Windows下Eclipse提交MR程序到HadoopCluster

时间：2023-03-09 04:00:18
Windows下Eclipse提交MR程序到HadoopCluster

作者：Syn良子 出处：http://www.cnblogs.com/cssdongl 欢迎转载,转载请注明出处.

以前Eclipse上写好的MapReduce项目经常是打好包上传到Hadoop测试集群来直接运行，运行遇到问题的话查看日志和修改相关代码来解决。找时间配置了Windows上Eclispe远程提交MR程序到集群方便调试.记录一些遇到的问题和解决方法.

系统环境:Windows7 64，Eclipse Mars,Maven3.3.9,Hadoop2.6.0-CDH5.4.0.

一.配置MapReduce Maven工程

新建一个Maven工程,将CDH集群的相关xml配置文件(主要是core-site.xml,hdfs-site.xml,mapred-site.xml和yarn-site.xml)拷贝到src/main/java下,因为需要连接的是CDH集群，所以配置pom.xml文件主要内容如下

<properties>

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

</properties>

<repositories>

        <repository>

            <id>cloudera</id>

            <url>https://repository.cloudera.com/artifactory/cloudera-repos/</url>

        </repository>

</repositories>

<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-cdh5.4.0</version>

            <type>jar</type>

            <exclusions>

                <exclusion>

                    <artifactId>kfs</artifactId>

                    <groupId>net.sf.kosmosfs</groupId>

                </exclusion>

            </exclusions>

            <scope>provided</scope>

        </dependency>

        <dependency>

            <groupId>org.apache.hadoop</groupId>

            <artifactId>hadoop-hdfs</artifactId>

            <version>2.6.0-cdh5.4.0</version>

            <type>jar</type>

            <scope>provided</scope>

        </dependency>

        <dependency>

            <groupId>org.apache.hadoop</groupId>

            <artifactId>hadoop-hdfs-nfs</artifactId>

            <version>2.6.0-cdh5.4.0</version>

            <type>jar</type>

            <scope>provided</scope>

        </dependency>

        <dependency>

            <groupId>org.apache.hadoop</groupId>

            <artifactId>hadoop-yarn-api</artifactId>

            <version>2.6.0-cdh5.4.0</version>

            <type>jar</type>

            <scope>provided</scope>

        </dependency>

        <dependency>

            <groupId>org.apache.hadoop</groupId>

            <artifactId>hadoop-yarn-applications-distributedshell</artifactId>

            <version>2.6.0-cdh5.4.0</version>

            <type>jar</type>

            <scope>provided</scope>

        </dependency>

        <dependency>

            <groupId>org.apache.hadoop</groupId>

            <artifactId>hadoop-yarn-server-resourcemanager</artifactId>

            <version>2.6.0-cdh5.4.0</version>

            <type>jar</type>

            <scope>provided</scope>

        </dependency>

        <dependency>

            <groupId>org.apache.hadoop</groupId>

            <artifactId>hadoop-yarn-applications-unmanaged-am-launcher</artifactId>

            <version>2.6.0-cdh5.4.0</version>

            <type>jar</type>

            <scope>provided</scope>

        </dependency>

        <dependency>

            <groupId>org.apache.hadoop</groupId>

            <artifactId>hbase-hadoop2-compat</artifactId>

            <version>1.0.0-cdh5.4.0</version>

            <type>jar</type>

            <scope>provided</scope>

        </dependency>

        <dependency>

            <groupId>org.apache.hadoop</groupId>

            <artifactId>hadoop-mapreduce-client-common</artifactId>

            <version>2.6.0-cdh5.4.0</version>

            <type>jar</type>

            <scope>provided</scope>

        </dependency>

        <dependency>

            <groupId>org.apache.hadoop</groupId>

            <artifactId>hadoop-mapreduce-client-jobclient</artifactId>

            <version>2.6.0-cdh5.4.0</version>

            <type>jar</type>

            <scope>provided</scope>

        </dependency>

        <dependency>

            <groupId>org.apache.hadoop</groupId>

            <artifactId>hadoop-mapreduce-client-app</artifactId>

            <version>2.6.0-cdh5.4.0</version>

            <type>jar</type>

            <scope>provided</scope>

        </dependency>

        <dependency>

            <groupId>org.apache.hadoop</groupId>

            <artifactId>hadoop-mapreduce-client-hs</artifactId>

            <version>2.6.0-cdh5.4.0</version>

            <type>jar</type>

            <scope>provided</scope>

