Book Image

Hadoop Beginner's Guide

Book Image

Hadoop Beginner's Guide

Overview of this book

Data is arriving faster than you can process it and the overall volumes keep growing at a rate that keeps you awake at night. Hadoop can help you tame the data beast. Effective use of Hadoop however requires a mixture of programming, design, and system administration skills."Hadoop Beginner's Guide" removes the mystery from Hadoop, presenting Hadoop and related technologies with a focus on building working systems and getting the job done, using cloud services to do so when it makes sense. From basic concepts and initial setup through developing applications and keeping the system running as the data grows, the book gives the understanding needed to effectively use Hadoop to solve real world problems.Starting with the basics of installing and configuring Hadoop, the book explains how to develop applications, maintain the system, and how to use additional products to integrate with other systems.While learning different ways to develop applications to run on Hadoop the book also covers tools such as Hive, Sqoop, and Flume that show how Hadoop can be integrated with relational databases and log collection.In addition to examples on Hadoop clusters on Ubuntu uses of cloud services such as Amazon, EC2 and Elastic MapReduce are covered.
Table of Contents (19 chapters)
Hadoop Beginner's Guide
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Getting data out of Hadoop


We said that the data flow between Hadoop and a relational database is rarely a linear single direction process. Indeed the situation where data is processed within Hadoop and then inserted into a relational database is arguably the more common case. We will explore this now.

Writing data from within the reducer

Thinking about how to copy the output of a MapReduce job into a relational database, we find similar considerations as when looking at the question of data import into Hadoop.

The obvious approach is to modify a reducer to generate the output for each key and its associated values and then to directly insert them into a database via JDBC. We do not have to worry about source column partitioning, as with the import case, but do still need to think about how much load we are placing on the database and whether we need to consider timeouts for long-running tasks. In addition, just as with the mapper situation, this approach tends to perform many single queries...