MapReduce实战案例(3)

案例三: MR实战之TOPN(自定义GroupingComparator)

项目准备

1. 需求+测试数据

有如下订单数据

订单id	商品id	成交金额
Order_0000001	Pdt_01	222.8
Order_0000001	Pdt_05	25.8
Order_0000002	Pdt_03	522.8
Order_0000002	Pdt_04	122.4
Order_0000002	Pdt_05	722.4
Order_0000003	Pdt_01	222.8

现在需要求出每一个订单中成交金额最大的一笔交易

1. 分析

   a) 利用“订单id和成交金额”作为key，可以将map阶段读取到的所有订单数据按照id分区，按照金额排序，发送到reduce

   b) 在reduce端利用groupingcomparator将订单id相同的kv聚合成组，然后取第一个即是最大值

项目实现

a)自定义groupingcomparator

/*** @Author 千锋大数据教学团队* @Company 千锋好程序员大数据* @Description 用于控制shuffle过程中reduce端对kv对的聚合逻辑*/
public class ItemidGroupingComparator extends WritableComparator {protected ItemidGroupingComparator() {super(OrderBean.class, true);}@Overridepublic 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());}
}
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b)定义订单信息bean

/*** @Author 千锋大数据教学团队* @Company 千锋好程序员大数据* @Description 订单信息bean，实现hadoop的序列化机制*/
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;}@Overridepublic int compareTo(OrderBean o) {int cmp = this.itemid.compareTo(o.getItemid());if (cmp == 0) {cmp = -this.amount.compareTo(o.getAmount());}return cmp;}@Overridepublic void write(DataOutput out) throws IOException {out.writeUTF(itemid.toString());out.writeDouble(amount.get());}@Overridepublic void readFields(DataInput in) throws IOException {String readUTF = in.readUTF();double readDouble = in.readDouble();this.itemid = new Text(readUTF);this.amount= new DoubleWritable(readDouble);}@Overridepublic String toString() {return itemid.toString() + "\t" + amount.get();}
}
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c) 编写MapReduce处理流程

/*** @Author 千锋大数据教学团队* @Company 千锋好程序员大数据* @Description 利用secondarysort机制输出每种item订单金额最大的记录*/public class SecondarySort {static class SecondarySortMapper extends Mapper<LongWritable, Text, OrderBean, NullWritable>{OrderBean bean = new OrderBean();@Overrideprotected 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@Overrideprotected 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);}}

 

 也可以观看视频：

千锋大数据Hadoop全新增强版-先导片