Stream
/**
* A sequence of elements supporting sequential and parallel aggregate
* operations. The following example illustrates an aggregate operation using
* {@link Stream} and {@link IntStream}:
*
* <pre>{@code // 举例：
*
int sum = widgets.stream()
*
.filter(w -> w.getColor() == RED)
*
.mapToInt(w -> w.getWeight())
*
.sum();
* }</pre>
*
* In this example, {@code widgets} is a {@code Collection<Widget>}. We create
* a stream of {@code Widget} objects via {@link Collection#stream Collection.stream()},
* filter it to produce a stream containing only the red widgets, and then
* transform it into a stream of {@code int} values representing the weight of
* each red widget. Then this stream is summed to produce a total weight.
*
* <p>In addition to {@code Stream}, which is a stream of object references,
* there are primitive specializations for {@link IntStream}, {@link LongStream},
* and {@link DoubleStream}, all of which are referred to as "streams" and
* conform to the characteristics and restrictions described here.
jdk提供了平行的 特化的流。
*
* <p>To perform a computation, stream
* <a href="package-summary.html#StreamOps">operations</a> are composed into a
* <em>stream pipeline</em>. A stream pipeline consists of a source (which
* might be an array, a collection, a generator function, an I/O channel,
* etc), zero or more <em>intermediate operations</em> (which transform a
* stream into another stream, such as {@link Stream#filter(Predicate)}), and a
* <em>terminal operation</em> (which produces a result or side-effect, such
* as {@link Stream#count()} or {@link Stream#forEach(Consumer)}).
* Streams are lazy; computation on the source data is only performed when the
* terminal operation is initiated, and source elements are consumed only
* as needed.
为了执行计算，流会被执行到一个流管道当中。
一个流管道包含了：
一个源。（数字来的地方）
0个或多个中间操作（将一个stream转换成另外一个Stream）。
一个终止操作（会生成一个结果，或者是一个副作用（求和，遍历））。
流是延迟的，只有当终止操作被发起的时候，才会执行中间操作。
* <p>Collections and streams, while bearing some superficial similarities,
* have different goals. Collections are primarily concerned with the efficient
* management of, and access to, their elements. By contrast, streams do not
* provide a means to directly access or manipulate their elements, and are
* instead concerned with declaratively describing their source and the
* computational operations which will be performed in aggregate on that source.
* However, if the provided stream operations do not offer the desired
* functionality, the {@link #iterator()} and {@link #spliterator()} operations
* can be used to perform a controlled traversal.
集合和流虽然有一些相似性，但是他们的差异是不同的。
集合是为了高效对于元素的管理和访问。流并不会提供方式去直接操作流里的元素。（集合关注的是数据的管理，流关注的是元素内容的计算）
如果流操作并没有提供我们需要的功能，那么我们可以使用传统的iterator or spliterator去执行操作。
* <p>A stream pipeline, like the "widgets" example above, can be viewed as
* a <em>query</em> on the stream source. Unless the source was explicitly
* designed for concurrent modification (such as a {@link ConcurrentHashMap}),
* unpredictable or erroneous behavior may result from modifying the stream
* source while it is being queried.
一个流管道，可以看做是对流源的查询，除非这个流被显示的设计成可以并发修改的。否则会抛出异常。
（如一个线程对流进行修改，另一个对流进行查询）
* <p>Most stream operations accept parameters that describe user-specified
* behavior, such as the lambda expression {@code w -> w.getWeight()} passed to
* {@code mapToInt} in the example above. To preserve correct behavior,
* these <em>behavioral parameters</em>:
//为了能满足结果，需满足下边的条件。
* <ul>
* <li>must be <a href="package-summary.html#NonInterference">non-interfering</a>
* (they do not modify the stream source); and</li>
* <li>in most cases must be <a href="package-summary.html#Statelessness">stateless</a>
* (their result should not depend on any state that might change during execution
* of the stream pipeline).</li>
* </ul>
行为上的参数，大多是无状态的。
* <p>Such parameters are always instances of a
* <a href="">functional interface</a> such
* as {@link java.util.function.Function}, and are often lambda expressions or
* method references. Unless otherwise specified these parameters must be
* <em>non-null</em>.
