MapReduce中的partitioner

1.日志源文件:

1363157985066     13726230503    00-FD-07-A4-72-B8:CMCC    120.196.100.82    i02.c.aliimg.com        24    27    2481    24681    200
1363157995052     13826544101    5C-0E-8B-C7-F1-E0:CMCC    120.197.40.4            4    0    264    0    200
1363157991076     13926435656    20-10-7A-28-CC-0A:CMCC    120.196.100.99            2    4    132    1512    200
1363154400022     13926251106    5C-0E-8B-8B-B1-50:CMCC    120.197.40.4            4    0    240    0    200
1363157993044     18211575961    94-71-AC-CD-E6-18:CMCC-EASY    120.196.100.99    iface.qiyi.com    视频网站    15    12    1527    2106    200
1363157995074     84138413    5C-0E-8B-8C-E8-20:7DaysInn    120.197.40.4    122.72.52.12        20    16    4116    1432    200
1363157993055     13560439658    C4-17-FE-BA-DE-D9:CMCC    120.196.100.99            18    15    1116    954    200
1363157995033     15920133257    5C-0E-8B-C7-BA-20:CMCC    120.197.40.4    sug.so.360.cn    信息安全    20    20    3156    2936    200
1363157983019     13719199419    68-A1-B7-03-07-B1:CMCC-EASY    120.196.100.82            4    0    240    0    200
1363157984041     13660577991    5C-0E-8B-92-5C-20:CMCC-EASY    120.197.40.4    s19.cnzz.com    站点统计    24    9    6960    690    200
1363157973098     15013685858    5C-0E-8B-C7-F7-90:CMCC    120.197.40.4    rank.ie.sogou.com    搜索引擎    28    27    3659    3538    200
1363157986029     15989002119    E8-99-C4-4E-93-E0:CMCC-EASY    120.196.100.99    www.umeng.com    站点统计    3    3    1938    180    200
1363157992093     13560439658    C4-17-FE-BA-DE-D9:CMCC    120.196.100.99            15    9    918    4938    200
1363157986041     13480253104    5C-0E-8B-C7-FC-80:CMCC-EASY    120.197.40.4            3    3    180    180    200
1363157984040     13602846565    5C-0E-8B-8B-B6-00:CMCC    120.197.40.4    2052.flash2-http.qq.com    综合门户    15    12    1938    2910    200
1363157995093     13922314466    00-FD-07-A2-EC-BA:CMCC    120.196.100.82    img.qfc.cn        12    12    3008    3720    200
1363157982040     13502468823    5C-0A-5B-6A-0B-D4:CMCC-EASY    120.196.100.99    y0.ifengimg.com    综合门户    57    102    7335    110349    200
1363157986072     18320173382    84-25-DB-4F-10-1A:CMCC-EASY    120.196.100.99    input.shouji.sogou.com    搜索引擎    21    18    9531    2412    200
1363157990043     13925057413    00-1F-64-E1-E6-9A:CMCC    120.196.100.55    t3.baidu.com    搜索引擎    69    63    11058    48243    200
1363157988072     13760778710    00-FD-07-A4-7B-08:CMCC    120.196.100.82            2    2    120    120    200
1363157985079     13823070001    20-7C-8F-70-68-1F:CMCC    120.196.100.99            6    3    360    180    200
1363157985069     13600217502    00-1F-64-E2-E8-B1:CMCC    120.196.100.55            18    138    1080    186852    200

2.写含有partitioner的MR代码:

 import java.io.DataInput;
 import java.io.DataOutput;
 import java.io.IOException;
 import java.util.HashMap;
 import java.util.Map;

 import org.apache.hadoop.conf.Configuration;
 import org.apache.hadoop.fs.Path;
 import org.apache.hadoop.io.LongWritable;
 import org.apache.hadoop.io.Text;
 import org.apache.hadoop.io.Writable;
 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.output.FileOutputFormat;

 public class TrafficApp {
     public static void main(String[] args) throws Exception {
         Job job = Job.getInstance(new Configuration(), TrafficApp.class.getSimpleName());
         job.setJarByClass(TrafficApp.class);

         FileInputFormat.setInputPaths(job, args[0]);

         job.setMapperClass(TrafficMapper.class);
         job.setMapOutputKeyClass(Text.class);
         job.setMapOutputValueClass(TrafficWritable.class);

         //job.setNumReduceTasks(2);//设定Reduce的数量为2  这个针对TrafficPartitioner.class
         //job.setPartitionerClass(TrafficPartitioner.class);//设定一个Partitioner的类.

