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Apache Oozie by Mohammad Kamrul Islam download in pdf, ePub, iPad

This led to several ad hoc solutions to manage the execution and interdependency of these multiple Hadoop jobs. It should be easy to troubleshot and recover jobs when something goes wrong. The join node waits until every concurrent execution path of the corresponding fork node arrives to it.

Tip Throughout the book

Tip Throughout the book, when referring to a MapReduce, Pig, Hive, or any other type of job that runs one or more MapReduce jobs on a Hadoop cluster, we refer to it as a Hadoop job. At workflow application deployment time, if Oozie detects a cycle in the workflow definition then it fails the deployment. Fork and Join Control Node - The fork and join control nodes are used in pairs and work as follows. Kill Control Node - The kill node allows a workflow job to kill itself.

At workflow application

The configuration section defines the Mapper class, the Reducer class, the input directory, and the output directory for the MapReduce job. Hadoop bundles the IdentityMapper class and IdentityReducer class, so we can use those classes for the example. We mention the job type explicitly only when there is a need to refer to a particular type of job. For now, we just need to know that we can run an Oozie job using the -run option.

Every Apache Oozie workflow definition must have one start node. Multiple MapReduce, Pig, or Hive jobs often need to be chained together, producing and consuming intermediate data and coordinating their flow of execution. Alejandro was in India at that time, and it seemed appropriate to use the Hindi name for elephant keeper, mahout. At runtime, these variables will be replaced with the actual values of these parameters. Something along the lines of an elephant keeper sounded ideal given that Hadoop was named after a stuffed toy elephant.