Hadoop基礎-MapReduce的數據傾斜解決方案

本文轉載自查看原文 2018-06-18 10:04 1489 Hadoop生態圈

　　　　　　　　　　　　　　　　　　　　　Hadoop基礎-MapReduce的數據傾斜解決方案

　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　作者：尹正傑

一.數據傾斜簡介

1>.什么是數據傾斜

　　答：大量數據涌入到某一節點，導致此節點負載過重，此時就產生了數據傾斜。

2>.處理數據傾斜的兩種方案

　　第一：重新設計key；

　　第二：設計隨機分區；

二.模擬數據傾斜

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screw.txt 文件內容

1>.App端代碼

 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         //實例化一個Configuration，它會自動去加載本地的core-site.xml配置文件的fs.defaultFS屬性。(該文件放在項目的resources目錄即可。)
20         Configuration conf = new Configuration();
21         //將hdfs寫入的路徑定義在本地，需要修改默認為文件系統，這樣就可以覆蓋到之前在core-site.xml配置文件讀取到的數據。
22         conf.set("fs.defaultFS","file:///");
23         //代碼的入口點，初始化HDFS文件系統，此時我們需要把讀取到的fs.defaultFS屬性傳給fs對象。
24         FileSystem fs = FileSystem.get(conf);
25         //創建一個任務對象job，別忘記把conf穿進去喲！
26         Job job = Job.getInstance(conf);
27         //給任務起個名字
28         job.setJobName("WordCount");
29         //指定main函數所在的類，也就是當前所在的類名
30         job.setJarByClass(ScrewApp.class);
31         //指定map的類名,這里指定咱們自定義的map程序即可
32         job.setMapperClass(ScrewMapper.class);
33         //指定reduce的類名，這里指定咱們自定義的reduce程序即可
34         job.setReducerClass(ScrewReduce.class);
35         //設置輸出key的數據類型
36         job.setOutputKeyClass(Text.class);
37         //設置輸出value的數據類型
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         //設置輸入路徑，需要傳遞兩個參數，即任務對象(job)以及輸入路徑
44         FileInputFormat.addInputPath(job,new Path("D:\\10.Java\\IDE\\yhinzhengjieData\\MyHadoop\\MapReduce\\screw.txt"));
45         //設置輸出路徑，需要傳遞兩個參數，即任務對象(job)以及輸出路徑
46         FileOutputFormat.setOutputPath(job,localPath);
47         //設置Reduce的個數為2.
48         job.setNumReduceTasks(2);
49         //等待任務執行結束,將里面的值設置為true。
50         job.waitForCompletion(true);
51     }
52 }

ScrewApp.java 文件內容

2>.Reduce端代碼

 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 }

ScrewReduce.java 文件內容

3>.Mapper端代碼

 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 }

ScrewMapper.java 文件內容

　　執行以上代碼，查看數據如下：

三.解決數據傾斜方案之重新設計key

1>.具體代碼如下

/*
@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> {
    //定義一個reduce變量
    int reduces;
    //定義一個隨機數生成器變量
    Random r;
    /**
     * setup方法是用於初始化值
     */
    @Override
    protected void setup(Context context) throws IOException, InterruptedException {
        //通過context.getNumReduceTasks()方法獲取到用戶配置的reduce個數。
        reduces = context.getNumReduceTasks();
        //生成一個隨機數生成器
        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) {
            //從reducs的范圍中獲取一個int類型的隨機數賦值給randVal
            int randVal = r.nextInt(reduces);
            //重新定義key
            String newWord = word+"_"+ randVal;
            //將自定義的key賦初始值為1發給reduce端
            context.write(new Text(newWord), new IntWritable(1));
        }
    }
}

ScrewMapper.java 文件內容

 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     //處理的數據類似於“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("\t");
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 }

ScrewMapper2.java 文件內容

 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.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 }

ScrewReducer.java 文件內容

 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.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         //實例化一個Configuration，它會自動去加載本地的core-site.xml配置文件的fs.defaultFS屬性。(該文件放在項目的resources目錄即可。)
20         Configuration conf = new Configuration();
21         //將hdfs寫入的路徑定義在本地，需要修改默認為文件系統，這樣就可以覆蓋到之前在core-site.xml配置文件讀取到的數據。
22         conf.set("fs.defaultFS","file:///");
23         //代碼的入口點，初始化HDFS文件系統，此時我們需要把讀取到的fs.defaultFS屬性傳給fs對象。
24         FileSystem fs = FileSystem.get(conf);
25         //創建一個任務對象job，別忘記把conf穿進去喲！
26         Job job = Job.getInstance(conf);
27         //給任務起個名字
28         job.setJobName("WordCount");
29         //指定main函數所在的類，也就是當前所在的類名
30         job.setJarByClass(ScrewApp.class);
31         //指定map的類名,這里指定咱們自定義的map程序即可
32         job.setMapperClass(ScrewMapper.class);
33         //指定reduce的類名，這里指定咱們自定義的reduce程序即可
34         job.setReducerClass(ScrewReducer.class);
35         //設置輸出key的數據類型
36         job.setOutputKeyClass(Text.class);
37         //設置輸出value的數據類型
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         //設置輸入路徑，需要傳遞兩個參數，即任務對象(job)以及輸入路徑
44         FileInputFormat.addInputPath(job,new Path("D:\\10.Java\\IDE\\yhinzhengjieData\\MyHadoop\\MapReduce\\screw.txt"));
45         //設置輸出路徑，需要傳遞兩個參數，即任務對象(job)以及輸出路徑
46         FileOutputFormat.setOutputPath(job,localPath);
47         //設置Reduce的個數為2.
48         job.setNumReduceTasks(2);
49         //等待任務執行結束,將里面的值設置為true。
50         if (job.waitForCompletion(true)) {
51             //當第一個MapReduce結束之后，我們這里又啟動了一個新的MapReduce，邏輯和上面類似。
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             //我們將第一個MapReduce的2個reducer的處理結果放在新的一個MapReduce中只啟用一個MapReduce。
66             job2.setNumReduceTasks(1);
67             job2.waitForCompletion(true);
68         }
69     }
70 }

ScrewApp.java 文件內容

2>.檢測實驗結果

　　“D:\\10.Java\\IDE\\yhinzhengjieData\\MyHadoop\\MapReduce\\out” 目錄內容如下：

　　“D:\\10.Java\\IDE\\yhinzhengjieData\\MyHadoop\\MapReduce\\out2” 目錄內容如下：

四.解決數據傾斜方案之使用隨機分區

1>.具體代碼如下

 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 }

Screw2Mapper.java 文件內容

 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.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         //返回的是分區的隨機的一個ID
19         return r.nextInt(numPartitions);
20     }
21 }

Screw2Partition.java 文件內容

 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.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 }

Screw2Reducer.java 文件內容

 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.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 }

Screw2App.java 文件內容

2>.檢測實驗結果

　　“D:\\10.Java\\IDE\\yhinzhengjieData\\MyHadoop\\MapReduce\\out” 目錄內容如下：

　　 “D:\\10.Java\\IDE\\yhinzhengjieData\\MyHadoop\\MapReduce\\out2” 目錄內容如下：

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猜您在找 Hadoop專業解決方案-第3章：MapReduce處理數據數據傾斜的原因以及解決方案數據傾斜的原因及解決方案 Spark 數據傾斜及其解決方案數據傾斜的原因和解決方案 MapReduce數據傾斜的解決方式 Hive千億級數據傾斜解決方案 Hive的HQL語句及數據傾斜解決方案最完整的數據傾斜解決方案(spark) Mapreduce怎么處理數據傾斜