运行第一个MapReduce程序

落日映苍穹つ 2022-01-17 01:07 353阅读 0赞

> 本文的环境基于CDH5的搭建[https://blog.csdn.net/songzehao/article/details/91044032][https_blog.csdn.net_songzehao_article_details_91044032]
> 
> 大数据环境CDH5已搭建成功，自带example也已跑通，总不能老跑人家的mapreduce程序吧，所以是时候跑跑自己写的mr了。

怎么写程序先不管，我们先本着拿来主义，或者好听一点说师夷长技以制夷，第一步只是为了成功走完部署运行jar包的流程，至于怎么实现mr，留到后面的步骤再去研究。所以，先用jdgui反编译hadoop自带的示例jar（/opt/cloudera/parcels/CDH-5.7.2-1.cdh5.7.2.p0.18/jars/hadoop-examples.jar），找到WordCount.class，拷贝出Java源码，放到自己新建的一个普通Java Project中，发现代码很多报错，原因是少jar包，hadoop编程需要哪些jar包，如下：

*  hadoop-client-3.2.0.jar
 *  hadoop-common-3.2.0.jar
 *  hadoop-hdfs-3.2.0.jar
 *  hadoop-mapreduce-client-core-3.2.0.jar
 *  commons-cli-1.2.jar

如下给出各自pom依赖：

<dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoop-common</artifactId>
        <version>3.2.0</version>
    </dependency>
    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoop-hdfs</artifactId>
        <version>3.2.0</version>
    </dependency>
    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoop-mapreduce-client-core</artifactId>
        <version>3.2.0</version>
    </dependency>
    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoop-client</artifactId>
        <version>3.2.0</version>
    </dependency>
    <dependency>
        <groupId>commons-cli</groupId>
        <artifactId>commons-cli</artifactId>
        <version>1.2</version>
    </dependency>

导入jar包后，代码已不报错，接下来准备生成jar包，方式有很多种，包括在Maven项目中用mvn命令打包等等。

第一种简便的方式，可以直接在Eclipse中Export为JAR FILE；

另一种方式使用javac命令编译，并使用jar命令打包，如下：

E:\J2EE_workspace\Test\src>javac -cp .;D:\maven-repository\commons-cli\commons-cli\1.2\commons-cli-1.2.jar;D:\maven-repository\org\apache\hadoop\hadoop-client\3.2.0\hadoop-client-3.2.0.jar;D:\maven-repository\org\apache\hadoop\hadoop-common\3.2.0\hadoop-common-3.2.0.jar;D:\maven-repository\org\apache\hadoop\hadoop-hdfs-client\3.2.0\hadoop-hdfs-client-3.2.0.jar;D:\maven-repository\org\apache\hadoop\hadoop-mapreduce-client-core\3.2.0\hadoop-mapreduce-client-core-3.2.0.jar WordCount.java
    E:\J2EE_workspace\Test\src>jar -cvf wc3.jar *.class
    已添加清单
    正在添加: WordCount$IntSumReducer.class(输入 = 1739) (输出 = 739)(压缩了 57%)
    正在添加: WordCount$TokenizerMapper.class(输入 = 1736) (输出 = 754)(压缩了 56%)
    正在添加: WordCount.class(输入 = 3037) (输出 = 1619)(压缩了 46%)

将生成的wc3.jar从本地上传到任一集群节点，考虑到主节点（10.1.4.18/b3/bi-zhaopeng03）起的服务太多导致OOM，所以这里将其放到一agent从节点（10.1.4.19/b4/bi-zhaopeng04）上的目录/root/songzehao/data下：

[root@bi-zhaopeng04 data]# pwd
    /root/songzehao/data
    [root@bi-zhaopeng04 data]# ll
    总用量 16
    -rwxrwxrwx 1 root root   90 6月  11 15:46 test.sh
    -rwxrwxrwx 1 hdfs hdfs 4261 6月  11 18:26 wc3.jar
    -rw-r--r-- 1 root root   61 6月  11 10:29 words.txt

