spring - Spring Task Executor 线程数不断增加

Question

以下是我设置的属性 -

spring.task.execution.pool.core-size=50
spring.task.execution.pool.max-size=200
spring.task.execution.pool.queue-capacity=100
spring.task.execution.shutdown.await-termination=true
spring.task.execution.shutdown.await-termination-period=10s
spring.task.execution.thread-name-prefix=async-task-exec-

我仍然看到线程名称为 - “async-task-exec-7200”

这是否意味着它正在创建 7200 个线程？

另外，我观察到的另一个问题是@Async 会等待超过 10 分钟才能获取线程并释放父线程。

标签： springspring-boot

Answer 1

You specified core size of 50 and max size of 200. So your pool will normally run with 50 threads, and when there is extra work, it will spawn additional threads, you'll see "async-task-exec-51", "async-task-exec-52" created and so on. Later, if there is not enough work for all the threads, the pool will kill some threads to get back to just 50. So it may kill thread "async-task-exec-52". The next time it has too much work for 50 threads, it will create a new thread "async-task-exec-53".

So the fact that you see "async-task-exec-7200" means that over the life time of the thread pool it has created 7200 threads, but it will still never have more than the max of 200 running at the same time.

If @Async method is waiting 10 minutes for a thread it means that you have put so much work into the pool that it has already spawned all 200 threads and they are processing, and you have filled up the queue capacity of 100, so now the parent thread has to block(wait) until there is at least a spot in the queue to put the task.

If you need to consistently handle more tasks, you will need a powerful enough machine and enough max threads in the pool. But if your work load is just very spiky, and you don't want to spend on a bigger machine and you are ok with tasks waiting longer sometimes, you might be able to get away with just raising your queue-capacity, so the work will queue up and eventually your threads might catch up (if the task creation gets slower).

Keep trying combinations of these settings to see what will be right for your workload.