谈谈stream的运行原理
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害，别误会，我这里说的stream不是流式编程，不是大数据处理框架。我这里说的是stream指的是jdk中的一个开发工具包stream. 该工具包在jdk8中出现，可以说已经是冷饭了，为何还要你说？只因各家一言，不算得自家理解，如若有空，何多听一版又何妨。
本篇主要从几个方面讲讲：1. 我们常见的stream都有哪些？ 2. stream包有哪些好处？ 3. stream包的实现原理？ 相信这些多少会解开大家的一些迷惑。
1. 我们常见的stream都有哪些？
stream直接翻译为流。何谓流？我们最常见的，比如网络中的数据传输，即tcp/udp那一套东西，都是建立在二进制流的基础上的。用流来形容这些数据或文件的传输，非常形象，因为数据总是源源不断地从一端流向另一端，这是不流是什么。只是，传输到另一端之后，我们再做解析，便有了数据或文件之说。其实这说的，便是高层协议了。
另说一个stream, 那就是jdk中的各种InputStream了，它用于读取文件数据，读取byte数据，其实也是源源不断将数据从一个设备流入到另一设备。jdk中有InputStream/OutputStream, 作为根基，其上则是各种 FileInputStream, FileOutputStream, FileReader, FileWriter,... 实际上，整个io包几乎都是在围绕流这个概念来展开的。可见，io是相当的重要啊。
再说一stream, 则是对大数据的处理了，stream，即是实时数据处理的重要技术实现，因与实时二字吻合，恰好又类似于数据从一设备流入另一设备，且是实时的。所以，stream在大数据领域也是大放异彩啊！
还有更多的流概念，更多的流实现，不必细说，也无法细说。单只知道，流无处不在，非常重要。
还有本文要议的stream包，到底是何生物，且看后续说来。
2. stream包有何好处？
stream包，在java中是以一个工具包的形式存在，即你用则以，不用亦可。
那么，用它到底有何好处？好处主要有二： 1.可以减少冗余代码的编写；比如要写一个过滤器则只需调用一filter()传入处理逻辑即可； 2.可以很方便的利用一些隐藏的升级好处或者多核带来的好处；（当然你可能用不上这些好处）
说实话，这两个功能，看起来实际没有太多的诱惑力，但凡我们封装几个方法，供外部调用，不也可以达到同等效果？是了！如果你有这等造诣，能够抽象出足够通用的方法，供各方使用，那你不算大牛何人算？说到底，stream也就是高手封装的工具包而已。
来几个应用实例，看看stream都如何使用的：
public class StreamUtilTest { @Test public void testArrayStream() { // 1. 过滤值；改变值；排序； Integer[] intArr = {1, 2, 3, 5, 22, 8, 5}; List<Integer> iArrList = Arrays.stream(intArr) .filter(r -> r < 20) .map(r -> r + 1) .sorted().collect(Collectors.toList()); System.out.println("result:" + iArrList); String[] strArr = {"a1,a2", "q,y,h", "ddd,bb,n", null}; // 2. 过滤数组；拆分值；输出； Arrays.stream(strArr).filter(Objects::nonNull) .flatMap(r -> Arrays.stream(r.split(","))) .forEach(System.out::println); } @Test public void testListStream() { List<String> list = new ArrayList<>(); list.add("ab"); list.add("ccc"); list.add("ddd"); // 3. 求list中的最大值 Optional<String> maxStr = list.stream().max(Comparator.naturalOrder()); System.out.println(maxStr); } }
害，不必纠结里面干的事情复不复杂，有没有意义，只知道有这用法即可。 反正就当你会这么用，即能解决这般问题。这也是我们高级语言使用必备技能，学会调用api.
