MapReduce v2 (MRv2/YARN) is designed to address which two issues?

Question

Options

• ASingle point of failure in the NameNode.
• BResource pressure on the JobTracker.
• CHDFS latency.
• DAbility to run frameworks other than MapReduce, such as MPI.
• EReduce complexity of the MapReduce APIs.
• FStandardize on a single MapReduce API.

• A
  6% (3)
• B
  75% (36)
• C
  13% (6)
• E
  4% (2)
• F
  2% (1)

Explanation

YARN (Yet Another Resource Negotiator), as an aspect of Hadoop, has two major kinds of * (D) The ability to use programming frameworks other than MapReduce. / MPI (Message Passing Interface) was mentioned as a paradigmatic example of a MapReduce * Scalability, no matter what programming framework you use. * The fundamental idea of MRv2 is to split up the two major functionalities of the JobTracker, resource management and job scheduling/monitoring, into separate daemons. The idea is to have a global ResourceManager (RM) and per-application ApplicationMaster (AM). An application is either a single job in the classical sense of Map-Reduce jobs or a DAG of jobs. * (B) The central goal of YARN is to clearly separate two things that are unfortunately smushed together in current Hadoop, specifically in (mainly) JobTracker: / Monitoring the status of the cluster with respect to which nodes have which resources available. Under YARN, this will be global. / Managing the parallelization execution of any specific job. Under YARN, this will be done separately for each job. The current Hadoop MapReduce system is fairly scalable -- Yahoo runs 5000 Hadoop jobs, truly concurrently, on a single cluster, for a total 1.5 - 2 millions jobs/cluster/month. Still, YARN will remove scalability bottlenecks

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