Changes between Initial Version and Version 1 of Hadoop_Lab5

Timestamp:

Aug 28, 2009, 6:22:35 PM (15 years ago)

Author:

waue

Comment:

--

Hadoop_Lab5

@@ +1 @@
+[[PageOutline]]
+{{{
+#!html
+<div style="text-align: center;"><big
+ style="font-weight: bold;"><big><big> hadoop 程式開發 (eclipse plugin) </big></big></big></div>
+}}}
+ = 零. 環境配置 =
+
+
+ == 0.1 環境說明 == 
+ * ubuntu 8.10
+ * sun-java-6
+   * [http://www.java.com/zh_TW/download/linux_manual.jsp?locale=zh_TW&host=www.java.com:80 java 下載處]
+   * [https://cds.sun.com/is-bin/INTERSHOP.enfinity/WFS/CDS-CDS_Developer-Site/en_US/-/USD/ViewProductDetail-Start?ProductRef=jdk-6u10-docs-oth-JPR@CDS-CDS_Developer JavaDoc ]
+ * eclipse 3.3.2
+   * eclipse 各版本下載點 [http://archive.eclipse.org/eclipse/downloads/]
+ * hadoop 0.18.3
+   * hadoop 各版本下載點 [http://ftp.twaren.net/Unix/Web/apache/hadoop/core/]
+
+ == 0.2 目錄說明 == 
+
+ * 使用者：hadoop
+ * 使用者家目錄： /home/hadooper
+ * 專案目錄 ： /home/hadooper/workspace
+ * hadoop目錄： /opt/hadoop
+
+ = 一、安裝 =
+
+安裝的部份沒必要都一模一樣，僅提供參考，反正只要安裝好java , hadoop , eclipse，並清楚自己的路徑就可以了
+
+ == 1.1. 安裝java ==
+ 
+首先安裝java 基本套件
+ 
+{{{
+$ sudo apt-get install java-common sun-java6-bin sun-java6-jdk sun-java6-jre
+}}}
+
+ == 1.1.1. 安裝sun-java6-doc ==
+ 
+ 1 將javadoc (jdk-6u10-docs.zip) 下載下來
+ [https://cds.sun.com/is-bin/INTERSHOP.enfinity/WFS/CDS-CDS_Developer-Site/en_US/-/USD/ViewProductDetail-Start?ProductRef=jdk-6u10-docs-oth-JPR@CDS-CDS_Developer 下載點]
+[[Image(wiki:waue/2009/0617:1-1.png)]]
+ 
+ 2 下載完後將檔案放在 /tmp/ 下 (假測已下載到 /home/hadooper/tools/ )
+{{{
+$ mv /home/hadooper/tools/jdk-*-docs.zip /tmp/
+}}}
+
+ 3 執行
+ 
+{{{
+$ sudo apt-get install sun-java6-doc
+}}}
+
+ == 1.2. ssh 安裝設定 ==
+
+
+ == 1.3. 安裝hadoop ==
+
+
+ == 1.4. 安裝eclipse ==
+
+ * 取得檔案 eclipse 3.3.2  (假設已經下載於/home/hadooper/tools/ 內)，執行下面指令：
+ 
+{{{
+$ cd ~/tools/
+$ tar -zxvf eclipse-SDK-3.3.2-linux-gtk.tar.gz
+$ sudo mv eclipse /opt
+$ sudo ln -sf /opt/eclipse/eclipse /usr/local/bin/
+}}}
+
+ = 二、 建立專案 = 
+
+ == 2.1 安裝hadoop 的 eclipse plugin ==
+
+ * 匯入hadoop eclipse plugin 
+ 
+{{{
+$ cd /opt/hadoop
+$ sudo cp /opt/hadoop/contrib/eclipse-plugin/hadoop-0.18.3-eclipse-plugin.jar /opt/eclipse/plugins
+}}}
+
+補充： 可斟酌參考eclipse.ini內容（非必要）
+
+{{{
+$ sudo vim /opt/eclipse/eclipse.ini
+}}}
+
+{{{
+#!sh
+-startup
+plugins/org.eclipse.equinox.launcher_1.0.101.R34x_v20081125.jar
+--launcher.library
+plugins/org.eclipse.equinox.launcher.gtk.linux.x86_1.0.101.R34x_v20080805
+-showsplash
+org.eclipse.platform
+--launcher.XXMaxPermSize
+512m
+-vmargs
+-Xms40m
+-Xmx512m
+}}}
+
+== 2.2 開啟eclipse ==
+
+ * 打開eclipse 
+ 
+{{{
+$ eclipse &
+}}}
+
+一開始會出現問你要將工作目錄放在哪裡：在這我們用預設值
+[[Image(wiki:waue/2009/0617:2-1.png)]] 
+-------
+
+'''PS: 之後的說明則是在eclipse 上的介面操作'''
+
+-------
+
+== 2.3 選擇視野 ==
+
+|| window -> || open pers.. -> || other.. -> || map/reduce|| 
+
+[[Image(wiki:waue/2009/0617:win-open-other.png)]] 
+
+-------
+
+設定要用 Map/Reduce 的視野
+[[Image(wiki:waue/2009/0617:2-2.png)]] 
+
+---------
+
+使用 Map/Reduce 的視野後的介面呈現
+[[Image(wiki:waue/2009/0617:2-3.png)]] 
+
+--------