不过需要说明的，java中有一句老话，叫做万事万物皆对象。 但见上面的写法，自然不太像对象。是了，这是lamda语法，虽说另一主题，但何妨在此处一题。但既然说到这，不妨来想想这lamda到底是何物？从某种角度来说，它可以看作是一种内部类，不过写法不太一样。但是当我们仔细观察class文件的变化情况时，发现它与内部类又不太一致，因为java的内部类会在class中生成$xx.class的类文件，而lamda表达式却不会。但是不管怎么样，它是可以使用内部类的表达方式获得同样的效果，只需将该类代入到其中，即可达到同样的效果。
但要细说lamda表达式，则可以反编译下class文件，可以见些许端倪。
# 调用lamda表达式示例 ... 59: invokestatic #4 // Method java/util/Arrays.stream:([Ljava/lang/Object;)Ljava/util/stream/Stream; 62: invokedynamic #5, 0 // InvokeDynamic #0:test:()Ljava/util/function/Predicate; 67: invokeinterface #6, 2 // InterfaceMethod java/util/stream/Stream.filter:(Ljava/util/function/Predicate;)Ljava/ut il/stream/Stream; 72: invokedynamic #7, 0 // InvokeDynamic #1:apply:()Ljava/util/function/Function; 77: invokeinterface #8, 2 // InterfaceMethod java/util/stream/Stream.map:(Ljava/util/function/Function;)Ljava/util/s tream/Stream; ... # 常量池定义，实际是定义了lamda的实现方式为 #0 号方法 #5 = InvokeDynamic #0:#102 // #0:test:()Ljava/util/function/Predicate; # lamda表达式的具体实现1示例 BootstrapMethods: 0: #98 invokestatic java/lang/invoke/LambdaMetafactory.metafactory:(Ljava/lang/invoke/MethodHandles$Lookup;Ljava/lang/String;Ljava/lan g/invoke/MethodType;Ljava/lang/invoke/MethodType;Ljava/lang/invoke/MethodHandle;Ljava/lang/invoke/MethodType;)Ljava/lang/invoke/CallSite ; Method arguments: #99 (Ljava/lang/Object;)Z // 此处为调用具体的实现方法 #100 invokestatic com/my/test/common/util/StreamUtilTest.lambda$testArrayStream$0:(Ljava/lang/Integer;)Z #101 (Ljava/lang/Integer;)Z 1: #98 invokestatic java/lang/invoke/LambdaMetafactory.metafactory:(Ljava/lang/invoke/MethodHandles$Lookup;Ljava/lang/String;Ljava/lang/invoke/MethodType;Ljava/lang/invoke/MethodType;Ljava/lang/invoke/MethodHandle;Ljava/lang/invoke/MethodType;)Ljava/lang/invoke/CallSite; Method arguments: #105 (Ljava/lang/Object;)Ljava/lang/Object; #106 invokestatic com/my/test/common/util/StreamUtilTest.lambda$testArrayStream$1:(Ljava/lang/Integer;)Ljava/lang/Integer; #107 (Ljava/lang/Integer;)Ljava/lang/Integer; # lamda表达式具体实现2, 上一步的静态调用 private static boolean lambda$testArrayStream$0(java.lang.Integer); descriptor: (Ljava/lang/Integer;)Z flags: ACC_PRIVATE, ACC_STATIC, ACC_SYNTHETIC Code: stack=2, locals=1, args_size=1 0: aload_0 1: invokevirtual #48 // Method java/lang/Integer.intValue:()I 4: bipush 20 6: if_icmpge 13 9: iconst_1 10: goto 14 13: iconst_0 14: ireturn LineNumberTable: line 16: 0 LocalVariableTable: Start Length Slot Name Signature 0 15 0 r Ljava/lang/Integer; StackMapTable: number_of_entries = 2 frame_type = 13 /* same */ frame_type = 64 /* same_locals_1_stack_item */ stack = [ int ] MethodParameters: Name Flags r synthetic
害，往深了就不说了。单说这lamda表达式，并非使用内部类来实现的，而是使用内部静态函数来做的，所以也叫函数式编程呢。烦话休提。
最后，再来看看，这stream包究竟有何神圣地方？其实，就是一个以一个 Stream 接口定义为核心展开的，且看如下：
/** * 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 * 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. * * <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. * * <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. * * <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. * * <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>: * <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> * * <p>Such parameters are always instances of a * <a href="../function/package-summary.html">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>. * * <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. * * <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.) * * <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. * * @param <T> the type of the stream elements * @since 1.8 * @see IntStream * @see LongStream * @see DoubleStream * @see <a href="package-summary.html">java.util.stream</a> */ public interface Stream<T> extends BaseStream<T, Stream<T>> { /** * Returns a stream consisting of the elements of this stream that match * the given predicate. * * <p>This is an <a href="package-summary.html#StreamOps">intermediate * operation</a>. * * @param predicate a <a href="package-summary.html#NonInterference">non-interfering</a>, * <a href="package-summary.html#Statelessness">stateless</a> * predicate to apply to each element to determine if it * should be included * @return the new stream */ Stream<T> filter(Predicate<? super T> predicate); /** * Returns a stream consisting of the results of applying the given * function to the elements of this stream. * * <p>This is an <a href="package-summary.html#StreamOps">intermediate * operation</a>. * * @param <R> The element type of the new stream * @param mapper a <a href="package-summary.html#NonInterference">non-interfering</a>, * <a href="package-summary.html#Statelessness">stateless</a> * function to apply to each element * @return the