JAVA8-Stream源码 Stream基本使用及特点Stream说明Stream操作分为：2组，//注意，流式管道操作，只能操作一次，否则报错。1、可连续操作： filter, map, and limit can be connected together to form a pipeline.2、操作中断collect causes the pipeline to be executed and closes it.使用范例：package com.example.study.java8.streams; import java.util.*; import java.util.stream.Stream; import static java.util.Comparator.comparing; import static java.util.stream.Collectors.toList; /** * Stream使用 * Stream操作分为：2组，//注意，流式管道操作，只能操作一次，否则报错 * You can see two groups of operations: * 1、可连续操作 *  filter, map, and limit can be connected together to form a pipeline. * 2、操作中断 *  collect causes the pipeline to be executed and closes it. */ public class SimpleStream { public static void main(String[] args) { List<Dish> menu = Arrays.asList( new Dish("pork", false, 800, Dish.Type.MEAT), new Dish("beef", false, 700, Dish.Type.MEAT), new Dish("chicken", false, 400, Dish.Type.MEAT), new Dish("french fries", true, 530, Dish.Type.OTHER), new Dish("rice", true, 350, Dish.Type.OTHER), new Dish("season fruit", true, 120, Dish.Type.OTHER), new Dish("pizza", true, 550, Dish.Type.OTHER), new Dish("prawns", false, 300, Dish.Type.FISH), new Dish("salmon", false, 450, Dish.Type.FISH)); Stream<Dish> stream = menu.stream(); stream.forEach(System.out::println); //注意，流式管道操作，只能操作一次，否则报错 //stream has already been operated upon or closed // stream.forEach(System.out::println); System.out.println("===================================="); //原始调用 List<String> namesByCollections = getDishNamesByCollections(menu); namesByCollections.stream().forEach(System.out::println); System.out.println("===================================="); //lambda stream调用 List<String> lambdaStreamNames = lambdaStream(menu); lambdaStreamNames.stream().forEach(System.out::println); } //原始写法 public static List<String> getDishNamesByCollections(List<Dish> menu) { List<Dish> caloriesDis = new ArrayList<>(); for (Dish dish : menu) { if (dish.getCalories() < 400) { caloriesDis.add(dish); } } Collections.sort(caloriesDis, (dish1, dish2) -> { return Integer.compare(dish1.getCalories(), dish2.getCalories()); }); List<String> names = new ArrayList<>(); for(Dish dish : caloriesDis){ names.add(dish.getName()); } return names; } //stream lambda处理 public static List<String> lambdaStream(List<Dish> menu){ return menu.stream().filter(dis -> dis.getCalories() < 400) .sorted(comparing(Dish::getCalories)) .map(Dish::getName).collect(toList()); } } 主要掌握Stream接口中常用方法。Stream源码：/* * Copyright (c) 2012, 2017, Oracle and/or its affiliates. All rights reserved. * ORACLE PROPRIETARY/CONFIDENTIAL. Use is subject to license terms. */ package java.util.stream; import java.nio.file.Files; import java.nio.file.Path; import java.util.Arrays; import java.util.Collection; import java.util.Comparator; import java.util.Objects; import java.util.Optional; import java.util.Spliterator; import java.util.Spliterators; import java.util.concurrent.ConcurrentHashMap; import java.util.function.BiConsumer; import java.util.function.BiFunction; import java.util.function.BinaryOperator; import java.util.function.Consumer; import java.util.function.Function; import java.util.function.IntFunction; import java.util.function.Predicate; import java.util.function.Supplier; import java.util.function.ToDoubleFunction; import java.util.function.ToIntFunction; import java.util.function.ToLongFunction; import java.util.function.UnaryOperator; /** * 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>A stream implementation is permitted significant latitude in optimizing * the computation of the result. For example, a stream implementation is free * to elide operations (or entire stages) from a stream pipeline -- and * therefore elide invocation of behavioral parameters -- if it can prove that * it would not affect the result of the computation. This means that * side-effects of behavioral parameters may not always be executed and should * not be relied upon, unless otherwise specified (such as by the terminal * operations {@code forEach} and {@code forEachOrdered}). (For a specific * example of such an optimization, see the API note documented on the * {@link #count} operation. For more detail, see the * <a href="package-summary.html#SideEffects">side-effects</a> section of the * stream package documentation.) * * <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}. * Operating on a stream after it has been closed will throw {@link IllegalStateException}. * Most stream instances do not actually need to be closed after use, as they * are backed by collections, arrays, or generating functions, which require no * special resource management. Generally, only streams whose source is an IO channel, * such as those returned by {@link Files#lines(Path)}, will require closing. If a * stream does require closing, it must be opened as a resource within a try-with-resources * statement or similar control structure to ensure that it is closed promptly after its * operations have completed. * * <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> * * <p>In cases where the stream implementation is able to optimize away the * production of some or all the elements (such as with short-circuiting * operations like {@code findFirst}, or in the example described in * {@link #count}), the action will not be invoked for those elements. * * @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); /** * Returns, if this stream is ordered, a stream consisting of the longest * prefix of elements taken from this stream that match the given predicate. * Otherwise returns, if this stream is unordered, a stream consisting of a * subset of elements taken from this stream that match the given predicate. * * <p>If this stream is ordered then the longest prefix is a contiguous * sequence of elements of this stream that match the given predicate. The * first element of the sequence is the first element of this stream, and * the element immediately following the last element of the sequence does * not match the given predicate. * * <p>If this stream is unordered, and some (but not all) elements of this * stream match the given predicate, then the behavior of this operation is * nondeterministic; it is free to take any subset of matching elements * (which includes the empty set). * * <p>Independent of whether this stream is ordered or unordered if all * elements of this stream match the given predicate then this operation * takes all elements (the result is the same as the input), or if no * elements of the stream match the given predicate then no elements are * taken (the result is an empty stream). * * <p>This is a <a href="package-summary.html#StreamOps">short-circuiting * stateful intermediate operation</a>. * * @implSpec * The default implementation obtains the {@link #spliterator() spliterator} * of this stream, wraps that spliterator so as to support the semantics * of this operation on traversal, and returns a new stream associated with * the wrapped spliterator. The returned stream preserves the execution * characteristics of this stream (namely parallel or sequential execution * as per {@link #isParallel()}) but the wrapped spliterator may choose to * not support splitting. When the returned stream is closed, the close * handlers for both the returned and this stream are invoked. * * @apiNote * While {@code takeWhile()} is generally a cheap operation on sequential * stream pipelines, it can be quite expensive on ordered parallel * pipelines, since the operation is constrained to return not just any * valid prefix, but the longest prefix of 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 takeWhile()} 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 takeWhile()} in parallel pipelines, switching to * sequential execution with {@link #sequential()} may improve performance. * * @param predicate a <a href="package-summary.html#NonInterference">non-interfering</a>, * <a href="package-summary.html#Statelessness">stateless</a> * predicate to apply to elements to determine the longest * prefix of elements. * @return the new stream * @since 9 */ default Stream<T> takeWhile(Predicate<? super T> predicate) { Objects.requireNonNull(predicate); // Reuses the unordered spliterator, which, when encounter is present, // is safe to use as long as it configured not to split return StreamSupport.stream( new WhileOps.UnorderedWhileSpliterator.OfRef.Taking<>(spliterator(), true, predicate), isParallel()).onClose(this::close); } /** * Returns, if this stream is ordered, a stream consisting of the remaining * elements of this stream after dropping the longest prefix of elements * that match the given predicate. Otherwise returns, if this stream is * unordered, a stream consisting of the remaining elements of this stream * after dropping a subset of elements that match the given predicate. * * <p>If this stream is ordered then the longest prefix is a contiguous * sequence of elements of this stream that match the given predicate. The * first element of the sequence is the first element of this stream, and * the element immediately following the last element of the sequence does * not match the given predicate. * * <p>If this stream is unordered, and some (but not all) elements of this * stream match the given predicate, then the behavior of this operation is * nondeterministic; it is free to drop any subset of matching elements * (which includes the empty set). * * <p>Independent of whether this stream is ordered or unordered if all * elements of this stream match the given predicate then this operation * drops all elements (the result is an empty stream), or if no elements of * the stream match the given predicate then no elements are dropped (the * result is the same as the input). * * <p>This is a <a href="package-summary.html#StreamOps">stateful * intermediate operation</a>. * * @implSpec * The default implementation obtains