hadoop平台运行WordCount程序-白红宇

hadoop平台运行WordCount程序

阅读量：4129 次

发布时间：2019-05-25

本文共 7693 字，大约阅读时间需要 25 分钟。

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
   
     {        private final static IntWritable one = new IntWritable(1);        private Text word = new Text();        public void map(LongWritable key, Text value,                OutputCollector
    
      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
     
       {        public void reduce(Text key, Iterator
      
        values,                OutputCollector
       
         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 
        
         ] [-r 
         
          ] 
           
          "); 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
           
             other_args = new ArrayList
            
             (); 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：

[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
/
16
 
22
:
49
:
43
 WARN mapred.JobClient: Use GenericOptionsParser 
for
 parsing the arguments. Applications should implement Tool 
for
 the same.
 
10
/
09
/
16
 
22
:
49
:
43
 INFO mapred.FileInputFormat: Total input paths to process :
2

 
10
/
09
/
16
 
22
:
49
:
43
 INFO mapred.JobClient: Running job: job_201008171228_76165
 
10
/
09
/
16
 
22
:
49
:
44
 INFO mapred.JobClient: map 
0
%
 reduce 
0
%

 
10
/
09
/
16
 
22
:
49
:
47
 INFO mapred.JobClient: map 
100
%
 reduce 
0
%

 
10
/
09
/
16
 
22
:
49
:
54
 INFO mapred.JobClient: map 
100
%
 reduce 
100
%

 
10
/
09
/
16
 
22
:
49
:
55
 INFO mapred.JobClient: Job complete: job_201008171228_76165
 
10
/
09
/
16
 
22
:
49
:
55
 INFO mapred.JobClient: Counters: 
16

 
10
/
09
/
16
 
22
:
49
:
55
 INFO mapred.JobClient: File Systems
 
10
/
09
/
16
 
22
:
49
:
55
 INFO mapred.JobClient: HDFS bytes read
=
62

 
10
/
09
/
16
 
22
:
49
:
55
 INFO mapred.JobClient: HDFS bytes written
=
73

 
10
/
09
/
16
 
22
:
49
:
55
 INFO mapred.JobClient: Local bytes read
=
152

 
10
/
09
/
16
 
22
:
49
:
55
 INFO mapred.JobClient: Local bytes written
=
366

 
10
/
09
/
16
 
22
:
49
:
55
 INFO mapred.JobClient: Job Counters
 
10
/
09
/
16
 
22
:
49
:
55
 INFO mapred.JobClient: Launched reduce tasks
=
1

 
10
/
09
/
16
 
22
:
49
:
55
 INFO mapred.JobClient: Rack
-
local map tasks
=
2

 
10
/
09
/
16
 
22
:
49
:
55
 INFO mapred.JobClient: Launched map tasks
=
2

 
10
/
09
/
16
 
22
:
49
:
55
 INFO mapred.JobClient: Map
-
Reduce Framework
 
10
/
09
/
16
 
22
:
49
:
55
 INFO mapred.JobClient: Reduce input groups
=
11

 
10
/
09
/
16
 
22
:
49
:
55
 INFO mapred.JobClient: Combine output records
=
14

 
10
/
09
/
16
 
22
:
49
:
55
 INFO mapred.JobClient: Map input records
=
4

 
10
/
09
/
16
 
22
:
49
:
55
 INFO mapred.JobClient: Reduce output records
=
11

 
10
/
09
/
16
 
22
:
49
:
55
 INFO mapred.JobClient: Map output bytes
=
118

 
10
/
09
/
16
 
22
:
49
:
55
 INFO mapred.JobClient: Map input bytes
=
62

 
10
/
09
/
16
 
22
:
49
:
55
 INFO mapred.JobClient: Combine input records
=
14

 
10
/
09
/
16
 
22
:
49
:
55
 INFO mapred.JobClient: Map output records
=
14

 
10
/
09
/
16
 
22
:
49
:
55
 INFO mapred.JobClient: Reduce input records
=
14

　　6. 查看运行结果

[admin@host WordCount]$ hadoop fs 
-
ls 
/
tmp
/
output
/

 Found 
2
 items
 drwxr
-
x
---
 
-
 admin admin 
0
 
2010
-
09
-
16
 
22
:
43
 
/
tmp
/
output
/
_logs
 
-
rw
-
r
-----
 
1
 admin admin 
102
 
2010
-
09
-
16
 
22
:
44
 
/
tmp
/
output
/
part
-
00000

 [admin@host WordCount]$ hadoop fs 
-
cat 
/
tmp
/
output
/
part
-
00000

 Hello, 
1

 You 
1

 are 
2

 china 
1

 hello, 
1

 i 
2

 love 
2

 ok 
1

 ok
?
 
1

 word 
1

 you 
1

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

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