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Hadoop Beginner's Guide

Hadoop Beginner's Guide

3.7 (13)
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Hadoop Beginner's Guide

Hadoop Beginner's Guide

3.7 (13)

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)
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Hadoop Beginner's Guide
Hadoop Beginner's Guide
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
What It's All About
What It's All About
Big data processing
Cloud computing with Amazon Web Services
Summary
2
Getting Hadoop Up and Running
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
3
Understanding MapReduce
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
4
Developing MapReduce Programs
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
5
Advanced MapReduce Techniques
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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
6
When Things Break
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
7
Keeping Things Running
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
8
A Relational View on Data with Hive
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
9
Working with Relational Databases
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
10
Data Collection with Flume
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
11
Where to Go Next
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
Pop Quiz Answers
Pop Quiz Answers
Chapter 3, Understanding MapReduce
Chapter 7, Keeping Things Running
Index
Index

Joins

Few problems use a single set of data. In many cases, there are easy ways to obviate the need to try and process numerous discrete yet related data sets within the MapReduce framework.

The analogy here is, of course, to the concept of join in a relational database. It is very natural to segment data into numerous tables and then use SQL statements that join tables together to retrieve data from multiple sources. The canonical example is where a main table has only ID numbers for particular facts, and joins against other tables are used to extract data about the information referred to by the unique ID.

When this is a bad idea

It is possible to implement joins in MapReduce. Indeed, as we'll see, the problem is less about the ability to do it and more the choice of which of many potential strategies to employ.

However, MapReduce joins are often difficult to write and easy to make inefficient. Work with Hadoop for any length of time, and you will come across a situation where you need to...

End of Section 3
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