MapReduce的数据流程、执行流程
MapReduce的数据流程：
-
- 预先加载本地的输入文件
- 经过MAP处理产生中间结果
- 经过shuffle程序将相同key的中间结果分发到同一节点上处理
- Recude处理产生结果输出
- 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
* <i>in-dir</i> <i>out-dir</i>
*/
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<IntPair> {
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<IntPair, IntWritable> {
@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<IntPair> {
// 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<LongWritable, Text, IntPair, IntWritable> {
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<IntPair, IntWritable, Text, IntWritable> {
private static final Text SEPARATOR = new Text(
"------------------------------------------------");
private final Text first = new Text();
@Override
public void reduce(IntPair key, Iterable<IntWritable> 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 <in> <out>");
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<Text,
// IntWritable>对Reduce的输入类型<IntPair, IntWritable>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);
}
}
