Sometimes it is necessary to impose certain order on the tasks in a threadpool. Issue 206 of the JavaSpecialists newsletter presents one such case: we have multiple connections from which we read using NIO. We need to ensure that events from a given connection are executed in-order but events between different connections can be freely mixed.
I would like to present a similar but slightly different situation: we have N clients. We would like to execute events from a given client in the order they were submitted, but events from different clients can be mixed freely. Also, from time to time, there are “rollup” tasks which involve more than one client. Such tasks should block the tasks for all involved clients (but not more!). Let’s see a diagram of the situation:
As you can see tasks from client A and client B are happily processed in parallel until a “rollup” task comes along. At that point no more tasks of type A or B can be processed but an unrelated task C can be executed (provided that there are enough threads). The skeleton of such an executor is available in my repository. The centerpiece is the following interface:
public interface OrderedTask extends Runnable {
boolean isCompatible(OrderedTask that);
}Using this interface the threadpool decides if two tasks may be run in parallel or not (A and B can be run in parallel if A.isCompatible(B) && B.isComaptible(A)). These methods should be implemented in a fast, non locking and time-invariant manner.
The algorithm behind this threadpool is as follows:
- If the task to be added doesn’t conflict with any existing tasks, add it to the thread with the fewest elements.
- If it conflicts with elements from exactly one thread, schedule it to be executed on that thread (and implicitly after the conflicting elements which ensures that the order of submission is maintained)
- If it conflicts with multiple threads, add tasks (shown with red below) on all but the first one of them on which a task on the first thread will wait, after which it will execute the original task.
More information about the implementation:
- The code is only a proof-of-concept, some more would would be needed to make it production quality (it needs code for exception handling in tasks, proper shutdown, etc)
- For maximum performance it uses lock-free* structures where available: each worker thread has an associated ConcurrentLinkedQueue. To achieve the sleep-until-work-is-available semantics, an additional Semaphore is used**
- To be able to compare a new OrderedTask with currently executing ones, a copy of their reference is kept. This list of copies is updated whenever new elements are enqueued (this is has the potential of memory leaks and if tasks are infrequent enough alternatives – like an additional timer for weak references – should be investigated)
- Compared to the solution in the JavaSpecialists newsletter, this is more similar to a fixed thread pool executor, while the solution from the newsletter is similar to a cached thread pool executor.
- This implementation is ideal if (a) the tasks are (mostly) short and (mostly) uniform and (b) there are few (one or two) threads submitting new tasks, since multiple submissions are mutually exclusive (but submission and execution isn’t)
- If immediately after a “rollup” is submitted (and before it can be executed) tasks of the same kind are submitted, they will unnecessarily be forced on one thread. We could add code rearrange tasks after the rollup task finished if this becomes an issue.
Have fun with the source code! (maybe some day I’ll find the time to remove all the rough edges).
* somewhat of a misnomer, since there are still locks, only at a lower – CPU not OS – level, but this is the accepted terminology
** – benchmarking indicated this to be the most performant solution. This was inspired from the implementation of the ThreadPoolExecutor.
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