Book Image

Hadoop Real-World Solutions Cookbook - Second Edition

By : Tanmay Deshpande
Book Image

Hadoop Real-World Solutions Cookbook - Second Edition

By: Tanmay Deshpande

Overview of this book

Big data is the current requirement. Most organizations produce huge amount of data every day. With the arrival of Hadoop-like tools, it has become easier for everyone to solve big data problems with great efficiency and at minimal cost. Grasping Machine Learning techniques will help you greatly in building predictive models and using this data to make the right decisions for your organization. Hadoop Real World Solutions Cookbook gives readers insights into learning and mastering big data via recipes. The book not only clarifies most big data tools in the market but also provides best practices for using them. The book provides recipes that are based on the latest versions of Apache Hadoop 2.X, YARN, Hive, Pig, Sqoop, Flume, Apache Spark, Mahout and many more such ecosystem tools. This real-world-solution cookbook is packed with handy recipes you can apply to your own everyday issues. Each chapter provides in-depth recipes that can be referenced easily. This book provides detailed practices on the latest technologies such as YARN and Apache Spark. Readers will be able to consider themselves as big data experts on completion of this book. This guide is an invaluable tutorial if you are planning to implement a big data warehouse for your business.
Table of Contents (18 chapters)
Hadoop Real-World Solutions Cookbook Second Edition
Credits
About the Author
Acknowledgements
About the Reviewer
www.PacktPub.com
Preface
Index

Implementing a user-defined counter in a Map Reduce program


In this recipe, we are going to learn how to add a user-defined counter so that we can keep track of certain events easily.

Getting ready

To perform this recipe, you should have a running Hadoop cluster as well as an eclipse that's similar to an IDE.

How to do it...

After every map reduce execution, you will see a set of system defined counters getting published, such as File System counters, Job counters, and Map Reduce Framework counters. These counters help us understand the execution in detail. They give very detailed information about the number of bytes written to HDFS, read from HDFS, the input given to a map, the output received from a map, and so on. Similar to this information, we can also add our own user-defined counters, which will help us track the execution in a better manner.

In earlier recipes, we considered the use case of log analytics. There can be chances that the input we receive might always not be in the same...