接下来要为wordcount程序创建一个文本文件words.txt，然后上传至hdfs上的某文件/tmp/songzehao/words\_input上。

words.txt内容如下：

[root@bi-zhaopeng04 data]# cat /root/songzehao/data/words.txt 
    szh dk tyn cj cj zp zp szh dk szh dk dk dk tyn

上传至hdfs指定文件/tmp/songzehao/words\_input中：

[root@bi-zhaopeng04 data]# pwd
    /root/songzehao/data
    [root@bi-zhaopeng04 data]# sudo -u hdfs hadoop fs -put words.txt /tmp/songzehao/words_input
    put: `words.txt': No such file or directory

结果报错put: \`words.txt': No such file or directory，注意这里不可以sudo –u hdfs，不然会报错说没有这个文件。奇怪了，明明有这个文件，路径也没问题，究竟是什么原因呢？这是因为Linux系统的用户权限机制导致，也就是说hdfs用户没有权限去操作/root/songzehao/data/words.txt或没有权限去打开此文件的所有上级目录，所以遇到Linux环境下的**文件不存在或找不到**的问题，应当重点排查用户是否有足够权限。不妨做个实验，我们切换到hdfs用户下，尝试去访问/root目录，可以发现hdfs并无权限访问/root：

[root@bi-zhaopeng04 data]# su hdfs
    上一次登录：三 6月 12 09:55:44 CST 2019pts/1 上
    [root@bi-zhaopeng04 data]$ ll /root
    ls: 无法打开目录/root: 权限不够

所以，我们以一个能操作此文件的Linux用户去执行hadoop put即可，可以看到内容也与words.txt一致：

[root@bi-zhaopeng04 data]# hadoop fs -put words.txt /tmp/songzehao/words_input
    [root@bi-zhaopeng04 data]# hadoop fs -cat /tmp/songzehao/words_input
    szh dk tyn cj cj zp zp szh dk szh dk dk dk tyn

至此，我们就具备了执行第一个mr任务的所有理论上的条件，那快来试试吧，跟CDH5搭建[https://blog.csdn.net/songzehao/article/details/91044032][https_blog.csdn.net_songzehao_article_details_91044032]的测试命令（sudo -u hdfs hadoop jar /opt/cloudera/parcels/CDH/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar pi 10 100）一样，我们尝试执行如下命令：

[root@bi-zhaopeng04 data]# sudo -u hdfs hadoop jar wc3.jar WordCount /tmp/songzehao/words_input /tmp/songzehao/words_output
    或者使用绝对路径
    [root@bi-zhaopeng04 data]# sudo -u hdfs hadoop jar /root/songzehao/data/wc3.jar WordCount  /tmp/songzehao/words_input /tmp/songzehao/words_output

