下面CompletableFuture例子中join的调用是否阻塞进程

Question

我试图理解 CompletableFutures 和 return 完成期货的调用链，我创建了下面的示例，它模拟了对数据库的两次调用。

第一个方法应该是用 userId 列表给出一个可完成的未来，然后我需要调用另一个提供 userId 的方法来获取用户（在本例中是一个字符串）。

总结一下：
1. 获取 ids
2. 获取与这些 ID 对应的用户列表。

我创建了简单的方法来模拟休眠线程的响应。请检查下面的代码

public class PipelineOfTasksExample {

    private Map<Long, String> db = new HashMap<>();

    PipelineOfTasksExample() {
        db.put(1L, "user1");
        db.put(2L, "user2");
        db.put(3L, "user3");
        db.put(4L, "user4");
    }


    private CompletableFuture<List<Long>> returnUserIdsFromDb() {
        try {
            Thread.sleep(500);
        } catch (InterruptedException e) {
            e.printStackTrace();
        }
        System.out.println("building the list of Ids" + " - thread: " + Thread.currentThread().getName());
        return CompletableFuture.supplyAsync(() -> Arrays.asList(1L, 2L, 3L, 4L));
    }

    private CompletableFuture<String> fetchById(Long id) {
        CompletableFuture<String> cfId = CompletableFuture.supplyAsync(() -> db.get(id));
        try {
            Thread.sleep(500);
        } catch (InterruptedException e) {
            e.printStackTrace();
        }
        System.out.println("fetching id: " + id + " -> " + db.get(id) + " thread: " + Thread.currentThread().getName());
        return cfId;
    }

    public static void main(String[] args) {

        PipelineOfTasksExample example = new PipelineOfTasksExample();

        CompletableFuture<List<String>> result = example.returnUserIdsFromDb()
                .thenCompose(listOfIds ->
                        CompletableFuture.supplyAsync(
                                () -> listOfIds.parallelStream()
                                        .map(id -> example.fetchById(id).join())
                                        .collect(Collectors.toList()
                                        )
                        )
                );

        System.out.println(result.join());
    }

}

我的问题是，连接调用 (example.fetchById(id).join()) 是否破坏了进程的非阻塞性质。如果答案是肯定的，我该如何解决这个问题？

提前致谢

Answer 1

你的例子有点奇怪，因为你在 returnUserIdsFromDb() 中减慢了主线程，甚至在任何操作开始之前，同样地，fetchById 减慢了调用者而不是异步操作，这违背了异步操作的全部目的。

此外，您可以简单地使用 .thenApplyAsync(listOfIds -> …).

而不是 .thenCompose(listOfIds -> CompletableFuture.supplyAsync(() -> …))

所以一个更好的例子可能是

public class PipelineOfTasksExample {
    private final Map<Long, String> db = LongStream.rangeClosed(1, 4).boxed()
        .collect(Collectors.toMap(id -> id, id -> "user"+id));

    PipelineOfTasksExample() {}

    private static <T> T slowDown(String op, T result) {
        LockSupport.parkNanos(TimeUnit.MILLISECONDS.toNanos(500));
        System.out.println(op + " -> " + result + " thread: "
            + Thread.currentThread().getName()+ ", "
            + POOL.getPoolSize() + " threads");
        return result;
    }
    private CompletableFuture<List<Long>> returnUserIdsFromDb() {
        System.out.println("trigger building the list of Ids - thread: "
            + Thread.currentThread().getName());
        return CompletableFuture.supplyAsync(
            () -> slowDown("building the list of Ids", Arrays.asList(1L, 2L, 3L, 4L)),
            POOL);
    }
    private CompletableFuture<String> fetchById(Long id) {
        System.out.println("trigger fetching id: " + id + " thread: "
            + Thread.currentThread().getName());
        return CompletableFuture.supplyAsync(
            () -> slowDown("fetching id: " + id , db.get(id)), POOL);
    }

    static ForkJoinPool POOL = new ForkJoinPool(2);

    public static void main(String[] args) {
        PipelineOfTasksExample example = new PipelineOfTasksExample();
        CompletableFuture<List<String>> result = example.returnUserIdsFromDb()
            .thenApplyAsync(listOfIds ->
                listOfIds.parallelStream()
                    .map(id -> example.fetchById(id).join())
                    .collect(Collectors.toList()
                ),
                POOL
            );
        System.out.println(result.join());
    }
}

