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25
26 /**
27 * Classes to support functional-style operations on streams of elements, such
28 * as map-reduce transformations on collections. For example:
29 *
30 * <pre>{@code
31 * int sum = widgets.stream()
32 * .filter(b -> b.getColor() == RED)
33 * .mapToInt(b -> b.getWeight())
34 * .sum();
35 * }</pre>
36 *
37 * <p>Here we use {@code widgets}, a {@code Collection<Widget>},
38 * as a source for a stream, and then perform a filter-map-reduce on the stream
39 * to obtain the sum of the weights of the red widgets. (Summation is an
40 * example of a <a href="package-summary.html#Reduction">reduction</a>
41 * operation.)
42 *
43 * <p>The key abstraction introduced in this package is <em>stream</em>. The
44 * classes {@link java.util.stream.Stream}, {@link java.util.stream.IntStream},
45 * {@link java.util.stream.LongStream}, and {@link java.util.stream.DoubleStream}
46 * are streams over objects and the primitive {@code int}, {@code long}, and
47 * {@code double} types. Streams differ from collections in several ways:
48 *
49 * <ul>
50 * <li>No storage. A stream is not a data structure that stores elements;
51 * instead, it conveys elements from a source such as a data structure,
52 * an array, a generator function, or an I/O channel, through a pipeline of
53 * computational operations.</li>
54 * <li>Functional in nature. An operation on a stream produces a result,
55 * but does not modify its source. For example, filtering a {@code Stream}
56 * obtained from a collection produces a new {@code Stream} without the
57 * filtered elements, rather than removing elements from the source
58 * collection.</li>
59 * <li>Laziness-seeking. Many stream operations, such as filtering, mapping,
60 * or duplicate removal, can be implemented lazily, exposing opportunities
61 * for optimization. For example, "find the first {@code String} with
62 * three consecutive vowels" need not examine all the input strings.
63 * Stream operations are divided into intermediate ({@code Stream}-producing)
64 * operations and terminal (value- or side-effect-producing) operations.
65 * Intermediate operations are always lazy.</li>
66 * <li>Possibly unbounded. While collections have a finite size, streams
67 * need not. Short-circuiting operations such as {@code limit(n)} or
68 * {@code findFirst()} can allow computations on infinite streams to
69 * complete in finite time.</li>
70 * <li>Consumable. The elements of a stream are only visited once during
71 * the life of a stream. Like an {@link java.util.Iterator}, a new stream
72 * must be generated to revisit the same elements of the source.
73 * </li>
74 * </ul>
75 *
76 * Streams can be obtained in a number of ways. Some examples include:
77 * <ul>
78 * <li>From a {@link java.util.Collection} via the {@code stream()} and
79 * {@code parallelStream()} methods;</li>
80 * <li>From an array via {@link java.util.Arrays#stream(Object[])};</li>
81 * <li>From static factory methods on the stream classes, such as
82 * {@link java.util.stream.Stream#of(Object[])},
83 * {@link java.util.stream.IntStream#range(int, int)}
84 * or {@link java.util.stream.Stream#iterate(Object, UnaryOperator)};</li>
85 * <li>The lines of a file can be obtained from {@link java.io.BufferedReader#lines()};</li>
86 * <li>Streams of file paths can be obtained from methods in {@link java.nio.file.Files};</li>
87 * <li>Streams of random numbers can be obtained from {@link java.util.Random#ints()};</li>
88 * <li>Numerous other stream-bearing methods in the JDK, including
89 * {@link java.util.BitSet#stream()},
90 * {@link java.util.regex.Pattern#splitAsStream(java.lang.CharSequence)},
91 * and {@link java.util.jar.JarFile#stream()}.</li>
92 * </ul>
93 *
94 * <p>Additional stream sources can be provided by third-party libraries using
95 * <a href="package-summary.html#StreamSources">these techniques</a>.
96 *
97 * <h2><a id="StreamOps">Stream operations and pipelines</a></h2>
98 *
99 * <p>Stream operations are divided into <em>intermediate</em> and
100 * <em>terminal</em> operations, and are combined to form <em>stream
101 * pipelines</em>. A stream pipeline consists of a source (such as a
102 * {@code Collection}, an array, a generator function, or an I/O channel);
103 * followed by zero or more intermediate operations such as
104 * {@code Stream.filter} or {@code Stream.map}; and a terminal operation such
105 * as {@code Stream.forEach} or {@code Stream.reduce}.
106 *
107 * <p>Intermediate operations return a new stream. They are always
108 * <em>lazy</em>; executing an intermediate operation such as
109 * {@code filter()} does not actually perform any filtering, but instead
110 * creates a new stream that, when traversed, contains the elements of
111 * the initial stream that match the given predicate. Traversal
112 * of the pipeline source does not begin until the terminal operation of the
113 * pipeline is executed.
