| 1 | [[PageOutline]] |
| 2 | {{{ |
| 3 | #!html |
| 4 | <div style="text-align: center;"><big |
| 5 | style="font-weight: bold;"><big><big> hadoop 0.20 程式開發 </big></big></big></div> |
| 6 | <div style="text-align: center;"> <big>eclipse plugin + Makefile</big> </div> |
| 7 | }}} |
| 8 | = 零. 前言 = |
| 9 | * 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... |
| 10 | * 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。 |
| 11 | * 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... |
| 12 | * 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''' |
| 13 | * 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 |
| 14 | |
| 15 | || 單位 || 作者 || Mail || |
| 16 | || 國家高速網路中心-格網技術組 || Wei-Yu Chen || waue @ nchc.org.tw || |
| 17 | |
| 18 | * Last Update: 2009/06/25 |
| 19 | |
| 20 | == 0.1 環境說明 == |
| 21 | * ubuntu 8.10 |
| 22 | * sun-java-6 |
| 23 | * eclipse 3.4.2 |
| 24 | * hadoop 0.20.0 |
| 25 | == 0.2 目錄說明 == |
| 26 | * 使用者:waue |
| 27 | * 使用者家目錄: /home/waue |
| 28 | * 專案目錄 : /home/waue/workspace |
| 29 | * hadoop目錄: /opt/hadoop |
| 30 | = 一、安裝 = |
| 31 | |
| 32 | 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 |
| 33 | |
| 34 | == 1.1. 安裝java == |
| 35 | |
| 36 | 首先安裝java 基本套件 |
| 37 | |
| 38 | {{{ |
| 39 | $ sudo apt-get install java-common sun-java6-bin sun-java6-jdk sun-java6-jre |
| 40 | }}} |
| 41 | |
| 42 | == 1.1.1. 安裝sun-java6-doc == |
| 43 | |
| 44 | 1 將javadoc (jdk-6u10-docs.zip) 下載下來 |
| 45 | [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] |
| 46 | [[Image(1-1.png)]] |
| 47 | |
| 48 | 2 下載完後將檔案放在 /tmp/ 下 |
| 49 | |
| 50 | 3 執行 |
| 51 | |
| 52 | {{{ |
| 53 | $ sudo apt-get install sun-java6-doc |
| 54 | }}} |
| 55 | |
| 56 | == 1.2. ssh 安裝設定 == |
| 57 | |
| 58 | {{{ |
| 59 | $ apt-get install ssh |
| 60 | $ ssh-keygen -t rsa -P '' -f ~/.ssh/id_rsa |
| 61 | $ cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys |
| 62 | $ ssh localhost |
| 63 | }}} |
| 64 | |
| 65 | 執行ssh localhost 沒有出現詢問密碼的訊息則無誤 |
| 66 | |
| 67 | == 1.3. 安裝hadoop == |
| 68 | |
| 69 | 安裝hadoop0.20到/opt/並取目錄名為hadoop |
| 70 | |
| 71 | {{{ |
| 72 | $ cd ~ |
| 73 | $ wget http://apache.ntu.edu.tw/hadoop/core/hadoop-0.20.0/hadoop-0.20.0.tar.gz |
| 74 | $ tar zxvf hadoop-0.20.0.tar.gz |
| 75 | $ sudo mv hadoop-0.20.0 /opt/ |
| 76 | $ sudo chown -R waue:waue /opt/hadoop-0.20.0 |
| 77 | $ sudo ln -sf /opt/hadoop-0.20.0 /opt/hadoop |
| 78 | }}} |
| 79 | |
| 80 | * 編輯 /opt/hadoop/conf/hadoop-env.sh |
| 81 | |
| 82 | {{{ |
| 83 | #!sh |
| 84 | export JAVA_HOME=/usr/lib/jvm/java-6-sun |
| 85 | export HADOOP_HOME=/opt/hadoop |
| 86 | export PATH=$PATH:/opt/hadoop/bin |
| 87 | }}} |
| 88 | |
| 89 | * 編輯 /opt/hadoop/conf/core-site.xml |
| 90 | |
| 91 | {{{ |
| 92 | #!sh |
| 93 | <configuration> |
| 94 | <property> |
| 95 | <name>fs.default.name</name> |
| 96 | <value>hdfs://localhost:9000</value> |
| 97 | </property> |
| 98 | <property> |
| 99 | <name>hadoop.tmp.dir</name> |
| 100 | <value>/tmp/hadoop/hadoop-${user.name}</value> |
