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>.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<Text,IntWritable,Text,IntWritable> { 15 @Override 16 protected void reduce(Text key, Iterable<IntWritable> 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<LongWritable,Text,Text,IntWritable> { 16 @Override 17 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 :yinzhengjie Blog: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<LongWritable, Text, Text, IntWritable> { //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<LongWritable,Text,Text,IntWritable> { 11 12 //处理的数据类似于“1_1 677” 13 @Override 14 protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { 15 String line = value.toString(); 16 // 17 String[] arr = line.split(" "); 18 19 //newKey 20 String newKey = arr[0].split("_")[0]; 21 22 //newVAl 23 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<Text,IntWritable,Text,IntWritable> { 15 @Override 16 protected void reduce(Text key, Iterable<IntWritable> 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<LongWritable,Text,Text,IntWritable> { 16 17 @Override 18 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<Text, IntWritable> { 15 @Override 16 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ID 19 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<Text,IntWritable,Text,IntWritable> { 15 @Override 16 protected void reduce(Text key, Iterable<IntWritable> 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: