什么是并行流队列行为？

Question

我正在使用parallelStream并行上传一些文件，有些是大文件，有些是小文件。我注意到并不是所有的工人都被使用。

一开始一切都运行良好，所有线程都被使用（我将并行选项设置为16）。然后在某一点上（一旦它到达更大的文件），它只使用一个线程

简化代码：

files.parallelStream().forEach((file) -> {
    try (FileInputStream fileInputStream = new FileInputStream(file)) {
                IDocumentStorageAdaptor uploader = null;

                try {
                    logger.debug("Adaptors before taking: " + uploaderPool.size());
                    uploader = uploaderPool.take();
                    logger.debug("Took an adaptor!");
                    logger.debug("Adaptors after taking: " + uploaderPool.size());
                    uploader.addNewFile(file);
                } finally {
                    if (uploader != null) {
                        logger.debug("Adding one back!");
                        uploaderPool.put(uploader);
                        logger.debug("Adaptors after putting: " + uploaderPool.size());
                    }
                }
            } catch (InterruptedException | IOException e) {
                throw new UploadException(e);
            }
});

uploaderPool是一个ArrayBlockingQueue。日志：

[ForkJoinPool.commonPool-worker-8] - Adaptors before taking: 0
[ForkJoinPool.commonPool-worker-15] - Adding one back!
[ForkJoinPool.commonPool-worker-8] - Took an adaptor!
[ForkJoinPool.commonPool-worker-15] - Adaptors after putting: 0
...
...
...
[ForkJoinPool.commonPool-worker-10] - Adding one back!
[ForkJoinPool.commonPool-worker-10] - Adaptors after putting: 16
[ForkJoinPool.commonPool-worker-10] - Adaptors before taking: 16
[ForkJoinPool.commonPool-worker-10] - Took an adaptor!
[ForkJoinPool.commonPool-worker-10] - Adaptors after taking: 15
[ForkJoinPool.commonPool-worker-10] - Adding one back!
[ForkJoinPool.commonPool-worker-10] - Adaptors after putting: 16
[ForkJoinPool.commonPool-worker-10] - Adaptors before taking: 16
[ForkJoinPool.commonPool-worker-10] - Took an adaptor!
[ForkJoinPool.commonPool-worker-10] - Adaptors after taking: 15

似乎所有的工作（列表中的项目）都分布在16个线程中，委托给一个线程的事情只会等待线程自由工作，而不是使用可用的线程。有没有办法改变parallelStream在排队时的工作方式？我读过forkjoinpool文档，其中提到了工作窃取，但只针对衍生的子任务。

我的另一个计划可能是随机排列我正在使用parallelStream的列表，这可能会平衡一些事情。

谢谢！

Answer 1

并行流的分割与计算启发式是针对数据并行操作进行调整的，而不是针对IO并行操作。（换句话说，它们是为了让CPU保持忙碌而调整的，但不会生成比你拥有的CPU多得多的任务。）因此，它们偏向于计算而不是分叉。目前没有覆盖这些选择的选项。