the {@link #spliterator() spliterator} * of this stream, wraps that spliterator so as to support the semantics * of this operation on traversal, and returns a new stream associated with * the wrapped spliterator. The returned stream preserves the execution * characteristics of this stream (namely parallel or sequential execution * as per {@link #isParallel()}) but the wrapped spliterator may choose to * not support splitting. When the returned stream is closed, the close * handlers for both the returned and this stream are invoked. * * @apiNote * While {@code dropWhile()} is generally a cheap operation on sequential * stream pipelines, it can be quite expensive on ordered parallel * pipelines, since the operation is constrained to return not just any * valid prefix, but the longest prefix of 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 dropWhile()} 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 dropWhile()} in parallel pipelines, switching to * sequential execution with {@link #sequential()} may improve performance. * * @param predicate a <a href="package-summary.html#NonInterference">non-interfering</a>, * <a href="package-summary.html#Statelessness">stateless</a> * predicate to apply to elements to determine the longest * prefix of elements. * @return the new stream * @since 9 */ default Stream<T> dropWhile(Predicate<? super T> predicate) { Objects.requireNonNull(predicate); // Reuses the unordered spliterator, which, when encounter is present, // is safe to use as long as it configured not to split return StreamSupport.stream( new WhileOps.UnorderedWhileSpliterator.OfRef.Dropping<>(spliterator(), true, predicate), isParallel()).onClose(this::close); } /** * 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, whose {@linkplain Class#getComponentType runtime component * type} is {@code Object}, 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 component 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 any element of this * stream is not assignable to the {@linkplain Class#getComponentType * runtime component type} of the generated array */ <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">reduction</a> on the * elements of this stream, using the provided identity, accumulation and * combining functions. This is equivalent to: * <pre>{@code * U 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 combiner * function. This means that for all {@code u}, {@code combiner(identity, u)} * is equal to {@code u}. Additionally, the {@code combiner} function * 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> * * <p>This is a <a href="package-summary.html#StreamOps">terminal * operation</a>. * * @apiNote Many reductions using this form can be represented more simply * by an explicit combination of {@code map} and {@code reduce} operations. * The {@code accumulator} function acts as a fused mapper and accumulator, * which can sometimes be more efficient than separate mapping and reduction, * such as when knowing the previously reduced value allows you to avoid * some computation. * * @param <U> The type of the result * @param identity the identity value for the combiner 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 incorporating an additional element into a result * @param combiner 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, which must be * compatible with the accumulator function * @return the result of the reduction * @see #reduce(BinaryOperator) * @see #reduce(Object, BinaryOperator) */ <U> U reduce(U identity, BiFunction<U, ? super T, U> accumulator, BinaryOperator<U> combiner); /** * Performs a <a href="package-summary.html#MutableReduction">mutable * reduction</a> operation on the elements of this stream. A mutable * reduction is one in which the reduced value is a mutable result container, * such as an {@code ArrayList}, and elements are incorporated by updating * the state of the result rather than by replacing the result. This * produces a result equivalent to: * <pre>{@code * R result = supplier.get(); * for (T element : this stream) * accumulator.accept(result, element); * return result; * }</pre> * * <p>Like {@link #reduce(Object, BinaryOperator)}, {@code collect} operations * can be parallelized without requiring additional synchronization. * * <p>This is a <a href="package-summary.html#StreamOps">terminal * operation</a>. * * @apiNote There are many existing classes in the JDK whose signatures are * well-suited for use with method references as arguments to {@code collect()}. * For example, the following will accumulate strings into an {@code ArrayList}: * <pre>{@code * List<String> asList = stringStream.collect(ArrayList::new, ArrayList::add, * ArrayList::addAll); * }</pre> * * <p>The following will take a stream of strings and concatenates them into a * single string: * <pre>{@code * String concat = stringStream.collect(StringBuilder::new, StringBuilder::append, * StringBuilder::append) * .toString(); * }</pre> * * @param <R> the type of the mutable result container * @param supplier a function that creates a new mutable result container. * For a parallel execution, this function may be called * multiple times and must return a fresh value each time. * @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 that must fold an element into a result * container. * @param combiner 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 that accepts two partial result containers * and merges them, which must be compatible with the * accumulator function. The combiner function must fold * the elements from the second result container into the * first result container. * @return the result of the reduction */ <R> R collect(Supplier<R> supplier, BiConsumer<R, ? super T> accumulator, BiConsumer<R, R> combiner); /** * Performs a <a href="package-summary.html#MutableReduction">mutable * reduction</a> operation on the elements of this stream using a * {@code Collector}. A {@code Collector} * encapsulates the functions used as arguments to * {@link #collect(Supplier, BiConsumer, BiConsumer)}, allowing for reuse of * collection strategies and composition of collect operations such as * multiple-level grouping or partitioning. * * <p>If the stream is parallel, and the {@code Collector} * is {@link Collector.Characteristics#CONCURRENT concurrent}, and * either the stream is unordered or the collector is * {@link Collector.Characteristics#UNORDERED unordered}, * then a concurrent reduction will be performed (see {@link Collector} for * details on concurrent reduction.) * * <p>This is a <a href="package-summary.html#StreamOps">terminal * operation</a>. * * <p>When executed in parallel, multiple intermediate results may be * instantiated, populated, and merged so as to maintain isolation of * mutable data structures. Therefore, even when executed in parallel * with non-thread-safe data structures (such as {@code ArrayList}), no * additional synchronization is needed for a parallel reduction. * * @apiNote * The following will accumulate strings into an ArrayList: * <pre>{@code * List<String> asList = stringStream.collect(Collectors.toList()); * }</pre> * * <p>The following will classify {@code Person} objects by city: * <pre>{@code * Map<String, List<Person>> peopleByCity * = personStream.collect(Collectors.groupingBy(Person::getCity)); * }</pre> * * <p>The following will classify {@code Person} objects by state and city, * cascading two {@code Collector}s together: * <pre>{@code * Map<String, Map<String, List<Person>>> peopleByStateAndCity * = personStream.collect(Collectors.groupingBy(Person::getState, * Collectors.groupingBy(Person::getCity))); * }</pre> * * @param <R> the type of the result * @param <A> the intermediate accumulation type of the {@code Collector} * @param collector the {@code Collector} describing the reduction * @return the result of the reduction * @see #collect(Supplier, BiConsumer, BiConsumer) * @see Collectors */ <R, A> R collect(Collector<? super T, A, R> collector); /** * Returns the minimum element of this stream according to the provided * {@code Comparator}. This is a special case of a * <a href="package-summary.html#Reduction">reduction</a>. * * <p>This is a <a href="package-summary.html#StreamOps">terminal 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 compare elements of this stream * @return an {@code Optional} describing the minimum element of this stream, * or an empty {@code Optional} if the stream is empty * @throws NullPointerException if the minimum element is null */ Optional<T> min(Comparator<? super T> comparator); /** * Returns the maximum element of this stream according to the provided * {@code Comparator}. This is a special case of a * <a href="package-summary.html#Reduction">reduction</a>. * * <p>This is a <a href="package-summary.html#StreamOps">terminal * 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 compare elements of this stream * @return an {@code Optional} describing the maximum element of this stream, * or an empty {@code Optional} if the stream is empty * @throws NullPointerException if the maximum element is null */ Optional<T> max(Comparator<? super T> comparator); /** * Returns the count of elements in this stream. This is a special case of * a <a href="package-summary.html#Reduction">reduction</a> and is * equivalent to: * <pre>{@code * return mapToLong(e -> 1L).sum(); * }</pre> * * <p>This is a <a href="package-summary.html#StreamOps">terminal operation</a>. * * @apiNote * An implementation may choose to not execute the stream pipeline (either * sequentially or in parallel) if it is capable of computing the count * directly from the stream source. In such cases no source elements will * be traversed and no intermediate operations will be evaluated. * Behavioral parameters with side-effects, which are strongly discouraged * except for harmless cases such as debugging, may be affected. For * example, consider the following stream: * <pre>{@code * List<String> l = Arrays.asList("A", "B", "C", "D"); * long count = l.stream().peek(System.out::println).count(); * }</pre> * The number of elements covered by the stream source, a {@code List}, is * known and the intermediate operation, {@code peek}, does not inject into * or remove elements from the stream (as may be the case for * {@code flatMap} or {@code filter} operations). Thus the count is the * size of the {@code List} and there is no need to execute the pipeline * and, as a side-effect, print out the list elements. * * @return the count of elements in this stream */ long count(); /** * Returns whether any elements of this stream match the provided * predicate. May not evaluate the predicate on all elements if not * necessary for determining the result. If the stream is empty then * {@code false} is returned and the predicate is not evaluated. * * <p>This is a <a href="package-summary.html#StreamOps">short-circuiting * terminal operation</a>. * * @apiNote * This method evaluates the <em>existential quantification</em> of the * predicate over the elements of the stream (for some x P(x)). * * @param predicate a <a href="package-summary.html#NonInterference">non-interfering</a>, * <a href="package-summary.html#Statelessness">stateless</a> * predicate to apply to elements of this stream * @return {@code true} if any elements of the stream match the provided * predicate, otherwise {@code false} */ boolean anyMatch(Predicate<? super T> predicate); /** * Returns whether all elements of this stream match the provided predicate. * May not evaluate the predicate on all elements if not necessary for * determining the result. If the stream is empty then {@code true} is * returned and the predicate is not evaluated. * * <p>This is a <a href="package-summary.html#StreamOps">short-circuiting * terminal operation</a>. * * @apiNote * This method evaluates the <em>universal quantification</em> of the * predicate over the elements of the stream (for all x P(x)). If the * stream is empty, the quantification is said to be <em>vacuously * satisfied</em> and is always {@code true} (regardless of P(x)). * * @param predicate a <a href="package-summary.html#NonInterference">non-interfering</a>, * <a href="package-summary.html#Statelessness">stateless</a> * predicate to apply to elements of this stream * @return {@code true} if either all elements of the stream match the * provided predicate or the stream is empty, otherwise {@code false} */ boolean allMatch(Predicate<? super T> predicate); /** * Returns whether no elements of this stream match the provided predicate. * May not evaluate the predicate on all elements if not necessary for * determining the result. If the stream is empty then {@code true} is * returned and the predicate is not evaluated. * * <p>This is a <a href="package-summary.html#StreamOps">short-circuiting * terminal operation</a>. * * @apiNote * This method evaluates the <em>universal quantification</em> of the * negated predicate over the elements of the stream (for all x ~P(x)). If * the stream is empty, the quantification is said to be vacuously satisfied * and is always {@code true}, regardless of P(x). * * @param predicate a <a href="package-summary.html#NonInterference">non-interfering</a>, * <a href="package-summary.html#Statelessness">stateless</a> * predicate to apply to elements of this stream * @return {@code true} if either no elements of the stream match the * provided predicate or the stream is empty, otherwise {@code false} */ boolean noneMatch(Predicate<? super T> predicate); /** * Returns an {@link Optional} describing the first element of this stream, * or an empty {@code Optional} if the stream is empty. If the stream has * no encounter order, then any element may be returned. * * <p>This is a <a href="package-summary.html#StreamOps">short-circuiting * terminal operation</a>. * * @return an {@code Optional} describing the first element of this stream, * or an empty {@code Optional} if the stream is empty * @throws NullPointerException if the element selected is null */ Optional<T> findFirst(); /** * Returns an {@link Optional} describing some element of the stream, or an * empty {@code Optional} if the stream is empty. * * <p>This is a <a href="package-summary.html#StreamOps">short-circuiting * terminal operation</a>. * * <p>The behavior of this operation is explicitly nondeterministic; it is * free to select any element in the stream. This is to allow for maximal * performance in parallel operations; the cost is that multiple invocations * on the same source may not return the same result. (If a stable result * is desired, use {@link #findFirst()} instead.) * * @return an {@code Optional} describing some element of this stream, or an * empty {@code Optional} if the stream is empty * @throws NullPointerException if the element selected is null * @see #findFirst() */ Optional<T> findAny(); // Static factories /** * Returns a builder for a {@code Stream}. * * @param <T> type of elements * @return a stream builder */ public static<T> Builder<T> builder() { return new Streams.StreamBuilderImpl<>(); } /** * Returns an empty sequential {@code Stream}. * * @param <T> the type of stream elements * @return an empty sequential stream */ public static<T> Stream<T> empty() { return StreamSupport.stream(Spliterators.<T>emptySpliterator(), false); } /** * Returns a sequential {@code Stream} containing a single element. * * @param t the single element * @param <T> the type of stream elements * @return a singleton sequential stream */ public static<T> Stream<T> of(T t) { return StreamSupport.stream(new Streams.StreamBuilderImpl<>(t), false); } /** * Returns a sequential {@code Stream} containing a single element, if * non-null, otherwise returns an empty {@code Stream}. * * @param t the single element * @param <T> the type of stream elements * @return a stream with a single element if the specified element * is non-null, otherwise an empty stream * @since 9 */ public static<T> Stream<T> ofNullable(T t) { return t == null ? Stream.empty() : StreamSupport.stream(new Streams.StreamBuilderImpl<>(t), false); } /** * Returns a sequential ordered stream whose elements are the specified values. * * @param <T> the type of stream elements * @param values the elements of the new stream * @return the new stream */ @SafeVarargs @SuppressWarnings("varargs") // Creating a stream from an array is safe public static<T> Stream<T> of(T... values) { return Arrays.stream(values); } /** * Returns an infinite sequential ordered {@code Stream} produced by iterative * application of a function {@code f} to an initial element {@code seed}, * producing a {@code Stream} consisting of {@code seed}, {@code f(seed)}, * {@code f(f(seed))}, etc. * * <p>The first element (position {@code 0}) in the {@code Stream} will be * the provided {@code seed}. For {@code n > 0}, the element at position * {@code n}, will be the result of applying the function {@code f} to the * element at position {@code n - 1}. * * <p>The action of applying {@code f} for one element * <a href="../concurrent/package-summary.html#MemoryVisibility"><i>happens-before</i></a> * the action of applying {@code f} for subsequent elements. For any given * element the action may be performed in whatever thread the library * chooses. * * @param <T> the type of stream elements * @param seed the initial element * @param f a function to be applied to the previous element to produce * a new element * @return a new sequential {@code Stream} */ public static<T> Stream<T> iterate(final T seed, final UnaryOperator<T> f) { Objects.requireNonNull(f); Spliterator<T> spliterator = new Spliterators.AbstractSpliterator<>(Long.MAX_VALUE, Spliterator.ORDERED | Spliterator.IMMUTABLE) { T prev; boolean started; @Override public boolean tryAdvance(Consumer<? super T> action) { Objects.requireNonNull(action); T t; if (started) t = f.apply(prev); else { t = seed; started = true; } action.accept(prev = t); return true; } }; return StreamSupport.stream(spliterator, false); } /** * Returns a sequential ordered {@code Stream} produced by iterative * application of the given {@code next} function to an initial element, * conditioned on satisfying the given {@code hasNext} predicate. The * stream terminates as soon as the {@code hasNext} predicate returns false. * * <p>{@code Stream.iterate} should produce the same sequence of elements as * produced by the corresponding for-loop: * <pre>{@code * for (T index=seed; hasNext.test(index); index = next.apply(index)) { * ... * } * }</pre> * * <p>The resulting sequence may be empty if the {@code hasNext} predicate * does not hold on the seed value. Otherwise the first element will be the * supplied {@code seed} value, the next element (if present) will be the * result of applying the {@code next} function to the {@code seed} value, * and so on iteratively until the {@code hasNext} predicate indicates that * the stream should terminate. * * <p>The action of applying the {@code hasNext} predicate to an element * <a href="../concurrent/package-summary.html#MemoryVisibility"><i>happens-before</i></a> * the action of applying the {@code next} function to that element. The * action of applying the {@code next} function for one element * <i>happens-before</i> the action of applying the {@code hasNext} * predicate for subsequent elements. For any given element an action may * be performed in whatever thread the library chooses. * * @param <T> the type of stream elements * @param seed the initial element * @param hasNext a predicate to apply to elements to determine when the * stream must terminate. * @param next a function to be applied to the previous element to produce * a new element * @return a new sequential {@code Stream} * @since 9 */ public static<T> Stream<T> iterate(T seed, Predicate<? super