| 1 | [[PageOutline]] |
| 2 | {{{ |
| 3 | #!html |
| 4 | <div style="text-align: center;"><big |
| 5 | style="font-weight: bold;"><big><big> hadoop 程式開發 (eclipse plugin) </big></big></big></div> |
| 6 | }}} |
| 7 | = 零. 環境配置 = |
| 8 | |
| 9 | |
| 10 | == 0.1 環境說明 == |
| 11 | * ubuntu 8.10 |
| 12 | * sun-java-6 |
| 13 | * [http://www.java.com/zh_TW/download/linux_manual.jsp?locale=zh_TW&host=www.java.com:80 java 下載處] |
| 14 | * [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 ] |
| 15 | * eclipse 3.3.2 |
| 16 | * eclipse 各版本下載點 [http://archive.eclipse.org/eclipse/downloads/] |
| 17 | * hadoop 0.18.3 |
| 18 | * hadoop 各版本下載點 [http://ftp.twaren.net/Unix/Web/apache/hadoop/core/] |
| 19 | |
| 20 | == 0.2 目錄說明 == |
| 21 | |
| 22 | * 使用者:hadoop |
| 23 | * 使用者家目錄: /home/hadooper |
| 24 | * 專案目錄 : /home/hadooper/workspace |
| 25 | * hadoop目錄: /opt/hadoop |
| 26 | |
| 27 | = 一、安裝 = |
| 28 | |
| 29 | 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 |
| 30 | |
| 31 | == 1.1. 安裝java == |
| 32 | |
| 33 | 首先安裝java 基本套件 |
| 34 | |
| 35 | {{{ |
| 36 | $ sudo apt-get install java-common sun-java6-bin sun-java6-jdk sun-java6-jre |
| 37 | }}} |
| 38 | |
| 39 | == 1.1.1. 安裝sun-java6-doc == |
| 40 | |
| 41 | 1 將javadoc (jdk-6u10-docs.zip) 下載下來放在 /tmp/ 下 |
| 42 | |
| 43 | * 教學環境內,已經存在於 /home/hadooper/tools/ ,將其複製到 /tmp |
| 44 | {{{ |
| 45 | $ cp /home/hadooper/tools/jdk-*-docs.zip /tmp/ |
| 46 | }}} |
| 47 | |
| 48 | * 或是到官方網站將javadoc (jdk-6u10-docs.zip) 下載下來放到 /tmp |
| 49 | [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] |
| 50 | [[Image(wiki:waue/2009/0617:1-1.png)]] |
| 51 | |
| 52 | 2 執行 |
| 53 | |
| 54 | {{{ |
| 55 | $ sudo apt-get install sun-java6-doc |
| 56 | $ sudo ln -sf /usr/share/doc/sun-java6-jdk/html /usr/lib/jvm/java-6-sun/docs |
| 57 | }}} |
| 58 | |
| 59 | == 1.2. ssh 安裝設定 == |
| 60 | |
| 61 | [http://trac.nchc.org.tw/cloud/wiki/Hadoop_Lab1 詳見實作一] |
| 62 | == 1.3. 安裝hadoop == |
| 63 | [http://trac.nchc.org.tw/cloud/wiki/Hadoop_Lab1 詳見實作一] |
| 64 | |
| 65 | == 1.4. 安裝eclipse == |
| 66 | |
| 67 | * 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: |
| 68 | |
| 69 | {{{ |
| 70 | $ cd ~/tools/ |
| 71 | $ tar -zxvf eclipse-SDK-3.3.2-linux-gtk.tar.gz |
| 72 | $ sudo mv eclipse /opt |
| 73 | $ sudo ln -sf /opt/eclipse/eclipse /usr/local/bin/ |
| 74 | }}} |
| 75 | |
| 76 | = 二、 建立專案 = |
| 77 | |
| 78 | == 2.1 安裝hadoop 的 eclipse plugin == |
| 79 | |
| 80 | * 匯入hadoop eclipse plugin |
| 81 | |
| 82 | {{{ |
| 83 | $ cd /opt/hadoop |
| 84 | $ sudo cp /opt/hadoop/contrib/eclipse-plugin/hadoop-0.18.3-eclipse-plugin.jar /opt/eclipse/plugins |
| 85 | }}} |
| 86 | |
| 87 | 補充: 可斟酌參考eclipse.ini內容(非必要) |
| 88 | |
| 89 | {{{ |
| 90 | $ sudo cat /opt/eclipse/eclipse.ini |
| 91 | }}} |
| 92 | |
| 93 | {{{ |
| 94 | #!sh |
| 95 | -showsplash |
| 96 | org.eclipse.platform |
| 97 | -vmargs |
| 98 | -Xms40m |
| 99 | -Xmx256m |
| 100 | }}} |
| 101 | |
| 102 | == 2.2 開啟eclipse == |
| 103 | |
| 104 | * 打開eclipse |
| 105 | |
| 106 | {{{ |
| 107 | $ eclipse & |
| 108 | }}} |
| 109 | |
| 110 | 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 |
| 111 | |
| 112 | |
| 113 | [[Image(wiki:waue/2009/0617:2-1.png)]] |
| 114 | ------- |
| 115 | |
| 116 | '''PS: 之後的說明則是在eclipse 上的介面操作''' |
