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Hadoop Real-World Solutions Cookbook- Second Edition

You're reading from   Hadoop Real-World Solutions Cookbook- Second Edition Over 90 hands-on recipes to help you learn and master the intricacies of Apache Hadoop 2.X, YARN, Hive, Pig, Oozie, Flume, Sqoop, Apache Spark, and Mahout

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Product type Paperback
Published in Mar 2016
Publisher
ISBN-13 9781784395506
Length 290 pages
Edition 2nd Edition
Tools
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Author (1):
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Tanmay Deshpande Tanmay Deshpande
Author Profile Icon Tanmay Deshpande
Tanmay Deshpande
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Table of Contents (12) Chapters Close

Preface 1. Getting Started with Hadoop 2.X FREE CHAPTER 2. Exploring HDFS 3. Mastering Map Reduce Programs 4. Data Analysis Using Hive, Pig, and Hbase 5. Advanced Data Analysis Using Hive 6. Data Import/Export Using Sqoop and Flume 7. Automation of Hadoop Tasks Using Oozie 8. Machine Learning and Predictive Analytics Using Mahout and R 9. Integration with Apache Spark 10. Hadoop Use Cases 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...

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