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
|
/*
* 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.streaming.api.java
import java.util
import java.lang.{Long => JLong}
import java.util.{List => JList}
import scala.collection.JavaConversions._
import scala.language.implicitConversions
import scala.reflect.ClassTag
import org.apache.spark.api.java.{JavaPairRDD, JavaRDD, JavaRDDLike}
import org.apache.spark.api.java.JavaPairRDD._
import org.apache.spark.api.java.JavaSparkContext.fakeClassTag
import org.apache.spark.api.java.function.{Function => JFunction, Function2 => JFunction2, Function3 => JFunction3, _}
import org.apache.spark.rdd.RDD
import org.apache.spark.streaming._
import org.apache.spark.streaming.api.java.JavaDStream._
import org.apache.spark.streaming.dstream.DStream
/**
* As a workaround for https://issues.scala-lang.org/browse/SI-8905, implementations
* of JavaDStreamLike should extend this dummy abstract class instead of directly inheriting
* from the trait. See SPARK-3266 for additional details.
*/
private[streaming]
abstract class AbstractJavaDStreamLike[T, This <: JavaDStreamLike[T, This, R],
R <: JavaRDDLike[T, R]] extends JavaDStreamLike[T, This, R]
trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T, R]]
extends Serializable {
implicit val classTag: ClassTag[T]
def dstream: DStream[T]
def wrapRDD(in: RDD[T]): R
implicit def scalaIntToJavaLong(in: DStream[Long]): JavaDStream[JLong] = {
in.map(new JLong(_))
}
/**
* Print the first ten elements of each RDD generated in this DStream. This is an output
* operator, so this DStream will be registered as an output stream and there materialized.
*/
def print(): Unit = {
print(10)
}
/**
* Print the first num elements of each RDD generated in this DStream. This is an output
* operator, so this DStream will be registered as an output stream and there materialized.
*/
def print(num: Int): Unit = {
dstream.print(num)
}
/**
* Return a new DStream in which each RDD has a single element generated by counting each RDD
* of this DStream.
*/
def count(): JavaDStream[JLong] = dstream.count()
/**
* Return a new DStream in which each RDD contains the counts of each distinct value in
* each RDD of this DStream. Hash partitioning is used to generate the RDDs with
* Spark's default number of partitions.
*/
def countByValue(): JavaPairDStream[T, JLong] = {
JavaPairDStream.scalaToJavaLong(dstream.countByValue())
}
/**
* Return a new DStream in which each RDD contains the counts of each distinct value in
* each RDD of this DStream. Hash partitioning is used to generate the RDDs with `numPartitions`
* partitions.
* @param numPartitions number of partitions of each RDD in the new DStream.
*/
def countByValue(numPartitions: Int): JavaPairDStream[T, JLong] = {
JavaPairDStream.scalaToJavaLong(dstream.countByValue(numPartitions))
}
/**
* Return a new DStream in which each RDD has a single element generated by counting the number
* of elements in a window over this DStream. windowDuration and slideDuration are as defined in
* the window() operation. This is equivalent to window(windowDuration, slideDuration).count()
*/
def countByWindow(windowDuration: Duration, slideDuration: Duration) : JavaDStream[JLong] = {
dstream.countByWindow(windowDuration, slideDuration)
}
/**
* Return a new DStream in which each RDD contains the count of distinct elements in
* RDDs in a sliding window over this DStream. Hash partitioning is used to generate the RDDs
* with Spark's default number of partitions.
* @param windowDuration width of the window; must be a multiple of this DStream's
* batching interval
* @param slideDuration sliding interval of the window (i.e., the interval after which
* the new DStream will generate RDDs); must be a multiple of this
* DStream's batching interval
*/
def countByValueAndWindow(windowDuration: Duration, slideDuration: Duration)
: JavaPairDStream[T, JLong] = {
JavaPairDStream.scalaToJavaLong(
dstream.countByValueAndWindow(windowDuration, slideDuration))
}
/**
* Return a new DStream in which each RDD contains the count of distinct elements in
* RDDs in a sliding window over this DStream. Hash partitioning is used to generate the RDDs
* with `numPartitions` partitions.
* @param windowDuration width of the window; must be a multiple of this DStream's
* batching interval
* @param slideDuration sliding interval of the window (i.e., the interval after which
* the new DStream will generate RDDs); must be a multiple of this
* DStream's batching interval
* @param numPartitions number of partitions of each RDD in the new DStream.
