第7章. Hadoop MapReduce介紹

7.1 wordCount.java介紹
在下列網址hadoop說明文件中有wordcount.java 的程式碼：

http://hadoop.apache.org/docs/current/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html

7.2 編輯wordCount.java
Step1 建立wordcount目錄

mkdir -p ~/wordcount/input
cd  ~/wordcount

Step2 編輯WordCount.java

gedit WordCount.java

Step3~6 編輯WordCount.java
在gedit 輸入 WordCount.java完整程式碼

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{

    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 {
    private IntWritable result = new IntWritable();

    public void reduce(Text key, Iterable 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);
  }
}

7.3 編譯wordCount.java
Step1 修改編譯所需要的環境變數檔

sudo gedit ~/.bashrc

輸入下列內容

export PATH=${JAVA_HOME}/bin:${PATH}
export HADOOP_CLASSPATH=${JAVA_HOME}/lib/tools.jar

Step2 讓 ~/.bashrc 修改的設定值生效

source ~/.bashrc

Step3 開始編譯

hadoop com.sun.tools.javac.Main WordCount.java
jar cf wc.jar WordCount*.class
ll

7.4 建立測試文字檔

cp /usr/local/hadoop/LICENSE.txt ~/wordcount/input
ll ~/wordcount/input
start-all.sh
hadoop fs -mkdir -p /user/hduser/wordcount/input
cd ~/wordcount/input
hadoop fs -copyFromLocal LICENSE.txt /user/hduser/wordcount/input
hadoop fs -ls /user/hduser/wordcount/input

7.5 執行wordCount.java

cd ~/wordcount
hadoop jar wc.jar WordCount /user/hduser/wordcount/input/LICENSE.txt /user/hduser/wordcount/output

7.6 查看執行結果

hadoop fs -ls /user/hduser/wordcount/output
hadoop fs -cat /user/hduser/wordcount/output/part-r-00000

 

以上內容節錄自這本書有詳細介紹:
  Hadoop+Spark大數據巨量分析與機器學習整合開發實戰 http://www.books.com.tw/products/0010695285

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