Hadoop Beginner's Guide

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Hadoop Beginner's Guide

3.7 (13)

Buy this Book

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.

Hadoop Beginner's Guide

Credits

About the Author

About the Reviewers

www.PacktPub.com

Preface

Free Chapter

What It's All About

Big data processing

Cloud computing with Amazon Web Services

Summary

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

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

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

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

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

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

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

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

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

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

Chapter 3, Understanding MapReduce

Chapter 7, Keeping Things Running

Index

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Hadoop Beginner's Guide

Hadoop Beginner's Guide

Overview of this book

Chapter 8. A Relational View on Data with Hive

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