T> hasNext, UnaryOperator<T> next) { Objects.requireNonNull(next); Objects.requireNonNull(hasNext); Spliterator<T> spliterator = new Spliterators.AbstractSpliterator<>(Long.MAX_VALUE, Spliterator.ORDERED | Spliterator.IMMUTABLE) { T prev; boolean started, finished; @Override public boolean tryAdvance(Consumer<? super T> action) { Objects.requireNonNull(action); if (finished) return false; T t; if (started) t = next.apply(prev); else { t = seed; started = true; } if (!hasNext.test(t)) { prev = null; finished = true; return false; } action.accept(prev = t); return true; } @Override public void forEachRemaining(Consumer<? super T> action) { Objects.requireNonNull(action); if (finished) return; finished = true; T t = started ? next.apply(prev) : seed; prev = null; while (hasNext.test(t)) { action.accept(t); t = next.apply(t); } } }; return StreamSupport.stream(spliterator, false); } /** * Returns an infinite sequential unordered stream where each element is * generated by the provided {@code Supplier}. This is suitable for * generating constant streams, streams of random elements, etc. * * @param <T> the type of stream elements * @param s the {@code Supplier} of generated elements * @return a new infinite sequential unordered {@code Stream} */ public static<T> Stream<T> generate(Supplier<? extends T> s) { Objects.requireNonNull(s); return StreamSupport.stream( new StreamSpliterators.InfiniteSupplyingSpliterator.OfRef<>(Long.MAX_VALUE, s), false); } /** * Creates a lazily concatenated stream whose elements are all the * elements of the first stream followed by all the elements of the * second stream. The resulting stream is ordered if both * of the input streams are ordered, and parallel if either of the input * streams is parallel. When the resulting stream is closed, the close * handlers for both input streams are invoked. * * <p>This method operates on the two input streams and binds each stream * to its source. As a result subsequent modifications to an input stream * source may not be reflected in the concatenated stream result. * * @implNote * Use caution when constructing streams from repeated concatenation. * Accessing an element of a deeply concatenated stream can result in deep * call chains, or even {@code StackOverflowError}. * * <p>Subsequent changes to the sequential/parallel execution mode of the * returned stream are not guaranteed to be propagated to the input streams. * * @apiNote * To preserve optimization opportunities this method binds each stream to * its source and accepts only two streams as parameters. For example, the * exact size of the concatenated stream source can be computed if the exact * size of each input stream source is known. * To concatenate more streams without binding, or without nested calls to * this method, try creating a stream of streams and flat-mapping with the * identity function, for example: * <pre>{@code * Stream<T> concat = Stream.of(s1, s2, s3, s4).flatMap(s -> s); * }</pre> * * @param <T> The type of stream elements * @param a the first stream * @param b the second stream * @return the concatenation of the two input streams */ public static <T> Stream<T> concat(Stream<? extends T> a, Stream<? extends T> b) { Objects.requireNonNull(a); Objects.requireNonNull(b); @SuppressWarnings("unchecked") Spliterator<T> split = new Streams.ConcatSpliterator.OfRef<>( (Spliterator<T>) a.spliterator(), (Spliterator<T>) b.spliterator()); Stream<T> stream = StreamSupport.stream(split, a.isParallel() || b.isParallel()); return stream.onClose(Streams.composedClose(a, b)); } /** * A mutable builder for a {@code Stream}. This allows the creation of a * {@code Stream} by generating elements individually and adding them to the * {@code Builder} (without the copying overhead that comes from using * an {@code ArrayList} as a temporary buffer.) * * <p>A stream builder has a lifecycle, which starts in a building * phase, during which elements can be added, and then transitions to a built * phase, after which elements may not be added. The built phase begins * when the {@link #build()} method is called, which creates an ordered * {@code Stream} whose elements are the elements that were added to the stream * builder, in the order they were added. * * @param <T> the type of stream elements * @see Stream#builder() * @since 1.8 */ public interface Builder<T> extends Consumer<T> { /** * Adds an element to the stream being built. * * @throws IllegalStateException if the builder has already transitioned to * the built state */ @Override void accept(T t); /** * Adds an element to the stream being built. * * @implSpec * The default implementation behaves as if: * <pre>{@code * accept(t) * return this; * }</pre> * * @param t the element to add * @return {@code this} builder * @throws IllegalStateException if the builder has already transitioned to * the built state */ default Builder<T> add(T t) { accept(t); return this; } /** * Builds the stream, transitioning this builder to the built state. * An {@code IllegalStateException} is thrown if there are further attempts * to operate on the builder after it has entered the built state. * * @return the built stream * @throws IllegalStateException if the builder has already transitioned to * the built state */ Stream<T> build(); } }

线程安全-并发容器J.U.C 线程安全-并发容器J.U.CArrayList -> CopyOnWriteArrayListHashSet、TreeSet -> CopyOnWriteArraySet 、ConcurrentSkipListSetHashMap、TreeMap -> ConcurrentHashMap 、ConcurrentSkipListMapCopyOnWriteArrayList适合读多写少的场景。读的时候再原数组读不需要加锁，写的时候会copy一份会单独加锁。代码示例：package com.yanxizhu.demo.concurrency.concurrent; import com.yanxizhu.demo.concurrency.annotation.ThreadSafety; import com.yanxizhu.demo.concurrency.annotation.UnThreadSafety; import lombok.extern.slf4j.Slf4j; import java.util.ArrayList; import java.util.List; import java.util.concurrent.*; /** * @description: 线程安全容器：CopyOnWriteArrayList * @author: <a href="mailto:batis@foxmail.com">清风</a> * @date: 2022/4/24 9:52 * @version: 1.0 */ @Slf4j @ThreadSafety public class DemoCopyOnWriteArrayList { // 请求总数 public static int clientTotal = 5000; // 同时并发执行的线程数 public static int threadTotal = 200; private static List<Integer> list = new CopyOnWriteArrayList<>(); public static void main(String[] args) throws InterruptedException { ExecutorService executorService = Executors.newCachedThreadPool(); final Semaphore semaphore = new Semaphore(threadTotal); final CountDownLatch countDownLatch = new CountDownLatch(clientTotal); for (int i = 0; i < clientTotal; i++) { final int count = i; executorService.execute(() -> { try { semaphore.acquire(); update(count); semaphore.release(); } catch (Exception e) { log.error("exception", e); } countDownLatch.countDown(); }); } countDownLatch.await(); executorService.shutdown(); log.info("size:{}", list.size()); } private static void update(int i) { list.add(i); } }输出结果：5000CopyOnWriteArraySet线程安全，代码示例package com.yanxizhu.demo.concurrency.concurrent; import com.yanxizhu.demo.concurrency.annotation.ThreadSafety; import lombok.extern.slf4j.Slf4j; import java.util.Set; import java.util.concurrent.*; /** * @description: 线程安全容器：CopyOnWriteArraySet * @author: <a href="mailto:batis@foxmail.com">清风</a> * @date: 2022/4/24 9:52 * @version: 1.0 */ @Slf4j @ThreadSafety public class DemoCopyOnWriteArraySet { // 请求总数 public static int clientTotal = 5000; // 同时并发执行的线程数 public static int threadTotal = 200; private static Set<Integer> set = new CopyOnWriteArraySet<>(); public static void main(String[] args) throws InterruptedException { ExecutorService executorService = Executors.newCachedThreadPool(); final Semaphore semaphore = new Semaphore(threadTotal); final CountDownLatch countDownLatch = new CountDownLatch(clientTotal); for (int i = 0; i < clientTotal; i++) { final int count = i; executorService.execute(() -> { try { semaphore.acquire(); update(count); semaphore.release(); } catch (Exception e) { log.error("exception", e); } countDownLatch.countDown(); }); } countDownLatch.await(); executorService.shutdown(); log.info("set size:{}", set.size()); } private static void update(int i) { set.add(i); } }输出结果：set size:5000，线程安全ConcurrentSkipListSet线程安全，代码示例package com.yanxizhu.demo.concurrency.concurrent; import com.yanxizhu.demo.concurrency.annotation.ThreadSafety; import lombok.extern.slf4j.Slf4j; import java.util.List; import java.util.Set; import java.util.concurrent.*; /** * @description: 线程安全容器：ConcurrentSkipListSet * @author: <a href="mailto:batis@foxmail.com">清风</a> * @date: 2022/4/24 9:52 * @version: 1.0 */ @Slf4j @ThreadSafety public class DemoConcurrentSkipListSet { // 请求总数 public static int clientTotal = 5000; // 同时并发执行的线程数 public static int threadTotal = 200; private static Set<Integer> set = new ConcurrentSkipListSet<>(); public static void main(String[] args) throws InterruptedException { ExecutorService executorService = Executors.newCachedThreadPool(); final Semaphore semaphore = new Semaphore(threadTotal); final CountDownLatch countDownLatch = new CountDownLatch(clientTotal); for (int i = 0; i < clientTotal; i++) { final int count = i; executorService.execute(() -> { try { semaphore.acquire(); update(count); semaphore.release(); } catch (Exception e) { log.error("exception", e); } countDownLatch.countDown(); }); } countDownLatch.await(); executorService.shutdown(); log.info("set size:{}", set.size()); } private static void update(int i) { set.add(i); } }输出结果：5000,线程安全。注意：单纯的add、remove是线程安全的，contains等是不安全的。ConcurrentHashMap线程安全，适合并发量大场景package com.yanxizhu.demo.concurrency.concurrent; import com.yanxizhu.demo.concurrency.annotation.UnThreadSafety; import lombok.extern.slf4j.Slf4j; import java.util.HashMap; import java.util.Map; import java.util.concurrent.