如何在maven项目里面编写mapreduce程序以及一个maven项目里面管理多个mapreduce程序

我们平时创建普通的mapreduce项目，在遍代码当你需要导包使用一些工具类的时候，

你需要自己找到对应的架包，再导进项目里面其实这样做非常不方便，我建议我们还是用maven项目来得方便多了

话不多说了，我们就开始吧

首先你在eclipse里把你本地安装的maven导进来

选择你本地安装的maven路径

勾选中你添加进来的maven

把本地安装的maven的setting文件添加进来

接下来创建一个maven项目

可以看到一个maven项目创建成功！！

现在我们来配置pom.xml文件，把mapreduce程序运行的一些架包通过maven导进来

这个是我的项目文件可以给大家作参考

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
  <modelVersion>4.0.0</modelVersion>

  <groupId>com.gong.fusion</groupId>
  <artifactId>Alert</artifactId>
  <version>0.0.1-SNAPSHOT</version>
  <packaging>jar</packaging>

  <name>Alert</name>
  <url>http://maven.apache.org</url>

  <properties>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
  </properties>

  <dependencies>
    <dependency>
            <groupId>junit</groupId>
            <artifactId>junit</artifactId>
            <version>3.8.1</version>
            <scope>test</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-common</artifactId>
            <version>2.6.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>2.6.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-hdfs</artifactId>
            <version>2.6.0</version>
        </dependency>
        <dependency>
            <groupId>jdk.tools</groupId>
            <artifactId>jdk.tools</artifactId>
            <version>1.7</version>
            <scope>system</scope>
            <systemPath>${JAVA_HOME}/lib/tools.jar</systemPath>
        </dependency>
         <dependency>
    <groupId>commons-lang</groupId>
    <artifactId>commons-lang</artifactId>
    <version>2.6</version>
    </dependency>
  </dependencies>
  <build>
        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-shade-plugin</artifactId>
                <version>2.4.1</version>
                <executions>
                    <!-- Run shade goal on package phase -->
                    <execution>
                        <phase>package</phase>
                        <goals>
                            <goal>shade</goal>
                        </goals>
                        <configuration>
                            <transformers>
                                <!-- add Main-Class to manifest file -->
                                <transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
                                    <mainClass>com.gong.fusion.Alert.MyDriver</mainClass> //这里是你自己项目的目录
                                </transformer>
                            </transformers>
                            <createDependencyReducedPom>false</createDependencyReducedPom>
                        </configuration>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>
</project>

下面我们来写一个经典例子wordcount代码来实验一下

如何新建一个类来写我就不说了，我直接把代码放上来

package com.gong.fusion.Alert;

import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class WordCount {

  public static class TokenizerMapper
       extends Mapper<Object, Text, Text, IntWritable>{

    private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();

    public void map(Object key, Text value, Context context
                    ) throws IOException, InterruptedException {
      StringTokenizer itr = new StringTokenizer(value.toString());
      while (itr.hasMoreTokens()) {
        word.set(itr.nextToken());
        context.write(word, one);
      }
    }
  }

  public static class IntSumReducer
       extends Reducer<Text,IntWritable,Text,IntWritable> {
    private IntWritable result = new IntWritable();

    public void reduce(Text key, Iterable<IntWritable> values,
                       Context context
                       ) throws IOException, InterruptedException {
      int sum = 0;
      for (IntWritable val : values) {
        sum += val.get();
      }
      result.set(sum);
      context.write(key, result);
    }
  }

  public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();
    Job job = Job.getInstance(conf, "word count");
    job.setJarByClass(WordCount.class);
    job.setMapperClass(TokenizerMapper.class);
    job.setCombinerClass(IntSumReducer.class);
    job.setReducerClass(IntSumReducer.class);
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(IntWritable.class);
    FileInputFormat.addInputPath(job, new Path("hdfs://cdh-master:9000/user/kfk/data/wc.input"));
    FileOutputFormat.setOutputPath(job, new Path("hdfs://cdh-master:9000/data/user/gong/wordcount-out1"));
    System.exit(job.waitForCompletion(true) ? 0 : 1);
  }
}

我的eclipse是已经跟我的大数据集群HDFS连接的.

大家记得添加这个文件

我们运行一下这个代码

运行成功！！！！！

我们在hdfs上查看运行结果

这样们就实现了在maven 项目里面运行mapreduce程序了

接下来要讲的就是怎么管理多个mapreduce程序

我们新建一个MyDriver类用来管理多个mapreduce程序的类，和再创建另外一个mapreduce程序类wordmean

wordmean的内容跟wordcount是一样的，我就是把名字和输出路径改了一下！！！

当然在实际的开发中不会有这样的情况的，我是方便测试才这样做

package com.gong.fusion.Alert;

import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.Reducer.Context;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import com.gong.fusion.Alert.WordCount.IntSumReducer;
import com.gong.fusion.Alert.WordCount.TokenizerMapper;

