MapReduce实战（二）自定义类型排序

需求：

基于上一道题，我想将结果按照总流量的大小由大到小输出。

思考：

默认mapreduce是对key字符串按照字母进行排序的，而我们想任意排序，只需要把key设成一个类，再对该类写一个compareTo（大于要比较对象返回1，等于返回0，小于返回-1）方法就可以了。

注：这里如果是实现java.lang.Comparable接口，最终报错，还是直接实现WritableComparable吧。

FlowBean.java更改如下：

package cn.darrenchan.hadoop.mr.flow;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;

import org.apache.hadoop.io.Writable;
import org.apache.hadoop.io.WritableComparable;

public class FlowBean implements WritableComparable<FlowBean> {
    private String phoneNum;// 手机号
    private long upFlow;// 上行流量
    private long downFlow;// 下行流量
    private long sumFlow;// 总流量

    public FlowBean() {
        super();
    }

    public FlowBean(String phoneNum, long upFlow, long downFlow) {
        super();
        this.phoneNum = phoneNum;
        this.upFlow = upFlow;
        this.downFlow = downFlow;
        this.sumFlow = upFlow + downFlow;
    }

    public String getPhoneNum() {
        return phoneNum;
    }

    public void setPhoneNum(String phoneNum) {
        this.phoneNum = phoneNum;
    }

    public long getUpFlow() {
        return upFlow;
    }

    public void setUpFlow(long upFlow) {
        this.upFlow = upFlow;
    }

    public long getDownFlow() {
        return downFlow;
    }

    public void setDownFlow(long downFlow) {
        this.downFlow = downFlow;
    }

    public long getSumFlow() {
        return sumFlow;
    }

    public void setSumFlow(long sumFlow) {
        this.sumFlow = sumFlow;
    }

    @Override
    public String toString() {
        return upFlow + "	" + downFlow + "	" + sumFlow;
    }

    // 从数据流中反序列出对象的数据
    // 从数据流中读出对象字段时，必须跟序列化时的顺序保持一致
    @Override
    public void readFields(DataInput in) throws IOException {
        phoneNum = in.readUTF();
        upFlow = in.readLong();
        downFlow = in.readLong();
        sumFlow = in.readLong();
    }

    // 将对象数据序列化到流中
    @Override
    public void write(DataOutput out) throws IOException {
        out.writeUTF(phoneNum);
        out.writeLong(upFlow);
        out.writeLong(downFlow);
        out.writeLong(sumFlow);
    }

    @Override
    public int compareTo(FlowBean flowBean) {
        return sumFlow > flowBean.getSumFlow() ? -1 : 1;
    }

}

建立文件SortMR.java:

package cn.darrenchan.hadoop.mr.flowsort;

import java.io.IOException;

import org.apache.commons.io.output.NullWriter;
import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import cn.darrenchan.hadoop.mr.flow.FlowBean;

//执行命令：hadoop jar flowsort.jar cn.darrenchan.hadoop.mr.flowsort.SortMR /flow/output /flow/outputsort
public class SortMR {
    public static class SortMapper extends
            Mapper<LongWritable, Text, FlowBean, NullWritable> {
        // 拿到一行数据，切分出各字段，封装为一个flowbean，作为key输出
        @Override
        protected void map(LongWritable key, Text value, Context context)
                throws IOException, InterruptedException {
            String line = value.toString();
            String[] words = StringUtils.split(line, "	");

            String phoneNum = words[0];
            long upFlow = Long.parseLong(words[1]);
            long downFlow = Long.parseLong(words[2]);

            context.write(new FlowBean(phoneNum, upFlow, downFlow),
                    NullWritable.get());
        }
    }

    public static class SortReducer extends
            Reducer<FlowBean, NullWritable, Text, FlowBean> {
        @Override
        protected void reduce(FlowBean key, Iterable<NullWritable> values,
                Context context) throws IOException, InterruptedException {
            String phoneNum = key.getPhoneNum();
            context.write(new Text(phoneNum), key);
        }
    }

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);

        job.setJarByClass(SortMR.class);

        job.setMapperClass(SortMapper.class);
        job.setReducerClass(SortReducer.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(FlowBean.class);

        job.setMapOutputKeyClass(FlowBean.class);
        job.setMapOutputValueClass(NullWritable.class);