尝试去执行，结果报错**文件找不到File /root/songzehao/data/wc3.jar does not exist**：

[root@bi-zhaopeng04 data]# sudo -u hdfs hadoop jar wc3.jar WordCount /tmp/songzehao/words_input /tmp/songzehao/words_output                     
    19/06/12 14:13:48 INFO client.RMProxy: Connecting to ResourceManager at bi-zhaopeng03/10.1.4.18:8032
    19/06/12 14:13:48 INFO mapreduce.JobSubmitter: Cleaning up the staging area /user/hdfs/.staging/job_1559721784881_0043
    19/06/12 14:13:48 WARN security.UserGroupInformation: PriviledgedActionException as:hdfs (auth:SIMPLE) cause:java.io.FileNotFoundException: File /root/songzehao/data/wc3.jar does not exist
    Exception in thread "main" java.io.FileNotFoundException: File /root/songzehao/data/wc3.jar does not exist
            at org.apache.hadoop.fs.RawLocalFileSystem.deprecatedGetFileStatus(RawLocalFileSystem.java:590)
            at org.apache.hadoop.fs.RawLocalFileSystem.getFileLinkStatusInternal(RawLocalFileSystem.java:803)
            at org.apache.hadoop.fs.RawLocalFileSystem.getFileStatus(RawLocalFileSystem.java:580)
            at org.apache.hadoop.fs.FilterFileSystem.getFileStatus(FilterFileSystem.java:425)
            at org.apache.hadoop.fs.FileUtil.copy(FileUtil.java:340)
            at org.apache.hadoop.fs.FileSystem.copyFromLocalFile(FileSystem.java:1949)
            at org.apache.hadoop.fs.FileSystem.copyFromLocalFile(FileSystem.java:1917)
            at org.apache.hadoop.fs.FileSystem.copyFromLocalFile(FileSystem.java:1882)
            at org.apache.hadoop.mapreduce.JobResourceUploader.copyJar(JobResourceUploader.java:210)
            at org.apache.hadoop.mapreduce.JobResourceUploader.uploadFiles(JobResourceUploader.java:166)
            at org.apache.hadoop.mapreduce.JobSubmitter.copyAndConfigureFiles(JobSubmitter.java:99)
            at org.apache.hadoop.mapreduce.JobSubmitter.submitJobInternal(JobSubmitter.java:194)
            at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1307)
            at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1304)
            at java.security.AccessController.doPrivileged(Native Method)
            at javax.security.auth.Subject.doAs(Subject.java:422)
            at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1693)
            at org.apache.hadoop.mapreduce.Job.submit(Job.java:1304)
            at org.apache.hadoop.mapreduce.Job.waitForCompletion(Job.java:1325)
            at WordCount.main(WordCount.java:49)
            at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
            at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
            at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
            at java.lang.reflect.Method.invoke(Method.java:498)
            at org.apache.hadoop.util.RunJar.run(RunJar.java:221)
            at org.apache.hadoop.util.RunJar.main(RunJar.java:136)

对呀，跟sudo –u hdfs put一样，先考虑hdfs用户是否有权限访问到任务jar包/root/songzehao/data/wc3.jar，显然也是没有的，但是正确规范的hadoop执行任务jar包又是要在hdfs用户下，解决此矛盾的方法，建议将wc3.jar放至hdfs用户有权限访问的目录下，如/home/hdfs/wc3.jar或/opt/\*\*等公共目录下即可，这也是为什么在搭建完CDH5后，去测试执行sudo -u hdfs hadoop jar /opt/cloudera/parcels/CDH/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar pi 10 100可以成功的原因（因为/opt/cloudera/parcels/CDH/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar已经在/opt这个系统自带的公共目录下了，hdfs用户已有权限访问）。Jar包放置得当，比如我放至/opt/szh下：

[root@bi-zhaopeng04 data]# cd /opt/szh/
    [root@bi-zhaopeng04 szh]# ll
    总用量 8
    -rw-r--r-- 1 root root 4910 6月  12 11:42 wc3.jar

之后，可以选择不切换至hdfs用户来直接执行，如下：

[root@bi-zhaopeng04 data]# sudo -u hdfs hadoop jar /opt/szh/wc3.jar WordCount /tmp/songzehao/words_input /tmp/songzehao/words_output

或者切换至hdfs用户来执行，如下：

[root@bi-zhaopeng04 data]# su hdfs
    [hdfs@bi-zhaopeng04 data]$ hadoop jar /opt/szh/wc3.jar WordCount /tmp/songzehao/words_input /tmp/songzehao/words_output

继续尝试去执行，这下总该没问题了吧？好吧，又报错了**类找不到Class WordCount$TokenizerMapper not found**，来看看具体的异常堆栈吧：

[root@bi-zhaopeng04 data]# sudo -u hdfs hadoop jar /opt/szh/wc3.jar WordCount /tmp/songzehao/words_input /tmp/songzehao/words_output
    19/06/10 19:39:57 INFO client.RMProxy: Connecting to ResourceManager at bi-zhaopeng03/10.1.4.18:8032
    19/06/10 19:39:57 WARN mapreduce.JobResourceUploader: No job jar file set.  User classes may not be found. See Job or Job#setJar(String).
    19/06/10 19:39:57 INFO input.FileInputFormat: Total input paths to process : 1
    19/06/10 19:39:58 INFO mapreduce.JobSubmitter: number of splits:1
    19/06/10 19:39:58 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1559721784881_0003
    19/06/10 19:39:58 INFO mapred.YARNRunner: Job jar is not present. Not adding any jar to the list of resources.
    19/06/10 19:39:58 INFO impl.YarnClientImpl: Submitted application application_1559721784881_0003
    19/06/10 19:39:58 INFO mapreduce.Job: The url to track the job: http://bi-zhaopeng03:8088/proxy/application_1559721784881_0003/
    19/06/10 19:39:58 INFO mapreduce.Job: Running job: job_1559721784881_0003
    19/06/10 19:40:02 INFO mapreduce.Job: Job job_1559721784881_0003 running in uber mode : false
    19/06/10 19:40:02 INFO mapreduce.Job:  map 0% reduce 0%
    19/06/10 19:40:05 INFO mapreduce.Job: Task Id : attempt_1559721784881_0003_m_000000_0, Status : FAILED
    Error: java.lang.RuntimeException: java.lang.ClassNotFoundException: Class WordCount$TokenizerMapper not found
            at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:2199)
            at org.apache.hadoop.mapreduce.task.JobContextImpl.getMapperClass(JobContextImpl.java:196)
            at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:745)
            at org.apache.hadoop.mapred.MapTask.run(MapTask.java:341)
            at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:164)
            at java.security.AccessController.doPrivileged(Native Method)
            at javax.security.auth.Subject.doAs(Subject.java:422)
            at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1693)
            at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:158)
    Caused by: java.lang.ClassNotFoundException: Class WordCount$TokenizerMapper not found
            at org.apache.hadoop.conf.Configuration.getClassByName(Configuration.java:2105)
            at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:2197)
            ... 8 more

该问题也比较明了，没有设置作业jar包，No job jar file set，则需要给conf设置mapred.jar，即，Java代码里加这么一句：

// wc3.jar为最后要执行的作业jar包名称
    conf.set("mapred.jar", System.getProperty("user.dir") + File.separator + "wc3.jar");

这里顺便给出全部的WordCount源码，包括自己修改添加的一些标准输出语句，如下：

import java.io.File;
    import java.io.IOException;
    import java.util.Arrays;
    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;
    import org.apache.hadoop.util.GenericOptionsParser;
    
    public class WordCount {
        @SuppressWarnings("deprecation")
        public static void main(String[] args) throws Exception {
            System.out.println("Args: " + Arrays.toString(args));
            Configuration conf = new Configuration();
            conf.set("mapred.jar", System.getProperty("user.dir") + File.separator + "wc3.jar");
            // mapred.jar已过期，也可使用新支持的mapreduce.job.jar
            // conf.set("mapreduce.job.jar", System.getProperty("user.dir") + File.separator + "wc3.jar");
            System.out.println("当前工作目录: " + System.getProperty("user.dir"));
            System.out.println("mapred.jar/mapreduce.job.jar: " + conf.get("mapred.jar"));
            File jarFile = new File(conf.get("mapred.jar"));
            System.out
                    .println("Jar to run: " + jarFile.getAbsolutePath() + "," + jarFile.isFile() + "," + jarFile.exists());
            String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
            System.out.println("OtherArgs: " + Arrays.toString(otherArgs));
            if (otherArgs.length < 2) {
                System.err.println("Usage: wordcount <in> [<in>...] <out>");
                System.exit(2);
            }
            Job job = new Job(conf, "szh's 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);
            for (int i = 0; i < otherArgs.length - 1; i++) {
                FileInputFormat.addInputPath(job, new Path(otherArgs[i]));
                System.out.println("Input path: " + otherArgs[i]);
            }
            FileOutputFormat.setOutputPath(job, new Path(otherArgs[(otherArgs.length - 1)]));
            System.out.println("Output path: " + otherArgs[(otherArgs.length - 1)]);
    