| 117 | |
| 118 | ------- |
| 119 | |
| 120 | == 2.3 選擇視野 == |
| 121 | |
| 122 | || window -> || open pers.. -> || other.. -> || map/reduce|| |
| 123 | |
| 124 | [[Image(wiki:waue/2009/0617:win-open-other.png)]] |
| 125 | |
| 126 | ------- |
| 127 | |
| 128 | 設定要用 Map/Reduce 的視野 |
| 129 | |
| 130 | |
| 131 | [[Image(wiki:waue/2009/0617:2-2.png)]] |
| 132 | |
| 133 | --------- |
| 134 | |
| 135 | 使用 Map/Reduce 的視野後的介面呈現 |
| 136 | |
| 137 | |
| 138 | [[Image(wiki:waue/2009/0617:2-3.png)]] |
| 139 | |
| 140 | -------- |
| 141 | |
| 142 | == 2.4 建立專案 == |
| 143 | |
| 144 | || file -> || new -> || project -> || Map/Reduce -> || Map/Reduce Project -> || next || |
| 145 | [[Image(wiki:waue/2009/0617:file-new-project.png)]] |
| 146 | |
| 147 | -------- |
| 148 | |
| 149 | 建立mapreduce專案(1) |
| 150 | |
| 151 | [[Image(wiki:waue/2009/0617:2-4.png)]] |
| 152 | |
| 153 | ----------- |
| 154 | |
| 155 | 建立mapreduce專案的(2) |
| 156 | {{{ |
| 157 | #!sh |
| 158 | project name-> 輸入 : icas (隨意) |
| 159 | use default hadoop -> Configur Hadoop install... -> 輸入: "/opt/hadoop" -> ok |
| 160 | Finish |
| 161 | }}} |
| 162 | |
| 163 | [[Image(wiki:waue/2009/0617:2-4-2.png)]] |
| 164 | |
| 165 | |
| 166 | -------------- |
| 167 | |
| 168 | == 2.5 設定專案 == |
| 169 | |
| 170 | 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 |
| 171 | |
| 172 | -------------- |
| 173 | |
| 174 | Step1. 右鍵點選project的properties做細部設定 |
| 175 | |
| 176 | [[Image(wiki:waue/2009/0617:2-5.png)]] |
| 177 | |
| 178 | ---------- |
| 179 | |
| 180 | Step2. 進入專案的細部設定頁 |
| 181 | |
| 182 | hadoop的javadoc的設定(1) |
| 183 | |
| 184 | |
| 185 | [[Image(wiki:waue/2009/0617:2-5-1.png)]] |
| 186 | |
| 187 | * java Build Path -> Libraries -> hadoop0.18.3-ant.jar |
| 188 | * java Build Path -> Libraries -> hadoop0.18.3-core.jar |
| 189 | * java Build Path -> Libraries -> hadoop0.18.3-tools.jar |
| 190 | * 以 hadoop0.18.3-core.jar 的設定內容如下,其他依此類推 |
| 191 | |
| 192 | {{{ |
| 193 | #!sh |
| 194 | source ...-> 輸入:/opt/hadoop/src/core |
| 195 | javadoc ...-> 輸入:file:/opt/hadoop/docs/api/ |
| 196 | }}} |
| 197 | |
| 198 | ------------ |
| 199 | Step3. hadoop的javadoc的設定完後(2) |
| 200 | [[Image(wiki:waue/2009/0617:2-5-2.png)]] |
| 201 | |
| 202 | ------------ |
| 203 | Step4. java本身的javadoc的設定(3) |
| 204 | |
| 205 | * javadoc location -> 輸入:file:/usr/lib/jvm/java-6-sun/docs/api/ |
| 206 | |
| 207 | [[Image(wiki:waue/2009/0617:2-5-3.png)]] |
| 208 | |
| 209 | ----- |
| 210 | 設定完後回到eclipse 主視窗 |
| 211 | |
| 212 | |
| 213 | == 2.6 連接hadoop server == |
| 214 | |
| 215 | -------- |
| 216 | 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: |
| 217 | [[Image(wiki:waue/2009/0617:2-6.png)]] |
| 218 | |
| 219 | ------------- |
| 220 | Step2. 進行eclipse 與 hadoop 間的設定(2) |
| 221 | [[Image(wiki:waue/2009/0617:2-6-1.png)]] |
| 222 | |
| 223 | {{{ |
| 224 | #!sh |
| 225 | Location Name -> 輸入:hadoop (隨意) |
| 226 | Map/Reduce Master |
| 227 | -> Host-> 輸入:localhost |
| 228 | -> Port-> 輸入:9001 |
| 229 | DFS Master |
| 230 | -> Host-> 輸入:9000 |
| 231 | Finish |
| 232 | }}} |
| 233 | ---------------- |
| 234 | |
| 235 | 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 |
| 236 | [[Image(wiki:waue/2009/0617:2-6-2.png)]] |
