Hadoop的mapreduce的簡單用法

　Mapreduce初析

　　Mapreduce是一個計算框架，既然是做計算的框架，那麼表現形式就是有個輸入（input），mapreduce操作這個輸入（input），通過本身定義好的計算模型，得到一個輸出（output），這個輸出就是我們所需要的結果。

　　我們要學習的就是這個計算模型的運行規則。在運行一個mapreduce計算任務時候，任務過程被分爲兩個階段：map階段和reduce階段，每個階段都是用鍵值對（key/value）作爲輸入（input）和輸出（output）。而程序員要做的就是定義好這兩個階段的函數：map函數和reduce函數。

  Mapreduce的基礎實例

  jar包依賴

<dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-client</artifactId>
      <version>2.7.6</version>
</dependency>

代碼實現

 map類

 

public 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);
		}
	}
}

 

reduce類

public 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);
	}

}

  main方法

   

public class WordCount {
	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);

	}
}

打成jar包放到hadoop環境下

./hadoop-2.7.6/bin/hadoop jar hadoop-mapreduce-1.0.0.jar com.dongpeng.hadoop.mapreduce.wordcount.WordCount /user/test.txt /user/in.txt