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  • Hadoop基础-MapReduce的排序

    　　　　　　　　　　　　　　　　　　　　Hadoop基础-MapReduce的排序

    　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　作者：尹正杰

    版权声明：原创作品，谢绝转载！否则将追究法律责任。

    一.MapReduce的排序分类

    1>.部分排序

    　　部分排序是对单个分区进行排序，举个简单的例子，第一个分区中的数据为1,3,5；而第二个分区为2,4,这两个分区的值看起来是没有连续性的，但是每个分区中的数据又是排序的！下面是我画的一个草图：

    2>.全排序

    　　全排序是对所有分区中的数据均排序，比如第一个分区的值为1,2,3,而第二个分区为4,5 很显然2个分区是经过排序的，可以明显的看清楚每个分区的具体的取值规范。下面是我画的一个草图：

    3>.二次排序

    　　二次排序是指对key排序的基础上，对value进行排序。

    二.全排序的实现方案

    26 May, 2017: Release 3.0.0-alpha3 available
This is a security release in the 3.0.0 release line. It consists of alpha2 plus security fixes, along with necessary build-related fixes. Users on 3.0.0-alpha1 and 3.0.0-alpha2 are encouraged to upgrade to 3.0.0-alpha3.

Please note that alpha releases come with no guarantees of quality or API stability, and are not intended for production use.

Users are encouraged to read the overview of major changes coming in 3.0.0. The alpha3 release notes and changelog detail the changes since 3.0.0-alpha2.
    wordCount.txt 文件内容

     1>.一个Reduce实现全排序

    　　缺点：单个节点负载较高！如果计算数据较大，那么会浪费很长的时间！不推荐使用！

     1 /*
 2 @author :yinzhengjie
 3 Blog:http://www.cnblogs.com/yinzhengjie/tag/Hadoop%E8%BF%9B%E9%98%B6%E4%B9%8B%E8%B7%AF/
 4 EMAIL:y1053419035@qq.com
 5 */
 6 package cn.org.yinzhengjie.mapreduce.wordcount;
 7 
 8 import org.apache.hadoop.io.IntWritable;
 9 import org.apache.hadoop.io.LongWritable;
10 import org.apache.hadoop.io.Text;
11 import org.apache.hadoop.mapreduce.Mapper;
12 
13 import java.io.IOException;
14 
15 /**
16  *      我们定义的map端类为WordCountMap，它需要继承“org.apache.hadoop.mapreduce.Mapper.Mapper”，
17  * 该Mapper有四个参数，前两个参数是指定map端输入key和value的数据类型，而后两个参数是指定map端输出
18  * key和value的数据类型。
19  */
20 public class WordCountMap extends Mapper<LongWritable,Text,Text,IntWritable> {
21 
22     /**
23      *
24      * @param key               ： 表示输入的key变量。
25      * @param value             ： 表示输入的value
26      * @param context           ： 表示map端的上下文，它是负责将map端数据传给reduce。
27      */
28     protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
29 
30         //得到一行数据
31         String line = value.toString();
32         //以空格切开一行的数据
33         String[] arr = line.split(" ");
34         for (String word:arr){
35             //遍历arr中的每个元素，并对每个元素赋初值为1，然后在将数据传给reduce端
36             context.write(new Text(word),new IntWritable(1));
37         }
38     }
39 }
    WordCountMap.java 文件内容
     1 /*
 2 @author :yinzhengjie
 3 Blog:http://www.cnblogs.com/yinzhengjie/tag/Hadoop%E8%BF%9B%E9%98%B6%E4%B9%8B%E8%B7%AF/
 4 EMAIL:y1053419035@qq.com
 5 */
 6 package cn.org.yinzhengjie.mapreduce.wordcount;
 7 
 8 import org.apache.hadoop.io.IntWritable;
 9 import org.apache.hadoop.io.Text;
10 import org.apache.hadoop.mapreduce.Reducer;
11 import java.io.IOException;
12 
13 /**
14  *      我们定义的reduce端类为WordCountReduce，它需要继承“org.apache.hadoop.mapreduce.Reducer.Reducer”，
15  * 该Reducer有四个参数，前两个参数是指定map端输入key和value的数据类型，而后两个参数是指定map端输出
16  * key和value的数据类型。
17  */
18 public class WordCountReduce extends Reducer<Text,IntWritable,Text,IntWritable> {
19     /**
20      *
21      * @param key               :  表示输入的key变量。
22      * @param values            ： 表示输入的value，这个变量是可迭代的，因此传递的是多个值。
23      * @param context           ： 表示reduce端的上下文，它是负责将reduce端数据传给调用者（调用者可以传递的数据输出到文件，也可以输出到下一个MapReduce程序。
24      */
25     protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
26         //定义一个单词计数器
27         Integer count = 0;
28         //由于输入端只有一个key，因此value的所有值都属于这个key的，我们需要做的是对value进行遍历并将所有数据进行相加操作，最终的结果就得到了同一个key的出现的次数。
29         for (IntWritable value : values){
30             //获取到value的get方法获取到value的值。
31             count += value.get();
32         }
33         //我们将key原封不动的返回，并将key的values的所有int类型的参数进行折叠，最终返回单词书以及该单词总共出现的次数。
34         context.write(key,new IntWritable(count));
35     }
36 }
    WordCountReduce.java 文件内容
     1 /*
 2 @author :yinzhengjie
 3 Blog:http://www.cnblogs.com/yinzhengjie/tag/Hadoop%E8%BF%9B%E9%98%B6%E4%B9%8B%E8%B7%AF/
 4 EMAIL:y1053419035@qq.com
 5 */
 6 package cn.org.yinzhengjie.mapreduce.wordcount;
 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.IntWritable;
12 import org.apache.hadoop.io.Text;
13 import org.apache.hadoop.mapreduce.Job;
14 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
15 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
16 
17 import java.io.IOException;
18 
19 public class WordCountApp {
20 
21     public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
22         //实例化一个Configuration，它会自动去加载本地的core-site.xml配置文件的fs.defaultFS属性。(该文件放在项目的resources目录即可。)
23         Configuration conf = new Configuration();
24         //将hdfs写入的路径定义在本地，需要修改默认为文件系统，这样就可以覆盖到之前在core-site.xml配置文件读取到的数据。
25         conf.set("fs.defaultFS","file:///");
26         //代码的入口点，初始化HDFS文件系统，此时我们需要把读取到的fs.defaultFS属性传给fs对象。
27         FileSystem fs = FileSystem.get(conf);
28         //创建一个任务对象job，别忘记把conf穿进去哟！
29         Job job = Job.getInstance(conf);
30         //给任务起个名字
31         job.setJobName("WordCount");
32         //指定main函数所在的类，也就是当前所在的类名
33         job.setJarByClass(WordCountApp.class);
34         //指定map的类名,这里指定咱们自定义的map程序即可
35         job.setMapperClass(WordCountMap.class);
36         //指定reduce的类名，这里指定咱们自定义的reduce程序即可
37         job.setReducerClass(WordCountReduce.class);
38         //设置输出key的数据类型
39         job.setOutputKeyClass(Text.class);
40         //设置输出value的数据类型
41         job.setOutputValueClass(IntWritable.class);
42         //设置输入路径，需要传递两个参数，即任务对象(job)以及输入路径
43         FileInputFormat.addInputPath(job,new Path("D:\10.Java\IDE\yhinzhengjieData\MyHadoop\MapReduce\wordCount.txt"));
44         //设置输出路径，需要传递两个参数，即任务对象(job)以及输出路径
45         Path localPath = new Path("D:\10.Java\IDE\yhinzhengjieData\MyHadoop\MapReduce\CountOut");
46         if (fs.exists(localPath)){
47             fs.delete(localPath,true);
48         }
49         FileOutputFormat.setOutputPath(job,localPath);
50         //设置1个Reduce任务，这样就可以生成的数据会被保存在1个文件中,从而实现了全排序！
51         job.setNumReduceTasks(1);
52         //等待任务执行结束,将里面的值设置为true。
53         job.waitForCompletion(true);
54     }
55 }
    WordCountApp.java 文件内容

