Hadoop分布式编程框架实战

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"《hadoop in action》是一本关于云计算教程的书籍，主要聚焦于Hadoop分布式编程框架。书中通过三个部分详细介绍了Hadoop的核心概念、实际应用以及在大规模环境中的运用。作者首先从Hadoop的基础知识入手，包括硬件组件构成的Hadoop集群，安装与配置以构建一个可工作的系统。接着深入探讨MapReduce框架，帮助读者编写基本的MapReduce程序。此外，还涵盖了高级MapReduce编程技巧、最佳实践、Hadoop操作管理以及在云端运行Hadoop的策略。书中的案例研究部分展示了Pig、Hive等工具在处理大数据时的应用，全面展现了Hadoop的生态体系和潜力。" 在《hadoop in action》中，作者首先在第一部分"分布式编程框架"中介绍Hadoop。这部分旨在帮助读者理解Hadoop的基本原理，讲解了用于构建Hadoop集群的硬件要素，以及如何设置和配置一个运行中的Hadoop系统。这一阶段的学习者将了解到如何设计出能够扩展并处理大量数据的分布式应用程序。第二部分"Hadoop实战"深入到MapReduce的核心，这是Hadoop处理数据的关键组件。在第4章，读者将学习编写基本的MapReduce程序，理解Mapper和Reducer的角色以及它们如何协同工作以实现数据处理。第5章则进一步深入MapReduce的高级技巧，探讨如何优化程序性能和解决复杂的数据处理问题。第6章则关注编程实践中的一些最佳策略，以提高代码的效率和可维护性。第三部分"Hadoop狂野之旅"探讨了Hadoop在大规模环境和云环境中的应用。第9章讲述了如何在云端部署和运行Hadoop，使读者理解云环境下的Hadoop管理和扩展性。第10章和第11章分别介绍了Pig和Hive，这两个工具是Hadoop生态系统中的重要组成部分，提供了更高级的数据处理和分析能力。这些章节展示了Hadoop在实际业务场景中的应用，如数据分析、报表生成和数据挖掘。此外，书中的附录还包含了HDFS文件命令，这为读者提供了一手的Hadoop文件系统操作指导。《hadoop in action》是一本全面而深入的Hadoop指南，适合对大数据处理感兴趣的开发者、数据分析师和IT专业人士阅读。通过本书，读者不仅可以掌握Hadoop的基本技术，还能了解如何在实际项目中有效地利用Hadoop进行大数据处理和分析。

14 C

HAPTER

1 Introducing Hadoop

for each value in values {

sum = sum + value;

}

emit ((String)token, (Integer) sum);

}

We’ve said before that the output of both map and reduce function are lists. As you

can see from the pseudo-code, in practice we use a special function in the framework

called emit() to generate the elements in the list one at a time. This emit() function

further relieves the programmer from managing a large list.

The code looks similar to what we have in section 1.5.1, except this time it will

actually work at scale. Hadoop makes building scalable distributed programs easy,

doesn’t it? Now let’s turn this pseudo-code into a Hadoop program.

1.6 Counting words with Hadoop—running your ﬁ rst program

Now that you know what the Hadoop and MapReduce framework is about, let’s get it

running. In this chapter, we’ll run Hadoop only on a single machine, which can be

your desktop or laptop computer. The next chapter will show you how to run Hadoop

over a cluster of machines, which is what you’d want for practical deployment. Run-

ning Hadoop on a single machine is mainly useful for development work.

Linux is the ofﬁ cial development and production platform for Hadoop, although

Windows is a supported development platform as well. For a Windows box, you’ll need

to install cygwin (http://www-cygwin.com/) to enable shell and Unix scripts.

NOTE

Many people have reported success in running Hadoop in development

mode on other variants of Unix, such as Solaris and Mac OS X . In fact,

MacBook Pro seems to be the laptop of choice among Hadoop developers, as

they’re ubiquitous in Hadoop conferences and user group meetings.

Running Hadoop requires Java (version 1.6 or higher). Mac users should get it from

Apple. You can download the latest JDK for other operating systems from Sun at

http://java.sun.com/javase/downloads/index.jsp. Install it and remember the root of

the Java installation, which we’ll need later.

To install Hadoop, ﬁ rst get the latest stable release at http://hadoop.apache.org/

core/releases.html. After you unpack the distribution, edit the script conf/hadoop-

env.sh to set JAVA_HOME to the root of the Java installation you have remembered

from earlier. For example, in Mac OS X, you’ll replace this line

# export JAVA_HOME=/usr/lib/j2sdk1.5-sun

with this line

export JAVA_HOME=/Library/Java/Home

You’ll be using the Hadoop script quite often. Let’s run it without any arguments to

see its usage documentation:

Counting words with Hadoop—running your ﬁ rst program 15

bin/hadoop

We get

Usage: hadoop [--conﬁ g confdir] COMMAND

where COMMAND is one of:

namenode -format format the DFS ﬁ lesystem

secondarynamenode run the DFS secondary namenode

namenode run the DFS namenode

datanode run a DFS datanode

dfsadmin run a DFS admin client

fsck run a DFS ﬁ lesystem checking utility

fs run a generic ﬁ lesystem user client

balancer run a cluster balancing utility

jobtracker run the MapReduce job Tracker node

pipes run a Pipes job

tasktracker run a MapReduce task Tracker node

job manipulate MapReduce jobs

version print the version

jar <jar> run a jar ﬁ le

distcp <srcurl> <desturl> copy ﬁ le or directories recursively

archive -archiveName NAME <src>* <dest> create a hadoop archive

daemonlog get/set the log level for each daemon

CLASSNAME run the class named CLASSNAME

Most commands print help when invoked w/o parameters.

