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
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
|
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.api.java
import java.util.{Comparator, List => JList, Map => JMap}
import java.lang.{Iterable => JIterable}
import scala.collection.JavaConverters._
import scala.language.implicitConversions
import scala.reflect.ClassTag
import com.google.common.base.Optional
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.io.compress.CompressionCodec
import org.apache.hadoop.mapred.{JobConf, OutputFormat}
import org.apache.hadoop.mapreduce.{OutputFormat => NewOutputFormat}
import org.apache.spark.{HashPartitioner, Partitioner}
import org.apache.spark.Partitioner._
import org.apache.spark.api.java.JavaSparkContext.fakeClassTag
import org.apache.spark.api.java.JavaUtils.mapAsSerializableJavaMap
import org.apache.spark.api.java.function.{Function => JFunction, Function2 => JFunction2, PairFunction}
import org.apache.spark.partial.{BoundedDouble, PartialResult}
import org.apache.spark.rdd.{OrderedRDDFunctions, RDD}
import org.apache.spark.rdd.RDD.rddToPairRDDFunctions
import org.apache.spark.serializer.Serializer
import org.apache.spark.storage.StorageLevel
import org.apache.spark.util.Utils
class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
(implicit val kClassTag: ClassTag[K], implicit val vClassTag: ClassTag[V])
extends AbstractJavaRDDLike[(K, V), JavaPairRDD[K, V]] {
override def wrapRDD(rdd: RDD[(K, V)]): JavaPairRDD[K, V] = JavaPairRDD.fromRDD(rdd)
override val classTag: ClassTag[(K, V)] = rdd.elementClassTag
import JavaPairRDD._
// Common RDD functions
/** Persist this RDD with the default storage level (`MEMORY_ONLY`). */
def cache(): JavaPairRDD[K, V] = new JavaPairRDD[K, V](rdd.cache())
/**
* Set this RDD's storage level to persist its values across operations after the first time
* it is computed. Can only be called once on each RDD.
*/
def persist(newLevel: StorageLevel): JavaPairRDD[K, V] =
new JavaPairRDD[K, V](rdd.persist(newLevel))
/**
* Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.
* This method blocks until all blocks are deleted.
*/
def unpersist(): JavaPairRDD[K, V] = wrapRDD(rdd.unpersist())
/**
* Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.
*
* @param blocking Whether to block until all blocks are deleted.
*/
def unpersist(blocking: Boolean): JavaPairRDD[K, V] = wrapRDD(rdd.unpersist(blocking))
// Transformations (return a new RDD)
/**
* Return a new RDD containing the distinct elements in this RDD.
*/
def distinct(): JavaPairRDD[K, V] = new JavaPairRDD[K, V](rdd.distinct())
/**
* Return a new RDD containing the distinct elements in this RDD.
*/
def distinct(numPartitions: Int): JavaPairRDD[K, V] =
new JavaPairRDD[K, V](rdd.distinct(numPartitions))
/**
* Return a new RDD containing only the elements that satisfy a predicate.
*/
def filter(f: JFunction[(K, V), java.lang.Boolean]): JavaPairRDD[K, V] =
new JavaPairRDD[K, V](rdd.filter(x => f.call(x).booleanValue()))
/**
* Return a new RDD that is reduced into `numPartitions` partitions.
*/
def coalesce(numPartitions: Int): JavaPairRDD[K, V] = fromRDD(rdd.coalesce(numPartitions))
/**
* Return a new RDD that is reduced into `numPartitions` partitions.
*/
def coalesce(numPartitions: Int, shuffle: Boolean): JavaPairRDD[K, V] =
fromRDD(rdd.coalesce(numPartitions, shuffle))
/**
* Return a new RDD that has exactly numPartitions partitions.
*
* Can increase or decrease the level of parallelism in this RDD. Internally, this uses
* a shuffle to redistribute data.
*
* If you are decreasing the number of partitions in this RDD, consider using `coalesce`,
* which can avoid performing a shuffle.
*/
def repartition(numPartitions: Int): JavaPairRDD[K, V] = fromRDD(rdd.repartition(numPartitions))
/**
* Return a sampled subset of this RDD.
*/
def sample(withReplacement: Boolean, fraction: Double): JavaPairRDD[K, V] =
sample(withReplacement, fraction, Utils.random.nextLong)
/**
* Return a sampled subset of this RDD.
*/
def sample(withReplacement: Boolean, fraction: Double, seed: Long): JavaPairRDD[K, V] =
new JavaPairRDD[K, V](rdd.sample(withReplacement, fraction, seed))
/**
* Return a subset of this RDD sampled by key (via stratified sampling).
*
* Create a sample of this RDD using variable sampling rates for different keys as specified by
* `fractions`, a key to sampling rate map, via simple random sampling with one pass over the
* RDD, to produce a sample of size that's approximately equal to the sum of
* math.ceil(numItems * samplingRate) over all key values.
*/
def sampleByKey(withReplacement: Boolean,
fractions: JMap[K, Double],
seed: Long): JavaPairRDD[K, V] =
new JavaPairRDD[K, V](rdd.sampleByKey(withReplacement, fractions.asScala, seed))
/**
* Return a subset of this RDD sampled by key (via stratified sampling).
*
* Create a sample of this RDD using variable sampling rates for different keys as specified by
* `fractions`, a key to sampling rate map, via simple random sampling with one pass over the
* RDD, to produce a sample of size that's approximately equal to the sum of
* math.ceil(numItems * samplingRate) over all key values.
*
* Use Utils.random.nextLong as the default seed for the random number generator.
