hadoop平台运行WordCount程序

1. 经典的WordCound程序（WordCount.java）

import java.io.IOException;
import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;
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.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.util.Tool;
import org.apache.hadoop.util.ToolRunner;

public class WordCount extends Configured implements Tool {

    public static class MapClass extends MapReduceBase implements
            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,
                OutputCollector<Text, IntWritable> output, Reporter reporter)
                throws IOException {
            String line = value.toString();
            StringTokenizer itr = new StringTokenizer(line);
            while (itr.hasMoreTokens()) {
                word.set(itr.nextToken());
                output.collect(word, one);
            }
        }
    }

    /**
     * A reducer class that just emits the sum of the input values.
     */
    public static class Reduce extends MapReduceBase implements
            Reducer<Text, IntWritable, Text, IntWritable> {

        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));
        }
    }

    static int printUsage() {
        System.out.println("wordcount [-m <maps>] [-r <reduces>] <input> <output>");
        ToolRunner.printGenericCommandUsage(System.out);
        return -1;
    }

    /**
     * The main driver for word count map/reduce program. Invoke this method to
     * submit the map/reduce job.
     *
     * @throws IOException
     *             When there is communication problems with the job tracker.
     */
    public int run(String[] args) throws Exception {
        JobConf conf = new JobConf(getConf(), WordCount.class);
        conf.setJobName("wordcount");

        // the keys are words (strings)
        conf.setOutputKeyClass(Text.class);
        // the values are counts (ints)
        conf.setOutputValueClass(IntWritable.class);

        conf.setMapperClass(MapClass.class);
        conf.setCombinerClass(Reduce.class);
        conf.setReducerClass(Reduce.class);

        List<String> other_args = new ArrayList<String>();
        for (int i = 0; i < args.length; ++i) {
            try {
                if ("-m".equals(args[i])) {
                    conf.setNumMapTasks(Integer.parseInt(args[++i]));
                } else if ("-r".equals(args[i])) {
                    conf.setNumReduceTasks(Integer.parseInt(args[++i]));
                } else {
                    other_args.add(args[i]);
                }
            } catch (NumberFormatException except) {
                System.out.println("ERROR: Integer expected instead of "
                        + args[i]);
                return printUsage();
            } catch (ArrayIndexOutOfBoundsException except) {
                System.out.println("ERROR: Required parameter missing from "
                        + args[i - 1]);
                return printUsage();
            }
        }

        // Make sure there are exactly 2 parameters left.
        if (other_args.size() != 2) {
            System.out.println("ERROR: Wrong number of parameters: "
                    + other_args.size() + " instead of 2.");
            return printUsage();
        }
        FileInputFormat.setInputPaths(conf, other_args.get(0));
        FileOutputFormat.setOutputPath(conf, new Path(other_args.get(1)));

        JobClient.runJob(conf);
        return 0;
    }

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

}

2. 保证hadoop集群是配置好了的，单机的也好。新建一个目录，比如 /home/admin/WordCount编译WordCount.java程序。

javac -classpath /home/admin/hadoop/hadoop-0.19.1-core.jar WordCount.java -d /home/admin/WordCount

3. 编译完后在/home/admin/WordCount目录会发现三个class文件 WordCount.class，WordCount$Map.class，WordCount$Reduce.class。
　　cd 进入 /home/admin/WordCount目录,然后执行：

jar cvf WordCount.jar*.class

　　就会生成 WordCount.jar 文件。

　　4. 构造一些输入数据
　　input1.txt和input2.txt的文件里面是一些单词。如下：

[[email protected] WordCount]$ cat input1.txt
Hello, i love china
are you ok?
[[email protected] WordCount]$ cat input2.txt
hello, i love word
You are ok

　　在hadoop上新建目录，和put程序运行所需要的输入文件:

hadoop fs-mkdir/tmp/input
hadoop fs-mkdir/tmp/output
hadoop fs-put input1.txt/tmp/input/
hadoop fs-put input2.txt/tmp/input/

　　5. 运行程序,会显示job运行时的一些信息。

[[email protected] WordCount]$ hadoop jar WordCount.jar WordCount/tmp/input/tmp/output
10/09/1622:49:43WARN mapred.JobClient: Use GenericOptionsParserforparsing the arguments. Applications should implement Toolforthe same.
10/09/1622:49:43INFO mapred.FileInputFormat: Total input paths to process :2
10/09/1622:49:43INFO mapred.JobClient: Running job: job_201008171228_76165
10/09/1622:49:44INFO mapred.JobClient: map0%reduce0%
10/09/1622:49:47INFO mapred.JobClient: map100%reduce0%
10/09/1622:49:54INFO mapred.JobClient: map100%reduce100%
10/09/1622:49:55INFO mapred.JobClient: Job complete: job_201008171228_76165
10/09/1622:49:55INFO mapred.JobClient: Counters:16
10/09/1622:49:55INFO mapred.JobClient: File Systems
10/09/1622:49:55INFO mapred.JobClient: HDFS bytes read=62
10/09/1622:49:55INFO mapred.JobClient: HDFS bytes written=73
10/09/1622:49:55INFO mapred.JobClient: Local bytes read=152
10/09/1622:49:55INFO mapred.JobClient: Local bytes written=366
10/09/1622:49:55INFO mapred.JobClient: Job Counters
10/09/1622:49:55INFO mapred.JobClient: Launched reduce tasks=1
10/09/1622:49:55INFO mapred.JobClient: Rack-local map tasks=2
10/09/1622:49:55INFO mapred.JobClient: Launched map tasks=2
10/09/1622:49:55INFO mapred.JobClient: Map-Reduce Framework
10/09/1622:49:55INFO mapred.JobClient: Reduce input groups=11
10/09/1622:49:55INFO mapred.JobClient: Combine output records=14
10/09/1622:49:55INFO mapred.JobClient: Map input records=4
10/09/1622:49:55INFO mapred.JobClient: Reduce output records=11
10/09/1622:49:55INFO mapred.JobClient: Map output bytes=118
10/09/1622:49:55INFO mapred.JobClient: Map input bytes=62
10/09/1622:49:55INFO mapred.JobClient: Combine input records=14
10/09/1622:49:55INFO mapred.JobClient: Map output records=14
10/09/1622:49:55INFO mapred.JobClient: Reduce input records=14

　　6. 查看运行结果

[[email protected] WordCount]$ hadoop fs-ls/tmp/output/
Found2items
drwxr-x----admin admin02010-09-1622:43/tmp/output/_logs
-rw-r-----1admin admin1022010-09-1622:44/tmp/output/part-00000
[[email protected] WordCount]$ hadoop fs-cat/tmp/output/part-00000
Hello,1
You1
are2
china1
hello,1
i2
love2
ok1
ok?1
word1
you1

其中可能出现的问题

1：java.io.FileNotFoundException

这个异常是因为目录创建上有问题，于是重新检查了下目录，发现自己弄成/opt/hadoop/tmp/inout。而是/tmp/input

2：org.apache.hadoop.mapred.FileAlreadyExistsException

这个异常主要是因为上一个导致的，因为hadoop 由于进行的是耗费资源的计算，生产的结果默认是不能被覆盖的，因此中间结果输出目录一定不能存在，否则出现这个错误。

于是就执行命令删除output文件 /opt/hadoop/bin/hadoop fs -rmr /tmp/output

3:ERROR namenode.NameNode: java.io.IOException: Cannot create directory /usr/local/hadoop-datastore/hadoop-hadoop/dfs/name/current

是因为hadoop-database 文件夹没有获取权限