    　　执行以上代码后，会生成1个分区文件，如下：

     1     2
 2 2017:    1
 3 26    1
 4 3.0.0    1
 5 3.0.0-alpha1    1
 6 3.0.0-alpha2    1
 7 3.0.0-alpha2.    1
 8 3.0.0-alpha3    1
 9 3.0.0-alpha3.    1
10 3.0.0.    1
11 API    1
12 It    1
13 May,    1
14 Please    1
15 Release    1
16 The    1
17 This    1
18 Users    2
19 a    1
20 along    1
21 alpha    1
22 alpha2    1
23 alpha3    1
24 and    3
25 are    3
26 available    1
27 build-related    1
28 changelog    1
29 changes    2
30 come    1
31 coming    1
32 consists    1
33 detail    1
34 encouraged    2
35 fixes,    1
36 fixes.    1
37 for    1
38 guarantees    1
39 in    2
40 intended    1
41 is    1
42 line.    1
43 major    1
44 necessary    1
45 no    1
46 not    1
47 note    1
48 notes    1
49 of    3
50 on    1
51 or    1
52 overview    1
53 plus    1
54 production    1
55 quality    1
56 read    1
57 release    3
58 releases    1
59 security    2
60 since    1
61 stability,    1
62 that    1
63 the    3
64 to    3
65 upgrade    1
66 use.    1
67 with    2
    part-r-00000 文件内容

    2>.自定义分区函数进行排序

    　　自定义分区函数需要划分key空间，按照范围指定分区索引。缺点：需要准确估算key的范围，否则容易导致数据倾斜。如果你对业务把控不是很准确的话，这种方法也不推荐使用！

     1 /*
 2 @author :yinzhengjie
 3 Blog:http://www.cnblogs.com/yinzhengjie/tag/Hadoop%E8%BF%9B%E9%98%B6%E4%B9%8B%E8%B7%AF/
 4 EMAIL:y1053419035@qq.com
 5 */
 6 package cn.org.yinzhengjie.mapreduce.full;
 7 
 8 import org.apache.hadoop.io.IntWritable;
 9 import org.apache.hadoop.io.LongWritable;
10 import org.apache.hadoop.io.Text;
11 import org.apache.hadoop.mapreduce.Mapper;
12 
13 import java.io.IOException;
14 
15 public class FullMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
16 
17     @Override
18     protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
19 
20         //得到一行数据
21         String line = value.toString();
22 
23         String[] arr = line.split(" ");
24 
25         for (String word : arr) {
26             context.write(new Text(word), new IntWritable(1));
27         }
28     }
29 }
    FullMapper.java　文件内容
     1 /*
 2 @author :yinzhengjie
 3 Blog:http://www.cnblogs.com/yinzhengjie/tag/Hadoop%E8%BF%9B%E9%98%B6%E4%B9%8B%E8%B7%AF/
 4 EMAIL:y1053419035@qq.com
 5 */
 6 package cn.org.yinzhengjie.mapreduce.full;
 7 
 8 import org.apache.hadoop.io.IntWritable;
 9 import org.apache.hadoop.io.Text;
10 import org.apache.hadoop.mapreduce.Reducer;
11 
12 import java.io.IOException;
13 
14 public class FullReducer extends Reducer<Text, IntWritable , Text, IntWritable> {
15     @Override
16     protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
17         Integer sum = 0;
18         for(IntWritable value : values){
19             sum += value.get();
20         }
21         context.write(key, new IntWritable(sum));
22     }
23 }
    FullReducer.java　文件内容
     1 /*
 2 @author :yinzhengjie
 3 Blog:http://www.cnblogs.com/yinzhengjie/tag/Hadoop%E8%BF%9B%E9%98%B6%E4%B9%8B%E8%B7%AF/
 4 EMAIL:y1053419035@qq.com
 5 */
 6 package cn.org.yinzhengjie.mapreduce.full;
 7 
 8 import org.apache.hadoop.io.IntWritable;
 9 import org.apache.hadoop.io.Text;
10 import org.apache.hadoop.mapreduce.Partitioner;
11 
12 public class FullPartition extends Partitioner<Text,IntWritable> {
13 
14     @Override
15     public int getPartition(Text text, IntWritable intWritable, int numPartitions) {
16 
17         String key = text.toString();
18 
19         if(key.compareTo("f") < 0){
20             return 0;
21         }
22         else {
23             return 1;
24         }
25 
26     }
27 }
    FullPartition.java 文件内容
     1 /*
 2 @author :yinzhengjie
 3 Blog:http://www.cnblogs.com/yinzhengjie/tag/Hadoop%E8%BF%9B%E9%98%B6%E4%B9%8B%E8%B7%AF/
 4 EMAIL:y1053419035@qq.com
 5 */
 6 package cn.org.yinzhengjie.mapreduce.full;
 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.IntWritable;
12 import org.apache.hadoop.io.Text;
13 import org.apache.hadoop.mapreduce.Job;
14 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
15 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
16 
17 import java.io.IOException;
18 
19 public class FullApp {
20 
21     public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
22         //实例化一个Configuration，它会自动去加载本地的core-site.xml配置文件的fs.defaultFS属性。(该文件放在项目的resources目录即可。)
23         Configuration conf = new Configuration();
24         //将hdfs写入的路径定义在本地，需要修改默认为文件系统，这样就可以覆盖到之前在core-site.xml配置文件读取到的数据。
25         conf.set("fs.defaultFS","file:///");
26         //代码的入口点，初始化HDFS文件系统，此时我们需要把读取到的fs.defaultFS属性传给fs对象。
27         FileSystem fs = FileSystem.get(conf);
28         //创建一个任务对象job，别忘记把conf穿进去哟！
29         Job job = Job.getInstance(conf);
30         //给任务起个名字
31         job.setJobName("WordCount");
32         //指定main函数所在的类，也就是当前所在的类名
33         job.setJarByClass(FullApp.class);
34         //指定map的类名,这里指定咱们自定义的map程序即可
35         job.setMapperClass(FullMapper.class);
36         //指定reduce的类名，这里指定咱们自定义的reduce程序即可
37         job.setReducerClass(FullReducer.class);
38         //指定Partition的类名，这里指定咱们自定义的Partition程序即可
39         job.setPartitionerClass(FullPartition.class);
40         //设置输出key的数据类型
41         job.setOutputKeyClass(Text.class);
42         //设置输出value的数据类型
43         job.setOutputValueClass(IntWritable.class);
44         //设置输入路径，需要传递两个参数，即任务对象(job)以及输入路径
45         FileInputFormat.addInputPath(job,new Path("D:\10.Java\IDE\yhinzhengjieData\MyHadoop\MapReduce\wordCount.txt"));
46         //设置输出路径，需要传递两个参数，即任务对象(job)以及输出路径
47         Path localPath = new Path("D:\10.Java\IDE\yhinzhengjieData\MyHadoop\MapReduce\CountOut");
48         if (fs.exists(localPath)){
49             fs.delete(localPath,true);
50         }
51         FileOutputFormat.setOutputPath(job,localPath);
52         //设置2个Reduce任务，这样就可以生成的数据会被保存在2个文件中
53         job.setNumReduceTasks(2);
54         //等待任务执行结束,将里面的值设置为true。
55         job.waitForCompletion(true);
56     }
57 }
    FullApp.java 文件内容