114 *
115 * <p>Terminal operations, such as {@code Stream.forEach} or
116 * {@code IntStream.sum}, may traverse the stream to produce a result or a
117 * side-effect. After the terminal operation is performed, the stream pipeline
118 * is considered consumed, and can no longer be used; if you need to traverse
119 * the same data source again, you must return to the data source to get a new
120 * stream. In almost all cases, terminal operations are <em>eager</em>,
121 * completing their traversal of the data source and processing of the pipeline
122 * before returning. Only the terminal operations {@code iterator()} and
123 * {@code spliterator()} are not; these are provided as an "escape hatch" to enable
124 * arbitrary client-controlled pipeline traversals in the event that the
125 * existing operations are not sufficient to the task.
126 *
127 * <p> Processing streams lazily allows for significant efficiencies; in a
128 * pipeline such as the filter-map-sum example above, filtering, mapping, and
129 * summing can be fused into a single pass on the data, with minimal
130 * intermediate state. Laziness also allows avoiding examining all the data
131 * when it is not necessary; for operations such as "find the first string
132 * longer than 1000 characters", it is only necessary to examine just enough
133 * strings to find one that has the desired characteristics without examining
134 * all of the strings available from the source. (This behavior becomes even
135 * more important when the input stream is infinite and not merely large.)
136 *
137 * <p>Intermediate operations are further divided into <em>stateless</em>
138 * and <em>stateful</em> operations. Stateless operations, such as {@code filter}
139 * and {@code map}, retain no state from previously seen element when processing
140 * a new element -- each element can be processed
141 * independently of operations on other elements. Stateful operations, such as
142 * {@code distinct} and {@code sorted}, may incorporate state from previously
143 * seen elements when processing new elements.
144 *
145 * <p>Stateful operations may need to process the entire input
146 * before producing a result. For example, one cannot produce any results from
147 * sorting a stream until one has seen all elements of the stream. As a result,
148 * under parallel computation, some pipelines containing stateful intermediate
149 * operations may require multiple passes on the data or may need to buffer
150 * significant data. Pipelines containing exclusively stateless intermediate
151 * operations can be processed in a single pass, whether sequential or parallel,
152 * with minimal data buffering.
153 *
154 * <p>Further, some operations are deemed <em>short-circuiting</em> operations.
155 * An intermediate operation is short-circuiting if, when presented with
156 * infinite input, it may produce a finite stream as a result. A terminal
157 * operation is short-circuiting if, when presented with infinite input, it may
158 * terminate in finite time. Having a short-circuiting operation in the pipeline
159 * is a necessary, but not sufficient, condition for the processing of an infinite
160 * stream to terminate normally in finite time.
161 *
162 * <h3><a id="Parallelism">Parallelism</a></h3>
163 *
164 * <p>Processing elements with an explicit {@code for-}loop is inherently serial.
165 * Streams facilitate parallel execution by reframing the computation as a pipeline of
166 * aggregate operations, rather than as imperative operations on each individual
167 * element. All streams operations can execute either in serial or in parallel.
168 * The stream implementations in the JDK create serial streams unless parallelism is
169 * explicitly requested. For example, {@code Collection} has methods
170 * {@link java.util.Collection#stream} and {@link java.util.Collection#parallelStream},
171 * which produce sequential and parallel streams respectively; other
172 * stream-bearing methods such as {@link java.util.stream.IntStream#range(int, int)}
173 * produce sequential streams but these streams can be efficiently parallelized by
174 * invoking their {@link java.util.stream.BaseStream#parallel()} method.
175 * To execute the prior "sum of weights of widgets" query in parallel, we would
176 * do:
177 *
178 * <pre>{@code
179 * int sumOfWeights = widgets.parallelStream()
180 * .filter(b -> b.getColor() == RED)
181 * .mapToInt(b -> b.getWeight())
182 * .sum();
183 * }</pre>
184 *
185 * <p>The only difference between the serial and parallel versions of this
186 * example is the creation of the initial stream, using "{@code parallelStream()}"
187 * instead of "{@code stream()}". The stream pipeline is executed sequentially or
188 * in parallel depending on the mode of the stream on which the terminal operation
189 * is invoked. The sequential or parallel mode of a stream can be determined with the
190 * {@link java.util.stream.BaseStream#isParallel()} method, and the
191 * stream's mode can be modified with the
192 * {@link java.util.stream.BaseStream#sequential()} and
193 * {@link java.util.stream.BaseStream#parallel()} operations.
194 * The most recent sequential or parallel mode setting applies to the
195 * execution of the entire stream pipeline.
