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

Coordinators


Coordinators allow us to run interdependent Workflows as data pipelines based on some starting criteria. They decide the when part of execution of Oozie job. Most of the Oozie jobs are triggered at a given scheduled time interval or when input Dataset is present for triggering the job. Here are a few important definitions related to Coordinators:

  • Nominal time: This the scheduled time at which job should execute. For example, we process press release every day at 8:00 P.M.

  • Actual time: This is the real time when the job runs. In some cases, if the input data does not arrive, the job might start late. This type of data-dependent job triggering is indicated by the <done-flag> tag (more on this later). The done-flag gives a signal to start the job execution.

The general skeleton template of Coordinator is shown in the following figure named Coordinator template XML:

Coordinator template XML

The <parameters> tag on line 2 in the preceding screenshot are any variables defined...