Cloudera CDH/CDP 및 Hadoop EcoSystem, Semantic IoT등의 개발/운영 기술을 정리합니다. gooper@gooper.com로 문의 주세요.
spark 테스트 프로그램 몇개
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 | package com.gooper.test;import java.io.Serializable;import java.util.ArrayList;import java.util.Arrays;import java.util.Iterator;import java.util.List;import java.util.Map;import java.util.Map.Entry;import org.apache.commons.lang.StringUtils;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.FileSystem;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.IntWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat;import org.apache.spark.Accumulator;import org.apache.spark.SparkConf;import org.apache.spark.api.java.JavaDoubleRDD;import org.apache.spark.api.java.JavaPairRDD;import org.apache.spark.api.java.JavaRDD;import org.apache.spark.api.java.JavaSparkContext;import org.apache.spark.api.java.Optional;import org.apache.spark.api.java.function.DoubleFunction;import org.apache.spark.api.java.function.FlatMapFunction;import org.apache.spark.api.java.function.Function;import org.apache.spark.api.java.function.Function2;import org.apache.spark.api.java.function.MapFunction;import org.apache.spark.api.java.function.PairFunction;import org.apache.spark.broadcast.Broadcast;import org.apache.spark.sql.Dataset;import org.apache.spark.sql.Encoders;import org.apache.spark.sql.Row;import org.apache.spark.sql.SQLContext;import org.apache.spark.sql.SparkSession;import org.apache.spark.sql.hive.HiveContext;import org.apache.spark.storage.StorageLevel;import scala.Tuple2;import com.google.gson.Gson;public final class JavaSparkPi { static final SparkConf sparkConf=new SparkConf().setAppName("JavaSparkPi"); static { sparkConf.setMaster("local[*]"); } static final JavaSparkContext jsc=new JavaSparkContext(sparkConf); static final Broadcast<List<String>> temp = jsc.broadcast(Arrays.asList("hello world", "", "hi park", "", "sss ff")); public static void main(String[] args) throws Exception { System.out.println("start................"); // PI값 구하기 int slices = (args.length == 1) ? Integer.parseInt(args[0]) : 2; int n = 100000 * slices; List<Integer> l = new ArrayList<Integer>(n); for (int i = 0; i < n; i++) { l.add(i); } JavaRDD<Integer> dataSet = jsc.parallelize(l, slices); int count = dataSet.map((z) -> { double x = Math.random() * 2 - 1; double y = Math.random() * 2 - 1; if(x * x + y * y <= 1) return 1; else return 0; }).reduce((s, s2) -> (s + s2)); System.out.println("Pi is roughly " + 4.0 * count / n); System.out.println("=========== test start ================================="); test(jsc); System.out.println("=========== test end ================================="); System.out.println("=========== test2 start ================================="); test2(jsc); System.out.println("=========== test2 end ================================="); System.out.println("=========== test3 start ================================="); test3(jsc); System.out.println("=========== test3 end ================================="); System.out.println("=========== test4 start ================================="); test4(jsc); System.out.println("=========== test4 end ================================="); System.out.println("=========== test5 start ================================="); test5(jsc); System.out.println("=========== test5 end ================================="); System.out.println("=========== test6 start ================================="); test6(jsc); System.out.println("=========== test6 end ================================="); System.out.println("=========== test7 start ================================="); test7(jsc); System.out.println("=========== test7 end ================================="); System.out.println("=========== test8 start ================================="); test8(jsc); System.out.println("=========== test8 end ================================="); System.out.println("=========== test9 start ================================="); test9(jsc); System.out.println("=========== test9 end ================================="); System.out.println("=========== test10 start ================================="); test10(jsc); System.out.println("=========== test10 end ================================="); System.out.println("=========== test11 start ================================="); test11(jsc); System.out.println("=========== test11 end ================================="); System.out.println("=========== test12 start ================================="); test12(jsc); System.out.println("=========== test12 end ================================="); System.out.println("=========== test13 start ================================="); test13(jsc); System.out.println("=========== test13 end ================================="); System.out.println("=========== test14 start ================================="); test14(jsc); System.out.println("=========== test14 end ================================="); System.out.println("=========== test15 start ================================="); test15(); System.out.println("=========== test15 end ================================="); System.out.println("=========== test16 start ================================="); test16(jsc); System.out.println("=========== test16 end ================================="); System.out.println("=========== test17 start ================================="); test17(jsc); System.out.println("=========== test17 end ================================="); System.out.println("=========== test18 start ================================="); test18(jsc); System.out.println("=========== test18 end ================================="); System.out.println("=========== test19 start ================================="); test19(jsc); System.out.println("=========== test19 end ================================="); jsc.stop(); jsc.close(); System.out.println("end................"); } // List를 RDD로 변환하고 map연상을 통해서 x*x한 값으로 구성된 JavaRDD를 만들어서 화면에 출력 static void test (JavaSparkContext sc) { JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1,2,3,4,5)); JavaRDD<Integer> result = rdd.map((x) -> {return x*x;}); System.out.println("result ==>"+StringUtils.join(result.collect(), ",")); } // 문자열을 space로 분리(람다식을 이용함)하여 화면에 출력 static void test2 (JavaSparkContext sc) { JavaRDD<String> lines = sc.parallelize(Arrays.asList("hello world", "hi")); JavaRDD<String> words = lines.flatMap( (line) -> {return Arrays.asList(line.split(" ")).iterator();}); System.out.println("result ==>"+StringUtils.join(words.collect(), ",")); } // RDD의 함수인 disticnt, union, intersection, subtract, cartesian, countByValue를 적용하여 화면에 결과 출력 static void test3 (JavaSparkContext sc) { JavaRDD<String> data1 = sc.parallelize(Arrays.asList("coffee", "coffee", "panda", "monkey", "tea")); JavaRDD<String> data2 = sc.parallelize(Arrays.asList("coffee", "monkey", "kitty")); JavaRDD<Integer> data3 = sc.parallelize(Arrays.asList(1,2,3)); System.out.println("distinct ==>"+data1.distinct().collect()); System.out.println("union ==>"+data1.union(data2).collect()); System.out.println("intersection ==>"+data1.intersection(data2).collect()); System.out.println("subtract ==>"+data1.subtract(data2).collect()); System.out.println("cartesion ==>"+data1.cartesian(data3).collect()); System.out.println("countByValue ==>"+data1.countByValue()); } // persist를 사용하여 reduce, fold등를 적용해보고, double형의 RDD로 변환하여 mean값을 구하여 출력함 static void test4(JavaSparkContext sc) { JavaRDD<Integer> data1 = sc.parallelize(Arrays.asList(1,2,3,4)); JavaRDD<Integer> data2 = sc.parallelize(Arrays.asList(3,4,5)); List<Integer> data3 = new ArrayList<Integer>(); data3.add(1); data3.add(2); data3.add(3); data3.add(4); JavaRDD<Integer> map = data1.map(x -> x+1); map.persist(StorageLevel.MEMORY_AND_DISK()); Function2<Integer, Integer, Integer> reduce = new Function2<Integer, Integer, Integer>() { public Integer call(Integer x, Integer y) { return x+y; } }; DoubleFunction<Integer> df = new DoubleFunction<Integer>() { public double call(Integer x) { return (double) x; } }; System.out.println("map==>"+data1.map(x -> x+1).reduce((x, y)->{return x+y;})); System.out.println("fold==>"+map.fold(0, reduce)); map.foreach((x)->System.out.println(x)); JavaDoubleRDD result = data1.mapToDouble((x) -> x); System.out.println("mean ===>"+result.mean()); result.foreach((x) -> System.out.println(x)); System.out.println("--------------------------"); JavaDoubleRDD result2 = map.mapToDouble(df); System.out.println("mean by DoubleFuntion()===>"+result2.mean()); result2.foreach((x) -> System.out.println(x)); } // 숫자형의 JavaRDD를 이용하여 aggregate함수를 사용하는 예제 static void test5(JavaSparkContext sc) { JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1,2,3,4,5)); Function2<AvgCount, Integer, AvgCount> addAndCount = new Function2<AvgCount, Integer, AvgCount> (){ private static final long serialVersionUID = 122222L; public AvgCount call(AvgCount a, Integer x) { a.total += x; a.num += 1; return a; } }; Function2<AvgCount, AvgCount, AvgCount> combine = new Function2<AvgCount, AvgCount, AvgCount> (){ private static final long serialVersionUID = 11111L; public AvgCount call(AvgCount a, AvgCount b) { a.total += b.total; a.num += b.num; return a; } }; AvgCount initial = new AvgCount(0, 0); AvgCount result = rdd.aggregate(initial, addAndCount, combine); System.out.println(result.avg()); } // 문자열 RDD를 key/value형태로 바꾸고 key를 기준을 작동하는 sortByKey, reduceByKey, groupByKey, sortByKey를 적용해보는 예제 static void test6(JavaSparkContext sc) { JavaRDD<String> lines = sc.parallelize(Arrays.asList("hello world", "hi", "hi park", "dk")); PairFunction<String, String, Integer> keyData = new PairFunction<String, String, Integer>() { public Tuple2<String, Integer> call(String x) { return new Tuple2(x.split(" ")[0], x.length()); } }; JavaPairRDD<String, Integer> pairs = lines.mapToPair(keyData); pairs.foreach(x->System.out.println(x)); JavaPairRDD<String, Integer> reduceByKey = pairs.reduceByKey( (x, y) -> { return (x+y);} ); JavaPairRDD<String, Iterable<Integer>> groupByKey = pairs.groupByKey(); JavaPairRDD<String, Integer> sortByKey = pairs.sortByKey(false); System.out.println("reduceByKey =>"+reduceByKey.collect() ); System.out.println("groupByKey =>"+groupByKey.collect() ); System.out.println("sortBykey => "+sortByKey.collect() ); } // Tuple2형의 RDD를 이용하여 mapToPair를 적용하여 JavaPairRDD를 만들면서 각각의 RDD를 출력해보는 예제 // 두개의 JavaPairRDD를 이용하여 subtractByKey, subtract, rightOuterJoin, leftOuterJoin, cogroup를 적용해보는 예제 static void test7(JavaSparkContext sc) { List<Tuple2<String, Integer>> data1 = new ArrayList<Tuple2<String, Integer>>(); List<Tuple2<String, Integer>> data2 = new ArrayList<Tuple2<String, Integer>>(); data1.add(new Tuple2("a",2)); data1.add(new Tuple2("c",4)); data1.add(new Tuple2("c",6)); data2.add(new Tuple2("c",4)); JavaRDD<Tuple2<String, Integer>> pdataa1 = sc.parallelize(data1); JavaPairRDD<String, Integer> pdataa11 = pdataa1.mapToPair( (x) -> {return new Tuple2(x._1, x._2);} ); System.out.println("pdataa1 ==>"+pdataa1); System.out.println("pdataa11 ==>"+pdataa11); System.out.println("pdataa1 ==>"+pdataa1.collect()); System.out.println("pdataa11 ==>"+pdataa11.collect()); JavaPairRDD<String, Integer> pdata1 = sc.parallelizePairs(data1); JavaPairRDD<String, Integer> pdata2 = sc.parallelizePairs(data2); System.out.println("pdata1 ==>"+pdata1.collect()); System.out.println("pdata2 ==>"+pdata2.collect()); System.out.println("subtractByKey =>"+pdataa11.subtractByKey(pdata2).collect()); System.out.println("subtract =>"+pdata1.subtract(pdata2).collect()); System.out.println("join =>"+pdata1.join(pdata2).collect()); System.out.println("rightOuterJoin =>"+pdata1.rightOuterJoin(pdata2).collect()); System.out.println("leftOuterJoin =>"+pdata1.leftOuterJoin(pdata2).collect()); System.out.println("cogroup =>"+pdata1.cogroup(pdata2).collect()); Function<Integer, Integer> ff = new Function<Integer, Integer>() { private static final long serialVersionUID = 11234L; int sum = 100; public Integer call (Integer x) { sum += x; return sum; } }; System.out.println("mapValues =>" + pdata1.mapValues(ff).collect()); //System.out.println("mapValues 1=>" + pdata1.reduce( (x2._2) -> {return x2._2;}) ); System.out.println("reduce =>" + pdata1.reduce( (x2, y2) -> {return new Tuple2("sum of all elements", (x2._2+ y2._2) );}) ); } // String형의 List를 JavaRDD를 만들고 이를 JavaPairRDD로 변환후 reduceByKey를 적용하는 예제 static void test8(JavaSparkContext sc) { List<String> data1 = new ArrayList<String>(); data1.add("ab"); data1.add("abcd"); data1.add("ab"); data1.add("cd"); JavaRDD<String> pdata = sc.parallelize(data1); JavaPairRDD<String, Integer> pdata1 = pdata.mapToPair(x-> {return new Tuple2(x, 1);}); System.out.println("mapToPair==>"+pdata1.collect()); JavaPairRDD<String, Integer> pdata2 = pdata1.reduceByKey( (x, y)-> { return (x+y); } ); System.out.println("reduceByKey==>"+pdata2.collect() ); } // sc.parallelizePairs를 이용하여 JavaPairRDD를 만들고 combineByKey 적용하고 Map으로 collect후에 출력하는 예제 static void test9(JavaSparkContext sc) { List<Tuple2<String, Integer>> data1 = new ArrayList<Tuple2<String, Integer>>(); data1.add(new Tuple2("a", 1)); data1.add(new Tuple2("b", 1)); data1.add(new Tuple2("a", 10)); data1.add(new Tuple2("c", 9)); JavaPairRDD<String, Integer> pdata = sc.parallelizePairs(data1); Function<Integer, AvgCount2> createCombiner = new Function<Integer, AvgCount2>() { public AvgCount2 call(Integer x) { return new AvgCount2(x, 1); } }; Function2<AvgCount2, Integer, AvgCount2> mergeValue = new Function2<AvgCount2, Integer, AvgCount2>() { public AvgCount2 call(AvgCount2 a, Integer x) { a.total_ += x; a.num_ += 1; return a; } }; Function2<AvgCount2, AvgCount2, AvgCount2> mergeCombiner = new Function2<AvgCount2, AvgCount2, AvgCount2>() { public AvgCount2 call(AvgCount2 a, AvgCount2 b) { a.total_ += b.total_; a.num_ += b.num_; return a; } }; JavaPairRDD<String, AvgCount2> avgCounts = pdata.combineByKey(createCombiner, mergeValue, mergeCombiner); Map<String, AvgCount2> countMap = avgCounts.collectAsMap(); for(Entry<String, AvgCount2> entry : countMap.entrySet()) { System.out.println(entry.getKey() + ":" + entry.getValue().avg()); } } // 두개의 Tuple2형 List를 이용하여 sc.paralledizePairs를 이용하여 JavaPairRDD로 변환하고 leftOuterJoin를 적용하는 예제 static void test10(JavaSparkContext sc) { List<Tuple2<String, Integer>> data1 = new ArrayList<Tuple2<String, Integer>>(); data1.add(new Tuple2("x", 10)); data1.add(new Tuple2("a", 1)); data1.add(new Tuple2("b", 1)); List<Tuple2<String, String>> data2 = new ArrayList<Tuple2<String, String>>(); data2.add(new Tuple2("a", "aa")); data2.add(new Tuple2("b", "bb")); JavaPairRDD<String, Integer> pdata1 = sc.parallelizePairs(data1); JavaPairRDD<String, String> pdata2 = sc.parallelizePairs(data2); pdata1.sortByKey(true); JavaPairRDD<String, Tuple2<Integer,Optional<String>>> result = pdata1.leftOuterJoin(pdata2); System.out.println("pdata1==>"+pdata1.collect()); System.out.println("pdata2==>"+pdata2.collect()); System.out.println("result==>"+result.collect()); } // test.json파일을 일거서 메모리에 적재하고 데이타를 파싱하여 Person1 객체에 담아 partition별로 map작업을 수행고 HDFS에 저장하는 예제 static void test11(JavaSparkContext sc) { String dir = "formatted-out"; JavaRDD<String> input = sc.textFile("file:///tmp/test.json", 5).persist(StorageLevel.MEMORY_ONLY_2()); JavaRDD<Person1> result = input.mapPartitions(new ParseJson()); System.out.println("persons from json ===>"+result.collect()); JavaRDD<String> formatted = result.mapPartitions(new WriteJson()); delete_dir(dir); //mkdir_dir(); formatted.saveAsTextFile(dir); } // Tuple2형의 List를 key/value형의 JavaPairRDD로 변환하고 ConvertToWritableTypes 객체를 이용하여 IntWritable형으로 변환하여 // SequenceFileOutputFormat으로 저장하는 예제 static void test12(JavaSparkContext sc) { String dir = "sequence-write"; List<Tuple2<String, Integer>> data1 = new ArrayList<Tuple2<String, Integer>>(); data1.add(new Tuple2("a",2)); data1.add(new Tuple2("c",4)); data1.add(new Tuple2("c",6)); JavaPairRDD<String, Integer> rdd = sc.parallelizePairs(data1); JavaPairRDD<Text, IntWritable> result = rdd.mapToPair(new ConvertToWritableTypes()); delete_dir(dir); System.out.println("Native Values before ==>"+data1.toString()); result.saveAsNewAPIHadoopFile(dir, Text.class, IntWritable.class, SequenceFileOutputFormat.class); System.out.println("Saved as SequenceFileOutputFormat.class"); } // Writable형의 저장되어 있는 sequence format의 data를 읽어서 원래의 값으로 변환하는 예제 static void test13(JavaSparkContext sc) { String fileName = "sequence-write"; JavaPairRDD<Text, IntWritable> input = sc.sequenceFile(fileName, Text.class, IntWritable.class); JavaPairRDD<String, Integer> result = input.mapToPair(new ConvertToNativeTypes()); System.out.println("Native Values after ====>"+result.collect()); } // HIveContext를 이용하여 hive테이블에 접근하여 데이타를 읽어 출력하는 예제(Spark 2.0기준으로 deprecated됨) static void test14(JavaSparkContext sc) { HiveContext ctx = new HiveContext(sc); Dataset<Row> rows = ctx.sql("select * from default.test_table"); Dataset<String> stringsDS = rows.map(new MapFunction<Row, String>() { @Override public String call(Row row) throws Exception { //return "Key : "+row.get(0) + ", Value : "+row.get(1); return "Value : "+row.get(0); } }, Encoders.STRING()); System.out.println("select result #1 =>"+stringsDS.toJavaRDD().collect()); } // SparkSession을 이용하여 hive테이블에 접근하여 데이타를 읽어 출력하는 예제(Spark 2.0이상에서는 이것만 유효함) static void test15() { SparkSession session = new SparkSession.Builder().appName("SparkJoinExample").master("local").enableHiveSupport().getOrCreate(); Dataset<Row> dset = session.sql("select * from default.test_table"); System.out.println("select result #2 =>"+dset.toJavaRDD().collect()); } // SQLContext를 이용하여 hive테이블에 접근하여 데이타를 읽어 출력하는 예제(Spark 2.0기준으로 deprecated됨) static void test16(JavaSparkContext sc) { SQLContext ctx = new SQLContext(sc); Dataset<Row> rows = ctx.sql("select * from default.test_table"); Dataset<String> stringsDS = rows.map(new MapFunction<Row, String>() { @Override public String call(Row row) throws Exception { //return "Key : "+row.get(0) + ", Value : "+row.get(1); return "Value : "+row.get(0); } }, Encoders.STRING()); System.out.println("select result #3 =>"+stringsDS.toJavaRDD().collect()); } // accumulator 변수를 사용하는 예제 static void test17(JavaSparkContext sc) { final Accumulator<Integer> blankLines = sc.accumulator(0); JavaRDD<String> lines = sc.parallelize(Arrays.asList("hello world", "", "hi park", "", "sss ff")); JavaRDD<String> callSigns = lines.flatMap( new FlatMapFunction<String, String>() { public Iterator<String> call(String line) { if(line.equals("")) { blankLines.add(1); } return Arrays.asList(line.split(" ")).iterator(); } }); delete_dir("output.txt"); callSigns.saveAsTextFile("output.txt"); System.out.println("Blank lines: "+blankLines.value()); } // accumulator와 broadcast변수를 동시에 사용하는 예제 static void test18(JavaSparkContext sc) { final Accumulator<Integer> blankLines = sc.accumulator(0); JavaRDD<String> lines = sc.parallelize(Arrays.asList("hello world", "", "hi park", "", "sss ff")); JavaRDD<String> callSigns = lines.flatMap( new FlatMapFunction<String, String>() { public Iterator<String> call(String line) { if(line.equals("")) { blankLines.add(1); } System.out.println("str in broadcasted ==>"+temp.value()); return Arrays.asList(line.split(" ")).iterator(); } }); delete_dir("output.txt2"); callSigns.saveAsTextFile("output.txt2"); System.out.println("Blank