public class WordMean {
     public static class TokenizerMapper
     extends Mapper<Object, Text, Text, IntWritable>{

  private final static IntWritable one = new IntWritable(1);
  private Text word = new Text();

  public void map(Object key, Text value, Context context
                  ) throws IOException, InterruptedException {
    StringTokenizer itr = new StringTokenizer(value.toString());
    while (itr.hasMoreTokens()) {
      word.set(itr.nextToken());
      context.write(word, one);
    }
  }
}

public static class IntSumReducer
     extends Reducer<Text,IntWritable,Text,IntWritable> {
  private IntWritable result = new IntWritable();

  public void reduce(Text key, Iterable<IntWritable> values,
                     Context context
                     ) throws IOException, InterruptedException {
    int sum = 0;
    for (IntWritable val : values) {
      sum += val.get();
    }
    result.set(sum);
    context.write(key, result);
  }
}

public static void main(String[] args) throws Exception {
  Configuration conf = new Configuration();
  Job job = Job.getInstance(conf, "word count");
  job.setJarByClass(WordCount.class);
  job.setMapperClass(TokenizerMapper.class);
  job.setCombinerClass(IntSumReducer.class);
  job.setReducerClass(IntSumReducer.class);
  job.setOutputKeyClass(Text.class);
  job.setOutputValueClass(IntWritable.class);
  FileInputFormat.addInputPath(job, new Path("hdfs://cdh-master:9000/user/kfk/data/wc.input"));
  FileOutputFormat.setOutputPath(job, new Path("hdfs://cdh-master:9000/data/user/gong/wordcount-out2"));
  System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}

package com.gong.fusion.Alert;
import org.apache.hadoop.util.ProgramDriver;
public class MyDriver {
     public static void main(String argv[]){
            int exitCode = -1;
            ProgramDriver pgd = new ProgramDriver();
            try {
              pgd.addClass("wordcount", WordCount.class,
                           "A map/reduce program that counts the words in the input files.");
              pgd.addClass("wordmean", WordMean.class,
                           "A map/reduce program that counts the average length of the words in the input files.");

              exitCode = pgd.run(argv);
            }
            catch(Throwable e){
              e.printStackTrace();
            }

            System.exit(exitCode);
          }
}

现在就通过Mydriver这个类来同时管理两个mapreduce代码了

我们现在把程序通过maven打包放到大数据集群上面运行一下

在我们的电脑打开cmd窗口，切换到你的项目路径下，用mvn clean清除一下

然后我们通过命令mvn package对项目进行打包

打包成功！！！

一般都会打包在target目录下的

我们把这个包上传到我们的大数据集群上面去，怎么上传我就不多说了，用工具上传，或者用rz命令上传就可以了

我们在集群上运行一下

我们直接在代码包后面加上其中一个mapreduce类的别名就可以了，这个别名在Mydiver类里面定义的

可以看到我们对两个不同的mapreduce都起了不同的别名

下面我们看看运行的结果

[hadoop@cdh-master hadoop]$ hadoop jar Alert-0.0.1-SNAPSHOT.jar wordcount
18/08/10 20:07:14 INFO client.RMProxy: Connecting to ResourceManager at cdh-master/192.168.211.13:8032
18/08/10 20:07:18 WARN mapreduce.JobSubmitter: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
18/08/10 20:08:02 INFO input.FileInputFormat: Total input paths to process : 1
18/08/10 20:08:03 INFO mapreduce.JobSubmitter: number of splits:1
18/08/10 20:08:05 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1533902197727_0001
18/08/10 20:08:07 INFO impl.YarnClientImpl: Submitted application application_1533902197727_0001
18/08/10 20:08:08 INFO mapreduce.Job: The url to track the job: http://cdh-master:8088/proxy/application_1533902197727_0001/
18/08/10 20:08:08 INFO mapreduce.Job: Running job: job_1533902197727_0001
18/08/10 20:09:16 INFO mapreduce.Job: Job job_1533902197727_0001 running in uber mode : false
18/08/10 20:09:16 INFO mapreduce.Job:  map 0% reduce 0%
18/08/10 20:11:28 INFO mapreduce.Job:  map 100% reduce 0%
18/08/10 20:11:52 INFO mapreduce.Job:  map 100% reduce 100%
18/08/10 20:11:54 INFO mapreduce.Job: Job job_1533902197727_0001 completed successfully
18/08/10 20:11:54 INFO mapreduce.Job: Counters: 49
    File System Counters
        FILE: Number of bytes read=136
        FILE: Number of bytes written=218031
        FILE: Number of read operations=0
        FILE: Number of large read operations=0
        FILE: Number of write operations=0
        HDFS: Number of bytes read=204
        HDFS: Number of bytes written=87
        HDFS: Number of read operations=6
        HDFS: Number of large read operations=0
        HDFS: Number of write operations=2
    Job Counters
        Launched map tasks=1
        Launched reduce tasks=1
        Data-local map tasks=1
        Total time spent by all maps in occupied slots (ms)=118978
        Total time spent by all reduces in occupied slots (ms)=20993
        Total time spent by all map tasks (ms)=118978
        Total time spent by all reduce tasks (ms)=20993
        Total vcore-seconds taken by all map tasks=118978
        Total vcore-seconds taken by all reduce tasks=20993
        Total megabyte-seconds taken by all map tasks=121833472
        Total megabyte-seconds taken by all reduce tasks=21496832
    Map-Reduce Framework
        Map input records=7
        Map output records=18
        Map output bytes=163
        Map output materialized bytes=132
        Input split bytes=110
        Combine input records=18
        Combine output records=12
        Reduce input groups=12
        Reduce shuffle bytes=132
        Reduce input records=12
        Reduce output records=12
        Spilled Records=24
        Shuffled Maps =1
        Failed Shuffles=0
        Merged Map outputs=1
        GC time elapsed (ms)=852
        CPU time spent (ms)=37740
        Physical memory (bytes) snapshot=316510208
        Virtual memory (bytes) snapshot=3017236480
        Total committed heap usage (bytes)=136122368
    Shuffle Errors
        BAD_ID=0
        CONNECTION=0
        IO_ERROR=0
        WRONG_LENGTH=0
        WRONG_MAP=0
        WRONG_REDUCE=0
    File Input Format Counters
        Bytes Read=94
    File Output Format Counters
        Bytes Written=87

