hadoop 初识,环境配置，运行demo

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本文介绍Hadoop在单机模式及伪分布模式下的安装配置步骤，并演示了使用MapReduce统计单词频率的示例。

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参考：http://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-common/SingleCluster.html

jdk安装以及配置先配置好

下载hadoop安装地址：http://apache.fayea.com/hadoop/common/stable/ 可以返回上级目录下载更新的

解压到目录，/usr/tools/ 没有此目录就先创建

切换到 hadoop安装目录 cd /usr/tools/hadoop-2.7.3

hadoop支持独立模式运行，以本地文件系统方式作为输入和输出

  $ mkdir input   //创建文件输入目录

切换到input目录创建几个文件

echo hello world, I am jungle. bye world>file1
echo hello hadoop. hello jungle. bye hadoop.>file2
echo the great software is hadoop.>>file2
 统计各个单词出现的次数 
$ bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.3.jar wordcount input output   $ cat output/*

展示输出结果，结果如下：

伪分布式方式：

修改配置文件

etc/hadoop/core-site.xml:
<configuration>
    <property>
        <name>fs.defaultFS</name>
        <value>hdfs://localhost:9000</value>
    </property>
</configuration>
etc/hadoop/hdfs-site.xml:
<configuration>
    <property>
        <name>dfs.replication</name>
        <value>1</value>
    </property>
</configuration>


No检查ssh登录是否不需要密码
  $ ssh localhost
如果需要密码， 执行下面操作
  $ ssh-keygen -t rsa -P '' -f ~/.ssh/id_rsa
  $ cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
  $ chmod 0600 ~/.ssh/authorized_keys

格式化文件系统
  $ bin/hdfs namenode -format
开启 NameNode 进程 and DataNode 进程:
  $ sbin/start-dfs.sh

  创建分布式文件系统目录 input  并且把本地文件夹input内的文件都复制到hdfs中input目录下

 [root@localhost hadoop-2.7.3]#hdfs dfs -mkdir input  
[root@localhost hadoop-2.7.3]# hdfs dfs -put /usr/tools/hadoop-2.7.3/input/* /input

执行mapreduce操作统计各个单词出现的次数

  $bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.3.jar wordcount input output

查看输出结果

$ bin/hdfs dfs -get output output
  $ cat output/*

或者

 $ bin/hdfs dfs -cat output/*

贴上示例中jar包的源码统计每个单词的个数

package org.apache.hadoop.examples;

import java.io.IOException;
import java.io.PrintStream;
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.Mapper.Context;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.Reducer.Context;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;

public class WordCount
{
  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);
  }

  public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable>
  {
    private IntWritable result = new IntWritable();

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

  public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable>
  {
    private static final IntWritable one = new IntWritable(1);
    private Text word = new Text();

    public void map(Object key, Text value, Mapper<Object, Text, Text, IntWritable>.Context context) throws IOException, InterruptedException
    {
      StringTokenizer itr = new StringTokenizer(value.toString());
      while (itr.hasMoreTokens()) {
        this.word.set(itr.nextToken());
        context.write(this.word, one);
      }
    }
  }
}