    　　执行以上代码后，会生成2个分区文件，如下：

     1     2
 2 2017:    1
 3 26    1
 4 3.0.0    1
 5 3.0.0-alpha1    1
 6 3.0.0-alpha2    1
 7 3.0.0-alpha2.    1
 8 3.0.0-alpha3    1
 9 3.0.0-alpha3.    1
10 3.0.0.    1
11 API    1
12 It    1
13 May,    1
14 Please    1
15 Release    1
16 The    1
17 This    1
18 Users    2
19 a    1
20 along    1
21 alpha    1
22 alpha2    1
23 alpha3    1
24 and    3
25 are    3
26 available    1
27 build-related    1
28 changelog    1
29 changes    2
30 come    1
31 coming    1
32 consists    1
33 detail    1
34 encouraged    2
    part-r-00000 文件内容
    fixes,    1
fixes.    1
for    1
guarantees    1
in    2
intended    1
is    1
line.    1
major    1
necessary    1
no    1
not    1
note    1
notes    1
of    3
on    1
or    1
overview    1
plus    1
production    1
quality    1
read    1
release    3
releases    1
security    2
since    1
stability,    1
that    1
the    3
to    3
upgrade    1
use.    1
with    2
    part-r-00001 文件内容

    3>.采样

    　　要使用采样的前提是不能使用文本类型，因为文本类型的Key为IntWritable。采样过程是在Mapper之前,采样的目的是为了让各个分区整体上实现全排序。

    　　a>.随机采样（性能差，适合乱序的源数据）

    1 循环的条件：
2 
3        当前已经扫描的分区数小于SplitToSample或者当前已经扫描的分区数超过了SplitToSample但是小于输出分区总数并且当前的采样数小于最大采样数numSamples。
4 
5 每个分区中记录采样的具体过程如下：
6 
7 　   从指定分区中取出一条记录，判断得到的随机浮点数是否小于等于采样频率freq，如果大于则放弃这条记录，然后判断当前你的采样是否小于最大采样数，如果小于则这条记录被选中，被放进采样集合中，否则从[0,numSamples]中选择一个随机数，如果这个随机数不等于最大采样数numSamples，则用这条记录替换掉采样集合随机数对应位置的记录，同时采样频率freq较少变为freq*(numSamples-1）/numSamples。然后依次遍历分区中的其它记录。
     1 /*
 2 @author :yinzhengjie
 3 Blog:http://www.cnblogs.com/yinzhengjie/tag/Hadoop%E8%BF%9B%E9%98%B6%E4%B9%8B%E8%B7%AF/
 4 EMAIL:y1053419035@qq.com
 5 */
 6 package cn.org.yinzhengjie.sequencefile;
 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.IntWritable;
12 import org.apache.hadoop.io.SequenceFile;
13 import org.apache.hadoop.io.Text;
14 
15 import java.io.IOException;
16 import java.util.Random;
17 
18 
19 public class TestSeq {
20 
21     public static void main(String[] args) throws IOException {
22         createSeq();
23     }
24 
25     public static void createSeq() throws IOException {
26 
27         Configuration conf = new Configuration();
28 
29         conf.set("fs.defaultFS", "file:///");
30 
31         FileSystem fs = FileSystem.get(conf);
32 
33         Path localPath = new Path("D:\BigData\JavaSE\yinzhengjieData\MyHadoop\MapReduce\block.seq");
34 
35         SequenceFile.Writer block_writer = SequenceFile.createWriter(fs, conf, localPath, Text.class, IntWritable.class,SequenceFile.CompressionType.BLOCK);
36 
37         for (int i = 0; i < 1000; i++) {
38             Random r = new Random();
39             int j = r.nextInt(1000);
40             Text key = new Text("yinzhengjie" + j);
41             IntWritable val = new IntWritable(j);
42             block_writer.append(key, val);
43         }
44         block_writer.close();
45     }
46 }
    生产SequenceFile的代码
     1 /*
 2 @author :yinzhengjie
 3 Blog:http://www.cnblogs.com/yinzhengjie/tag/Hadoop%E8%BF%9B%E9%98%B6%E4%B9%8B%E8%B7%AF/
 4 EMAIL:y1053419035@qq.com
 5 */
 6 package cn.org.yinzhengjie.totalsampler;
 7 
 8 import org.apache.hadoop.io.IntWritable;
 9 import org.apache.hadoop.io.LongWritable;
10 import org.apache.hadoop.io.Text;
11 import org.apache.hadoop.mapreduce.Mapper;
12 
13 import java.io.IOException;
14 
15 public class SamplerMapper extends Mapper<Text, IntWritable, Text, IntWritable> {
16 
17     @Override
18     protected void map(Text key, IntWritable value, Context context) throws IOException, InterruptedException {
19         context.write(key, value);
20     }
21 }
    SamplerMapper.java 文件内容
     1 /*
 2 @author :yinzhengjie
 3 Blog:http://www.cnblogs.com/yinzhengjie/tag/Hadoop%E8%BF%9B%E9%98%B6%E4%B9%8B%E8%B7%AF/
 4 EMAIL:y1053419035@qq.com
 5 */
 6 package cn.org.yinzhengjie.totalsampler;
 7 
 8 import org.apache.hadoop.io.IntWritable;
 9 import org.apache.hadoop.io.Text;
10 import org.apache.hadoop.mapreduce.Reducer;
11 
12 import java.io.IOException;
13 
14 public class SamplerReducer extends Reducer<Text, IntWritable , Text, IntWritable> {
15     @Override
16     protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
17         Integer sum = 0;
18         for(IntWritable value : values){
19             sum += value.get();
20         }
21         context.write(key, new IntWritable(sum));
22     }
23 }
    SamplerReducer.java 文件内容
     1 /*
 2 @author :yinzhengjie
 3 Blog:http://www.cnblogs.com/yinzhengjie/tag/Hadoop%E8%BF%9B%E9%98%B6%E4%B9%8B%E8%B7%AF/
 4 EMAIL:y1053419035@qq.com
 5 */
 6 package cn.org.yinzhengjie.totalsampler;
 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.IntWritable;
12 import org.apache.hadoop.io.Text;
13 import org.apache.hadoop.mapreduce.Job;
14 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
15 import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat;
16 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
17 import org.apache.hadoop.mapreduce.lib.partition.InputSampler;
18 import org.apache.hadoop.mapreduce.lib.partition.TotalOrderPartitioner;
19 
20 public class SamplerApp {
21 
22     public static void main(String[] args) throws Exception {
23 
24         Configuration conf = new Configuration();
25         conf.set("fs.defaultFS","file:///");
26 
27         Job job = Job.getInstance(conf);
28         FileSystem fs = FileSystem.get(conf);
29 
30         job.setJobName("Wordcount");
31         job.setJarByClass(SamplerApp.class);
32 
33         job.setMapperClass(SamplerMapper.class);
34         job.setReducerClass(SamplerReducer.class);
35 
36         //设置输入类型 ===> sequenceFile
37         job.setInputFormatClass(SequenceFileInputFormat.class);
38 
39         job.setOutputKeyClass(Text.class);
40         job.setOutputValueClass(IntWritable.class);
41 
42         Path p = new Path("D:\BigData\JavaSE\yinzhengjieData\MyHadoop\MapReduce\out");
43         if (fs.exists(p)) {
44             fs.delete(p, true);
45         }
46 
47         FileInputFormat.addInputPath(job, new Path("D:\BigData\JavaSE\yinzhengjieData\MyHadoop\MapReduce\block.seq"));
48         FileOutputFormat.setOutputPath(job, p);
49         //设置Reduce任务的个数，你也可以理解为分区个数！
50         job.setNumReduceTasks(5);
51 
52         //设置全排序分区类
53         job.setPartitionerClass(TotalOrderPartitioner.class);
54 
55         /**
56          * 采样代码一定要到job设置的最后部分
57          */
58         //设置分区文件
59         TotalOrderPartitioner.setPartitionFile(job.getConfiguration(),new Path("D:\BigData\JavaSE\yinzhengjieData\MyHadoop\MapReduce\par\par.dat"));
60 
61         /**
62          * 初始化采样器
63          * freq             每个Key被选中的概率     freq x key > 分区数(我上面设置的分区数是5个，这里的key应该是你的条目数，如果这个freq设置的值较小，就会抛异常：ArrayIndexOutOfBoundsException)
64          * numSamples       需要的样本数           numSamples  > 分区数(我上面设置的分区数是5个，如果numSamples的个数小于分区数也会抛异常：ArrayIndexOutOfBoundsException)
65          * maxSplitsSampled 文件最大切片数         maxSplitsSampled > 当前切片数
66          *
67          */
68         InputSampler.RandomSampler sampler = new InputSampler.RandomSampler(0.01,5,3);
69 
70         //写入分区文件
71         InputSampler.writePartitionFile(job, sampler);
72 
73         job.waitForCompletion(true);
74     }
75 }
    SamplerApp.java 文件内容