lines: "+blankLines.value()); } // 문자형 List를 JavaRDD로 변환하고 mapToDobule를 이용하여 double형으로 변환하고 집계함수인 sum, mean, variance, stdev등을 적용해보는 예제 static void test19(JavaSparkContext sc) { JavaRDD<String> age = sc.parallelize(Arrays.asList("1","2","3","4","5")); JavaDoubleRDD doubleAge = age.mapToDouble(new DoubleFunction<String>() { public double call(String value) { return Double.parseDouble(value); }}); System.out.println("sum = "+doubleAge.sum()); System.out.println("mean ="+doubleAge.mean()); System.out.println("variance ="+doubleAge.variance()); System.out.println("stdev ="+doubleAge.stdev()); } // Tuple2<Text, IntWritable>값을 받아서 String형 key와 Integer형 value로 변환하는 클래스 static class ConvertToNativeTypes implements PairFunction<Tuple2<Text, IntWritable>, String, Integer> { public Tuple2<String, Integer> call(Tuple2<Text, IntWritable> record) { return new Tuple2(record._1.toString(), record._2.get()); } } // String형의 key와 Integer형의 Tuple2를 받아서 Text형의 key와 IntWritable형의 Tuple2값으로 변환하는 클래스 static class ConvertToWritableTypes implements PairFunction<Tuple2<String, Integer>, Text, IntWritable> { public Tuple2<Text, IntWritable> call(Tuple2<String, Integer> record) { return new Tuple2(new Text(record._1), new IntWritable(record._2)); } } // HDFS상의 폴더밑 하위 파일을 지우는 함수 static void delete_dir(String ff) { Configuration conf = new Configuration(); try { FileSystem dfs = FileSystem.get(conf); System.out.println("Home Path : " + dfs.getHomeDirectory()); System.out.println("Work Path : " + dfs.getWorkingDirectory()); Path dir = new Path (ff); if(dfs.exists(dir)) { dfs.delete(dir, true); } } catch (Exception e) { System.out.println("delete dir error ==>"+e); } } // HDFS상의 폴더를 생성하는 함수 static void mkdir_dir() { Configuration conf = new Configuration(); try { FileSystem dfs = FileSystem.get(conf); Path dir = new Path ("formatted-out"); if( ! dfs.exists(dir)) { dfs.mkdirs(dir); } } catch (Exception e) { System.out.println("mk dir error ==>"+e); } } }// json데이타를 Person1형의 iterator로 변환하는 클래스class ParseJson implements FlatMapFunction<Iterator<String>, Person1> { public Iterator<Person1> call(Iterator<String> lines) throws Exception { ArrayList<Person1> people = new ArrayList<Person1>(); Gson mapper = new Gson(); while (lines.hasNext()) { String line = lines.next(); try { System.out.println("line => "+line); Person1 person1 = mapper.fromJson(line, Person1.class); System.out.println("person1=>"+person1); people.add(person1); } catch (Exception e) { // 무시함 } } return people.iterator(); }}// Person1형의 데이타를 String형의 iterator로 변환하는 클래스class WriteJson implements FlatMapFunction<Iterator<Person1>, String> { public Iterator<String> call(Iterator<Person1> people) throws Exception { ArrayList<String> text = new ArrayList<String>(); while(people.hasNext()) { Person1 person = people.next(); text.add("new string =>"+person.toString()); } return text.iterator(); }}class Person1 implements Serializable { String name; int age; public String getName() { return name; } public void setName(String name) { this.name = name; } public int getAge() { return age; } public void setAge(int age) { this.age = age; } @Override public String toString() { return "Person1 [name=" + name + ", age=" + age + "]"; } }// 평균값을 구하는 클래스 1class AvgCount implements Serializable { private static final long serialVersionUID = 134444L; public AvgCount (int total, int num) { this.total = total; this.num = num; } public int total; public int num; public double avg() { return total / (double) num; }}//평균값을 구하는 클래스 2class AvgCount2 implements Serializable { private static final long serialVersionUID = -1683922668212126392L; public AvgCount2(int total, int num) { total_= total; num_=num;} public int total_; public int num_; public float avg() { return total_ / (float) num_; }} |