| 237 | ------------- |
| 238 | |
| 239 | = 三、 撰寫範例程式 = |
| 240 | |
| 241 | * 之前在eclipse上已經開了個專案icas,因此這個目錄在: |
| 242 | * /home/hadooper/workspace/icas |
| 243 | * 在這個目錄內有兩個資料夾: |
| 244 | * src : 用來裝程式原始碼 |
| 245 | * bin : 用來裝編譯後的class檔 |
| 246 | * 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 |
| 247 | * 在這我們編輯一個範例程式 : WordCount |
| 248 | |
| 249 | == 3.1 mapper.java == |
| 250 | |
| 251 | 1. new |
| 252 | |
| 253 | || File -> || new -> || mapper || |
| 254 | [[Image(wiki:waue/2009/0617:file-new-mapper.png)]] |
| 255 | |
| 256 | ----------- |
| 257 | |
| 258 | 2. create |
| 259 | |
| 260 | [[Image(wiki:waue/2009/0617:3-1.png)]] |
| 261 | {{{ |
| 262 | #!sh |
| 263 | source folder-> 輸入: icas/src |
| 264 | Package : Sample |
| 265 | Name -> : mapper |
| 266 | }}} |
| 267 | ---------- |
| 268 | |
| 269 | 3. modify |
| 270 | |
| 271 | {{{ |
| 272 | #!java |
| 273 | package Sample; |
| 274 | |
| 275 | import java.io.IOException; |
| 276 | import java.util.StringTokenizer; |
| 277 | |
| 278 | import org.apache.hadoop.io.IntWritable; |
| 279 | import org.apache.hadoop.io.LongWritable; |
| 280 | import org.apache.hadoop.io.Text; |
| 281 | import org.apache.hadoop.mapred.MapReduceBase; |
| 282 | import org.apache.hadoop.mapred.Mapper; |
| 283 | import org.apache.hadoop.mapred.OutputCollector; |
| 284 | import org.apache.hadoop.mapred.Reporter; |
| 285 | |
| 286 | public class mapper extends MapReduceBase implements Mapper<LongWritable, Text, Text, IntWritable> { |
| 287 | private final static IntWritable one = new IntWritable(1); |
| 288 | private Text word = new Text(); |
| 289 | |
| 290 | public void map(LongWritable key, Text value, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException { |
| 291 | String line = value.toString(); |
| 292 | StringTokenizer tokenizer = new StringTokenizer(line); |
| 293 | while (tokenizer.hasMoreTokens()) { |
| 294 | word.set(tokenizer.nextToken()); |
| 295 | output.collect(word, one); |
| 296 | } |
| 297 | } |
| 298 | } |
| 299 | |
| 300 | }}} |
| 301 | |
| 302 | 建立mapper.java後,貼入程式碼 |
| 303 | [[Image(wiki:waue/2009/0617:3-2.png)]] |
| 304 | |
| 305 | ------------ |
| 306 | |
| 307 | == 3.2 reducer.java == |
| 308 | |
| 309 | 1. new |
| 310 | |
| 311 | * File -> new -> reducer |
| 312 | [[Image(wiki:waue/2009/0617:file-new-reducer.png)]] |
| 313 | |
| 314 | ------- |
| 315 | 2. create |
| 316 | [[Image(wiki:waue/2009/0617:3-3.png)]] |
| 317 | |
| 318 | {{{ |
| 319 | #!sh |
| 320 | source folder-> 輸入: icas/src |
| 321 | Package : Sample |
| 322 | Name -> : reducer |
| 323 | }}} |
| 324 | |
| 325 | ----------- |
| 326 | |
| 327 | 3. modify |
| 328 | |
| 329 | {{{ |
| 330 | #!java |
| 331 | package Sample; |
| 332 | |
| 333 | import java.io.IOException; |
| 334 | import java.util.Iterator; |
| 335 | |
| 336 | import org.apache.hadoop.io.IntWritable; |
| 337 | import org.apache.hadoop.io.Text; |
| 338 | import org.apache.hadoop.mapred.MapReduceBase; |
| 339 | import org.apache.hadoop.mapred.OutputCollector; |
| 340 | import org.apache.hadoop.mapred.Reducer; |
| 341 | import org.apache.hadoop.mapred.Reporter; |
| 342 | |
| 343 | public class reducer extends MapReduceBase implements Reducer<Text, IntWritable, Text, IntWritable> { |