196 *
197 * <p>Except for operations identified as explicitly nondeterministic, such
198 * as {@code findAny()}, whether a stream executes sequentially or in parallel
199 * should not change the result of the computation.
200 *
201 * <p>Most stream operations accept parameters that describe user-specified
202 * behavior, which are often lambda expressions. To preserve correct behavior,
203 * these <em>behavioral parameters</em> must be <em>non-interfering</em>, and in
204 * most cases must be <em>stateless</em>. Such parameters are always instances
205 * of a <a href="../function/package-summary.html">functional interface</a> such
206 * as {@link java.util.function.Function}, and are often lambda expressions or
207 * method references.
208 *
209 * <h3><a id="NonInterference">Non-interference</a></h3>
210 *
211 * Streams enable you to execute possibly-parallel aggregate operations over a
212 * variety of data sources, including even non-thread-safe collections such as
213 * {@code ArrayList}. This is possible only if we can prevent
214 * <em>interference</em> with the data source during the execution of a stream
215 * pipeline. Except for the escape-hatch operations {@code iterator()} and
216 * {@code spliterator()}, execution begins when the terminal operation is
217 * invoked, and ends when the terminal operation completes. For most data
218 * sources, preventing interference means ensuring that the data source is
219 * <em>not modified at all</em> during the execution of the stream pipeline.
220 * The notable exception to this are streams whose sources are concurrent
221 * collections, which are specifically designed to handle concurrent modification.
222 * Concurrent stream sources are those whose {@code Spliterator} reports the
223 * {@code CONCURRENT} characteristic.
224 *
225 * <p>Accordingly, behavioral parameters in stream pipelines whose source might
226 * not be concurrent should never modify the stream's data source.
227 * A behavioral parameter is said to <em>interfere</em> with a non-concurrent
228 * data source if it modifies, or causes to be
229 * modified, the stream's data source. The need for non-interference applies
230 * to all pipelines, not just parallel ones. Unless the stream source is
231 * concurrent, modifying a stream's data source during execution of a stream
232 * pipeline can cause exceptions, incorrect answers, or nonconformant behavior.
233 *
234 * For well-behaved stream sources, the source can be modified before the
235 * terminal operation commences and those modifications will be reflected in
236 * the covered elements. For example, consider the following code:
237 *
238 * <pre>{@code
239 * List<String> l = new ArrayList(Arrays.asList("one", "two"));
240 * Stream<String> sl = l.stream();
241 * l.add("three");
242 * String s = sl.collect(joining(" "));
243 * }</pre>
244 *
245 * First a list is created consisting of two strings: "one" and "two". Then a
246 * stream is created from that list. Next the list is modified by adding a third
247 * string: "three". Finally the elements of the stream are collected and joined
248 * together. Since the list was modified before the terminal {@code collect}
249 * operation commenced the result will be a string of "one two three". All the
250 * streams returned from JDK collections, and most other JDK classes,
251 * are well-behaved in this manner; for streams generated by other libraries, see
252 * <a href="package-summary.html#StreamSources">Low-level stream
253 * construction</a> for requirements for building well-behaved streams.
254 *
255 * <h3><a id="Statelessness">Stateless behaviors</a></h3>
256 *
257 * Stream pipeline results may be nondeterministic or incorrect if the behavioral
258 * parameters to the stream operations are <em>stateful</em>. A stateful lambda
259 * (or other object implementing the appropriate functional interface) is one
260 * whose result depends on any state which might change during the execution
261 * of the stream pipeline. An example of a stateful lambda is the parameter
262 * to {@code map()} in:
263 *
264 * <pre>{@code
265 * Set<Integer> seen = Collections.synchronizedSet(new HashSet<>());
266 * stream.parallel().map(e -> { if (seen.add(e)) return 0; else return e; })...
267 * }</pre>
268 *
269 * Here, if the mapping operation is performed in parallel, the results for the
270 * same input could vary from run to run, due to thread scheduling differences,
271 * whereas, with a stateless lambda expression the results would always be the
272 * same.
273 *
274 * <p>Note also that attempting to access mutable state from behavioral parameters
275 * presents you with a bad choice with respect to safety and performance; if
276 * you do not synchronize access to that state, you have a data race and
277 * therefore your code is broken, but if you do synchronize access to that
278 * state, you risk having contention undermine the parallelism you are seeking
279 * to benefit from. The best approach is to avoid stateful behavioral
280 * parameters to stream operations entirely; there is usually a way to
281 * restructure the stream pipeline to avoid statefulness.
282 *
283 * <h3><a id="SideEffects">Side-effects</a></h3>
284 *
285 * Side-effects in behavioral parameters to stream operations are, in general,
286 * discouraged, as they can often lead to unwitting violations of the
287 * statelessness requirement, as well as other thread-safety hazards.