| 101 | </property> |
| 102 | </configuration> |
| 103 | |
| 104 | }}} |
| 105 | |
| 106 | * 編輯 /opt/hadoop/conf/hdfs-site.xml |
| 107 | |
| 108 | {{{ |
| 109 | #!sh |
| 110 | <configuration> |
| 111 | <property> |
| 112 | <name>dfs.replication</name> |
| 113 | <value>1</value> |
| 114 | </property> |
| 115 | </configuration> |
| 116 | }}} |
| 117 | |
| 118 | * 編輯 /opt/hadoop/conf/mapred-site.xml |
| 119 | |
| 120 | {{{ |
| 121 | #!sh |
| 122 | <configuration> |
| 123 | <property> |
| 124 | <name>mapred.job.tracker</name> |
| 125 | <value>localhost:9001</value> |
| 126 | </property> |
| 127 | </configuration> |
| 128 | }}} |
| 129 | |
| 130 | * 啟動 |
| 131 | {{{ |
| 132 | $ cd /opt/hadoop |
| 133 | $ source /opt/hadoop/conf/hadoop-env.sh |
| 134 | $ hadoop namenode -format |
| 135 | $ start-all.sh |
| 136 | $ hadoop fs -put conf input |
| 137 | $ hadoop fs -ls |
| 138 | }}} |
| 139 | |
| 140 | * 沒有錯誤訊息則代表無誤 |
| 141 | |
| 142 | == 1.4. 安裝eclipse == |
| 143 | |
| 144 | * 在此提供兩個方法來下載檔案 |
| 145 | * 方法一: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],並且放這檔案到家目錄 |
| 146 | * 方法二:貼上指令 |
| 147 | {{{ |
| 148 | $ cd ~ |
| 149 | $ wget http://ftp.cs.pu.edu.tw/pub/eclipse/eclipse/downloads/drops/R-3.4.2-200902111700/eclipse-SDK-3.4.2-linux-gtk.tar.gz |
| 150 | }}} |
| 151 | |
| 152 | * eclipse 檔已下載到家目錄後,執行下面指令: |
| 153 | |
| 154 | {{{ |
| 155 | $ cd ~ |
| 156 | $ tar -zxvf eclipse-SDK-3.4.2-linux-gtk.tar.gz |
| 157 | $ sudo mv eclipse /opt |
| 158 | $ sudo ln -sf /opt/eclipse/eclipse /usr/local/bin/ |
| 159 | |
| 160 | }}} |
| 161 | |
| 162 | = 二、 建立專案 = |
| 163 | |
| 164 | == 2.1 安裝hadoop 的 eclipse plugin == |
| 165 | |
| 166 | * 匯入hadoop 0.20.0 eclipse plugin |
| 167 | |
| 168 | {{{ |
| 169 | $ cd /opt/hadoop |
| 170 | $ sudo cp /opt/hadoop/contrib/eclipse-plugin/hadoop-0.20.0-eclipse-plugin.jar /opt/eclipse/plugins |
| 171 | }}} |
| 172 | |
| 173 | {{{ |
| 174 | $ sudo vim /opt/eclipse/eclipse.ini |
| 175 | }}} |
| 176 | |
| 177 | * 可斟酌參考eclipse.ini內容(非必要) |
| 178 | |
| 179 | {{{ |
| 180 | #!sh |
| 181 | -startup |
| 182 | plugins/org.eclipse.equinox.launcher_1.0.101.R34x_v20081125.jar |
| 183 | --launcher.library |
| 184 | plugins/org.eclipse.equinox.launcher.gtk.linux.x86_1.0.101.R34x_v20080805 |
| 185 | -showsplash |
| 186 | org.eclipse.platform |
| 187 | --launcher.XXMaxPermSize |
| 188 | 512m |
| 189 | -vmargs |
| 190 | -Xms40m |
| 191 | -Xmx512m |
| 192 | }}} |
| 193 | |
| 194 | == 2.2 開啟eclipse == |
| 195 | |
| 196 | * 打開eclipse |
| 197 | |
| 198 | {{{ |
| 199 | $ eclipse & |
| 200 | }}} |
| 201 | |
| 202 | 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 |
| 203 | [[Image(2-1.png)]] |
| 204 | ------- |
| 205 | |
| 206 | '''PS: 之後的說明則是在eclipse 上的介面操作''' |
| 207 | |
| 208 | ------- |
| 209 | |
| 210 | == 2.3 選擇視野 == |
| 211 | |
| 212 | || window -> || open pers.. -> || other.. -> || map/reduce|| |
| 213 | |
| 214 | [[Image(win-open-other.png)]] |
| 215 | |
| 216 | ------- |