*/
def sampleByKey(withReplacement: Boolean,
fractions: JMap[K, Double]): JavaPairRDD[K, V] =
sampleByKey(withReplacement, fractions, Utils.random.nextLong)
/**
* Return a subset of this RDD sampled by key (via stratified sampling) containing exactly
* math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).
*
* This method differs from [[sampleByKey]] in that we make additional passes over the RDD to
* create a sample size that's exactly equal to the sum of math.ceil(numItems * samplingRate)
* over all key values with a 99.99% confidence. When sampling without replacement, we need one
* additional pass over the RDD to guarantee sample size; when sampling with replacement, we need
* two additional passes.
*/
def sampleByKeyExact(withReplacement: Boolean,
fractions: JMap[K, Double],
seed: Long): JavaPairRDD[K, V] =
new JavaPairRDD[K, V](rdd.sampleByKeyExact(withReplacement, fractions.asScala, seed))
/**
* Return a subset of this RDD sampled by key (via stratified sampling) containing exactly
* math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).
*
* This method differs from [[sampleByKey]] in that we make additional passes over the RDD to
* create a sample size that's exactly equal to the sum of math.ceil(numItems * samplingRate)
* over all key values with a 99.99% confidence. When sampling without replacement, we need one
* additional pass over the RDD to guarantee sample size; when sampling with replacement, we need
* two additional passes.
*
* Use Utils.random.nextLong as the default seed for the random number generator.
*/
def sampleByKeyExact(withReplacement: Boolean, fractions: JMap[K, Double]): JavaPairRDD[K, V] =
sampleByKeyExact(withReplacement, fractions, Utils.random.nextLong)
/**
* Return the union of this RDD and another one. Any identical elements will appear multiple
* times (use `.distinct()` to eliminate them).
*/
def union(other: JavaPairRDD[K, V]): JavaPairRDD[K, V] =
new JavaPairRDD[K, V](rdd.union(other.rdd))
/**
* Return the intersection of this RDD and another one. The output will not contain any duplicate
* elements, even if the input RDDs did.
*
* Note that this method performs a shuffle internally.
*/
def intersection(other: JavaPairRDD[K, V]): JavaPairRDD[K, V] =
new JavaPairRDD[K, V](rdd.intersection(other.rdd))
// first() has to be overridden here so that the generated method has the signature
// 'public scala.Tuple2 first()'; if the trait's definition is used,
// then the method has the signature 'public java.lang.Object first()',
// causing NoSuchMethodErrors at runtime.
override def first(): (K, V) = rdd.first()
// Pair RDD functions
/**
* Generic function to combine the elements for each key using a custom set of aggregation
* functions. Turns a JavaPairRDD[(K, V)] into a result of type JavaPairRDD[(K, C)], for a
* "combined type" C. Note that V and C can be different -- for example, one might group an
* RDD of type (Int, Int) into an RDD of type (Int, List[Int]). Users provide three
* functions:
*
* - `createCombiner`, which turns a V into a C (e.g., creates a one-element list)
* - `mergeValue`, to merge a V into a C (e.g., adds it to the end of a list)
* - `mergeCombiners`, to combine two C's into a single one.
*
* In addition, users can control the partitioning of the output RDD, the serializer that is use
* for the shuffle, and whether to perform map-side aggregation (if a mapper can produce multiple
* items with the same key).
*/
def combineByKey[C](createCombiner: JFunction[V, C],
mergeValue: JFunction2[C, V, C],
mergeCombiners: JFunction2[C, C, C],
partitioner: Partitioner,
mapSideCombine: Boolean,
serializer: Serializer): JavaPairRDD[K, C] = {
implicit val ctag: ClassTag[C] = fakeClassTag
fromRDD(rdd.combineByKeyWithClassTag(
createCombiner,
mergeValue,
mergeCombiners,
partitioner,
mapSideCombine,
serializer
))
}
/**
* Generic function to combine the elements for each key using a custom set of aggregation
* functions. Turns a JavaPairRDD[(K, V)] into a result of type JavaPairRDD[(K, C)], for a
* "combined type" C. Note that V and C can be different -- for example, one might group an
* RDD of type (Int, Int) into an RDD of type (Int, List[Int]). Users provide three
* functions:
*
* - `createCombiner`, which turns a V into a C (e.g., creates a one-element list)
* - `mergeValue`, to merge a V into a C (e.g., adds it to the end of a list)
* - `mergeCombiners`, to combine two C's into a single one.
*
* In addition, users can control the partitioning of the output RDD. This method automatically
* uses map-side aggregation in shuffling the RDD.
*/
def combineByKey[C](createCombiner: JFunction[V, C],
mergeValue: JFunction2[C, V, C],
mergeCombiners: JFunction2[C, C, C],
partitioner: Partitioner): JavaPairRDD[K, C] = {
combineByKey(createCombiner, mergeValue, mergeCombiners, partitioner, true, null)
}
/**
* Simplified version of combineByKey that hash-partitions the output RDD and uses map-side
* aggregation.
*/
def combineByKey[C](createCombiner: JFunction[V, C],
mergeValue: JFunction2[C, V, C],
mergeCombiners: JFunction2[C, C, C],
numPartitions: Int): JavaPairRDD[K, C] =
combineByKey(createCombiner, mergeValue, mergeCombiners, new HashPartitioner(numPartitions))
/**
* Merge the values for each key using an associative reduce function. This will also perform
* the merging locally on each mapper before sending results to a reducer, similarly to a
* "combiner" in MapReduce.
*/
def reduceByKey(partitioner: Partitioner, func: JFunction2[V, V, V]): JavaPairRDD[K, V] =
fromRDD(rdd.reduceByKey(partitioner, func))
/**
* Merge the values for each key using an associative reduce function, but return the results
* immediately to the master as a Map. This will also perform the merging locally on each mapper
* before sending results to a reducer, similarly to a "combiner" in MapReduce.
