jdk-sandbox: comparison jdk/src/share/classes/java/util/stream/package-info.java

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+/*
+* Copyright (c) 2012, 2013, Oracle and/or its affiliates. All rights reserved.
+* DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
+*
+* This code is free software; you can redistribute it and/or modify it
+* under the terms of the GNU General Public License version 2 only, as
+* published by the Free Software Foundation.  Oracle designates this
+* particular file as subject to the "Classpath" exception as provided
+* by Oracle in the LICENSE file that accompanied this code.
+*
+* This code is distributed in the hope that it will be useful, but WITHOUT
+* ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
+* FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License
+* version 2 for more details (a copy is included in the LICENSE file that
+* accompanied this code).
+*
+* You should have received a copy of the GNU General Public License version
+* 2 along with this work; if not, write to the Free Software Foundation,
+* Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
+*
+* Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA
+* or visit www.oracle.com if you need additional information or have any
+* questions.
+*/
+/**
+* <h1>java.util.stream</h1>
+*
+* Classes to support functional-style operations on streams of values, as in the following:
+*
+* <pre>{@code
+*     int sumOfWeights = blocks.stream().filter(b -> b.getColor() == RED)
+*                                       .mapToInt(b -> b.getWeight())
+*                                       .sum();
+* }</pre>
+*
+* <p>Here we use {@code blocks}, which might be a {@code Collection}, as a source for a stream,
+* and then perform a filter-map-reduce ({@code sum()} is an example of a <a href="package-summary.html#Reduction">reduction</a>
+* operation) on the stream to obtain the sum of the weights of the red blocks.
+*
+* <p>The key abstraction used in this approach is {@link java.util.stream.Stream}, as well as its primitive
+* specializations {@link java.util.stream.IntStream}, {@link java.util.stream.LongStream},
+* and {@link java.util.stream.DoubleStream}.  Streams differ from Collections in several ways:
+*
+* <ul>
+*     <li>No storage.  A stream is not a data structure that stores elements; instead, they
+*     carry values from a source (which could be a data structure, a generator, an IO channel, etc)
+*     through a pipeline of computational operations.</li>
+*     <li>Functional in nature.  An operation on a stream produces a result, but does not modify
+*     its underlying data source.  For example, filtering a {@code Stream} produces a new {@code Stream},
+*     rather than removing elements from the underlying source.</li>
+*     <li>Laziness-seeking.  Many stream operations, such as filtering, mapping, or duplicate removal,
+*     can be implemented lazily, exposing opportunities for optimization.  (For example, "find the first
+*     {@code String} matching a pattern" need not examine all the input strings.)  Stream operations
+*     are divided into intermediate ({@code Stream}-producing) operations and terminal (value-producing)
+*     operations; all intermediate operations are lazy.</li>
+*     <li>Possibly unbounded.  While collections have a finite size, streams need not.  Operations
+*     such as {@code limit(n)} or {@code findFirst()} can allow computations on infinite streams
+*     to complete in finite time.</li>
+* </ul>
+*
+* <h2><a name="StreamPipelines">Stream pipelines</a></h2>
+*
+* <p>Streams are used to create <em>pipelines</em> of <a href="package-summary.html#StreamOps">operations</a>.  A
+* complete stream pipeline has several components: a source (which may be a {@code Collection},
+* an array, a generator function, or an IO channel); zero or more <em>intermediate operations</em>
+* such as {@code Stream.filter} or {@code Stream.map}; and a <em>terminal operation</em> such
+* as {@code Stream.forEach} or {@code java.util.stream.Stream.reduce}.  Stream operations may take as parameters
+* <em>function values</em> (which are often lambda expressions, but could be method references
+* or objects) which parameterize the behavior of the operation, such as a {@code Predicate}
+* passed to the {@code Stream#filter} method.
+*
+* <p>Intermediate operations return a new {@code Stream}.  They are lazy; executing an
+* intermediate operation such as {@link java.util.stream.Stream#filter Stream.filter} does
+* not actually perform any filtering, instead creating a new {@code Stream} that, when
+* traversed, contains the elements of the initial {@code Stream} that match the
+* given {@code Predicate}.  Consuming elements from the  stream source does not
+* begin until the terminal operation is executed.
+*
+* <p>Terminal operations consume the {@code Stream} and produce a result or a side-effect.
