36 MAPREDUCE自定义GroupingComparator

/**
 * 用于控制shuffle过程中reduce端对kv对的聚合逻辑
 * @author duanhaitao@itcast.cn
 *
 */
public class ItemidGroupingComparator extends WritableComparator {
  protected ItemidGroupingComparator() {
    super(OrderBean.class, true);
  }
  @Override
  public int compare(WritableComparable a, WritableComparable b) {
    OrderBean abean = (OrderBean) a;
    OrderBean bbean = (OrderBean) b;
    //将item_id相同的bean都视为相同，从而聚合为一组
    return abean.getItemid().compareTo(bbean.getItemid());
  }
}

/**
 * 订单信息bean，实现hadoop的序列化机制
 * @author duanhaitao@itcast.cn
 *
 */
public class OrderBean implements WritableComparable<OrderBean>{
  private Text itemid;
  private DoubleWritable amount;
  public OrderBean() {
  }
  public OrderBean(Text itemid, DoubleWritable amount) {
    set(itemid, amount);
  }
  public void set(Text itemid, DoubleWritable amount) {
    this.itemid = itemid;
    this.amount = amount;
  }
  public Text getItemid() {
    return itemid;
  }
  public DoubleWritable getAmount() {
    return amount;
  }
  @Override
  public int compareTo(OrderBean o) {
    int cmp = this.itemid.compareTo(o.getItemid());
    if (cmp == 0) {
      cmp = -this.amount.compareTo(o.getAmount());
    }
    return cmp;
  }
  @Override
  public void write(DataOutput out) throws IOException {
    out.writeUTF(itemid.toString());
    out.writeDouble(amount.get());
  }
  @Override
  public void readFields(DataInput in) throws IOException {
    String readUTF = in.readUTF();
    double readDouble = in.readDouble();
    this.itemid = new Text(readUTF);
    this.amount= new DoubleWritable(readDouble);
  }
  @Override
  public String toString() {
    return itemid.toString() + "\t" + amount.get();
  }
}

/**
 * 利用secondarysort机制输出每种item订单金额最大的记录
 * @author duanhaitao@itcast.cn
 *
 */
public class SecondarySort {
  static class SecondarySortMapper extends Mapper<LongWritable, Text, OrderBean, NullWritable>{
    OrderBean bean = new OrderBean();
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
      String line = value.toString();
      String[] fields = StringUtils.split(line, "\t");
      bean.set(new Text(fields[0]), new DoubleWritable(Double.parseDouble(fields[1])));
      context.write(bean, NullWritable.get());
    }
  }
  static class SecondarySortReducer extends Reducer<OrderBean, NullWritable, OrderBean, NullWritable>{
    //在设置了groupingcomparator以后，这里收到的kv数据 就是：  <1001 87.6>,null  <1001 76.5>,null  .... 
    //此时，reduce方法中的参数key就是上述kv组中的第一个kv的key：<1001 87.6>
    //要输出同一个item的所有订单中最大金额的那一个，就只要输出这个key
    @Override
    protected void reduce(OrderBean key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {
      context.write(key, NullWritable.get());
    }
  }
  public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();
    Job job = Job.getInstance(conf);
    job.setJarByClass(SecondarySort.class);
    job.setMapperClass(SecondarySortMapper.class);
    job.setReducerClass(SecondarySortReducer.class);
    job.setOutputKeyClass(OrderBean.class);
    job.setOutputValueClass(NullWritable.class);
    FileInputFormat.setInputPaths(job, new Path(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));
    //指定shuffle所使用的GroupingComparator类
    job.setGroupingComparatorClass(ItemidGroupingComparator.class);
    //指定shuffle所使用的partitioner类
    job.setPartitionerClass(ItemIdPartitioner.class);
    job.setNumReduceTasks(3);
    job.waitForCompletion(true);
  }
}