打印类似

的内容

trigger building the list of Ids - thread: main
building the list of Ids -> [1, 2, 3, 4] thread: ForkJoinPool-1-worker-1, 1 threads
trigger fetching id: 2 thread: ForkJoinPool-1-worker-0
trigger fetching id: 3 thread: ForkJoinPool-1-worker-1
trigger fetching id: 4 thread: ForkJoinPool-1-worker-2
fetching id: 4 -> user4 thread: ForkJoinPool-1-worker-3, 4 threads
fetching id: 2 -> user2 thread: ForkJoinPool-1-worker-3, 4 threads
fetching id: 3 -> user3 thread: ForkJoinPool-1-worker-2, 4 threads
trigger fetching id: 1 thread: ForkJoinPool-1-worker-3
fetching id: 1 -> user1 thread: ForkJoinPool-1-worker-2, 4 threads
[user1, user2, user3, user4]

乍一看，线程数可能令人惊讶。

答案是join()可能会阻塞线程，但是如果这种情况发生在Fork/Join池的工作线程内部，这种情况会被检测到，并启动一个新的补偿线程，以确保配置的目标并行度。

作为一种特殊情况，当使用默认的 Fork/Join 池时，实现可能会在 join() 方法中选取新的待处理任务，以确保同一线程内的进度。

所以代码总是会取得进展，偶尔调用 join() 没有错，如果备选方案要复杂得多，但如果过度使用，就会有资源消耗过多的危险。毕竟之所以要用线程池，就是为了限制线程数。

替代方法是尽可能使用链式依赖操作。

public class PipelineOfTasksExample {
    private final Map<Long, String> db = LongStream.rangeClosed(1, 4).boxed()
        .collect(Collectors.toMap(id -> id, id -> "user"+id));

    PipelineOfTasksExample() {}

    private static <T> T slowDown(String op, T result) {
        LockSupport.parkNanos(TimeUnit.MILLISECONDS.toNanos(500));
        System.out.println(op + " -> " + result + " thread: "
            + Thread.currentThread().getName()+ ", "
            + POOL.getPoolSize() + " threads");
        return result;
    }
    private CompletableFuture<List<Long>> returnUserIdsFromDb() {
        System.out.println("trigger building the list of Ids - thread: "
            + Thread.currentThread().getName());
        return CompletableFuture.supplyAsync(
            () -> slowDown("building the list of Ids", Arrays.asList(1L, 2L, 3L, 4L)),
            POOL);
    }
    private CompletableFuture<String> fetchById(Long id) {
        System.out.println("trigger fetching id: " + id + " thread: "
            + Thread.currentThread().getName());
        return CompletableFuture.supplyAsync(
            () -> slowDown("fetching id: " + id , db.get(id)), POOL);
    }

    static ForkJoinPool POOL = new ForkJoinPool(2);

    public static void main(String[] args) {
        PipelineOfTasksExample example = new PipelineOfTasksExample();

        CompletableFuture<List<String>> result = example.returnUserIdsFromDb()
            .thenComposeAsync(listOfIds -> {
                List<CompletableFuture<String>> jobs = listOfIds.parallelStream()
                    .map(id -> example.fetchById(id))
                    .collect(Collectors.toList());
                return CompletableFuture.allOf(jobs.toArray(new CompletableFuture<?>[0]))
                    .thenApply(_void -> jobs.stream()
                        .map(CompletableFuture::join).collect(Collectors.toList()));
                },
                POOL
            );

        System.out.println(result.join());
        System.out.println(ForkJoinPool.commonPool().getPoolSize());
    }
}

不同之处在于，首先提交所有异步作业，然后调度调用它们的依赖操作 join，仅在所有作业完成后执行，因此这些 join调用永远不会阻塞。只有在 main 方法末尾的最终 join 调用可能会阻塞主线程。

所以这会打印类似

的内容

trigger building the list of Ids - thread: main
building the list of Ids -> [1, 2, 3, 4] thread: ForkJoinPool-1-worker-1, 1 threads
trigger fetching id: 3 thread: ForkJoinPool-1-worker-1
trigger fetching id: 2 thread: ForkJoinPool-1-worker-0
trigger fetching id: 4 thread: ForkJoinPool-1-worker-1
trigger fetching id: 1 thread: ForkJoinPool-1-worker-0
fetching id: 4 -> user4 thread: ForkJoinPool-1-worker-1, 2 threads
fetching id: 3 -> user3 thread: ForkJoinPool-1-worker-0, 2 threads
fetching id: 2 -> user2 thread: ForkJoinPool-1-worker-1, 2 threads
fetching id: 1 -> user1 thread: ForkJoinPool-1-worker-0, 2 threads
[user1, user2, user3, user4]

显示无需创建补偿线程，因此线程数与配置的目标并行度匹配。

请注意，如果实际工作是在后台线程中完成的，而不是在 fetchById 方法本身中完成的，那么您现在不再需要并行流，因为没有阻塞 join()称呼。对于这种情况，仅使用 stream() 通常会获得更高的性能。

下面CompletableFuture例子中join的调用是否阻塞进程

Does the call of join in the following CompletableFuture example block the process

java

nonblocking

completable-future