+* After a terminal operation is performed, the stream can no longer be used and you must
+* return to the data source, or select a new data source, to get a new stream. For example,
+* obtaining the sum of weights of all red blocks, and then of all blue blocks, requires a
+* filter-map-reduce on two different streams:
+* <pre>{@code
+*     int sumOfRedWeights  = blocks.stream().filter(b -> b.getColor() == RED)
+*                                           .mapToInt(b -> b.getWeight())
+*                                           .sum();
+*     int sumOfBlueWeights = blocks.stream().filter(b -> b.getColor() == BLUE)
+*                                           .mapToInt(b -> b.getWeight())
+*                                           .sum();
+* }</pre>
+*
+* <p>However, there are other techniques that allow you to obtain both results in a single
+* pass if multiple traversal is impractical or inefficient.  TODO provide link
+*
+* <h3><a name="StreamOps">Stream operations</a></h3>
+*
+* <p>Intermediate stream operation (such as {@code filter} or {@code sorted}) always produce a
+* new {@code Stream}, and are always<em>lazy</em>.  Executing a lazy operations does not
+* trigger processing of the stream contents; all processing is deferred until the terminal
+* operation commences.  Processing streams lazily allows for significant efficiencies; in a
+* pipeline such as the filter-map-sum example above, filtering, mapping, and addition can be
+* fused into a single pass, with minimal intermediate state.  Laziness also enables us to avoid
+* examining all the data when it is not necessary; for operations such as "find the first
+* string longer than 1000 characters", one need not examine all the input strings, just enough
+* to find one that has the desired characteristics.  (This behavior becomes even more important
+* when the input stream is infinite and not merely large.)
+*
+* <p>Intermediate operations are further divided into <em>stateless</em> and <em>stateful</em>
+* operations.  Stateless operations retain no state from previously seen values when processing
+* a new value; examples of stateless intermediate operations include {@code filter} and
+* {@code map}.  Stateful operations may incorporate state from previously seen elements in
+* processing new values; examples of stateful intermediate operations include {@code distinct}
+* and {@code sorted}.  Stateful operations may need to process the entire input before
+* producing a result; for example, one cannot produce any results from sorting a stream until
+* one has seen all elements of the stream.  As a result, under parallel computation, some
+* pipelines containing stateful intermediate operations have to be executed in multiple passes.
+* Pipelines containing exclusively stateless intermediate operations can be processed in a
+* single pass, whether sequential or parallel.
+*
+* <p>Further, some operations are deemed <em>short-circuiting</em> operations.  An intermediate
+* operation is short-circuiting if, when presented with infinite input, it may produce a
+* finite stream as a result.  A terminal operation is short-circuiting if, when presented with
+* infinite input, it may terminate in finite time.  (Having a short-circuiting operation is a
+* necessary, but not sufficient, condition for the processing of an infinite stream to
+* terminate normally in finite time.)
+*
+* Terminal operations (such as {@code forEach} or {@code findFirst}) are always eager
+* (they execute completely before returning), and produce a non-{@code Stream} result, such
+* as a primitive value or a {@code Collection}, or have side-effects.
+*
+* <h3>Parallelism</h3>
+*
+* <p>By recasting aggregate operations as a pipeline of operations on a stream of values, many
+* aggregate operations can be more easily parallelized.  A {@code Stream} can execute either
+* in serial or in parallel.  When streams are created, they are either created as sequential
+* or parallel streams; the parallel-ness of streams can also be switched by the
+* {@link java.util.stream Stream#sequential()} and {@link java.util.stream.Stream#parallel()}
+* operations.  The {@code Stream} implementations in the JDK create serial streams unless
+* parallelism is explicitly requested.  For example, {@code Collection} has methods
+* {@link java.util.Collection#stream} and {@link java.util.Collection#parallelStream},
+* which produce sequential and parallel streams respectively; other stream-bearing methods
+* such as {@link java.util.stream.Streams#intRange(int, int)} produce sequential
+* streams but these can be efficiently parallelized by calling {@code parallel()} on the
+* result. The set of operations on serial and parallel streams is identical. To execute the
+* "sum of weights of blocks" query in parallel, we would do:
+*
+* <pre>{@code
+*     int sumOfWeights = blocks.parallelStream().filter(b -> b.getColor() == RED)
+*                                               .mapToInt(b -> b.getWeight())
+*                                               .sum();
+* }</pre>
+*
+* <p>The only difference between the serial and parallel versions of this example code is
+* the creation of the initial {@code Stream}.  Whether a {@code Stream} will execute in serial
+* or parallel can be determined by the {@code Stream#isParallel} method.  When the terminal
+* operation is initiated, the entire stream pipeline is either executed sequentially or in
+* parallel, determined by the last operation that affected the stream's serial-parallel
+* orientation (which could be the stream source, or the {@code sequential()} or
+* {@code parallel()} methods.)
