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Home > Data > Data Processing > Hadoop Beginner's Guide
Hadoop Beginner's Guide
Hadoop Beginner's Guide

Hadoop Beginner's Guide: Get your mountain of data under control with Hadoop. This guide requires no prior knowledge of the software or cloud services – just a willingness to learn the basics from this practical step-by-step tutorial.

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Hadoop Beginner's Guide

Chapter 2. Getting Hadoop Up and Running

Now that we have explored the opportunities and challenges presented by large-scale data processing and why Hadoop is a compelling choice, it's time to get things set up and running.

In this chapter, we will do the following:

  • Learn how to install and run Hadoop on a local Ubuntu host

  • Run some example Hadoop programs and get familiar with the system

  • Set up the accounts required to use Amazon Web Services products such as EMR

  • Create an on-demand Hadoop cluster on Elastic MapReduce

  • Explore the key differences between a local and hosted Hadoop cluster

Hadoop on a local Ubuntu host

For our exploration of Hadoop outside the cloud, we shall give examples using one or more Ubuntu hosts. A single machine (be it a physical computer or a virtual machine) will be sufficient to run all the parts of Hadoop and explore MapReduce. However, production clusters will most likely involve many more machines, so having even a development Hadoop cluster deployed on multiple hosts will be good experience. However, for getting started, a single host will suffice.

Nothing we discuss will be unique to Ubuntu, and Hadoop should run on any Linux distribution. Obviously, you may have to alter how the environment is configured if you use a distribution other than Ubuntu, but the differences should be slight.

Other operating systems

Hadoop does run well on other platforms. Windows and Mac OS X are popular choices for developers. Windows is supported only as a development platform and Mac OS X is not formally supported at all.

If you choose to use such a platform, the...

Time for action – checking the prerequisites

Hadoop is written in Java, so you will need a recent Java Development Kit (JDK) installed on the Ubuntu host. Perform the following steps to check the prerequisites:

  1. First, check what's already available by opening up a terminal and typing the following:

    $ javac
$ java -version

  2. If either of these commands gives a no such file or directory or similar error, or if the latter mentions "Open JDK", it's likely you need to download the full JDK. Grab this from the Oracle download page at http://www.oracle.com/technetwork/java/javase/downloads/index.html; you should get the latest release.

  3. Once Java is installed, add the JDK/bin directory to your path and set the JAVA_HOME environment variable with commands such as the following, modified for your specific Java version:

    $ export JAVA_HOME=/opt/jdk1.6.0_24
$ export PATH=$JAVA_HOME/bin:${PATH}

What just happened?

These steps ensure the right version of Java is installed and available from the command line...

Time for action – downloading Hadoop

Carry out the following steps to download Hadoop:

  1. Go to the Hadoop download page at http://hadoop.apache.org/common/releases.html and retrieve the latest stable version of the 1.0.x branch; at the time of this writing, it was 1.0.4.

  2. You'll be asked to select a local mirror; after that you need to download the file with a name such as hadoop-1.0.4 -bin.tar.gz.

  3. Copy this file to the directory where you want Hadoop to be installed (for example, /usr/local), using the following command:

    $ cp Hadoop-1.0.4.bin.tar.gz /usr/local

  4. Decompress the file by using the following command:

    $ tar –xf hadoop-1.0.4-bin.tar.gz

  5. Add a convenient symlink to the Hadoop installation directory.

    $ ln -s /usr/local/hadoop-1.0.4 /opt/hadoop

  6. Now you need to add the Hadoop binary directory to your path and set the HADOOP_HOME environment variable, just as we did earlier with Java.

