hadoop2.7.0实践- WordCount

Posted 2020-10-01 yutingliuyl

环境要求
说明：本文档为wordcount的mapreduce job编写及执行文档。

操作系统：Ubuntu14 x64位
Hadoop：Hadoop 2.7.0
Hadoop官网：http://hadoop.apache.org/releases.html
MapReduce參照官网步骤：
http://hadoop.apache.org/docs/current/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html#Source_Code

本章基于前一篇文章《hadoop2.7.0实践-环境搭建》。

1.安装Eclipse
1)下载eclipse
官网：http://www.eclipse.org/

2)解压eclipse包

$tar -xvf eclipse-jee-mars-R-linux-gtk-x86_64.tar.gz

3)启动eclipse
4)写測试程序

public class TestMore {

    public static void main(String[] args) {
        System.out.println("hello world!");
        System.out.println("I‘m so glad to see that");
    }
}

2.编写wordcount
1)jar包引入
eclipse的lib中引入的jar包
hadoop包下的share/hadoop下的各个文件夹都有jar包
hadoop-2.7.0/share/hadoop/common/hadoop-common-2.7.0.jar
hadoop-2.7.0/share/hadoop/mapreduce/hadoop-mapreduce-client-core-2.7.0.jar

2)编写worcount程序
相应源代码

import java.io.IOException;
import java.util.StringTokenizer;

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.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

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();
    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);
    FileInputFormat.addInputPath(job, new Path(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));
    System.exit(job.waitForCompletion(true) ? 0 : 1);
  }
}

3)导出jar包
取名wc.jar，直接导出到hadoop文件夹下。

3.执行wordcount
1)启动dfs服务
參照文件《hadoop2.7.0实践-环境搭建》。
进入hadoop文件夹，用cd命令。

$sbin/start-dfs.sh

相应查看网页：http://localhost:50070/
2)准备文件
hadoop-2.7.0/wctest/input文件夹中放入待统计文件file01
输入内容：hello world bye world

//创建hdfs文件夹。操作命令相似本地操作

$ bin/hdfs fs -mkdir /user
$ bin/hdfs fs -mkdir /user/a

//复制本地文件到hdfs中

$ bin/hdfs fs -put wctest/input /user/a/input

//备注：相应文件夹删除命令例如以下

delete dir：bin/hadoop fs -rm -f -r /user/a/input

相应文件http://localhost:50070/
3)启动yarn服务

$ sbin/start-yarn.sh

4)执行wordcount程序

$ bin/hadoop jar wc.jar WordCount /user/a/input /user/a/output

5)查看结果

$ bin/hadoop fs -cat /user/a/output/part-r-00000
bye 1
hello   1
world   2

常见错误及说明
1)未启动yarn时执行MapReduce程序

原因：已经配置了yarn,但没有启动引起的
调整：启动一下yarn

$ sbin/start-yarn.sh