+*
+* <p>In order for the results of parallel operations to be deterministic and consistent with
+* their serial equivalent, the function values passed into the various stream operations should
+* be <a href="#NonInteference"><em>stateless</em></a>.
+*
+* <h3><a name="Ordering">Ordering</a></h3>
+*
+* <p>Streams may or may not have an <em>encounter order</em>.  An encounter
+* order specifies the order in which elements are provided by the stream to the
+* operations pipeline.  Whether or not there is an encounter order depends on
+* the source, the intermediate  operations, and the terminal operation.
+* Certain stream sources (such as {@code List} or arrays) are intrinsically
+* ordered, whereas others (such as {@code HashSet}) are not.  Some intermediate
+* operations may impose an encounter order on an otherwise unordered stream,
+* such as {@link java.util.stream.Stream#sorted()}, and others may render an
+* ordered stream unordered (such as {@link java.util.stream.Stream#unordered()}).
+* Some terminal operations may ignore encounter order, such as
+* {@link java.util.stream.Stream#forEach}.
+*
+* <p>If a Stream is ordered, most operations are constrained to operate on the
+* elements in their encounter order; if the source of a stream is a {@code List}
+* containing {@code [1, 2, 3]}, then the result of executing {@code map(x -> x*2)}
+* must be {@code [2, 4, 6]}.  However, if the source has no defined encounter
+* order, than any of the six permutations of the values {@code [2, 4, 6]} would
+* be a valid result. Many operations can still be efficiently parallelized even
+* under ordering constraints.
+*
+* <p>For sequential streams, ordering is only relevant to the determinism
+* of operations performed repeatedly on the same source.  (An {@code ArrayList}
+* is constrained to iterate elements in order; a {@code HashSet} is not, and
+* repeated iteration might produce a different order.)
+*
+* <p>For parallel streams, relaxing the ordering constraint can enable
+* optimized implementation for some operations.  For example, duplicate
+* filtration on an ordered stream must completely process the first partition
+* before it can return any elements from a subsequent partition, even if those
+* elements are available earlier.  On the other hand, without the constraint of
+* ordering, duplicate filtration can be done more efficiently by using
+* a shared {@code ConcurrentHashSet}.  There will be cases where the stream
+* is structurally ordered (the source is ordered and the intermediate
+* operations are order-preserving), but the user does not particularly care
+* about the encounter order.  In some cases, explicitly de-ordering the stream
+* with the {@link java.util.stream.Stream#unordered()} method may result in
+* improved parallel performance for some stateful or terminal operations.
+*
+* <h3><a name="Non-Interference">Non-interference</a></h3>
+*
+* The {@code java.util.stream} package enables you to execute possibly-parallel
+* bulk-data operations over a variety of data sources, including even non-thread-safe
+* collections such as {@code ArrayList}.  This is possible only if we can
+* prevent <em>interference</em> with the data source during the execution of a
+* stream pipeline.  (Execution begins when the terminal operation is invoked, and ends
+* when the terminal operation completes.)  For most data sources, preventing interference
+* means ensuring that the data source is <em>not modified at all</em> during the execution
+* of the stream pipeline.  (Some data sources, such as concurrent collections, are
+* specifically designed to handle concurrent modification.)
+*
+* <p>Accordingly, lambda expressions (or other objects implementing the appropriate functional
+* interface) passed to stream methods should never modify the stream's data source.  An
+* implementation is said to <em>interfere</em> with the data source if it modifies, or causes
+* to be modified, the stream's data source.  The need for non-interference applies to all
+* pipelines, not just parallel ones.  Unless the stream source is concurrent, modifying a
+* stream's data source during execution of a stream pipeline can cause exceptions, incorrect
+* answers, or nonconformant results.
+*
+* <p>Further, results may be nondeterministic or incorrect if the lambda expressions passed to
+* stream operations are <em>stateful</em>.  A stateful lambda (or other object implementing the
+* appropriate functional interface) is one whose result depends on any state which might change
+* during the execution of the stream pipeline.  An example of a stateful lambda is:
+* <pre>{@code
+*     Set<Integer> seen = Collections.synchronizedSet(new HashSet<>());
+*     stream.parallel().map(e -> { if (seen.add(e)) return 0; else return e; })...
+* }</pre>
+* Here, if the mapping operation is performed in parallel, the results for the same input
+* could vary from run to run, due to thread scheduling differences, whereas, with a stateless
+* lambda expression the results would always be the same.
+*
+* <h3>Side-effects</h3>
+*
+* <h2><a name="Reduction">Reduction operations</a></h2>
+*
+* A <em>reduction</em> operation takes a stream of elements and processes them in a way
+* that reduces to a single value or summary description, such as finding the sum or maximum
+* of a set of numbers.  (In more complex scenarios, the reduction operation might need to
+* extract data from the elements before reducing that data to a single value, such as
+* finding the sum of weights of a set of blocks.  This would require extracting the weight
+* from each block before summing up the weights.)
+*
+* <p>Of course, such operations can be readily implemented as simple sequential loops, as in:
+* <pre>{@code
+*    int sum = 0;
+*    for (int x : numbers) {
+*       sum += x;
+*    }
+* }</pre>
+* However, there may be a significant advantage to preferring a {@link java.util.stream.Stream#reduce reduce operation}
+* over a mutative accumulation such as the above -- a properly constructed reduce operation is
+* inherently parallelizable so long as the
+* {@link java.util.function.BinaryOperator reduction operaterator}
+* has the right characteristics. Specifically the operator must be
+* <a href="#Associativity">associative</a>.  For example, given a
+* stream of numbers for which we want to find the sum, we can write:
+* <pre>{@code
+*    int sum = numbers.reduce(0, (x,y) -> x+y);
+* }</pre>
+* or more succinctly:
+* <pre>{@code
+*    int sum = numbers.reduce(0, Integer::sum);
+* }</pre>
+*
+* <p>(The primitive specializations of {@link java.util.stream.Stream}, such as
+* {@link java.util.stream.IntStream}, even have convenience methods for common reductions,
+* such as {@link java.util.stream.IntStream#sum() sum} and {@link java.util.stream.IntStream#max() max},
+* which are implemented as simple wrappers around reduce.)
+*
+* <p>Reduction parallellizes well since the implementation of {@code reduce} can operate on
+* subsets of the stream in parallel, and then combine the intermediate results to get the final
+* correct answer.  Even if you were to use a parallelizable form of the
+* {@link java.util.stream.Stream#forEach(Consumer) forEach()} method
+* in place of the original for-each loop above, you would still have to provide thread-safe
+* updates to the shared accumulating variable {@code sum}, and the required synchronization
+* would likely eliminate any performance gain from parallelism. Using a {@code reduce} method
+* instead removes all of the burden of parallelizing the reduction operation, and the library
+* can provide an efficient parallel implementation with no additional synchronization needed.
+*
+* <p>The "blocks" examples shown earlier shows how reduction combines with other operations
+* to replace for loops with bulk operations.  If {@code blocks} is a collection of {@code Block}
+* objects, which have a {@code getWeight} method, we can find the heaviest block with:
+* <pre>{@code
+*     OptionalInt heaviest = blocks.stream()
+*                                  .mapToInt(Block::getWeight)
+*                                  .reduce(Integer::max);
+* }</pre>
+*
+* <p>In its more general form, a {@code reduce} operation on elements of type {@code <T>}
+* yielding a result of type {@code <U>} requires three parameters:
+* <pre>{@code
+* <U> U reduce(U identity,
+*              BiFunction<U, ? super T, U> accumlator,
+*              BinaryOperator<U> combiner);
+* }</pre>
+* Here, the <em>identity</em> element is both an initial seed for the reduction, and a default
+* result if there are no elements. The <em>accumulator</em> function takes a partial result and
+* the next element, and produce a new partial result. The <em>combiner</em> function combines
+* the partial results of two accumulators to produce a new partial result, and eventually the
+* final result.
