a timely manner, while still allowing users who are running

concurrent smaller ad hoc queries to get results back in a reasonable time.

 

With the Capacity Scheduler (ii in Figure 4-3), a separate dedicated queue allows the small

job to start as soon as it is submitted, although this is at the cost of overall cluster

utilization since the queue capacity is reserved for jobs in that queue. This means that the

large job finishes later than when using the FIFO Scheduler.

 

With the Fair Scheduler (iii in Figure 4-3), there is no need to reserve a set amount of

capacity, since it will dynamically balance resources between all running jobs. Just after

the first (large) job starts, it is the only job running, so it gets all the resources in the

cluster. When the second (small) job starts, it is allocated half of the cluster resources so

that each job is using its fair share of resources.

Note that there is a lag between the time the second job starts and when it receives its fair

share, since it has to wait for resources to free up as containers used by the first job

complete. After the small job completes and no longer requires resources, the large job

goes back to using the full cluster capacity again. The overall effect is both high cluster

utilization and timely small job completion