我觉得你犯了个错误：

/usr/local/hadoop$ bin/hadoop jar wc.jar WordCount /home/hduser/gutenberg /home/hduser/gutenberg-output/sample.txt

请改为：

/usr/local/hadoop$ bin/hadoop jar wc.jar org.myorg.WordCount /home/hduser/gutenberg /home/hduser/gutenberg-output/sample.txt

这应该管用。
@aswinalagappan：原因是jar文件包含了你的路径。jvm无法在jar文件中找到您的类，因为它位于“jar\org\myorg”路径中。明白吗？

尝试显式地包含嵌套类（即。 TokenizerMapper 以及 IntSumReducer )在jar文件中。我是这样做的：

jar cvf WordCount.jar WordCount.class WordCount\$TokenizerMapper.class WordCount\$IntSumReducer.class

试试这个，

import java.io.IOException;
import java.util.Iterator;
import java.util.StringTokenizer;
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.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.TextInputFormat;
import org.apache.hadoop.mapred.TextOutputFormat;
public class WordCount {
    public static class Map extends MapReduceBase implements
            Mapper<LongWritable, Text, Text, IntWritable> {
        @Override
        public void map(LongWritable key, Text value, OutputCollector<Text, IntWritable> output, Reporter reporter)
                throws IOException {
            String line = value.toString();
            StringTokenizer tokenizer = new StringTokenizer(line);
            while (tokenizer.hasMoreTokens()) {
                value.set(tokenizer.nextToken());
                output.collect(value, new IntWritable(1));
            }
        }
    }
    public static class Reduce extends MapReduceBase implements
            Reducer<Text, IntWritable, Text, IntWritable> {
        @Override
        public void reduce(Text key, Iterator<IntWritable> values,
                OutputCollector<Text, IntWritable> output, Reporter reporter)
                throws IOException {
            int sum = 0;
            while (values.hasNext()) {
                sum += values.next().get();
            }
            output.collect(key, new IntWritable(sum));
        }
    }
    public static void main(String[] args) throws Exception {
        JobConf conf = new JobConf(WordCount.class);
        conf.setJobName("wordcount");
        conf.setOutputKeyClass(Text.class);
        conf.setOutputValueClass(IntWritable.class);
        conf.setMapperClass(Map.class);
        conf.setReducerClass(Reduce.class);
        conf.setInputFormat(TextInputFormat.class);
        conf.setOutputFormat(TextOutputFormat.class);
        FileInputFormat.setInputPaths(conf, new Path(args[0]));
        FileOutputFormat.setOutputPath(conf, new Path(args[1]));
        JobClient.runJob(conf);
    }
}

然后运行命令

bin/hadoop jar WordCount.jar WordCount /hdfs_Input_filename /output_filename

如果您的代码是特定的包，那么您必须用类名来表示包名

bin/hadoop jar WordCount.jar PakageName.WordCount /hdfs_Input_filename /output_filename

kishore的回答，让我朝着正确的方向前进，如果可能的话，我想证实这一点，报告我用java代码做的关于稀疏矩阵MoltiApplication的实验：
1） 源代码（下载自https://github.com/marufaytekin/matrixmultiply/tree/master/src/main/java/com/lendap/hadoop)，并保存在/home/hduser/playground/src/matrixmult中
2） 下载的数据集（矩阵m和n来自https://github.com/marufaytekin/matrixmultiply/tree/master/input)，然后保存在hdfs中，路径如下：/user/hduser/inmatrix
3） 编译hadoop类，在playground/classes5中创建java类：javac-classpath$hadoop\u home/share/hadoop/common/lib/activation-1.1.jar:$hadoop\u home/share/hadoop/common/hadoop-common-2.7.1.jar:/usr/hadoop/hadoop-2.7.1/share/hadoop/mapreduce/-d playground/classes5 playground/src/matrixmult/
4） 使用以下命令创建jar文件matrixmultiply.jar:jar-cvf playground/matrixmultiply.jar-c playground/classes5/。
5） hadoop mapreduce命令（来自$hadoop\u home路径，在我的例子中是/usr/hadoop/hadoop-2.7.1$hadoop jar/home/hduser/playground/matrixmultiply.jar com.lendap.hadoop.matrixmultiply/user/hduser/inmatrix/outputmatrix
6） 在我的4节点群集上正确执行mapreduce作业。这里是最终输出的一部分：
0,375,890.0 0,376,1005.0 0,377,1377.0 0,378,604.0 0,379,924.0 0,38,476.0 0,380,621.0 0,381,730.0
990,225,542.0 990,226,639.0 990,227,466.0 990,228,406.0 990,229,343.0 990,23,397.0 990,230,794.0

这听起来可能很疯狂。我补充道 package org.myorg; 然后重新编译。我将类文件放在org/myorg文件夹中，并使用它们创建jar文件。然后我就用那个 jar wc.jar org.myorg.WordCount 命令并成功执行。如果有人能告诉我它是怎么跑的那就太好了。无论如何，谢谢你们帮我。

你正在课堂上使用package。所以你的命令应该是

bin/hadoop jar wc.jar org.myorg.WordCount /home/hduser/gutenberg /home/hduser/gutenberg-output/sample.txt