*/
def countByValueAndWindow(windowDuration: Duration, slideDuration: Duration, numPartitions: Int)
: JavaPairDStream[T, JLong] = {
JavaPairDStream.scalaToJavaLong(
dstream.countByValueAndWindow(windowDuration, slideDuration, numPartitions))
}
/**
* Return a new DStream in which each RDD is generated by applying glom() to each RDD of
* this DStream. Applying glom() to an RDD coalesces all elements within each partition into
* an array.
*/
def glom(): JavaDStream[JList[T]] =
new JavaDStream(dstream.glom().map(x => new java.util.ArrayList[T](x.toSeq)))
/** Return the [[org.apache.spark.streaming.StreamingContext]] associated with this DStream */
def context(): StreamingContext = dstream.context
/** Return a new DStream by applying a function to all elements of this DStream. */
def map[R](f: JFunction[T, R]): JavaDStream[R] = {
new JavaDStream(dstream.map(f)(fakeClassTag))(fakeClassTag)
}
/** Return a new DStream by applying a function to all elements of this DStream. */
def mapToPair[K2, V2](f: PairFunction[T, K2, V2]): JavaPairDStream[K2, V2] = {
def cm: ClassTag[(K2, V2)] = fakeClassTag
new JavaPairDStream(dstream.map[(K2, V2)](f)(cm))(fakeClassTag[K2], fakeClassTag[V2])
}
/**
* Return a new DStream by applying a function to all elements of this DStream,
* and then flattening the results
*/
def flatMap[U](f: FlatMapFunction[T, U]): JavaDStream[U] = {
import scala.collection.JavaConverters._
def fn: (T) => Iterable[U] = (x: T) => f.call(x).asScala
new JavaDStream(dstream.flatMap(fn)(fakeClassTag[U]))(fakeClassTag[U])
}
/**
* Return a new DStream by applying a function to all elements of this DStream,
* and then flattening the results
*/
def flatMapToPair[K2, V2](f: PairFlatMapFunction[T, K2, V2]): JavaPairDStream[K2, V2] = {
import scala.collection.JavaConverters._
def fn: (T) => Iterable[(K2, V2)] = (x: T) => f.call(x).asScala
def cm: ClassTag[(K2, V2)] = fakeClassTag
new JavaPairDStream(dstream.flatMap(fn)(cm))(fakeClassTag[K2], fakeClassTag[V2])
}
/**
* Return a new DStream in which each RDD is generated by applying mapPartitions() to each RDDs
* of this DStream. Applying mapPartitions() to an RDD applies a function to each partition
* of the RDD.
*/
def mapPartitions[U](f: FlatMapFunction[java.util.Iterator[T], U]): JavaDStream[U] = {
def fn: (Iterator[T]) => Iterator[U] = {
(x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator())
}
new JavaDStream(dstream.mapPartitions(fn)(fakeClassTag[U]))(fakeClassTag[U])
}
/**
* Return a new DStream in which each RDD is generated by applying mapPartitions() to each RDDs
* of this DStream. Applying mapPartitions() to an RDD applies a function to each partition
* of the RDD.
*/
def mapPartitionsToPair[K2, V2](f: PairFlatMapFunction[java.util.Iterator[T], K2, V2])
: JavaPairDStream[K2, V2] = {
def fn: (Iterator[T]) => Iterator[(K2, V2)] = {
(x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator())
}
new JavaPairDStream(dstream.mapPartitions(fn))(fakeClassTag[K2], fakeClassTag[V2])
}
/**
* Return a new DStream in which each RDD has a single element generated by reducing each RDD
* of this DStream.
*/
def reduce(f: JFunction2[T, T, T]): JavaDStream[T] = dstream.reduce(f)
/**
* Return a new DStream in which each RDD has a single element generated by reducing all
* elements in a sliding window over this DStream.
* @param reduceFunc associative reduce function
* @param windowDuration width of the window; must be a multiple of this DStream's
* batching interval
* @param slideDuration sliding interval of the window (i.e., the interval after which
* the new DStream will generate RDDs); must be a multiple of this
* DStream's batching interval
* @deprecated As this API is not Java compatible.
*/
@deprecated("Use Java-compatible version of reduceByWindow", "1.3.0")
def reduceByWindow(
reduceFunc: (T, T) => T,
windowDuration: Duration,
slideDuration: Duration
): DStream[T] = {
dstream.reduceByWindow(reduceFunc, windowDuration, slideDuration)
}
/**
* Return a new DStream in which each RDD has a single element generated by reducing all
* elements in a sliding window over this DStream.