288 *
289 * <p>If the behavioral parameters do have side-effects, unless explicitly
290 * stated, there are no guarantees as to:
291 * <ul>
292 * <li>the <a href="../concurrent/package-summary.html#MemoryVisibility">
293 * <i>visibility</i></a> of those side-effects to other threads;</li>
294 * <li>that different operations on the "same" element within the same stream
295 * pipeline are executed in the same thread; and</li>
296 * <li>that behavioral parameters are always invoked, since a stream
297 * implementation is free to elide operations (or entire stages) from a
298 * stream pipeline if it can prove that it would not affect the result of the
299 * computation.
300 * </li>
301 * </ul>
302 * <p>The ordering of side-effects may be surprising. Even when a pipeline is
303 * constrained to produce a <em>result</em> that is consistent with the
304 * encounter order of the stream source (for example,
305 * {@code IntStream.range(0,5).parallel().map(x -> x*2).toArray()}
306 * must produce {@code [0, 2, 4, 6, 8]}), no guarantees are made as to the order
307 * in which the mapper function is applied to individual elements, or in what
308 * thread any behavioral parameter is executed for a given element.
309 *
310 * <p>The eliding of side-effects may also be surprising. With the exception of
311 * terminal operations {@link java.util.stream.Stream#forEach forEach} and
312 * {@link java.util.stream.Stream#forEachOrdered forEachOrdered}, side-effects
313 * of behavioral parameters may not always be executed when the stream
314 * implementation can optimize away the execution of behavioral parameters
315 * without affecting the result of the computation. (For a specific example
316 * see the API note documented on the {@link java.util.stream.Stream#count count}
317 * operation.)
318 *
319 * <p>Many computations where one might be tempted to use side-effects can be more
320 * safely and efficiently expressed without side-effects, such as using
321 * <a href="package-summary.html#Reduction">reduction</a> instead of mutable
322 * accumulators. However, side-effects such as using {@code println()} for debugging
323 * purposes are usually harmless. A small number of stream operations, such as
324 * {@code forEach()} and {@code peek()}, can operate only via side-effects;
325 * these should be used with care.
326 *
327 * <p>As an example of how to transform a stream pipeline that inappropriately
328 * uses side-effects to one that does not, the following code searches a stream
329 * of strings for those matching a given regular expression, and puts the
330 * matches in a list.
331 *
332 * <pre>{@code
333 * ArrayList<String> results = new ArrayList<>();
334 * stream.filter(s -> pattern.matcher(s).matches())
335 * .forEach(s -> results.add(s)); // Unnecessary use of side-effects!
336 * }</pre>
337 *
338 * This code unnecessarily uses side-effects. If executed in parallel, the
339 * non-thread-safety of {@code ArrayList} would cause incorrect results, and
340 * adding needed synchronization would cause contention, undermining the
341 * benefit of parallelism. Furthermore, using side-effects here is completely
342 * unnecessary; the {@code forEach()} can simply be replaced with a reduction
343 * operation that is safer, more efficient, and more amenable to
344 * parallelization:
345 *
346 * <pre>{@code
347 * List<String> results =
348 * stream.filter(s -> pattern.matcher(s).matches())
349 * .collect(Collectors.toList()); // No side-effects!
350 * }</pre>
351 *
352 * <h3><a id="Ordering">Ordering</a></h3>
353 *
354 * <p>Streams may or may not have a defined <em>encounter order</em>. Whether
355 * or not a stream has an encounter order depends on the source and the
356 * intermediate operations. Certain stream sources (such as {@code List} or
357 * arrays) are intrinsically ordered, whereas others (such as {@code HashSet})
358 * are not. Some intermediate operations, such as {@code sorted()}, may impose
359 * an encounter order on an otherwise unordered stream, and others may render an
360 * ordered stream unordered, such as {@link java.util.stream.BaseStream#unordered()}.
361 * Further, some terminal operations may ignore encounter order, such as
362 * {@code forEach()}.
363 *
364 * <p>If a stream is ordered, most operations are constrained to operate on the
365 * elements in their encounter order; if the source of a stream is a {@code List}
366 * containing {@code [1, 2, 3]}, then the result of executing {@code map(x -> x*2)}
367 * must be {@code [2, 4, 6]}. However, if the source has no defined encounter
368 * order, then any permutation of the values {@code [2, 4, 6]} would be a valid
369 * result.
370 *
371 * <p>For sequential streams, the presence or absence of an encounter order does
372 * not affect performance, only determinism. If a stream is ordered, repeated
373 * execution of identical stream pipelines on an identical source will produce
374 * an identical result; if it is not ordered, repeated execution might produce
375 * different results.