+
+== 2.4 建立專案 ==
+
+ || file ->  || new ->  || project ->  || Map/Reduce ->  || Map/Reduce Project -> ||  next || 
+[[Image(wiki:waue/2009/0617:file-new-project.png)]]
+
+--------
+
+建立mapreduce專案(1)
+
+[[Image(wiki:waue/2009/0617:2-4.png)]] 
+
+-----------
+
+建立mapreduce專案的(2)
+{{{
+#!sh
+project name-> 輸入 : icas (隨意)
+use default hadoop -> Configur Hadoop install... -> 輸入： "/opt/hadoop" -> ok
+Finish
+}}}
+
+[[Image(wiki:waue/2009/0617:2-4-2.png)]]
+
+
+--------------
+
+== 2.5 設定專案 ==
+
+由於剛剛建立了icas這個專案，因此eclipse已經建立了新的專案，出現在左邊視窗，右鍵點選該資料夾，並選properties
+
+--------------
+
+ Step1. 右鍵點選project的properties做細部設定
+
+[[Image(wiki:waue/2009/0617:2-5.png)]]
+
+----------
+
+ Step2. 進入專案的細部設定頁
+
+hadoop的javadoc的設定(1)
+[[Image(wiki:waue/2009/0617:2-5-1.png)]]
+
+ * java Build Path -> Libraries -> hadoop0.18.3-ant.jar
+ * java Build Path -> Libraries -> hadoop0.18.3-core.jar 
+ * java Build Path -> Libraries ->  hadoop0.18.3-tools.jar
+   * 以 hadoop0.18.3-core.jar 的設定內容如下，其他依此類推
+   
+{{{
+#!sh
+source ...-> 輸入：/opt/opt/hadoop0.18.3/src/core
+javadoc ...-> 輸入：file:/opt/hadoop/docs/api/
+}}}
+
+------------
+ Step3. hadoop的javadoc的設定完後(2)
+[[Image(wiki:waue/2009/0617:2-5-2.png)]]
+
+------------
+ Step4. java本身的javadoc的設定(3)
+ 
+ * javadoc location -> 輸入：file:/usr/lib/jvm/java-6-sun/docs/api/
+ 
+[[Image(wiki:waue/2009/0617:2-5-3.png)]]
+
+-----
+設定完後回到eclipse 主視窗
+
+
+== 2.6 連接hadoop server ==
+
+--------
+ Step1. 視窗右下角黃色大象圖示"Map/Reduce Locations tag" -> 點選齒輪右邊的藍色大象圖示：
+[[Image(wiki:waue/2009/0617:2-6.png)]]
+
+-------------
+ Step2. 進行eclipse 與 hadoop 間的設定(2)
+[[Image(wiki:waue/2009/0617:2-6-1.png)]]
+
+{{{
+#!sh
+Location Name -> 輸入：hadoop  (隨意)
+Map/Reduce Master -> Host-> 輸入：localhost 
+Map/Reduce Master -> Port-> 輸入：9001 
+DFS Master -> Host-> 輸入：9000
+Finish
+}}}
+----------------
+
+設定完後，可以看到下方多了一隻藍色大象，左方展開資料夾也可以秀出在hdfs內的檔案結構
+[[Image(wiki:waue/2009/0617:2-6-2.png)]]
+-------------
+
+ = 三、 撰寫範例程式 =
+
+ * 之前在eclipse上已經開了個專案icas，因此這個目錄在：
+   * /home/hadooper/workspace/icas
+ * 在這個目錄內有兩個資料夾： 
+   * src ： 用來裝程式原始碼 
+   * bin ： 用來裝編譯後的class檔
+ * 如此一來原始碼和編譯檔就不會混在一起，對之後產生jar檔會很有幫助
+ * 在這我們編輯一個範例程式 : WordCount 
+
+ == 3.1 mapper.java ==
+ 
+ 1. new
+ 
+ || File ->  || new ->  || mapper || 
+[[Image(wiki:waue/2009/0617:file-new-mapper.png)]]
+
+-----------
+
+ 2. create
+ 
+[[Image(wiki:waue/2009/0617:3-1.png)]]
+{{{
+#!sh
+source folder-> 輸入： icas/src
+Package : Sample
+Name -> : mapper
+}}}
+----------
+
+ 3. modify
+ 
+{{{
+#!java
+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 Map 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
+[[Image(wiki:waue/2009/0617:3-2.png)]]
+
+------------
+
+== 3.2 reducer.java ==
+
+ 1. new
+
+ * File -> new -> reducer
+[[Image(wiki:waue/2009/0617:file-new-reducer.png)]]
+
+-------
+ 2. create
+[[Image(wiki:waue/2009/0617:3-3.png)]]
+
+{{{
+#!sh
+source folder-> 輸入： icas/src
+Package : Sample
+Name -> : reducer
+}}}
+
+-----------
+
+ 3. modify 
+ 
+{{{
+#!java
+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 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));
