自定义排序及Hadoop序列化

Posted 2020-09-08 ahu-lichang

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自定义排序

将两列数据进行排序，第一列按照升序排列，当第一列相同时，第二列升序排列。

在map和reduce阶段进行排序时，比较的是k2。v2是不参与排序比较的。如果要想让v2也进行排序，需要把k2和v2组装成新的类，作为k2，才能参与比较。

  1 package sort;
  2 
  3 import java.io.DataInput;
  4 import java.io.DataOutput;
  5 import java.io.IOException;
  6 import java.net.URI;
  7 
  8 import org.apache.hadoop.conf.Configuration;
  9 import org.apache.hadoop.fs.FileSystem;
 10 import org.apache.hadoop.fs.Path;
 11 import org.apache.hadoop.io.LongWritable;
 12 import org.apache.hadoop.io.Text;
 13 import org.apache.hadoop.io.WritableComparable;
 14 import org.apache.hadoop.mapreduce.Job;
 15 import org.apache.hadoop.mapreduce.Mapper;
 16 import org.apache.hadoop.mapreduce.Reducer;
 17 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
 18 import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
 19 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
 20 import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
 21 import org.apache.hadoop.mapreduce.lib.partition.HashPartitioner;
 22 
 23 public class SortApp {
 24     static final String INPUT_PATH = "hdfs://chaoren:9000/input";
 25     static final String OUT_PATH = "hdfs://chaoren:9000/out";
 26 
 27     public static void main(String[] args) throws Exception {
 28         final Configuration configuration = new Configuration();
 29 
 30         final FileSystem fileSystem = FileSystem.get(new URI(INPUT_PATH),
 31                 configuration);
 32         if (fileSystem.exists(new Path(OUT_PATH))) {
 33             fileSystem.delete(new Path(OUT_PATH), true);
 34         }
 35 
 36         final Job job = new Job(configuration, SortApp.class.getSimpleName());
 37 
 38         // 1.1 指定输入文件路径
 39         FileInputFormat.setInputPaths(job, INPUT_PATH);
 40         // 指定哪个类用来格式化输入文件
 41         job.setInputFormatClass(TextInputFormat.class);
 42 
 43         // 1.2指定自定义的Mapper类
 44         job.setMapperClass(MyMapper.class);
 45         // 指定输出<k2,v2>的类型
 46         job.setMapOutputKeyClass(NewK2.class);
 47         job.setMapOutputValueClass(LongWritable.class);
 48 
 49         // 1.3 指定分区类
 50         job.setPartitionerClass(HashPartitioner.class);
 51         job.setNumReduceTasks(1);
 52 
 53         // 1.4 TODO 排序、分区
 54 
 55         // 1.5 TODO （可选）合并
 56 
 57         // 2.2 指定自定义的reduce类
 58         job.setReducerClass(MyReducer.class);
 59         // 指定输出<k3,v3>的类型
 60         job.setOutputKeyClass(LongWritable.class);
 61         job.setOutputValueClass(LongWritable.class);
 62 
 63         // 2.3 指定输出到哪里
 64         FileOutputFormat.setOutputPath(job, new Path(OUT_PATH));
 65         // 设定输出文件的格式化类
 66         job.setOutputFormatClass(TextOutputFormat.class);
 67 
 68         // 把代码提交给JobTracker执行
 69         job.waitForCompletion(true);
 70     }
 71 
 72     static class MyMapper extends
 73             Mapper<LongWritable, Text, NewK2, LongWritable> {
 74         protected void map(
 75                 LongWritable key,
 76                 Text value,
 77                 org.apache.hadoop.mapreduce.Mapper<LongWritable, Text, NewK2, LongWritable>.Context context)
 78                 throws java.io.IOException, InterruptedException {
 79             final String[] splited = value.toString().split("\\t");
 80             final NewK2 k2 = new NewK2(Long.parseLong(splited[0]),
 81                     Long.parseLong(splited[1]));
 82             final LongWritable v2 = new LongWritable(Long.parseLong(splited[1]));
 83             context.write(k2, v2);
 84         };
 85     }
 86 
 87     static class MyReducer extends
 88             Reducer<NewK2, LongWritable, LongWritable, LongWritable> {
 89         protected void reduce(
 90                 NewK2 k2,
 91                 java.lang.Iterable<LongWritable> v2s,
 92                 org.apache.hadoop.mapreduce.Reducer<NewK2, LongWritable, LongWritable, LongWritable>.Context context)
 93                 throws java.io.IOException, InterruptedException {
 94             context.write(new LongWritable(k2.first), new LongWritable(
 95                     k2.second));
 96         };
 97     }
 98 
 99     /**
100      * 问：为什么实现该类？ 答：因为原来的v2不能参与排序，把原来的k2和v2封装到一个类中，作为新的k2
101      * 
102      */
103     // WritableComparable：Hadoop的序列化
104     static class NewK2 implements WritableComparable<NewK2> {
105         Long first;
106         Long second;
107 
108         public NewK2() {
109         }
110 
111         public NewK2(long first, long second) {
112             this.first = first;
113             this.second = second;
114         }
115 
116         public void readFields(DataInput in) throws IOException {
117             this.first = in.readLong();
118             this.second = in.readLong();
119         }
120 
121         public void write(DataOutput out) throws IOException {
122             out.writeLong(first);
123             out.writeLong(second);
124         }
125 
126         /**
127          * 当k2进行排序时，会调用该方法. 当第一列不同时，升序；当第一列相同时，第二列升序
128          */
129         public int compareTo(NewK2 o) {
130             final long minus = this.first - o.first;
131             if (minus != 0) {
132                 return (int) minus;
133             }
134             return (int) (this.second - o.second);
135         }
136 
137         @Override
138         public int hashCode() {
139             return this.first.hashCode() + this.second.hashCode();
140         }
141 
142         @Override
143         public boolean equals(Object obj) {
144             if (!(obj instanceof NewK2)) {
145                 return false;
146             }
147             NewK2 oK2 = (NewK2) obj;
148             return (this.first == oK2.first) && (this.second == oK2.second);
149         }
150     }
151 
152 }