            System.exit(job.waitForCompletion(true) ? 0 : 1);
        }
    
        public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
            private IntWritable result = new IntWritable();
    
            public void reduce(Text key, Iterable<IntWritable> values,
                    Reducer<Text, IntWritable, Text, IntWritable>.Context context)
                    throws IOException, InterruptedException {
                int sum = 0;
                for (IntWritable val : values) {
                    sum += val.get();
                }
                this.result.set(sum);
                context.write(key, this.result);
            }
        }
    
        public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> {
            private static final IntWritable one = new IntWritable(1);
            private Text word = new Text();
    
            public void map(Object key, Text value, Mapper<Object, Text, Text, IntWritable>.Context context)
                    throws IOException, InterruptedException {
                StringTokenizer itr = new StringTokenizer(value.toString());
                while (itr.hasMoreTokens()) {
                    this.word.set(itr.nextToken());
                    context.write(this.word, one);
                }
            }
        }
    }

折腾了这么久，这下重新导出为jar包，重新执行总该SUCCESS了吧，终于成功了，如下：

[root@bi-zhaopeng04 ~]# sudo -u hdfs hadoop jar /opt/szh/wc3.jar WordCount /tmp/songzehao/words_input /tmp/songzehao/words_output
    Args: [/tmp/songzehao/words_input, /tmp/songzehao/words_output]
    当前工作目录: /tmp/hsperfdata_hdfs
    mapred.jar/mapreduce.job.jar: /tmp/hsperfdata_hdfs/wc3.jar
    Jar to run: /tmp/hsperfdata_hdfs/wc3.jar,false,false
    OtherArgs: [/tmp/songzehao/words_input, /tmp/songzehao/words_output]
    Input path: /tmp/songzehao/words_input
    Output path: /tmp/songzehao/words_output
    19/06/12 14:57:38 INFO client.RMProxy: Connecting to ResourceManager at bi-zhaopeng03/10.1.4.18:8032
    19/06/12 14:57:38 INFO input.FileInputFormat: Total input paths to process : 1
    19/06/12 14:57:38 INFO mapreduce.JobSubmitter: number of splits:1
    19/06/12 14:57:38 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar
    19/06/12 14:57:38 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1559721784881_0045
    19/06/12 14:57:38 INFO impl.YarnClientImpl: Submitted application application_1559721784881_0045
    19/06/12 14:57:38 INFO mapreduce.Job: The url to track the job: http://bi-zhaopeng03:8088/proxy/application_1559721784881_0045/
    19/06/12 14:57:38 INFO mapreduce.Job: Running job: job_1559721784881_0045
    19/06/12 14:57:43 INFO mapreduce.Job: Job job_1559721784881_0045 running in uber mode : false
    19/06/12 14:57:43 INFO mapreduce.Job:  map 0% reduce 0%
    19/06/12 14:57:47 INFO mapreduce.Job:  map 100% reduce 0%
    19/06/12 14:57:51 INFO mapreduce.Job:  map 100% reduce 13%
    19/06/12 14:57:55 INFO mapreduce.Job:  map 100% reduce 25%
    19/06/12 14:57:59 INFO mapreduce.Job:  map 100% reduce 38%
    19/06/12 14:58:03 INFO mapreduce.Job:  map 100% reduce 50%
    19/06/12 14:58:07 INFO mapreduce.Job:  map 100% reduce 63%
    19/06/12 14:58:11 INFO mapreduce.Job:  map 100% reduce 75%
    19/06/12 14:58:15 INFO mapreduce.Job:  map 100% reduce 88%
    19/06/12 14:58:19 INFO mapreduce.Job:  map 100% reduce 100%
    19/06/12 14:58:19 INFO mapreduce.Job: Job job_1559721784881_0045 completed successfully
    19/06/12 14:58:19 INFO mapreduce.Job: Counters: 49
            File System Counters
                    FILE: Number of bytes read=367
                    FILE: Number of bytes written=2018974
                    FILE: Number of read operations=0
                    FILE: Number of large read operations=0
                    FILE: Number of write operations=0
                    HDFS: Number of bytes read=163
                    HDFS: Number of bytes written=27
                    HDFS: Number of read operations=51
                    HDFS: Number of large read operations=0
                    HDFS: Number of write operations=32
            Job Counters 
                    Launched map tasks=1
                    Launched reduce tasks=16
                    Data-local map tasks=1
                    Total time spent by all maps in occupied slots (ms)=1703
                    Total time spent by all reduces in occupied slots (ms)=28315
                    Total time spent by all map tasks (ms)=1703
                    Total time spent by all reduce tasks (ms)=28315
                    Total vcore-seconds taken by all map tasks=1703
                    Total vcore-seconds taken by all reduce tasks=28315
                    Total megabyte-seconds taken by all map tasks=1743872
                    Total megabyte-seconds taken by all reduce tasks=28994560
            Map-Reduce Framework
                    Map input records=1
                    Map output records=14
                    Map output bytes=103
                    Map output materialized bytes=303
                    Input split bytes=116
                    Combine input records=14
                    Combine output records=5
                    Reduce input groups=5
                    Reduce shuffle bytes=303
                    Reduce input records=5
                    Reduce output records=5
                    Spilled Records=10
                    Shuffled Maps =16
                    Failed Shuffles=0
                    Merged Map outputs=16
                    GC time elapsed (ms)=895
                    CPU time spent (ms)=12940
                    Physical memory (bytes) snapshot=3937447936
                    Virtual memory (bytes) snapshot=48442486784
                    Total committed heap usage (bytes)=3520069632
            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=47
            File Output Format Counters 
                    Bytes Written=27