+*
+* <p>This form is a generalization of the two-argument form, and is also a generalization of
+* the map-reduce construct illustrated above.  If we wanted to re-cast the simple {@code sum}
+* example using the more general form, {@code 0} would be the identity element, while
+* {@code Integer::sum} would be both the accumulator and combiner. For the sum-of-weights
+* example, this could be re-cast as:
+* <pre>{@code
+*     int sumOfWeights = blocks.stream().reduce(0,
+*                                               (sum, b) -> sum + b.getWeight())
+*                                               Integer::sum);
+* }</pre>
+* though the map-reduce form is more readable and generally preferable.  The generalized form
+* is provided for cases where significant work can be optimized away by combining mapping and
+* reducing into a single function.
+*
+* <p>More formally, the {@code identity} value must be an <em>identity</em> for the combiner
+* function. This means that for all {@code u}, {@code combiner.apply(identity, u)} is equal
+* to {@code u}. Additionally, the {@code combiner} function must be
+* <a href="#Associativity">associative</a> and must be compatible with the {@code accumulator}
+* function; for all {@code u} and {@code t}, the following must hold:
+* <pre>{@code
+*     combiner.apply(u, accumulator.apply(identity, t)) == accumulator.apply(u, t)
+* }</pre>
+*
+* <h3><a name="MutableReduction">Mutable Reduction</a></h3>
+*
+* A <em>mutable</em> reduction operation is similar to an ordinary reduction, in that it reduces
+* a stream of values to a single value, but instead of producing a distinct single-valued result, it
+* mutates a general <em>result container</em>, such as a {@code Collection} or {@code StringBuilder},
+* as it processes the elements in the stream.
+*
+* <p>For example, if we wanted to take a stream of strings and concatenate them into a single
+* long string, we <em>could</em> achieve this with ordinary reduction:
+* <pre>{@code
+*     String concatenated = strings.reduce("", String::concat)
+* }</pre>
+*
+* We would get the desired result, and it would even work in parallel.  However, we might not
+* be happy about the performance!  Such an implementation would do a great deal of string
+* copying, and the run time would be <em>O(n^2)</em> in the number of elements.  A more
+* performant approach would be to accumulate the results into a {@link java.lang.StringBuilder}, which
+* is a mutable container for accumulating strings.  We can use the same technique to
+* parallelize mutable reduction as we do with ordinary reduction.
+*
+* <p>The mutable reduction operation is called {@link java.util.stream.Stream#collect(Collector) collect()}, as it
+* collects together the desired results into a result container such as {@code StringBuilder}.
+* A {@code collect} operation requires three things: a factory function which will construct
+* new instances of the result container, an accumulating function that will update a result
+* container by incorporating a new element, and a combining function that can take two
+* result containers and merge their contents.  The form of this is very similar to the general
+* form of ordinary reduction:
+* <pre>{@code
+* <R> R collect(Supplier<R> resultFactory,
+*               BiConsumer<R, ? super T> accumulator,
+*               BiConsumer<R, R> combiner);
+* }</pre>
+* As with {@code reduce()}, the benefit of expressing {@code collect} in this abstract way is
+* that it is directly amenable to parallelization: we can accumulate partial results in parallel
+* and then combine them.  For example, to collect the String representations of the elements
+* in a stream into an {@code ArrayList}, we could write the obvious sequential for-each form:
+* <pre>{@code
+*     ArrayList<String> strings = new ArrayList<>();
+*     for (T element : stream) {
+*         strings.add(element.toString());
+*     }
+* }</pre>
+* Or we could use a parallelizable collect form:
+* <pre>{@code
+*     ArrayList<String> strings = stream.collect(() -> new ArrayList<>(),
+*                                                (c, e) -> c.add(e.toString()),
+*                                                (c1, c2) -> c1.addAll(c2));
+* }</pre>
+* or, noting that we have buried a mapping operation inside the accumulator function, more
+* succinctly as:
+* <pre>{@code
+*     ArrayList<String> strings = stream.map(Object::toString)
+*                                       .collect(ArrayList::new, ArrayList::add, ArrayList::addAll);
+* }</pre>
+* Here, our supplier is just the {@link java.util.ArrayList#ArrayList() ArrayList constructor}, the
+* accumulator adds the stringified element to an {@code ArrayList}, and the combiner simply
+* uses {@link java.util.ArrayList#addAll addAll} to copy the strings from one container into the other.
