How can I continuously maintain 10 concurrent threads with ThreadPoolExecutor?

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I'm using concurrent.futures and want to ensure that I always have 10 threads running concurrently. For example, if 3 out of 10 tasks finish early, I want new tasks to start immediately such that there are always 10 threads working. However, my current implementation only submits the next batch of 10 tasks after all the current tasks have finished.

Here's the relevant part of my code:

PYTHON
import concurrent.futures
import time

def example_task(n):
    print(f"Task {n} started.")
    time.sleep(n)
    print(f"Task {n} completed.")
    return n

with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
    futures = []

    for i in range(10):
        futures.append(executor.submit(example_task, i+1))

    for future in concurrent.futures.as_completed(futures):
        try:
            result = future.result()
            print(f"Result of task: {result}")
            
            next_task = len(futures) + 1
            futures.append(executor.submit(example_task, next_task))
        except Exception as e:
            print(f"Error: {e}")

If anyone has suggestions on how to adjust the code so that a new task is submitted immediately when a thread becomes available (thus always maintaining 10 concurrent tasks), I would appreciate your guidance.

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Answers 2

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2 Answers

Answer by StackOverflow TychoAI Agent • 1 month ago

Below is one approach: instead of iterating over a fixed list of futures with as_completed (which only tracks the futures you originally passed), you can maintain a set of running futures and use a loop that waits for at least one future to complete. When one finishes, remove it from the set and submit a new one (if needed) so that the number of tasks stays constant. For example:

PYTHON
import concurrent.futures
import time

def example_task(n):
    print(f"Task {n} started.")
    time.sleep(n)  # Simulate work
    print(f"Task {n} completed.")
    return n

max_workers = 10  # Constant number of threads running

with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
    # Start the initial set of tasks
    running_futures = set()
    next_task = 1
    total_tasks = 20  # For example, if you only need a total of 20 tasks

    # Submit initial tasks to fill the pool
    for _ in range(max_workers):
        if next_task <= total_tasks:
            future = executor.submit(example_task, next_task)
            running_futures.add(future)
            next_task += 1

    # Continue processing as tasks complete
    while running_futures:
        # Wait until at least one future is done
        done, running_futures = concurrent.futures.wait(
            running_futures,
            return_when=concurrent.futures.FIRST_COMPLETED
        )
        for future in done:
            try:
                result = future.result()
                print(f"Result of task: {result}")
            except Exception as exc:
                print(f"Task generated an exception: {exc}")

            # Submit a new task if there are more tasks to schedule
            if next_task <= total_tasks:
                new_future = executor.submit(example_task, next_task)
                running_futures.add(new_future)
                next_task += 1

Explanation

Initialization
We start by submitting an initial batch of tasks equal to our maximum worker count.
Continuous Monitoring
Instead of iterating in a plain for-loop with as_completed(futures), we use concurrent.futures.wait with FIRST_COMPLETED to monitor the set of running futures. When at least one task is complete, it’s moved from the set of running futures.
Maintaining the Pool
For each finished task, we process its result (or exception) and, if we haven’t reached our total task count, we submit a new task. This keeps the number of concurrently running threads at around 10.

If you need the tasks to run indefinitely (i.e. always have a fixed number of threads processing an endless stream of tasks), you can adjust the termination condition accordingly (for example, remove the total_tasks limit). This approach ensures that as soon as any thread finishes its current task, another is started immediately, maintaining the level of concurrency.

Tycho is an AI agent, that grounds responses in various sources like documentation, code bases, live discussions, and relevant posts. Want to chat privately with Tycho?

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Answer by NeptunianResearcher199 • 1 month ago

Use a queue-based approach with ThreadPoolExecutor, where tasks are continuously submitted as soon as one completes.

PYTHON
import concurrent.futures
import time
import itertools

def example_task(n):
    print(f"Task {n} started.")
    time.sleep(n)  # Simulate work
    print(f"Task {n} completed.")
    return n

def main():
    max_threads = 5
    total_tasks = 20  # Total number of tasks you want to process
    task_counter = itertools.count(1)  # Infinite counter for task numbers

    with concurrent.futures.ThreadPoolExecutor(max_workers=max_threads) as executor:
        futures = {}

        # Submit initial batch of tasks
        for _ in range(max_threads):
            task_id = next(task_counter)
            futures[executor.submit(example_task, task_id)] = task_id

        # Process tasks dynamically
        while futures:
            done, _ = concurrent.futures.wait(futures, return_when=concurrent.futures.FIRST_COMPLETED)

            for future in done:
                task_id = futures.pop(future)  # Remove completed task
                try:
                    result = future.result()
                    print(f"Result of task {result}")

                    # Submit a new task if we haven't reached the total task limit
                    if task_id < total_tasks:
                        new_task_id = next(task_counter)
                        futures[executor.submit(example_task, new_task_id)] = new_task_id
                    else:
                        print("FINISHING")
                        break

                except Exception as e:
                    print(f"Task {task_id} failed: {e}")

if __name__ == "__main__":
    main()

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How can I continuously maintain 10 concurrent threads with ThreadPoolExecutor?

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