趁热赶快去看hdfs中有没有在指定输出目录/tmp/songzehao/words\_output下生成文件，可通过web界面浏览hdfs目录，地址：masterIp:50070 /explorer.html\#/，如下：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3Nvbmd6ZWhhbw_size_16_color_FFFFFF_t_70][]

查看一下输出的文件内容吧，是否正确统计出来单词个数，一种方法是直接可以点击文件大小非零的reduce任务输出文件part-r-xxxxx，然后Download查看；另一种可通过hadoop命令行对应查看，如：

[root@bi-zhaopeng04 ~]# hadoop fs -cat /tmp/songzehao/words_output/part-r-00000 /tmp/songzehao/words_output/part-r-00007 /tmp/songzehao/words_output/part-r-00008
    szh     3
    zp      2
    cj      2
    dk      5
    tyn     2

至此，就算我们成功运行了第一个mr任务了。可是每次执行hadoop jar都要指定主类名吗？这样也有些小麻烦，jar包里面的META-INF/MANIFEST.MF不就可以保存主类信息吗？Hadoop这么优秀的分布式系统架构，应该也已经实现这么一个小小的需求了吧，如果可以的话，那每次执行hadoop jar就不用手动指定主类了。其实Hadoop确实早已想到并实现了这一点，如果我们故意不指定主类名，会发生什么呢？如下：

[root@bi-zhaopeng04 ~]# sudo -u hdfs hadoop jar /opt/szh/wc3.jar /tmp/songzehao/words_input /tmp/songzehao/words_output          
    Exception in thread "main" java.lang.ClassNotFoundException: /tmp/songzehao/words_input
            at java.lang.Class.forName0(Native Method)
            at java.lang.Class.forName(Class.java:348)
            at org.apache.hadoop.util.RunJar.run(RunJar.java:214)
            at org.apache.hadoop.util.RunJar.main(RunJar.java:136)

理所当然地报错说类找不到，至少从异常堆栈信息中，我们已找到翻看源码的突破口RunJar.java，可见Hadoop运行作业jar包，都会进入此入口类。接下来看看它的main方法和run方法：