         //job.setNumReduceTasks(3);//设定Reduce的数量为3 这个针对ProviderPartitioner
         //也可以通过参数指定
         job.setNumReduceTasks(Integer.parseInt(args[2]));
         job.setPartitionerClass(ProviderPartitioner.class);

         /*
          *Partitioner是如何实现不同的Map输出分配到不同的Reduce中?
          *在不适用指定的Partitioner时,有 一个默认的Partitioner.
          *就是HashPartitioner.
          *其只有一行代码,其意思就是过来的key,不管是什么,模numberReduceTasks之后 返回值就是reduce任务的编号.
          *numberReduceTasks的默认值是1.  任何一个数模1(取余数)都是0.
          *这个地方0就是取编号为0的Reduce.(Reduce从0开始编号.)
          */

         job.setReducerClass(TrafficReducer.class);
         job.setOutputKeyClass(Text.class);
         job.setOutputValueClass(TrafficWritable.class);

         FileOutputFormat.setOutputPath(job, new Path(args[1]));
         job.waitForCompletion(true);
     }

     public static class TrafficPartitioner extends Partitioner<Text,TrafficWritable>{//k2,v2

         @Override
         public int getPartition(Text key, TrafficWritable value,int numPartitions) {
             long phoneNumber = Long.parseLong(key.toString());
             return (int)(phoneNumber%numPartitions);
         }

     }

     //根据号码所属的运营商进行分区,号码的前三位(也可以根据号码所在的行政区域进行分区,号码的前七位)
     public static class ProviderPartitioner extends Partitioner<Text,TrafficWritable>{//k2,v2
         //初始化映射关系
         /*
          * 这两个静态的static的执行的先后顺序是 从上往下,先执行providerMap 再 执行static静态块.
          */
         private static Map<String,Integer> providerMap = new HashMap<String,Integer>();
         static{
             providerMap.put("135", 1);//1是移动,2是联通,3是电信
             providerMap.put("136", 1);
             providerMap.put("137", 1);
             providerMap.put("138", 1);
             providerMap.put("139", 1);
             providerMap.put("134", 2);
             providerMap.put("150", 2);
             providerMap.put("159", 2);
             providerMap.put("183", 3);
             providerMap.put("182", 3);
         }
         @Override
         public int getPartition(Text key, TrafficWritable value,int numPartitions) {
             //
             String account = key.toString();
             //
             String sub_account = account.substring(0,3);//从第0位开始取,取三位...这个东西不需要记,忘了就自己写个代码试一下.
             //
             Integer code = providerMap.get(sub_account);
             if(code == null){
                 code = 0;//代表是其他的运行商.
             }
             return code;
         }
     }

     /**
      * 第一个参数是LongWritable类型是文本一行数据开头的字节数
      * 第二个参数是文本中的一行数据  Text类型
      * 第三个参数是要输出的手机号  Text类型
      * 第四个参数是需要我们自定义的流量类型TrafficWritable
      * @author ABC
      *
      */
     public static class TrafficMapper extends Mapper<LongWritable, Text, Text, TrafficWritable>{
         Text k2 = new Text();
         TrafficWritable v2 = null;
         @Override
         protected void map(LongWritable key,Text value,    Mapper<LongWritable, Text, Text, TrafficWritable>.Context context)
                 throws IOException, InterruptedException {
             String line = value.toString();
             String[] splited = line.split("\t");

             k2.set(splited[1]);//这个值对应的是手机号码
             v2 = new TrafficWritable(splited[6], splited[7], splited[8], splited[9]);
             context.write(k2, v2);
         }

