YARN集群的mapreduce测试（三）

将user表、group表、order表关；（类似于多表关联查询）

测试准备:

首先同步时间，然后开启hdfs集群，开启yarn集群；在本地"/home/hadoop/test/"目录创建user表、group表、order表的文件；

user文件：

group文件：

order文件：

测试目标：

得到3张表关联后的结果；

测试代码：

一定要把握好输出键值的类型，否则有可能造成有输出目录，但是没有文件内容的问题；

package com.mmzs.bigdata.yarn.mapreduce;

import java.io.IOException;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;

public class UserGroupMapper01 extends Mapper<LongWritable, Text, Text, Text> {
    
    private Text outKey;
    private Text outValue;
    
    @Override
    protected void setup(Mapper<LongWritable, Text, Text, Text>.Context context)
            throws IOException, InterruptedException {
        outKey = new Text();
        outValue = new Text();
    }

    @Override
    protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, Text>.Context context)
            throws IOException, InterruptedException {
        FileSplit fp = (FileSplit) context.getInputSplit();
        String fileName = fp.getPath().getName();
        
        String line = value.toString();
        String[] fields = line.split("\s+");
        
        String keyStr = null;
        String valueStr = null;
        if ("group".equalsIgnoreCase(fileName)) {
            keyStr = fields[0];
            valueStr = new StringBuilder(fields[1]).append("-->").append(fileName).toString();
        } else {
            keyStr = fields[2];
            //加“-->”;后以此标识符作为分割符，进行文件区分
            valueStr = new StringBuilder(fields[0]).append("	").append(fields[1]).append("-->").append(fileName).toString();
        }
        
        outKey.set(keyStr);
        outValue.set(valueStr);
        context.write(outKey, outValue);
        
        
    }
    
    @Override
    protected void cleanup(Mapper<LongWritable, Text, Text, Text>.Context context)
            throws IOException, InterruptedException {
        outKey = null;
        outValue = null;
    }
    
}

UserGroupMapper01

 1 package com.mmzs.bigdata.yarn.mapreduce;
 2 
 3 import java.io.IOException;
 4 import java.util.ArrayList;
 5 import java.util.Iterator;
 6 import java.util.List;
 7 import org.apache.hadoop.io.Text;
 8 import org.apache.hadoop.mapreduce.Reducer;
 9 
10 public class UserGroupReducer01 extends Reducer<Text, Text, Text, Text> {
11     
12     private Text outValue;
13     
14     @Override
15     protected void setup(Reducer<Text, Text, Text, Text>.Context context) throws IOException, InterruptedException {
16         outValue = new Text();
17     }
18 
19     @Override
20     protected void reduce(Text key, Iterable<Text> values, Reducer<Text, Text, Text, Text>.Context context)
21             throws IOException, InterruptedException {
22         Iterator<Text> its = values.iterator();
23         
24         String masterRecord = null;
25         List<String> slaveRecords = new ArrayList<String>();
26         
27         //拆分出主表记录和从表记录
28         while (its.hasNext()) {
29             String[] rowAndFileName = its.next().toString().split("-->");
30             if (rowAndFileName[1].equalsIgnoreCase("group")) {
31                 masterRecord = rowAndFileName[0];
32                 continue;
33             }
34             slaveRecords.add(rowAndFileName[0]);
35         }
36         
37         for (String slaveRecord : slaveRecords) {
38             String valueStr = new StringBuilder(masterRecord).append("	").append(slaveRecord).toString();
39             outValue.set(valueStr);
40             context.write(key, outValue);
41         }
42         
43     }
44     
45     @Override
46     protected void cleanup(Reducer<Text, Text, Text, Text>.Context context) throws IOException, InterruptedException {
47         outValue = null;
48     }
49 
50 
51 }

