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hadoop 程式開發 (eclipse plugin)
}}} = 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) 下載下來放在 /tmp/ 下 * 教學環境內,已經存在於 /home/hadooper/tools/ ,將其複製到 /tmp {{{ $ cp /home/hadooper/tools/jdk-*-docs.zip /tmp/ }}} * 或是到官方網站將javadoc (jdk-6u10-docs.zip) 下載下來放到 /tmp [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 執行 {{{ $ sudo apt-get install sun-java6-doc $ sudo ln -sf /usr/share/doc/sun-java6-jdk/html /usr/lib/jvm/java-6-sun/docs }}} == 1.2. ssh 安裝設定 == [http://trac.nchc.org.tw/cloud/wiki/Hadoop_Lab1 詳見實作一] == 1.3. 安裝hadoop == [http://trac.nchc.org.tw/cloud/wiki/Hadoop_Lab1 詳見實作一] == 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 cat /opt/eclipse/eclipse.ini }}} {{{ #!sh -showsplash org.eclipse.platform -vmargs -Xms40m -Xmx256m }}} == 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/hadoop/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 -> 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 mapper 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 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 reducer 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)); } } }}} * 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(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 [[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 (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 [http://trac.nchc.org.tw/cloud/raw-attachment/wiki/Hadoop_Lab5/Makefile Makefile] 吧 {{{ $ cd /home/hadooper/workspace/icas/ $ wget http://trac.nchc.org.tw/cloud/raw-attachment/wiki/Hadoop_Lab5/Makefile }}} === 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 )] = 六、練習:匯入專案 = * 將 [http://trac.nchc.org.tw/cloud/raw-attachment/wiki/Hadoop_Lab5/hadoop_sample_codes.zip nchc-sample] 給匯入到eclipse 內開發吧!