*/
def reduceByKeyLocally(func: JFunction2[V, V, V]): java.util.Map[K, V] =
mapAsSerializableJavaMap(rdd.reduceByKeyLocally(func))
/** Count the number of elements for each key, and return the result to the master as a Map. */
def countByKey(): java.util.Map[K, Long] = mapAsSerializableJavaMap(rdd.countByKey())
/**
* Approximate version of countByKey that can return a partial result if it does
* not finish within a timeout.
*/
def countByKeyApprox(timeout: Long): PartialResult[java.util.Map[K, BoundedDouble]] =
rdd.countByKeyApprox(timeout).map(mapAsSerializableJavaMap)
/**
* Approximate version of countByKey that can return a partial result if it does
* not finish within a timeout.
*/
def countByKeyApprox(timeout: Long, confidence: Double = 0.95)
: PartialResult[java.util.Map[K, BoundedDouble]] =
rdd.countByKeyApprox(timeout, confidence).map(mapAsSerializableJavaMap)
/**
* Aggregate the values of each key, using given combine functions and a neutral "zero value".
* This function can return a different result type, U, than the type of the values in this RDD,
* V. Thus, we need one operation for merging a V into a U and one operation for merging two U's,
* as in scala.TraversableOnce. The former operation is used for merging values within a
* partition, and the latter is used for merging values between partitions. To avoid memory
* allocation, both of these functions are allowed to modify and return their first argument
* instead of creating a new U.
*/
def aggregateByKey[U](zeroValue: U, partitioner: Partitioner, seqFunc: JFunction2[U, V, U],
combFunc: JFunction2[U, U, U]): JavaPairRDD[K, U] = {
implicit val ctag: ClassTag[U] = fakeClassTag
fromRDD(rdd.aggregateByKey(zeroValue, partitioner)(seqFunc, combFunc))
}
/**
* Aggregate the values of each key, using given combine functions and a neutral "zero value".
* This function can return a different result type, U, than the type of the values in this RDD,
* V. Thus, we need one operation for merging a V into a U and one operation for merging two U's,
* as in scala.TraversableOnce. The former operation is used for merging values within a
* partition, and the latter is used for merging values between partitions. To avoid memory
* allocation, both of these functions are allowed to modify and return their first argument
* instead of creating a new U.
*/
def aggregateByKey[U](zeroValue: U, numPartitions: Int, seqFunc: JFunction2[U, V, U],
combFunc: JFunction2[U, U, U]): JavaPairRDD[K, U] = {
implicit val ctag: ClassTag[U] = fakeClassTag
fromRDD(rdd.aggregateByKey(zeroValue, numPartitions)(seqFunc, combFunc))
}
/**
* Aggregate the values of each key, using given combine functions and a neutral "zero value".
* This function can return a different result type, U, than the type of the values in this RDD,
* V. Thus, we need one operation for merging a V into a U and one operation for merging two U's.
* The former operation is used for merging values within a partition, and the latter is used for
* merging values between partitions. To avoid memory allocation, both of these functions are
* allowed to modify and return their first argument instead of creating a new U.
*/
def aggregateByKey[U](zeroValue: U, seqFunc: JFunction2[U, V, U], combFunc: JFunction2[U, U, U]):
JavaPairRDD[K, U] = {
implicit val ctag: ClassTag[U] = fakeClassTag
fromRDD(rdd.aggregateByKey(zeroValue)(seqFunc, combFunc))
}
/**
* Merge the values for each key using an associative function and a neutral "zero value" which
* may be added to the result an arbitrary number of times, and must not change the result
* (e.g ., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
*/
def foldByKey(zeroValue: V, partitioner: Partitioner, func: JFunction2[V, V, V])
: JavaPairRDD[K, V] = fromRDD(rdd.foldByKey(zeroValue, partitioner)(func))
/**
* Merge the values for each key using an associative function and a neutral "zero value" which
* may be added to the result an arbitrary number of times, and must not change the result
* (e.g ., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
*/
def foldByKey(zeroValue: V, numPartitions: Int, func: JFunction2[V, V, V]): JavaPairRDD[K, V] =
fromRDD(rdd.foldByKey(zeroValue, numPartitions)(func))
/**
* Merge the values for each key using an associative function and a neutral "zero value"
* which may be added to the result an arbitrary number of times, and must not change the result
* (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
*/
def foldByKey(zeroValue: V, func: JFunction2[V, V, V]): JavaPairRDD[K, V] =
fromRDD(rdd.foldByKey(zeroValue)(func))
/**
* Merge the values for each key using an associative reduce function. This will also perform
* the merging locally on each mapper before sending results to a reducer, similarly to a
* "combiner" in MapReduce. Output will be hash-partitioned with numPartitions partitions.
*/
def reduceByKey(func: JFunction2[V, V, V], numPartitions: Int): JavaPairRDD[K, V] =
fromRDD(rdd.reduceByKey(func, numPartitions))
/**
* Group the values for each key in the RDD into a single sequence. Allows controlling the
* partitioning of the resulting key-value pair RDD by passing a Partitioner.
*
* Note: If you are grouping in order to perform an aggregation (such as a sum or average) over
* each key, using [[JavaPairRDD.reduceByKey]] or [[JavaPairRDD.combineByKey]]
* will provide much better performance.
*/
def groupByKey(partitioner: Partitioner): JavaPairRDD[K, JIterable[V]] =
fromRDD(groupByResultToJava(rdd.groupByKey(partitioner)))
/**
* Group the values for each key in the RDD into a single sequence. Hash-partitions the
* resulting RDD with into `numPartitions` partitions.