     　　执行以上代码后，会生成5个分区文件，如下：

    　　b>.间隔采样（性能较好，适合有序的源数据）

     　　　　固定采样间隔， 当数量达到numSamples，停止采样。

     1 /*
 2 @author :yinzhengjie
 3 Blog:http://www.cnblogs.com/yinzhengjie/tag/Hadoop%E8%BF%9B%E9%98%B6%E4%B9%8B%E8%B7%AF/
 4 EMAIL:y1053419035@qq.com
 5 */
 6 package cn.org.yinzhengjie.totalsampler;
 7 
 8 import org.apache.hadoop.io.IntWritable;
 9 import org.apache.hadoop.io.Text;
10 import org.apache.hadoop.mapreduce.Mapper;
11 
12 import java.io.IOException;
13 
14 public class SamplerMapper extends Mapper<Text, IntWritable, Text, IntWritable> {
15 
16     @Override
17     protected void map(Text key, IntWritable value, Context context) throws IOException, InterruptedException {
18         context.write(key, value);
19     }
20 }
    SamplerMapper.java 文件内容
     1 /*
 2 @author :yinzhengjie
 3 Blog:http://www.cnblogs.com/yinzhengjie/tag/Hadoop%E8%BF%9B%E9%98%B6%E4%B9%8B%E8%B7%AF/
 4 EMAIL:y1053419035@qq.com
 5 */
 6 package cn.org.yinzhengjie.totalsampler;
 7 
 8 import org.apache.hadoop.io.IntWritable;
 9 import org.apache.hadoop.io.Text;
10 import org.apache.hadoop.mapreduce.Reducer;
11 
12 import java.io.IOException;
13 
14 public class SamplerReducer extends Reducer<Text, IntWritable , Text, IntWritable> {
15     @Override
16     protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
17         Integer sum = 0;
18         for(IntWritable value : values){
19             sum += value.get();
20         }
21         context.write(key, new IntWritable(sum));
22     }
23 }
    SamplerReducer.java 文件内容
     1 /*
 2 @author :yinzhengjie
 3 Blog:http://www.cnblogs.com/yinzhengjie/tag/Hadoop%E8%BF%9B%E9%98%B6%E4%B9%8B%E8%B7%AF/
 4 EMAIL:y1053419035@qq.com
 5 */
 6 package cn.org.yinzhengjie.totalsampler;
 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.IntWritable;
12 import org.apache.hadoop.io.Text;
13 import org.apache.hadoop.mapreduce.Job;
14 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
15 import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat;
16 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
17 import org.apache.hadoop.mapreduce.lib.partition.InputSampler;
18 import org.apache.hadoop.mapreduce.lib.partition.TotalOrderPartitioner;
19 
20 public class SamplerApp {
21 
22     public static void main(String[] args) throws Exception {
23 
24         Configuration conf = new Configuration();
25         conf.set("fs.defaultFS","file:///");
26 
27         Job job = Job.getInstance(conf);
28         FileSystem fs = FileSystem.get(conf);
29 
30         job.setJobName("Wordcount");
31         job.setJarByClass(SamplerApp.class);
32 
33         job.setMapperClass(SamplerMapper.class);
34         job.setReducerClass(SamplerReducer.class);
35         //job.setCombinerClass(WCReducer.class);
36 
37         //设置输入类型 ===> sequenceFile
38         job.setInputFormatClass(SequenceFileInputFormat.class);
39 
40         job.setOutputKeyClass(Text.class);
41         job.setOutputValueClass(IntWritable.class);
42 
43         Path localPath = new Path("D:\10.Java\IDE\yhinzhengjieData\MyHadoop\MapReduce\out");
44         if (fs.exists(localPath)) {
45             fs.delete(localPath, true);
46         }
47         FileInputFormat.addInputPath(job, new Path("D:\10.Java\IDE\yhinzhengjieData\MyHadoop\MapReduce\block.seq"));
48         FileOutputFormat.setOutputPath(job, localPath);
49 
50         job.setNumReduceTasks(5);
51 
52 
53         //设置全排序分区类
54         job.setPartitionerClass(TotalOrderPartitioner.class);
55 
56 
57         /**
58          * 采样代码一定要到job设置的最后部分
59          */
60         //设置分区文件,该文件为采样文件，作用是用于判断分区个数，比如有4个采样，那么就可以生成5个分区！
61         TotalOrderPartitioner.setPartitionFile(job.getConfiguration(),new Path("D:\10.Java\IDE\yhinzhengjieData\MyHadoop\MapReduce\par\par.dat"));
62 
63         /**
64          * 初始化采样器
65          * freq             每个Key被选中的概率     freq x key > 分区数
66          * maxSplitsSampled 文件最大切片数         maxSplitsSampled > 当前切片数
67          *
68          */
69         InputSampler.IntervalSampler sampler = new InputSampler.IntervalSampler(0.01,10);
70 
71         //写入分区文件
72         InputSampler.writePartitionFile(job, sampler);
73 
74         job.waitForCompletion(true);
75     }
76 }
    SamplerApp.java 文件内容