        </dependency>

        <dependency>

            <groupId>org.apache.hadoop</groupId>

            <artifactId>hadoop-mapreduce-client-hs-plugins</artifactId>

            <version>2.6.0-cdh5.4.0</version>

            <type>jar</type>

            <scope>provided</scope>

        </dependency>

        <dependency>

            <groupId>org.apache.hadoop</groupId>

            <artifactId>hbase-client</artifactId>

            <version>1.0.0-cdh5.4.0</version>

            <type>jar</type>

            <scope>provided</scope>

        </dependency>

        <dependency>

            <groupId>org.apache.hadoop</groupId>

            <artifactId>hbase-common</artifactId>

            <version>1.0.0-cdh5.4.0</version>

            <type>jar</type>

            <scope>provided</scope>

        </dependency>

        <dependency>

            <groupId>org.apache.hadoop</groupId>

            <artifactId>hbase-server</artifactId>

            <version>1.0.0-cdh5.4.0</version>

            <type>jar</type>

            <scope>provided</scope>

        </dependency>

        <dependency>

            <groupId>org.apache.hadoop</groupId>

            <artifactId>hbase-protocol</artifactId>

            <version>1.0.0-cdh5.4.0</version>

            <type>jar</type>

            <scope>provided</scope>

        </dependency>

        <dependency>

            <groupId>org.apache.hadoop</groupId>

            <artifactId>hbase-prefix-tree</artifactId>

            <version>1.0.0-cdh5.4.0</version>

            <type>jar</type>

            <scope>provided</scope>

        </dependency>

        <dependency>

            <groupId>org.apache.hadoop</groupId>

            <artifactId>hbase-hadoop-compat</artifactId>

            <version>1.0.0-cdh5.4.0</version>

            <type>jar</type>

            <scope>provided</scope>

        </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>org.apache.maven.surefire</groupId>

            <artifactId>surefire-booter</artifactId>

            <version>2.12.4</version>

        </dependency>

</dependencies>

如果CDH是其他版本，请参考CDH官方Maven Artifacts,配置好对应的dependency(修改version之类的属性).如果是原生Hadoop,remove掉上面Cloudera的repositroy,配置好对应的dependency.

配置好以后保存pom文件，等待相关jar包下载完成.

二.配置Eclipse提交MR到集群

最简单的莫过于WordCount了，贴代码先.

package org.ldong.test;

import java.io.File;

import java.io.IOException;

import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.conf.Configured;

import org.apache.hadoop.fs.FileSystem;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapred.JobConf;

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.Tool;

import org.apache.hadoop.util.ToolRunner;

import org.apache.hadoop.yarn.conf.YarnConfiguration;

public class WordCount1 extends Configured implements Tool {

    public static class TokenizerMapper 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 {

            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> {

        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 static void main(String[] args) throws Exception {

        System.setProperty("hadoop.home.dir", "C:\\hadoop-2.6.0");

        ToolRunner.run(new WordCount1(), args);

    }

    public int run(String[] args) throws Exception {

        String input = "hdfs://littleNameservice/test/input";

        String output = "hdfs://littleNameservice/test/output";

        Configuration conf = new YarnConfiguration();

        conf.addResource("core-site.xml");

        conf.addResource("hdfs-site.xml");

        conf.addResource("mapred-site.xml");

        conf.addResource("yarn-site.xml");

        Job job = Job.getInstance(conf, "word count");

        job.setJarByClass(WordCount1.class);

        job.setOutputKeyClass(Text.class);

        job.setOutputValueClass(IntWritable.class);

        job.setMapperClass(TokenizerMapper.class);

        job.setCombinerClass(IntSumReducer.class);

        job.setReducerClass(IntSumReducer.class);

        job.setNumReduceTasks(1);

        FileInputFormat.addInputPath(job, new Path(input));

        FileSystem fs = FileSystem.get(conf);

        if (fs.exists(new Path(output))) {

            fs.delete(new Path(output), true);