376 *
377 * <p>For parallel streams, relaxing the ordering constraint can sometimes enable
378 * more efficient execution. Certain aggregate operations,
379 * such as filtering duplicates ({@code distinct()}) or grouped reductions
380 * ({@code Collectors.groupingBy()}) can be implemented more efficiently if ordering of elements
381 * is not relevant. Similarly, operations that are intrinsically tied to encounter order,
382 * such as {@code limit()}, may require
383 * buffering to ensure proper ordering, undermining the benefit of parallelism.
384 * In cases where the stream has an encounter order, but the user does not
385 * particularly <em>care</em> about that encounter order, explicitly de-ordering
386 * the stream with {@link java.util.stream.BaseStream#unordered() unordered()} may
387 * improve parallel performance for some stateful or terminal operations.
388 * However, most stream pipelines, such as the "sum of weight of blocks" example
389 * above, still parallelize efficiently even under ordering constraints.
390 *
391 * <h2><a id="Reduction">Reduction operations</a></h2>
392 *
393 * A <em>reduction</em> operation (also called a <em>fold</em>) takes a sequence
394 * of input elements and combines them into a single summary result by repeated
395 * application of a combining operation, such as finding the sum or maximum of
396 * a set of numbers, or accumulating elements into a list. The streams classes have
397 * multiple forms of general reduction operations, called
398 * {@link java.util.stream.Stream#reduce(java.util.function.BinaryOperator) reduce()}
399 * and {@link java.util.stream.Stream#collect(java.util.stream.Collector) collect()},
400 * as well as multiple specialized reduction forms such as
401 * {@link java.util.stream.IntStream#sum() sum()}, {@link java.util.stream.IntStream#max() max()},
402 * or {@link java.util.stream.IntStream#count() count()}.
403 *
404 * <p>Of course, such operations can be readily implemented as simple sequential
405 * loops, as in:
406 * <pre>{@code
407 * int sum = 0;
408 * for (int x : numbers) {
409 * sum += x;
410 * }
411 * }</pre>
412 * However, there are good reasons to prefer a reduce operation
413 * over a mutative accumulation such as the above. Not only is a reduction
414 * "more abstract" -- it operates on the stream as a whole rather than individual
415 * elements -- but a properly constructed reduce operation is inherently
416 * parallelizable, so long as the function(s) used to process the elements
417 * are <a href="package-summary.html#Associativity">associative</a> and
418 * <a href="package-summary.html#Statelessness">stateless</a>.
419 * For example, given a stream of numbers for which we want to find the sum, we
420 * can write:
421 * <pre>{@code
422 * int sum = numbers.stream().reduce(0, (x,y) -> x+y);
423 * }</pre>
424 * or:
425 * <pre>{@code
426 * int sum = numbers.stream().reduce(0, Integer::sum);
427 * }</pre>
428 *
429 * <p>These reduction operations can run safely in parallel with almost no
430 * modification:
431 * <pre>{@code
432 * int sum = numbers.parallelStream().reduce(0, Integer::sum);
433 * }</pre>
434 *
435 * <p>Reduction parallellizes well because the implementation
436 * can operate on subsets of the data in parallel, and then combine the
437 * intermediate results to get the final correct answer. (Even if the language
438 * had a "parallel for-each" construct, the mutative accumulation approach would
439 * still required the developer to provide
440 * thread-safe updates to the shared accumulating variable {@code sum}, and
441 * the required synchronization would then likely eliminate any performance gain from
442 * parallelism.) Using {@code reduce()} instead removes all of the
443 * burden of parallelizing the reduction operation, and the library can provide
444 * an efficient parallel implementation with no additional synchronization
445 * required.
446 *
447 * <p>The "widgets" examples shown earlier shows how reduction combines with
448 * other operations to replace for-loops with bulk operations. If {@code widgets}
449 * is a collection of {@code Widget} objects, which have a {@code getWeight} method,
450 * we can find the heaviest widget with:
451 * <pre>{@code
452 * OptionalInt heaviest = widgets.parallelStream()
453 * .mapToInt(Widget::getWeight)
454 * .max();
455 * }</pre>
456 *
457 * <p>In its more general form, a {@code reduce} operation on elements of type
458 * {@code <T>} yielding a result of type {@code <U>} requires three parameters:
459 * <pre>{@code
460 * <U> U reduce(U identity,
461 * BiFunction<U, ? super T, U> accumulator,
462 * BinaryOperator<U> combiner);
463 * }</pre>
464 * Here, the <em>identity</em> element is both an initial seed value for the reduction
465 * and a default result if there are no input elements. The <em>accumulator</em>
466 * function takes a partial result and the next element, and produces a new
467 * partial result. The <em>combiner</em> function combines two partial results
468 * to produce a new partial result. (The combiner is necessary in parallel
469 * reductions, where the input is partitioned, a partial accumulation computed
470 * for each partition, and then the partial results are combined to produce a
471 * final result.)