    　　　　执行以上代码后，会生成5个分区文件，如下：

    　　c>.切片采样（性能较好，适合有序的源数据）

    　　　　　对每个切片的前n个数据进行采样。

     1 /*
 2 @author :yinzhengjie
 3 Blog:http://www.cnblogs.com/yinzhengjie/tag/Hadoop%E8%BF%9B%E9%98%B6%E4%B9%8B%E8%B7%AF/
 4 EMAIL:y1053419035@qq.com
 5 */
 6 package cn.org.yinzhengjie.totalsampler;
 7 
 8 import org.apache.hadoop.io.IntWritable;
 9 import org.apache.hadoop.io.Text;
10 import org.apache.hadoop.mapreduce.Mapper;
11 
12 import java.io.IOException;
13 
14 public class SamplerMapper extends Mapper<Text, IntWritable, Text, IntWritable> {
15 
16     @Override
17     protected void map(Text key, IntWritable value, Context context) throws IOException, InterruptedException {
18         context.write(key, value);
19     }
20 }
    SamplerMapper.java 文件内容
     1 /*
 2 @author :yinzhengjie
 3 Blog:http://www.cnblogs.com/yinzhengjie/tag/Hadoop%E8%BF%9B%E9%98%B6%E4%B9%8B%E8%B7%AF/
 4 EMAIL:y1053419035@qq.com
 5 */
 6 package cn.org.yinzhengjie.totalsampler;
 7 
 8 import org.apache.hadoop.io.IntWritable;
 9 import org.apache.hadoop.io.Text;
10 import org.apache.hadoop.mapreduce.Reducer;
11 
12 import java.io.IOException;
13 
14 public class SamplerReducer extends Reducer<Text, IntWritable , Text, IntWritable> {
15     @Override
16     protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
17         Integer sum = 0;
18         for(IntWritable value : values){
19             sum += value.get();
20         }
21         context.write(key, new IntWritable(sum));
22     }
23 }
    SamplerReducer.java 文件内容
     1 /*
 2 @author :yinzhengjie
 3 Blog:http://www.cnblogs.com/yinzhengjie/tag/Hadoop%E8%BF%9B%E9%98%B6%E4%B9%8B%E8%B7%AF/
 4 EMAIL:y1053419035@qq.com
 5 */
 6 package cn.org.yinzhengjie.totalsampler;
 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.IntWritable;
12 import org.apache.hadoop.io.Text;
13 import org.apache.hadoop.mapreduce.Job;
14 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
15 import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat;
16 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
17 import org.apache.hadoop.mapreduce.lib.partition.InputSampler;
18 import org.apache.hadoop.mapreduce.lib.partition.TotalOrderPartitioner;
19 
20 public class SamplerApp {
21 
22     public static void main(String[] args) throws Exception {
23 
24         Configuration conf = new Configuration();
25         conf.set("fs.defaultFS","file:///");
26 
27         Job job = Job.getInstance(conf);
28         FileSystem fs = FileSystem.get(conf);
29 
30         job.setJobName("Wordcount");
31         job.setJarByClass(SamplerApp.class);
32 
33         job.setMapperClass(SamplerMapper.class);
34         job.setReducerClass(SamplerReducer.class);
35         //job.setCombinerClass(WCReducer.class);
36 
37         //设置输入类型 ===> sequenceFile
38         job.setInputFormatClass(SequenceFileInputFormat.class);
39 
40         job.setOutputKeyClass(Text.class);
41         job.setOutputValueClass(IntWritable.class);
42 
43         Path localPath = new Path("D:\10.Java\IDE\yhinzhengjieData\MyHadoop\MapReduce\out");
44         if (fs.exists(localPath)) {
45             fs.delete(localPath, true);
46         }
47         FileInputFormat.addInputPath(job, new Path("D:\10.Java\IDE\yhinzhengjieData\MyHadoop\MapReduce\block.seq"));
48         FileOutputFormat.setOutputPath(job, localPath);
49 
50         job.setNumReduceTasks(5);
51 
52 
53         //设置全排序分区类
54         job.setPartitionerClass(TotalOrderPartitioner.class);
55 
56 
57         /**
58          * 采样代码一定要到job设置的最后部分
59          */
60         //设置分区文件,该文件为采样文件，作用是用于判断分区个数，比如有4个采样，那么就可以生成5个分区！
61         TotalOrderPartitioner.setPartitionFile(job.getConfiguration(),new Path("D:\10.Java\IDE\yhinzhengjieData\MyHadoop\MapReduce\par\par.dat"));
62 
63         /**
64          * 初始化采样器
65          * numSamples       需要的样本数           numSamples  > 分区数
66          * maxSplitsSampled 文件最大切片数         maxSplitsSampled > 当前切片数
67          *
68          */
69         InputSampler.SplitSampler sampler = new InputSampler.SplitSampler(10,5);
70 
71         //写入分区文件
72         InputSampler.writePartitionFile(job, sampler);
73 
74         job.waitForCompletion(true);
75     }
76 }
    SamplerApp.java 文件内容