        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

我们现在处理的结果是上一次实验的输出结果，打成jar包flowsort.jar，执行命令：

hadoop jar flowsort.jar cn.darrenchan.hadoop.mr.flowsort.SortMR /flow/output /flow/outputsort

得到的处理信息如下：

17/02/26 05:22:36 INFO client.RMProxy: Connecting to ResourceManager at weekend110/192.168.230.134:8032
17/02/26 05:22:36 WARN mapreduce.JobSubmitter: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
17/02/26 05:22:36 INFO input.FileInputFormat: Total input paths to process : 1
17/02/26 05:22:36 INFO mapreduce.JobSubmitter: number of splits:1
17/02/26 05:22:37 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1488112052214_0003
17/02/26 05:22:37 INFO impl.YarnClientImpl: Submitted application application_1488112052214_0003
17/02/26 05:22:37 INFO mapreduce.Job: The url to track the job: http://weekend110:8088/proxy/application_1488112052214_0003/
17/02/26 05:22:37 INFO mapreduce.Job: Running job: job_1488112052214_0003
17/02/26 05:24:16 INFO mapreduce.Job: Job job_1488112052214_0003 running in uber mode : false
17/02/26 05:24:16 INFO mapreduce.Job: map 0% reduce 0%
17/02/26 05:24:22 INFO mapreduce.Job: map 100% reduce 0%
17/02/26 05:24:28 INFO mapreduce.Job: map 100% reduce 100%
17/02/26 05:24:28 INFO mapreduce.Job: Job job_1488112052214_0003 completed successfully
17/02/26 05:24:28 INFO mapreduce.Job: Counters: 49
File System Counters
FILE: Number of bytes read=933
FILE: Number of bytes written=187799
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=735
HDFS: Number of bytes written=623
HDFS: Number of read operations=6
HDFS: Number of large read operations=0
HDFS: Number of write operations=2
Job Counters 
Launched map tasks=1
Launched reduce tasks=1
Data-local map tasks=1
Total time spent by all maps in occupied slots (ms)=3077
Total time spent by all reduces in occupied slots (ms)=2350
Total time spent by all map tasks (ms)=3077
Total time spent by all reduce tasks (ms)=2350
Total vcore-seconds taken by all map tasks=3077
Total vcore-seconds taken by all reduce tasks=2350
Total megabyte-seconds taken by all map tasks=3150848
Total megabyte-seconds taken by all reduce tasks=2406400
Map-Reduce Framework
Map input records=22
Map output records=22
Map output bytes=883
Map output materialized bytes=933
Input split bytes=112
Combine input records=0
Combine output records=0
Reduce input groups=22
Reduce shuffle bytes=933
Reduce input records=22
Reduce output records=22
Spilled Records=44
Shuffled Maps =1
Failed Shuffles=0
Merged Map outputs=1
GC time elapsed (ms)=142
CPU time spent (ms)=1280
Physical memory (bytes) snapshot=218406912
Virtual memory (bytes) snapshot=726446080
Total committed heap usage (bytes)=137433088
Shuffle Errors
BAD_ID=0
CONNECTION=0
IO_ERROR=0
WRONG_LENGTH=0
WRONG_MAP=0
WRONG_REDUCE=0
File Input Format Counters 
Bytes Read=623
File Output Format Counters 
Bytes Written=623

最终结果如下，可以看到是排序好的。

1363157985069 186852 200 187052
1363157985066 2481 24681 27162
1363157990043 63 11058 11121
1363157986072 18 9531 9549
1363157982040 102 7335 7437
1363157984041 9 6960 6969
1363157995093 3008 3720 6728
1363157995074 4116 1432 5548
1363157992093 4938 200 5138
1363157973098 27 3659 3686
1363157995033 20 3156 3176
1363157984040 12 1938 1950
1363157986029 3 1938 1941
1363157991076 1512 200 1712
1363157993044 12 1527 1539
1363157993055 954 200 1154
1363157985079 180 200 380
1363157986041 180 200 380
1363157988072 120 200 320
1363154400022 0 200 200
1363157983019 0 200 200
1363157995052 0 200 200