UserGroupReducer01

  1 package com.mmzs.bigdata.yarn.mapreduce;
  2 
  3 import java.io.IOException;
  4 import java.net.URI;
  5 import java.net.URISyntaxException;
  6 
  7 import org.apache.hadoop.conf.Configuration;
  8 import org.apache.hadoop.fs.FileSystem;
  9 import org.apache.hadoop.fs.Path;
 10 import org.apache.hadoop.io.LongWritable;
 11 import org.apache.hadoop.io.Text;
 12 import org.apache.hadoop.mapreduce.Job;
 13 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
 14 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
 15 
 16 /**
 17  * @author hadoop
 18  *
 19  */
 20 public class UserGroupDriver01 {
 21 
 22     private static FileSystem fs;
 23     private static Configuration conf;
 24     static {
 25         String uri = "hdfs://master01:9000/";
 26         conf = new Configuration();
 27         try {
 28             fs = FileSystem.get(new URI(uri), conf, "hadoop");
 29         } catch (IOException e) {
 30             e.printStackTrace();
 31         } catch (InterruptedException e) {
 32             e.printStackTrace();
 33         } catch (URISyntaxException e) {
 34             e.printStackTrace();
 35         }
 36     }
 37     
 38     public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
 39 
 40         Job ugJob01 = getJob(args);
 41         if (null == ugJob01) {
 42             return;
 43         }
 44         //提交Job到集群并等待Job运行完成，参数true表示将Job运行时的状态信息返回到客户端
 45         boolean flag = false;
 46         flag = ugJob01.waitForCompletion(true);
 47         System.exit(flag?0:1);
 48     }
 49     
 50     /**
 51      * 获取Job实例
 52      * @param args
 53      * @return
 54      * @throws IOException
 55      */
 56     public static Job getJob(String[] args) throws IOException {
 57         if (null==args || args.length<2) return null;
 58         //放置需要处理的数据所在的HDFS路径
 59         Path inputPath = new Path(args[0]);
 60         //放置Job作业执行完成之后其处理结果的输出路径
 61         Path outputPath = new Path(args[1]);
 62         //主机文件路径
 63         Path userPath = new Path("/home/hadoop/test/user");
 64         Path groupPath = new Path("/home/hadoop/test/group");
 65 
 66         //如果输入的集群路径存在，则删除
 67         if (fs.exists(inputPath)) {
 68             fs.delete(inputPath, true);//true表示递归删除
 69         }
 70         if (fs.exists(outputPath)) {
 71             fs.delete(outputPath, true);//true表示递归删除
 72         }
 73         
 74         //创建并且将数据文件拷贝到创建的集群路径
 75         fs.mkdirs(inputPath);
 76         fs.copyFromLocalFile(false, false, new Path[]{userPath, groupPath}, inputPath);
 77         
 78         
 79         //获取Job实例
 80         Job ugJob01 = Job.getInstance(conf, "UserGroupJob01");
 81         //设置运行此jar包入口类
 82         //ugJob01的入口是WordCountDriver类
 83         ugJob01.setJarByClass(UserGroupDriver01.class);
 84         //设置Job调用的Mapper类
 85         ugJob01.setMapperClass(UserGroupMapper01.class);
 86         //设置Job调用的Reducer类（如果一个Job没有Reducer则可以不调用此条语句）
 87         ugJob01.setReducerClass(UserGroupReducer01.class);
 88         
 89         //设置MapTask的输出键类型
 90         ugJob01.setMapOutputKeyClass(Text.class);
 91         //设置MapTask的输出值类型
 92         ugJob01.setMapOutputValueClass(Text.class);
 93         
 94         //设置整个Job的输出键类型（如果一个Job没有Reducer则可以不调用此条语句）
 95         ugJob01.setOutputKeyClass(Text.class);
 96         //设置整个Job的输出值类型（如果一个Job没有Reducer则可以不调用此条语句）
 97         ugJob01.setOutputValueClass(Text.class);
 98         
 99         //设置整个Job需要处理数据的输入路径
100         FileInputFormat.setInputPaths(ugJob01, inputPath);
101         //设置整个Job计算结果的输出路径
102         FileOutputFormat.setOutputPath(ugJob01, outputPath);
103         
104         return ugJob01;
105     }
106     
107 }

UserGroupDriver01

 1 package com.mmzs.bigdata.yarn.mapreduce;
 2 
 3 import java.io.IOException;
 4 
 5 import org.apache.hadoop.io.LongWritable;
 6 import org.apache.hadoop.io.Text;
 7 import org.apache.hadoop.mapreduce.Mapper;
 8 import org.apache.hadoop.mapreduce.lib.input.FileSplit;
 9 
10 public class UserGroupMapper02 extends Mapper<LongWritable, Text, Text, Text> {
11     
12     private Text outKey;
13     private Text outValue;
14     
15     @Override
16     protected void setup(Mapper<LongWritable, Text, Text, Text>.Context context)
17             throws IOException, InterruptedException {
18         outKey = new Text();
19         outValue = new Text();
20     }
21 
22     @Override
23     protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, Text>.Context context)
24             throws IOException, InterruptedException {
25         FileSplit fp = (FileSplit) context.getInputSplit();
26         String fileName = fp.getPath().getName();
27         
28         String line = value.toString();
29         String[] fields = line.split("\s+");
30         
31         String keyStr = fields[2];
32         String valueStr = null;
33         valueStr = new StringBuilder(fields[0]).append("	").append(fields[1]).append("	").append(fields[3]).append("-->").append(fileName).toString();
34         
35         outKey.set(keyStr);
36         outValue.set(valueStr);
37         context.write(outKey, outValue);
38         
39         
40     }
41     
42     @Override
43     protected void cleanup(Mapper<LongWritable, Text, Text, Text>.Context context)
44             throws IOException, InterruptedException {
45         outKey = null;
46         outValue = null;
47     }
48     
49 }

