solution:No job file jar和ClassNotFoundException(hadoop,mapreduce)

时间：2014-12-10 18:11:28 阅读：300 评论：0 收藏：0 [点我收藏+]

hadoop-1.2.1伪分布式搭建好了，也只是用命令跑过hadoop-example.jar包的wordcount，这一切看起来so easy。
但没想到的是，自己的mr程序，运行起来却遇到了No job file jar和ClassNotFoundException的问题。
经过一番周折，自己写的mapreduce 终于成功运行了。
我没有将第三方jar包（hadoop-core，commons-cli，commons-xxx等6个jar包）和自己的代码的jar包全部都添加到远程集群上，在本地也没有将第三方jar包打成third-party.jar，也没有用到“-libjars”参数，连GenericOptionsParser也没使用（网上很多solution都说这个用来解析hadoop的命令参数），，
关键代码：
        Job job = new Job(getConf());
        job.setJarByClass(WordCountJob.class)；
和
       int res = ToolRunner.run(new WordCountJob(),args);

source code：
package wordcount2;

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.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.util.GenericOptionsParser;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class WordCountJob extends Configured implements Tool {


    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);
        }
    }





    @Override
    public int run(String[] args) throws Exception {
        // TODO Auto-generated method stub


//        Configuration conf = new Configuration();
//        String[] otherArgs = new GenericOptionsParser(conf,args).getRemainingArgs();
        if(args.length !=2){
            System.err.println("Usage:wordcount <in> <out>");
            System.exit(2);
        }


//        Job job = new Job(conf,"wordcountmr");
        Job job = new Job(getConf());

        job.setJarByClass(WordCountJob.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(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

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

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

编译成jar包，可以使用命令（javac -classpath /home/lzc/hadoop-1.2.1/hadoop-core-1.2.1.jar:/home/lzc/hadoop-1.2.1/lib/commons-cli-1.2.jar -d ./classes/ ./src/WordCountJob.java以及jar -cvfm wordcountjob.jar -C ./classes/两个命令），最简单的方式是使用eclipse的导出jar文件功能，单独将该class生成一个jar文件。

