Mapreduce实例——二次排序
在电商网站中，用户进入页面浏览商品时会产生访问日志，记录用户对商品的访问情况，现有goods_visit2表，包含（goods_id,click_num）两个字段，数据内容如下：
goods_id click_num
1010037 100
1010102 100
1010152 97
1010178 96
1010280 104
1010320 103
1010510 104
1010603 96
1010637 97
编写MapReduce代码，功能为根据商品的点击次数(click_num)进行降序排序，再根据goods_id升序排序，并输出所有商品：
package mapreduce8; 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.Text; import org.apache.hadoop.io.WritableComparable; import org.apache.hadoop.io.WritableComparator; 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.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.input.TextInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat; //07.Mapreduce实例——二次排序 public class SecondarySort { public static class IntPair implements WritableComparable<IntPair> { int first; int second; public void set(int left, int right) { first = left; second = right; } public int getFirst() { return first; } public int getSecond() { return second; } @Override public void readFields(DataInput in) throws IOException { // TODO Auto-generated method stub first = in.readInt(); second = in.readInt(); } @Override public void write(DataOutput out) throws IOException { // TODO Auto-generated method stub out.writeInt(first); out.writeInt(second); } @Override public int compareTo(IntPair o) { // TODO Auto-generated method stub if (first != o.first) { return first < o.first ? 1 : -1; } else if (second != o.second) { return second < o.second ? -1 : 1; } else { return 0; } } @Override public int hashCode() { return first * 157 + second; } @Override public boolean equals(Object right) { if (right == null) return false; if (this == right) return true; if (right instanceof IntPair) { IntPair r = (IntPair) right; return r.first == first && r.second == second; } else { return false; } } } 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; } } public static class GroupingComparator extends WritableComparator { protected GroupingComparator() { super(IntPair.class, true); } @Override //Compare two WritableComparables. public int compare(WritableComparable w1, WritableComparable w2) { IntPair ip1 = (IntPair) w1; IntPair ip2 = (IntPair) w2; int l = ip1.getFirst(); int r = ip2.getFirst(); return l == r ? 0 : (l < r ? -1 : 1); } } public static class Map extends Mapper<LongWritable, Text, IntPair, IntWritable> { private final IntPair intkey = new IntPair(); private final IntWritable intvalue = new IntWritable(); public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { String line = value.toString(); StringTokenizer tokenizer = new StringTokenizer(line); int left = 0; int right = 0; if (tokenizer.hasMoreTokens()) { left = Integer.parseInt(tokenizer.nextToken()); if (tokenizer.hasMoreTokens()) right = Integer.parseInt(tokenizer.nextToken()); intkey.set(right, left); intvalue.set(left); context.write(intkey, intvalue); } } } public static class Reduce extends Reducer<IntPair, IntWritable, Text, IntWritable> { private final Text left = new Text(); private static final Text SEPARATOR = new Text("------------------------------------------------"); public void reduce(IntPair key, Iterable<IntWritable> values,Context context) throws IOException, InterruptedException { context.write(SEPARATOR, null); left.set(Integer.toString(key.getFirst())); System.out.println(left); for (IntWritable val : values) { context.write(left, val); //System.out.println(val); } } } public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException { Configuration conf = new Configuration(); Job job = new Job(conf, "secondarysort"); job.setJarByClass(SecondarySort.class); job.setMapperClass(Map.class); job.setReducerClass(Reduce.class); job.setPartitionerClass(FirstPartitioner.class); job.setGroupingComparatorClass(GroupingComparator.class); job.setMapOutputKeyClass(IntPair.class); job.setMapOutputValueClass(IntWritable.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); job.setInputFormatClass(TextInputFormat.class); job.setOutputFormatClass(TextOutputFormat.class); String[] otherArgs=new String[2]; otherArgs[0]="hdfs://192.168.51.100:8020/mymapreduce8/in/goods_visit2"; otherArgs[1]="hdfs://192.168.51.100:8020/mymapreduce8/out"; FileInputFormat.setInputPaths(job, new Path(otherArgs[0])); FileOutputFormat.setOutputPath(job, new Path(otherArgs[1])); System.exit(job.waitForCompletion(true) ? 0 : 1); } }
结果：
原理：
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。本实验中使用的是TextInputFormat，他提供的RecordReder会将文本的字节偏移量作为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>键值对输入给Map的map方法。注意输出应该符合自定义Map中定义的输出<IntPair, IntWritable>。最终是生成一个List<IntPair, IntWritable>。在map阶段的最后，会先调用job.setPartitionerClass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，则可以使用key实现的compareTo方法进行排序。 在本实验中，就使用了IntPair实现的compareTo方法。
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job.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，而这个迭代器的key使用属于同一个组的所有key的第一个key。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。
