Linux下MapReduce编程WordCount练习——使用命令行编译打包运行MapReduce程序

一、WordCount练习

要在 Eclipse 上编译和运行 MapReduce 程序，需要安装 hadoop-eclipse-plugin，参见厦大网址。

1、查看 HDFS 中的文件列表

2、在 Eclipse 中创建 MapReduce 项目WordCount

3、新建类 Class，在 Package 处填写 org.apache.hadoop.examples；在 Name 处填写 WordCount，如图

4、 WordCount.java 这个文件的代码如下

    package org.apache.hadoop.examples;
     
    import java.io.IOException;
    import java.util.Iterator;
    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 {
        public WordCount() {
        }
     
        public static void main(String[] args) throws Exception {
            Configuration conf = new Configuration();
            String[] otherArgs = (new GenericOptionsParser(conf, args)).getRemainingArgs();
            if(otherArgs.length < 2) {
                System.err.println("Usage: wordcount <in> [<in>...] <out>");
                System.exit(2);
            }
     
            Job job = Job.getInstance(conf, "word count");
            job.setJarByClass(WordCount.class);
            job.setMapperClass(WordCount.TokenizerMapper.class);
            job.setCombinerClass(WordCount.IntSumReducer.class);
            job.setReducerClass(WordCount.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]));
            }
     
            FileOutputFormat.setOutputPath(job, new 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 IntSumReducer() {
            }
     
            public void reduce(Text key, Iterable<IntWritable> values, Reducer<Text, IntWritable, Text, IntWritable>.Context context) throws IOException, InterruptedException {
                int sum = 0;
     
                IntWritable val;
                for(Iterator i$ = values.iterator(); i$.hasNext(); sum += val.get()) {
                    val = (IntWritable)i$.next();
                }
     
                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 TokenizerMapper() {
            }
     
            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);
                }
     
            }
        }
    }

5、复制配置文件解决参数设置问题

    cp /usr/local/hadoop/etc/hadoop/core-site.xml ~/workspace/WordCount/src
    cp /usr/local/hadoop/etc/hadoop/hdfs-site.xml ~/workspace/WordCount/src
    cp /usr/local/hadoop/etc/hadoop/log4j.properties ~/workspace/WordCount/src

6、复制完成后，右键点击 WordCount 选择 refresh 进行刷新，可以看到文件结构如下所示

7、设置运行时的相关参数，如图所示

也可以直接在代码中设置好输入参数，如图所示

8、设定参数后，再次运行程序，可以看到运行成功的提示如下所示

9、做到这里，就可以使用 Eclipse 方便的进行 MapReduce程序的开发了

二、编译、打包 Hadoop MapReduce 程序

10、将 Hadoop 的 classhpath 信息添加到 CLASSPATH 变量，执行 source ~/.bashrc 使变量生效

vim  ~/.bashrc
source ~/.bashrc

11、通过 javac 命令编译 WordCount.java
(这里要到你的WordCount.java目录下运行！)

javac WordCount.java

12、把 .class 文件打包成 jar
（这里也在相关目录下运行）

jar -cvf WordCount.jar ./WordCount*.class

如图（生成的jar包）

（我把它拷贝到了workspace/WordCount目录下，方便！）

13、打包完成后，运行，创建几个输入文件

    mkdir input
    echo "echo of the rainbow" > ./input/file0
    echo "the waiting game" > ./input/file1

14、查看input下的文件

ls ./input

15、把本地文件上传到伪分布式HDFS上

/usr/local/hadoop/bin/hadoop fs -put ./input input

16、开始运行(代码中设置了package包名，这里要写全！)

/usr/local/hadoop/bin/hadoop jar WordCount.jar org/apache/hadoop/examples/WordCount input output

终端运行结果

17、Localhost:50070端口查看/user/hadoop/output/结果

终端查看伪分布式下/user/hadoop/output/结果

cd /usr/local/hadoop
./bin/hdfs dfs -ls /user/hadoop/output

18、查看伪分布式下/user/hadoop/output/part-r-00000结果

./bin/hdfs dfs -cat /user/hadoop/output/part-r-00000

三、遇见的问题及解决办法

问题一：出现找不到类的错误

解决办法：这是因为在代码中设置了package包名，这里也要写全（正确的命令：/usr/local/hadoop/bin/hadoop jar WordCount.jar org/apache/hadoop/examples/WordCount input output）
问题二：Exception in thread “main” org.apache.hadoop.mapred.FileAlreadyExistsException: Output directory hdfs://localhost:9000/user/hadoop/output already exists

解决办法：伪分布式下删除运行时自动创建的output

./bin/hdfs dfs -rm -r /user/hadoop/output