java - 从另一个 Java 文件动态编译和运行 Hadoop 作业

Question

我正在尝试编写一个 Java 文件来接收 MapReduce 作业的源代码，对其进行动态编译并在 Hadoop 集群上运行该作业。为此，我编写了 3 个方法，分别称为 compile()、makeJAR() 和 run_Hadoop_Job()。 JAR 文件的编译和创建一切正常。然而，当作业提交到 Hadoop 时，一旦作业开始，它就会面临查找所需的 Mapper/Reducer 类的问题，并为 Mapper_Class 和 Reducer_Class *(java.lang.ClassNotFoundException: reza. rCloud.Mapper_Reducer_Classes$Mapper_Class.class)* 。我知道我引用所需的 Mapper/Reducer 类的方式应该有问题，但我在几次之后无法弄清楚。非常感谢任何有关如何解决问题的帮助/建议。

关于项目的详细信息: 我有一个名为“rCloud_test/src/reza/Mapper_Reducer_Classes.java”的文件，其中包含 Mapper_Class 和 Reducer_Class 的源代码。该文件最终在运行时收到，但现在我复制了其中的 Hadoop WordCount 示例，并将其本地存储在与我的主类文件相同的文件夹中:rCloud_test/src/reza/Platform2.java。

在下面您可以看到 Platform2.java 的 main() 方法，这是我在这个项目中的主要类:

    public static void main(String[] args){
    System.out.println("Code Execution Started");
    String className = "Mapper_Reducer_Classes";
    Platform2 myPlatform = new Platform2();

    //step 1: compile the received class file dynamically:
    boolean compResult = myPlatform.compile(className); 
    System.out.println(className + ".java compilation result: "+compResult);

    //step 2: make a JAR file out of the compiled file:
    if (compResult) {
        compResult  = myPlatform.makeJAR("jar_file", myPlatform.compilation_Output_Folder);
        System.out.println("JAR creation result: "+compResult);
    } 
    //step 3: Now let's run the Hadoop job:
    if (compResult) {       
        compResult = myPlatform.run_Hadoop_Job(className);
        System.out.println("Running on Hadoop result: "+compResult);
    }

导致我出现所有问题的方法是 run_Hadoop_Job()，如下所示:

private boolean run_Hadoop_Job(String className){
try{
    System.out.println("*Starting to run the code on Hadoop...");
    String[] argsTemp = { "project_test/input", "project_test/output" };

    Configuration conf = new Configuration();
    conf.set("fs.default.name", "hdfs://localhost:54310");
    conf.set("mapred.job.tracker", "localhost:54311");

    conf.set("mapred.jar", jar_Output_Folder + "/jar_file"+".jar");

    conf.set("libjars", required_Execution_Classes);

    //THIS IS WHERE IT CAN'T FIND THE MENTIONED CLASSES, ALTHOUGH THEY EXIST BOTH ON DISK 
    // AND IN THE CREATED JAR FILE:??????
    System.out.println("Getting Mapper/Reducer package name: " + 
                        Mapper_Reducer_Classes.class.getName());
    conf.set("mapreduce.map.class", "reza.rCloud.Mapper_Reducer_Classes$Mapper_Class");
    conf.set("mapreduce.reduce.class", "reza.rCloud.Mapper_Reducer_Classes$Reducer_Class");

    Job job = new Job(conf, "Hadoop Example for dynamically and programmatically compiling-running a job");
    job.setJarByClass(Platform2.class);

    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(IntWritable.class);

    FileInputFormat.addInputPath(job, new Path(argsTemp[0]));
    FileSystem fs = FileSystem.get(conf);
    Path out = new Path(argsTemp[1]);
    fs.delete(out, true);
    FileOutputFormat.setOutputPath(job, new Path(argsTemp[1]));

    //job.submit();
    System.out.println("*and now submitting the job to Hadoop...");
    System.exit(job.waitForCompletion(true) ? 0 : 1); 
    System.out.println("Job Finished!");        
} catch (Exception e) {         
            System.out.println("****************Exception!" ); 
            e.printStackTrace();
            return false; 
    }
return true;
}

如果需要，这里是 compile() 方法的源代码:

private boolean compile(String className) {
    String fileToCompile =  JOB_FOLDER   + "/" +className+".java";
    JavaCompiler compiler = ToolProvider.getSystemJavaCompiler();       
    FileOutputStream errorStream = null;        
    try{
        errorStream = new FileOutputStream(JOB_FOLDER + "/logs/Errors.txt");
    } catch(FileNotFoundException e){ 
        //if problem creating the file, default wil be console 
    }   

    int compilationResult = 
            compiler.run(   null, null, errorStream, 
                            "-classpath", required_Compilation_Classes,
                            "-d", compilation_Output_Folder,
                            fileToCompile);
    if (compilationResult == 0) {
        //Compilation is successful:
        return true;
    } else {
        //Compilation Failed:
        return false;
    }   
}