+*
+* <p>As with the regular reduction operation, the ability to parallelize only comes if an
+* <a href="package-summary.html#Associativity">associativity</a> condition is met. The {@code combiner} is associative
+* if for result containers {@code r1}, {@code r2}, and {@code r3}:
+* <pre>{@code
+*    combiner.accept(r1, r2);
+*    combiner.accept(r1, r3);
+* }</pre>
+* is equivalent to
+* <pre>{@code
+*    combiner.accept(r2, r3);
+*    combiner.accept(r1, r2);
+* }</pre>
+* where equivalence means that {@code r1} is left in the same state (according to the meaning
+* of {@link java.lang.Object#equals equals} for the element types). Similarly, the {@code resultFactory}
+* must act as an <em>identity</em> with respect to the {@code combiner} so that for any result
+* container {@code r}:
+* <pre>{@code
+*     combiner.accept(r, resultFactory.get());
+* }</pre>
+* does not modify the state of {@code r} (again according to the meaning of
+* {@link java.lang.Object#equals equals}). Finally, the {@code accumulator} and {@code combiner} must be
+* compatible such that for a result container {@code r} and element {@code t}:
+* <pre>{@code
+*    r2 = resultFactory.get();
+*    accumulator.accept(r2, t);
+*    combiner.accept(r, r2);
+* }</pre>
+* is equivalent to:
+* <pre>{@code
+*    accumulator.accept(r,t);
+* }</pre>
+* where equivalence means that {@code r} is left in the same state (again according to the
+* meaning of {@link java.lang.Object#equals equals}).
+*
+* <p> The three aspects of {@code collect}: supplier, accumulator, and combiner, are often very
+* tightly coupled, and it is convenient to introduce the notion of a {@link java.util.stream.Collector} as
+* being an object that embodies all three aspects. There is a {@link java.util.stream.Stream#collect(Collector) collect}
+* method that simply takes a {@code Collector} and returns the resulting container.
+* The above example for collecting strings into a {@code List} can be rewritten using a
+* standard {@code Collector} as:
+* <pre>{@code
+*     ArrayList<String> strings = stream.map(Object::toString)
+*                                       .collect(Collectors.toList());
+* }</pre>
+*
+* <h3><a name="ConcurrentReduction">Reduction, Concurrency, and Ordering</a></h3>
+*
+* With some complex reduction operations, for example a collect that produces a
+* {@code Map}, such as:
+* <pre>{@code
+*     Map<Buyer, List<Transaction>> salesByBuyer
+*         = txns.parallelStream()
+*               .collect(Collectors.groupingBy(Transaction::getBuyer));
+* }</pre>
+* (where {@link java.util.stream.Collectors#groupingBy} is a utility function
+* that returns a {@link java.util.stream.Collector} for grouping sets of elements based on some key)
+* it may actually be counterproductive to perform the operation in parallel.
+* This is because the combining step (merging one {@code Map} into another by key)
+* can be expensive for some {@code Map} implementations.
+*
+* <p>Suppose, however, that the result container used in this reduction
+* was a concurrently modifiable collection -- such as a
+* {@link java.util.concurrent.ConcurrentHashMap ConcurrentHashMap}. In that case,
+* the parallel invocations of the accumulator could actually deposit their results
+* concurrently into the same shared result container, eliminating the need for the combiner to
+* merge distinct result containers. This potentially provides a boost
+* to the parallel execution performance. We call this a <em>concurrent</em> reduction.
+*
+* <p>A {@link java.util.stream.Collector} that supports concurrent reduction is marked with the
+* {@link java.util.stream.Collector.Characteristics#CONCURRENT} characteristic.
+* Having a concurrent collector is a necessary condition for performing a
+* concurrent reduction, but that alone is not sufficient. If you imagine multiple
+* accumulators depositing results into a shared container, the order in which
+* results are deposited is non-deterministic. Consequently, a concurrent reduction
+* is only possible if ordering is not important for the stream being processed.
+* The {@link java.util.stream.Stream#collect(Collector)}
+* implementation will only perform a concurrent reduction if
+* <ul>
+* <li>The stream is parallel;</li>
+* <li>The collector has the
+* {@link java.util.stream.Collector.Characteristics#CONCURRENT} characteristic,
+* and;</li>
+* <li>Either the stream is unordered, or the collector has the
+* {@link java.util.stream.Collector.Characteristics#UNORDERED} characteristic.
