远程调用执行Hadoop Map/Reduce

在Web项目中，由用户下发任务后，后台服务器远程调用JobTracker所在服务器，运行Map/Reduce更符合B/S架构的习惯。

由于网上没有相关资料，所以自己实现了一个，现在分享一下。

注：基于Hadoop1.1.2版本

转发请注明地址：http://sgq0085.iteye.com/admin/blogs/1879442

一个常见的WordCount如下：

 

package com.gqshao.hadoop.remote;

import java.io.IOException;
import java.util.*;

import org.apache.hadoop.conf.*;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapreduce.*;
import org.apache.hadoop.mapreduce.lib.input.*;
import org.apache.hadoop.mapreduce.lib.output.*;
import org.apache.hadoop.util.*;

public class WordCount extends Configured implements Tool {
    public static class Map extends Mapper<LongWritable, Text, Text, IntWritable> {
        private final static IntWritable one = new IntWritable(1);
        private Text word = new Text();

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

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

    public int run(String[] args) throws Exception {
        this.getClass().getResource("/hadoop/");
        Configuration conf = getConf();
        Job job = new Job(conf);
        conf.set("mapred.job.tracker", "192.168.0.128:9001");
        conf.set("fs.default.name", "hdfs://192.168.0.128:9000");
        conf.set("hadoop.job.ugi", "hadoop");
        conf.set("Hadoop.tmp.dir", "/user/gqshao/temp/");

        job.setJarByClass(WordCount.class);
        job.setJobName("wordcount");

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

        job.setMapperClass(Map.class);
        job.setReducerClass(Reduce.class);

        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);
        String hdfs = "hdfs://192.168.0.128:9000";
        args = new String[] { hdfs + "/user/gqshao/input/big", hdfs + "/user/gqshao/output/WordCount/" + new Date().getTime() };
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        boolean success = job.waitForCompletion(true);
        return success ? 0 : 1;
    }

    public static void main(String[] args) throws Exception {
        int ret = ToolRunner.run(new WordCount(), args);
        System.exit(ret);
    }
}

 在这里输入和输出目录都是指向HDFS上的，但实际运行的时候(一般 -Xms128m -Xmx512m -XX:MaxPermSize=128M)发现输出中有如下信息：

信息: Running job: job_local_0001

证明该Map/Reduce程序运行在Local中。也就是说，这种方式只能提前打好Jar包，放到Cluster服务器上，在通过Jar运行。

转发请注明地址：http://sgq0085.iteye.com/admin/blogs/1879442

如何远程运行Map/Reduce程序，经研究发现两点。

1.需要将Hadoop的配置文件加载到当前进程的ClassLoader中，或将配置文件放到/bin目录下。

通过跟踪 job.waitForCompletion(true);→submit();→info = jobClient.submitJobInternal(conf);→status = jobSubmitClient.submitJob(jobId, submitJobDir.toString(), jobCopy.getCredentials());

发现private JobSubmissionProtocol jobSubmitClient;分别有两个实现

在org.apache.hadoop.mapred.JobClient中init()方法中可以看到如果设置了conf中如果设置了mapred.job.tracker则在Hadoop Cluster中运行，否则是Local

 

  public void init(JobConf conf) throws IOException {
    String tracker = conf.get("mapred.job.tracker", "local");
    tasklogtimeout = conf.getInt(
      TASKLOG_PULL_TIMEOUT_KEY, DEFAULT_TASKLOG_TIMEOUT);
    this.ugi = UserGroupInformation.getCurrentUser();
    if ("local".equals(tracker)) {
      conf.setNumMapTasks(1);
      this.jobSubmitClient = new LocalJobRunner(conf);
    } else {
      this.rpcJobSubmitClient = 
          createRPCProxy(JobTracker.getAddress(conf), conf);
      this.jobSubmitClient = createProxy(this.rpcJobSubmitClient, conf);
    }        
  }

 

所以需要在运行时加载某目录下配置文件

方法如下：

    /**
     * 加载配置文件
     */
    public static void setConf(Class<?> clazz, Thread thread, String path) {
        URL url = clazz.getResource(path);
        try {
            File confDir = new File(url.toURI());
            if (!confDir.exists()) {
                return;
            }
            URL key = confDir.getCanonicalFile().toURI().toURL();
            ClassLoader classLoader = thread.getContextClassLoader();
            classLoader = new URLClassLoader(new URL[] { key }, classLoader);
            thread.setContextClassLoader(classLoader);
        } catch (Exception e) {
            e.printStackTrace();
        }
    }