*
* Note: If you are grouping in order to perform an aggregation (such as a sum or average) over
* each key, using [[JavaPairRDD.reduceByKey]] or [[JavaPairRDD.combineByKey]]
* will provide much better performance.
*/
def groupByKey(numPartitions: Int): JavaPairRDD[K, JIterable[V]] =
fromRDD(groupByResultToJava(rdd.groupByKey(numPartitions)))
/**
* Return an RDD with the elements from `this` that are not in `other`.
*
* Uses `this` partitioner/partition size, because even if `other` is huge, the resulting
* RDD will be <= us.
*/
def subtract(other: JavaPairRDD[K, V]): JavaPairRDD[K, V] =
fromRDD(rdd.subtract(other))
/**
* Return an RDD with the elements from `this` that are not in `other`.
*/
def subtract(other: JavaPairRDD[K, V], numPartitions: Int): JavaPairRDD[K, V] =
fromRDD(rdd.subtract(other, numPartitions))
/**
* Return an RDD with the elements from `this` that are not in `other`.
*/
def subtract(other: JavaPairRDD[K, V], p: Partitioner): JavaPairRDD[K, V] =
fromRDD(rdd.subtract(other, p))
/**
* Return an RDD with the pairs from `this` whose keys are not in `other`.
*
* Uses `this` partitioner/partition size, because even if `other` is huge, the resulting
* RDD will be <= us.
*/
def subtractByKey[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, V] = {
implicit val ctag: ClassTag[W] = fakeClassTag
fromRDD(rdd.subtractByKey(other))
}
/** Return an RDD with the pairs from `this` whose keys are not in `other`. */
def subtractByKey[W](other: JavaPairRDD[K, W], numPartitions: Int): JavaPairRDD[K, V] = {
implicit val ctag: ClassTag[W] = fakeClassTag
fromRDD(rdd.subtractByKey(other, numPartitions))
}
/** Return an RDD with the pairs from `this` whose keys are not in `other`. */
def subtractByKey[W](other: JavaPairRDD[K, W], p: Partitioner): JavaPairRDD[K, V] = {
implicit val ctag: ClassTag[W] = fakeClassTag
fromRDD(rdd.subtractByKey(other, p))
}
/**
* Return a copy of the RDD partitioned using the specified partitioner.
*/
def partitionBy(partitioner: Partitioner): JavaPairRDD[K, V] =
fromRDD(rdd.partitionBy(partitioner))
/**
* Merge the values for each key using an associative reduce function. This will also perform
* the merging locally on each mapper before sending results to a reducer, similarly to a
* "combiner" in MapReduce.
*/
def join[W](other: JavaPairRDD[K, W], partitioner: Partitioner): JavaPairRDD[K, (V, W)] =
fromRDD(rdd.join(other, partitioner))
/**
* Perform a left outer join of `this` and `other`. For each element (k, v) in `this`, the
* resulting RDD will either contain all pairs (k, (v, Some(w))) for w in `other`, or the
* pair (k, (v, None)) if no elements in `other` have key k. Uses the given Partitioner to
* partition the output RDD.
*/
def leftOuterJoin[W](other: JavaPairRDD[K, W], partitioner: Partitioner)
: JavaPairRDD[K, (V, Optional[W])] = {
val joinResult = rdd.leftOuterJoin(other, partitioner)
fromRDD(joinResult.mapValues{case (v, w) => (v, JavaUtils.optionToOptional(w))})
}
/**
* Perform a right outer join of `this` and `other`. For each element (k, w) in `other`, the
* resulting RDD will either contain all pairs (k, (Some(v), w)) for v in `this`, or the
* pair (k, (None, w)) if no elements in `this` have key k. Uses the given Partitioner to
* partition the output RDD.
*/
def rightOuterJoin[W](other: JavaPairRDD[K, W], partitioner: Partitioner)
: JavaPairRDD[K, (Optional[V], W)] = {
val joinResult = rdd.rightOuterJoin(other, partitioner)
fromRDD(joinResult.mapValues{case (v, w) => (JavaUtils.optionToOptional(v), w)})
}
/**
* Perform a full outer join of `this` and `other`. For each element (k, v) in `this`, the
* resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in `other`, or
* the pair (k, (Some(v), None)) if no elements in `other` have key k. Similarly, for each
* element (k, w) in `other`, the resulting RDD will either contain all pairs
* (k, (Some(v), Some(w))) for v in `this`, or the pair (k, (None, Some(w))) if no elements
* in `this` have key k. Uses the given Partitioner to partition the output RDD.
*/
def fullOuterJoin[W](other: JavaPairRDD[K, W], partitioner: Partitioner)
: JavaPairRDD[K, (Optional[V], Optional[W])] = {
val joinResult = rdd.fullOuterJoin(other, partitioner)
fromRDD(joinResult.mapValues{ case (v, w) =>
(JavaUtils.optionToOptional(v), JavaUtils.optionToOptional(w))
})
}
/**
* Simplified version of combineByKey that hash-partitions the resulting RDD using the existing
* partitioner/parallelism level and using map-side aggregation.
*/
def combineByKey[C](createCombiner: JFunction[V, C],
mergeValue: JFunction2[C, V, C],
mergeCombiners: JFunction2[C, C, C]): JavaPairRDD[K, C] = {
implicit val ctag: ClassTag[C] = fakeClassTag
fromRDD(combineByKey(createCombiner, mergeValue, mergeCombiners, defaultPartitioner(rdd)))
}
/**
* Merge the values for each key using an associative reduce function. This will also perform
* the merging locally on each mapper before sending results to a reducer, similarly to a
* "combiner" in MapReduce. Output will be hash-partitioned with the existing partitioner/
* parallelism level.