+* </ul>
+* For example:
+* <pre>{@code
+*     Map<Buyer, List<Transaction>> salesByBuyer
+*         = txns.parallelStream()
+*               .unordered()
+*               .collect(groupingByConcurrent(Transaction::getBuyer));
+* }</pre>
+* (where {@link java.util.stream.Collectors#groupingByConcurrent} is the concurrent companion
+* to {@code groupingBy}).
+*
+* <p>Note that if it is important that the elements for a given key appear in the
+* order they appear in the source, then we cannot use a concurrent reduction,
+* as ordering is one of the casualties of concurrent insertion.  We would then
+* be constrained to implement either a sequential reduction or a merge-based
+* parallel reduction.
+*
+* <h2><a name="Associativity">Associativity</a></h2>
+*
+* An operator or function {@code op} is <em>associative</em> if the following holds:
+* <pre>{@code
+*     (a op b) op c == a op (b op c)
+* }</pre>
+* The importance of this to parallel evaluation can be seen if we expand this to four terms:
+* <pre>{@code
+*     a op b op c op d == (a op b) op (c op d)
+* }</pre>
+* So we can evaluate {@code (a op b)} in parallel with {@code (c op d)} and then invoke {@code op} on
+* the results.
+* TODO what does associative mean for mutative combining functions?
+* FIXME: we described mutative associativity above.
+*
+* <h2><a name="StreamSources">Stream sources</a></h2>
+* TODO where does this section go?
+*
+* XXX - change to section to stream construction gradually introducing more
+*       complex ways to construct
+*     - construction from Collection
+*     - construction from Iterator
+*     - construction from array
+*     - construction from generators
+*     - construction from spliterator
+*
+* XXX - the following is quite low-level but important aspect of stream constriction
+*
+* <p>A pipeline is initially constructed from a spliterator (see {@link java.util.Spliterator}) supplied by a stream source.
+* The spliterator covers elements of the source and provides element traversal operations
+* for a possibly-parallel computation.  See methods on {@link java.util.stream.Streams} for construction
+* of pipelines using spliterators.
+*
+* <p>A source may directly supply a spliterator.  If so, the spliterator is traversed, split, or queried
+* for estimated size after, and never before, the terminal operation commences. It is strongly recommended
+* that the spliterator report a characteristic of {@code IMMUTABLE} or {@code CONCURRENT}, or be
+* <em>late-binding</em> and not bind to the elements it covers until traversed, split or queried for
+* estimated size.
+*
+* <p>If a source cannot directly supply a recommended spliterator then it may indirectly supply a spliterator
+* using a {@code Supplier}.  The spliterator is obtained from the supplier after, and never before, the terminal
+* operation of the stream pipeline commences.
+*
+* <p>Such requirements significantly reduce the scope of potential interference to the interval starting
+* with the commencing of the terminal operation and ending with the producing a result or side-effect.  See
+* <a href="package-summary.html#Non-Interference">Non-Interference</a> for
+* more details.
+*
+* XXX - move the following to the non-interference section
+*
+* <p>A source can be modified before the terminal operation commences and those modifications will be reflected in
+* the covered elements.  Afterwards, and depending on the properties of the source, further modifications
+* might not be reflected and the throwing of a {@code ConcurrentModificationException} may occur.
+*
+* <p>For example, consider the following code:
+* <pre>{@code
+*     List<String> l = new ArrayList(Arrays.asList("one", "two"));
+*     Stream<String> sl = l.stream();
+*     l.add("three");
+*     String s = sl.collect(toStringJoiner(" ")).toString();
+* }</pre>
+* First a list is created consisting of two strings: "one"; and "two". Then a stream is created from that list.
+* Next the list is modified by adding a third string: "three".  Finally the elements of the stream are collected
+* and joined together.  Since the list was modified before the terminal {@code collect} operation commenced
+* the result will be a string of "one two three". However, if the list is modified after the terminal operation
+* commences, as in:
+* <pre>{@code
+*     List<String> l = new ArrayList(Arrays.asList("one", "two"));
+*     Stream<String> sl = l.stream();
+*     String s = sl.peek(s -> l.add("BAD LAMBDA")).collect(toStringJoiner(" ")).toString();
+* }</pre>
+* then a {@code ConcurrentModificationException} will be thrown since the {@code peek} operation will attempt
+* to add the string "BAD LAMBDA" to the list after the terminal operation has commenced.
+*/
+package java.util.stream;

changeset 17167	87067e3340d3
child 18156	edb590d448c5