Hadoop 实战之MapReduce链接作业之预处理

环境：Vmware 8.0 和Ubuntu11.04

Hadoop 实战之MapReduce链接作业之预处理

第一步：首先创建一个工程命名为HadoopTest.目录结构如下图:

第二步: 在/home/tanglg1987目录下新建一个start.sh脚本文件，每次启动虚拟机都要删除/tmp目录下的全部文件，重新格式化namenode，代码如下：

 sudo rm -rf /tmp/*
 rm -rf /home/tanglg1987/hadoop-0.20.2/logs
 hadoop namenode -format
 hadoop datanode -format
 start-all.sh
 hadoop fs -mkdir input
 hadoop dfsadmin -safemode leave

 

  第三步：给start.sh增加执行权限并启动hadoop伪分布式集群，代码如下:

 chmod 777 /home/tanglg1987/start.sh
 ./start.sh

执行过程如下：

第四步：上传本地文件到hdfs

在/home/tanglg1987目录下新建Customer.txt内容如下：

 100 tom 90
 101 mary 85
 102 kate 60

上传本地文件到hdfs：

 hadoop fs -put /home/tanglg1987/ChainMapper.txt input

第五步：新建一个ChainMapperDemo.java，代码如下：

 package com.baison.action;
 import java.io.IOException;
 import java.util.*;
 import java.lang.String;
 import org.apache.hadoop.fs.Path;
 import org.apache.hadoop.conf.*;
 import org.apache.hadoop.io.*;
 import org.apache.hadoop.mapred.*;
 import org.apache.hadoop.util.*;
 import org.apache.hadoop.mapred.lib.*;
 public class ChainMapperDemo {
     public static class Map00 extends MapReduceBase implements
             Mapper<Text, Text, Text, Text> {
         public void map(Text key, Text value, OutputCollector output,
                 Reporter reporter) throws IOException {
             Text ft = new Text("100");
             if (!key.equals(ft)) {
                 output.collect(key, value);
             }
         }
     }
     public static class Map01 extends MapReduceBase implements
             Mapper<Text, Text, Text, Text> {
         public void map(Text key, Text value, OutputCollector output,
                 Reporter reporter) throws IOException {
             Text ft = new Text("101");
             if (!key.equals(ft)) {
                 output.collect(key, value);
             }
         }
     }
     public static class Reduce extends MapReduceBase implements
             Reducer<Text, Text, Text, Text> {
         public void reduce(Text key, Iterator values, OutputCollector output,
                 Reporter reporter) throws IOException {
             while (values.hasNext()) {
                 output.collect(key, values.next());
             }

         }
     }
     public static void main(String[] args) throws Exception {
         String[] arg = { "hdfs://localhost:9100/user/tanglg1987/input/ChainMapper.txt",
                 "hdfs://localhost:9100/user/tanglg1987/output" };
         JobConf conf = new JobConf(ChainMapperDemo.class);
         conf.setJobName("ChainMapperDemo");
         conf.setInputFormat(KeyValueTextInputFormat.class);
         conf.setOutputFormat(TextOutputFormat.class);
         ChainMapper cm = new ChainMapper();
         JobConf mapAConf = new JobConf(false);
         cm.addMapper(conf, Map00.class, Text.class, Text.class, Text.class,
                 Text.class, true, mapAConf);
         JobConf mapBConf = new JobConf(false);
         cm.addMapper(conf, Map01.class, Text.class, Text.class, Text.class,
                 Text.class, true, mapBConf);
         conf.setReducerClass(Reduce.class);
         conf.setOutputKeyClass(Text.class);
         conf.setOutputValueClass(Text.class);
         FileInputFormat.setInputPaths(conf, new Path(arg[0]));
         FileOutputFormat.setOutputPath(conf, new Path(arg[1]));
         JobClient.runJob(conf);
     }
 }