        }

        String classDirToPackage = "D:\\workspace\\performance-statistics-mvn\\target\\classes";

        File jarFile = EJob.createTempJar(classDirToPackage);

        ClassLoader classLoader = EJob.getClassLoader();

        Thread.currentThread().setContextClassLoader(classLoader);

        ((JobConf) job.getConfiguration()).setJar(jarFile.toString());

        FileOutputFormat.setOutputPath(job, new Path(output));

        return job.waitForCompletion(true) ? 0 : 1;

    }

}

ok,这里主要配置好代码中标红部分的configuration(和自己的连接集群保持一致),开始运行该程序,直接run as java application ,不出意外的话，肯定报错如下

java.io.IOException: Could not locate executable null\bin\winutils.exe in the Hadoop binaries.

这个错误很常见，在windows下运行MR就会出现,主要是hadoop需要的一些native library在windows上找不到.作为强迫症患者肯定不能视而不见了，解决方式如下:

1.去官网下载hadoop2.6.0-.tar.gz解压到windows本地,配置环境变量，指向解压以后的hadoop根目录下bin文件夹;

2.将本文最后链接中的压缩包下载解压，将其中的文件都解压到步骤1中hadoop的bin目录下.

3.如果没有立刻生效的，加上临时代码，如上面代码中 System.setProperty("hadoop.home.dir", "C:\\hadoop-2.6.0")，下次重启生效后好可以去掉这行

重新运行,该错误消失.出现另外一个错误

Windows下Eclipse提交MR程序到HadoopCluster

其实这个异常是Hadoop2.4之前的一个windows提交MR任务到Hadoop Cluster的严重bug,跟不同系统的classes path有关系,已经在hadoop2.4修复了，见如下链接

http://hadoop.apache.org/docs/r2.4.1/hadoop-project-dist/hadoop-common/releasenotes.html

其中 MAPREDUCE-4052 :“Windows eclipse cannot submit job from Windows client to Linux/Unix Hadoop cluster.”

如果有同学是Hadoop2.4之前版本的，解决方式参考下面几个链接(其实4052和5655是duplicate的):

https://issues.apache.org/jira/browse/MAPREDUCE-4052
https://issues.apache.org/jira/browse/MAPREDUCE-5655
https://issues.apache.org/jira/browse/YARN-1298
http://www.aboutyun.com/thread-8498-1-1.html
http://blog.****.net/fansy1990/article/details/22896249

而我的环境是Hadoop2.6.0-CDH5.4.0的，解决方式没有上面这么麻烦,直接修改工程下mapred-site.xml文件,修改属性如下(如没有则添加)

<property>

    <name>mapreduce.app-submission.cross-platform</name>

    <value>true</value>

</property>

继续运行，上面那个”no job control”错误消失，出现如下错误

Windows下Eclipse提交MR程序到HadoopCluster

没完没了了，肯定还是配置的问题，最后查阅资料发现还是直接修改工程的配置文件

对于mapred-site.xml，添加或者修改属性如下，注意自己的集群保持一一致,如下

<property>

    <name>mapreduce.application.classpath</name>

    <value>$HADOOP_MAPRED_HOME/*,$HADOOP_MAPRED_HOME/lib/*,$MR2_CLASSPATH</value>

 </property>

对于yarn-site.xml，添加或者修改属性如下，注意自己的集群保持一一致,如下

<property>

    <name>yarn.application.classpath</name>

    <value>$HADOOP_CLIENT_CONF_DIR,$HADOOP_CONF_DIR,$HADOOP_COMMON_HOME/*,$HADOOP_COMMON_HOME/lib/*,$HADOOP_HDFS_HOME/*,$HADOOP_HDFS_HOME/lib/*,$HADOOP_YARN_HOME/*,$HADOOP_YARN_HOME/lib/*</value>

 </property>

重新运行MR，上面错误消失,出现新的错误找不到MR的class.汗了一地，好吧，2种方式，一种就是我代码中标黄的部分自动把MR打成jar包上传到集群分布式运行

String classDirToPackage = "D:\\workspace\\performance-statistics-mvn\\target\\classes";