| 344 | public void reduce(Text key, Iterator<IntWritable> values, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException { |
| 345 | int sum = 0; |
| 346 | while (values.hasNext()) { |
| 347 | sum += values.next().get(); |
| 348 | } |
| 349 | output.collect(key, new IntWritable(sum)); |
| 350 | } |
| 351 | } |
| 352 | }}} |
| 353 | |
| 354 | * File -> new -> Map/Reduce Driver |
| 355 | [[Image(wiki:waue/2009/0617:file-new-mr-driver.png)]] |
| 356 | ---------- |
| 357 | |
| 358 | == 3.3 WordCount.java (main function) == |
| 359 | |
| 360 | 1. new |
| 361 | |
| 362 | 建立WordCount.java,此檔用來驅動mapper 與 reducer,因此選擇 Map/Reduce Driver |
| 363 | |
| 364 | |
| 365 | [[Image(wiki:waue/2009/0617:3-4.png)]] |
| 366 | ------------ |
| 367 | |
| 368 | 2. create |
| 369 | |
| 370 | {{{ |
| 371 | #!sh |
| 372 | source folder-> 輸入: icas/src |
| 373 | Package : Sample |
| 374 | Name -> : WordCount.java |
| 375 | }}} |
| 376 | |
| 377 | ------- |
| 378 | 3. modify |
| 379 | |
| 380 | {{{ |
| 381 | #!java |
| 382 | package Sample; |
| 383 | import org.apache.hadoop.fs.Path; |
| 384 | import org.apache.hadoop.io.IntWritable; |
| 385 | import org.apache.hadoop.io.Text; |
| 386 | import org.apache.hadoop.mapred.FileInputFormat; |
| 387 | import org.apache.hadoop.mapred.FileOutputFormat; |
| 388 | import org.apache.hadoop.mapred.JobClient; |
| 389 | import org.apache.hadoop.mapred.JobConf; |
| 390 | import org.apache.hadoop.mapred.TextInputFormat; |
| 391 | import org.apache.hadoop.mapred.TextOutputFormat; |
| 392 | |
| 393 | public class WordCount { |
| 394 | |
| 395 | public static void main(String[] args) throws Exception { |
| 396 | JobConf conf = new JobConf(WordCount.class); |
| 397 | conf.setJobName("wordcount"); |
| 398 | |
| 399 | conf.setOutputKeyClass(Text.class); |
| 400 | conf.setOutputValueClass(IntWritable.class); |
| 401 | |
| 402 | conf.setMapperClass(mapper.class); |
| 403 | conf.setCombinerClass(reducer.class); |
| 404 | conf.setReducerClass(reducer.class); |
| 405 | |
| 406 | conf.setInputFormat(TextInputFormat.class); |
| 407 | conf.setOutputFormat(TextOutputFormat.class); |
| 408 | |
| 409 | FileInputFormat.setInputPaths(conf, new Path("/user/hadooper/input")); |
| 410 | FileOutputFormat.setOutputPath(conf, new Path("lab5_out2")); |
| 411 | |
| 412 | JobClient.runJob(conf); |
| 413 | } |
| 414 | } |
| 415 | }}} |
| 416 | |
| 417 | 三個檔完成後並存檔後,整個程式建立完成 |
| 418 | [[Image(wiki:waue/2009/0617:3-5.png)]] |
| 419 | |
| 420 | ------- |
| 421 | |
| 422 | * 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 |
| 423 | |
| 424 | {{{ |
| 425 | $ cd workspace/icas |
| 426 | $ ls src/Sample/ |
| 427 | mapper.java reducer.java WordCount.java |
| 428 | $ ls bin/Sample/ |
| 429 | mapper.class reducer.class WordCount.class |
| 430 | }}} |
| 431 | |
| 432 | = 四、測試範例程式 = |
| 433 | |
| 434 | 在此提供兩種方法來run我們從eclipse 上編譯出的code。 |
| 435 | |
| 436 | 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 |
| 437 | |
| 438 | 方法二是產生jar檔後搭配自動編譯程式Makefile,參閱4.2 |
| 439 | |
| 440 | |
| 441 | == 4.1 法一:在eclipse上操作 == |
| 442 | |
| 443 | * 右鍵點選專案資料夾:icas -> run as -> run on Hadoop |
| 444 | |
| 445 | [[Image(wiki:waue/2009/0617:run-on-hadoop.png)]] |
| 446 | |
| 447 | |
| 448 | |