Hadoop基础-MapReduce的数据倾斜解决方案

　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　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.数据倾斜简介

1>.什么是数据倾斜

　　答：大量数据涌入到某一节点，导致此节点负载过重，此时就产生了数据倾斜。

2>.处理数据倾斜的两种方案

　　第一：重新设计key；

　　第二：设计随机分区； 

 

二.模拟数据倾斜

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1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9

1>.App端代码

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.srew; 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 public class ScrewApp {18     public static void main(String[] args) throws Exception {19         //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。)20         Configuration conf = new Configuration();21         //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。22         conf.set("fs.defaultFS","file:///");23         //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。24         FileSystem fs = FileSystem.get(conf);25         //创建一个任务对象job，别忘记把conf穿进去哟！26         Job job = Job.getInstance(conf);27         //给任务起个名字28         job.setJobName("WordCount");29         //指定main函数所在的类，也就是当前所在的类名30         job.setJarByClass(ScrewApp.class);31         //指定map的类名,这里指定咱们自定义的map程序即可32         job.setMapperClass(ScrewMapper.class);33         //指定reduce的类名，这里指定咱们自定义的reduce程序即可34         job.setReducerClass(ScrewReduce.class);35         //设置输出key的数据类型36         job.setOutputKeyClass(Text.class);37         //设置输出value的数据类型38         job.setOutputValueClass(IntWritable.class);39         Path localPath = new Path("D:\\10.Java\\IDE\\yhinzhengjieData\\MyHadoop\\MapReduce\\out");40         if (fs.exists(localPath)){41             fs.delete(localPath,true);42         }43         //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44         FileInputFormat.addInputPath(job,new Path("D:\\10.Java\\IDE\\yhinzhengjieData\\MyHadoop\\MapReduce\\screw.txt"));45         //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46         FileOutputFormat.setOutputPath(job,localPath);47         //设置Reduce的个数为2.48         job.setNumReduceTasks(2);49         //等待任务执行结束,将里面的值设置为true。50         job.waitForCompletion(true);51     }52 }

2>.Reduce端代码

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.srew; 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 ScrewReduce extends Reducer
      
        {15     @Override16     protected void reduce(Text key, Iterable
       
         values, Context context) throws IOException, InterruptedException {17         int count = 0;18         for (IntWritable value : values) {19             count += value.get();20         }21         context.write(key,new IntWritable(count));22     }23 }
       
      

3>.Mapper端代码

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.srew; 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 ScrewMapper extends Mapper
      
        {16     @Override17     protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {18         String line = value.toString();19 20         String[] arr = line.split(" ");21 22         for (String word : arr) {23             context.write(new Text(word),new IntWritable(1));24         }25     }26 }
      

 　　执行以上代码，查看数据如下：

 

三.解决数据倾斜方案之重新设计key

1>.具体代码如下

/*@author :yinzhengjieBlog:http://www.cnblogs.com/yinzhengjie/tag/Hadoop%E8%BF%9B%E9%98%B6%E4%B9%8B%E8%B7%AF/EMAIL:y1053419035@qq.com*/package cn.org.yinzhengjie.srew;import org.apache.hadoop.io.IntWritable;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Mapper;import java.io.IOException;import java.util.Random;public class ScrewMapper extends Mapper
      
        {    //定义一个reduce变量    int reduces;    //定义一个随机数生成器变量    Random r;    /**     * setup方法是用于初始化值     */    @Override    protected void setup(Context context) throws IOException, InterruptedException {        //通过context.getNumReduceTasks()方法获取到用户配置的reduce个数。        reduces = context.getNumReduceTasks();        //生成一个随机数生成器        r = new Random();    }    @Override    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {        String line = value.toString();        String[] arr = line.split(" ");        for (String word : arr) {            //从reducs的范围中获取一个int类型的随机数赋值给randVal            int randVal = r.nextInt(reduces);            //重新定义key            String newWord = word+"_"+ randVal;            //将自定义的key赋初始值为1发给reduce端            context.write(new Text(newWord), new IntWritable(1));        }    }}
      

1 package cn.org.yinzhengjie.srew; 2  3 import org.apache.hadoop.io.IntWritable; 4 import org.apache.hadoop.io.LongWritable; 5 import org.apache.hadoop.io.Text; 6 import org.apache.hadoop.mapreduce.Mapper; 7  8 import java.io.IOException; 9 10 public class ScrewMapper2 extends Mapper
      
        {11 12     //处理的数据类似于“1_1    677”13     @Override14     protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {15         String line = value.toString();16         //17         String[] arr = line.split("\t");18 19         //newKey20         String newKey = arr[0].split("_")[0];21 22         //newVAl23         int newVal = Integer.parseInt(arr[1]);24 25         context.write(new Text(newKey), new IntWritable(newVal));26 27 28     }29 }
      

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.srew; 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 ScrewReducer extends Reducer
      
        {15     @Override16     protected void reduce(Text key, Iterable
       
         values, Context context) throws IOException, InterruptedException {17         int count = 0;18         for (IntWritable value : values) {19             count += value.get();20         }21         context.write(key,new IntWritable(count));22     }23 }
       
      