    $ export HADOOP_HOME=/usr/local/Hadoop
$ export PATH=$HADOOP_HOME/bin:$PATH

  7. Go into the conf directory within...

Time for action – setting up SSH

Carry out the following steps to set up SSH:

  1. Create a new OpenSSL key pair with the following commands:

    $ ssh-keygen
Generating public/private rsa key pair.
Enter file in which to save the key (/home/hadoop/.ssh/id_rsa): 
Created directory '/home/hadoop/.ssh'.
Enter passphrase (empty for no passphrase): 
Enter same passphrase again: 
Your identification has been saved in /home/hadoop/.ssh/id_rsa.
Your public key has been saved in /home/hadoop/.ssh/id_rsa.pub.
…

  2. Copy the new public key to the list of authorized keys by using the following command:

    $ cp .ssh/id _rsa.pub  .ssh/authorized_keys

  3. Connect to the local host.

    $ ssh localhost
The authenticity of host 'localhost (127.0.0.1)' can't be established.
RSA key fingerprint is b6:0c:bd:57:32:b6:66:7c:33:7b:62:92:61:fd:ca:2a.
Are you sure you want to continue connecting (yes/no)? yes
Warning: Permanently added 'localhost' (RSA) to the list of known hosts.

  4. Confirm that the password-less SSH is working.

    $ ssh localhost...

Time for action – using Hadoop to calculate Pi

We will now use a sample Hadoop program to calculate the value of Pi. Right now, this is primarily to validate the installation and to show how quickly you can get a MapReduce job to execute. Assuming the HADOOP_HOME/bin directory is in your path, type the following commands:

$ Hadoop jar hadoop/hadoop-examples-1.0.4.jar  pi 4 1000
Number of Maps  = 4
Samples per Map = 1000
Wrote input for Map #0
Wrote input for Map #1
Wrote input for Map #2
Wrote input for Map #3
Starting Job
12/10/26 22:56:11 INFO jvm.JvmMetrics: Initializing JVM Metrics with processName=JobTracker, sessionId=
12/10/26 22:56:11 INFO mapred.FileInputFormat: Total input paths to process : 4
12/10/26 22:56:12 INFO mapred.JobClient: Running job: job_local_0001
12/10/26 22:56:12 INFO mapred.FileInputFormat: Total input paths to process : 4
12/10/26 22:56:12 INFO mapred.MapTask: numReduceTasks: 1
…
12/10/26 22:56:14 INFO mapred.JobClient:  map 100% reduce 100%
12/10/26 22:56:14...

Time for action – configuring the pseudo-distributed mode

Take a look in the conf directory within the Hadoop distribution. There are many configuration files, but the ones we need to modify are core-site.xml, hdfs-site.xml and mapred-site.xml.

  1. Modify core-site.xml to look like the following code:

    <?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>

<!-- Put site-specific property overrides in this file. -->

<configuration>
<property>
<name>fs.default.name</name>
<value>hdfs://localhost:9000</value>
</property>
</configuration>

  2. Modify hdfs-site.xml to look like the following code:

    <?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>

<!-- Put site-specific property overrides in this file. -->

<configuration>
<property>
<name>dfs.replication</name>
<value>1</value>
</property>
</configuration>

  3. Modify mapred...

Time for action – changing the base HDFS directory

Let's first set the base directory that specifies the location on the local filesystem under which Hadoop will keep all its data. Carry out the following steps:

  1. Create a directory into which Hadoop will store its data:

    $ mkdir /var/lib/hadoop

  2. Ensure the directory is writeable by any user:

    $ chmod 777 /var/lib/hadoop

  3. Modify core-site.xml once again to add the following property:

    <property>
<name>hadoop.tmp.dir</name>
<value>/var/lib/hadoop</value>
</property>

What just happened?

As we will be storing data in Hadoop and all the various components are running on our local host, this data will need to be stored on our local filesystem somewhere. Regardless of the mode, Hadoop by default uses the hadoop.tmp.dir property as the base directory under which all files and data are written.

MapReduce, for example, uses a /mapred directory under this base directory; HDFS uses /dfs. The danger is that the default value...