    　　　　执行以上代码后，会生成5个分区文件，如下：

    三.二次排序实现方案

       1 1901    -172
   2 1901    -172
   3 1901    -72
   4 1901    -89
   5 1901    -117
   6 1901    -56
   7 1901    -111
   8 1901    -156
   9 1901    -39
  10 1901    -56
  11 1901    -33
  12 1901    11
  13 1901    6
  14 1901    17
  15 1901    17
  16 1901    0
  17 1901    -39
  18 1901    0
  19 1901    -6
  20 1901    11
  21 1901    28
  22 1901    17
  23 1901    6
  24 1901    17
  25 1901    6
  26 1901    -11
  27 1901    0
  28 1901    -22
  29 1901    -44
  30 1901    -6
  31 1901    -39
  32 1901    -50
  33 1901    22
  34 1901    6
  35 1901    0
  36 1901    22
  37 1901    17
  38 1901    11
  39 1901    33
  40 1901    22
  41 1901    0
  42 1901    28
  43 1901    -6
  44 1901    -6
  45 1901    -11
  46 1901    -17
  47 1901    -11
  48 1901    0
  49 1901    -28
  50 1901    -28
  51 1901    -28
  52 1901    -28
  53 1901    -56
  54 1901    -50
  55 1901    -50
  56 1901    -61
  57 1901    -44
  58 1901    -61
  59 1901    -39
  60 1901    0
  61 1901    0
  62 1901    6
  63 1901    6
  64 1901    -6
  65 1901    -6
  66 1901    0
  67 1901    -28
  68 1901    -11
  69 1901    -6
  70 1901    -17
  71 1901    -11
  72 1901    -6
  73 1901    -17
  74 1901    0
  75 1901    17
  76 1901    0
  77 1901    17
  78 1901    11
  79 1901    -11
  80 1901    -17
  81 1901    17
  82 1901    -11
  83 1901    11
  84 1901    28
  85 1901    -11
  86 1901    0
  87 1901    22
  88 1901    22
  89 1901    6
  90 1901    28
  91 1901    0
  92 1901    11
  93 1901    33
  94 1901    0
  95 1901    11
  96 1901    22
  97 1901    17
  98 1901    11
  99 1901    28
 100 1901    17
 101 1901    11
 102 1901    44
 103 1901    17
 104 1901    6
 105 1901    50
 106 1901    6
 107 1901    22
 108 1901    39
 109 1901    17
 110 1901    -11
 111 1901    0
 112 1901    -22
 113 1901    -28
 114 1901    0
 115 1901    -17
 116 1901    -22
 117 1901    -11
 118 1901    -11
 119 1901    -22
 120 1901    0
 121 1901    -6
 122 1901    -6
 123 1901    33
 124 1901    -17
 125 1901    17
 126 1901    44
 127 1901    6
 128 1901    17
 129 1901    33
 130 1901    33
 131 1901    33
 132 1901    72
 133 1901    33
 134 1901    72
 135 1901    72
 136 1901    56
 137 1901    72
 138 1901    89
 139 1901    83
 140 1901    67
 141 1901    50
 142 1901    67
 143 1901    67
 144 1901    83
 145 1901    39
 146 1901    22
 147 1901    33
 148 1901    17
 149 1901    11
 150 1901    17
 151 1901    17
 152 1901    17
 153 1901    28
 154 1901    6
 155 1901    17
 156 1901    33
 157 1901    17
 158 1901    11
 159 1901    50
 160 1901    17
 161 1901    22
 162 1901    44
 163 1901    39
 164 1901    33
 165 1901    50
 166 1901    33
 167 1901    33
 168 1901    50
 169 1901    33
 170 1901    33
 171 1901    50
 172 1901    33
 173 1901    33
 174 1901    56
 175 1901    33
 176 1901    50
 177 1901    61
 178 1901    39
 179 1901    33
 180 1901    39
 181 1901    28
 182 1901    33
 183 1901    44
 184 1901    28
 185 1901    22
 186 1901    33
 187 1901    28
 188 1901    44
 189 1901    50
 190 1901    33
 191 1901    44
 192 1901    67
 193 1901    44
 194 1901    56
 195 1901    50
 196 1901    44
 197 1901    61
 198 1901    94
 199 1901    67
 200 1901    44
 201 1901    61
 202 1901    44
 203 1901    50
 204 1901    67
 205 1901    61
 206 1901    94
 207 1901    128
 208 1901    122
 209 1901    94
 210 1901    89
 211 1901    78
 212 1901    72
 213 1901    100
 214 1901    72
 215 1901    72
 216 1901    100
 217 1901    89
 218 1901    100
 219 1901    117
 220 1901    128
 221 1901    156
 222 1901    117
 223 1901    117
 224 1901    94
 225 1901    94
 226 1901    106
 227 1901    100
 228 1901    89
 229 1901    72
 230 1901    72
 231 1901    94
 232 1901    50
 233 1901    67
 234 1901    78
 235 1901    56
 236 1901    61
 237 1901    78
 238 1901    67
 239 1901    67
 240 1901    78
 241 1901    67
 242 1901    78
 243 1901    111
 244 1901    83
 245 1901    100
 246 1901    111
 247 1901    100
 248 1901    100
 249 1901    111
 250 1901    94
 251 1901    100
 252 1901    122
 253 1901    100
 254 1901    83
 255 1901    106
 256 1901    83
 257 1901    100
 258 1901    133
 259 1901    117
 260 1901    128
 261 1901    161
 262 1901    156
 263 1901    172
 264 1901    200
 265 1901    183
 266 1901    172
 267 1901    194
 268 1901    144
 269 1901    150
 270 1901    178
 271 1901    144
 272 1901    133
 273 1901    128
 274 1901    111
 275 1901    111
 276 1901    128
 277 1901    100
 278 1901    100
 279 1901    111
 280 1901    94
 281 1901    106
 282 1901    128
 283 1901    117
 284 1901    111
 285 1901    128
 286 1901    111
 287 1901    111
 288 1901    111
 289 1901    117
 290 1901    111
 291 1901    117
 292 1901    122
 293 1901    111
 294 1901    122
 295 1901    133
 296 1901    122
 297 1901    128
 298 1901    128
 299 1901    150
 300 1901    161
 301 1901    133
 302 1901    139
 303 1901    172
 304 1901    156
 305 1901    133
 306 1901    144
 307 1901    139
 308 1901    133
 309 1901    167
 310 1901    150
 311 1901    133
 312 1901    178
 313 1901    161
 314 1901    172
 315 1901    167
 316 1901    161
 317 1901    150
 318 1901    144
 319 1901    150
 320 1901    156
 321 1901    178
 322 1901    161
 323 1901    178
 324 1901    167
 325 1901    172
 326 1901    156
 327 1901    161
 328 1901    150
 329 1901    167
 330 1901    161
 331 1901    156
 332 1901    161
 333 1901    189
 334 1901    144
 335 1901    156
 336 1901    178
 337 1901    167
 338 1901    172
 339 1901    200
 340 1901    194
 341 1901    183
 342 1901    211
 343 1901    178
 344 1901    178
 345 1901    200
 346 1901    183
 347 1901    178
 348 1901    206
 349 1901    194
 350 1901    183
 351 1901    239
 352 1901    222
 353 1901    200
 354 1901    222
 355 1901    194
 356 1901    200
 357 1901    222
 358 1901    206
 359 1901    200
 360 1901    211
 361 1901    194
 362 1901    194
 363 1901    206
 364 1901    194
 365 1901    194
 366 1901    228
 367 1901    206
 368 1901    200
 369 1901    217
 370 1901    206
 371 1901    189
 372 1901    194
 373 1901    194
 374 1901    172
 375 1901    194
 376 1901    172
 377 1901    150
 378 1901    183
 379 1901    172
 380 1901    183
 381 1901    211
 382 1901    189
 383 1901    133
 384 1901    167
 385 1901    156
 386 1901    150
 387 1901    189
 388 1901    172
 389 1901    161
 390 1901    156
 391 1901    156
 392 1901    144
 393 1901    167
 394 1901    156
 395 1901    144
 396 1901    156
 397 1901    150
 398 1901    156
 399 1901    172
 400 1901    167
 401 1901    144
 402 1901    156
 403 1901    144
 404 1901    150
 405 1901    200
 406 1901    178
 407 1901    183
 408 1901    194
 409 1901    167
 410 1901    150
 411 1901    161
 412 1901    150
 413 1901    156
 414 1901    172
 415 1901    178
 416 1901    156
 417 1901    167
 418 1901    167
 419 1901    178
 420 1901    200
 421 1901    178
 422 1901    183
 423 1901    217
 424 1901    194
 425 1901    183
 426 1901    217
 427 1901    183
 428 1901    183
 429 1901    183
 430 1901    178
 431 1901    172
 432 1901    189
 433 1901    161
 434 1901    144
 435 1901    156
 436 1901    139
 437 1901    139
 438 1901    139
 439 1901    133
 440 1901    150
 441 1901    161
 442 1901    161
 443 1901    161
 444 1901    172
 445 1901    161
