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hadoop 0.20 程式開發
eclipse plugin + Makefile
}}} = 零. 前言 = * 開發hadoop 需要用到許多的物件導向語法,包括繼承關係、介面類別,而且需要匯入正確的classpath,否則寫hadoop程式只是打字練習... * 用類 vim 來處理這種複雜的程式,有可能會變成一場惡夢,因此用eclipse開發,搭配mapreduce-plugin會事半功倍。 * 早在hadoop 0.19~0.16之間的版本,筆者就試過各個plugin,每個版本的plugin都確實有大大小小的問題,如:hadoop plugin 無法正確使用、無法run as mapreduce。hadoop0.16搭配IBM的hadoop_plugin 可以提供完整的功能,但是,老兵不死,只是凋零... * 子曰:"逝者如斯夫,不捨晝夜",以前寫的文件也落伍了,要跟上潮流,因此此篇的重點在:'''用eclipse 3.4.2 開發hadoop 0.20程式,並且測試撰寫的程式運作在hadoop平台上''' * 以下是我的作法,如果你有更好的作法,或有需要更正的地方,請與我聯絡 || 單位 || 作者 || Mail || || 國家高速網路中心-格網技術組 || Wei-Yu Chen || waue @ nchc.org.tw || == 0.1 環境說明 == * ubuntu 8.10 * sun-java-6 * eclipse 3.4.2 * hadoop 0.20.0 == 0.2 目錄說明 == * 使用者:waue * 使用者家目錄: /home/waue * 專案目錄 : /home/waue/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 == * 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] * 2 放在 /tmp/ 下 * 3 執行 {{{ $ sudo apt-get install sun-java6-doc }}} == 1.2. ssh 安裝設定 == {{{ $ apt-get install ssh $ ssh-keygen -t rsa -P '' -f ~/.ssh/id_rsa $ cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys $ ssh localhost }}} * 執行ssh localhost 沒有出現詢問密碼的訊息則無誤 == 1.3. 安裝hadoop == * 安裝hadoop0.20到/opt/並取目錄名為hadoop {{{ $ cd ~ $ wget http://apache.ntu.edu.tw/hadoop/core/hadoop-0.20.0/hadoop-0.20.0.tar.gz $ tar zxvf hadoop-0.20.0.tar.gz $ sudo mv hadoop-0.20.0 /opt/ $ sudo chown -R waue:waue /opt/hadoop-0.20.0 $ sudo ln -sf /opt/hadoop-0.20.0 /opt/hadoop }}} * 編輯 /opt/hadoop/conf/hadoop-env.sh {{{ #!sh export JAVA_HOME=/usr/lib/jvm/java-6-sun export HADOOP_HOME=/opt/hadoop export PATH=$PATH:/opt/hadoop/bin }}} * 編輯 /opt/hadoop/conf/core-site.xml {{{ #!sh fs.default.name hdfs://localhost:9000 hadoop.tmp.dir /tmp/hadoop/hadoop-${user.name} }}} * 編輯 /opt/hadoop/conf/hdfs-site.xml {{{ #!sh dfs.replication 1 }}} * 編輯 /opt/hadoop/conf/mapred-site.xml {{{ #!sh mapred.job.tracker localhost:9001 }}} * 啟動 {{{ $ cd /opt/hadoop $ source /opt/hadoop/conf/hadoop-env.sh $ hadoop namenode -format $ start-all.sh $ hadoop fs -put conf input $ hadoop fs -ls }}} * 沒有錯誤訊息則代表無誤 == 1.4. 安裝eclipse == * 下載 [http://www.eclipse.org/downloads/download.php?file=/eclipse/downloads/drops/R-3.4.2-200902111700/eclipse-SDK-3.4.2-linux-gtk.tar.gz eclipse SDK 3.4.2 Classic] * 放這檔案到家目錄 {{{ $ cd ~ $ tar -zxvf eclipse-SDK-3.4.2-linux-gtk.tar.gz $ sudo mv eclipse /opt $ sudo ln -sf /opt/eclipse/bin/eclipse /usr/local/bin/ }}} = 二、 建立專案 = == 2.1 安裝hadoop 的 eclipse plugin == * 匯入hadoop 0.20.0 eclipse plugin {{{ $ cd /opt/hadoop $ sudo cp /opt/hadoop/contrib/eclipse-plugin/hadoop-0.20.0-eclipse-plugin.jar /opt/eclipse/plugins }}} {{{ $ sudo vim /opt/eclipse/eclipse.ini }}} * 可斟酌參考eclipse.ini內容(非必要) {{{ #!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 & 設定 == * 打開eclipse {{{ $ eclipse & }}} ------- 之後的說明則是在eclipse 上的介面操作 * window -> open pers.. -> other.. -> map/reduce * file -> new -> project -> Map/Reduce -> Map/Reduce Project -> next {{{ #!sh project name-> 輸入 : icas (隨意) use default hadoop -> Configur Hadoop install... -> 輸入: "/opt/hadoop" -> ok Finish }}} * Project -> properties -> * java Build Path -> Libraries -> hadoop-0.20.0-ant.jar * java Build Path -> Libraries -> hadoop-0.20.0-core.jar * java Build Path -> Libraries -> hadoop-0.20.0-tools.jar * 以 hadoop-0.20.0-core.jar 的設定內容如下,其他依此類推 {{{ #!sh source ...