*; /** * @description: 线程安全容器：ConcurrentHashMap适合高并发场景 * @author: <a href="mailto:batis@foxmail.com">清风</a> * @date: 2022/4/24 9:52 * @version: 1.0 */ @Slf4j @UnThreadSafety public class DemoConcurrentHashMap { private static Map<Integer, Integer> map = new ConcurrentHashMap<>(); //用户数量 private static final int clientsTotal = 5000; //并发数量 private static final int concurrencyTotal = 200; public static void main(String[] args) throws InterruptedException { ExecutorService executorService = Executors.newCachedThreadPool(); //信号量 final Semaphore semaphore = new Semaphore(concurrencyTotal); //闭锁 final CountDownLatch countDownLatch = new CountDownLatch(clientsTotal); for (int i = 0; i < clientsTotal; i++) { final int count = i; executorService.execute(()->{ try { semaphore.acquire(); update(count); semaphore.release(); } catch (InterruptedException e) { log.error("出现错误:【{}】", e.getMessage()); } countDownLatch.countDown(); }); } countDownLatch.await(); executorService.shutdown(); log.info("map size:{}", map.size()); } /** * 通过线程安全对象dateTimeFormatter处理 */ private static void update(int count){ map.put(count, count); } }输出结果：5000,线程安全ConcurrentSkipListMap线程安全，随着并发量增加性能表现越强，大并发下使用ConcurrentHashMap，并发一直增加推荐使用ConcurrentSkipListMap。package com.yanxizhu.demo.concurrency.concurrent; import com.yanxizhu.demo.concurrency.annotation.UnThreadSafety; import lombok.extern.slf4j.Slf4j; import java.util.Map; import java.util.concurrent.*; /** * @description: 线程安全容器：ConcurrentSkipListMap,随着并发的增加性能表现的越好 * @author: <a href="mailto:batis@foxmail.com">清风</a> * @date: 2022/4/24 9:52 * @version: 1.0 */ @Slf4j @UnThreadSafety public class DemoConcurrentHashMap1 { private static Map<Integer, Integer> map = new ConcurrentSkipListMap<>(); //用户数量 private static final int clientsTotal = 5000; //并发数量 private static final int concurrencyTotal = 200; public static void main(String[] args) throws InterruptedException { ExecutorService executorService = Executors.newCachedThreadPool(); //信号量 final Semaphore semaphore = new Semaphore(concurrencyTotal); //闭锁 final CountDownLatch countDownLatch = new CountDownLatch(clientsTotal); for (int i = 0; i < clientsTotal; i++) { final int count = i; executorService.execute(()->{ try { semaphore.acquire(); update(count); semaphore.release(); } catch (InterruptedException e) { log.error("出现错误:【{}】", e.getMessage()); } countDownLatch.countDown(); }); } countDownLatch.await(); executorService.shutdown(); log.info("map size:{}", map.size()); } /** * 通过线程安全对象dateTimeFormatter处理 */ private static void update(int count){ map.put(count, count); } }输出结果:5000,线程安全。注意：ConcurrentHashMap与ConcurrentSkipListMap的使用场景。小总结：线程限制：一个被线程限制的对象，由线程独占，并且只能被占有它的线程修改共享只读：一个共享只读的对象，在没有额外同步的情况下，可以被多个线程并发访问，但是任何线程都不能修改它。线程安全对象：一个线程安全的对象或者容器，在内部通过同步机制来保证线程安全，所以其他线程无需额外的同步就可以通过公共接口随意访问它被守护对象：被守护对象只能通过获取特定的锁来访问

线程安全-同步容器 线程安全-同步容器主要包括ArrayList -> Vector，Stack HashMap->HashTable（key、value不能为null）Collections.synchronizedXXX（List、Set、Map）都是使用Synchronized进行修饰的，性能不是特别好。可以使用并发容器代替。注意：同步容器也可能是线程步安全的。同步容器线程步安全，代码示例：package com.yanxizhu.demo.concurrency.synContainer; import com.yanxizhu.demo.concurrency.annotation.UnThreadSafety; import java.util.Vector; /** * @description: 同步容器，也可能出现线程不安全情况 * @author: <a href="mailto:vip@foxmail.com">清风</a> * @date: 2022/4/24 10:32 * @version: 1.0 */ @UnThreadSafety public class DemoVecotNo { private static final Vector<Integer> vector = new Vector<>(); public static void main(String[] args) { //一直循环 while(true) { //向vector容器放入值 for(int i=0; i <10; i++){ vector.add(i); } //线程1，向vector容器移除值 new Thread(()->{ for(int i=0; i <vector.size(); i++){ vector.remove(i); } }).start(); //线程2，向vector中获取值 new Thread(()->{ for(int i=0; i <vector.size(); i++){ vector.get(i); } }).start(); } } }输出结果：Exception in thread "Thread-478" Exception in thread "Thread-1724" Exception in thread "Thread-1918" Exception in thread "Thread-1769" java.lang.ArrayIndexOutOfBoundsException: Array index out of range: 10 at java.base/java.util.Vector.remove(Vector.java:875) at com.yanxizhu.demo.concurrency.synContainer.DemoVecotNo.lambda$main$0(DemoVecotNo.java:27) at java.base/java.lang.Thread.run(Thread.java:834) java.lang.ArrayIndexOutOfBoundsException: Array index out of range: 62 at java.base/java.util.Vector.remove(Vector.java:875) at com.yanxizhu.demo.concurrency.synContainer.DemoVecotNo.lambda$main$0(DemoVecotNo.java:27) at java.base/java.lang.Thread.run(Thread.java:834) java.lang.ArrayIndexOutOfBoundsException: Array index out of range: 2 at java.base/java.util.Vector.get(Vector.java:781) at com.yanxizhu.demo.concurrency.synContainer.DemoVecotNo.lambda$main$1(DemoVecotNo.java:34) at java.base/java.lang.Thread.run(Thread.java:834) java.lang.ArrayIndexOutOfBoundsException: Array index out of range: 10 at java.base/java.util.Vector.remove(Vector.java:875) at com.yanxizhu.demo.concurrency.synContainer.DemoVecotNo.lambda$main$0(DemoVecotNo.java:27) at java.base/java.lang.Thread.run(Thread.java:834) Exception in thread "Thread-4507" java.lang.ArrayIndexOutOfBoundsException: Array index out of range: 12 at java.base/java.util.Vector.get(Vector.java:781) at com.yanxizhu.demo.concurrency.synContainer.DemoVecotNo.lambda$main$1(DemoVecotNo.java:34) at java.base/java.lang.Thread.run(Thread.java:834)说明：add、remove、get防范都是通过synchronized修饰了的，为什么还是会出现线程不安全，因为当remove和get运行时，如果i相等，remove移除i时，get再获取i就包错了。Vector线程安全代码示例package com.yanxizhu.demo.concurrency.synContainer; import com.yanxizhu.demo.concurrency.annotation.ThreadSafety; import lombok.extern.slf4j.Slf4j; import java.util.Vector; import java.util.concurrent.CountDownLatch; import java.util.concurrent.ExecutorService; import java.util.concurrent.Executors; import java.util.concurrent.Semaphore; /** * @description: 线程安全容器：Vector * @author: <a href="mailto:vip@foxmail.com">清风</a> * @date: 2022/4/24 10:18 * @version: 1.0 */ @Slf4j @ThreadSafety public class DeomVecotr { private static Vector<Integer> vector = new Vector<>(); //用户数量 private static final int clientsTotal = 5000; //并发数量 private static final int concurrencyTotal = 200; public static void main(String[] args) throws InterruptedException { ExecutorService executorService = Executors.newCachedThreadPool(); //信号量 final Semaphore semaphore = new Semaphore(concurrencyTotal); //闭锁 final CountDownLatch countDownLatch = new CountDownLatch(clientsTotal); for (int i = 0; i < clientsTotal; i++) { final int count = i; executorService.execute(()->{ try { semaphore.acquire(); update(count); semaphore.release(); } catch (InterruptedException e) { log.error("出现错误:【{}】", e.getMessage()); } countDownLatch.countDown(); }); } countDownLatch.await(); executorService.shutdown(); log.info("vector size:{}", vector.size()); } /** * 通过线程安全对象dateTimeFormatter处理 */ private static void update(int count){ vector.add(count); } }输出结果：每次是输出5000Hashtable线程安全，代码示例：package com.yanxizhu.demo.concurrency.synContainer; import com.yanxizhu.demo.concurrency.annotation.ThreadSafety; import com.yanxizhu.demo.concurrency.annotation.UnThreadSafety; import lombok.extern.slf4j.Slf4j; import java.util.HashMap; import java.util.Hashtable; import java.util.Map; import java.util.concurrent.CountDownLatch; import java.util.concurrent.ExecutorService; import java.util.concurrent.Executors; import java.util.concurrent.Semaphore; /** * @description: 线程安全容器：Hashtable * @author: <a href="mailto:vip@foxmail.com">清风</a> * @date: 2022/4/24 9:52 * @version: 1.0 */ @Slf4j @ThreadSafety public class DemoHashTable { private static Map<Integer, Integer> map = new Hashtable<>(); //用户数量 private static final int clientsTotal = 5000; //并发数量 private static final int concurrencyTotal = 200; public static void main(String[] args) throws InterruptedException { ExecutorService executorService = Executors.newCachedThreadPool(); //信号量 final Semaphore semaphore = new Semaphore(concurrencyTotal); //闭锁 final CountDownLatch countDownLatch = new CountDownLatch(clientsTotal); for (int i = 0; i < clientsTotal; i++) { final int count = i; executorService.execute(()->{ try { semaphore.acquire(); update(count); semaphore.release(); } catch (InterruptedException e) { log.error("出现错误:【{}】", e.getMessage()); } countDownLatch.countDown(); }); } countDownLatch.await(); executorService.shutdown(); log.info("map size:{}", map.size()); } private static void update(int count){ map.put(count, count); } }输出结果：每次都是5000.线程安全。Collections.synchronizedList线程安全，同步容器package com.yanxizhu.demo.concurrency.synContainer; import com.yanxizhu.demo.concurrency.annotation.ThreadSafety; import lombok.extern.slf4j.Slf4j; import java.util.ArrayList; import java.util.Collections; import java.util.List; import java.util.concurrent.CountDownLatch; import java.util.concurrent.ExecutorService; import java.util.concurrent.Executors; import java.util.concurrent.Semaphore; /** * @description: 安全容器，Collections下的同步容器，线程安全 * @author: <a href="mailto:vip@foxmail.com">清风</a> * @date: 2022/4/24 10:47 * @version: 1.0 */ @Slf4j @ThreadSafety public class DemoCollections { private static List<Integer> list = Collections.synchronizedList(new ArrayList<>()); //用户数量 private static final int clientsTotal = 5000; //并发数量 private static final int concurrencyTotal = 200; public static void main(String[] args) throws InterruptedException { ExecutorService executorService = Executors.newCachedThreadPool(); //信号量 final Semaphore semaphore = new Semaphore(concurrencyTotal); //闭锁 final CountDownLatch countDownLatch = new CountDownLatch(clientsTotal); for (int i = 0; i < clientsTotal; i++) { final int count = i; executorService.execute(()->{ try { semaphore.acquire(); update(count); semaphore.release(); } catch (InterruptedException e) { log.error("出现错误:【{}】", e.getMessage()); } countDownLatch.countDown(); }); } countDownLatch.await(); executorService.shutdown(); log.info("list size:{}", list.size()); } /** * 通过线程安全对象dateTimeFormatter处理 */ private static void update(int count){ list.add(count); } }输出结果：每次输出5000，线程安全。