 446 1901    144
 447 1901    150
 448 1901    150
 449 1901    139
 450 1901    144
 451 1901    128
 452 1901    122
 453 1901    150
 454 1901    144
 455 1901    133
 456 1901    161
 457 1901    156
 458 1901    167
 459 1901    183
 460 1901    156
 461 1901    133
 462 1901    161
 463 1901    133
 464 1901    100
 465 1901    133
 466 1901    122
 467 1901    117
 468 1901    106
 469 1901    106
 470 1901    78
 471 1901    100
 472 1901    83
 473 1901    56
 474 1901    72
 475 1901    89
 476 1901    72
 477 1901    89
 478 1901    100
 479 1901    94
 480 1901    106
 481 1901    106
 482 1901    83
 483 1901    106
 484 1901    94
 485 1901    83
 486 1901    106
 487 1901    100
 488 1901    72
 489 1901    111
 490 1901    94
 491 1901    78
 492 1901    111
 493 1901    94
 494 1901    106
 495 1901    117
 496 1901    106
 497 1901    94
 498 1901    117
 499 1901    100
 500 1901    106
 501 1901    106
 502 1901    83
 503 1901    94
 504 1901    100
 505 1901    89
 506 1901    89
 507 1901    106
 508 1901    100
 509 1901    100
 510 1901    111
 511 1901    111
 512 1901    100
 513 1901    111
 514 1901    100
 515 1901    100
 516 1901    117
 517 1901    106
 518 1901    106
 519 1901    122
 520 1901    106
 521 1901    106
 522 1901    117
 523 1901    106
 524 1901    106
 525 1901    117
 526 1901    106
 527 1901    111
 528 1901    122
 529 1901    106
 530 1901    100
 531 1901    94
 532 1901    94
 533 1901    83
 534 1901    111
 535 1901    106
 536 1901    111
 537 1901    111
 538 1901    106
 539 1901    111
 540 1901    117
 541 1901    111
 542 1901    111
 543 1901    106
 544 1901    106
 545 1901    111
 546 1901    111
 547 1901    100
 548 1901    100
 549 1901    117
 550 1901    117
 551 1901    100
 552 1901    117
 553 1901    117
 554 1901    117
 555 1901    117
 556 1901    100
 557 1901    106
 558 1901    122
 559 1901    117
 560 1901    100
 561 1901    94
 562 1901    78
 563 1901    67
 564 1901    89
 565 1901    94
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5056 1903    -78
5057 1903    -122
5058 1903    -39
5059 1903    -94
5060 1903    -100
5061 1903    -78
5062 1903    -61
5063 1903    -44
5064 1903    -44
5065 1903    -61
5066 1903    -72
5067 1903    -78
5068 1903    -83
5069 1903    -89
5070 1903    -83
5071 1903    -61
5072 1903    -39
5073 1903    -11
5074 1903    6
5075 1903    17
5076 1903    28
5077 1903    28
5078 1903    6
5079 1903    22
5080 1903    0
5081 1903    -17
5082 1903    -6
5083 1903    -17
5084 1903    11
5085 1903    33
5086 1903    6
5087 1903    -6
5088 1903    0
5089 1903    6
5090 1903    22
5091 1903    22
5092 1903    6
5093 1903    11
5094 1903    17
5095 1903    6
5096 1903    -22
5097 1903    6
5098 1903    -44
5099 1903    -17
5100 1903    -11
5101 1903    -6
5102 1903    -22
5103 1903    0
5104 1903    22
5105 1903    11
5106 1903    11
5107 1903    17
5108 1903    11
5109 1903    11
5110 1903    17
5111 1903    22
5112 1903    11
5113 1903    0
5114 1903    -6
5115 1903    28
5116 1903    11
5117 1903    -33
5118 1903    -6
5119 1903    0
5120 1903    33
5121 1903    39
5122 1903    22
5123 1903    -11
5124 1903    -17
5125 1903    -44
5126 1903    -111
5127 1903    -61
5128 1903    -122
5129 1903    -106
5130 1903    -50
5131 1903    -106
5132 1903    -150
5133 1903    -111
5134 1903    -150
5135 1903    -139
5136 1903    -67
5137 1903    -94
5138 1903    -11
5139 1903    -17
5140 1903    -100
5141 1903    -150
5142 1903    -100
5143 1903    -50
5144 1903    11
5145 1903    22
5146 1903    11
5147 1903    28
5148 1903    39
5149 1903    28
5150 1903    22
5151 1903    22
5152 1903    22
5153 1903    6
5154 1903    28
5155 1903    33
5156 1903    -11
5157 1903    6
5158 1903    0
5159 1903    -6
5160 1903    6
5161 1903    -11
5162 1903    -78
5163 1903    11
5164 1903    17
5165 1903    11
5166 1903    28
5167 1903    22
5168 1903    22
5169 1903    28
5170 1903    11
5171 1903    28
5172 1903    33
5173 1903    22
5174 1903    11
5175 1903    11
5176 1903    -11
5177 1903    28
5178 1903    33
5179 1903    0
5180 1903    -17
5181 1903    -11
5182 1903    6
5183 1903    17
5184 1903    22
5185 1903    17
5186 1903    17
5187 1903    28
5188 1903    11
5189 1903    6
5190 1903    28
5191 1903    6
5192 1903    0
5193 1903    28
5194 1903    11
5195 1903    6
5196 1903    11
5197 1903    -11
5198 1903    6
5199 1903    33
5200 1903    -6
5201 1903    -6
5202 1903    39
5203 1903    -17
5204 1903    -6
5205 1903    0
5206 1903    -6
5207 1903    -6
5208 1903    6
5209 1903    -6
5210 1903    6
5211 1903    17
5212 1903    -17
5213 1903    -28
5214 1903    6
5215 1903    -11
5216 1903    -11
5217 1903    17
5218 1903    6
5219 1903    6
5220 1903    39
5221 1903    -11
5222 1903    -28
5223 1903    -11
5224 1903    -44
5225 1903    -44
5226 1903    6
5227 1903    -22
5228 1903    -28
5229 1903    11
5230 1903    6
5231 1903    17
5232 1903    28
5233 1903    33
5234 1903    17
5235 1903    44
5236 1903    11
5237 1903    17
5238 1903    44
5239 1903    50
5240 1903    50
5241 1903    100
5242 1903    56
5243 1903    72
5244 1903    50
5245 1903    22
5246 1903    -22
5247 1903    6
5248 1903    -28
5249 1903    0
5250 1903    44
5251 1903    56
5252 1903    44
5253 1903    106
5254 1903    33
5255 1903    17
5256 1903    56
5257 1903    39
5258 1903    39
5259 1903    78
5260 1903    39
5261 1903    44
5262 1903    94
5263 1903    39
5264 1903    11
5265 1903    83
5266 1903    44
5267 1903    -33
5268 1903    -33
5269 1903    -28
5270 1903    -56
5271 1903    6
5272 1903    -39
5273 1903    -33
5274 1903    -11
5275 1903    -33
5276 1903    -50
5277 1903    -11
5278 1903    -39
5279 1903    -39
5280 1903    0
5281 1903    -6
5282 1903    22
5283 1903    44
5284 1903    11
5285 1903    22
5286 1903    11
5287 1903    22
5288 1903    17
5289 1903    50
5290 1903    22
5291 1903    11
5292 1903    67
5293 1903    22
5294 1903    17
5295 1903    67
5296 1903    6
5297 1903    11
5298 1903    33
5299 1903    -6
5300 1903    11
5301 1903    44
5302 1903    39
5303 1903    22
5304 1903    17
5305 1903    11
5306 1903    22
5307 1903    39
5308 1903    11
5309 1903    17
5310 1903    28
5311 1903    11
5312 1903    22
5313 1903    39
5314 1903    17
5315 1903    17
5316 1903    39
5317 1903    6
5318 1903    6
5319 1903    39
5320 1903    11
5321 1903    11
5322 1903    22
5323 1903    39
5324 1903    28
5325 1903    56
5326 1903    28
5327 1903    17
5328 1903    22
5329 1903    11
5330 1903    22
5331 1903    44
5332 1903    22
5333 1903    11
5334 1903    83
5335 1903    33
5336 1903    22
5337 1903    89
5338 1903    94
5339 1903    117
5340 1903    172
5341 1903    117
5342 1903    122
5343 1903    156
5344 1903    94
5345 1903    89
5346 1903    183
5347 1903    122
5348 1903    100
5349 1903    150
5350 1903    100
5351 1903    106
5352 1903    189
5353 1903    133
5354 1903    117
5355 1903    183
5356 1903    128
5357 1903    122
5358 1903    167
5359 1903    100
5360 1903    44
5361 1903    67
5362 1903    61
5363 1903    67
5364 1903    100
5365 1903    67
5366 1903    61
5367 1903    100
5368 1903    72
5369 1903    67
5370 1903    117
5371 1903    72
5372 1903    67
5373 1903    50
5374 1903    50
5375 1903    44
5376 1903    50
5377 1903    44
5378 1903    44
5379 1903    83
5380 1903    56
5381 1903    83
5382 1903    106
5383 1903    44
    yinzhengjie.txt 文件内容