-> 輸入:/opt/opt/hadoop-0.20.0/src/core javadoc ...-> 輸入:file:/opt/hadoop/docs/api/ }}} * Project -> properties * javadoc location -> 輸入:file:/usr/lib/jvm/java-6-sun/docs/api/ * 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: {{{ #!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 }}} = 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/waue/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 == 3.1 我的第一隻程式 == * File -> new -> mapper {{{ #!sh source folder-> 輸入: icas/src Package : Sample Name -> : mapper }}} * 編輯mapper.java {{{ #!java package Sample; import java.io.IOException; import java.util.StringTokenizer; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Mapper; public class mapper extends Mapper { private final static IntWritable one = new IntWritable(1); private Text word = new Text(); public void map(Object 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); } } } }}} * File -> new -> reducer {{{ #!sh source folder-> 輸入: icas/src Package : Sample Name -> : reducer }}} {{{ #!java package Sample; import java.io.IOException; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Reducer; public class reducer extends Reducer { private IntWritable result = new IntWritable(); public void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException { int sum = 0; for (IntWritable val : values) { sum += val.get(); } result.set(sum); context.write(key, result); } } }}} * File -> new -> Map/Reduce Driver {{{ #!sh source folder-> 輸入: icas/src Package : Sample Name -> : WordCount.java }}} * 編輯 WordCount.java 檔 {{{ #!java package Sample; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.hadoop.util.GenericOptionsParser; public class WordCount { public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); String[] otherArgs = new GenericOptionsParser(conf, args) .getRemainingArgs(); if (otherArgs.length != 2) { System.err.println("Usage: wordcount "); System.exit(2); } Job job = new Job(conf, "word count"); job.setJarByClass(WordCount.class); job.setMapperClass(mapper.class); job.setCombinerClass(reducer.class); job.setReducerClass(reducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); FileInputFormat.addInputPath(job, new Path(otherArgs[0])); FileOutputFormat.setOutputPath(job, new Path(otherArgs[1])); System.exit(job.waitForCompletion(true) ? 0 : 1); } } }}} * 三個檔都存檔後,可以看到icas專案下的src,bin都有檔案產生 {{{ $ cd workspace/icas $ ls src/Sample/ mapper.java reducer.java WordCount.java $ ls bin/Sample/ mapper.class reducer.class WordCount.class }}} = 四、編譯 = * 由於hadoop 0.20 此版本的eclipse-plugin依舊不完整,如:1. 齒輪圖示沒有作用 2. 右鍵點選WordCount.java -> run as -> run on Hadoop :沒有效果 * 因此編譯hadoop程式就要靠指令來編輯,然而用一行一行指令來產生太沒效率,在此介紹用Makefile來編譯 == 4.1 產生Makefile == {{{ $ cd /home/waue/workspace/icas/ $ gedit Makefile }}} {{{ #!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. 執行 == {{{ $ cd /home/waue/workspace/icas/ $ make jar; make run; make output; make clean }}} == 4.3 screenshot == * 圖一: 完成後的eclipse視窗快照 [[Image(1.png)]] ------ * 圖二: 因為有設定完整的javadoc, 因此可以得到詳細的解說與輔助 [[Image(2.png)]]