精通Hadoop：分布式编程实战

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"Hadoop in Action" 是一本关于Hadoop分布式编程框架的专业书籍，涵盖了Hadoop的基础、实战应用以及大规模环境下的使用。在【标题】"Hadoop in Action" 中，关键词“Hadoop”指的是一个开源的分布式计算框架，它允许在廉价硬件上处理大量数据。该书可能详细介绍了如何利用Hadoop进行大数据处理。【描述】虽然没有提供具体内容，但通常"Action"一词暗示了本书不仅理论介绍，还将深入实践，教导读者如何实际操作Hadoop。【标签】同样为"Hadoop in Action"，与标题一致，意味着内容会围绕Hadoop的实际应用展开。【部分内容】提到了书的结构分为三个部分： 1. 第一部分是Hadoop分布式编程框架的介绍，讲解了构成Hadoop集群的硬件组件，包括安装配置Hadoop以创建一个可工作的系统，以及对MapReduce框架的高层次理解，并提供了编写第一个MapReduce程序的指导。 2. 第二部分“Hadoop in Action”深入讨论如何编写基本和高级的MapReduce程序，良好的编程实践，还包含了一本“Cookbook”，可能包含解决特定问题的实例和技巧。 3. 第三部分“Hadoop Gone Wild”探讨了Hadoop在云环境中的运行，介绍了使用Pig进行编程，Hive和Hadoop的交互，以及一些实际案例研究，这些章节将扩展到更复杂和大规模的数据处理场景。书中详细内容包括： - 第1章“Introducing Hadoop”介绍了如何编写可扩展的分布式数据密集型应用程序的基础知识。 - 第2章“Starting Hadoop”涉及启动Hadoop集群的步骤，包括硬件需求和集群设置。 - 第3章“Components of Hadoop”讨论了Hadoop的主要组件，如NameNode、DataNode、TaskTracker和JobTracker等。 - 第4章至第6章深入MapReduce，从基础到高级，提供了编程实践和建议。 - 第7章“Cookbook”可能包含了一系列实用的MapReduce代码示例和解决方案。 - 第8章“Managing Hadoop”涵盖了Hadoop集群的管理和监控，可能包括日志分析、性能优化和故障排除等内容。 - 第9章“Running Hadoop in the cloud”讨论了如何在云计算平台如Amazon Web Services (AWS) 上部署和管理Hadoop。 - 第10章“Programming with Pig”介绍了Pig Latin语言，一种用于Hadoop的数据分析工具。 - 第11章“Hive and the Hadoop herd”讲述了Hive，它是基于Hadoop的数据仓库工具，允许用户通过SQL-like查询语言处理数据。 - 第12章“Case studies”提供了实际应用场景，展示了Hadoop在不同行业的应用和成功案例。 - 附录包括HDFS文件命令，帮助读者更好地理解和操作Hadoop的分布式文件系统。 "Hadoop in Action" 是一本全面且深度探索Hadoop的指南，适合希望掌握Hadoop技术，从基础知识到高级应用，再到大规模数据处理和云环境部署的读者。

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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