*/
def reduceByKey(func: JFunction2[V, V, V]): JavaPairRDD[K, V] = {
fromRDD(reduceByKey(defaultPartitioner(rdd), func))
}
/**
* Group the values for each key in the RDD into a single sequence. Hash-partitions the
* resulting RDD with the existing partitioner/parallelism level.
*
* Note: If you are grouping in order to perform an aggregation (such as a sum or average) over
* each key, using [[JavaPairRDD.reduceByKey]] or [[JavaPairRDD.combineByKey]]
* will provide much better performance.
*/
def groupByKey(): JavaPairRDD[K, JIterable[V]] =
fromRDD(groupByResultToJava(rdd.groupByKey()))
/**
* Return an RDD containing all pairs of elements with matching keys in `this` and `other`. Each
* pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in `this` and
* (k, v2) is in `other`. Performs a hash join across the cluster.
*/
def join[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (V, W)] =
fromRDD(rdd.join(other))
/**
* Return an RDD containing all pairs of elements with matching keys in `this` and `other`. Each
* pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in `this` and
* (k, v2) is in `other`. Performs a hash join across the cluster.
*/
def join[W](other: JavaPairRDD[K, W], numPartitions: Int): JavaPairRDD[K, (V, W)] =
fromRDD(rdd.join(other, numPartitions))
/**
* Perform a left outer join of `this` and `other`. For each element (k, v) in `this`, the
* resulting RDD will either contain all pairs (k, (v, Some(w))) for w in `other`, or the
* pair (k, (v, None)) if no elements in `other` have key k. Hash-partitions the output
* using the existing partitioner/parallelism level.
*/
def leftOuterJoin[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (V, Optional[W])] = {
val joinResult = rdd.leftOuterJoin(other)
fromRDD(joinResult.mapValues{case (v, w) => (v, JavaUtils.optionToOptional(w))})
}
/**
* Perform a left outer join of `this` and `other`. For each element (k, v) in `this`, the
* resulting RDD will either contain all pairs (k, (v, Some(w))) for w in `other`, or the
* pair (k, (v, None)) if no elements in `other` have key k. Hash-partitions the output
* into `numPartitions` partitions.
*/
def leftOuterJoin[W](other: JavaPairRDD[K, W], numPartitions: Int)
: JavaPairRDD[K, (V, Optional[W])] = {
val joinResult = rdd.leftOuterJoin(other, numPartitions)
fromRDD(joinResult.mapValues{case (v, w) => (v, JavaUtils.optionToOptional(w))})
}
/**
* Perform a right outer join of `this` and `other`. For each element (k, w) in `other`, the
* resulting RDD will either contain all pairs (k, (Some(v), w)) for v in `this`, or the
* pair (k, (None, w)) if no elements in `this` have key k. Hash-partitions the resulting
* RDD using the existing partitioner/parallelism level.
*/
def rightOuterJoin[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (Optional[V], W)] = {
val joinResult = rdd.rightOuterJoin(other)
fromRDD(joinResult.mapValues{case (v, w) => (JavaUtils.optionToOptional(v), w)})
}
/**
* Perform a right outer join of `this` and `other`. For each element (k, w) in `other`, the
* resulting RDD will either contain all pairs (k, (Some(v), w)) for v in `this`, or the
* pair (k, (None, w)) if no elements in `this` have key k. Hash-partitions the resulting
* RDD into the given number of partitions.
*/
def rightOuterJoin[W](other: JavaPairRDD[K, W], numPartitions: Int)
: JavaPairRDD[K, (Optional[V], W)] = {
val joinResult = rdd.rightOuterJoin(other, numPartitions)
fromRDD(joinResult.mapValues{case (v, w) => (JavaUtils.optionToOptional(v), w)})
}
/**
* Perform a full outer join of `this` and `other`. For each element (k, v) in `this`, the
* resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in `other`, or
* the pair (k, (Some(v), None)) if no elements in `other` have key k. Similarly, for each
* element (k, w) in `other`, the resulting RDD will either contain all pairs
* (k, (Some(v), Some(w))) for v in `this`, or the pair (k, (None, Some(w))) if no elements
* in `this` have key k. Hash-partitions the resulting RDD using the existing partitioner/
* parallelism level.
*/
def fullOuterJoin[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (Optional[V], Optional[W])] = {
val joinResult = rdd.fullOuterJoin(other)
fromRDD(joinResult.mapValues{ case (v, w) =>
(JavaUtils.optionToOptional(v), JavaUtils.optionToOptional(w))
})
}
/**
* Perform a full outer join of `this` and `other`. For each element (k, v) in `this`, the
* resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in `other`, or
* the pair (k, (Some(v), None)) if no elements in `other` have key k. Similarly, for each
* element (k, w) in `other`, the resulting RDD will either contain all pairs
* (k, (Some(v), Some(w))) for v in `this`, or the pair (k, (None, Some(w))) if no elements
* in `this` have key k. Hash-partitions the resulting RDD into the given number of partitions.
*/
def fullOuterJoin[W](other: JavaPairRDD[K, W], numPartitions: Int)
: JavaPairRDD[K, (Optional[V], Optional[W])] = {
val joinResult = rdd.fullOuterJoin(other, numPartitions)
fromRDD(joinResult.mapValues{ case (v, w) =>
(JavaUtils.optionToOptional(v), JavaUtils.optionToOptional(w))
})
}
/**
* Return the key-value pairs in this RDD to the master as a Map.
