Hadoop的mapreduce的简单用法

　Mapreduce初析

　　Mapreduce是一个计算框架，既然是作计算的框架，那么表现形式就是有个输入（input），mapreduce操做这个输入（input），经过自己定义好的计算模型，获得一个输出（output），这个输出就是咱们所须要的结果。

　　咱们要学习的就是这个计算模型的运行规则。在运行一个mapreduce计算任务时候，任务过程被分为两个阶段：map阶段和reduce阶段，每一个阶段都是用键值对（key/value）做为输入（input）和输出（output）。而程序员要作的就是定义好这两个阶段的函数：map函数和reduce函数。

  Mapreduce的基础实例

  jar包依赖

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

代码实现

 map类

 

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类

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环境下

./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