把生成的jar包cp到hadoop_home下，执行以下命令。

hadoop121@ubuntu:~/Dolphin/hadoop-1.2.1$ bin/hadoop jar wc2.jar wordcount2.WordCountJob input/file*.txt output
14/12/10 15:48:59 INFO input.FileInputFormat: Total input paths to process : 2
14/12/10 15:48:59 INFO util.NativeCodeLoader: Loaded the native-hadoop library
14/12/10 15:48:59 WARN snappy.LoadSnappy: Snappy native library not loaded
14/12/10 15:49:00 INFO mapred.JobClient: Running job: job_201412080836_0026
14/12/10 15:49:01 INFO mapred.JobClient: map 0% reduce 0%
14/12/10 15:49:06 INFO mapred.JobClient: map 100% reduce 0%
14/12/10 15:49:13 INFO mapred.JobClient: map 100% reduce 33%
14/12/10 15:49:15 INFO mapred.JobClient: map 100% reduce 100%
14/12/10 15:49:15 INFO mapred.JobClient: Job complete: job_201412080836_0026
14/12/10 15:49:15 INFO mapred.JobClient: Counters: 29
14/12/10 15:49:15 INFO mapred.JobClient:   Job Counters
14/12/10 15:49:15 INFO mapred.JobClient:     Launched reduce tasks=1
14/12/10 15:49:15 INFO mapred.JobClient:     SLOTS_MILLIS_MAPS=7921
14/12/10 15:49:15 INFO mapred.JobClient:     Total time spent by all reduces waiting after reserving slots (ms)=0
14/12/10 15:49:15 INFO mapred.JobClient:     Total time spent by all maps waiting after reserving slots (ms)=0
14/12/10 15:49:15 INFO mapred.JobClient:     Launched map tasks=2
14/12/10 15:49:15 INFO mapred.JobClient:     Data-local map tasks=2
14/12/10 15:49:15 INFO mapred.JobClient:     SLOTS_MILLIS_REDUCES=9018
14/12/10 15:49:15 INFO mapred.JobClient:   File Output Format Counters
14/12/10 15:49:15 INFO mapred.JobClient:     Bytes Written=48
14/12/10 15:49:15 INFO mapred.JobClient:   FileSystemCounters
14/12/10 15:49:15 INFO mapred.JobClient:     FILE_BYTES_READ=102
14/12/10 15:49:15 INFO mapred.JobClient:     HDFS_BYTES_READ=284
14/12/10 15:49:15 INFO mapred.JobClient:     FILE_BYTES_WRITTEN=190665
14/12/10 15:49:15 INFO mapred.JobClient:     HDFS_BYTES_WRITTEN=48
14/12/10 15:49:15 INFO mapred.JobClient:   File Input Format Counters
14/12/10 15:49:15 INFO mapred.JobClient:     Bytes Read=48
14/12/10 15:49:15 INFO mapred.JobClient:   Map-Reduce Framework
14/12/10 15:49:15 INFO mapred.JobClient:     Map output materialized bytes=108
14/12/10 15:49:15 INFO mapred.JobClient:     Map input records=2
14/12/10 15:49:15 INFO mapred.JobClient:     Reduce shuffle bytes=108
14/12/10 15:49:15 INFO mapred.JobClient:     Spilled Records=16
14/12/10 15:49:15 INFO mapred.JobClient:     Map output bytes=80
14/12/10 15:49:15 INFO mapred.JobClient:     CPU time spent (ms)=2420
14/12/10 15:49:15 INFO mapred.JobClient:     Total committed heap usage (bytes)=390004736
14/12/10 15:49:15 INFO mapred.JobClient:     Combine input records=8
14/12/10 15:49:15 INFO mapred.JobClient:     SPLIT_RAW_BYTES=236
14/12/10 15:49:15 INFO mapred.JobClient:     Reduce input records=8
14/12/10 15:49:15 INFO mapred.JobClient:     Reduce input groups=6
14/12/10 15:49:15 INFO mapred.JobClient:     Combine output records=8
14/12/10 15:49:15 INFO mapred.JobClient:     Physical memory (bytes) snapshot=436707328
14/12/10 15:49:15 INFO mapred.JobClient:     Reduce output records=6
14/12/10 15:49:15 INFO mapred.JobClient:     Virtual memory (bytes) snapshot=1908416512
14/12/10 15:49:15 INFO mapred.JobClient:     Map output records=8
hadoop121@ubuntu:~/Dolphin/hadoop-1.2.1$ bin/hadoop fs -ls output
Found 3 items
-rw-r--r--   2 hadoop121 supergroup          0 2014-12-10 15:49 /user/hadoop121/output/_SUCCESS
drwxr-xr-x   - hadoop121 supergroup          0 2014-12-10 15:49 /user/hadoop121/output/_logs
-rw-r--r--   2 hadoop121 supergroup         48 2014-12-10 15:49 /user/hadoop121/output/part-r-00000
hadoop121@ubuntu:~/Dolphin/hadoop-1.2.1$ bin/hadoop fs -cat output/part-r-00000
Hadoop    1
Hello    2
Word    1
hadoop    1
hello    2
word    1

