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

Spark action


The Spark action has been recently added in Oozie and the general XSD is shown in the following figure:

Spark SVG action

The general schema is as follows:

<action>
  <job-tracker>        // Job tracker details
  <name-node>          // Name node details
  <prepare>            // Create or Delete directory
  <job-xml>            // Any job xml properties
  <configuration>      // Hadoop job configuration
  <master>             // Spark master details
  <mode>               // Spark driver mode
  <name>               // Spark Job name
  <class>              // Spark main class
  <spark-opts>         // Spark Job options
  <arg>                // Arguments for the job
</action>

The <master> element tells about the URL of Spark master. Spark can run in different cluster configurations, namely Spark standalone, Mesos, and Yarn. Depending on which cluster manager you are using, the master URL will change...