     }

     public static class TrafficReducer extends Reducer <Text, TrafficWritable, Text, TrafficWritable>{
         @Override
         protected void reduce(Text k2,Iterable<TrafficWritable> v2s,
                 Reducer<Text, TrafficWritable, Text, TrafficWritable>.Context context)
                 throws IOException, InterruptedException {
             //遍历v2s 流量都这个集合里面
             long t1 = 0L;
             long t2 = 0L;
             long t3 = 0L;
             long t4 = 0L;

             for (TrafficWritable v2 : v2s) {
                 t1 += v2.getT1();
                 t2 += v2.getT2();
                 t3 += v2.getT3();
                 t4 += v2.getT4();
             }
             TrafficWritable v3 = new TrafficWritable(t1, t2, t3, t4);
             context.write(k2, v3);
         }
     }

     public static class TrafficWritable implements Writable{
         private long t1;
         private long t2;
         private long t3;
         private long t4;
         //写两个构造方法,一个是有参数的构造方法,一个是无参数的构造方法.
         //必须要有 一个无参数的构造方法,否则程序运行会报错.

         public TrafficWritable(){
             super();
         }

         public TrafficWritable(long t1, long t2, long t3, long t4) {
             super();
             this.t1 = t1;
             this.t2 = t2;
             this.t3 = t3;
             this.t4 = t4;
         }
         //在程序中读取文本穿过来的都是字符串,所以再搞一个字符串类型的构造方法
         public TrafficWritable(String t1, String t2, String t3, String t4) {
             super();
             this.t1 = Long.parseLong(t1);
             this.t2 = Long.parseLong(t2);
             this.t3 = Long.parseLong(t3);
             this.t4 = Long.parseLong(t4);
         }

         public void write(DataOutput out) throws IOException {
             //对各个成员变量进行序列化
             out.writeLong(t1);
             out.writeLong(t2);
             out.writeLong(t3);
             out.writeLong(t4);
         }

         public void readFields(DataInput in) throws IOException {
             //对成员变量进行反序列化
             this.t1 = in.readLong();
             this.t2 = in.readLong();
             this.t3 = in.readLong();
             this.t4 = in.readLong();
         }

         public long getT1() {
             return t1;
         }

         public void setT1(long t1) {
             this.t1 = t1;
         }

         public long getT2() {
             return t2;
         }

         public void setT2(long t2) {
             this.t2 = t2;
         }

         public long getT3() {
             return t3;
         }

         public void setT3(long t3) {
             this.t3 = t3;
         }

         public long getT4() {
             return t4;
         }

         public void setT4(long t4) {
             this.t4 = t4;
         }

         @Override
         public String toString() {
             return t1 + "\t" + t2 + "\t" + t3 + "\t" + t4 ;
         }

     }
 }

3.命令执行:

hadoop jar /root/itcastmr.jar mapreduce.TrafficApp /files/traffic /traffic_provider3 4

产生的结果文件:

代码中的逻辑是对应4个分区,设置了4个分区,就产生了4个分区文件...

查看各个文件中的内容:

4.其他情况:

①

如果代码中不设置分区的数量: job.setNumReduceTasks(Integer.parseInt(args[2]));   运行命令执行会发现只产生了一个结果文件.

因为MR默认只启动一个reducer...一个reducer对应一个结果文件. 不是分区所要的效果.

②

如果代码中设置的分区数量大于实际的产生的分区数量. 比如以上代码根据数据情况只产生4个分区,但是设置6个分区.

同样会产生6个结果文件,但是后两个结果文件中是没有值的.

③

代码中的分区数量小于实际产生的分区数量. 比如以上代码根据数据情况会产生4个分区,但是只设置2个分区.

报错:(就是因为182对应应该在3号分区,但是实际是只设置了2个分区)

还有设置分区之后代码执行会变慢:因为之前只需要之前只需要把结果发给一个reducer,现在要根据某个属性把mapper的结果分发到不同的reducer中.