有人说hdfs不能访问本地文件，有权限问题，但我特意试了下，本地一样成功执行。
hadoop121@ubuntu:~/Dolphin/hadoop-1.2.1$ bin/hadoop jar /home/lzc/workspace/wordcount1/wc2.jar wordcount2.WordCountJob input/file*.txt output
14/12/10 16:08:26 INFO input.FileInputFormat: Total input paths to process : 2
14/12/10 16:08:26 INFO util.NativeCodeLoader: Loaded the native-hadoop library
14/12/10 16:08:26 WARN snappy.LoadSnappy: Snappy native library not loaded
14/12/10 16:08:27 INFO mapred.JobClient: Running job: job_201412080836_0027
14/12/10 16:08:28 INFO mapred.JobClient: map 0% reduce 0%
14/12/10 16:08:33 INFO mapred.JobClient: map 100% reduce 0%
14/12/10 16:08:40 INFO mapred.JobClient: map 100% reduce 33%
14/12/10 16:08:41 INFO mapred.JobClient: map 100% reduce 100%
14/12/10 16:08:42 INFO mapred.JobClient: Job complete: job_201412080836_0027
14/12/10 16:08:42 INFO mapred.JobClient: Counters: 29
14/12/10 16:08:42 INFO mapred.JobClient:   Job Counters
14/12/10 16:08:42 INFO mapred.JobClient:     Launched reduce tasks=1
14/12/10 16:08:42 INFO mapred.JobClient:     SLOTS_MILLIS_MAPS=7221
14/12/10 16:08:42 INFO mapred.JobClient:     Total time spent by all reduces waiting after reserving slots (ms)=0
14/12/10 16:08:42 INFO mapred.JobClient:     Total time spent by all maps waiting after reserving slots (ms)=0
14/12/10 16:08:42 INFO mapred.JobClient:     Launched map tasks=2
14/12/10 16:08:42 INFO mapred.JobClient:     Data-local map tasks=2
14/12/10 16:08:42 INFO mapred.JobClient:     SLOTS_MILLIS_REDUCES=8677
14/12/10 16:08:42 INFO mapred.JobClient:   File Output Format Counters
14/12/10 16:08:42 INFO mapred.JobClient:     Bytes Written=48
14/12/10 16:08:42 INFO mapred.JobClient:   FileSystemCounters
14/12/10 16:08:42 INFO mapred.JobClient:     FILE_BYTES_READ=102
14/12/10 16:08:42 INFO mapred.JobClient:     HDFS_BYTES_READ=284
14/12/10 16:08:42 INFO mapred.JobClient:     FILE_BYTES_WRITTEN=190665
14/12/10 16:08:42 INFO mapred.JobClient:     HDFS_BYTES_WRITTEN=48
14/12/10 16:08:42 INFO mapred.JobClient:   File Input Format Counters
14/12/10 16:08:42 INFO mapred.JobClient:     Bytes Read=48
14/12/10 16:08:42 INFO mapred.JobClient:   Map-Reduce Framework
14/12/10 16:08:42 INFO mapred.JobClient:     Map output materialized bytes=108
14/12/10 16:08:42 INFO mapred.JobClient:     Map input records=2
14/12/10 16:08:42 INFO mapred.JobClient:     Reduce shuffle bytes=108
14/12/10 16:08:42 INFO mapred.JobClient:     Spilled Records=16
14/12/10 16:08:42 INFO mapred.JobClient:     Map output bytes=80
14/12/10 16:08:42 INFO mapred.JobClient:     CPU time spent (ms)=2280
14/12/10 16:08:42 INFO mapred.JobClient:     Total committed heap usage (bytes)=373489664
14/12/10 16:08:42 INFO mapred.JobClient:     Combine input records=8
14/12/10 16:08:42 INFO mapred.JobClient:     SPLIT_RAW_BYTES=236
14/12/10 16:08:42 INFO mapred.JobClient:     Reduce input records=8
14/12/10 16:08:42 INFO mapred.JobClient:     Reduce input groups=6
14/12/10 16:08:42 INFO mapred.JobClient:     Combine output records=8
14/12/10 16:08:42 INFO mapred.JobClient:     Physical memory (bytes) snapshot=433147904
14/12/10 16:08:42 INFO mapred.JobClient:     Reduce output records=6
14/12/10 16:08:42 INFO mapred.JobClient:     Virtual memory (bytes) snapshot=1911033856
14/12/10 16:08:42 INFO mapred.JobClient:     Map output records=8
hadoop121@ubuntu:~/Dolphin/hadoop-1.2.1$

references：

1.http://dongxicheng.org/mapreduce/run-hadoop-job-problems/

2.http://lucene.472066.n3.nabble.com/Trouble-with-Word-Count-example-td4023269.html

3.http://stackoverflow.com/questions/22850532/warn-mapred-jobclient-no-job-jar-file-set-user-classes-may-not-be-found

solution:No job file jar和ClassNotFoundException(hadoop,mapreduce)

标签：mapreduce hdfs ubuntu 集群

原文地址：http://blog.csdn.net/cleverlzc/article/details/41847781

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