* @param reduceFunc associative reduce function
* @param windowDuration width of the window; must be a multiple of this DStream's
* batching interval
* @param slideDuration sliding interval of the window (i.e., the interval after which
* the new DStream will generate RDDs); must be a multiple of this
* DStream's batching interval
*/
def reduceByWindow(
reduceFunc: JFunction2[T, T, T],
windowDuration: Duration,
slideDuration: Duration
): JavaDStream[T] = {
dstream.reduceByWindow(reduceFunc, windowDuration, slideDuration)
}
/**
* Return a new DStream in which each RDD has a single element generated by reducing all
* elements in a sliding window over this DStream. However, the reduction is done incrementally
* using the old window's reduced value :
* 1. reduce the new values that entered the window (e.g., adding new counts)
* 2. "inverse reduce" the old values that left the window (e.g., subtracting old counts)
* This is more efficient than reduceByWindow without "inverse reduce" function.
* However, it is applicable to only "invertible reduce functions".
* @param reduceFunc associative reduce function
* @param invReduceFunc inverse reduce function
* @param windowDuration width of the window; must be a multiple of this DStream's
* batching interval
* @param slideDuration sliding interval of the window (i.e., the interval after which
* the new DStream will generate RDDs); must be a multiple of this
* DStream's batching interval
*/
def reduceByWindow(
reduceFunc: JFunction2[T, T, T],
invReduceFunc: JFunction2[T, T, T],
windowDuration: Duration,
slideDuration: Duration
): JavaDStream[T] = {
dstream.reduceByWindow(reduceFunc, invReduceFunc, windowDuration, slideDuration)
}
/**
* Return all the RDDs between 'fromDuration' to 'toDuration' (both included)
*/
def slice(fromTime: Time, toTime: Time): JList[R] = {
new util.ArrayList(dstream.slice(fromTime, toTime).map(wrapRDD(_)).toSeq)
}
/**
* Apply a function to each RDD in this DStream. This is an output operator, so
* 'this' DStream will be registered as an output stream and therefore materialized.
*
* @deprecated As of release 0.9.0, replaced by foreachRDD
*/
@deprecated("Use foreachRDD", "0.9.0")
def foreach(foreachFunc: JFunction[R, Void]) {
foreachRDD(foreachFunc)
}
/**
* Apply a function to each RDD in this DStream. This is an output operator, so
* 'this' DStream will be registered as an output stream and therefore materialized.
*
* @deprecated As of release 0.9.0, replaced by foreachRDD
*/
@deprecated("Use foreachRDD", "0.9.0")
def foreach(foreachFunc: JFunction2[R, Time, Void]) {
foreachRDD(foreachFunc)
}
/**
* Apply a function to each RDD in this DStream. This is an output operator, so
* 'this' DStream will be registered as an output stream and therefore materialized.
*/
def foreachRDD(foreachFunc: JFunction[R, Void]) {
dstream.foreachRDD(rdd => foreachFunc.call(wrapRDD(rdd)))
}
/**
* Apply a function to each RDD in this DStream. This is an output operator, so
* 'this' DStream will be registered as an output stream and therefore materialized.
*/
def foreachRDD(foreachFunc: JFunction2[R, Time, Void]) {
dstream.foreachRDD((rdd, time) => foreachFunc.call(wrapRDD(rdd), time))
}
/**
* Return a new DStream in which each RDD is generated by applying a function
* on each RDD of 'this' DStream.
*/
def transform[U](transformFunc: JFunction[R, JavaRDD[U]]): JavaDStream[U] = {
implicit val cm: ClassTag[U] = fakeClassTag
def scalaTransform (in: RDD[T]): RDD[U] =
transformFunc.call(wrapRDD(in)).rdd
dstream.transform(scalaTransform(_))
}
/**
* Return a new DStream in which each RDD is generated by applying a function
* on each RDD of 'this' DStream.
*/
def transform[U](transformFunc: JFunction2[R, Time, JavaRDD[U]]): JavaDStream[U] = {
implicit val cm: ClassTag[U] = fakeClassTag
def scalaTransform (in: RDD[T], time: Time): RDD[U] =
transformFunc.call(wrapRDD(in), time).rdd
dstream.transform(scalaTransform(_, _))
}
/**
* Return a new DStream in which each RDD is generated by applying a function
* on each RDD of 'this' DStream.
*/
def transformToPair[K2, V2](transformFunc: JFunction[R, JavaPairRDD[K2, V2]]):
JavaPairDStream[K2, V2] = {
implicit val cmk: ClassTag[K2] = fakeClassTag
implicit val cmv: ClassTag[V2] = fakeClassTag
def scalaTransform (in: RDD[T]): RDD[(K2, V2)] =
transformFunc.call(wrapRDD(in)).rdd
dstream.transform(scalaTransform(_))
}
/**
* Return a new DStream in which each RDD is generated by applying a function
* on each RDD of 'this' DStream.