Time for action – formatting the NameNode

Before starting Hadoop in either pseudo-distributed or fully distributed mode for the first time, we need to format the HDFS filesystem that it will use. Type the following:

$  hadoop namenode -format

The output of this should look like the following:

$ hadoop namenode -format
12/10/26 22:45:25 INFO namenode.NameNode: STARTUP_MSG: 
/************************************************************
STARTUP_MSG: Starting NameNode
STARTUP_MSG:   host = vm193/10.0.0.193
STARTUP_MSG:   args = [-format]
…
12/10/26 22:45:25 INFO namenode.FSNamesystem: fsOwner=hadoop,hadoop
12/10/26 22:45:25 INFO namenode.FSNamesystem: supergroup=supergroup
12/10/26 22:45:25 INFO namenode.FSNamesystem: isPermissionEnabled=true
12/10/26 22:45:25 INFO common.Storage: Image file of size 96 saved in 0 seconds.
12/10/26 22:45:25 INFO common.Storage: Storage directory /var/lib/hadoop-hadoop/dfs/name has been successfully formatted.
12/10/26 22:45:26 INFO namenode.NameNode: SHUTDOWN_MSG...

Time for action – starting Hadoop

Unlike the local mode of Hadoop, where all the components run only for the lifetime of the submitted job, with the pseudo-distributed or fully distributed mode of Hadoop, the cluster components exist as long-running processes. Before we use HDFS or MapReduce, we need to start up the needed components. Type the following commands; the output should look as shown next, where the commands are included on the lines prefixed by $:

  1. Type in the first command:

    $ start-dfs.sh
starting namenode, logging to /home/hadoop/hadoop/bin/../logs/hadoop-hadoop-namenode-vm193.out
localhost: starting datanode, logging to /home/hadoop/hadoop/bin/../logs/hadoop-hadoop-datanode-vm193.out
localhost: starting secondarynamenode, logging to /home/hadoop/hadoop/bin/../logs/hadoop-hadoop-secondarynamenode-vm193.out

  2. Type in the second command:

    $ jps
9550 DataNode
9687 Jps
9638 SecondaryNameNode
9471 NameNode

  3. Type in the third command:

    $ hadoop dfs -ls /
Found 2 items
drwxr-xr-x   - hadoop...

Time for action – using HDFS

As the preceding example shows, there is a familiar-looking interface to HDFS that allows us to use commands similar to those in Unix to manipulate files and directories on the filesystem. Let's try it out by typing the following commands:

Type in the following commands:

$ hadoop -mkdir /user
$ hadoop -mkdir /user/hadoop
$ hadoop fs -ls /user
Found 1 items
drwxr-xr-x   - hadoop supergroup          0 2012-10-26 23:09 /user/Hadoop
$ echo "This is a test." >> test.txt
$ cat test.txt
This is a test.
$ hadoop dfs -copyFromLocal test.txt  .
$ hadoop dfs -ls
Found 1 items
-rw-r--r--   1 hadoop supergroup         16 2012-10-26 23:19/user/hadoop/test.txt
$ hadoop dfs -cat test.txt
This is a test.
$ rm test.txt 
$ hadoop dfs -cat test.txt
This is a test.
$ hadoop fs -copyToLocal test.txt
$ cat test.txt
This is a test.

What just happened?

This example shows the use of the fs subcommand to the Hadoop utility. Note that both dfs and fs commands are equivalent). Like...

Time for action – WordCount, the Hello World of MapReduce

Many applications, over time, acquire a canonical example that no beginner's guide should be without. For Hadoop, this is WordCount – an example bundled with Hadoop that counts the frequency of words in an input text file.

  1. First execute the following commands:

    $ hadoop dfs -mkdir data
$ hadoop dfs -cp test.txt data
$ hadoop dfs -ls data
Found 1 items
-rw-r--r--   1 hadoop supergroup         16 2012-10-26 23:20 /user/hadoop/data/test.txt

  2. Now execute these commands:

    $ Hadoop Hadoop/hadoop-examples-1.0.4.jar  wordcount data out
12/10/26 23:22:49 INFO input.FileInputFormat: Total input paths to process : 1
12/10/26 23:22:50 INFO mapred.JobClient: Running job: job_201210262315_0002
12/10/26 23:22:51 INFO mapred.JobClient:  map 0% reduce 0%
12/10/26 23:23:03 INFO mapred.JobClient:  map 100% reduce 0%
12/10/26 23:23:15 INFO mapred.JobClient:  map 100% reduce 100%
12/10/26 23:23:17 INFO mapred.JobClient: Job complete: job_201210262315_0002...