new stream */ <R> Stream<R> map(Function<? super T, ? extends R> mapper); /** * Returns an {@code IntStream} consisting of the results of applying the * given function to the elements of this stream. * * <p>This is an <a href="package-summary.html#StreamOps"> * intermediate operation</a>. * * @param mapper a <a href="package-summary.html#NonInterference">non-interfering</a>, * <a href="package-summary.html#Statelessness">stateless</a> * function to apply to each element * @return the new stream */ IntStream mapToInt(ToIntFunction<? super T> mapper); /** * Returns a {@code LongStream} consisting of the results of applying the * given function to the elements of this stream. * * <p>This is an <a href="package-summary.html#StreamOps">intermediate * operation</a>. * * @param mapper a <a href="package-summary.html#NonInterference">non-interfering</a>, * <a href="package-summary.html#Statelessness">stateless</a> * function to apply to each element * @return the new stream */ LongStream mapToLong(ToLongFunction<? super T> mapper); /** * Returns a {@code DoubleStream} consisting of the results of applying the * given function to the elements of this stream. * * <p>This is an <a href="package-summary.html#StreamOps">intermediate * operation</a>. * * @param mapper a <a href="package-summary.html#NonInterference">non-interfering</a>, * <a href="package-summary.html#Statelessness">stateless</a> * function to apply to each element * @return the new stream */ DoubleStream mapToDouble(ToDoubleFunction<? super T> mapper); /** * Returns a stream consisting of the results of replacing each element of * this stream with the contents of a mapped stream produced by applying * the provided mapping function to each element. Each mapped stream is * {@link java.util.stream.BaseStream#close() closed} after its contents * have been placed into this stream. (If a mapped stream is {@code null} * an empty stream is used, instead.) * * <p>This is an <a href="package-summary.html#StreamOps">intermediate * operation</a>. * * @apiNote * The {@code flatMap()} operation has the effect of applying a one-to-many * transformation to the elements of the stream, and then flattening the * resulting elements into a new stream. * * <p><b>Examples.</b> * * <p>If {@code orders} is a stream of purchase orders, and each purchase * order contains a collection of line items, then the following produces a * stream containing all the line items in all the orders: * <pre>{@code * orders.flatMap(order -> order.getLineItems().stream())... * }</pre> * * <p>If {@code path} is the path to a file, then the following produces a * stream of the {@code words} contained in that file: * <pre>{@code * Stream<String> lines = Files.lines(path, StandardCharsets.UTF_8); * Stream<String> words = lines.flatMap(line -> Stream.of(line.split(" +"))); * }</pre> * The {@code mapper} function passed to {@code flatMap} splits a line, * using a simple regular expression, into an array of words, and then * creates a stream of words from that array. * * @param <R> The element type of the new stream * @param mapper a <a href="package-summary.html#NonInterference">non-interfering</a>, * <a href="package-summary.html#Statelessness">stateless</a> * function to apply to each element which produces a stream * of new values * @return the new stream */ <R> Stream<R> flatMap(Function<? super T, ? extends Stream<? extends R>> mapper); /** * Returns an {@code IntStream} consisting of the results of replacing each * element of this stream with the contents of a mapped stream produced by * applying the provided mapping function to each element. Each mapped * stream is {@link java.util.stream.BaseStream#close() closed} after its * contents have been placed into this stream. (If a mapped stream is * {@code null} an empty stream is used, instead.) * * <p>This is an <a href="package-summary.html#StreamOps">intermediate * operation</a>. * * @param mapper a <a href="package-summary.html#NonInterference">non-interfering</a>, * <a href="package-summary.html#Statelessness">stateless</a> * function to apply to each element which produces a stream * of new values * @return the new stream * @see #flatMap(Function) */ IntStream flatMapToInt(Function<? super T, ? extends IntStream> mapper); /** * Returns an {@code LongStream} consisting of the results of replacing each * element of this stream with the contents of a mapped stream produced by * applying the provided mapping function to each element. Each mapped * stream is {@link java.util.stream.BaseStream#close() closed} after its * contents have been placed into this stream. (If a mapped stream is * {@code null} an empty stream