We’ll cover the various Hadoop commands in the course of this book. For our current

purpose, we only need to know that the command to run a ( Java) Hadoop program is

bin/hadoop jar <jar>

. As the command implies, Hadoop programs written in Java

are packaged in jar ﬁ les for execution.

Fortunately for us, we don’t need to write a Hadoop program ﬁ rst; the default

installation already has several sample programs we can use. The following command

shows what is available in the examples jar ﬁ le:

bin/hadoop jar hadoop-*-examples.jar

You’ll see about a dozen example programs prepackaged with Hadoop, and one

of them is a word counting program called...

wordcount

! The important (inner)

classes of that program are shown in listing 1.2. We’ll see how this Java program

implements the word counting map and reduce functions we had in pseudo-code

in listing 1.1. We’ll modify this program to understand how to vary its behavior. For

now we’ll assume it works as expected and only follow the mechanics of executing a

Hadoop program.

Without specifying any arguments, executing

wordcount

will show its usage

information:

bin/hadoop jar hadoop-*-examples.jar wordcount

which shows the arguments list:

wordcount [-m <maps>] [-r <reduces>] <input> <output>

16 C

HAPTER

1 Introducing Hadoop

The only parameters are an input directory (

<input>

) of text documents you want to

analyze and an output directory (

) where the program will dump its output.

To execute

wordcount

, we need to ﬁ rst create an input directory:

mkdir input

and put some documents in it. You can add any text document to the directory. For

illustration, let’s put the text version of the 2002 State of the Union address, obtained

from http://www.gpoaccess.gov/sou/. We now analyze its word counts and see the

results:

bin/hadoop jar hadoop-*-examples.jar wordcount input output

more output/*

You’ll see a word count of every word used in the document, listed in alphabetical or-

der. This is not bad considering you have not written a single line of code yet! But, also

note a number of shortcomings in the included

wordcount

program. Tokenization

is based purely on whitespace characters and not punctuation marks, making States,

States., and States: separate words. The same is true for capitalization, where States and

states appear as separate words. Furthermore, we would like to leave out words that

show up in the document only once or twice.

Fortunately, the source code for

wordcount

is available and included in the

installation at src/examples/org/apache/hadoop/examples/WordCount.java. We

can modify it as per our requirements. Let’s ﬁ rst set up a directory structure for our

playground and make a copy of the program.

mkdir playground

mkdir playground/src

mkdir playground/classes

cp src/examples/org/apache/hadoop/examples/WordCount.java

➥

playground/src/WordCount.java

Before we make changes to the program, let’s go through compiling and executing

this new copy in the Hadoop framework.

javac -classpath hadoop-*-core.jar -d playground/classes

➥

playground/src/WordCount.java

jar -cvf playground/wordcount.jar -C playground/classes/ .

You’ll have to remove the output directory each time you run this Hadoop command,

because it is created automatically.

bin/hadoop jar playground/wordcount.jar

➥

org.apache.hadoop.examples.WordCount input output

Look at the ﬁ les in your output directory again. As we haven’t changed any program

code, the result should be the same as before. We’ve only compiled our own copy

rather than running the precompiled version.

Now we are ready to modify

WordCount

to add some extra features. Listing 1.2 is

a partial view of the WordCount.java program. Comments and supporting code are

stripped out.

Listing 1.2 WordCount.java

public class WordCount extends Conﬁ gured implements Tool {

public static class MapClass extends MapReduceBase

implements Mapper<LongWritable, Text, Text, IntWritable> {

private ﬁ nal static IntWritable one = new IntWritable(1);

private Text word = new Text();

public void map(LongWritable key, Text value,

OutputCollector<Text, IntWritable> output,

Reporter reporter) throws IOException {

String line = value.toString();

StringTokenizer itr = new StringTokenizer(line);

while (itr.hasMoreTokens()) {

word.set(itr.nextToken());

output.collect(word, one);

}

public static class Reduce extends MapReduceBase

implements Reducer<Text, IntWritable, Text, IntWritable> {

public void reduce(Text key, Iterator<IntWritable> values,

OutputCollector<Text, IntWritable> output,

Reporter reporter) throws IOException {

int sum = 0;

while (values.hasNext()) {

sum += values.next().get();

}

output.collect(key, new IntWritable(sum));

}

...

}

The main functional distinction between

WordCount.java

and our MapReduce pseudo-

code is that in

WordCount.java

map()

processes one line of text at a time whereas our

pseudo-code processes a document at a time. This distinction may not even be apparent

from looking at

WordCount.java

as it’s Hadoop’s default conﬁ guration.

The code in listing 1.2 is virtually identical to our pseudo-code in listing 1.1 though

the Java syntax makes it more verbose. The map and reduce functions are inside inner

classes of

WordCount

. You may notice we use special classes such as

LongWritable

IntWritable

, and

Text

instead of the more familiar

Long

Integer

, and

String

classes of Java. Consider these implementation details for now. The new classes have

additional serialization capabilities needed by Hadoop’s internal.

The changes we want to make to the program are easy to spot. We see

that

WordCount

uses Java’s

StringTokenizer

in its default setting, which tokenizes based

only on whitespaces. To ignore standard punctuation marks, we add them to the

StringTokenizer

’s list of delimiter characters:

StringTokenizer itr = new StringTokenizer(line, “ \t\n\r\f,.:;?![]`”);

Counting words with Hadoop—running your ﬁ rst program 17

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