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

Map Reduce program to find the top X


In this recipe, we are going to learn how to write a map reduce program to find the top X records from the given set of values.

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...

A lot of the time, we might need to find the top X values from the given set of values. A simple example could be to find the top 10 trending topics from a Twitter dataset. In this case, we will need to use two map reduce jobs. First of all, find out all the words that start with # and the number of times each hashtag has occurred in a given set of data. The first map reduce program is quite simple, which is pretty similar to the word count program. But for the second program, we need to use some logic. In this recipe, we'll explore how we can write a map reduce program to find the top X values from the given set. Now, though, lets try to understand the logic behind this.

As shown in the preceding...