尚硅谷大數(shù)據(jù)技術(shù)之Hadoop（MapReduce）（新）第1章 MapReduce概述
1.7?MapReduce編程規(guī)范
用戶編寫的程序分成三個(gè)部分：Mapper、Reducer和Driver。
1.8?WordCount案例實(shí)操
1．需求
在給定的文本文件中統(tǒng)計(jì)輸出每一個(gè)單詞出現(xiàn)的總次數(shù)
（1）輸入數(shù)據(jù)
atguigu atguigu
ss ss
cls cls
jiao
banzhang
xue
hadoop
（2）期望輸出數(shù)據(jù)
atguigu 2
banzhang 1
cls 2
hadoop 1
jiao 1
ss 2
xue 1
2．需求分析
按照MapReduce編程規(guī)范，分別編寫Mapper，Reducer，Driver，如圖4-2所示。
圖4-2?需求分析
3．環(huán)境準(zhǔn)備
（1）創(chuàng)建maven工程
（2）在pom.xml文件中添加如下依賴
<dependencies>
<dependency>
<groupId>junit</groupId>
<artifactId>junit</artifactId>
<version>RELEASE</version>
</dependency>
<dependency>
<groupId>org.apache.logging.log4j</groupId>
<artifactId>log4j-core</artifactId>
<version>2.8.2</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-common</artifactId>
<version>2.7.2</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>2.7.2</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-hdfs</artifactId>
<version>2.7.2</version>
</dependency>
</dependencies>
（2）在項(xiàng)目的src/main/resources目錄下，新建一個(gè)文件，命名為“l(fā)og4j.properties”，在文件中填入。
|
log4j.rootLogger=INFO, stdout log4j.appender.stdout=org.apache.log4j.ConsoleAppender log4j.appender.stdout.layout=org.apache.log4j.PatternLayout log4j.appender.stdout.layout.ConversionPattern=%d %p [%c] - %m%n log4j.appender.logfile=org.apache.log4j.FileAppender log4j.appender.logfile.File=target/spring.log log4j.appender.logfile.layout=org.apache.log4j.PatternLayout log4j.appender.logfile.layout.ConversionPattern=%d %p [%c] - %m%n |
4．編寫程序
（1）編寫Mapper類
|
package com.atguigu.mapreduce; import java.io.IOException; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Mapper;
public class WordcountMapper extends Mapper<LongWritable, Text, Text, IntWritable>{ Text k = new Text(); IntWritable v = new IntWritable(1); @Override protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { // 1 獲取一行 String line = value.toString(); // 2 切割 String[] words = line.split(" "); // 3 輸出 for (String word : words) { k.set(word); context.write(k, v); } } } |
（2）編寫Reducer類
|
package com.atguigu.mapreduce.wordcount; import java.io.IOException; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Reducer;
public class WordcountReducer extends Reducer<Text, IntWritable, Text, IntWritable>{
int sum; IntWritable v = new IntWritable();
@Override protected void reduce(Text key, Iterable<IntWritable> values,Context context) throws IOException, InterruptedException { // 1 累加求和 sum = 0; for (IntWritable count : values) { sum += count.get(); } // 2 輸出 ???????v.set(sum); context.write(key,v); } } |
（3）編寫Driver驅(qū)動(dòng)類
|
package com.atguigu.mapreduce.wordcount; import java.io.IOException; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class WordcountDriver {
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
// 1 獲取配置信息以及封裝任務(wù) Configuration configuration = new Configuration(); Job job = Job.getInstance(configuration);
// 2 設(shè)置jar加載路徑 job.setJarByClass(WordcountDriver.class);
// 3 設(shè)置map和reduce類 job.setMapperClass(WordcountMapper.class); job.setReducerClass(WordcountReducer.class);
// 4 設(shè)置map輸出 job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(IntWritable.class);
// 5 設(shè)置最終輸出kv類型 job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); // 6 設(shè)置輸入和輸出路徑 FileInputFormat.setInputPaths(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1]));
// 7 提交 boolean result = job.waitForCompletion(true);
System.exit(result ? 0 : 1); } } |
5．本地測(cè)試
（1）如果電腦系統(tǒng)是win7的就將win7的hadoop jar包解壓到非中文路徑，并在Windows環(huán)境上配置HADOOP_HOME環(huán)境變量。如果是電腦win10操作系統(tǒng)，就解壓win10的hadoop jar包，并配置HADOOP_HOME環(huán)境變量。
注意：win8電腦和win10家庭版操作系統(tǒng)可能有問題，需要重新編譯源碼或者更改操作系統(tǒng)。
（2）在Eclipse/Idea上運(yùn)行程序
6．集群上測(cè)試
（0）用maven打jar包，需要添加的打包插件依賴
注意：標(biāo)記紅顏色的部分需要替換為自己工程主類
<build>
<plugins>
<plugin>
<artifactId>maven-compiler-plugin</artifactId>
<version>2.3.2</version>
<configuration>
<source>1.8</source>
<target>1.8</target>
</configuration>
</plugin>
<plugin>
<artifactId>maven-assembly-plugin </artifactId>
<configuration>
<descriptorRefs>
<descriptorRef>jar-with-dependencies</descriptorRef>
</descriptorRefs>
<archive>
<manifest>
<mainClass>com.atguigu.mr.WordcountDriver</mainClass>
</manifest>
</archive>
</configuration>
<executions>
<execution>
<id>make-assembly</id>
<phase>package</phase>
<goals>
<goal>single</goal>
</goals>
</execution>
</executions>
</plugin>
</plugins>
</build>
注意：如果工程上顯示紅叉。在項(xiàng)目上右鍵->maven->update project即可。
（1）將程序打成jar包，然后拷貝到Hadoop集群中
步驟詳情：右鍵->Run as->maven install。等待編譯完成就會(huì)在項(xiàng)目的target文件夾中生成jar包。如果看不到。在項(xiàng)目上右鍵-》Refresh，即可看到。修改不帶依賴的jar包名稱為wc.jar，并拷貝該jar包到Hadoop集群。
（2）啟動(dòng)Hadoop集群
（3）執(zhí)行WordCount程序
[atguigu@hadoop102 software]$ hadoop jar ?wc.jar
?com.atguigu.wordcount.WordcountDriver /user/atguigu/input /user/atguigu/output