我们运行一下另外一个mapreduce程序

[hadoop@cdh-master hadoop]$ hadoop jar Alert-0.0.1-SNAPSHOT.jar wordmean
18/08/10 20:13:22 INFO client.RMProxy: Connecting to ResourceManager at cdh-master/192.168.211.13:8032
18/08/10 20:13:24 WARN mapreduce.JobSubmitter: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
18/08/10 20:13:33 INFO input.FileInputFormat: Total input paths to process : 1
18/08/10 20:13:33 INFO mapreduce.JobSubmitter: number of splits:1
18/08/10 20:13:34 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1533902197727_0002
18/08/10 20:13:35 INFO impl.YarnClientImpl: Submitted application application_1533902197727_0002
18/08/10 20:13:35 INFO mapreduce.Job: The url to track the job: http://cdh-master:8088/proxy/application_1533902197727_0002/
18/08/10 20:13:35 INFO mapreduce.Job: Running job: job_1533902197727_0002
18/08/10 20:15:22 INFO mapreduce.Job: Job job_1533902197727_0002 running in uber mode : false
18/08/10 20:15:22 INFO mapreduce.Job:  map 0% reduce 0%
18/08/10 20:16:30 INFO mapreduce.Job:  map 100% reduce 0%
18/08/10 20:16:56 INFO mapreduce.Job:  map 100% reduce 100%
18/08/10 20:16:57 INFO mapreduce.Job: Job job_1533902197727_0002 completed successfully
18/08/10 20:16:58 INFO mapreduce.Job: Counters: 49
    File System Counters
        FILE: Number of bytes read=136
        FILE: Number of bytes written=218025
        FILE: Number of read operations=0
        FILE: Number of large read operations=0
        FILE: Number of write operations=0
        HDFS: Number of bytes read=204
        HDFS: Number of bytes written=87
        HDFS: Number of read operations=6
        HDFS: Number of large read operations=0
        HDFS: Number of write operations=2
    Job Counters
        Launched map tasks=1
        Launched reduce tasks=1
        Data-local map tasks=1
        Total time spent by all maps in occupied slots (ms)=65084
        Total time spent by all reduces in occupied slots (ms)=23726
        Total time spent by all map tasks (ms)=65084
        Total time spent by all reduce tasks (ms)=23726
        Total vcore-seconds taken by all map tasks=65084
        Total vcore-seconds taken by all reduce tasks=23726
        Total megabyte-seconds taken by all map tasks=66646016
        Total megabyte-seconds taken by all reduce tasks=24295424
    Map-Reduce Framework
        Map input records=7
        Map output records=18
        Map output bytes=163
        Map output materialized bytes=132
        Input split bytes=110
        Combine input records=18
        Combine output records=12
        Reduce input groups=12
        Reduce shuffle bytes=132
        Reduce input records=12
        Reduce output records=12
        Spilled Records=24
        Shuffled Maps =1
        Failed Shuffles=0
        Merged Map outputs=1
        GC time elapsed (ms)=493
        CPU time spent (ms)=8170
        Physical memory (bytes) snapshot=312655872
        Virtual memory (bytes) snapshot=3007705088
        Total committed heap usage (bytes)=150081536
    Shuffle Errors
        BAD_ID=0
        CONNECTION=0
        IO_ERROR=0
        WRONG_LENGTH=0
        WRONG_MAP=0
        WRONG_REDUCE=0
    File Input Format Counters
        Bytes Read=94
    File Output Format Counters
        Bytes Written=87
[hadoop@cdh-master hadoop]$ 

可以看到两个不同的输出路径上，是两个程序分别运行的结果