is used, instead.) * * <p>This is an <a href="package-summary.html#StreamOps">intermediate * operation</a>. * * @param mapper a <a href="package-summary.html#NonInterference">non-interfering</a>, * <a href="package-summary.html#Statelessness">stateless</a> * function to apply to each element which produces a stream * of new values * @return the new stream * @see #flatMap(Function) */ LongStream flatMapToLong(Function<? super T, ? extends LongStream> mapper); /** * Returns an {@code DoubleStream} consisting of the results of replacing * each element of this stream with the contents of a mapped stream produced * by applying the provided mapping function to each element. Each mapped * stream is {@link java.util.stream.BaseStream#close() closed} after its * contents have placed been into this stream. (If a mapped stream is * {@code null} an empty stream is used, instead.) * * <p>This is an <a href="package-summary.html#StreamOps">intermediate * operation</a>. * * @param mapper a <a href="package-summary.html#NonInterference">non-interfering</a>, * <a href="package-summary.html#Statelessness">stateless</a> * function to apply to each element which produces a stream * of new values * @return the new stream * @see #flatMap(Function) */ DoubleStream flatMapToDouble(Function<? super T, ? extends DoubleStream> mapper); /** * Returns a stream consisting of the distinct elements (according to * {@link Object#equals(Object)}) of this stream. * * <p>For ordered streams, the selection of distinct elements is stable * (for duplicated elements, the element appearing first in the encounter * order is preserved.) For unordered streams, no stability guarantees * are made. * * <p>This is a <a href="package-summary.html#StreamOps">stateful * intermediate operation</a>. * * @apiNote * Preserving stability for {@code distinct()} in parallel pipelines is * relatively expensive (requires that the operation act as a full barrier, * with substantial buffering overhead), and stability is often not needed. * Using an unordered stream source (such as {@link #generate(Supplier)}) * or removing the ordering constraint with {@link #unordered()} may result * in significantly more efficient execution for {@code distinct()} in parallel * pipelines, if the semantics of your situation permit. If consistency * with encounter order is required, and you are experiencing poor performance * or memory utilization with {@code distinct()} in parallel pipelines, * switching to sequential execution with {@link #sequential()} may improve * performance. * * @return the new stream */ Stream<T> distinct(); /** * Returns a stream consisting of the elements of this stream, sorted * according to natural order. If the elements of this stream are not * {@code Comparable}, a {@code java.lang.ClassCastException} may be thrown * when the terminal operation is executed. * * <p>For ordered streams, the sort is stable. For unordered streams, no * stability guarantees are made. * * <p>This is a <a href="package-summary.html#StreamOps">stateful * intermediate operation</a>. * * @return the new stream */ Stream<T> sorted(); /** * Returns a stream consisting of the elements of this stream, sorted * according to the provided {@code Comparator}. * * <p>For ordered streams, the sort is stable. For unordered streams, no * stability guarantees are made. * * <p>This is a <a href="package-summary.html#StreamOps">stateful * intermediate operation</a>. * * @param comparator a <a href="package-summary.html#NonInterference">non-interfering</a>, * <a href="package-summary.html#Statelessness">stateless</a> * {@code Comparator} to be used to compare stream elements * @return the new stream */ Stream<T> sorted(Comparator<? super T> comparator); /** * Returns a stream consisting of the elements of this stream, additionally * performing the provided action on each element as elements are consumed * from the resulting stream. * * <p>This is an <a href="package-summary.html#StreamOps">intermediate * operation</a>. * * <p>For parallel stream pipelines, the action may be called at * whatever time and in whatever thread the element is made available by the * upstream operation. If the action modifies shared state, * it is responsible for providing the required synchronization. * * @apiNote This method exists mainly to support debugging, where you want * to see the elements as they flow past a certain point in a pipeline: * <pre>{@code * Stream.of("one", "two", "three", "four") * .filter(e -> e.length() > 3) * .peek(e -> System.out.println("Filtered value: " + e)) * .map(String::toUpperCase) * .peek(e -> System.out.println("Mapped value: " + e)) * .collect(Collectors.toList()); * }</pre> * * @param action a <a href="package-summary.html#NonInterference"> * non-interfering</a> action to perform on the elements as * they are consumed from the stream * @return the new stream */ Stream<T> peek(Consumer<? super T> action); /** * Returns a stream consisting of the elements of this stream, truncated * to be no longer than {@code maxSize} in length. * * <p>This is a <a href="package-summary.html#StreamOps">short-circuiting * stateful intermediate operation</a>. * * @apiNote * While {@code limit()} is generally a cheap operation on sequential * stream pipelines, it can be quite expensive on ordered parallel pipelines, * especially for large values of {@code maxSize}, since {@code limit(n)} * is constrained to return not just any <em>n</em> elements, but the * <em>first n</em> elements in the encounter order. Using an unordered * stream source (such as {@link #generate(Supplier)}) or removing the * ordering constraint with {@link #unordered()} may result in significant * speedups of {@code limit()} in parallel pipelines, if the semantics of * your situation permit. If consistency with encounter order is required, * and you are experiencing poor performance or memory utilization with * {@code limit()} in parallel pipelines, switching to sequential execution * with {@link #sequential()} may improve performance. * * @param maxSize the number of elements the stream should be limited to * @return the new stream * @throws IllegalArgumentException if {@code maxSize} is negative */ Stream<T> limit(long maxSize); /** * Returns a stream consisting of the remaining elements of this stream * after discarding the first {@code n} elements of the stream. * If this stream contains fewer than {@code n} elements then an * empty stream will be returned. * * <p>This is a <a href="package-summary.html#StreamOps">stateful * intermediate operation</a>. * * @apiNote * While {@code skip()} is generally a cheap operation on sequential * stream pipelines, it can be quite expensive on ordered parallel pipelines, * especially for large values of {@code n}, since {@code skip(n)} * is constrained to skip not just any <em>n</em> elements, but the * <em>first n</em> elements in the encounter order. Using an unordered * stream source (such as {@link #generate(Supplier)}) or removing the * ordering constraint with {@link #unordered()} may result in significant * speedups of {@code skip()} in parallel pipelines, if the semantics of * your situation permit. If consistency with encounter order is required, * and you are experiencing poor performance or memory utilization with * {@code skip()} in parallel pipelines, switching to sequential execution * with {@link #sequential()} may improve performance. * * @param n the number of leading elements to skip * @return the new stream * @throws IllegalArgumentException if {@code n} is negative */ Stream<T> skip(long n); /** * Performs an action for each element of this stream. * * <p>This is a <a href="package-summary.html#StreamOps">terminal * operation</a>. * * <p>The behavior of this operation is explicitly nondeterministic. * For parallel stream pipelines, this operation does <em>not</em> * guarantee to respect the encounter order of the stream, as doing so * would sacrifice the benefit of parallelism. For any given element, the * action may be performed at whatever time and in whatever thread the * library chooses. If the action accesses shared state, it is * responsible for providing the required synchronization. * * @param action a <a href="package-summary.html#NonInterference"> * non-interfering</a> action to perform on the elements */ void forEach(Consumer<? super T> action); /** * Performs an action for each element of this stream, in the encounter * order of the stream if the stream has a defined encounter order. * * <p>This is a <a href="package-summary.html#StreamOps">terminal * operation</a>. * * <p>This operation processes the elements one at a time, in encounter * order if one exists. Performing the action for one element * <a href="../concurrent/package-summary.html#MemoryVisibility"><i>happens-before</i></a> * performing the action for subsequent elements, but for any given element, * the action may be performed in whatever thread the library chooses. * * @param action a <a href="package-summary.html#NonInterference"> * non-interfering</a> action to perform on the elements * @see #forEach(Consumer) */ void forEachOrdered(Consumer<? super T> action); /** * Returns an array containing the elements of this stream. * * <p>This is a <a href="package-summary.html#StreamOps">terminal * operation</a>. * * @return an array