| 217 | |
| 218 | 設定要用 Map/Reduce 的視野 |
| 219 | [[Image(2-2.png)]] |
| 220 | |
| 221 | --------- |
| 222 | |
| 223 | 使用 Map/Reduce 的視野後的介面呈現 |
| 224 | [[Image(2-3.png)]] |
| 225 | |
| 226 | -------- |
| 227 | |
| 228 | == 2.4 建立專案 == |
| 229 | |
| 230 | || file -> || new -> || project -> || Map/Reduce -> || Map/Reduce Project -> || next || |
| 231 | [[Image(file-new-project.png)]] |
| 232 | |
| 233 | -------- |
| 234 | |
| 235 | 建立mapreduce專案(1) |
| 236 | |
| 237 | [[Image(2-4.png)]] |
| 238 | |
| 239 | ----------- |
| 240 | |
| 241 | 建立mapreduce專案的(2) |
| 242 | {{{ |
| 243 | #!sh |
| 244 | project name-> 輸入 : icas (隨意) |
| 245 | use default hadoop -> Configur Hadoop install... -> 輸入: "/opt/hadoop" -> ok |
| 246 | Finish |
| 247 | }}} |
| 248 | |
| 249 | [[Image(2-4-2.png)]] |
| 250 | |
| 251 | |
| 252 | -------------- |
| 253 | |
| 254 | == 2.5 設定專案 == |
| 255 | |
| 256 | 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 |
| 257 | |
| 258 | -------------- |
| 259 | |
| 260 | Step1. 右鍵點選project的properties做細部設定 |
| 261 | |
| 262 | [[Image(2-5.png)]] |
| 263 | |
| 264 | ---------- |
| 265 | |
| 266 | Step2. 進入專案的細部設定頁 |
| 267 | |
| 268 | hadoop的javadoc的設定(1) |
| 269 | [[Image(2-5-1.png)]] |
| 270 | |
| 271 | * java Build Path -> Libraries -> hadoop-0.20.0-ant.jar |
| 272 | * java Build Path -> Libraries -> hadoop-0.20.0-core.jar |
| 273 | * java Build Path -> Libraries -> hadoop-0.20.0-tools.jar |
| 274 | * 以 hadoop-0.20.0-core.jar 的設定內容如下,其他依此類推 |
| 275 | |
| 276 | {{{ |
| 277 | #!sh |
| 278 | source ...-> 輸入:/opt/opt/hadoop-0.20.0/src/core |
| 279 | javadoc ...-> 輸入:file:/opt/hadoop/docs/api/ |
| 280 | }}} |
| 281 | |
| 282 | ------------ |
| 283 | Step3. hadoop的javadoc的設定完後(2) |
| 284 | [[Image(2-5-2.png)]] |
| 285 | |
| 286 | ------------ |
| 287 | Step4. java本身的javadoc的設定(3) |
| 288 | |
| 289 | * javadoc location -> 輸入:file:/usr/lib/jvm/java-6-sun/docs/api/ |
| 290 | |
| 291 | [[Image(2-5-3.png)]] |
| 292 | |
| 293 | ----- |
| 294 | 設定完後回到eclipse 主視窗 |
| 295 | |
| 296 | |
| 297 | == 2.6 連接hadoop server == |
| 298 | |
| 299 | -------- |
| 300 | 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: |
| 301 | [[Image(2-6.png)]] |
| 302 | |
| 303 | ------------- |
| 304 | Step2. 進行eclipse 與 hadoop 間的設定(2) |
| 305 | [[Image(2-6-1.png)]] |
| 306 | |
| 307 | {{{ |
| 308 | #!sh |
| 309 | Location Name -> 輸入:hadoop (隨意) |
| 310 | Map/Reduce Master -> Host-> 輸入:localhost |
| 311 | Map/Reduce Master -> Port-> 輸入:9001 |
| 312 | DFS Master -> Host-> 輸入:9000 |
| 313 | Finish |
| 314 | }}} |
| 315 | ---------------- |
| 316 | |
| 317 | 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 |
| 318 | [[Image(2-6-2.png)]] |
| 319 | ------------- |
| 320 | |
| 321 | = 三、 撰寫範例程式 = |
| 322 | |
| 323 | * 之前在eclipse上已經開了個專案icas,因此這個目錄在: |
| 324 | * /home/waue/workspace/icas |
| 325 | * 在這個目錄內有兩個資料夾: |
| 326 | * src : 用來裝程式原始碼 |
| 327 | * bin : 用來裝編譯後的class檔 |
| 328 | * 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 |
