jvm+windows+cygwin+eclipse+hadoop配置篇

1 整个过程视频教程：http://v.youku.com/v_show/id_XMzc5MzM1NDQw.html

下载地址：http://pan.baidu.com/share/link?shareid=211927&uk=1678594189

2 cygwin的下载网址：http://www.cygwin.com

3 cygwin的vim设置：http://blog.163.com/xjx_user/blog/static/21493137720130104037220/

注意".vimrc" 放在自己的目录下 首先通过cd ~ 切换到自己的目录 然以后vi .vimrc 然后设置

截图： 

打开.c文件后为：

4 Cygwin下运行ssh-host-config（安全外壳协议，secureshell 加密后传输 一般的ftp,pop telnet是没有加密的）参考网址

http://blog.sina.com.cn/s/blog_62adf3670101c0bw.html

http://www.cnblogs.com/xjx-user/archive/2013/01/09/2852201.html

登录ssh方式为：ssh localhost 就可以使用who命令了。

5 cygin上安装gcc工具链：http://www.cnblogs.com/xjx-user/archive/2013/01/09/2852204.html

注意，一般下载与安装要分开重做一遍。否则容易出错。即使下载完全也可能提示出错。

6 hadoop下载地址：http://www.apache.org/dist/hadoop/core/ 

7 在eclipse中配置hadoop插件:

http://www.cnblogs.com/xjx-user/archive/2013/01/09/2852205.html

8 windows7下eclipse与hadoop连接时产生的没有权限需要更改的文件hadoop-core-1.0.4.jar

网址：http://download.csdn.net/download/snow_eagle_howard/4842134 

免费下载地址：http://pan.baidu.com/share/link?shareid=211924&uk=1678594189

9 hadoop启动的代码：到hadoop目录下   ./start-all.sh      然后就可以在bin目录下运行./hadoop dfsadmin -report

10 wordcount的代码：http://www.cnblogs.com/xjx-user/archive/2013/01/09/2852205.html

11  wordcount个人运行结果：

注意 运行前要在cygwin下先启动hadoop 同时保证cygwin服务已启动 同时保证ssh可用 如果之前已经有输出文件 output/1目录已经存在 要先删除