*/
def collectAsMap(): java.util.Map[K, V] = mapAsSerializableJavaMap(rdd.collectAsMap())
/**
* Pass each value in the key-value pair RDD through a map function without changing the keys;
* this also retains the original RDD's partitioning.
*/
def mapValues[U](f: JFunction[V, U]): JavaPairRDD[K, U] = {
implicit val ctag: ClassTag[U] = fakeClassTag
fromRDD(rdd.mapValues(f))
}
/**
* Pass each value in the key-value pair RDD through a flatMap function without changing the
* keys; this also retains the original RDD's partitioning.
*/
def flatMapValues[U](f: JFunction[V, java.lang.Iterable[U]]): JavaPairRDD[K, U] = {
import scala.collection.JavaConverters._
def fn: (V) => Iterable[U] = (x: V) => f.call(x).asScala
implicit val ctag: ClassTag[U] = fakeClassTag
fromRDD(rdd.flatMapValues(fn))
}
/**
* For each key k in `this` or `other`, return a resulting RDD that contains a tuple with the
* list of values for that key in `this` as well as `other`.
*/
def cogroup[W](other: JavaPairRDD[K, W], partitioner: Partitioner)
: JavaPairRDD[K, (JIterable[V], JIterable[W])] =
fromRDD(cogroupResultToJava(rdd.cogroup(other, partitioner)))
/**
* For each key k in `this` or `other1` or `other2`, return a resulting RDD that contains a
* tuple with the list of values for that key in `this`, `other1` and `other2`.
*/
def cogroup[W1, W2](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2],
partitioner: Partitioner): JavaPairRDD[K, (JIterable[V], JIterable[W1], JIterable[W2])] =
fromRDD(cogroupResult2ToJava(rdd.cogroup(other1, other2, partitioner)))
/**
* For each key k in `this` or `other1` or `other2` or `other3`,
* return a resulting RDD that contains a tuple with the list of values
* for that key in `this`, `other1`, `other2` and `other3`.
*/
def cogroup[W1, W2, W3](other1: JavaPairRDD[K, W1],
other2: JavaPairRDD[K, W2],
other3: JavaPairRDD[K, W3],
partitioner: Partitioner)
: JavaPairRDD[K, (JIterable[V], JIterable[W1], JIterable[W2], JIterable[W3])] =
fromRDD(cogroupResult3ToJava(rdd.cogroup(other1, other2, other3, partitioner)))
/**
* For each key k in `this` or `other`, return a resulting RDD that contains a tuple with the
* list of values for that key in `this` as well as `other`.
*/
def cogroup[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (JIterable[V], JIterable[W])] =
fromRDD(cogroupResultToJava(rdd.cogroup(other)))
/**
* For each key k in `this` or `other1` or `other2`, return a resulting RDD that contains a
* tuple with the list of values for that key in `this`, `other1` and `other2`.
*/
def cogroup[W1, W2](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2])
: JavaPairRDD[K, (JIterable[V], JIterable[W1], JIterable[W2])] =
fromRDD(cogroupResult2ToJava(rdd.cogroup(other1, other2)))
/**
* For each key k in `this` or `other1` or `other2` or `other3`,
* return a resulting RDD that contains a tuple with the list of values
* for that key in `this`, `other1`, `other2` and `other3`.
*/
def cogroup[W1, W2, W3](other1: JavaPairRDD[K, W1],
other2: JavaPairRDD[K, W2],
other3: JavaPairRDD[K, W3])
: JavaPairRDD[K, (JIterable[V], JIterable[W1], JIterable[W2], JIterable[W3])] =
fromRDD(cogroupResult3ToJava(rdd.cogroup(other1, other2, other3)))
/**
* For each key k in `this` or `other`, return a resulting RDD that contains a tuple with the
* list of values for that key in `this` as well as `other`.
*/
def cogroup[W](other: JavaPairRDD[K, W], numPartitions: Int)
: JavaPairRDD[K, (JIterable[V], JIterable[W])] =
fromRDD(cogroupResultToJava(rdd.cogroup(other, numPartitions)))
/**
* For each key k in `this` or `other1` or `other2`, return a resulting RDD that contains a
* tuple with the list of values for that key in `this`, `other1` and `other2`.
*/
def cogroup[W1, W2](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2], numPartitions: Int)
: JavaPairRDD[K, (JIterable[V], JIterable[W1], JIterable[W2])] =
fromRDD(cogroupResult2ToJava(rdd.cogroup(other1, other2, numPartitions)))
/**
* For each key k in `this` or `other1` or `other2` or `other3`,
* return a resulting RDD that contains a tuple with the list of values
* for that key in `this`, `other1`, `other2` and `other3`.
*/
def cogroup[W1, W2, W3](other1: JavaPairRDD[K, W1],
other2: JavaPairRDD[K, W2],
other3: JavaPairRDD[K, W3],
numPartitions: Int)
: JavaPairRDD[K, (JIterable[V], JIterable[W1], JIterable[W2], JIterable[W3])] =
fromRDD(cogroupResult3ToJava(rdd.cogroup(other1, other2, other3, numPartitions)))
/** Alias for cogroup. */
def groupWith[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (JIterable[V], JIterable[W])] =
fromRDD(cogroupResultToJava(rdd.groupWith(other)))
/** Alias for cogroup. */
def groupWith[W1, W2](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2])
: JavaPairRDD[K, (JIterable[V], JIterable[W1], JIterable[W2])] =
fromRDD(cogroupResult2ToJava(rdd.groupWith(other1, other2)))
/** Alias for cogroup. */
def groupWith[W1, W2, W3](other1: JavaPairRDD[K, W1],
other2: JavaPairRDD[K, W2],
other3: JavaPairRDD[K, W3])
: JavaPairRDD[K, (JIterable[V], JIterable[W1], JIterable[W2], JIterable[W3])] =
fromRDD(cogroupResult3ToJava(rdd.groupWith(other1, other2, other3)))
/**
* Return the list of values in the RDD for key `key`. This operation is done efficiently if the
* RDD has a known partitioner by only searching the partition that the key maps to.