UserGroupMapper02

 1 package com.mmzs.bigdata.yarn.mapreduce;
 2 
 3 import java.io.IOException;
 4 import java.util.ArrayList;
 5 import java.util.Iterator;
 6 import java.util.List;
 7 
 8 import org.apache.hadoop.io.Text;
 9 import org.apache.hadoop.mapreduce.Reducer;
10 
11 public class UserGroupReducer02 extends Reducer<Text, Text, Text, Text> {
12     
13     private Text outValue;
14     
15     @Override
16     protected void setup(Reducer<Text, Text, Text, Text>.Context context) throws IOException, InterruptedException {
17         outValue = new Text();
18     }
19 
20     @Override
21     protected void reduce(Text key, Iterable<Text> values, Reducer<Text, Text, Text, Text>.Context context)
22             throws IOException, InterruptedException {
23         String masterRecord = null;
24         List<String> slaveRecords = new ArrayList<String>();
25         
26         //拆分出主表记录和从表记录
27         Iterator<Text> its = values.iterator();
28         while (its.hasNext()) {
29             String[] rowAndFileName = its.next().toString().split("-->");
30             if (!rowAndFileName[1].equalsIgnoreCase("order")) {
31                 masterRecord = rowAndFileName[0];
32                 continue;
33             }
34             slaveRecords.add(rowAndFileName[0]);
35         }
36         
37         for (String slaveRecord : slaveRecords) {
38             String valueStr = new StringBuilder(masterRecord).append("	").append(slaveRecord).toString();
39             outValue.set(valueStr);
40             context.write(key, outValue);
41         }
42         
43     }
44     
45     @Override
46     protected void cleanup(Reducer<Text, Text, Text, Text>.Context context) throws IOException, InterruptedException {
47         outValue = null;
48     }
49 
50 
51 }

UserGroupReducer02

  1 package com.mmzs.bigdata.yarn.mapreduce;
  2 
  3 import java.io.IOException;
  4 import java.net.URI;
  5 import java.net.URISyntaxException;
  6 import org.apache.hadoop.conf.Configuration;
  7 import org.apache.hadoop.fs.FileSystem;
  8 import org.apache.hadoop.fs.Path;
  9 import org.apache.hadoop.io.Text;
 10 import org.apache.hadoop.mapreduce.Job;
 11 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
 12 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
 13 
 14 /**
 15  * @author hadoop
 16  *
 17  */
 18 public class UserGroupDriver02 {
 19 
 20     private static FileSystem fs;
 21     private static Configuration conf;
 22     static {
 23         String uri = "hdfs://master01:9000/";
 24         conf = new Configuration();
 25         try {
 26             fs = FileSystem.get(new URI(uri), conf, "hadoop");
 27         } catch (IOException e) {
 28             e.printStackTrace();
 29         } catch (InterruptedException e) {
 30             e.printStackTrace();
 31         } catch (URISyntaxException e) {
 32             e.printStackTrace();
 33         }
 34     }
 35     
 36     public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
 37 
 38         Job ugJob02 = getJob(new String[] {args[1], args[2]});
 39         if (null == ugJob02) {
 40             return;
 41         }
 42         //提交Job到集群并等待Job运行完成，参数true表示将Job运行时的状态信息返回到客户端
 43         boolean flag = false;
 44         flag = ugJob02.waitForCompletion(true);
 45         System.exit(flag?0:1);
 46     }
 47     
 48     /**
 49      * 获取Job实例
 50      * @param args
 51      * @return
 52      * @throws IOException
 53      */
 54     public static Job getJob(String[] args) throws IOException {
 55         if (null==args || args.length<2) return null;
 56         //放置需要处理的数据所在的HDFS路径
 57         Path inputPath = new Path(args[1]);
 58         //放置Job作业执行完成之后其处理结果的输出路径
 59         Path outputPath = new Path(args[2]);
 60         //主机文件路径
 61         Path orderPath = new Path("/home/hadoop/test/order");
 62 
 63         //输入的集群路径存在，在第一次已创建
 64         if (!fs.exists(inputPath)) return null;
 65         if (fs.exists(outputPath)) {
 66             fs.delete(outputPath, true);//true表示递归删除
 67         }
 68         
 69         //将数据文件拷贝到创建的集群路径
 70         fs.copyFromLocalFile(false, false, orderPath, inputPath);
 71         
 72         
 73         //获取Job实例
 74         Job ugJob02 = Job.getInstance(conf, "UserGroupJob02");
 75         //设置运行此jar包入口类
 76         //ugJob02的入口是WordCountDriver类
 77         ugJob02.setJarByClass(UserGroupDriver02.class);
 78         //设置Job调用的Mapper类
 79         ugJob02.setMapperClass(UserGroupMapper02.class);
 80         //设置Job调用的Reducer类（如果一个Job没有Reducer则可以不调用此条语句）
 81         ugJob02.setReducerClass(UserGroupReducer02.class);
 82         
 83         //设置MapTask的输出键类型
 84         ugJob02.setMapOutputKeyClass(Text.class);
 85         //设置MapTask的输出值类型
 86         ugJob02.setMapOutputValueClass(Text.class);
 87         
 88         //设置整个Job的输出键类型（如果一个Job没有Reducer则可以不调用此条语句）
 89         ugJob02.setOutputKeyClass(Text.class);
 90         //设置整个Job的输出值类型（如果一个Job没有Reducer则可以不调用此条语句）
 91         ugJob02.setOutputValueClass(Text.class);
 92         
 93         //设置整个Job需要处理数据的输入路径
 94         FileInputFormat.setInputPaths(ugJob02, inputPath);
 95         //设置整个Job计算结果的输出路径
 96         FileOutputFormat.setOutputPath(ugJob02, outputPath);
 97         
 98         return ugJob02;
 99     }
100     
101 }