| 329 | * 在這我們編輯一個範例程式 : WordCount |
| 330 | |
| 331 | == 3.1 mapper.java == |
| 332 | |
| 333 | 1. new |
| 334 | |
| 335 | || File -> || new -> || mapper || |
| 336 | [[Image(file-new-mapper.png)]] |
| 337 | |
| 338 | ----------- |
| 339 | |
| 340 | 2. create |
| 341 | |
| 342 | [[Image(3-1.png)]] |
| 343 | {{{ |
| 344 | #!sh |
| 345 | source folder-> 輸入: icas/src |
| 346 | Package : Sample |
| 347 | Name -> : mapper |
| 348 | }}} |
| 349 | ---------- |
| 350 | |
| 351 | 3. modify |
| 352 | |
| 353 | {{{ |
| 354 | #!java |
| 355 | package Sample; |
| 356 | |
| 357 | import java.io.IOException; |
| 358 | import java.util.StringTokenizer; |
| 359 | |
| 360 | import org.apache.hadoop.io.IntWritable; |
| 361 | import org.apache.hadoop.io.Text; |
| 362 | import org.apache.hadoop.mapreduce.Mapper; |
| 363 | |
| 364 | public class mapper extends Mapper<Object, Text, Text, IntWritable> { |
| 365 | |
| 366 | private final static IntWritable one = new IntWritable(1); |
| 367 | private Text word = new Text(); |
| 368 | |
| 369 | public void map(Object key, Text value, Context context) |
| 370 | throws IOException, InterruptedException { |
| 371 | StringTokenizer itr = new StringTokenizer(value.toString()); |
| 372 | while (itr.hasMoreTokens()) { |
| 373 | word.set(itr.nextToken()); |
| 374 | context.write(word, one); |
| 375 | } |
| 376 | } |
| 377 | } |
| 378 | }}} |
| 379 | |
| 380 | 建立mapper.java後,貼入程式碼 |
| 381 | [[Image(3-2.png)]] |
| 382 | |
| 383 | ------------ |
| 384 | |
| 385 | == 3.2 reducer.java == |
| 386 | |
| 387 | 1. new |
| 388 | |
| 389 | * File -> new -> reducer |
| 390 | [[Image(file-new-reducer.png)]] |
| 391 | |
| 392 | ------- |
| 393 | 2. create |
| 394 | [[Image(3-3.png)]] |
| 395 | |
| 396 | {{{ |
| 397 | #!sh |
| 398 | source folder-> 輸入: icas/src |
| 399 | Package : Sample |
| 400 | Name -> : reducer |
| 401 | }}} |
| 402 | |
| 403 | ----------- |
| 404 | |
| 405 | 3. modify |
| 406 | |
| 407 | {{{ |
| 408 | #!java |
| 409 | package Sample; |
| 410 | |
| 411 | import java.io.IOException; |
| 412 | |
| 413 | import org.apache.hadoop.io.IntWritable; |
| 414 | import org.apache.hadoop.io.Text; |
| 415 | import org.apache.hadoop.mapreduce.Reducer; |
| 416 | |
| 417 | public class reducer extends Reducer<Text, IntWritable, Text, IntWritable> { |
| 418 | private IntWritable result = new IntWritable(); |
| 419 | |
| 420 | public void reduce(Text key, Iterable<IntWritable> values, Context context) |
| 421 | throws IOException, InterruptedException { |
| 422 | int sum = 0; |
| 423 | for (IntWritable val : values) { |
| 424 | sum += val.get(); |
| 425 | } |
| 426 | result.set(sum); |
| 427 | context.write(key, result); |
| 428 | } |
| 429 | } |
| 430 | }}} |
| 431 | |
| 432 | * File -> new -> Map/Reduce Driver |
| 433 | [[Image(file-new-mr-driver.png)]] |
| 434 | ---------- |
| 435 | |
| 436 | == 3.3 WordCount.java (main function) == |
| 437 | |
| 438 | 1. new |
| 439 | |
