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

Time for action – killing a TaskTracker process


We've abused HDFS and its DataNode enough; now let's see what damage we can do to MapReduce by killing some TaskTracker processes.

Though there is an mradmin command, it does not give the sort of status reports we are used to with HDFS. So we'll use the MapReduce web UI (located by default on port 50070 on the JobTracker host) to monitor the MapReduce cluster health.

Perform the following steps:

  1. Ensure everything is running via the start-all.sh script then point your browser at the MapReduce web UI. The page should look like the following screenshot:

  2. Start a long-running MapReduce job; the example pi estimator with large values is great for this:

    $ Hadoop jar Hadoop/Hadoop-examples-1.0.4.jar pi 2500 2500
    
  3. Now log onto a cluster node and use jps to identify the TaskTracker process:

    $ jps
    21822 TaskTracker
    3918 Jps
    3891 DataNode
    
  4. Kill the TaskTracker process:

    $ kill -9 21822
    
  5. Verify that the TaskTracker is no longer running:

    $jps
    3918 Jps
    3891 DataNode...