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Hadoop: The Definitive Guide, 4th Edition by Tom White

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Chapter 7. How MapReduce Works

In this chapter, we look at how MapReduce in Hadoop works in detail. This knowledge provides a good foundation for writing more advanced MapReduce programs, which we will cover in the following two chapters.

Anatomy of a MapReduce Job Run

You can run a MapReduce job with a single method call: submit() on a Job object (you can also call waitForCompletion(), which submits the job if it hasn’t been submitted already, then waits for it to finish).[51] This method call conceals a great deal of processing behind the scenes. This section uncovers the steps Hadoop takes to run a job.

The whole process is illustrated in Figure 7-1. At the highest level, there are five independent entities:[52]

  • The client, which submits the MapReduce job.

  • The YARN resource manager, which coordinates the allocation of compute resources on the cluster.

  • The YARN node managers, which launch and monitor the compute containers on machines in the cluster.

  • The MapReduce application master, which coordinates the tasks running the MapReduce job. The application master and the MapReduce tasks run in containers that are scheduled by the resource manager and managed by the node managers.

  • The distributed filesystem (normally HDFS, covered in Chapter 3), which is used for sharing job files between the other entities.

How Hadoop runs a MapReduce job
Figure 7-1. How Hadoop runs a MapReduce job

Job Submission

The submit() method on Job ...

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