MapReduce的数据流程：

1. 1. 预先加载本地的输入文件
   2. 经过MAP处理产生中间结果
   3. 经过shuffle程序将相同key的中间结果分发到同一节点上处理
   4. Recude处理产生结果输出
   5. 将结果输出保存在hdfs上

MAP

在map阶段，使用job.setInputFormatClass定义的InputFormat将输入的数据集分割成小数据块splites，

同时InputFormat提供一个RecordReder的实现。默认的是TextInputFormat， 他提供的RecordReder会将文本的一行的偏移量作为key，这一行的文本作为value。 这就是自定义Map的输入是<LongWritable, Text>的原因。 然后调用自定义Map的map方法，将一个个<LongWritable, Text>对输入给Map的map方法。

最终是按照自定义的MAP的输出key类，输出class类生成一个List<MapOutputKeyClass, MapOutputValueClass>。

Partitioner

在map阶段的最后，会先调用job.setPartitionerClass设置的类对这个List进行分区，

每个分区映射到一个reducer。每个分区内又调用job.setSortComparatorClass设置的key比较函数类排序。

可以看到，这本身就是一个二次排序。

如果没有通过job.setSortComparatorClass设置key比较函数类，则使用key的实现的compareTo方法。

Shuffle：

将每个分区根据一定的规则，分发到reducer处理

Sort

在reduce阶段，reducer接收到所有映射到这个reducer的map输出后，

也是会调用job.setSortComparatorClass设置的key比较函数类对所有数据对排序。 然后开始构造一个key对应的value迭代器。这时就要用到分组， 使用jobjob.setGroupingComparatorClass设置的分组函数类。只要这个比较器比较的两个key相同， 他们就属于同一个组，它们的value放在一个value迭代器

Reduce

最后就是进入Reducer的reduce方法，reduce方法的输入是所有的（key和它的value迭代器）。 同样注意输入与输出的类型必须与自定义的Reducer中声明的一致。

一个更为详细的流程图

具体的例子：

是hadoop mapreduce example中的例子，自己改写了一下并加入的注释

import java.io.DataInput;import java.io.DataOutput;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.LongWritable;import org.apache.hadoop.io.RawComparator;import org.apache.hadoop.io.Text;import org.apache.hadoop.io.WritableComparable;import org.apache.hadoop.io.WritableComparator;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;import org.apache.hadoop.mapreduce.Job;import org.apache.hadoop.mapreduce.Mapper;import org.apache.hadoop.mapreduce.Partitioner;import org.apache.hadoop.mapreduce.Reducer;import org.apache.hadoop.util.GenericOptionsParser;import com.catt.cdh.mr.example.SecondarySort2.FirstPartitioner;import com.catt.cdh.mr.example.SecondarySort2.Reduce;/** * This is an example Hadoop Map/Reduce application. * It reads the text input files that must contain two integers per a line. * The output is sorted by the first and second number and grouped on the * first number. *  * To run: bin/hadoop jar build/hadoop-examples.jar secondarysort * in-dir out-dir */public class SecondarySort {	/**	 * Define a pair of integers that are writable.	 * They are serialized in a byte comparable format.	 */	public static class IntPair implements WritableComparable
    
      {		private int first = 0;		private int second = 0;		/**		 * Set the left and right values.		 */		public void set(int left, int right) {			first = left;			second = right;		}		public int getFirst() {			return first;		}		public int getSecond() {			return second;		}		/**		 * Read the two integers.		 * Encoded as: MIN_VALUE -> 0, 0 -> -MIN_VALUE, MAX_VALUE-> -1		 */		@Override		public void readFields(DataInput in) throws IOException {			first = in.readInt() + Integer.MIN_VALUE;			second = in.readInt() + Integer.MIN_VALUE;		}		@Override		public void write(DataOutput out) throws IOException {			out.writeInt(first - Integer.MIN_VALUE);			out.writeInt(second - Integer.MIN_VALUE);		}		@Override		// The hashCode() method is used by the HashPartitioner (the default		// partitioner in MapReduce)		public int hashCode() {			return first * 157 + second;		}		@Override		public boolean equals(Object right) {			if (right instanceof IntPair) {				IntPair r = (IntPair) right;				return r.first == first && r.second == second;			} else {				return false;			}		}		/** A Comparator that compares serialized IntPair. */		public static class Comparator extends WritableComparator {			public Comparator() {				super(IntPair.class);			}			// 针对key进行比较，调用多次			public int compare(byte[] b1, int s1, int l1, byte[] b2, int s2,					int l2) {				return compareBytes(b1, s1, l1, b2, s2, l2);			}		}		static {			// 注意：如果不进行注册，则使用key.compareTo方法进行key的比较			// register this comparator			WritableComparator.define(IntPair.class, new Comparator());		}		// 如果不注册WritableComparator，则使用此方法进行key的比较		@Override		public int compareTo(IntPair o) {			if (first != o.first) {				return first < o.first ? -1 : 1;			} else if (second != o.second) {				return second < o.second ? -1 : 1;			} else {				return 0;			}		}	}	/**	 * Partition based on the first part of the pair.	 */	public static class FirstPartitioner extends			Partitioner
     