Using Elastic MapReduce

We will now turn to Hadoop in the cloud, the Elastic MapReduce service offered by Amazon Web Services. There are multiple ways to access EMR, but for now we will focus on the provided web console to contrast a full point-and-click approach to Hadoop with the previous command-line-driven examples.

Setting up an account in Amazon Web Services

Before using Elastic MapReduce, we need to set up an Amazon Web Services account and register it with the necessary services.

Creating an AWS account

Amazon has integrated their general accounts with AWS, meaning that if you already have an account for any of the Amazon retail websites, this is the only account you will need to use AWS services.

Note that AWS services have a cost; you will need an active credit card associated with the account to which charges can be made.

If you require a new Amazon account, go to http://aws.amazon.com, select create a new AWS account, and follow the prompts. Amazon has added a free tier for some services...

Time for action – WordCount on EMR using the management console

Let's jump straight into an example on EMR using some provided example code. Carry out the following steps:

  1. Browse to http://aws.amazon.com, go to Developers | AWS Management Console, and then click on the Sign in to the AWS Console button. The default view should look like the following screenshot. If it does not, click on Amazon S3 from within the console.

  2. As shown in the preceding screenshot, click on the Create bucket button and enter a name for the new bucket. Bucket names must be globally unique across all AWS users, so do not expect obvious bucket names such as mybucket or s3test to be available.

  3. Click on the Region drop-down menu and select the geographic area nearest to you.

  4. Click on the Elastic MapReduce link and click on the Create a new Job Flow button. You should see a screen like the following screenshot:

  5. You should now see a screen like the preceding screenshot. Select the Run a sample application radio button and...

Comparison of local versus EMR Hadoop

After our first experience of both a local Hadoop cluster and its equivalent in EMR, this is a good point at which we can consider the differences of the two approaches.

As may be apparent, the key differences are not really about capability; if all we want is an environment to run MapReduce jobs, either approach is completely suited. Instead, the distinguishing characteristics revolve around a topic we touched on in Chapter 1, What It's All About, that being whether you prefer a cost model that involves upfront infrastructure costs and ongoing maintenance effort over one with a pay-as-you-go model with a lower maintenance burden along with rapid and conceptually infinite scalability. Other than the cost decisions, there are a few things to keep in mind:

  • EMR supports specific versions of Hadoop and has a policy of upgrading over time. If you have a need for a specific version, in particular if you need the latest and greatest versions immediately after...

Summary

We covered a lot of ground in this chapter, in regards to getting a Hadoop cluster up and running and executing MapReduce programs on it.

Specifically, we covered the prerequisites for running Hadoop on local Ubuntu hosts. We also saw how to install and configure a local Hadoop cluster in either standalone or pseudo-distributed modes. Then, we looked at how to access the HDFS filesystem and submit MapReduce jobs. We then moved on and learned what accounts are needed to access Elastic MapReduce and other AWS services.

We saw how to browse and create S3 buckets and objects using the AWS management console, and also how to create a job flow and use it to execute a MapReduce job on an EMR-hosted Hadoop cluster. We also discussed other ways of accessing AWS services and studied the differences between local and EMR-hosted Hadoop.

Now that we have learned about running Hadoop locally or on EMR, we are ready to start writing our own MapReduce programs, which is the topic of the next chapter...

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Key benefits

  • Learn tools and techniques that let you approach big data with relish and not fear
  • Shows how to build a complete infrastructure to handle your needs as your data grows
  • Hands-on examples in each chapter give the big picture while also giving direct experience

Description

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.

Who is this book for?

This book assumes no existing experience with Hadoop or cloud services. It assumes you have familiarity with a programming language such as Java or Ruby but gives you the needed background on the other topics.

