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 – changing job priorities and killing a job


Let's explore job priorities by changing them dynamically and watching the result of killing a job.

  1. Start a relatively long-running job on the cluster.

    $ hadoop jar hadoop-examples-1.0.4.jar pi 100 1000
    
  2. Open another window and submit a second job.

    $ hadoop jar hadoop-examples-1.0.4.jar wordcount test.txt out1
    
  3. Open another window and submit a third.

    $ hadoop jar hadoop-examples-1.0.4.jar wordcount test.txt out2
    
  4. List the running jobs.

    $ Hadoop job -list
    

    You'll see the following lines on the screen:

    3 jobs currently running
    JobId  State  StartTime  UserName  Priority  SchedulingInfo
    job_201201111540_0005  1  1326325810671  hadoop  NORMAL  NA
    job_201201111540_0006  1  1326325938781  hadoop  NORMAL  NA
    job_201201111540_0007  1  1326325961700  hadoop  NORMAL  NA
    
  5. Check the status of the running job.

    $ Hadoop job -status job_201201111540_0005
    

    You'll see the following lines on the screen:

    Job: job_201201111540_0005
    file: hdfs://head:9000...