Book Image

Apache Oozie Essentials

By : Jagat Singh
Book Image

Apache Oozie Essentials

By: Jagat Singh

Overview of this book

As more and more organizations are discovering the use of big data analytics, interest in platforms that provide storage, computation, and analytic capabilities is booming exponentially. This calls for data management. Hadoop caters to this need. Oozie fulfils this necessity for a scheduler for a Hadoop job by acting as a cron to better analyze data. Apache Oozie Essentials starts off with the basics right from installing and configuring Oozie from source code on your Hadoop cluster to managing your complex clusters. You will learn how to create data ingestion and machine learning workflows. This book is sprinkled with the examples and exercises to help you take your big data learning to the next level. You will discover how to write workflows to run your MapReduce, Pig ,Hive, and Sqoop scripts and schedule them to run at a specific time or for a specific business requirement using a coordinator. This book has engaging real-life exercises and examples to get you in the thick of things. Lastly, you’ll get a grip of how to embed Spark jobs, which can be used to run your machine learning models on Hadoop. By the end of the book, you will have a good knowledge of Apache Oozie. You will be capable of using Oozie to handle large Hadoop workflows and even improve the availability of your Hadoop environment.
Table of Contents (16 chapters)
Apache Oozie Essentials
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Running MapReduce jobs from Oozie


We will see how to write a simple MapReduce job for word count and schedule it via Oozie. Later, we will wrap this in our first Coordinator job. Along this journey, we will learn some concepts and apply them in examples.

I have already saved one word count Java MapReduce code, which we will try to run over our input data. Let's dive into the code. You can check out the mapreduce folder in Book_Code_Folder/learn_oozie/ch04/.

Note

Check the workflow_0.5.xsd file in the xsd_svg folder and note the inputs needed for the MapReduce action to run.

The Workflow is shown in the following code and we can see the arguments are the same as the one we need in the Hadoop jar command for running a MapReduce job. At the start of the job, we delete the output folder as Hadoop fails the job if the output folder already exists.

The mapper that we need is life.jugnu.learnoozie.ch04.WordCountMapper and the reducer is life.jugnu.learnoozie.ch04.WordCountReducer. Both of them are present...