hadoop平台运行WordCount程序

吸毒的虫子

　　 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)) {
conf.setNumMapTasks(Integer.parseInt(args[++i]));
} else if ("-r".equals(args)) {
conf.setNumReduceTasks(Integer.parseInt(args[++i]));
} else {
other_args.add(args);
}
} catch (NumberFormatException except) {
System.out.println("ERROR: Integer expected instead of "
+ args);
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：

[admin@host WordCount]$ cat input1.txt

Hello, i love china

are you ok?

[admin@host 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。



[admin@host 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



[admin@host 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

[admin@host 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