这个地方请改成自己工程下的classes文件夹(如果是maven工程，请暂时删除classes下META-INF文件夹，否则后续无法打包)，随后的代码用EJob类来打成Jar包，最终提交给集群来运行.EJob类如下

package org.ldong.test;

import java.io.File;

import java.io.FileInputStream;

import java.io.FileOutputStream;

import java.io.IOException;

import java.net.URL;

import java.net.URLClassLoader;

import java.util.ArrayList;

import java.util.List;

import java.util.jar.JarEntry;

import java.util.jar.JarOutputStream;

import java.util.jar.Manifest;

public class EJob {

    private static List<URL> classPath = new ArrayList<URL>();

    public static File createTempJar(String root) throws IOException {

        if (!new File(root).exists()) {

            return null;

        }

        Manifest manifest = new Manifest();

        manifest.getMainAttributes().putValue("Manifest-Version", "1.0");

        final File jarFile = File.createTempFile("EJob-", ".jar", new File(

                System.getProperty("java.io.tmpdir")));

        Runtime.getRuntime().addShutdownHook(new Thread() {

            public void run() {

                jarFile.delete();

            }

        });

        JarOutputStream out = new JarOutputStream(

                new FileOutputStream(jarFile), manifest);

        createTempJarInner(out, new File(root), "");

        out.flush();

        out.close();

        return jarFile;

    }

    private static void createTempJarInner(JarOutputStream out, File f,

            String base) throws IOException {

        if (f.isDirectory()) {

            File[] fl = f.listFiles();

            if (base.length() > 0) {

                base = base + "/";

            }

            for (int i = 0; i < fl.length; i++) {

                createTempJarInner(out, fl[i], base + fl[i].getName());

            }

        } else {

            out.putNextEntry(new JarEntry(base));

            FileInputStream in = new FileInputStream(f);

            byte[] buffer = new byte[1024];

            int n = in.read(buffer);

            while (n != -1) {

                out.write(buffer, 0, n);

                n = in.read(buffer);

            }

            in.close();

        }

    }

    public static ClassLoader getClassLoader() {

        ClassLoader parent = Thread.currentThread().getContextClassLoader();

        System.out .println(parent);

        URL[]  urls = classPath.toArray(new URL[0]);

        for( URL url : urls)

        {

            System.out .println(url);

        }

        System.out.println(classPath.toArray(new URL[0]));

        if (parent == null) {

            parent = EJob.class.getClassLoader();

        }

        if (parent == null) {

            parent = ClassLoader.getSystemClassLoader();

        }

        return new URLClassLoader(classPath.toArray(new URL[0]), parent);

    }

    public static void addClasspath(String component) {

        if ((component != null) && (component.length() > 0)) {

            try {

                File f = new File(component);

                if (f.exists()) {

                    URL key = f.getCanonicalFile().toURL();

                    if (!classPath.contains(key)) {

                        classPath.add(key);

                    }

                }

            } catch (IOException e) {

            }

        }

    }

}

另外一种方式就是直接手动将MR工程打包，放到集群每个节点上可以加载到的class path下,执行eclipse中程序触发MR分布式运行,也可以实现.

好吧，接着运行，这次没什么问题成功了，如下图

Windows下Eclipse提交MR程序到HadoopCluster

去集群上验证jobhistory和hdfs输出目录，结果正确.

其他可能会遇到的错误如

org.apache.hadoop.security.AccessControlException: Permission denied….

其实就是没有hdfs权限,解决方法有以下几种

1.在系统环境变量中增加HADOOP_USER_NAME，其值为hadoop；或者通过java程序动态添加，如下：System.setProperty("HADOOP_USER_NAME", "hadoop"),这里hadoop为对hdfs具有读写权限的hadoop用户.

2.或者对需要操作的目录开放权限，例如 hadoop fs -chmod 777 /test.

3.或者修改hadoop的配置文件：hdfs-site.xml，添加或者修改 dfs.permissions 的值为 false.

4.或者将Eclipse所在机器的用户的名称修改为hadoop，即与服务器上运行hadoop的用户一致,这里hadoop为对hdfs具有读写权限的hadoop用户.