472 *
473 * <p>More formally, the {@code identity} value must be an <em>identity</em> for
474 * the combiner function. This means that for all {@code u},
475 * {@code combiner.apply(identity, u)} is equal to {@code u}. Additionally, the
476 * {@code combiner} function must be <a href="package-summary.html#Associativity">associative</a> and
477 * must be compatible with the {@code accumulator} function: for all {@code u}
478 * and {@code t}, {@code combiner.apply(u, accumulator.apply(identity, t))} must
479 * be {@code equals()} to {@code accumulator.apply(u, t)}.
480 *
481 * <p>The three-argument form is a generalization of the two-argument form,
482 * incorporating a mapping step into the accumulation step. We could
483 * re-cast the simple sum-of-weights example using the more general form as
484 * follows:
485 * <pre>{@code
486 * int sumOfWeights = widgets.stream()
487 * .reduce(0,
488 * (sum, b) -> sum + b.getWeight(),
489 * Integer::sum);
490 * }</pre>
491 * though the explicit map-reduce form is more readable and therefore should
492 * usually be preferred. The generalized form is provided for cases where
493 * significant work can be optimized away by combining mapping and reducing
494 * into a single function.
495 *
496 * <h3><a id="MutableReduction">Mutable reduction</a></h3>
497 *
498 * A <em>mutable reduction operation</em> accumulates input elements into a
499 * mutable result container, such as a {@code Collection} or {@code StringBuilder},
500 * as it processes the elements in the stream.
501 *
502 * <p>If we wanted to take a stream of strings and concatenate them into a
503 * single long string, we <em>could</em> achieve this with ordinary reduction:
504 * <pre>{@code
505 * String concatenated = strings.reduce("", String::concat)
506 * }</pre>
507 *
508 * <p>We would get the desired result, and it would even work in parallel. However,
509 * we might not be happy about the performance! Such an implementation would do
510 * a great deal of string copying, and the run time would be <em>O(n^2)</em> in
511 * the number of characters. A more performant approach would be to accumulate
512 * the results into a {@link java.lang.StringBuilder}, which is a mutable
513 * container for accumulating strings. We can use the same technique to
514 * parallelize mutable reduction as we do with ordinary reduction.
515 *
516 * <p>The mutable reduction operation is called
517 * {@link java.util.stream.Stream#collect(Collector) collect()},
518 * as it collects together the desired results into a result container such
519 * as a {@code Collection}.
520 * A {@code collect} operation requires three functions:
521 * a supplier function to construct new instances of the result container, an
522 * accumulator function to incorporate an input element into a result
523 * container, and a combining function to merge the contents of one result
524 * container into another. The form of this is very similar to the general
525 * form of ordinary reduction:
526 * <pre>{@code
527 * <R> R collect(Supplier<R> supplier,
528 * BiConsumer<R, ? super T> accumulator,
529 * BiConsumer<R, R> combiner);
530 * }</pre>
531 * <p>As with {@code reduce()}, a benefit of expressing {@code collect} in this
532 * abstract way is that it is directly amenable to parallelization: we can
533 * accumulate partial results in parallel and then combine them, so long as the
534 * accumulation and combining functions satisfy the appropriate requirements.
535 * For example, to collect the String representations of the elements in a
536 * stream into an {@code ArrayList}, we could write the obvious sequential
537 * for-each form:
538 * <pre>{@code
539 * ArrayList<String> strings = new ArrayList<>();
540 * for (T element : stream) {
541 * strings.add(element.toString());
542 * }
543 * }</pre>
544 * Or we could use a parallelizable collect form:
545 * <pre>{@code
546 * ArrayList<String> strings = stream.collect(() -> new ArrayList<>(),
547 * (c, e) -> c.add(e.toString()),
548 * (c1, c2) -> c1.addAll(c2));
549 * }</pre>
550 * or, pulling the mapping operation out of the accumulator function, we could
551 * express it more succinctly as:
552 * <pre>{@code
553 * List<String> strings = stream.map(Object::toString)
554 * .collect(ArrayList::new, ArrayList::add, ArrayList::addAll);
555 * }</pre>
556 * Here, our supplier is just the {@link java.util.ArrayList#ArrayList()
557 * ArrayList constructor}, the accumulator adds the stringified element to an
558 * {@code ArrayList}, and the combiner simply uses {@link java.util.ArrayList#addAll addAll}
559 * to copy the strings from one container into the other.