View Code 
13/01/09 01:26:13 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
13/01/09 01:26:13 WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
13/01/09 01:26:13 INFO input.FileInputFormat: Total input paths to process : 5
13/01/09 01:26:14 WARN snappy.LoadSnappy: Snappy native library not loaded
13/01/09 01:26:14 INFO mapred.JobClient: Running job: job_local_0001
13/01/09 01:26:14 INFO mapred.Task:  Using ResourceCalculatorPlugin : null
13/01/09 01:26:14 INFO mapred.MapTask: io.sort.mb = 100
13/01/09 01:26:14 INFO mapred.MapTask: data buffer = 79691776/99614720
13/01/09 01:26:14 INFO mapred.MapTask: record buffer = 262144/327680
13/01/09 01:26:14 INFO mapred.MapTask: Starting flush of map output
13/01/09 01:26:14 INFO mapred.MapTask: Finished spill 0
13/01/09 01:26:14 INFO mapred.Task: Task:attempt_local_0001_m_000000_0 is done. And is in the process of commiting
13/01/09 01:26:15 INFO mapred.JobClient:  map 0% reduce 0%
13/01/09 01:26:17 INFO mapred.LocalJobRunner: 
13/01/09 01:26:17 INFO mapred.Task: Task 'attempt_local_0001_m_000000_0' done.
13/01/09 01:26:17 INFO mapred.Task:  Using ResourceCalculatorPlugin : null
13/01/09 01:26:17 INFO mapred.MapTask: io.sort.mb = 100
13/01/09 01:26:17 INFO mapred.MapTask: data buffer = 79691776/99614720
13/01/09 01:26:17 INFO mapred.MapTask: record buffer = 262144/327680
13/01/09 01:26:17 INFO mapred.MapTask: Starting flush of map output
13/01/09 01:26:17 INFO mapred.MapTask: Finished spill 0
13/01/09 01:26:17 INFO mapred.Task: Task:attempt_local_0001_m_000001_0 is done. And is in the process of commiting
13/01/09 01:26:18 INFO mapred.JobClient:  map 100% reduce 0%
13/01/09 01:26:20 INFO mapred.LocalJobRunner: 
13/01/09 01:26:20 INFO mapred.Task: Task 'attempt_local_0001_m_000001_0' done.
13/01/09 01:26:20 INFO mapred.Task:  Using ResourceCalculatorPlugin : null
13/01/09 01:26:20 INFO mapred.MapTask: io.sort.mb = 100
13/01/09 01:26:20 INFO mapred.MapTask: data buffer = 79691776/99614720
13/01/09 01:26:20 INFO mapred.MapTask: record buffer = 262144/327680
13/01/09 01:26:20 INFO mapred.MapTask: Starting flush of map output
13/01/09 01:26:20 INFO mapred.MapTask: Finished spill 0
13/01/09 01:26:20 INFO mapred.Task: Task:attempt_local_0001_m_000002_0 is done. And is in the process of commiting
13/01/09 01:26:23 INFO mapred.LocalJobRunner: 
13/01/09 01:26:23 INFO mapred.Task: Task 'attempt_local_0001_m_000002_0' done.
13/01/09 01:26:23 INFO mapred.Task:  Using ResourceCalculatorPlugin : null
13/01/09 01:26:23 INFO mapred.MapTask: io.sort.mb = 100
13/01/09 01:26:23 INFO mapred.MapTask: data buffer = 79691776/99614720
13/01/09 01:26:23 INFO mapred.MapTask: record buffer = 262144/327680
13/01/09 01:26:23 INFO mapred.MapTask: Starting flush of map output
13/01/09 01:26:23 INFO mapred.MapTask: Finished spill 0
13/01/09 01:26:23 INFO mapred.Task: Task:attempt_local_0001_m_000003_0 is done. And is in the process of commiting
13/01/09 01:26:26 INFO mapred.LocalJobRunner: 
13/01/09 01:26:26 INFO mapred.Task: Task 'attempt_local_0001_m_000003_0' done.
13/01/09 01:26:26 INFO mapred.Task:  Using ResourceCalculatorPlugin : null
13/01/09 01:26:26 INFO mapred.MapTask: io.sort.mb = 100
13/01/09 01:26:26 INFO mapred.MapTask: data buffer = 79691776/99614720
13/01/09 01:26:26 INFO mapred.MapTask: record buffer = 262144/327680
13/01/09 01:26:26 INFO mapred.MapTask: Starting flush of map output
13/01/09 01:26:26 INFO mapred.MapTask: Finished spill 0
13/01/09 01:26:26 INFO mapred.Task: Task:attempt_local_0001_m_000004_0 is done. And is in the process of commiting
13/01/09 01:26:29 INFO mapred.LocalJobRunner: 
13/01/09 01:26:29 INFO mapred.Task: Task 'attempt_local_0001_m_000004_0' done.
13/01/09 01:26:29 INFO mapred.Task:  Using ResourceCalculatorPlugin : null
13/01/09 01:26:29 INFO mapred.LocalJobRunner: 
13/01/09 01:26:29 INFO mapred.Merger: Merging 5 sorted segments
13/01/09 01:26:29 INFO mapred.Merger: Down to the last merge-pass, with 5 segments left of total size: 2065 bytes
13/01/09 01:26:29 INFO mapred.LocalJobRunner: 
13/01/09 01:26:29 INFO mapred.Task: Task:attempt_local_0001_r_000000_0 is done. And is in the process of commiting
13/01/09 01:26:29 INFO mapred.LocalJobRunner: 
13/01/09 01:26:29 INFO mapred.Task: Task attempt_local_0001_r_000000_0 is allowed to commit now
13/01/09 01:26:29 INFO output.FileOutputCommitter: Saved output of task 'attempt_local_0001_r_000000_0' to /mapreduce/wordcount/output/1
13/01/09 01:26:32 INFO mapred.LocalJobRunner: reduce > reduce
13/01/09 01:26:32 INFO mapred.Task: Task 'attempt_local_0001_r_000000_0' done.
13/01/09 01:26:33 INFO mapred.JobClient:  map 100% reduce 100%
13/01/09 01:26:33 INFO mapred.JobClient: Job complete: job_local_0001
13/01/09 01:26:33 INFO mapred.JobClient: Counters: 19
13/01/09 01:26:33 INFO mapred.JobClient:   File Output Format Counters 
13/01/09 01:26:33 INFO mapred.JobClient:     Bytes Written=1485
13/01/09 01:26:33 INFO mapred.JobClient:   FileSystemCounters
13/01/09 01:26:33 INFO mapred.JobClient:     FILE_BYTES_READ=6117827
13/01/09 01:26:33 INFO mapred.JobClient:     HDFS_BYTES_READ=4960
13/01/09 01:26:33 INFO mapred.JobClient:     FILE_BYTES_WRITTEN=6423845
13/01/09 01:26:33 INFO mapred.JobClient:     HDFS_BYTES_WRITTEN=1485
13/01/09 01:26:33 INFO mapred.JobClient:   File Input Format Counters 
13/01/09 01:26:33 INFO mapred.JobClient:     Bytes Read=1036
13/01/09 01:26:33 INFO mapred.JobClient:   Map-Reduce Framework
13/01/09 01:26:33 INFO mapred.JobClient:     Map output materialized bytes=2085
13/01/09 01:26:33 INFO mapred.JobClient:     Map input records=15
13/01/09 01:26:33 INFO mapred.JobClient:     Reduce shuffle bytes=0
13/01/09 01:26:33 INFO mapred.JobClient:     Spilled Records=216
13/01/09 01:26:33 INFO mapred.JobClient:     Map output bytes=1835
13/01/09 01:26:33 INFO mapred.JobClient:     Total committed heap usage (bytes)=986734592
13/01/09 01:26:33 INFO mapred.JobClient:     SPLIT_RAW_BYTES=605
13/01/09 01:26:33 INFO mapred.JobClient:     Combine input records=0
13/01/09 01:26:33 INFO mapred.JobClient:     Reduce input records=108
13/01/09 01:26:33 INFO mapred.JobClient:     Reduce input groups=87
13/01/09 01:26:33 INFO mapred.JobClient:     Combine output records=0
13/01/09 01:26:33 INFO mapred.JobClient:     Reduce output records=87
13/01/09 01:26:33 INFO mapred.JobClient:     Map output records=108