| 440 | 建立WordCount.java,此檔用來驅動mapper 與 reducer,因此選擇 Map/Reduce Driver |
| 441 | [[Image(3-4.png)]] |
| 442 | ------------ |
| 443 | |
| 444 | 2. create |
| 445 | |
| 446 | {{{ |
| 447 | #!sh |
| 448 | source folder-> 輸入: icas/src |
| 449 | Package : Sample |
| 450 | Name -> : WordCount.java |
| 451 | }}} |
| 452 | |
| 453 | ------- |
| 454 | 3. modify |
| 455 | |
| 456 | {{{ |
| 457 | #!java |
| 458 | package Sample; |
| 459 | |
| 460 | import org.apache.hadoop.conf.Configuration; |
| 461 | import org.apache.hadoop.fs.Path; |
| 462 | import org.apache.hadoop.io.IntWritable; |
| 463 | import org.apache.hadoop.io.Text; |
| 464 | import org.apache.hadoop.mapreduce.Job; |
| 465 | import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; |
| 466 | import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; |
| 467 | import org.apache.hadoop.util.GenericOptionsParser; |
| 468 | |
| 469 | public class WordCount { |
| 470 | |
| 471 | public static void main(String[] args) throws Exception { |
| 472 | Configuration conf = new Configuration(); |
| 473 | String[] otherArgs = new GenericOptionsParser(conf, args) |
| 474 | .getRemainingArgs(); |
| 475 | if (otherArgs.length != 2) { |
| 476 | System.err.println("Usage: wordcount <in> <out>"); |
| 477 | System.exit(2); |
| 478 | } |
| 479 | Job job = new Job(conf, "word count"); |
| 480 | job.setJarByClass(WordCount.class); |
| 481 | job.setMapperClass(mapper.class); |
| 482 | |
| 483 | job.setCombinerClass(reducer.class); |
| 484 | job.setReducerClass(reducer.class); |
| 485 | job.setOutputKeyClass(Text.class); |
| 486 | job.setOutputValueClass(IntWritable.class); |
| 487 | FileInputFormat.addInputPath(job, new Path(otherArgs[0])); |
| 488 | FileOutputFormat.setOutputPath(job, new Path(otherArgs[1])); |
| 489 | System.exit(job.waitForCompletion(true) ? 0 : 1); |
| 490 | } |
| 491 | } |
| 492 | }}} |
| 493 | |
| 494 | 三個檔完成後並存檔後,整個程式建立完成 |
| 495 | [[Image(3-5.png)]] |
| 496 | |
| 497 | ------- |
| 498 | |
| 499 | * 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 |
| 500 | |
| 501 | {{{ |
| 502 | $ cd workspace/icas |
| 503 | $ ls src/Sample/ |
| 504 | mapper.java reducer.java WordCount.java |
| 505 | $ ls bin/Sample/ |
| 506 | mapper.class reducer.class WordCount.class |
| 507 | }}} |
| 508 | |
| 509 | = 四、測試範例程式 = |
| 510 | |
| 511 | * 由於hadoop 0.20 此版本的eclipse-plugin依舊不完整 ,如: |
| 512 | * 右鍵點選WordCount.java -> run as -> run on Hadoop :沒有效果 |
| 513 | |
| 514 | [[Image(run-on-hadoop.png)]] |
| 515 | |
| 516 | * 因此,4.1 提供一個eclipse 上解除 run-on-hadoop 封印的方法。而4.2 則是避開run-on-hadoop 這個功能,用command mode端指令的方法執行。 |
| 517 | |
| 518 | |
| 519 | == 4.1 解除run-on-hadoop封印 == |
| 520 | |
| 521 | 有一熱心的hadoop使用者提供一個能讓 run-on-hadoop 這個功能恢復的方法。 |
| 522 | |
| 523 | 原因是hadoop 的 eclipse-plugin 也許是用eclipse europa 這個版本開發的,而eclipse 的各版本 3.2 , 3.3, 3.4 間也都有或多或少的差異性存在。 |
| 524 | |
| 525 | 因此如果先用eclipse europa 來建立一個新專案,之後把europa的eclipse這個版本關掉,換用eclipse 3.4開啟,之後這個專案就能用run-on-mapreduce 這個功能囉! |
