CentOS 7.6 下利用 Hadoop2.6.0 运行 MapReduce 案例 WordCount

Hadoop 安装可以参考文章：CentOS 7.6 安装 Hadoop2.6.0

零、前言

1. 运行环境

CentOS 7.6
Hadoop 2.6.0
# 一个主节点、一个数据节点
jdk1.7.0_75
hadoop安装目录：/home/hadoop/hadoop-2.6.0

2. Wordcount程序
   Hadoop 自带 wordcount 程序，我们只需要编写测试用例后运行即可，当然也可以自定义 WordCount程序，该案例可参考官网程序：Apache Hadoop 3.2.1 – MapReduce Tutorial。

# hadoop 自带 WordCount 程序位置
/home/hadoop/hadoop-2.6.0/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.6.0.jar

一、启动 Hadoop

# 先关闭所有节点的防火墙设置
systemctl stop firewalld
/home/hadoop/hadoop-2.6.0/sbin/start-dfs.sh

# 停止 hadoop 命令
# /home/hadoop/hadoop-2.6.0/sbin/stop-dfs.sh

二、创建测试文件

vi test.txt

# 添加以下内容
hello summer hi summer try happy hi summer

# 将测试文件复制到 hadoop 文件系统下
hadoop fs -put test.txt /test.txt
hadoop fs -ls /test.txt
hadoop fs -cat /test.txt

三、运行程序

hadoop jar /home/hadoop/hadoop-2.6.0/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.6.0.jar wordcount /test.txt /wc_output

四、查看结果

hadoop fs -ls /wc_output
hadoop fs -cat /wc_output/part-r-00000

五、自定义 WordCount 程序

5.1 创建 WordCount.java

vi WordCount.java

添加以下代码：

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);
  }
}

5.2 编译运行

# 编译生成 class
# Hadoop2.x已经没有了 hadoop-core.jar
# 所以需要单独配置hadoop-common-2.6.0.jar、 hadoop-annotations-2.6.0.jar、hadoop-mapreduce-client-core-2.6.0.jar
# 这些文件都在 /home/hadoop/hadoop-2.6.0/share/hadoop 目录下
# 也可以将这些设置到环境变量中
# javac -classpath 所需jar包路径  WordCount.java
javac -classpath /home/hadoop/hadoop-2.6.0/share/hadoop/common/hadoop-common-2.6.0.jar:/home/hadoop/hadoop-2.6.0/share/hadoop/common/lib/hadoop-annotations-2.6.0.jar:/home/hadoop/hadoop-2.6.0/share/hadoop/mapreduce/hadoop-mapreduce-client-core-2.6.0.jar WordCount.java
# 打包成 jar 文件
jar cf wc.jar WordCount*.class
# 利用 hadoop 运行程序
hadoop jar wc.jar WordCount /test.txt /wc_output

5.3 查看结果

hadoop fs -ls /wc_output
hadoop fs -cat /wc_output/part-r-00000

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参考文章：【1】运行Hadoop自带的wordcount单词统计程序-香飘叶子-51CTO博客
     【2】Hadoop第一个样例Wordcount运行笔记 - Joe’s Blog
     【3】Apache Hadoop 3.2.1 – MapReduce Tutorial
     【4】Hadoop 2.x 下使用javac编译java文件 - midori的博客 - CSDN博客