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.srew; 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 public class ScrewApp {18     public static void main(String[] args) throws Exception {19         //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。)20         Configuration conf = new Configuration();21         //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。22         conf.set("fs.defaultFS","file:///");23         //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。24         FileSystem fs = FileSystem.get(conf);25         //创建一个任务对象job，别忘记把conf穿进去哟！26         Job job = Job.getInstance(conf);27         //给任务起个名字28         job.setJobName("WordCount");29         //指定main函数所在的类，也就是当前所在的类名30         job.setJarByClass(ScrewApp.class);31         //指定map的类名,这里指定咱们自定义的map程序即可32         job.setMapperClass(ScrewMapper.class);33         //指定reduce的类名，这里指定咱们自定义的reduce程序即可34         job.setReducerClass(ScrewReducer.class);35         //设置输出key的数据类型36         job.setOutputKeyClass(Text.class);37         //设置输出value的数据类型38         job.setOutputValueClass(IntWritable.class);39         Path localPath = new Path("D:\\10.Java\\IDE\\yhinzhengjieData\\MyHadoop\\MapReduce\\out");40         if (fs.exists(localPath)){41             fs.delete(localPath,true);42         }43         //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44         FileInputFormat.addInputPath(job,new Path("D:\\10.Java\\IDE\\yhinzhengjieData\\MyHadoop\\MapReduce\\screw.txt"));45         //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46         FileOutputFormat.setOutputPath(job,localPath);47         //设置Reduce的个数为2.48         job.setNumReduceTasks(2);49         //等待任务执行结束,将里面的值设置为true。50         if (job.waitForCompletion(true)) {51             //当第一个MapReduce结束之后，我们这里又启动了一个新的MapReduce，逻辑和上面类似。52             Job job2 = Job.getInstance(conf);53             job2.setJobName("Wordcount2");54             job2.setJarByClass(ScrewApp.class);55             job2.setMapperClass(ScrewMapper2.class);56             job2.setReducerClass(ScrewReducer.class);57             job2.setOutputKeyClass(Text.class);58             job2.setOutputValueClass(IntWritable.class);59             Path p2 = new Path("D:\\10.Java\\IDE\\yhinzhengjieData\\MyHadoop\\MapReduce\\out2");60             if (fs.exists(p2)) {61                 fs.delete(p2, true);62             }63             FileInputFormat.addInputPath(job2, localPath);64             FileOutputFormat.setOutputPath(job2, p2);65             //我们将第一个MapReduce的2个reducer的处理结果放在新的一个MapReduce中只启用一个MapReduce。66             job2.setNumReduceTasks(1);67             job2.waitForCompletion(true);68         }69     }70 }

2>.检测实验结果

　　“D:\\10.Java\\IDE\\yhinzhengjieData\\MyHadoop\\MapReduce\\out” 目录内容如下：

 

　　“D:\\10.Java\\IDE\\yhinzhengjieData\\MyHadoop\\MapReduce\\out2” 目录内容如下：

 

四.解决数据倾斜方案之使用随机分区

 1>.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.screwpartition; 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 Screw2Mapper extends Mapper
      
        {16 17     @Override18     protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {19 20         String line = value.toString();21 22         String[] arr = line.split(" ");23 24         for(String word : arr){25             context.write(new Text(word), new IntWritable(1));26 27         }28 29 30     }31 }
      

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.screwpartition; 7  8 import org.apache.hadoop.io.IntWritable; 9 import org.apache.hadoop.io.Text;10 import org.apache.hadoop.mapreduce.Partitioner;11 12 import java.util.Random;13 14 public class Screw2Partition extends Partitioner
      
        {15     @Override16     public int getPartition(Text text, IntWritable intWritable, int numPartitions) {17         Random r = new Random();18         //返回的是分区的随机的一个ID19         return r.nextInt(numPartitions);20     }21 }
      

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.screwpartition; 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 Screw2Reducer extends Reducer
      
        {15     @Override16     protected void reduce(Text key, Iterable
       
         values, Context context) throws IOException, InterruptedException {17         int sum = 0;18         for(IntWritable value : values){19             sum += value.get();20         }21         context.write(key,new IntWritable(sum));22     }23 }
       
      

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.screwpartition; 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 public class Screw2App {18     public static void main(String[] args) throws Exception {19         Configuration conf = new Configuration();20         conf.set("fs.defaultFS", "file:///");21         FileSystem fs = FileSystem.get(conf);22         Job job = Job.getInstance(conf);23         job.setJobName("Wordcount");24         job.setJarByClass(Screw2App.class);25         job.setMapperClass(Screw2Mapper.class);26         job.setReducerClass(Screw2Reducer.class);27         job.setPartitionerClass(Screw2Partition.class);28         job.setOutputKeyClass(Text.class);29         job.setOutputValueClass(IntWritable.class);30         Path p = new Path("D:\\10.Java\\IDE\\yhinzhengjieData\\MyHadoop\\MapReduce\\out");31         if (fs.exists(p)) {32             fs.delete(p, true);33         }34         FileInputFormat.addInputPath(job, new Path("D:\\10.Java\\IDE\\yhinzhengjieData\\MyHadoop\\MapReduce\\screw.txt"));35         FileOutputFormat.setOutputPath(job, p);36         job.setNumReduceTasks(2);37         job.waitForCompletion(true);38     }39 }

2>.检测实验结果

 　　“D:\\10.Java\\IDE\\yhinzhengjieData\\MyHadoop\\MapReduce\\out” 目录内容如下：

　　 “D:\\10.Java\\IDE\\yhinzhengjieData\\MyHadoop\\MapReduce\\out2” 目录内容如下：