       {		@Override		public int getPartition(IntPair key, IntWritable value,				int numPartitions) {			return Math.abs(key.getFirst() * 127) % numPartitions;		}	}	/**	 * Compare only the first part of the pair, so that reduce is called once	 * for each value of the first part.	 */	public static class FirstGroupingComparator implements			RawComparator
      
        {		// 针对key调用，调用多次		@Override		public int compare(byte[] b1, int s1, int l1, byte[] b2, int s2, int l2) {			return WritableComparator.compareBytes(b1, s1, Integer.SIZE / 8,					b2, s2, Integer.SIZE / 8);		}		// 没有监控到被调用，不知道有什么用		@Override		public int compare(IntPair o1, IntPair o2) {			int l = o1.getFirst();			int r = o2.getFirst();			return l == r ? 0 : (l < r ? -1 : 1);		}	}	/**	 * Read two integers from each line and generate a key, value pair	 * as ((left, right), right).	 */	public static class MapClass extends			Mapper
       
         {		private final IntPair key = new IntPair();		private final IntWritable value = new IntWritable();		@Override		public void map(LongWritable inKey, Text inValue, Context context)				throws IOException, InterruptedException {			StringTokenizer itr = new StringTokenizer(inValue.toString());			int left = 0;			int right = 0;			if (itr.hasMoreTokens()) {				left = Integer.parseInt(itr.nextToken());				if (itr.hasMoreTokens()) {					right = Integer.parseInt(itr.nextToken());				}				key.set(left, right);				value.set(right);				context.write(key, value);			}		}	}	/**	 * A reducer class that just emits the sum of the input values.	 */	public static class Reduce extends			Reducer
        
          {		private static final Text SEPARATOR = new Text(				"------------------------------------------------");		private final Text first = new Text();		@Override		public void reduce(IntPair key, Iterable
         
           values, Context context) throws IOException, InterruptedException { context.write(SEPARATOR, null); first.set(Integer.toString(key.getFirst())); for (IntWritable value : values) { context.write(first, value); } } } public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); String[] ars = new String[] { "hdfs://data2.kt:8020/test/input", "hdfs://data2.kt:8020/test/output" }; conf.set("fs.default.name", "hdfs://data2.kt:8020/"); String[] otherArgs = new GenericOptionsParser(conf, ars) .getRemainingArgs(); if (otherArgs.length != 2) { System.err.println("Usage: secondarysort 
           
           
            "); System.exit(2); } Job job = new Job(conf, "secondary sort"); job.setJarByClass(SecondarySort.class); job.setMapperClass(MapClass.class); // 不再需要Combiner类型，因为Combiner的输出类型
            
             对Reduce的输入类型
             
              不适用 // job.setCombinerClass(Reduce.class); // Reducer类型 job.setReducerClass(Reduce.class); // 分区函数 job.setPartitionerClass(FirstPartitioner.class); // 设置setSortComparatorClass，在partition后， // 每个分区内又调用job.setSortComparatorClass设置的key比较函数类排序 // 另外，在reducer接收到所有映射到这个reducer的map输出后， // 也是会调用job.setSortComparatorClass设置的key比较函数类对所有数据对排序 // job.setSortComparatorClass(GroupingComparator2.class); // 分组函数 job.setGroupingComparatorClass(FirstGroupingComparator.class); // the map output is IntPair, IntWritable // 针对自定义的类型，需要指定MapOutputKeyClass job.setMapOutputKeyClass(IntPair.class); // job.setMapOutputValueClass(IntWritable.class); // the reduce output is Text, IntWritable job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); FileInputFormat.addInputPath(job, new Path(otherArgs[0])); FileOutputFormat.setOutputPath(job, new Path(otherArgs[1])); System.exit(job.waitForCompletion(true) ? 0 : 1); }}