*/
def transformToPair[K2, V2](transformFunc: JFunction2[R, Time, JavaPairRDD[K2, V2]]):
JavaPairDStream[K2, V2] = {
implicit val cmk: ClassTag[K2] = fakeClassTag
implicit val cmv: ClassTag[V2] = fakeClassTag
def scalaTransform (in: RDD[T], time: Time): RDD[(K2, V2)] =
transformFunc.call(wrapRDD(in), time).rdd
dstream.transform(scalaTransform(_, _))
}
/**
* Return a new DStream in which each RDD is generated by applying a function
* on each RDD of 'this' DStream and 'other' DStream.
*/
def transformWith[U, W](
other: JavaDStream[U],
transformFunc: JFunction3[R, JavaRDD[U], Time, JavaRDD[W]]
): JavaDStream[W] = {
implicit val cmu: ClassTag[U] = fakeClassTag
implicit val cmv: ClassTag[W] = fakeClassTag
def scalaTransform (inThis: RDD[T], inThat: RDD[U], time: Time): RDD[W] =
transformFunc.call(wrapRDD(inThis), other.wrapRDD(inThat), time).rdd
dstream.transformWith[U, W](other.dstream, scalaTransform(_, _, _))
}
/**
* Return a new DStream in which each RDD is generated by applying a function
* on each RDD of 'this' DStream and 'other' DStream.
*/
def transformWithToPair[U, K2, V2](
other: JavaDStream[U],
transformFunc: JFunction3[R, JavaRDD[U], Time, JavaPairRDD[K2, V2]]
): JavaPairDStream[K2, V2] = {
implicit val cmu: ClassTag[U] = fakeClassTag
implicit val cmk2: ClassTag[K2] = fakeClassTag
implicit val cmv2: ClassTag[V2] = fakeClassTag
def scalaTransform (inThis: RDD[T], inThat: RDD[U], time: Time): RDD[(K2, V2)] =
transformFunc.call(wrapRDD(inThis), other.wrapRDD(inThat), time).rdd
dstream.transformWith[U, (K2, V2)](other.dstream, scalaTransform(_, _, _))
}
/**
* Return a new DStream in which each RDD is generated by applying a function
* on each RDD of 'this' DStream and 'other' DStream.
*/
def transformWith[K2, V2, W](
other: JavaPairDStream[K2, V2],
transformFunc: JFunction3[R, JavaPairRDD[K2, V2], Time, JavaRDD[W]]
): JavaDStream[W] = {
implicit val cmk2: ClassTag[K2] = fakeClassTag
implicit val cmv2: ClassTag[V2] = fakeClassTag
implicit val cmw: ClassTag[W] = fakeClassTag
def scalaTransform (inThis: RDD[T], inThat: RDD[(K2, V2)], time: Time): RDD[W] = {
transformFunc.call(wrapRDD(inThis), other.wrapRDD(inThat), time).rdd
}
dstream.transformWith[(K2, V2), W](other.dstream, scalaTransform(_, _, _))
}
/**
* Return a new DStream in which each RDD is generated by applying a function
* on each RDD of 'this' DStream and 'other' DStream.
*/
def transformWithToPair[K2, V2, K3, V3](
other: JavaPairDStream[K2, V2],
transformFunc: JFunction3[R, JavaPairRDD[K2, V2], Time, JavaPairRDD[K3, V3]]
): JavaPairDStream[K3, V3] = {
implicit val cmk2: ClassTag[K2] = fakeClassTag
implicit val cmv2: ClassTag[V2] = fakeClassTag
implicit val cmk3: ClassTag[K3] = fakeClassTag
implicit val cmv3: ClassTag[V3] = fakeClassTag
def scalaTransform (inThis: RDD[T], inThat: RDD[(K2, V2)], time: Time): RDD[(K3, V3)] =
transformFunc.call(wrapRDD(inThis), other.wrapRDD(inThat), time).rdd
dstream.transformWith[(K2, V2), (K3, V3)](other.dstream, scalaTransform(_, _, _))
}
/**
* Enable periodic checkpointing of RDDs of this DStream.
* @param interval Time interval after which generated RDD will be checkpointed
*/
def checkpoint(interval: Duration): DStream[T] = {
dstream.checkpoint(interval)
}
}
|