Hadoop-MapReduce

Posted 2020-09-14

在之前建立的HDFS基础上，自己编写MapReduce程序，打包，并运行。

重新打包WordCount并执行

新建一个Maven项目，将示例程序中WordCount.java的复制到新项目中，使用mvn clean package打包为jar文件并复制到服务器。

WordCount.java内容如下：

public class WordCount {

  public static class TokenizerMapper 
       extends Mapper<Object, Text, Text, IntWritable>{
    
    private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();
      
    public void map(Object key, Text value, Context context
                    ) throws IOException, InterruptedException {
      StringTokenizer itr = new StringTokenizer(value.toString());
      while (itr.hasMoreTokens()) {
        word.set(itr.nextToken());
        context.write(word, one);
      }
    }
  }
  
  public static class IntSumReducer 
       extends Reducer<Text,IntWritable,Text,IntWritable> {
    private IntWritable result = new IntWritable();

    public void reduce(Text key, Iterable<IntWritable> values, 
                       Context context
                       ) throws IOException, InterruptedException {
      int sum = 0;
      for (IntWritable val : values) {
        sum += val.get();
      }
      result.set(sum);
      context.write(key, result);
    }
  }

  public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();
    String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
    if (otherArgs.length < 2) {
      System.err.println("Usage: wordcount <in> [<in>...] <out>");
      System.exit(2);
    }
    Job job = Job.getInstance(conf, "word count");
    job.setJarByClass(WordCount.class);
    job.setMapperClass(TokenizerMapper.class);
    job.setCombinerClass(IntSumReducer.class);
    job.setReducerClass(IntSumReducer.class);
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(IntWritable.class);
    for (int i = 0; i < otherArgs.length - 1; ++i) {
      FileInputFormat.addInputPath(job, new Path(otherArgs[i]));
    }
    FileOutputFormat.setOutputPath(job,
      new Path(otherArgs[otherArgs.length - 1]));
    System.exit(job.waitForCompletion(true) ? 0 : 1);
  }
}

在服务器上创建一个test.txt文件，内容为：

This is a test.

将文件复制到HDFS中：

hadoop/bin/hdfs dfs -put test.txt /mrtest/input

使用下面的命令执行WorkCount：

hadoop/bin/hadoop jar hadoop-mapreduce-demo-0.0.1-SNAPSHOT.jar com.u3dspace.hadoop.mapreduce.demo.WordCount /mrtest/input /mrtest/output

查看输出结果：

hadoop/bin/hdfs dfs -cat /mrtest/output/part-r-00000

This    1
a       1
is      1
test.   1

自定义Writable

定义一个类CountWritable：

public class CountWritable implements Writable {
    private int count;
    
    public CountWritable() {
        this.count = 0;
    }
    public CountWritable(int count) {
        this.count = count;
    }
    
    public int getCount() {
        return count;
    }
    public void setCount(int count) {
        this.count = count;
    }
    
    public void readFields(DataInput in) throws IOException {
        this.count = in.readInt();
    }
    
    public void write(DataOutput out) throws IOException {
        out.writeInt(this.count);
    }
    
    @Override
    public String toString() {
        return Integer.toString(this.count);
    }
}

将刚才示例中的IntWritable换成CountWritable，打包到服务器执行，输出的结果和上一次相同。

依赖第三方jar包

当需要使用第三方jar包时，简单的方法是在打包时将第三方jar包也打进去，Maven中配置一个plugin，如下：

<build>
    <plugins>
        <plugin>
            <artifactId>maven-assembly-plugin</artifactId>
            <configuration>
                <descriptorRefs>
                    <descriptorRef>jar-with-dependencies</descriptorRef>
                </descriptorRefs>
                <archive>
                    <manifest>
                        <mainClass></mainClass>
                    </manifest>
                </archive>
            </configuration>
            <executions>
                <execution>
                    <id>make-assembly</id>
                    <phase>package</phase>
                    <goals>
                        <goal>single</goal>
                    </goals>
                </execution>
            </executions>
        </plugin>
    </plugins>
</build>

然后使用mvn package打包后，在target/目录下会多一个以-jar-with-dependencies.jar为后缀的jar包，在服务器执行这个jar包即可。

使用yarn执行MapReduce任务

修改配置文件

修改hadoop/etc/hadoop/mapred-site.xml，使其configuration节点内容如下：

<configuration>
    <property>
        <name>mapreduce.framework.name</name>
        <value>yarn</value>
    </property>
</configuration>

修改hadoop/etc/hadoop/yarn-site.xml，使其configuration节点内容如下：

<configuration>
    <property>
        <name>yarn.nodemanager.aux-services</name>
        <value>mapreduce_shuffle</value>
    </property>
</configuration>

启动yarn

使用下面的命令启动yarn：

hadoop/sbin/start-yarn.sh

浏览器访问yarn用户界面

默认端口为8088，如使用后面的地址访问：http://52.69.38.114:8088/

提交MapReduce任务

使用之前的命令提交一个MapReduce任务，如：

hadoop/bin/hadoop jar hadoop-mapreduce-demo-0.0.1-SNAPSHOT.jar com.u3dspace.hadoop.mapreduce.demo.WordCount /mrtest/input /mrtest/output

在浏览器的yarn界面下，可看到提交的任务及执行情况。