Hadoop入门（十五）Mapreduce的数据排序程序

ゝ一纸荒年。 2023-06-07 05:50 67阅读 0赞

"数据排序"是许多实际任务执行时要完成的第一项工作，比如学生成绩评比、数据建立索引等。这个实例和数据去重类似，都是先对原始数据进行初步处理，为进一步的数据操作打好基础

### 1 实例描述 ###

对输入文件中数据进行排序。输入文件中的每行内容均为一个数字，即一个数据。要求在输出中每行有两个间隔的数字，其中，第一个代表原始数据在原始数据集中的位次，第二个代表原始数据。  
样例输入如下所示：   
1）file1

2
    32
    654
    32
    15
    756
    65223

2）file2

5956
    22
    650
    92

3）file3

26
    54
    6

期望输出：

1    2
    2    6
    3    15
    4    22
    5    26
    6    32
    7    32
    8    54
    9    92
    10    650
    11    654
    12    756
    13    5956
    14    65223

### 2 问题分析 ###

这个实例仅仅要求对输入数据进行排序

分析：  
   MapReduce过程中就有排序,它的默认排序规则按照key值进行排序的，如果key为封装int的IntWritable类型，那么MapReduce按照数字大小对key排序，如果key为封装为String的Text类型，那么MapReduce按照字典顺序对字符串排序。  
　  
使用封装int的IntWritable型数据结构了。也就是在map中将读入的数据转化成IntWritable型，然后作为key值输出（value任意）。reduce拿到<key，value-list>之后，将输入的key作为value输出，并根据value-list中元素的个数决定输出的次数。输出的key（即代码中的linenum）是一个全局变量，它统计当前key的位次。需要注意的是这个程序中没有配置Combiner，也就是在MapReduce过程中不使用Combiner。这主要是因为使用map和reduce就已经能够完成任务了。

### 3.实现步骤 ###

1.  在map中将读入的数据转化成IntWritable型，然后作为key值输出（value任意）。 
2.  reduce拿到<key，value-list>之后，将输入的key作为value输出，并根据value-list中元素的个数决定输出的次数
3.  输出的key是一个全局变量，它统计当前key的位次

### 4.关键代码 ###

正序：

package com.mk.mapreduce;
    
    
    import org.apache.hadoop.conf.Configuration;
    import org.apache.hadoop.fs.FileSystem;
    import org.apache.hadoop.fs.Path;
    import org.apache.hadoop.io.IntWritable;
    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 java.io.IOException;
    import java.net.URI;
    
    public class Sort {
    
        public static class SortMapper extends Mapper<LongWritable, Text, IntWritable, IntWritable> {
            @Override
            protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
    
               IntWritable v = new IntWritable(Integer.parseInt(value.toString().trim()));
                context.write(v, new IntWritable(1));
            }
        }
    
        public static class SortReducer extends Reducer<IntWritable, IntWritable, IntWritable, IntWritable> {
    
            int count = 1;
            @Override
            protected void reduce(IntWritable key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
    
                for (IntWritable v: values) {
                    context.write(new IntWritable(count ++), key);
                }
    
    
            }
        }
        public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
    
            String uri = "hdfs://192.168.150.128:9000";
            String input = "/sort/input";
            String output = "/sort/output";
            Configuration conf = new Configuration();
            if(System.getProperty("os.name").toLowerCase().contains("win"))
                conf.set("mapreduce.app-submission.cross-platform","true");
    
            FileSystem fileSystem = FileSystem.get(URI.create(uri), conf);
            Path path = new Path(output);
            fileSystem.delete(path,true);
    
            Job job = new Job(conf,"Sort");
            job.setJar("./out/artifacts/hadoop_test_jar/hadoop-test.jar");
            job.setJarByClass(Sort.class);
            job.setMapperClass(SortMapper.class);
            job.setReducerClass(SortReducer.class);
            job.setMapOutputKeyClass(IntWritable.class);
            job.setMapOutputValueClass(IntWritable.class);
            job.setOutputKeyClass(IntWritable.class);
            job.setOutputValueClass(IntWritable.class);
            FileInputFormat.addInputPaths(job, uri + input);
            FileOutputFormat.setOutputPath(job, new Path(uri + output));
    
    
    
            boolean ret = job.waitForCompletion(true);
            System.out.println(job.getJobName() + "-----" +ret);
        }
    }

逆序：

package com.mk.mapreduce;
    
    
    import org.apache.hadoop.conf.Configuration;
    import org.apache.hadoop.fs.FileSystem;
    import org.apache.hadoop.fs.Path;
    import org.apache.hadoop.io.*;
    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 java.io.IOException;
    import java.net.URI;
    
    public class Sort {
    
        public static class SortMapper extends Mapper<LongWritable, Text, IntWritable, IntWritable> {
            @Override
            protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
    
               IntWritable v = new IntWritable(Integer.parseInt(value.toString().trim()));
                context.write(v, new IntWritable(1));
            }
        }
    
        public static class SortReducer extends Reducer<IntWritable, IntWritable, IntWritable, IntWritable> {
    
            int count = 1;
            @Override
            protected void reduce(IntWritable key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
    
                for (IntWritable v: values) {
                    context.write(new IntWritable(count ++), key);
                }
    
    
            }
        }
    
        public static class SortComparator implements RawComparator<IntWritable> {
    
            @Override
            public int compare(byte[] b1, int s1, int l1, byte[] b2, int s2, int l2) {
                return IntWritable.Comparator.compareBytes(b2, s2, l2, b1, s1, l1);
            }
    
            @Override
            public int compare(IntWritable o1, IntWritable o2) {
                return o2.get() - o1.get();
            }
        }
        public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
    
            String uri = "hdfs://192.168.150.128:9000";
            String input = "/sort/input";
            String output = "/sort/output";
            Configuration conf = new Configuration();
            if(System.getProperty("os.name").toLowerCase().contains("win"))
                conf.set("mapreduce.app-submission.cross-platform","true");
    
            FileSystem fileSystem = FileSystem.get(URI.create(uri), conf);
            Path path = new Path(output);
            fileSystem.delete(path,true);
    
            Job job = new Job(conf,"Sort");
            job.setJar("./out/artifacts/hadoop_test_jar/hadoop-test.jar");
            job.setJarByClass(Sort.class);
            job.setMapperClass(SortMapper.class);
            job.setReducerClass(SortReducer.class);
            job.setMapOutputKeyClass(IntWritable.class);
            job.setMapOutputValueClass(IntWritable.class);
            job.setOutputKeyClass(IntWritable.class);
            job.setOutputValueClass(IntWritable.class);
            FileInputFormat.addInputPaths(job, uri + input);
            FileOutputFormat.setOutputPath(job, new Path(uri + output));
            job.setSortComparatorClass(SortComparator.class);
    
    
            boolean ret = job.waitForCompletion(true);
            System.out.println(job.getJobName() + "-----" +ret);
        }
    }