Apache Oozie -Part 4: Oozie workflow with java mapreduce action

What's covered in the blog?

1. Documentation on the Oozie map reduce action
2. A sample workflow that includes oozie map-reduce action to process some syslog generated log files.  Instructions on loading sample data and running the workflow are provided, along with some notes based on my learnings.

Versions covered:
Oozie 3.3.0; Map reduce new API

Related blogs:

Blog 1: Oozie workflow - hdfs and email actions
Blog 2: Oozie workflow - hdfs, email and hive actions
Blog 3: Oozie workflow - sqoop action (Hive-mysql; sqoop export)
Blog 4: Oozie workflow - java map-reduce (new API) action
Blog 5: Oozie workflow - streaming map-reduce (python) action 
Blog 6: Oozie workflow - java main action
Blog 7: Oozie workflow - Pig action
Blog 8: Oozie sub-workflow
Blog 9a: Oozie coordinator job - time-triggered sub-workflow, fork-join control and decision control
Blog 9b: Oozie coordinator jobs - file triggered 
Blog 9c: Oozie coordinator jobs - dataset availability triggered
Blog 10: Oozie bundle jobs
Blog 11: Oozie Java API for interfacing with oozie workflows
Blog 12: Oozie workflow - shell action +passing output from one action to another
Blog 13: Oozie workflow - SSH action

Your thoughts/updates:
If you want to share your thoughts/updates, email me at airawat.blog@gmail.com.

About the Oozie MapReduce action
Excerpt from Apache Oozie documentation...

The map-reduce action starts a Hadoop map/reduce job from a workflow. Hadoop jobs can be Java Map/Reduce jobs or streaming jobs.

A map-reduce action can be configured to perform file system cleanup and directory creation before starting the map reduce job. This capability enables Oozie to retry a Hadoop job in the situation of a transient failure (Hadoop checks the non-existence of the job output directory and then creates it when the Hadoop job is starting, thus a retry without cleanup of the job output directory would fail).

The workflow job will wait until the Hadoop map/reduce job completes before continuing to the next action in the workflow execution path.

The counters of the Hadoop job and job exit status (=FAILED=, KILLED or SUCCEEDED ) must be available to the workflow job after the Hadoop jobs ends. This information can be used from within decision nodes and other actions configurations.

The map-reduce action has to be configured with all the necessary Hadoop JobConf properties to run the Hadoop map/reduce job.

Hadoop JobConf properties can be specified in a JobConf XML file bundled with the workflow application or they can be indicated inline in the map-reduce action configuration.

The configuration properties are loaded in the following order, streaming , job-xml and configuration , and later values override earlier values.

Streaming and inline property values can be parameterized (templatized) using EL expressions.

The Hadoop mapred.job.tracker and fs.default.name properties must not be present in the job-xml and inline configuration.


Apache Oozie documentation:
http://oozie.apache.org/docs/3.3.0/WorkflowFunctionalSpec.html#a3.2.2_Map-Reduce_Action


Components of a workflow with java map reduce action:

Sample workflow

Highlights

The sample workflow application runs a java map reduce program that parses log files (syslog generated) in HDFS and generates a report on the same.

The following is a pictorial representation of the workflow.

Workflow application details

Oozie web GUI - screenshots

http://YourOozieServer:TypicallyPort11000/oozie/

Do share, if you have any additional insights that can be addd to the blog.

References

Map reduce cookbook
https://cwiki.apache.org/OOZIE/map-reduce-cookbook.html

How to use a sharelib in Oozie
http://blog.cloudera.com/blog/2012/12/how-to-use-the-sharelib-in-apache-oozie/

Everything-you-wanted-to-know-but-were-afraid-to-ask-about-oozie

http://www.slideshare.net/ChicagoHUG/everything-you-wanted-to-know-but-were-afraid-to-ask-about-oozie

Oozie workflow use cases

https://github.com/yahoo/oozie/wiki/Oozie-WF-use-cases