三.配置MR程序本地运行

如果仅仅是需要本地调试测试MR逻辑来读写HDFS，不提交MR到集群运行,那配置比上面简单很多

同样新建一个Maven工程，这次不需要拷贝那些*-site.xml,直接简单配置pom文件主要内容如下

<properties>

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

</properties>

<repositories>

        <repository>

            <id>cloudera</id>

            <url>https://repository.cloudera.com/artifactory/cloudera-repos/</url>

        </repository>

</repositories>

<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-cdh5.4.0</version>

            <type>jar</type>

            <exclusions>

                <exclusion>

                    <artifactId>kfs</artifactId>

                    <groupId>net.sf.kosmosfs</groupId>

                </exclusion>

            </exclusions>

            <scope>provided</scope>

        </dependency>

        <dependency>

            <groupId>org.apache.hadoop</groupId>

            <artifactId>hadoop-hdfs</artifactId>

            <version>2.6.0-cdh5.4.0</version>

            <type>jar</type>

            <scope>provided</scope>

        </dependency>

        <dependency>

            <groupId>org.apache.hadoop</groupId>

            <artifactId>hadoop-core</artifactId>

            <version>2.6.0-mr1-cdh5.4.0</version>

            <type>jar</type>

            <scope>provided</scope>

        </dependency>

        <dependency>

            <groupId>org.apache.hadoop</groupId>

            <artifactId>hadoop-yarn-api</artifactId>

            <version>2.6.0-cdh5.4.0</version>

            <type>jar</type>

            <scope>provided</scope>

        </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>org.apache.maven.surefire</groupId>

            <artifactId>surefire-booter</artifactId>

            <version>2.12.4</version>

        </dependency>

</dependencies>

编写WordCount如下

import java.io.IOException;

import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.conf.Configured;

import org.apache.hadoop.fs.FileSystem;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.LongWritable;

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;

import org.apache.hadoop.util.Tool;

import org.apache.hadoop.util.ToolRunner;

import org.apache.hadoop.yarn.conf.YarnConfiguration;

public class WordCount extends Configured implements Tool {

    public static class TokenizerMapper 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 {

            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> {

        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 static void main(String[] args) throws Exception {

        System.setProperty("hadoop.home.dir", "C:\\hadoop-2.6.0");

        ToolRunner.run(new WordCount(), args);

    }

    public int run(String[] args) throws Exception {

        String input = "hdfs://master01.jj.wl:8020/test/input";

        String output = "hdfs://master01.jj.wl:8020/test/output";

        Configuration conf = new YarnConfiguration();

        conf.set("fs.defaultFS", "hdfs://master01.jj.wl:8020");

        conf.set("mapreduce.framework.name", "yarn");

        conf.set("yarn.resourcemanager.address", "master01.jj.wl:8032");

        Job job = new Job(conf, "word count");

        job.setJarByClass(WordCount.class);

        job.setOutputKeyClass(Text.class);

        job.setOutputValueClass(IntWritable.class);

        job.setMapperClass(TokenizerMapper.class);

        job.setCombinerClass(IntSumReducer.class);

        job.setReducerClass(IntSumReducer.class);

        FileInputFormat.addInputPath(job, new Path(input));

        FileSystem fs = FileSystem.get(conf);

        if (fs.exists(new Path(output))) {

            fs.delete(new Path(output), true);

        }

        FileOutputFormat.setOutputPath(job, new Path(output));

        return job.waitForCompletion(true) ? 0 : 1;

    }

}

配置好集群的连接，如标红部分,然后运行,报错如下

Could not locate executable null\bin\winutils.exe in the Hadoop binaries

和上面的解决方式一样，不废话，解决掉，接着运行，成功

Windows下Eclipse提交MR程序到HadoopCluster

眼尖的同学应该会发现这次MR调用了LocalJobRunner来运行,阅读源码会发现其实就是一个本地的JVM在运行MR程序，根本没有提交到集群，但是读写的是集群的HDFS,所以去集群上检查依然能看到output的输出结果，但是去Web UI上查看集群的任务列表会发现根本找不到刚才运行成功的任务.

winutils相关文件下载链接:http://files.cnblogs.com/files/cssdongl/hadoop2.6%28x64%29.zip

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