containing the elements of this stream */ Object[] toArray(); /** * Returns an array containing the elements of this stream, using the * provided {@code generator} function to allocate the returned array, as * well as any additional arrays that might be required for a partitioned * execution or for resizing. * * <p>This is a <a href="package-summary.html#StreamOps">terminal * operation</a>. * * @apiNote * The generator function takes an integer, which is the size of the * desired array, and produces an array of the desired size. This can be * concisely expressed with an array constructor reference: * <pre>{@code * Person[] men = people.stream() * .filter(p -> p.getGender() == MALE) * .toArray(Person[]::new); * }</pre> * * @param <A> the element type of the resulting array * @param generator a function which produces a new array of the desired * type and the provided length * @return an array containing the elements in this stream * @throws ArrayStoreException if the runtime type of the array returned * from the array generator is not a supertype of the runtime type of every * element in this stream */ <A> A[] toArray(IntFunction<A[]> generator); /** * Performs a <a href="package-summary.html#Reduction">reduction</a> on the * elements of this stream, using the provided identity value and an * <a href="package-summary.html#Associativity">associative</a> * accumulation function, and returns the reduced value. This is equivalent * to: * <pre>{@code * T result = identity; * for (T element : this stream) * result = accumulator.apply(result, element) * return result; * }</pre> * * but is not constrained to execute sequentially. * * <p>The {@code identity} value must be an identity for the accumulator * function. This means that for all {@code t}, * {@code accumulator.apply(identity, t)} is equal to {@code t}. * The {@code accumulator} function must be an * <a href="package-summary.html#Associativity">associative</a> function. * * <p>This is a <a href="package-summary.html#StreamOps">terminal * operation</a>. * * @apiNote Sum, min, max, average, and string concatenation are all special * cases of reduction. Summing a stream of numbers can be expressed as: * * <pre>{@code * Integer sum = integers.reduce(0, (a, b) -> a+b); * }</pre> * * or: * * <pre>{@code * Integer sum = integers.reduce(0, Integer::sum); * }</pre> * * <p>While this may seem a more roundabout way to perform an aggregation * compared to simply mutating a running total in a loop, reduction * operations parallelize more gracefully, without needing additional * synchronization and with greatly reduced risk of data races. * * @param identity the identity value for the accumulating function * @param accumulator an <a href="package-summary.html#Associativity">associative</a>, * <a href="package-summary.html#NonInterference">non-interfering</a>, * <a href="package-summary.html#Statelessness">stateless</a> * function for combining two values * @return the result of the reduction */ T reduce(T identity, BinaryOperator<T> accumulator); /** * Performs a <a href="package-summary.html#Reduction">reduction</a> on the * elements of this stream, using an * <a href="package-summary.html#Associativity">associative</a> accumulation * function, and returns an {@code Optional} describing the reduced value, * if any. This is equivalent to: * <pre>{@code * boolean foundAny = false; * T result = null; * for (T element : this stream) { * if (!foundAny) { * foundAny = true; * result = element; * } * else * result = accumulator.apply(result, element); * } * return foundAny ? Optional.of(result) : Optional.empty(); * }</pre> * * but is not constrained to execute sequentially. * * <p>The {@code accumulator} function must be an * <a href="package-summary.html#Associativity">associative</a> function. * * <p>This is a <a href="package-summary.html#StreamOps">terminal * operation</a>. * * @param accumulator an <a href="package-summary.html#Associativity">associative</a>, * <a href="package-summary.html#NonInterference">non-interfering</a>, * <a href="package-summary.html#Statelessness">stateless</a> * function for combining two values * @return an {@link Optional} describing the result of the reduction * @throws NullPointerException if the result of the reduction is null * @see #reduce(Object, BinaryOperator) * @see #min(Comparator) * @see #max(Comparator) */ Optional<T> reduce(BinaryOperator<T> accumulator); /** * Performs a <a href="package-summary.html#Reduction以上是关于谈谈stream的运行原理的主要内容，如果未能解决你的问题，请参考以下文章
[三]java8 函数式编程Stream 概念深入理解 Stream 运行原理 Stream设计思路
read ECONNRESET at TLSWrap.onStreamRead (internal/stream_base_commons.js:209:20) { errno: -4077(代码片段