 

2.设置运行时Jar包

继续看jobClient.submitJobInternal(conf);可以发现client在提交作业到Hadoop时需要把作业打包成jar，然后copy到fs的submitJarFile路径中。所以必须指定conf中的运行的Jar包。

方法如下：

    /**
     * 动态生成Jar包
     */
    public static File createJar(Class<?> clazz) throws Exception {
        String fqn = clazz.getName();
        String base = fqn.substring(0, fqn.lastIndexOf("."));
        base = "/" + base.replaceAll("\\.", Matcher.quoteReplacement("/"));
        URL root = clazz.getResource("");

        JarOutputStream out = null;
        final File jar = File.createTempFile("HadoopRunningJar-", ".jar", new File(System.getProperty("java.io.tmpdir")));
        System.out.println(jar.getAbsolutePath());
        Runtime.getRuntime().addShutdownHook(new Thread() {
            public void run() {
                jar.delete();
            }
        });
        try {
            File path = new File(root.toURI());
            Manifest manifest = new Manifest();
            manifest.getMainAttributes().putValue("Manifest-Version", "1.0");
            manifest.getMainAttributes().putValue("Created-By", "RemoteHadoopUtil");
            out = new JarOutputStream(new FileOutputStream(jar), manifest);
            writeBaseFile(out, path, base);
        } finally {
            out.flush();
            out.close();
        }
        return jar;
    }

    /**
     * 递归添加.class文件
     */
    private static void writeBaseFile(JarOutputStream out, File file, String base) throws IOException {
        if (file.isDirectory()) {
            File[] fl = file.listFiles();
            if (base.length() > 0) {
                base = base + "/";
            }
            for (int i = 0; i < fl.length; i++) {
                writeBaseFile(out, fl[i], base + fl[i].getName());
            }
        } else {
            out.putNextEntry(new JarEntry(base));
            FileInputStream in = null;
            try {
                in = new FileInputStream(file);
                byte[] buffer = new byte[1024];
                int n = in.read(buffer);
                while (n != -1) {
                    out.write(buffer, 0, n);
                    n = in.read(buffer);
                }
            } finally {
                in.close();
            }  
        }
    }

 

修改后的WordCount如下：

public class WordCount extends Configured implements Tool {
    public static class Map extends Mapper<LongWritable, Text, Text, IntWritable> {
        private final static IntWritable one = new IntWritable(1);
        private Text word = new Text();

        public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            String line = value.toString();
            System.out.println("line===>" + line);
            StringTokenizer tokenizer = new StringTokenizer(line);
            while (tokenizer.hasMoreTokens()) {
                word.set(tokenizer.nextToken());
                context.write(word, one);
            }
        }
    }

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

    public int run(String[] args) throws Exception {
        Configuration conf = getConf();
        Job job = new Job(conf);
        System.out.println(conf.get("mapred.job.tracker"));
        System.out.println(conf.get("fs.default.name"));
        /**
         * TODO:调用二
         */
        File jarFile = RemoteHadoopUtil.createJar(WordCount.class);
        ((JobConf) job.getConfiguration()).setJar(jarFile.toString());
        job.setJarByClass(WordCount.class);
        job.setJobName("wordcount");

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

        job.setMapperClass(Map.class);
        job.setReducerClass(Reduce.class);

        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);
        String hdfs = "hdfs://192.168.0.128:9000";
        args = new String[] { hdfs + "/user/gqshao/input/WordCount/", hdfs + "/user/gqshao/output/WordCount/" + new Date().getTime() };
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        boolean success = job.waitForCompletion(true);
        System.out.println(job.isComplete());
        System.out.println("JobID: " + job.getJobID());
        return success ? 0 : 1;
    }

    public static void main(String[] args) throws Exception {
        /**
         * TODO:调用一
         */
        RemoteHadoopUtil.setConf(WordCount.class, Thread.currentThread(), "/hadoop");
        int ret = ToolRunner.run(new WordCount(), args);
        System.exit(ret);
    }
}

 转发请注明地址：http://sgq0085.iteye.com/admin/blogs/1879442

 附件中有完整代码和测试用例，欢迎讨论。解压后在文件目录中运行mvn eclipse:clean eclipse:eclipse即可(前提是需要有Maven)