| 526 | |
| 527 | 有興趣的話可以試試!(感謝逢甲資工所謝同學) |
| 528 | |
| 529 | == 4.2 運用終端指令 == |
| 530 | === 4.2.1 產生Makefile 檔 === |
| 531 | {{{ |
| 532 | |
| 533 | $ cd /home/waue/workspace/icas/ |
| 534 | $ gedit Makefile |
| 535 | }}} |
| 536 | |
| 537 | * 輸入以下Makefile的內容 |
| 538 | {{{ |
| 539 | #!sh |
| 540 | |
| 541 | JarFile="sample-0.1.jar" |
| 542 | MainFunc="Sample.WordCount" |
| 543 | LocalOutDir="/tmp/output" |
| 544 | |
| 545 | all:help |
| 546 | jar: |
| 547 | jar -cvf ${JarFile} -C bin/ . |
| 548 | |
| 549 | run: |
| 550 | hadoop jar ${JarFile} ${MainFunc} input output |
| 551 | |
| 552 | clean: |
| 553 | hadoop fs -rmr output |
| 554 | |
| 555 | output: |
| 556 | rm -rf ${LocalOutDir} |
| 557 | hadoop fs -get output ${LocalOutDir} |
| 558 | gedit ${LocalOutDir}/part-r-00000 & |
| 559 | |
| 560 | help: |
| 561 | @echo "Usage:" |
| 562 | @echo " make jar - Build Jar File." |
| 563 | @echo " make clean - Clean up Output directory on HDFS." |
| 564 | @echo " make run - Run your MapReduce code on Hadoop." |
| 565 | @echo " make output - Download and show output file" |
| 566 | @echo " make help - Show Makefile options." |
| 567 | @echo " " |
| 568 | @echo "Example:" |
| 569 | @echo " make jar; make run; make output; make clean" |
| 570 | |
| 571 | }}} |
| 572 | |
| 573 | === 4.2.2 執行 === |
| 574 | |
| 575 | * 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 |
| 576 | * make 的用法說明 |
| 577 | |
| 578 | {{{ |
| 579 | $ cd /home/waue/workspace/icas/ |
| 580 | $ make |
| 581 | Usage: |
| 582 | make jar - Build Jar File. |
| 583 | make clean - Clean up Output directory on HDFS. |
| 584 | make run - Run your MapReduce code on Hadoop. |
| 585 | make output - Download and show output file |
| 586 | make help - Show Makefile options. |
| 587 | |
| 588 | Example: |
| 589 | make jar; make run; make output; make clean |
| 590 | }}} |
| 591 | |
| 592 | * 下面提供各種make 的參數 |
| 593 | |
| 594 | === make jar === |
| 595 | * 1. 編譯產生jar檔 |
| 596 | |
| 597 | {{{ |
| 598 | $ make jar |
| 599 | }}} |
| 600 | |
| 601 | === make run === |
| 602 | * 2. 跑我們的wordcount 於hadoop上 |
| 603 | |
| 604 | {{{ |
| 605 | $ make run |
| 606 | }}} |
| 607 | |
| 608 | * 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hadoop0.20的平台上運行。 |
| 609 | |
| 610 | * 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 |
| 611 | |
| 612 | [[Image(4-1.png)]] |
| 613 | ------ |
| 614 | * 因為有設定完整的javadoc, 因此可以得到詳細的解說與輔助 |
| 615 | [[Image(4-2.png)]] |
| 616 | |
| 617 | === make output === |
| 618 | * 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 |
| 619 | |
| 620 | {{{ |
| 621 | $ make output |
| 622 | }}} |
| 623 | |
| 624 | === make clean === |
| 625 | * 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! |
| 626 | |
| 627 | {{{ |
| 628 | $ make clean |
| 629 | }}} |
| 630 | |
| 631 | = 五、結論 = |
| 632 | |
| 633 | * 搭配eclipse ,我們可以更有效率的開發hadoop |
| 634 | * hadoop 0.20 與之前的版本api以及設定都有些改變,可以看 [wiki:waue/2009/0618 hadoop 0.20 coding (eclipse )] |