*/
def lookup(key: K): JList[V] = rdd.lookup(key).asJava
/** Output the RDD to any Hadoop-supported file system. */
def saveAsHadoopFile[F <: OutputFormat[_, _]](
path: String,
keyClass: Class[_],
valueClass: Class[_],
outputFormatClass: Class[F],
conf: JobConf) {
rdd.saveAsHadoopFile(path, keyClass, valueClass, outputFormatClass, conf)
}
/** Output the RDD to any Hadoop-supported file system. */
def saveAsHadoopFile[F <: OutputFormat[_, _]](
path: String,
keyClass: Class[_],
valueClass: Class[_],
outputFormatClass: Class[F]) {
rdd.saveAsHadoopFile(path, keyClass, valueClass, outputFormatClass)
}
/** Output the RDD to any Hadoop-supported file system, compressing with the supplied codec. */
def saveAsHadoopFile[F <: OutputFormat[_, _]](
path: String,
keyClass: Class[_],
valueClass: Class[_],
outputFormatClass: Class[F],
codec: Class[_ <: CompressionCodec]) {
rdd.saveAsHadoopFile(path, keyClass, valueClass, outputFormatClass, codec)
}
/** Output the RDD to any Hadoop-supported file system. */
def saveAsNewAPIHadoopFile[F <: NewOutputFormat[_, _]](
path: String,
keyClass: Class[_],
valueClass: Class[_],
outputFormatClass: Class[F],
conf: Configuration) {
rdd.saveAsNewAPIHadoopFile(path, keyClass, valueClass, outputFormatClass, conf)
}
/**
* Output the RDD to any Hadoop-supported storage system, using
* a Configuration object for that storage system.
*/
def saveAsNewAPIHadoopDataset(conf: Configuration) {
rdd.saveAsNewAPIHadoopDataset(conf)
}
/** Output the RDD to any Hadoop-supported file system. */
def saveAsNewAPIHadoopFile[F <: NewOutputFormat[_, _]](
path: String,
keyClass: Class[_],
valueClass: Class[_],
outputFormatClass: Class[F]) {
rdd.saveAsNewAPIHadoopFile(path, keyClass, valueClass, outputFormatClass)
}
/**
* Output the RDD to any Hadoop-supported storage system, using a Hadoop JobConf object for
* that storage system. The JobConf should set an OutputFormat and any output paths required
* (e.g. a table name to write to) in the same way as it would be configured for a Hadoop
* MapReduce job.
*/
def saveAsHadoopDataset(conf: JobConf) {
rdd.saveAsHadoopDataset(conf)
}
/**
* Repartition the RDD according to the given partitioner and, within each resulting partition,
* sort records by their keys.
*
* This is more efficient than calling `repartition` and then sorting within each partition
* because it can push the sorting down into the shuffle machinery.
*/
def repartitionAndSortWithinPartitions(partitioner: Partitioner): JavaPairRDD[K, V] = {
val comp = com.google.common.collect.Ordering.natural().asInstanceOf[Comparator[K]]
repartitionAndSortWithinPartitions(partitioner, comp)
}
/**
* Repartition the RDD according to the given partitioner and, within each resulting partition,
* sort records by their keys.
*
* This is more efficient than calling `repartition` and then sorting within each partition
* because it can push the sorting down into the shuffle machinery.
*/
def repartitionAndSortWithinPartitions(partitioner: Partitioner, comp: Comparator[K])
: JavaPairRDD[K, V] = {
implicit val ordering = comp // Allow implicit conversion of Comparator to Ordering.
fromRDD(
new OrderedRDDFunctions[K, V, (K, V)](rdd).repartitionAndSortWithinPartitions(partitioner))
}
/**
* Sort the RDD by key, so that each partition contains a sorted range of the elements in
* ascending order. Calling `collect` or `save` on the resulting RDD will return or output an
* ordered list of records (in the `save` case, they will be written to multiple `part-X` files
* in the filesystem, in order of the keys).
*/
def sortByKey(): JavaPairRDD[K, V] = sortByKey(true)
/**
* Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling
* `collect` or `save` on the resulting RDD will return or output an ordered list of records
* (in the `save` case, they will be written to multiple `part-X` files in the filesystem, in
* order of the keys).
*/
def sortByKey(ascending: Boolean): JavaPairRDD[K, V] = {
val comp = com.google.common.collect.Ordering.natural().asInstanceOf[Comparator[K]]
sortByKey(comp, ascending)
}
/**
* Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling
* `collect` or `save` on the resulting RDD will return or output an ordered list of records
* (in the `save` case, they will be written to multiple `part-X` files in the filesystem, in
* order of the keys).
*/
def sortByKey(ascending: Boolean, numPartitions: Int): JavaPairRDD[K, V] = {
val comp = com.google.common.collect.Ordering.natural().asInstanceOf[Comparator[K]]
sortByKey(comp, ascending, numPartitions)
}
/**
* Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling
* `collect` or `save` on the resulting RDD will return or output an ordered list of records
* (in the `save` case, they will be written to multiple `part-X` files in the filesystem, in
* order of the keys).