560 *
561 * <p>The three aspects of {@code collect} -- supplier, accumulator, and
562 * combiner -- are tightly coupled. We can use the abstraction of a
563 * {@link java.util.stream.Collector} to capture all three aspects. The
564 * above example for collecting strings into a {@code List} can be rewritten
565 * using a standard {@code Collector} as:
566 * <pre>{@code
567 * List<String> strings = stream.map(Object::toString)
568 * .collect(Collectors.toList());
569 * }</pre>
570 *
571 * <p>Packaging mutable reductions into a Collector has another advantage:
572 * composability. The class {@link java.util.stream.Collectors} contains a
573 * number of predefined factories for collectors, including combinators
574 * that transform one collector into another. For example, suppose we have a
575 * collector that computes the sum of the salaries of a stream of
576 * employees, as follows:
577 *
578 * <pre>{@code
579 * Collector<Employee, ?, Integer> summingSalaries
580 * = Collectors.summingInt(Employee::getSalary);
581 * }</pre>
582 *
583 * (The {@code ?} for the second type parameter merely indicates that we don't
584 * care about the intermediate representation used by this collector.)
585 * If we wanted to create a collector to tabulate the sum of salaries by
586 * department, we could reuse {@code summingSalaries} using
587 * {@link java.util.stream.Collectors#groupingBy(java.util.function.Function, java.util.stream.Collector) groupingBy}:
588 *
589 * <pre>{@code
590 * Map<Department, Integer> salariesByDept
591 * = employees.stream().collect(Collectors.groupingBy(Employee::getDepartment,
592 * summingSalaries));
593 * }</pre>
594 *
595 * <p>As with the regular reduction operation, {@code collect()} operations can
596 * only be parallelized if appropriate conditions are met. For any partially
597 * accumulated result, combining it with an empty result container must
598 * produce an equivalent result. That is, for a partially accumulated result
599 * {@code p} that is the result of any series of accumulator and combiner
600 * invocations, {@code p} must be equivalent to
601 * {@code combiner.apply(p, supplier.get())}.
602 *
603 * <p>Further, however the computation is split, it must produce an equivalent
604 * result. For any input elements {@code t1} and {@code t2}, the results
605 * {@code r1} and {@code r2} in the computation below must be equivalent:
606 * <pre>{@code
607 * A a1 = supplier.get();
608 * accumulator.accept(a1, t1);
609 * accumulator.accept(a1, t2);
610 * R r1 = finisher.apply(a1); // result without splitting
611 *
612 * A a2 = supplier.get();
613 * accumulator.accept(a2, t1);
614 * A a3 = supplier.get();
615 * accumulator.accept(a3, t2);
616 * R r2 = finisher.apply(combiner.apply(a2, a3)); // result with splitting
617 * }</pre>
618 *
619 * <p>Here, equivalence generally means according to {@link java.lang.Object#equals(Object)}.
620 * but in some cases equivalence may be relaxed to account for differences in
621 * order.
622 *
623 * <h3><a id="ConcurrentReduction">Reduction, concurrency, and ordering</a></h3>
624 *
625 * With some complex reduction operations, for example a {@code collect()} that
626 * produces a {@code Map}, such as:
627 * <pre>{@code
628 * Map<Buyer, List<Transaction>> salesByBuyer
629 * = txns.parallelStream()
630 * .collect(Collectors.groupingBy(Transaction::getBuyer));
631 * }</pre>
632 * it may actually be counterproductive to perform the operation in parallel.
633 * This is because the combining step (merging one {@code Map} into another by
634 * key) can be expensive for some {@code Map} implementations.
635 *
636 * <p>Suppose, however, that the result container used in this reduction
637 * was a concurrently modifiable collection -- such as a
638 * {@link java.util.concurrent.ConcurrentHashMap}. In that case, the parallel
639 * invocations of the accumulator could actually deposit their results
640 * concurrently into the same shared result container, eliminating the need for
641 * the combiner to merge distinct result containers. This potentially provides
642 * a boost to the parallel execution performance. We call this a
643 * <em>concurrent</em> reduction.
644 *
645 * <p>A {@link java.util.stream.Collector} that supports concurrent reduction is
646 * marked with the {@link java.util.stream.Collector.Characteristics#CONCURRENT}
647 * characteristic. However, a concurrent collection also has a downside. If
648 * multiple threads are depositing results concurrently into a shared container,
649 * the order in which results are deposited is non-deterministic. Consequently,
650 * a concurrent reduction is only possible if ordering is not important for the
651 * stream being processed. The {@link java.util.stream.Stream#collect(Collector)}
652 * implementation will only perform a concurrent reduction if
653 * <ul>
654 * <li>The stream is parallel;</li>
655 * <li>The collector has the
656 * {@link java.util.stream.Collector.Characteristics#CONCURRENT} characteristic,
657 * and;</li>
658 * <li>Either the stream is unordered, or the collector has the
659 * {@link java.util.stream.Collector.Characteristics#UNORDERED} characteristic.
