深入理解Hadoop：分布式编程框架实战

需积分: 0 90 浏览量更新于2024-07-27 收藏 5.05MB PDF 举报

"Hadoop-in-action" 《Hadoop实战》这本书深入介绍了分布式编程框架Hadoop，旨在帮助读者理解和应用这一强大的大数据处理工具。全书分为三个部分：第一部分：Hadoop - 分布式编程框架这部分从基础开始，阐述了构成Hadoop集群的硬件组件以及如何安装和配置来创建一个可工作的系统。它对MapReduce框架进行了高层次的概述，并指导读者编写并运行第一个MapReduce程序。在《介绍Hadoop》这一章中，主要讨论了以下几个关键点： 1. 可扩展和分布式数据处理的基础：Hadoop的设计目标是处理海量数据，其分布式架构允许在多台服务器上并行处理任务，实现横向扩展。 2. Hadoop集群硬件组件：包括数据节点、名称节点、任务跟踪器和JobTracker等，这些组件协同工作，确保数据的存储和计算效率。 3. 安装与配置：详述了搭建Hadoop环境的步骤，包括硬件选择、软件依赖、网络配置等。第二部分：Hadoop 实战这部分进一步深入到Hadoop的实际应用，通过实例教学如何编写和优化MapReduce程序，以及最佳实践。具体章节包括： 4. 编写基本的MapReduce程序：讲解了Map函数和Reduce函数的工作原理，以及如何设计输入输出格式。 5. 高级MapReduce：涵盖了Combiner、Partitioner、自定义排序和数据本地化等进阶主题，以提高MapReduce程序的性能。 6. 编程实践：分享了开发Hadoop应用程序时的经验和技巧，如错误处理、日志记录和调试方法。 7. 烹饪书（Cookbook）：提供了多种常见问题的解决方案，帮助读者解决实际开发中遇到的问题。 8. 管理Hadoop：讨论了监控、维护、故障排查和集群扩展等管理方面的内容。第三部分：Hadoop 走向野外随着Hadoop的普及，它已经不再局限于传统的数据中心，而是进入了云环境。这部分涵盖了以下主题： 9. 在云端运行Hadoop：探讨了如何在Amazon Web Services (AWS) 或其他云平台上部署和管理Hadoop集群，以及云环境中的优化策略。 10. 使用Pig编程：Pig是Hadoop上的一个高级数据流语言，简化了复杂的数据分析任务。 11. Hive和Hadoop牧群：介绍了Hive，一种基于Hadoop的数据仓库工具，用于查询和管理大规模数据集。 12. 案例研究：通过实际案例展示了Hadoop在不同行业和场景中的应用。附录：HDFS文件命令这部分提供了一份Hadoop分布式文件系统（HDFS）的常用命令参考，帮助用户更好地操作和管理HDFS。总体来说，《Hadoop实战》全面覆盖了从理论到实践，从基础到高级的Hadoop技术，是学习和掌握Hadoop不可或缺的参考资料。无论你是初学者还是有经验的开发者，都能从中获得宝贵的知识和实践经验。

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

charly2018

粉丝: 3
资源: 8

深入理解Hadoop：分布式编程框架实战

hadoop-in-action:gradle 的 Hadoop 示例

Hadoop - Hadoop in Action

Hadoop-in-Action:使用脚本语言和java实现Hadoop

word源码java-hadoop-practice:一些hadoop相关的练习

hadoop in action

Hadoop in Action

Hadoop In Action

学生信息管理系统-----------无数据库版本

2024年福建省村级（居委会）行政区划shp数据集

win32汇编环境，对话框中显示bmp图像文件

最新资源