 

12 编程实现对hdfs中文件的操作

代码：

View Code 
 import java.io.IOException;
 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.LongWritable;
 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;
 
 public class WordCount {
     public static class TokenizerMapper extends 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, Context context)
                 throws IOException, InterruptedException {
             StringTokenizer itr = new StringTokenizer(value.toString());
             while (itr.hasMoreTokens()) {
                 word.set(itr.nextToken());
                 context.write(word, one);
             }
         }
     }
 
     public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
         private IntWritable result = new IntWritable();
 
         public void reduce(Text key, Iterable<IntWritable> values, Context context)
                 throws IOException, InterruptedException {
             int sum = 0;
             for (IntWritable val : values) {
                 sum += val.get();
             }
             result.set(sum);
             context.write(key, result);
         }
     }
 
     public static void main(String[] args) throws Exception {
         Configuration conf = new Configuration();
         if (args.length != 2) {
             System.err.println("Usage: wordcount  ");
             System.exit(2);
         }
 
         Job job = new Job(conf, "word count");
         job.setJarByClass(WordCount.class);
         job.setMapperClass(TokenizerMapper.class);
         job.setReducerClass(IntSumReducer.class);
         job.setMapOutputKeyClass(Text.class);
         job.setMapOutputValueClass(IntWritable.class);
         job.setOutputKeyClass(Text.class);
         job.setOutputValueClass(IntWritable.class);
 
         FileInputFormat.addInputPath(job, new Path(args[0]));
         FileOutputFormat.setOutputPath(job, new Path(args[1]));
 
         System.exit(job.waitForCompletion(true) ? 0 : 1);
 
     }
 
 }

 运行结果

13 sequenceFile（顺序文件）的读写 这里只实现了写（mapfile文件的读写则类似）：

代码：

View Code 
import java.net.URI;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataInputStream;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IOUtils;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.SequenceFile;
import org.apache.hadoop.io.Text;
public class SequenceFileWriteDemo {
    private static final String[] DATA=
        {
        "one,teo,buckle my shoe",
        "Three,four,shut the door",
        "Five,six,pick up sticks",
        "Seven,eight,lay them straight",
        "Nine,ten,a big fat hen"
        };
    public static void main(String[] args) throws Exception{
        String uri=args[0];
        Configuration conf =new Configuration();
        FileSystem fs=FileSystem.get(URI.create(uri),conf);
        Path path=new Path(uri);
        IntWritable key=new IntWritable();
        Text value=new Text();
        SequenceFile.Writer writer=null;
        try
        {
            writer=SequenceFile.createWriter(fs, conf, path,key.getClass(),value.getClass());
            for(int i=0;i<100;i++)
            {
            key.set(100-i);
            value.set(DATA[i%DATA.length]);
            System.out.printf("[%s]\t%s\t%s\n",writer.getLength(),key,value);
            writer.append(key, value);
            }
        }
        
        finally{
        IOUtils.closeStream(writer);
        }
        }
}

运行eclipse结果：

 

 之后通过cygin的读命令来查看(也可以通过编程来实现查看，注意是sequencefile文件，所以直接在windwos下记事本打开会出现乱码)：

 

hadoop的网络用户界面：

JobTracker：（http://jobtracker-host:50030),方便跟踪Job工作进程，查看工作统计和日志；http://localhost:50030/

NameNode: （http://jobtracker-host:50070),查看NameNode的基本情况，HDFS中的内容，NameNode日志   http://localhost:50070/

posted @ 2013-01-09 01:41  xjx_user  阅读(802)  评论(0)  编辑  收藏  举报