660 * </ul>
661 * You can ensure the stream is unordered by using the
662 * {@link java.util.stream.BaseStream#unordered()} method. For example:
663 * <pre>{@code
664 * Map<Buyer, List<Transaction>> salesByBuyer
665 * = txns.parallelStream()
666 * .unordered()
667 * .collect(groupingByConcurrent(Transaction::getBuyer));
668 * }</pre>
669 * (where {@link java.util.stream.Collectors#groupingByConcurrent} is the
670 * concurrent equivalent of {@code groupingBy}).
671 *
672 * <p>Note that if it is important that the elements for a given key appear in
673 * the order they appear in the source, then we cannot use a concurrent
674 * reduction, as ordering is one of the casualties of concurrent insertion.
675 * We would then be constrained to implement either a sequential reduction or
676 * a merge-based parallel reduction.
677 *
678 * <h3><a id="Associativity">Associativity</a></h3>
679 *
680 * An operator or function {@code op} is <em>associative</em> if the following
681 * holds:
682 * <pre>{@code
683 * (a op b) op c == a op (b op c)
684 * }</pre>
685 * The importance of this to parallel evaluation can be seen if we expand this
686 * to four terms:
687 * <pre>{@code
688 * a op b op c op d == (a op b) op (c op d)
689 * }</pre>
690 * So we can evaluate {@code (a op b)} in parallel with {@code (c op d)}, and
691 * then invoke {@code op} on the results.
692 *
693 * <p>Examples of associative operations include numeric addition, min, and
694 * max, and string concatenation.
695 *
696 * <h2><a id="StreamSources">Low-level stream construction</a></h2>
697 *
698 * So far, all the stream examples have used methods like
699 * {@link java.util.Collection#stream()} or {@link java.util.Arrays#stream(Object[])}
700 * to obtain a stream. How are those stream-bearing methods implemented?
701 *
702 * <p>The class {@link java.util.stream.StreamSupport} has a number of
703 * low-level methods for creating a stream, all using some form of a
704 * {@link java.util.Spliterator}. A spliterator is the parallel analogue of an
705 * {@link java.util.Iterator}; it describes a (possibly infinite) collection of
706 * elements, with support for sequentially advancing, bulk traversal, and
707 * splitting off some portion of the input into another spliterator which can
708 * be processed in parallel. At the lowest level, all streams are driven by a
709 * spliterator.
710 *
711 * <p>There are a number of implementation choices in implementing a
712 * spliterator, nearly all of which are tradeoffs between simplicity of
713 * implementation and runtime performance of streams using that spliterator.
714 * The simplest, but least performant, way to create a spliterator is to
715 * create one from an iterator using
716 * {@link java.util.Spliterators#spliteratorUnknownSize(java.util.Iterator, int)}.
717 * While such a spliterator will work, it will likely offer poor parallel
718 * performance, since we have lost sizing information (how big is the
719 * underlying data set), as well as being constrained to a simplistic
720 * splitting algorithm.
721 *
722 * <p>A higher-quality spliterator will provide balanced and known-size
723 * splits, accurate sizing information, and a number of other
724 * {@link java.util.Spliterator#characteristics() characteristics} of the
725 * spliterator or data that can be used by implementations to optimize
726 * execution.
727 *
728 * <p>Spliterators for mutable data sources have an additional challenge;
729 * timing of binding to the data, since the data could change between the time
730 * the spliterator is created and the time the stream pipeline is executed.
731 * Ideally, a spliterator for a stream would report a characteristic of
732 * {@code IMMUTABLE} or {@code CONCURRENT}; if not it should be
733 * <a href="../Spliterator.html#binding"><em>late-binding</em></a>. If a source
734 * cannot directly supply a recommended spliterator, it may indirectly supply
735 * a spliterator using a {@code Supplier}, and construct a stream via the
736 * {@code Supplier}-accepting versions of
737 * {@link java.util.stream.StreamSupport#stream(Supplier, int, boolean) stream()}.
738 * The spliterator is obtained from the supplier only after the terminal
739 * operation of the stream pipeline commences.
740 *
741 * <p>These requirements significantly reduce the scope of potential
742 * interference between mutations of the stream source and execution of stream
743 * pipelines. Streams based on spliterators with the desired characteristics,
744 * or those using the Supplier-based factory forms, are immune to
745 * modifications of the data source prior to commencement of the terminal
746 * operation (provided the behavioral parameters to the stream operations meet
747 * the required criteria for non-interference and statelessness). See
748 * <a href="package-summary.html#NonInterference">Non-Interference</a>
749 * for more details.
750 *
751 * @since 1.8
752 */
753 package java.util.stream;
754
755 import java.util.function.BinaryOperator;
756 import java.util.function.UnaryOperator;