以及 makeJAR() 方法的源代码:

private boolean makeJAR(String outputFileName, String inputDirectory) {
    Manifest manifest = new Manifest();
    manifest.getMainAttributes().put(Attributes.Name.MANIFEST_VERSION,
            "1.0");

    JarOutputStream target = null;                      
    try {       
        target = new JarOutputStream(new FileOutputStream(
                jar_Output_Folder+ "/"  
                + outputFileName+".jar"                      ), manifest);
        add(new File(inputDirectory), target);
    } catch (Exception e) { return false; }
    finally {
        if (target != null)
            try{
                target.close();
            } catch (Exception e) { return false; }
    }
    return true;
}

    private void add(File source, JarOutputStream target) throws IOException
{
  BufferedInputStream in = null;
  try
  {
    if (source.isDirectory())
    {
      String name = source.getPath().replace("\\", "/");
      if (!name.isEmpty())
      {
        if (!name.endsWith("/"))
          name += "/";
        JarEntry entry = new JarEntry(name);
        entry.setTime(source.lastModified());
        target.putNextEntry(entry);
        target.closeEntry();
      }
      for (File nestedFile: source.listFiles())
        add(nestedFile, target);
      return;
    }

    JarEntry entry = new JarEntry(source.getPath().replace("\\", "/"));
    entry.setTime(source.lastModified());
    target.putNextEntry(entry);
    in = new BufferedInputStream(new FileInputStream(source));

    byte[] buffer = new byte[1024];
    while (true)
    {
      int count = in.read(buffer);
      if (count == -1)
        break;
      target.write(buffer, 0, count);
    }
    target.closeEntry();
  }
  finally
  {
    if (in != null)
      in.close();
  }
}

最后是用于访问文件的固定参数:

private String JOB_FOLDER = "/Users/reza/My_Software/rCloud_test/src/reza/rCloud";
private String HADOOP_SOURCE_FOLDER = "/Users/reza/My_Software/hadoop-0.20.2";
private String required_Compilation_Classes = HADOOP_SOURCE_FOLDER + "/hadoop-0.20.2-core.jar";
private String required_Execution_Classes = required_Compilation_Classes + "," +
     "/Users/reza/My_Software/ActorFoundry_dist_ver/lib/commons-cli-1.1.jar," +
     "/Users/reza/My_Software/ActorFoundry_dist_ver/lib/commons-logging-1.1.1.jar";
public String compilation_Output_Folder = "/Users/reza/My_Software/rCloud_test/dyn_classes";
private String jar_Output_Folder = "/Users/reza/My_Software/rCloud_test/dyn_jar";

作为运行 Platform2 的结果，磁盘上的项目结构如下所示:

rCloud_test/classes/reza/rCloud/Platform2.class:包含 Platform2 类rCloud_test/dyn_classes/reza/rCloud/包含 Mapper_Reducer_Classes.class、Mapper_Reducer_Classes$Mapper_Class.class 和 Mapper_Reducer_Classes$Reducer_Class.class 的类rCloud_test/dyn_jar/jar_file.jar 包含创建的jar文件

REVSED:这里是 rCloud_test/src/reza/rCloud/Mapper_Reducer_Classes.java 的源代码:

package reza.rCloud;

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

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
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;
import org.apache.hadoop.util.GenericOptionsParser;

public class Mapper_Reducer_Classes {
/**
 * The map class of WordCount.
 */
public static class Mapper_Class
    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);
        }
    }
}
/**
 * The reducer class of WordCount
 */
public static class Reducer_Class
    extends Reducer<Text, IntWritable, Text, IntWritable> {
    public void reduce(Text key, Iterable<IntWritable> values, Context context)
        throws IOException, InterruptedException {
        int sum = 0;
        for (IntWritable value : values) {
            sum += value.get();
        }
        context.write(key, new IntWritable(sum));
    }
}
}

Answer 1

尝试使用 setClass() 设置它们方法:

conf.setClass("mapreduce.map.class", 
              Class.forName("reza.rCloud.Mapper_Reducer_Classes$Mapper_Class"),
              Mapper.class);

conf.setClass("mapreduce.reduce.class",
              Class.forName("reza.rCloud.Mapper_Reducer_Classes$Reducer_Class"),
              Reducer.class);