hadoop 程式開發 (eclipse plugin) (進階)

個別編譯程式

1 mapper.java

1. new

File ->

new ->

mapper

---

2. create

source folder-> 輸入： icas/src
Package : Sample
Name -> : mapper

---

3. modify

package Sample;

import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reporter;

public class mapper 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 tokenizer = new StringTokenizer(line);
      while (tokenizer.hasMoreTokens()) {
        word.set(tokenizer.nextToken());
        output.collect(word, one);
      }
    }
  }

建立mapper.java後，貼入程式碼

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2 reducer.java

1. new

• File -> new -> reducer

---

2. create

source folder-> 輸入： icas/src
Package : Sample
Name -> : reducer

---

3. modify

package Sample;

import java.io.IOException;
import java.util.Iterator;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;

public class reducer 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));
    }
  }

• File -> new -> Map/Reduce? Driver

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3 WordCount.java (main function)

1. new

建立WordCount.java，此檔用來驅動mapper 與 reducer，因此選擇 Map/Reduce? Driver

---

2. create

source folder-> 輸入： icas/src
Package : Sample
Name -> : WordCount.java

---

3. modify

package Sample;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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.TextInputFormat;
import org.apache.hadoop.mapred.TextOutputFormat;

public class WordCount {

   public static void main(String[] args) throws Exception {
     JobConf conf = new JobConf(WordCount.class);
     conf.setJobName("wordcount");

     conf.setOutputKeyClass(Text.class);
     conf.setOutputValueClass(IntWritable.class);

     conf.setMapperClass(mapper.class);
     conf.setCombinerClass(reducer.class);
     conf.setReducerClass(reducer.class);

     conf.setInputFormat(TextInputFormat.class);
     conf.setOutputFormat(TextOutputFormat.class);

    FileInputFormat.setInputPaths(conf, new Path("/user/hadooper/input"));
    FileOutputFormat.setOutputPath(conf, new Path("lab5_out2"));

     JobClient.runJob(conf);
   }
}

三個檔完成後並存檔後，整個程式建立完成

---

• 三個檔都存檔後，可以看到icas專案下的src，bin都有檔案產生，我們用指令來check

$ cd workspace/icas
$ ls src/Sample/
mapper.java  reducer.java  WordCount.java
$ ls bin/Sample/
mapper.class  reducer.class  WordCount.class

eclipse 可以產生出jar檔

File -> Export -> java -> JAR file
-> next ->

---

選擇要匯出的專案 -> jarfile: /home/hadooper/mytest.jar ->
next ->

---

next ->

---

main class: 選擇有Main的class ->
Finish

---

• 以上的步驟就可以在/home/hadooper/ 產生出你的 mytest.jar

用Makefile 來更快速編譯

• 程式常常修改，每次都做這些動作也很累很煩，讓我們來體驗一下用指令比用圖形介面操作還方便吧

1 產生Makefile 檔

$ cd /home/hadooper/workspace/icas/
$ gedit Makefile

• 輸入以下Makefile的內容 (注意 ":" 後面要接 "tab" 而不是 "空白")

  JarFile="sample-0.1.jar"
  MainFunc="Sample.WordCount"
  LocalOutDir="/tmp/output"
  HADOOP_BIN="/opt/hadoop/bin"
  
  all:jar run output clean
  
  jar:
  	jar -cvf ${JarFile} -C bin/ .
  
  run:
  	${HADOOP_BIN}/hadoop jar ${JarFile} ${MainFunc} input output
  
  clean:
  	${HADOOP_BIN}/hadoop fs -rmr output
  
  output:
  	rm -rf ${LocalOutDir}
  	${HADOOP_BIN}/hadoop fs -get output ${LocalOutDir}
  	gedit ${LocalOutDir}/part-r-00000 & 
  
  help:
  	@echo "Usage:"
  	@echo " make jar     - Build Jar File."
  	@echo " make clean   - Clean up Output directory on HDFS."
  	@echo " make run     - Run your MapReduce code on Hadoop."
  	@echo " make output  - Download and show output file"
  	@echo " make help    - Show Makefile options."
  	@echo " "
  	@echo "Example:"
  	@echo " make jar; make run; make output; make clean"
  

• 或是直接下載 Makefile 吧

  $ cd /home/hadooper/workspace/icas/
  $ wget http://trac.nchc.org.tw/cloud/raw-attachment/wiki/Hadoop_Lab5/Makefile
  

2 執行

• 執行Makefile，可以到該目錄下，執行make [參數]，若不知道參數為何，可以打make 或 make help
• make 的用法說明

$ cd /home/hadooper/workspace/icas/
$ make
Usage:
 make jar     - Build Jar File.
 make clean   - Clean up Output directory on HDFS.
 make run     - Run your MapReduce code on Hadoop.
 make output  - Download and show output file
 make help    - Show Makefile options.
 
Example:
 make jar; make run; make output; make clean

• 下面提供各種make 的參數

make jar

• 1. 編譯產生jar檔

$ make jar

make run

• 2. 跑我們的wordcount 於hadoop上

$ make run

• make run基本上能正確無誤的運作到結束，因此代表我們在eclipse編譯的程式可以順利在hadoop0.18.3的平台上運行。

• 而回到eclipse視窗，我們可以看到下方視窗run完的job會呈現出來；左方視窗也多出output資料夾，part-r-00000就是我們的結果檔

---

• 因為有設定完整的javadoc, 因此可以得到詳細的解說與輔助

make output

• 3. 這個指令是幫助使用者將結果檔從hdfs下載到local端，並且用gedit來開啟你的結果檔

$ make output

make clean

• 4. 這個指令用來把hdfs上的output資料夾清除。如果你還想要在跑一次make run，請先執行make clean，否則hadoop會告訴你，output資料夾已經存在，而拒絕工作喔！

$ make clean

練習：匯入專案

• 將 nchc-sample 給匯入到eclipse 內開發吧！