Hadoop worldcount

之前的公司和如今的公司，都用到了hadoop和hdfs。一直沒入門，今天照着官網寫了一個hadoop worldcount demo

1. hadoop是一個框架，什麼是框架，spring是一個框架、mybatis是一個框架，框架是把系統中通用的功能寫進去，減小開發工做量。好比基於spring boot開發一個web應用，直接寫一個java類，加一些註解，打成jar包，java -jar demo.java即完成應用開發。

　　spring boot也是基於java serlet、tomcat、jetty等封裝的一個框架，有了這個框架，咱們就不用再寫servlet實現類，配置web.xml等重複工做

2. hadoop須要的數據存放在hdfs裏面，這裏參照官網，在本機運行了一個僞分佈式的hdfs

3. demo組成，寫worldcount類，打成jar包，放到本機hadoop運行，從hdfs讀文件內容，把結果寫到hdfs中

4. 注意參考官網

　　mapreduce官網: http://hadoop.apache.org/docs/stable/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html#Example:_WordCount_v1.0

　　hdfs官網：http://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-common/SingleCluster.html#Standalone_Operation

pom.xml

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <groupId>com.gxf</groupId>
    <artifactId>hadoop_demo</artifactId>
    <version>1.0-SNAPSHOT</version>

    <dependencies>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-core</artifactId>
            <version>1.2.1</version>
        </dependency>
    </dependencies>
    
</project>

WordCount.java這個直接從官網copy過來的

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);
  }
}

這裏沒有加package，由於我搞不定，因此去掉了包名

接着就是打成jar包、準備文本文件放到hdfs、使用hadoop運行jar、查看結果。這些步驟在官網上有