Collections.synchronizedSet线程安全，同步容器，代码示例package com.yanxizhu.demo.concurrency.synContainer; import com.google.common.collect.Sets; import com.yanxizhu.demo.concurrency.annotation.ThreadSafety; import lombok.extern.slf4j.Slf4j; import java.util.Collections; import java.util.Set; import java.util.concurrent.CountDownLatch; import java.util.concurrent.ExecutorService; import java.util.concurrent.Executors; import java.util.concurrent.Semaphore; /** * @description: 安全容器，Collections下的同步容器，synchronizedSet,线程安全 * @author: <a href="mailto:vip@foxmail.com">清风</a> * @date: 2022/4/24 10:47 * @version: 1.0 */ @Slf4j @ThreadSafety public class DemoCollectionsSynSet { private static Set<Integer> set = Collections.synchronizedSet(Sets.newHashSet()); //用户数量 private static final int clientsTotal = 5000; //并发数量 private static final int concurrencyTotal = 200; public static void main(String[] args) throws InterruptedException { ExecutorService executorService = Executors.newCachedThreadPool(); //信号量 final Semaphore semaphore = new Semaphore(concurrencyTotal); //闭锁 final CountDownLatch countDownLatch = new CountDownLatch(clientsTotal); for (int i = 0; i < clientsTotal; i++) { final int count = i; executorService.execute(()->{ try { semaphore.acquire(); update(count); semaphore.release(); } catch (InterruptedException e) { log.error("出现错误:【{}】", e.getMessage()); } countDownLatch.countDown(); }); } countDownLatch.await(); executorService.shutdown(); log.info("set size:{}", set.size()); } /** * 通过线程安全对象dateTimeFormatter处理 */ private static void update(int count){ set.add(count); } }输出结果：每次输出5000，线程安全Collections.synchronizedMap线程安全同步容器，代码示例：package com.yanxizhu.demo.concurrency.synContainer; import com.yanxizhu.demo.concurrency.annotation.UnThreadSafety; import lombok.extern.slf4j.Slf4j; import java.util.Collections; import java.util.HashMap; import java.util.Map; import java.util.concurrent.CountDownLatch; import java.util.concurrent.ExecutorService; import java.util.concurrent.Executors; import java.util.concurrent.Semaphore; /** * @description: 安全容器，Collections.synchronizedMap，线程安全 * @author: <a href="mailto:vip@foxmail.com">清风</a> * @date: 2022/4/24 9:52 * @version: 1.0 */ @Slf4j @UnThreadSafety public class DemoCollectionsSynHashMap { private static Map<Integer, Integer> map = Collections.synchronizedMap(new HashMap()); //用户数量 private static final int clientsTotal = 5000; //并发数量 private static final int concurrencyTotal = 200; public static void main(String[] args) throws InterruptedException { ExecutorService executorService = Executors.newCachedThreadPool(); //信号量 final Semaphore semaphore = new Semaphore(concurrencyTotal); //闭锁 final CountDownLatch countDownLatch = new CountDownLatch(clientsTotal); for (int i = 0; i < clientsTotal; i++) { final int count = i; executorService.execute(()->{ try { semaphore.acquire(); update(count); semaphore.release(); } catch (InterruptedException e) { log.error("出现错误:【{}】", e.getMessage()); } countDownLatch.countDown(); }); } countDownLatch.await(); executorService.shutdown(); log.info("map size:{}", map.size()); } private static void update(int count){ map.put(count, count); } }输出结果：每次输出5000，线程安全。常用操作错误package com.yanxizhu.demo.concurrency.synContainer; import java.util.Iterator; import java.util.Vector; /** * @description: 常见容器使用错误，解决办法 * @author: <a href="mailto:vip@foxmail.com">清风</a> * @date: 2022/4/24 11:04 * @version: 1.0 */ public class DemoErrorUser { //增强for循环中修改，报错：java.util.ConcurrentModificationException public static void test1(Vector<Integer> vector) { for (Integer integer: vector) { //使用增强for循环 if (integer.equals(3)) { // vector.remove(integer); vector.add(4); } } } //迭代器中循环修改，报错：java.util.ConcurrentModificationException public static void test2(Vector<Integer> vector) { Iterator<Integer> iterator = vector.iterator(); while (iterator.hasNext()){ Integer integer = iterator.next(); if( integer.equals(3)) { vector.remove(integer); } } } //普通for循环中修改，正常。 public static void test3(Vector<Integer> vector) { for(int i=0; i< vector.size(); i++) { if(i==3) { vector.remove(i); } } } //总结：增强for、迭代器循环中修改，可以先标记，然后最后进行修改。 public static void main(String[] args) { Vector<Integer> vector = new Vector<>(); vector.add(1); vector.add(2); vector.add(3); test1(vector); // test2(vector); // test3(vector); } }输出结果：ConcurrentModificationExceptionException in thread "main" java.util.ConcurrentModificationException at java.base/java.util.Vector$Itr.checkForComodification(Vector.java:1321) at java.base/java.util.Vector$Itr.next(Vector.java:1277) at com.yanxizhu.demo.concurrency.synContainer.DemoErrorUser.test1(DemoErrorUser.java:16) at com.yanxizhu.demo.concurrency.synContainer.DemoErrorUser.main(DemoErrorUser.java:52)增强for、迭代器中循环修改线程报错，正常for循环，正常，解决办法，通过标记后，再单独进行移除操作。

线程不安全的类与写法 线程不安全的类与写法线程不安全的类：类的对象可以同时被多个线程访问，比如抛出异常，逻辑错误。下面介绍一些常见的类。StringBuilderStringBuilder线程不安全的类代码示例package com.yanxizhu.demo.concurrency.threadUnSafetyClass; import com.yanxizhu.demo.concurrency.annotation.UnThreadSafety; import lombok.extern.slf4j.Slf4j; import java.util.concurrent.CountDownLatch; import java.util.concurrent.ExecutorService; import java.util.concurrent.Executors; import java.util.concurrent.Semaphore; /** * @description: 线程不安全的类：StringBuilder * @author: <a href="mailto:batis@foxmail.com">清风</a> * @date: 2022/4/24 9:01 * @version: 1.0 */ @Slf4j @UnThreadSafety public class DemoStringBuilder { //用户数量 private static final int clientsTotal = 5000; //并发数量 private static final int concurrencyTotal = 200; //累加总和 private static StringBuilder stringBuilder = new StringBuilder(); public static void main(String[] args) throws InterruptedException { ExecutorService executorService = Executors.newCachedThreadPool(); //信号量 final Semaphore semaphore = new Semaphore(concurrencyTotal); //闭锁 final CountDownLatch countDownLatch = new CountDownLatch(clientsTotal); for (int i = 0; i < clientsTotal; i++) { executorService.execute(()->{ try { semaphore.acquire(); update(); semaphore.release(); } catch (InterruptedException e) { log.error("出现错误:【{}】", e.getMessage()); } countDownLatch.countDown(); }); } countDownLatch.await(); executorService.shutdown(); log.info("字符长度Length=【{}】",stringBuilder.length()); } /** * 累加 */ private static void update(){ stringBuilder.append(1); } }每次输出字符长度不一样，也不等于5000StringBuffer线程安全的类代码示例package com.yanxizhu.demo.concurrency.threadUnSafetyClass; import com.yanxizhu.demo.concurrency.annotation.ThreadSafety; import lombok.extern.slf4j.Slf4j; import java.util.concurrent.CountDownLatch; import java.util.concurrent.ExecutorService; import java.util.concurrent.Executors; import java.util.concurrent.Semaphore; /** * @description: 线程安全的类：StringBuffer * @author: <a href="mailto:batis@foxmail.com">清风</a> * @date: 2022/4/24 9:06 * @version: 1.0 */ @Slf4j @ThreadSafety public class DemoStringBuffer { //用户数量 private static final int clientsTotal = 5000; //并发数量 private static final int concurrencyTotal = 200; //累加总和 private static StringBuffer stringBuffer = new StringBuffer(); public static void main(String[] args) throws InterruptedException { ExecutorService executorService = Executors.newCachedThreadPool(); //信号量 final Semaphore semaphore = new Semaphore(concurrencyTotal); //闭锁 final CountDownLatch countDownLatch = new CountDownLatch(clientsTotal); for (int i = 0; i < clientsTotal; i++) { executorService.execute(()->{ try { semaphore.acquire(); update(); semaphore.release(); } catch (InterruptedException e) { log.error("出现错误:【{}】", e.getMessage()); } countDownLatch.countDown(); }); } countDownLatch.await(); executorService.shutdown(); log.info("字符长度Length=【{}】", stringBuffer.length()); } /** * 累加 */ private static void update(){ stringBuffer.append(1); } }输出结果：每次都是长度为5000.StringBuffer为什么线程安全，看源码 @Override @HotSpotIntrinsicCandidate public synchronized StringBuffer append(int i) { toStringCache = null; super.append(i); return this; }因为StringBuffer所有的方法操作都加了synchronized。区别StringBuffer由于加了synchronized，性能上有损耗，没有StringBuilder性能高，在线程封闭中，堆栈封闭时，一般处理字符串局部变量时用StringBuilfer就行了。SimpleDateFormat线程不安全的类代码示例package com.yanxizhu.demo.concurrency.threadUnSafetyClass; import com.yanxizhu.demo.concurrency.annotation.UnThreadSafety; import lombok.extern.slf4j.Slf4j; import java.text.ParseException; import java.text.SimpleDateFormat; import java.util.concurrent.CountDownLatch; import java.util.concurrent.ExecutorService; import java.util.concurrent.Executors; import java.util.concurrent.Semaphore; /** * @description: 线程不安全类： SimpleDateFormat * @author: <a href="mailto:batis@foxmail.com">清风</a> * @date: 2022/4/24 9:18 * @version: 1.0 */ @Slf4j @UnThreadSafety public class DemoSimpleDateFormat { private static SimpleDateFormat simpleDateFormat = new SimpleDateFormat("yyyyyMMMdd"); //用户数量 private static final int clientsTotal = 5000; //并发数量 private static final int concurrencyTotal = 200; public static void main(String[] args) throws InterruptedException { ExecutorService executorService = Executors.newCachedThreadPool(); //信号量 final Semaphore semaphore = new Semaphore(concurrencyTotal); //闭锁 final CountDownLatch countDownLatch = new CountDownLatch(clientsTotal); for (int i = 0; i < clientsTotal; i++) { executorService.execute(()->{ try { semaphore.acquire(); update(); semaphore.release(); } catch (InterruptedException e) { log.error("出现错误:【{}】", e.getMessage()); } countDownLatch.countDown(); }); } countDownLatch.await(); executorService.shutdown(); } /** * 累加 */ private static void update(){ try { simpleDateFormat.parse("20220424"); } catch (ParseException e) { log.error(e.getMessage()); } } }输出结果： at java.base/java.lang.Double.parseDouble(Double.java:543) at java.base/java.text.DigitList.getDouble(DigitList.java:169) at java.base/java.text.DecimalFormat.parse(DecimalFormat.java:2126) at java.base/java.text.SimpleDateFormat.subParse(SimpleDateFormat.java:1933) at java.base/java.text.SimpleDateFormat.parse(SimpleDateFormat.java:1541) at java.base/java.text.DateFormat.parse(DateFormat.java:393) at com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoSimpleDateFormat.update(DemoSimpleDateFormat.java:57) at