/** Run a Hadoop job jar.  If the main class is not in the jar's manifest,
       * then it must be provided on the command line. */
      public static void main(String[] args) throws Throwable {
        new RunJar().run(args);
      }
    
      public void run(String[] args) throws Throwable {
        String usage = "RunJar jarFile [mainClass] args...";
    
        if (args.length < 1) {
          System.err.println(usage);
          System.exit(-1);
        }
    
        int firstArg = 0;
        String fileName = args[firstArg++];
        File file = new File(fileName);
        if (!file.exists() || !file.isFile()) {
          System.err.println("JAR does not exist or is not a normal file: " +
              file.getCanonicalPath());
          System.exit(-1);
        }
        String mainClassName = null;
    
        JarFile jarFile;
        try {
          jarFile = new JarFile(fileName);
        } catch (IOException io) {
          throw new IOException("Error opening job jar: " + fileName)
            .initCause(io);
        }
    
        Manifest manifest = jarFile.getManifest();
        if (manifest != null) {
          mainClassName = manifest.getMainAttributes().getValue("Main-Class");
        }
        jarFile.close();
    
        if (mainClassName == null) {
          if (args.length < 2) {
            System.err.println(usage);
            System.exit(-1);
          }
          mainClassName = args[firstArg++];
        }
        mainClassName = mainClassName.replaceAll("/", ".");
    
        File tmpDir = new File(System.getProperty("java.io.tmpdir"));
        ensureDirectory(tmpDir);
    
        final File workDir;
        try {
          workDir = File.createTempFile("hadoop-unjar", "", tmpDir);
        } catch (IOException ioe) {
          // If user has insufficient perms to write to tmpDir, default
          // "Permission denied" message doesn't specify a filename.
          System.err.println("Error creating temp dir in java.io.tmpdir "
                             + tmpDir + " due to " + ioe.getMessage());
          System.exit(-1);
          return;
        }
    
        if (!workDir.delete()) {
          System.err.println("Delete failed for " + workDir);
          System.exit(-1);
        }
        ensureDirectory(workDir);
    
        ShutdownHookManager.get().addShutdownHook(
            new Runnable() {
              @Override
              public void run() {
                FileUtil.fullyDelete(workDir);
              }
            }, SHUTDOWN_HOOK_PRIORITY);
    
        if (!skipUnjar()) {
          unJar(file, workDir);
        }
    
        ClassLoader loader = createClassLoader(file, workDir);
    
        Thread.currentThread().setContextClassLoader(loader);
        Class<?> mainClass = Class.forName(mainClassName, true, loader);
        Method main = mainClass.getMethod("main", String[].class);
        List<String> newArgsSubList = Arrays.asList(args)
            .subList(firstArg, args.length);
        String[] newArgs = newArgsSubList
            .toArray(new String[newArgsSubList.size()]);
        try {
          main.invoke(null, new Object[] {newArgs});
        } catch (InvocationTargetException e) {
          throw e.getTargetException();
        }
      }

从main方法的方法注释或run方法的实现可以了解到，“运行Hadoop作业jar。 如果主类不在jar的清单Manifest中，那么它必须在命令行中提供。”，所以，我们需要在导出jar的时候，指定其主类Main-Class即可，在Eclipse中注意这一步：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3Nvbmd6ZWhhbw_size_16_color_FFFFFF_t_70 1][]

导出后的新jar，查看META-INF/MANIFEST.MF如下：

Manifest-Version: 1.0
    Main-Class: WordCount

From now on，我们执行hadoop jar便不用在命令行手动指定主类名了。

--------------------

❀完结撒花❀

[https_blog.csdn.net_songzehao_article_details_91044032]: https://blog.csdn.net/songzehao/article/details/91044032
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3Nvbmd6ZWhhbw_size_16_color_FFFFFF_t_70]: /images/20220117/3cef5aad1b35483085d36e410385980d.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3Nvbmd6ZWhhbw_size_16_color_FFFFFF_t_70 1]: /images/20220117/7e51c579387740a8bf3ebd1f908b61bb.png