What you will learn

  • The trends that led to Hadoop and cloud services, giving the background to know when to use the technology
  • Best practices for setup and configuration of Hadoop clusters, tailoring the system to the problem at hand
  • Developing applications to run on Hadoop with examples in Java and Ruby
  • How Amazon Web Services can be used to deliver a hosted Hadoop solution and how this differs from directly-managed environments
  • Integration with relational databases, using Hive for SQL queries and Sqoop for data transfer
  • How Flume can collect data from multiple sources and deliver it to Hadoop for processing
  • What other projects and tools make up the broader Hadoop ecosystem and where to go next
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Publication date : Feb 22, 2013
Length: 398 pages
Edition : 1st
Language : English
ISBN-13 : 9781849517300
Vendor :
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Category :
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Concepts :
Data Processing
Tools :
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Product Details

Publication date : Feb 22, 2013
Length: 398 pages
Edition : 1st
Language : English
ISBN-13 : 9781849517300
Vendor :
Apache
Category :
Data
Concepts :
Data Processing
Tools :
Hadoop

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Table of Contents

11 Chapters
What It's All About Chevron down icon Chevron up icon
What It's All About
Big data processing
Cloud computing with Amazon Web Services
Summary
Getting Hadoop Up and Running Chevron down icon Chevron up icon
Getting Hadoop Up and Running
Hadoop on a local Ubuntu host
Time for action – checking the prerequisites
Time for action – downloading Hadoop
Time for action – setting up SSH
Time for action – using Hadoop to calculate Pi
Time for action – configuring the pseudo-distributed mode
Time for action – changing the base HDFS directory
Time for action – formatting the NameNode
Time for action – starting Hadoop
Time for action – using HDFS
Time for action – WordCount, the Hello World of MapReduce
Using Elastic MapReduce
Time for action – WordCount on EMR using the management console
Comparison of local versus EMR Hadoop
Summary
Understanding MapReduce Chevron down icon Chevron up icon
Understanding MapReduce
Key/value pairs
The Hadoop Java API for MapReduce
Writing MapReduce programs
Time for action – setting up the classpath
Time for action – implementing WordCount
Time for action – building a JAR file
Time for action – running WordCount on a local Hadoop cluster
Time for action – running WordCount on EMR
Time for action – WordCount the easy way
Walking through a run of WordCount
Time for action – WordCount with a combiner
Time for action – fixing WordCount to work with a combiner
Hadoop-specific data types
Time for action – using the Writable wrapper classes
Input/output
Summary
Developing MapReduce Programs Chevron down icon Chevron up icon
Developing MapReduce Programs
Using languages other than Java with Hadoop
Time for action – implementing WordCount using Streaming
Analyzing a large dataset
Time for action – summarizing the UFO data
Time for action – summarizing the shape data
Time for action – correlating of sighting duration to UFO shape
Time for action – performing the shape/time analysis from the command line
Time for action – using ChainMapper for field validation/analysis
Time for action – using the Distributed Cache to improve location output
Counters, status, and other output
Time for action – creating counters, task states, and writing log output
Summary
Advanced MapReduce Techniques Chevron down icon Chevron up icon
Advanced MapReduce Techniques
Simple, advanced, and in-between
Joins
Time for action – reduce-side join using MultipleInputs
Graph algorithms
Time for action – representing the graph
Time for action – creating the source code
Time for action – the first run
Time for action – the second run
Time for action – the third run
Time for action – the fourth and last run
Using language-independent data structures
Time for action – getting and installing Avro
Time for action – defining the schema
Time for action – creating the source Avro data with Ruby
Time for action – consuming the Avro data with Java
Time for action – generating shape summaries in MapReduce
Time for action – examining the output data with Ruby
Time for action – examining the output data with Java
Summary
When Things Break Chevron down icon Chevron up icon
When Things Break
Failure
Time for action – killing a DataNode process
Time for action – the replication factor in action
Time for action – intentionally causing missing blocks
Time for action – killing a TaskTracker process
Time for action – killing the JobTracker
Time for action – killing the NameNode process
Time for action – causing task failure
Time for action – handling dirty data by using skip mode
Summary
Keeping Things Running Chevron down icon Chevron up icon
Keeping Things Running
A note on EMR
Hadoop configuration properties
Time for action – browsing default properties
Setting up a cluster
Time for action – examining the default rack configuration
Time for action – adding a rack awareness script
Cluster access control
Time for action – demonstrating the default security
Managing the NameNode
Time for action – adding an additional fsimage location
Time for action – swapping to a new NameNode host
Managing HDFS
MapReduce management
Time for action – changing job priorities and killing a job
Scaling
Summary
A Relational View on Data with Hive Chevron down icon Chevron up icon
A Relational View on Data with Hive
Overview of Hive
Setting up Hive
Time for action – installing Hive
Using Hive
Time for action – creating a table for the UFO data
Time for action – inserting the UFO data
Time for action – validating the table
Time for action – redefining the table with the correct column separator
Time for action – creating a table from an existing file
Time for action – performing a join
Time for action – using views
Time for action – exporting query output
Time for action – making a partitioned UFO sighting table
Time for action – adding a new User Defined Function (UDF)
Hive on Amazon Web Services
Time for action – running UFO analysis on EMR
Summary
Working with Relational Databases Chevron down icon Chevron up icon
Working with Relational Databases
Common data paths
Setting up MySQL
Time for action – installing and setting up MySQL
Time for action – configuring MySQL to allow remote connections
Time for action – setting up the employee database
Getting data into Hadoop
Time for action – downloading and configuring Sqoop
Time for action – exporting data from MySQL to HDFS
Time for action – exporting data from MySQL into Hive
Time for action – a more selective import
Time for action – using a type mapping
Time for action – importing data from a raw query
Getting data out of Hadoop
Time for action – importing data from Hadoop into MySQL
Time for action – importing Hive data into MySQL
Time for action – fixing the mapping and re-running the export
AWS considerations
Summary
Data Collection with Flume Chevron down icon Chevron up icon
Data Collection with Flume
A note about AWS
Data data everywhere...
Time for action – getting web server data into Hadoop
Introducing Apache Flume
Time for action – installing and configuring Flume
Time for action – capturing network traffic in a log file
Time for action – logging to the console
Time for action – capturing the output of a command to a flat file
Time for action – capturing a remote file in a local flat file
Time for action – writing network traffic onto HDFS
Time for action – adding timestamps
Time for action – multi level Flume networks
Time for action – writing to multiple sinks
The bigger picture
Summary
Where to Go Next Chevron down icon Chevron up icon
Where to Go Next
What we did and didn't cover in this book
Upcoming Hadoop changes
Alternative distributions
Other Apache projects
Other programming abstractions
AWS resources
Sources of information
Summary