+    }
+  }
+}}}
+
+ * File -> new -> Map/Reduce Driver
+[[Image(wiki:waue/2009/0617:file-new-mr-driver.png)]]
+----------
+
+== 3.3 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
+[[Image(wiki:waue/2009/0617:3-4.png)]]
+------------
+
+ 2. create
+
+{{{
+#!sh
+source folder-> 輸入： icas/src
+Package : Sample
+Name -> : WordCount.java
+}}}
+
+-------
+ 3. modify
+
+{{{
+#!java
+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(Map.class);
+     conf.setCombinerClass(Reduce.class);
+     conf.setReducerClass(Reduce.class);
+
+     conf.setInputFormat(TextInputFormat.class);
+     conf.setOutputFormat(TextOutputFormat.class);
+
+     FileInputFormat.setInputPaths(conf, new Path(args[0]));
+     FileOutputFormat.setOutputPath(conf, new Path(args[1]));
+
+     JobClient.runJob(conf);
+   }
+}
+}}}
+
+三個檔完成後並存檔後，整個程式建立完成
+[[Image(wiki:waue/2009/0617:3-5.png)]]
+
+-------
+
+ * 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
+}}}
+
+ = 四、測試範例程式 =
+
+在此提供兩種方法來run我們從eclipse 上編譯出的code。 
+
+方法一是直接在eclipse上用圖形介面操作，參閱 4.1  在eclipse上操作
+
+方法二是產生jar檔後搭配自動編譯程式Makefile，參閱4.2
+
+
+ == 4.1 法一：在eclipse上操作 ==
+
+ * 右鍵點選專案資料夾：icas -> run as -> run on Hadoop 
+   
+[[Image(wiki:waue/2009/0617:run-on-hadoop.png)]]
+
+
+ == 4.2 法二：jar檔搭配自動編譯程式 ==
+ 
+ * eclipse 可以產生出jar檔 :
+ 
+File -> Export -> java -> JAR file [[br]]
+-> next -> 
+--------
+選擇要匯出的專案 ->
+jarfile: /home/hadooper/mytest.jar -> [[br]]
+next ->
+--------
+next ->
+--------
+main class: 選擇有Main的class   -> [[br]]
+Finish
+--------
+
+ * 以上的步驟就可以在/home/hadooper/ 產生出你的 mytest.jar
+ * 不過程式常常修改，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
+ 
+ === 4.2.1 產生Makefile 檔 ===
+{{{
+
+$ cd /home/hadooper/workspace/icas/
+$ gedit Makefile
+}}}
+
+ * 輸入以下Makefile的內容
+{{{
+#!sh
+
+JarFile="sample-0.1.jar"
+MainFunc="Sample.WordCount"
+LocalOutDir="/tmp/output"
+
+all:help
+jar:
+  jar -cvf ${JarFile} -C bin/ .
+
+run:
+  hadoop jar ${JarFile} ${MainFunc} input output
+
+clean:
+  hadoop fs -rmr output
+
+output:
+  rm -rf ${LocalOutDir}
+  hadoop fs -get output ${LocalOutDir}
+  gedit ${LocalOutDir}/part-r-00000 & 
+
+help:
+  @echo "Usage:"
+  @echo " make jar     - Build Jar File."
+  @echo " make clean   - Clean up Output directory on HDFS."
+  @echo " make run     - Run your MapReduce code on Hadoop."
+  @echo " make output  - Download and show output file"
+  @echo " make help    - Show Makefile options."
+  @echo " "
+  @echo "Example:"
+  @echo " make jar; make run; make output; make clean"
+
+}}}
+
+ === 4.2.2 執行 ===
+ 
+ * 執行Makefile，可以到該目錄下，執行make [參數]，若不知道參數為何，可以打make 或 make help
+ * make 的用法說明
+
+{{{
+$ 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
+ 
+[[Image(wiki:waue/2009/0617:4-1.png)]]
+------
+ * 因為有設定完整的javadoc, 因此可以得到詳細的解說與輔助
+[[Image(wiki:waue/2009/0617:4-2.png)]]
+
+ === 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
+}}}
+
+= 五、結論 =
+
+ * 搭配eclipse ，我們可以更有效率的開發hadoop
+ * hadoop 0.20 與之前的版本api以及設定都有些改變，可以看 [wiki:waue/2009/0617 hadoop 0.20 coding (eclipse )]