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/**
* <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.IntStream#range(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;