com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoSimpleDateFormat.lambda$main$0(DemoSimpleDateFormat.java:40) at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128) at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628) at java.base/java.lang.Thread.run(Thread.java:834) Exception in thread "pool-1-thread-824" java.lang.NumberFormatException: For input string: "E.0220E0" at java.base/jdk.internal.math.FloatingDecimal.readJavaFormatString(FloatingDecimal.java:2054) at java.base/jdk.internal.math.FloatingDecimal.parseDouble(FloatingDecimal.java:110) at java.base/java.lang.Double.parseDouble(Double.java:543) at java.base/java.text.DigitList.getDouble(DigitList.java:169) at java.base/java.text.DecimalFormat.parse(DecimalFormat.java:2126) at java.base/java.text.SimpleDateFormat.subParse(SimpleDateFormat.java:1933) at java.base/java.text.SimpleDateFormat.parse(SimpleDateFormat.java:1541) at java.base/java.text.DateFormat.parse(DateFormat.java:393) at com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoSimpleDateFormat.update(DemoSimpleDateFormat.java:57) at com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoSimpleDateFormat.lambda$main$0(DemoSimpleDateFormat.java:40) at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128) at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628) at java.base/java.lang.Thread.run(Thread.java:834) Exception in thread "pool-1-thread-717" java.lang.NumberFormatException: multiple points at java.base/jdk.internal.math.FloatingDecimal.readJavaFormatString(FloatingDecimal.java:1890) at java.base/jdk.internal.math.FloatingDecimal.parseDouble(FloatingDecimal.java:110) at java.base/java.lang.Double.parseDouble(Double.java:543) at java.base/java.text.DigitList.getDouble(DigitList.java:169) at java.base/java.text.DecimalFormat.parse(DecimalFormat.java:2126) at java.base/java.text.SimpleDateFormat.subParse(SimpleDateFormat.java:1933) at java.base/java.text.SimpleDateFormat.parse(SimpleDateFormat.java:1541) at java.base/java.text.DateFormat.parse(DateFormat.java:393) at com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoSimpleDateFormat.update(DemoSimpleDateFormat.java:57) at com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoSimpleDateFormat.lambda$main$0(DemoSimpleDateFormat.java:40) at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128) at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628) at java.base/java.lang.Thread.run(Thread.java:834) Exception in thread "pool-1-thread-671" java.lang.NumberFormatException: For input string: "" at java.base/java.lang.NumberFormatException.forInputString(NumberFormatException.java:65) at java.base/java.lang.Long.parseLong(Long.java:702) at java.base/java.lang.Long.parseLong(Long.java:817) at java.base/java.text.DigitList.getLong(DigitList.java:195) at java.base/java.text.DecimalFormat.parse(DecimalFormat.java:2121) at java.base/java.text.SimpleDateFormat.subParse(SimpleDateFormat.java:1933) at java.base/java.text.SimpleDateFormat.parse(SimpleDateFormat.java:1541) at java.base/java.text.DateFormat.parse(DateFormat.java:393) at com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoSimpleDateFormat.update(DemoSimpleDateFormat.java:57) at com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoSimpleDateFormat.lambda$main$0(DemoSimpleDateFormat.java:40) at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128) at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628) at java.base/java.lang.Thread.run(Thread.java:834) Exception in thread "pool-1-thread-805" java.lang.NumberFormatException: multiple points at java.base/jdk.internal.math.FloatingDecimal.readJavaFormatString(FloatingDecimal.java:1914) at java.base/jdk.internal.math.FloatingDecimal.parseDouble(FloatingDecimal.java:110) at java.base/java.lang.Double.parseDouble(Double.java:543) at java.base/java.text.DigitList.getDouble(DigitList.java:169) at java.base/java.text.DecimalFormat.parse(DecimalFormat.java:2126) at java.base/java.text.SimpleDateFormat.subParse(SimpleDateFormat.java:1933) at java.base/java.text.SimpleDateFormat.parse(SimpleDateFormat.java:1541) at java.base/java.text.DateFormat.parse(DateFormat.java:393) at com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoSimpleDateFormat.update(DemoSimpleDateFormat.java:57) at com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoSimpleDateFormat.lambda$main$0(DemoSimpleDateFormat.java:40) at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128) at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628) at java.base/java.lang.Thread.run(Thread.java:834) Exception in thread "pool-1-thread-736" java.lang.NumberFormatException: For input string: ".20220424" at java.base/java.lang.NumberFormatException.forInputString(NumberFormatException.java:65) at java.base/java.lang.Long.parseLong(Long.java:678) at java.base/java.lang.Long.parseLong(Long.java:817) at java.base/java.text.DigitList.getLong(DigitList.java:195) at java.base/java.text.DecimalFormat.parse(DecimalFormat.java:2121) at java.base/java.text.SimpleDateFormat.subParse(SimpleDateFormat.java:1933) at java.base/java.text.SimpleDateFormat.parse(SimpleDateFormat.java:1541) at java.base/java.text.DateFormat.parse(DateFormat.java:393) at com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoSimpleDateFormat.update(DemoSimpleDateFormat.java:57) at com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoSimpleDateFormat.lambda$main$0(DemoSimpleDateFormat.java:40) at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128) at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628) at java.base/java.lang.Thread.run(Thread.java:834) Exception in thread "pool-1-thread-740" java.lang.NumberFormatException: multiple points at java.base/jdk.internal.math.FloatingDecimal.readJavaFormatString(FloatingDecimal.java:1914) at java.base/jdk.internal.math.FloatingDecimal.parseDouble(FloatingDecimal.java:110) at java.base/java.lang.Double.parseDouble(Double.java:543) at java.base/java.text.DigitList.getDouble(DigitList.java:169) at java.base/java.text.DecimalFormat.parse(DecimalFormat.java:2126) at java.base/java.text.SimpleDateFormat.subParse(SimpleDateFormat.java:1933) at java.base/java.text.SimpleDateFormat.parse(SimpleDateFormat.java:1541) at java.base/java.text.DateFormat.parse(DateFormat.java:393) at com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoSimpleDateFormat.update(DemoSimpleDateFormat.java:57) at com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoSimpleDateFormat.lambda$main$0(DemoSimpleDateFormat.java:40) at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128) at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628) at java.base/java.lang.Thread.run(Thread.java:834) Exception in thread "pool-1-thread-695" java.lang.NumberFormatException: For input string: "E.0220" at java.base/jdk.internal.math.FloatingDecimal.readJavaFormatString(FloatingDecimal.java:2054) at java.base/jdk.internal.math.FloatingDecimal.parseDouble(FloatingDecimal.java:110) at java.base/java.lang.Double.parseDouble(Double.java:543) at java.base/java.text.DigitList.getDouble(DigitList.java:169) at java.base/java.text.DecimalFormat.parse(DecimalFormat.java:2126) at java.base/java.text.SimpleDateFormat.subParse(SimpleDateFormat.java:1933) at java.base/java.text.SimpleDateFormat.parse(SimpleDateFormat.java:1541) at java.base/java.text.DateFormat.parse(DateFormat.java:393) at com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoSimpleDateFormat.update(DemoSimpleDateFormat.java:57) at com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoSimpleDateFormat.lambda$main$0(DemoSimpleDateFormat.java:40) at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128) at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628) at java.base/java.lang.Thread.run(Thread.java:834) Exception in thread "pool-1-thread-888" java.lang.NumberFormatException: For input string: "202204242022042420220424" at java.base/java.lang.NumberFormatException.forInputString(NumberFormatException.java:65) at java.base/java.lang.Long.parseLong(Long.java:692) at java.base/java.lang.Long.parseLong(Long.java:817) at java.base/java.text.DigitList.getLong(DigitList.java:195) at java.base/java.text.DecimalFormat.parse(DecimalFormat.java:2121) at java.base/java.text.SimpleDateFormat.subParse(SimpleDateFormat.java:1933) at java.base/java.text.SimpleDateFormat.parse(SimpleDateFormat.java:1541) at java.base/java.text.DateFormat.parse(DateFormat.java:393) at com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoSimpleDateFormat.update(DemoSimpleDateFormat.java:57) at com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoSimpleDateFormat.lambda$main$0(DemoSimpleDateFormat.java:40) at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128) at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628) at java.base/java.lang.Thread.run(Thread.java:834)虽然SimpleDateFormat是线程不安全的类，但是可以通过线程封闭中，堆栈封闭(栈封闭)方法，将SimpleDateFormat定义到局部变量中，每次是一个新对象，这样来保证线程安全。package com.yanxizhu.demo.concurrency.threadUnSafetyClass; import com.yanxizhu.demo.concurrency.annotation.ThreadSafety; import com.yanxizhu.demo.concurrency.annotation.UnThreadSafety; import lombok.extern.slf4j.Slf4j; import java.text.ParseException; import java.text.SimpleDateFormat; import java.util.Date; import java.util.concurrent.CountDownLatch; import java.util.concurrent.ExecutorService; import java.util.concurrent.Executors; import java.util.concurrent.Semaphore; /** * @description: 线程不安全类： SimpleDateFormat,通过堆栈封闭，实现线程安全。 * @author: <a href="mailto:batis@foxmail.com">清风</a> * @date: 2022/4/24 9:18 * @version: 1.0 */ @Slf4j @ThreadSafety public class DemoSimpleDateFormatOk { //用户数量 private static final int clientsTotal = 5000; //并发数量 private static final int concurrencyTotal = 200; public static void main(String[] args) throws InterruptedException { ExecutorService executorService = Executors.newCachedThreadPool(); //信号量 final Semaphore semaphore = new Semaphore(concurrencyTotal); //闭锁 final CountDownLatch countDownLatch = new CountDownLatch(clientsTotal); for (int i = 0; i < clientsTotal; i++) { executorService.execute(()->{ try { semaphore.acquire(); update(); semaphore.release(); } catch (InterruptedException e) { log.error("出现错误:【{}】", e.getMessage()); } countDownLatch.countDown(); }); } countDownLatch.await(); executorService.shutdown(); } /** * 通过线程封闭，将SimpleDateFormat定义在方法内，每次都是一个新的对象，来保证线程安全 */ private static void update(){ try { SimpleDateFormat simpleDateFormat = new SimpleDateFormat("yyyyyMMMdd"); simpleDateFormat.parse("20220424"); } catch (ParseException e) { log.error(e.getMessage()); } } }输出结果：输出5000条，一样的。09:27:17.633 [pool-1-thread-461] ERROR com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoSimpleDateFormatOk - Unparseable date: "20220424" 09:27:17.633 [pool-1-thread-460] ERROR com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoSimpleDateFormatOk - Unparseable date: "20220424" 09:27:17.634 [pool-1-thread-452] ERROR com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoSimpleDateFormatOk - Unparseable date: "20220424" 09:27:17.634 [pool-1-thread-445] ERROR com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoSimpleDateFormatOk - Unparseable date: "20220424" 09:27:17.634 [pool-1-thread-453] ERROR com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoSimpleDateFormatOk - Unparseable date: "20220424" 。。。。。joda.time第三方包中DateTimeFormatter代码示例package com.yanxizhu.demo.concurrency.threadUnSafetyClass; import com.yanxizhu.demo.concurrency.annotation.ThreadSafety; import lombok.extern.slf4j.Slf4j; import org.joda.time.DateTime; import org.joda.time.format.DateTimeFormat; import org.joda.time.format.DateTimeFormatter; import java.util.Date; import java.util.concurrent.CountDownLatch; import java.util.concurrent.ExecutorService; import java.util.concurrent.Executors; import java.util.concurrent.Semaphore; /** * @description: 线程安全的类：Joda-time包里面的DateTimeFormatter需要单独导入 * @author: <a href="mailto:batis@foxmail.com">清风</a> * @date: 2022/4/24 9:31 * @version: 1.0 */ @Slf4j @ThreadSafety public class DemoJodaTime { private static DateTimeFormatter dateTimeFormatter = DateTimeFormat.forPattern("yyyyMMdd"); //用户数量 private static final int clientsTotal = 5000; //并发数量 private static final int concurrencyTotal = 200; public static void main(String[] args) throws InterruptedException { ExecutorService executorService = Executors.newCachedThreadPool(); //信号量 final Semaphore semaphore = new Semaphore(clientsTotal); //闭锁 final CountDownLatch countDownLatch = new CountDownLatch(concurrencyTotal); for (int i = 0; i < clientsTotal; i++) { final int count = i; executorService.execute(()->{ try { semaphore.acquire(); update(count); semaphore.release(); } catch (InterruptedException e) { log.error("出现错误:【{}】", e.getMessage()); } countDownLatch.countDown(); }); } countDownLatch.await(); executorService.shutdown(); } /** * 通过线程安全对象dateTimeFormatter处理 */ private static void update(int count){ Date date = DateTime.parse("20220424", dateTimeFormatter).toDate(); log.info("{},{}", count, date); } }输出结果：有5000条，线程安全的09:45:06.164 [pool-1-thread-1947] INFO com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoJodaTime - 1946,Sun Apr 24 00:00:00 CST 2022 09:45:06.154 [pool-1-thread-491] INFO com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoJodaTime - 490,Sun Apr 24 00:00:00 CST 2022 09:45:06.184 [pool-1-thread-2943] INFO com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoJodaTime - 2942,Sun Apr 24 00:00:00 CST 2022 09:45:06.169 [pool-1-thread-1911] INFO com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoJodaTime - 1910,Sun Apr 24 00:00:00 CST 2022 09:45:06.169 [pool-1-thread-616] INFO com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoJodaTime - 615,Sun Apr 24 00:00:00 CST 2022 09:45:06.168 [pool-1-thread-2551] INFO com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoJodaTime - 2550,Sun Apr 24 00:00:00 CST 2022 09:45:06.144 [pool-1-thread-1415] INFO com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoJodaTime - 1414,Sun Apr 24 00:00:00 CST 2022 09:45:06.154 [pool-1-thread-4611] INFO com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoJodaTime - 4610,Sun Apr 24 00:00:00 CST 2022 09:45:06.169 [pool-1-thread-4760] INFO com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoJodaTime - 4759,Sun Apr 24 00:00:00 CST 2022 09:45:06.167 [pool-1-thread-2388] INFO com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoJodaTime - 2387,Sun Apr 24 00:00:00 CST 2022 09:45:06.184 [pool-1-thread-3990] INFO com.yanxizhu.demo.concurrency.threadUnSafetyClass.DemoJodaTime - 3989,Sun Apr 24 00:00:00 CST 2022 。。。。。ArrayList线程不安全的代码示例package com.yanxizhu.demo.concurrency.threadUnSafetyClass; import com.yanxizhu.demo.concurrency.annotation.UnThreadSafety; import lombok.extern.slf4j.Slf4j; import org.checkerframework.checker.units.qual.A; import org.joda.time.DateTime; import java.util.ArrayList; import java.util.Date; import java.util.List; import java.util.concurrent.CountDownLatch; import java.util.concurrent.ExecutorService; import java.util.concurrent.Executors; import java.util.concurrent.Semaphore; /** * @description: 线程不安全类：ArrayList * @author: <a href="mailto:batis@foxmail.com">清风</a> * @date: 2022/4/24 9:52 * @version: 1.0 */ @Slf4j @UnThreadSafety public class DemoArrayList { private static List<Integer> list = new ArrayList<>(); //用户数量 private static final int clientsTotal = 5000; //并发数量 private static final int concurrencyTotal = 200; public static void main(String[] args) throws InterruptedException { ExecutorService executorService = Executors.newCachedThreadPool(); //信号量 final Semaphore semaphore = new Semaphore(concurrencyTotal); //闭锁 final CountDownLatch countDownLatch = new CountDownLatch(clientsTotal); for (int i = 0; i < clientsTotal; i++) { final int count = i; executorService.execute(()->{ try { semaphore.acquire(); update(count); semaphore.release(); } catch (InterruptedException e) { log.error("出现错误:【{}】", e.getMessage()); } countDownLatch.countDown(); }); } countDownLatch.await(); executorService.shutdown(); log.info("list size:{}", list.size()); } /** * 通过线程安全对象dateTimeFormatter处理 */ private static void update(int count){ list.add(count); } }输出结果：每次输出ist长度不同，线程不安全的。HashSet线程不安全类代码示例package com.yanxizhu.demo.concurrency.threadUnSafetyClass; import com.yanxizhu.demo.concurrency.annotation.UnThreadSafety; import lombok.extern.slf4j.Slf4j; import java.util.HashSet; import java.util.Set; import java.util.concurrent.CountDownLatch; import java.util.concurrent.ExecutorService; import java.util.concurrent.Executors; import java.util.concurrent.Semaphore; /** * @description: 线程不安全类：HashSet * @author: <a href="mailto:batis@foxmail.com">清风</a> * @date: 2022/4/24 9:52 * @version: 1.0 */ @Slf4j @UnThreadSafety public class DemoHashSet { private static Set<Integer> set = new HashSet<>(); //用户数量 private static final int clientsTotal = 5000; //并发数量 private static final int concurrencyTotal = 200; public static void main(String[] args) throws InterruptedException { ExecutorService executorService = Executors.newCachedThreadPool(); //信号量 final Semaphore semaphore = new Semaphore(concurrencyTotal); //闭锁 final CountDownLatch countDownLatch = new CountDownLatch(clientsTotal); for (int i = 0; i < clientsTotal; i++) { final int count = i; executorService.execute(()->{ try { semaphore.acquire(); update(count); semaphore.release(); } catch (InterruptedException e) { log.error("出现错误:【{}】", e.getMessage()); } countDownLatch.countDown(); }); } countDownLatch.await(); executorService.shutdown(); log.info("set size:{}", set.size()); } /** * 通过线程安全对象dateTimeFormatter处理 */ private static void update(int count){ set.add(count); } }输出结果：每次长度不同，不是5000，线程不安全。HashMap线程不安全的代码示例package com.yanxizhu.demo.concurrency.threadUnSafetyClass; import com.yanxizhu.demo.concurrency.annotation.UnThreadSafety; import lombok.extern.slf4j.Slf4j; import java.util.*; import java.util.concurrent.CountDownLatch; import java.util.concurrent.ExecutorService; import java.util.concurrent.Executors; import java.util.concurrent.Semaphore; /** * @description: 线程不安全类：HashMap * @author: <a href="mailto:batis@foxmail.com">清风</a> * @date: 2022/4/24 9:52 * @version: 1.0 */ @Slf4j @UnThreadSafety public class DemoHashMap { private static Map<Integer, Integer> map = new HashMap(); //用户数量 private static final int clientsTotal = 5000; //并发数量 private static final int concurrencyTotal = 200; public static void main(String[] args) throws InterruptedException { ExecutorService executorService = Executors.newCachedThreadPool(); //信号量 final Semaphore semaphore = new Semaphore(concurrencyTotal); //闭锁 final CountDownLatch countDownLatch = new CountDownLatch(clientsTotal); for (int i = 0; i < clientsTotal; i++) { final int count = i; executorService.execute(()->{ try { semaphore.acquire(); update(count); semaphore.release(); } catch (InterruptedException e) { log.error("出现错误:【{}】", e.getMessage()); } countDownLatch.countDown(); }); } countDownLatch.await(); executorService.shutdown(); log.info("map size:{}", map.size()); } /** * 通过线程安全对象dateTimeFormatter处理 */ private static void update(int count){ map.put(count, count); } }输出结果： 长度不是5000，每次长度不一样，线程不安全。总结：线程安全类对应的不只一个。后面专门说明。注意：先检查再执行这种类是线程不安全的，因为被分成2步了，if (condition(a)) { handle(1);}

高并发中集合问题 第一代线程安全集合类Vector、Hashtable是怎么保证线程安排的：使用synchronized修饰方法缺点：效率低下第二代线程非安全集合类ArrayList、HashMap线程不安全，但是性能好，用来替代Vector、Hashtable使用ArrayList、HashMap，需要线程安全怎么办呢？使用Collections.synchronizedList（list）；Collections.synchronizedMap（m）；底层使用synchronized代码块锁虽然也是锁住了所有的代码，但是锁在方法里边，并所在方法外边性能可以理解为稍有提高吧。毕竟进方法本身就要分配资源的第三代线程安全集合类在大量并发情况下如何提高集合的效率和安全呢？java.util.concurrent.*ConcurrentHashMap：CopyOnWriteArrayList：CopyOnWriteArraySet：注意不是CopyOnWriteHashSet*底层大都采用Lock锁（1.8的ConcurrentHashMap不使用Lock锁），保证安全的同时，性能也很高。