无一例外的。这种参数总是函数式接口的形式。也就是lambda表达式。除非特别指定，这些参数必须是非空的。
* <p>A stream should be operated on (invoking an intermediate or terminal stream
* operation) only once. This rules out, for example, "forked" streams, where
* the same source feeds two or more pipelines, or multiple traversals of the
* same stream. A stream implementation may throw {@link IllegalStateException}
* if it detects that the stream is being reused. However, since some stream
* operations may return their receiver rather than a new stream object, it may
* not be possible to detect reuse in all cases.
一个流只能被使用一次。对相同的流进行多次操作，需要创建多个流管道。
* <p>Streams have a {@link #close()} method and implement {@link AutoCloseable},
* but nearly all stream instances do not actually need to be closed after use.
* Generally, only streams whose source is an IO channel (such as those returned
* by {@link Files#lines(Path, Charset)}) will require closing. Most streams
* are backed by collections, arrays, or generating functions, which require no
* special resource management. (If a stream does require closing, it can be
* declared as a resource in a {@code try}-with-resources statement.)
流拥有一个closed方法，实现了AutoCloseable，在他的父类里。 最上面以举例实现。
但是一个流 除了是I/O流（因为持有句柄等资源）才需要被关闭外，是不需要被关闭的。
大多数的流底层是集合、数组或者是生成器函数。 他们并不需要特别的资源管理。如果需要被关闭，可以用try（）操作。
* <p>Stream pipelines may execute either sequentially or in
* <a href="package-summary.html#Parallelism">parallel</a>. This
* execution mode is a property of the stream. Streams are created
* with an initial choice of sequential or parallel execution. (For example,
* {@link Collection#stream() Collection.stream()} creates a sequential stream,
* and {@link Collection#parallelStream() Collection.parallelStream()} creates
* a parallel one.) This choice of execution mode may be modified by the
* {@link #sequential()} or {@link #parallel()} methods, and may be queried with
* the {@link #isParallel()} method.
流管道可以被串行或者并行操作。这种模式只是一个属性而已。 初始化的时候会进行一个选择。
比如说 stream() 是串行流。parallelStream()是并行流。
还可以通过sequential（）or parallel() 来进行修改。 以最后一个被调用的方法为准。
也可以用isParallel（）来进行查询流是否是并行流。
* @param <T> the type of the stream elements
* @since 1.8
* @see IntStream
* @see LongStream
* @see DoubleStream
* @see <a href="http://www.likecs.com/package-summary.html">java.util.stream</a>
*/
public interface Stream<T> extends BaseStream<T, Stream<T>> {
// 具体举例， 源码中有例子
Stream<T> filter(Predicate<? super T> predicate); // 过滤
<R> Stream<R> map(Function<? super T, ? extends R> mapper); //映射
IntStream mapToInt(ToIntFunction<? super T> mapper);
LongStream mapToLong(ToLongFunction<? super T> mapper);
DoubleStream mapToDouble(ToDoubleFunction<? super T> mapper);
<R> Stream<R> flatMap(Function<? super T, ? extends Stream<? extends R>> mapper); //压平
IntStream flatMapToInt(Function<? super T, ? extends IntStream> mapper);
LongStream flatMapToLong(Function<? super T, ? extends LongStream> mapper);
DoubleStream flatMapToDouble(Function<? super T, ? extends DoubleStream> mapper);、
Stream<T> distinct();// 去重
Stream<T> sorted(); //排序
Stream<T> sorted(Comparator<? super T> comparator);
Stream<T> peek(Consumer<? super T> action);
Stream<T> limit(long maxSize); // 截断
void forEach(Consumer<? super T> action); // 遍历
void forEachOrdered(Consumer<? super T> action); // 遍历时执行操作
Object[] toArray(); // 转数组
T reduce(T identity, BinaryOperator<T> accumulator); // 汇聚， 返回一个汇聚的结果
<R> R collect(Supplier<R> supplier,
BiConsumer<R, ? super T> accumulator,
BiConsumer<R, R> combiner);
// 收集器
。。。
}
自行参考父接口中的方法；
Stream中具体方法的详解