    　　对以上文件的value进行二次排序（降序排序），具体代码如下：

     1 /*
 2 @author :yinzhengjie
 3 Blog:http://www.cnblogs.com/yinzhengjie/tag/Hadoop%E8%BF%9B%E9%98%B6%E4%B9%8B%E8%B7%AF/
 4 EMAIL:y1053419035@qq.com
 5 */
 6 package cn.org.yinzhengjie.mapreduce.SecondReducer;
 7 
 8 import org.apache.hadoop.io.WritableComparable;
 9 
10 import java.io.DataInput;
11 import java.io.DataOutput;
12 import java.io.IOException;
13 
14 /**
15  * 定义组合key
16  */
17 public class CompKey implements WritableComparable<CompKey>{
18 
19     private String year;
20     private int temp;
21 
22     public int compareTo(CompKey o) {
23         //如果年份相等的情况下
24         if( this.getYear().equals(o.getYear())){
25             //返回temp的比较
26             return  o.getTemp() - this.getTemp() ;
27         }
28         else {
29             //年分不等，返回年份比较
30             return this.getYear().compareTo(o.getYear());
31         }
32     }
33 
34     //序列化方式
35     public void write(DataOutput out) throws IOException {
36         out.writeUTF(year);
37         out.writeInt(temp);
38     }
39 
40     //反序列化方式
41     public void readFields(DataInput in) throws IOException {
42         year = in.readUTF();
43         temp = in.readInt();
44     }
45 
46 
47 
48     public String getYear() {
49         return year;
50     }
51 
52     public void setYear(String year) {
53         this.year = year;
54     }
55 
56     public int getTemp() {
57         return temp;
58     }
59 
60     public void setTemp(int temp) {
61         this.temp = temp;
62     }
63 
64     @Override
65     public String toString() {
66         return year + '	' + temp;
67     }
68 }
    CompKey.java 文件内容
     1 /*
 2 @author :yinzhengjie
 3 Blog:http://www.cnblogs.com/yinzhengjie/tag/Hadoop%E8%BF%9B%E9%98%B6%E4%B9%8B%E8%B7%AF/
 4 EMAIL:y1053419035@qq.com
 5 */
 6 package cn.org.yinzhengjie.mapreduce.SecondReducer;
 7 
 8 import org.apache.hadoop.io.WritableComparable;
 9 import org.apache.hadoop.io.WritableComparator;
10 
11 
12 /**
13  * 定义分组对比器
14  */
15 public class MyGroupComparator extends WritableComparator {
16 
17     //标准写法，
18     public MyGroupComparator() {
19         //将我们定义的组合key类传递进去，并创建实例
20         super(CompKey.class, true);
21     }
22 
23     @Override
24     public int compare(WritableComparable a, WritableComparable b) {
25         //将a和b进行向下转型,将其类型转化为CompKey（因为我们已经实现了CompKey的WritableComparable接口）。这样就可以调用getYear()方法了！
26         CompKey ck1 = (CompKey)a;
27         CompKey ck2 = (CompKey)b;
28         return ck1.getYear().compareTo(ck2.getYear());
29     }
30 }
    MyGroupComparator.java　文件内容
     1 /*
 2 @author :yinzhengjie
 3 Blog:http://www.cnblogs.com/yinzhengjie/tag/Hadoop%E8%BF%9B%E9%98%B6%E4%B9%8B%E8%B7%AF/
 4 EMAIL:y1053419035@qq.com
 5 */
 6 package cn.org.yinzhengjie.mapreduce.SecondReducer;
 7 
 8 import org.apache.hadoop.io.NullWritable;
 9 import org.apache.hadoop.io.Text;
10 import org.apache.hadoop.mapreduce.Mapper;
11 
12 import java.io.IOException;
13 
14 public class SecondMapper extends Mapper<Text,Text,CompKey,NullWritable> {
15 
16     @Override
17     protected void map(Text key, Text value, Context context) throws IOException, InterruptedException {
18 
19         String year = key.toString();
20         int temp = Integer.parseInt(value.toString());
21 
22         CompKey ck = new CompKey();
23         ck.setYear(year);
24         ck.setTemp(temp);
25 
26         context.write(ck,NullWritable.get());
27 
28     }
29 }
    SecondMapper.java 文件内容
     1 /*
 2 @author :yinzhengjie
 3 Blog:http://www.cnblogs.com/yinzhengjie/tag/Hadoop%E8%BF%9B%E9%98%B6%E4%B9%8B%E8%B7%AF/
 4 EMAIL:y1053419035@qq.com
 5 */
 6 package cn.org.yinzhengjie.mapreduce.SecondReducer;
 7 
 8 import org.apache.hadoop.io.NullWritable;
 9 import org.apache.hadoop.mapreduce.Reducer;
10 
11 import java.io.IOException;
12 
13 public class SecondReducer extends Reducer<CompKey,NullWritable,CompKey,NullWritable> {
14 
15     @Override
16     protected void reduce(CompKey key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {
17 
18         String Delimiter = "==================== 我是分隔符 ====================";
19         CompKey ck2 = new CompKey();
20         //下面事物给ck2传递了两个参数，一个是String，一个是int类型，做这个操作的目的就是为了在每个key输出玩之后打印一下这个分隔符！
21         ck2.setYear(Delimiter);
22         ck2.setTemp(2018);
23 
24         for (NullWritable value : values) {
25             context.write(key,value);
26         }
27         //将变量Delimiter在每次循环后进行打印
28         context.write(ck2,NullWritable.get());
29     }
30 }
    SecondReducer.java 文件内容
     1 /*
 2 @author :yinzhengjie
 3 Blog:http://www.cnblogs.com/yinzhengjie/tag/Hadoop%E8%BF%9B%E9%98%B6%E4%B9%8B%E8%B7%AF/
 4 EMAIL:y1053419035@qq.com
 5 */
 6 package cn.org.yinzhengjie.mapreduce.SecondReducer;
 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.NullWritable;
12 import org.apache.hadoop.mapreduce.Job;
13 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
14 import org.apache.hadoop.mapreduce.lib.input.KeyValueTextInputFormat;
15 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
16 
17 public class App {
18 
19     public static void main(String[] args) throws Exception {
20 
21 
22         Configuration conf = new Configuration();
23         conf.set("fs.defaultFS","file:///");
24 
25         //如果conf设置了新值，一定要在初始化job时将conf传进去
26         Job job = Job.getInstance(conf);
27         FileSystem fs = FileSystem.get(conf);
28 
29         job.setJobName("SecondarySort");
30         job.setJarByClass(App.class);
31 
32         job.setInputFormatClass(KeyValueTextInputFormat.class);
33 
34         job.setOutputKeyClass(CompKey.class);
35         job.setOutputValueClass(NullWritable.class);
36 
37         job.setMapperClass(SecondMapper.class);
38         job.setReducerClass(SecondReducer.class);
39         //设置分组对比器的类，这里指定我们自定义的分组对比器即可
40         job.setGroupingComparatorClass(MyGroupComparator.class);
41 
42 
43         FileInputFormat.addInputPath(job,new Path("D:\10.Java\IDE\yhinzhengjieData\MyHadoop\MapReduce\yinzhengjie.txt"));
44         Path localPath = new Path("D:\10.Java\IDE\yhinzhengjieData\MyHadoop\MapReduce\second");
45         if (fs.exists(localPath)) {
46             fs.delete(localPath, true);
47         }
48         FileOutputFormat.setOutputPath(job,localPath);
49 
50         job.waitForCompletion(true);
51 
52 
53 
54     }
55 
56 
57 }
    App.java 文件内容

     　　执行以上代码会生成一个分区文件，如下：
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  • 原文地址：https://www.cnblogs.com/yinzhengjie/p/9226218.html
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