UserGroupDriver02

 1 package com.mmzs.bigdata.yarn.mapreduce;
 2 
 3 import java.io.IOException;
 4 import java.net.URI;
 5 import java.net.URISyntaxException;
 6 
 7 import org.apache.hadoop.conf.Configuration;
 8 import org.apache.hadoop.fs.FileSystem;
 9 import org.apache.hadoop.fs.Path;
10 import org.apache.hadoop.mapreduce.Job;
11 
12 public class UserGroupDriver {
13 
14     private static FileSystem fs;
15     private static Configuration conf;
16     private static final String TEMP= "hdfs://master01:9000/data/usergrouporder/tmp";
17     static {
18         String uri = "hdfs://master01:9000/";
19         conf = new Configuration();
20         try {
21             fs = FileSystem.get(new URI(uri), conf, "hadoop");
22         } catch (IOException e) {
23             e.printStackTrace();
24         } catch (InterruptedException e) {
25             e.printStackTrace();
26         } catch (URISyntaxException e) {
27             e.printStackTrace();
28         }
29     }
30     
31     public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
32         
33         String[] params = {args[0], TEMP, args[1]};
34         
35         //运行第1个Job
36         Job ugJob01 = UserGroupDriver01.getJob(params);
37         //提交Job到集群并等待Job运行完成，参数true表示将Job运行时的状态信息返回到客户端
38         boolean flag01 = ugJob01.waitForCompletion(true);
39         if (!flag01) {
40             System.out.println("job2 running failure......");
41             System.exit(1);
42         }
43         
44         //运行第2个Job
45         Job ugJob02 = UserGroupDriver02.getJob(params);
46         //提交Job到集群并等待Job运行完成，参数true表示将Job运行时的状态信息返回到客户端
47         boolean flag02 = ugJob02.waitForCompletion(true);
48         if (flag02) {//等待Job02完成后就删掉中间目录并退出；
49 //            fs.delete(new Path(TEMP), true);
50             System.out.println("job2 running success......");
51             System.exit(0);
52         }
53         System.out.println("job2 running failure......");
54         System.exit(1);
55     }
56 
57 }

UserGroupDriver

为了更好的测试，可以先屏蔽删除中间输出结果的语句；

//总Driver
String[] params = {args[0], TEMP, args[1]};    
//运行第1个Job
Job ugJob01 = UserGroupDriver01.getJob(params);
//运行第2个Job
Job ugJob02 = UserGroupDriver02.getJob(params);

//分Driver01
//放置需要处理的数据所在的HDFS路径
Path inputPath = new Path(args[0]);//params中的args[0]
//放置Job作业执行完成之后其处理结果的输出路径
Path outputPath = new Path(args[1]);//params中的TEMP

//分Driver02
//params中的TEMP和args[2]//放置需要处理的数据所在的HDFS路径
Path inputPath = new Path(args[1]);
//放置Job作业执行完成之后其处理结果的输出路径
Path outputPath = new Path(args[2]);

测试结果：

运行时传入参数是：

如果在eclipse上运行：传参需要加上集群的master的uri即 hdfs://master01:9000

输入路径参数： /data/usergrouporder/src

输出路径参数： /data/usergrouporder/dst

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原文地址：https://www.cnblogs.com/mmzs/p/8040158.html