*/
def sortByKey(comp: Comparator[K]): JavaPairRDD[K, V] = sortByKey(comp, true)
/**
* Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling
* `collect` or `save` on the resulting RDD will return or output an ordered list of records
* (in the `save` case, they will be written to multiple `part-X` files in the filesystem, in
* order of the keys).
*/
def sortByKey(comp: Comparator[K], ascending: Boolean): JavaPairRDD[K, V] = {
implicit val ordering = comp // Allow implicit conversion of Comparator to Ordering.
fromRDD(new OrderedRDDFunctions[K, V, (K, V)](rdd).sortByKey(ascending))
}
/**
* Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling
* `collect` or `save` on the resulting RDD will return or output an ordered list of records
* (in the `save` case, they will be written to multiple `part-X` files in the filesystem, in
* order of the keys).
*/
def sortByKey(comp: Comparator[K], ascending: Boolean, numPartitions: Int): JavaPairRDD[K, V] = {
implicit val ordering = comp // Allow implicit conversion of Comparator to Ordering.
fromRDD(new OrderedRDDFunctions[K, V, (K, V)](rdd).sortByKey(ascending, numPartitions))
}
/**
* Return an RDD with the keys of each tuple.
*/
def keys(): JavaRDD[K] = JavaRDD.fromRDD[K](rdd.map(_._1))
/**
* Return an RDD with the values of each tuple.
*/
def values(): JavaRDD[V] = JavaRDD.fromRDD[V](rdd.map(_._2))
/**
* Return approximate number of distinct values for each key in this RDD.
*
* The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice:
* Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available
* <a href="http://dx.doi.org/10.1145/2452376.2452456">here</a>.
*
* @param relativeSD Relative accuracy. Smaller values create counters that require more space.
* It must be greater than 0.000017.
* @param partitioner partitioner of the resulting RDD.
*/
def countApproxDistinctByKey(relativeSD: Double, partitioner: Partitioner): JavaPairRDD[K, Long] =
{
fromRDD(rdd.countApproxDistinctByKey(relativeSD, partitioner))
}
/**
* Return approximate number of distinct values for each key in this RDD.
*
* The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice:
* Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available
* <a href="http://dx.doi.org/10.1145/2452376.2452456">here</a>.
*
* @param relativeSD Relative accuracy. Smaller values create counters that require more space.
* It must be greater than 0.000017.
* @param numPartitions number of partitions of the resulting RDD.
*/
def countApproxDistinctByKey(relativeSD: Double, numPartitions: Int): JavaPairRDD[K, Long] = {
fromRDD(rdd.countApproxDistinctByKey(relativeSD, numPartitions))
}
/**
* Return approximate number of distinct values for each key in this RDD.
*
* The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice:
* Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available
* <a href="http://dx.doi.org/10.1145/2452376.2452456">here</a>.
*
* @param relativeSD Relative accuracy. Smaller values create counters that require more space.
* It must be greater than 0.000017.
*/
def countApproxDistinctByKey(relativeSD: Double): JavaPairRDD[K, Long] = {
fromRDD(rdd.countApproxDistinctByKey(relativeSD))
}
/** Assign a name to this RDD */
def setName(name: String): JavaPairRDD[K, V] = {
rdd.setName(name)
this
}
}
object JavaPairRDD {
private[spark]
def groupByResultToJava[K: ClassTag, T](rdd: RDD[(K, Iterable[T])]): RDD[(K, JIterable[T])] = {
rddToPairRDDFunctions(rdd).mapValues(_.asJava)
}
private[spark]
def cogroupResultToJava[K: ClassTag, V, W](
rdd: RDD[(K, (Iterable[V], Iterable[W]))]): RDD[(K, (JIterable[V], JIterable[W]))] = {
rddToPairRDDFunctions(rdd).mapValues(x => (x._1.asJava, x._2.asJava))
}
private[spark]
def cogroupResult2ToJava[K: ClassTag, V, W1, W2](
rdd: RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))])
: RDD[(K, (JIterable[V], JIterable[W1], JIterable[W2]))] = {
rddToPairRDDFunctions(rdd).mapValues(x => (x._1.asJava, x._2.asJava, x._3.asJava))
}
private[spark]
def cogroupResult3ToJava[K: ClassTag, V, W1, W2, W3](
rdd: RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))])
: RDD[(K, (JIterable[V], JIterable[W1], JIterable[W2], JIterable[W3]))] = {
rddToPairRDDFunctions(rdd).mapValues(x => (x._1.asJava, x._2.asJava, x._3.asJava, x._4.asJava))
}
def fromRDD[K: ClassTag, V: ClassTag](rdd: RDD[(K, V)]): JavaPairRDD[K, V] = {
new JavaPairRDD[K, V](rdd)
}
implicit def toRDD[K, V](rdd: JavaPairRDD[K, V]): RDD[(K, V)] = rdd.rdd
private[spark]
implicit def toScalaFunction2[T1, T2, R](fun: JFunction2[T1, T2, R]): Function2[T1, T2, R] = {
(x: T1, x1: T2) => fun.call(x, x1)
}
private[spark] implicit def toScalaFunction[T, R](fun: JFunction[T, R]): T => R = x => fun.call(x)
private[spark]
implicit def pairFunToScalaFun[A, B, C](x: PairFunction[A, B, C]): A => (B, C) = y => x.call(y)
/** Convert a JavaRDD of key-value pairs to JavaPairRDD. */
def fromJavaRDD[K, V](rdd: JavaRDD[(K, V)]): JavaPairRDD[K, V] = {
implicit val ctagK: ClassTag[K] = fakeClassTag
implicit val ctagV: ClassTag[V] = fakeClassTag
new JavaPairRDD[K, V](rdd.rdd)
}
}
|