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Customer reviews

Most Recent
  • Top Reviews
  • Most Recent
Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7
(13 Ratings)
5 star 15.4%
4 star 46.2%
3 star 30.8%
2 star 7.7%
1 star 0%
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  • Most Recent

Filter reviews by

pc May 16, 2016
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
I got several access errors for folders and tables. There were no solutions given or those situations accounted for in this book. This book also asks to download UFO sighting dataset from InfoChimps website, which is used in several chapters. This dataset is no longer available in InfoChimps website. There was no response from the publisher when I sent them an email. The book is otherwise good.
Amazon Verified review Amazon
Lyle W Gilbert Apr 23, 2015
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
A good read with one large exception, the code bundle for the book on the website and I quot " Code bundle not present for the chapter 1, 2, and 11" its in the readme after you download it. Also, not the fault of the authors, but Hadoop has changed drastically from the version used in the book, but with a few workarounds it does seem to be very informative.
Amazon Verified review Amazon
Amazon Customer Nov 08, 2014
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
I like this book, it is very clear and has good examples to try out. I don't give it 5 stars because many of the examples have mistakes, many of them very obvious.
Amazon Verified review Amazon
Cheng Sep 24, 2014
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A very good book for the beginner
Amazon Verified review Amazon
Asad Abdullah Apr 29, 2014
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
The good: The explanations are very clear and the author is very good at explaining what hadoop and mapreduce are and their applications. He bases his explanations on the premise that the reader has never dealt with hadoop before.The bad: There are many many mistakes in the code examples which gets very frustrating. Although these mistakes can be overcome by a google search, it does slow down the learning process.Hence I gave it a 3 star rating.One more thing to note is that this book uses hadoop 1.0.4. Although hadoop is now past 2.0, there are no major problems with downloading the latest version (in my case 2.4.0) and still using the book.
Amazon Verified review Amazon

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