第六步：Run On Hadoop，运行过程如下：

12/10/17 21:05:53 INFO jvm.JvmMetrics: Initializing JVM Metrics with processName=JobTracker, sessionId=
12/10/17 21:05:53 WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
12/10/17 21:05:53 WARN mapred.JobClient: No job jar file set.  User classes may not be found. See JobConf(Class) or JobConf#setJar(String).
12/10/17 21:05:54 INFO mapred.FileInputFormat: Total input paths to process : 1
12/10/17 21:05:54 INFO mapred.JobClient: Running job: job_local_0001
12/10/17 21:05:54 INFO mapred.FileInputFormat: Total input paths to process : 1
12/10/17 21:05:54 INFO mapred.MapTask: numReduceTasks: 1
12/10/17 21:05:54 INFO mapred.MapTask: io.sort.mb = 100
12/10/17 21:05:54 INFO mapred.MapTask: data buffer = 79691776/99614720
12/10/17 21:05:54 INFO mapred.MapTask: record buffer = 262144/327680
12/10/17 21:05:54 INFO mapred.MapTask: Starting flush of map output
12/10/17 21:05:54 INFO mapred.MapTask: Finished spill 0
12/10/17 21:05:54 INFO mapred.TaskRunner: Task:attempt_local_0001_m_000000_0 is done. And is in the process of commiting
12/10/17 21:05:54 INFO mapred.LocalJobRunner: hdfs://localhost:9100/user/tanglg1987/input/ChainMapper.txt:0+35
12/10/17 21:05:54 INFO mapred.TaskRunner: Task 'attempt_local_0001_m_000000_0' done.
12/10/17 21:05:54 INFO mapred.LocalJobRunner: 
12/10/17 21:05:54 INFO mapred.Merger: Merging 1 sorted segments
12/10/17 21:05:54 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 16 bytes
12/10/17 21:05:54 INFO mapred.LocalJobRunner: 
12/10/17 21:05:54 INFO mapred.TaskRunner: Task:attempt_local_0001_r_000000_0 is done. And is in the process of commiting
12/10/17 21:05:54 INFO mapred.LocalJobRunner: 
12/10/17 21:05:54 INFO mapred.TaskRunner: Task attempt_local_0001_r_000000_0 is allowed to commit now
12/10/17 21:05:54 INFO mapred.FileOutputCommitter: Saved output of task 'attempt_local_0001_r_000000_0' to hdfs://localhost:9100/user/tanglg1987/output
12/10/17 21:05:54 INFO mapred.LocalJobRunner: reduce > reduce
12/10/17 21:05:54 INFO mapred.TaskRunner: Task 'attempt_local_0001_r_000000_0' done.
12/10/17 21:05:55 INFO mapred.JobClient:  map 100% reduce 100%
12/10/17 21:05:55 INFO mapred.JobClient: Job complete: job_local_0001
12/10/17 21:05:55 INFO mapred.JobClient: Counters: 15
12/10/17 21:05:55 INFO mapred.JobClient:   FileSystemCounters
12/10/17 21:05:55 INFO mapred.JobClient:     FILE_BYTES_READ=36152
12/10/17 21:05:55 INFO mapred.JobClient:     HDFS_BYTES_READ=70
12/10/17 21:05:55 INFO mapred.JobClient:     FILE_BYTES_WRITTEN=73202
12/10/17 21:05:55 INFO mapred.JobClient:     HDFS_BYTES_WRITTEN=12
12/10/17 21:05:55 INFO mapred.JobClient:   Map-Reduce Framework
12/10/17 21:05:55 INFO mapred.JobClient:     Reduce input groups=1
12/10/17 21:05:55 INFO mapred.JobClient:     Combine output records=0
12/10/17 21:05:55 INFO mapred.JobClient:     Map input records=3
12/10/17 21:05:55 INFO mapred.JobClient:     Reduce shuffle bytes=0
12/10/17 21:05:55 INFO mapred.JobClient:     Reduce output records=1
12/10/17 21:05:55 INFO mapred.JobClient:     Spilled Records=2
12/10/17 21:05:55 INFO mapred.JobClient:     Map output bytes=12
12/10/17 21:05:55 INFO mapred.JobClient:     Map input bytes=35
12/10/17 21:05:55 INFO mapred.JobClient:     Combine input records=0
12/10/17 21:05:55 INFO mapred.JobClient:     Map output records=1
12/10/17 21:05:55 INFO mapred.JobClient:     Reduce input records=1

第七步：查看结果集，运行结果如下：