Mapreduce 订单分组案例
程序执行流程如下:
map()-->getPartition()分区--->write()(序列化,每一行都顺序执行这三个方法)--->readFields()---->compareTo()排序---->readFields()--->分组compare--->reduce()
对于每一行的内容,依次执行map()-->getPartition()---->write()(序列化,会在指定的输出目录下生成temporary目录),全部序列化完成之后执行readFields(),之后进行排序,全部排序完毕后才会再次执行readFields(),
之后进行分组,分出一组,执行一次reduce()写出数据
1.OrderBean(分了区之后就不需要对OrderId进行排序了,因为同一分区OrderId相同)
1 package mapreduce.order;
2
3 import java.io.DataInput;
4 import java.io.DataOutput;
5 import java.io.IOException;
6 import org.apache.hadoop.io.WritableComparable;
7
8 public class OrderBean implements WritableComparable<OrderBean>{
9 private String orderId;
10 private Double price;
11
12 public String getOrderId() {
13 return orderId;
14 }
15
16 public void setOrderId(String orderId) {
17 this.orderId = orderId;
18 }
19
20 public Double getPrice() {
21 return price;
22 }
23
24 public void setPrice(Double price) {
25 this.price = price;
26 }
27
28 public OrderBean() {
29 super();
30 }
31
32 /**
33 * 序列化
34 * @param out
35 * @throws IOException
36 */
37 @Override
38 public void write(DataOutput out) throws IOException {
39 out.writeUTF(orderId);
40 out.writeDouble(price);
41 }
42
43 /**
44 * 反序列化
45 * @param in
46 * @throws IOException
47 */
48 @Override
49 public void readFields(DataInput in) throws IOException {
50 orderId = in.readUTF();
51 price = in.readDouble();
52 }
53
54
55 @Override
56 public String toString() {
57 return orderId + "\t" + price;
58 }
59
60 /**
61 * 二次排序(如果进行了分区,可以直接对price排序,不再需要二次排序)
62 * @param o
63 * @return
64 */
65 @Override
66 public int compareTo(OrderBean o) {
67 //先比较orderId(字符串)
68 // int result = this.getOrderId().compareTo(o.getOrderId());
69
70 //相同再比较price
71 // if(result == 0) {
72 //降序,数字的compareTo() -1表示小于
73 int result = this.getPrice() > o.getPrice()?-1:1;
74 // }
75 return result;
76 }
77 }
2.OrderMapper
1 package mapreduce.order;
2
3 import java.io.IOException;
4 import org.apache.hadoop.io.LongWritable;
5 import org.apache.hadoop.io.NullWritable;
6 import org.apache.hadoop.io.Text;
7 import org.apache.hadoop.mapreduce.Mapper;
8
9 public class OrderMapper extends Mapper<LongWritable, Text, OrderBean, NullWritable> {
10 OrderBean order = new OrderBean();
11
12 @Override
13 protected void map(LongWritable key, Text value,
14 Mapper<LongWritable, Text, OrderBean, NullWritable>.Context context)
15 throws IOException, InterruptedException {
16
17 // 1.读取一行
18 String line = value.toString();
19
20 // 2.切割并封装对象
21 String[] split = line.split("\t");
22 order.setOrderId(split[0]);
23 order.setPrice(Double.parseDouble(split[2]));
24
25 // 3.输出
26 context.write(order, NullWritable.get());
27 }
28 }
3.OrderPartioner
1 package mapreduce.order;
2
3 import org.apache.hadoop.io.NullWritable;
4 import org.apache.hadoop.mapreduce.Partitioner;
5
6 /**
7 * 分区
8 * @author tele
9 *
10 */
11 public class OrderPartioner extends Partitioner<OrderBean, NullWritable> {
12 /**
13 * 默认是根据orderBean的hashcode进行分区
14 * 现在更改为orderBean对象的orderId进行分区,这样相同orderId的即可进入同一分区
15 */
16 @Override
17 public int getPartition(OrderBean key, NullWritable value, int numReduceTasks) {
18 return (key.getOrderId().hashCode() & Integer.MAX_VALUE) % numReduceTasks;
19 }
20 }
4.OrderGroupComparator
1 package mapreduce.order;
2
3 import org.apache.hadoop.io.WritableComparable;
4 import org.apache.hadoop.io.WritableComparator;
5
6 /**
7 * reduce分组的方法
8 *
9 * @author tele
10 *
11 */
12 public class OrderGroupComparator extends WritableComparator {
13 /**
14 * 确保类型转换成功,能够创建实例,如果是false,创建的实例为null
15 */
16 protected OrderGroupComparator() {
17 super(OrderBean.class, true);
18 }
19
20 /**
21 * reduce默认是根据key进行分组,即此处的orderBean,重写之后
22 * 根据orderBean的orderId进行分组,相同的orderId的orderBean将会分到同一组
23 */
24 @Override
25 public int compare(WritableComparable a, WritableComparable b) {
26 OrderBean bean1 = (OrderBean) a;
27 OrderBean bean2 = (OrderBean) b;
28 return bean1.getOrderId().compareTo(bean2.getOrderId());
29 }
30 }
5.OrderReducer(reduce()每次读入一组)
1 package mapreduce.order;
2
3 import java.io.IOException;
4 import org.apache.hadoop.io.NullWritable;
5 import org.apache.hadoop.mapreduce.Reducer;
6
7 public class OrderReducer extends Reducer<OrderBean, NullWritable, OrderBean, NullWritable> {
8 @Override
9 protected void reduce(OrderBean key, Iterable<NullWritable> value,
10 Reducer<OrderBean, NullWritable, OrderBean, NullWritable>.Context context)
11 throws IOException, InterruptedException {
12 context.write(key, NullWritable.get());
13 }
14 }
6.OrderDriver(必须设置分组类)
1 package mapreduce.order;
2
3 import java.io.IOException;
4 import org.apache.hadoop.conf.Configuration;
5 import org.apache.hadoop.fs.Path;
6 import org.apache.hadoop.io.NullWritable;
7 import org.apache.hadoop.mapreduce.Job;
8 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
9 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
10
11 /**
12 *
13 * @author tele
14 *
15 */
16 public class OrderDriver {
17 public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
18 // 1.获得job实例
19 Configuration conf = new Configuration();
20 Job job = Job.getInstance(conf);
21
22 // 2.设置driverclass
23 job.setJarByClass(OrderDriver.class);
24 // mapclass
25 job.setMapperClass(OrderMapper.class);
26 // reduceclass
27 job.setReducerClass(OrderReducer.class);
28
29 // 3.map输入输出数据类型
30 job.setMapOutputKeyClass(OrderBean.class);
31 job.setMapOutputValueClass(NullWritable.class);
32
33 // 4.最终输出数据类型
34 job.setOutputKeyClass(OrderBean.class);
35 job.setOutputValueClass(NullWritable.class);
36
37 // 7.设置分区类
38 job.setPartitionerClass(OrderPartioner.class);
39 job.setNumReduceTasks(3);
40
41 // 8.设置分组类
42 job.setGroupingComparatorClass(OrderGroupComparator.class);
43
44 // 5.输入与输出路径
45 FileInputFormat.setInputPaths(job, new Path(args[0]));
46 FileOutputFormat.setOutputPath(job, new Path(args[1]));
47
48 // 6.提交与退出
49 boolean result = job.waitForCompletion(true);
50 System.exit(result ? 0 : 1);
51
52 }
53 }
