探索Hadoop：从入门到云计算实战

需积分: 10 13 浏览量更新于2024-07-29 收藏 5.09MB PDF 举报

《Hadoop in Action》是一本深入讲解Apache Hadoop的实用指南，分为三个部分：一个分布式编程框架的基础介绍、在实际操作中的应用以及更高级的主题和案例研究。本书首先在PART1 "Hadoop - A Distributed Programming Framework" 中，引导读者了解Hadoop的基本概念和构成。在第一章 "Introducing Hadoop" 中，作者会详述Hadoop的核心理念，即如何设计和编写能够处理大规模数据集的并行计算系统。它强调了Hadoop的可扩展性和容错性，以及其在处理海量数据时的优势，比如使用廉价硬件和分布式存储来实现高效处理。第二章 "Starting Hadoop" 介绍了如何设置和配置Hadoop集群，包括硬件需求、软件安装过程以及搭建一个基本的工作环境。读者可以在此部分了解到如何初始化Hadoop集群，并学习到必要的命令行工具和配置参数。紧接着在第三章 "Components of Hadoop"，作者详细剖析了Hadoop的组件，包括Hadoop Distributed File System (HDFS)、MapReduce框架以及YARN资源管理器等。这些组件在Hadoop生态系统中的作用和相互关系将被逐一讲解。进入PART2 "Hadoop in Action"，读者将深入实践Hadoop技术。第四章 "Writing basic MapReduce programs" 介绍了MapReduce编程模型，通过编写和运行简单的程序，让读者掌握这个核心计算框架。第五章 "Advanced MapReduce" 探讨了MapReduce的高级特性，如自定义分割函数、归约操作的优化以及任务调度策略等。第六章 "Programming practices" 针对实际开发中的最佳实践，涵盖性能调优、代码组织、错误处理等内容，帮助读者写出健壮且高效的Hadoop代码。第七章 "Cookbook" 提供一系列实用技巧和解决常见问题的方法，以便于快速解决问题。第八章 "Managing Hadoop" 讨论如何管理和维护Hadoop集群，包括监控、日志分析、故障排查以及安全设置等，确保系统的稳定运行。在PART3 "Hadoop Gone Wild"，作者探讨了Hadoop在云计算环境下的部署和扩展。第九章 "Running Hadoop in the cloud" 介绍如何在云端部署Hadoop，如Amazon Web Services（AWS）或Google Cloud Platform（GCP）上实现。第十章 "Programming with Pig" 引导读者使用Pig Latin编程语言进行数据处理，这是一种简洁的查询语言，适用于Hadoop。第十一章 "Hive and the Hadoop herd" 着重于Hive，一个SQL-like的数据仓库工具，如何与Hadoop集成，提供易于使用的数据查询和分析能力。最后一章 "Case studies" 通过具体的案例分析，展示了Hadoop在不同场景下的应用实例，加深理解和实践经验。附录部分 "HDFS file commands" 列出了一系列Hadoop分布式文件系统（HDFS）的命令行操作，方便读者在实际操作中查阅和学习。《Hadoop in Action》是一本全面而深入的指南，无论是初学者还是经验丰富的开发者，都能从中获得构建、优化和管理大规模数据处理系统的宝贵知识和技能。

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

剩余298页未读，继续阅读

韭菜2014

粉丝: 3

探索Hadoop：从入门到云计算实战

Hadoop_in_Action.pdf

Hadoop In Action2

Hadoop权威指南中文版（第二版）+Hadoop in Action

结合hadoop的书籍推荐代码

hadoop集群脚本

ssm用hadoop上传文件

Hadoop监视文件夹启动命令

Hadoop，habse，spark 参考文献

hbase in action 英文版

最新资源