剖解MapReduce

Hadoop数据类型

为了能让MapReduce的key/value对能够在集群中移动，MapReduce框架提供了一个序列化key/value对的方法

但MapReduce并不允许任意的类都能做为key，只有实现了WriableComparable或者Wirable接口（说明，Wriable也实现了WriableComparable接口）的类才能做为键，因为在reduce阶段要根据key来进行排序，并将相同key的值进行归并。

所以要想自己写的类能做为key的话，则此类必须实现Comparable接口。

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下面我们来写一个这样的类代表两个城市之间的航

package csdn.jtlyuan;import java.io.DataInput;import java.io.DataOutput;import java.io.IOException;import org.apache.hadoop.io.WritableComparable;public class Edge implements WritableComparable<Edge>{private String startNode;private String endNode;@Overridepublic void readFields(DataInput in) throws IOException {this.startNode=in.readUTF();this.endNode=in.readUTF();}@Overridepublic void write(DataOutput out) throws IOException {out.writeUTF(this.startNode);out.writeUTF(this.endNode);}@Overridepublic int compareTo(Edge o) {return (this.startNode.compareTo(o.startNode) != 0)?this.startNode.compareTo(o.startNode):this.endNode.compareTo(o.endNode);}}

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MapReduce程序的各种阶段：

Mapper：把输入映射为key value形式

Reducer：接受来自各个mapper的输出，它按照key/value对的key进行排序，并将相同key的值归并放在同一个reducer里。

Partition：重定向Mapper输出，默认的作法是对键的散列来觉得reducer，hadoop可以通过HashPartitioner类改变这种策略。

Combiner：本地reduce

Shuffle：当第一个map任务完成后，节点可能还要继续执行更多的map任务，但这时候也开始把map任务的中间输出交换到需要它们的reducer那里去，这个移动map输出到reducer的过程叫做shuffle

举例：WordCount程序：

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;import org.apache.hadoop.util.GenericOptionsParser;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);//Context接受reduce阶段的输出    }  }  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> <out>");      System.exit(2);    }    Job job = new Job(conf, "word count");//根据配置来实例化一个Job,并给Job设置名字       job.setJarByClass(WordCount.class);//Set the Jar by finding where a given class came from.    job.setMapperClass(TokenizerMapper.class);//Set the Mapper for the job    job.setCombinerClass(IntSumReducer.class);//Set the combiner class for the job.    job.setReducerClass(IntSumReducer.class);//Set the Reducer for the job        job.setOutputKeyClass(Text.class);//Set the key class for the job output data.    job.setOutputValueClass(IntWritable.class);//Set the value class for job outputs.        FileInputFormat.addInputPath(job, new Path(otherArgs[0]));//设置输入路径    FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));//设置输出位置 (在运行之前，该目录不应该存在，否则会报错并拒绝运行该任务)       System.exit(job.waitForCompletion(true) ? 0 : 1);//job.waitForCompletion Submit the job to the cluster and wait for it to finish.  }}

下面的图可以清晰得开到怎样对两个文件的单词进行统计的。