To run multiple Python jobs in parallel, submit each job to a concurrent.futures executor, keep its Future paired with a stable job ID, and collect each result or exception in the controlling thread. Start with a thread pool for blocking I/O or try a process pool for suitable CPU-bound work; neither choice guarantees a speedup without testing your actual workload.
Define what the job runner promises
A small runner needs more than a pool of workers. Before choosing an executor, decide what callers submit and what they get back. Give every job a stable ID, a callable, and its arguments. Also decide whether results are reported in submission order or as jobs finish, what happens when one job fails, and how the caller closes the runner.
- Identity: keep a job ID or input associated with every submitted job.
- Result order: choose input order for predictable batch output, or completion order when prompt reporting matters.
- Failure policy: decide whether to continue collecting independent jobs, stop reporting after a failure, or return an aggregate of successes and failures.
- Shutdown: make clear when the caller waits for outstanding work to finish.
Prefer having the controlling thread gather results rather than having workers mutate a shared result collection. That keeps the boundary between executing a job and recording its outcome simple.
Start with the standard-library executor and Future
Python’s concurrent.futures API provides a shared Executor interface. Calling submit(fn, *args, **kwargs) schedules a callable and immediately returns a Future: an object that represents the work, which may complete later. The abstract Executor is not itself a pool; concrete implementations such as ThreadPoolExecutor and ProcessPoolExecutor provide the execution backend.
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Store the association between each future and its job ID so a result can be identified even when jobs finish out of order:
from concurrent.futures import ThreadPoolExecutor, as_completed
def run_job(value):
return value * 2
jobs = [("job-a", 10), ("job-b", 20), ("job-c", 30)]
with ThreadPoolExecutor() as executor:
future_to_job_id = {
executor.submit(run_job, value): job_id
for job_id, value in jobs
}
for future in as_completed(future_to_job_id):
job_id = future_to_job_id[future]
try:
result = future.result()
except Exception as exc:
print(job_id, "failed:", exc)
else:
print(job_id, "succeeded:", result)This first version reports jobs in completion order. as_completed() yields each future as it finishes, and future.result() returns the callable’s value or raises the exception the callable raised. Catching Exception around an individual result lets the loop continue to collect other independent jobs; remove that recovery behavior or implement a different policy if a job failure should stop the batch.
Choose result order deliberately
The collection method is part of the runner’s observable behavior, not just an implementation detail.
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Completion order with as_completed()
Use the future-to-ID mapping and as_completed() when consumers should hear about fast jobs without waiting for slower earlier submissions. Because completion order differs from submission order, the mapping is essential for associating each outcome with its job.
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Input order with map()
Executor.map() yields results corresponding to input order, even if later jobs finish first. It can make ordered batch processing concise, but a slow early job can delay access to later completed results. A task exception is raised when its corresponding result is retrieved. In Python 3.13, map() collects its input iterables immediately, so it is not a safe assumption for an arbitrarily large or unbounded input stream.
How do I choose between ThreadPoolExecutor and ProcessPoolExecutor?
Choose a first backend based on the workload and programming style, then measure with representative jobs on the target Python version and hardware. The Python concurrency overview describes the relevant choice as depending on whether work is CPU- or I/O-bound and on whether the preferred style is event-driven cooperative multitasking or preemptive multitasking (Python concurrency overview). That is a decision framework, not a promise that one executor will be faster for every application.
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| Option | Good first fit | Important trade-off |
|---|---|---|
ThreadPoolExecutor |
Synchronous callables that spend substantial time waiting on blocking I/O. | Threads run in the same process. Test the real workload rather than assuming a thread pool improves CPU-bound Python computation. |
ProcessPoolExecutor |
CPU-bound work where running jobs in separate processes is appropriate. | Worker functions and transferred arguments must be picklable, and the worker process must be able to import the main module. |
asyncio |
Applications structured around event-driven coroutine code. | It is a different concurrency model, not a drop-in executor choice for ordinary synchronous callables. |
The executors share the submit/Future interface, so changing the backend can be a small code change. The operating model is still different: process pools cross a process boundary and impose serialization and importability requirements, while threads remain within one process. No general worker count or numerical speedup follows from the API choice alone.
Bound the number of submitted jobs when input is large
A runner that submits every item from a very large source at once may consume substantial memory and create a large backlog. In Python 3.13, Executor.map() eagerly collects its iterables. For a finite but large or streaming source, keep only a limited number of futures in flight, then submit another job as one completes.
One basic pattern is to maintain a set of pending futures and replenish it after each completion. The maximum should be an explicit application choice based on the work, memory budget, and acceptable queueing; the documentation does not establish a universal value.
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from concurrent.futures import ThreadPoolExecutor, wait, FIRST_COMPLETED
def run_bounded(executor, jobs, limit):
jobs = iter(jobs)
pending = {}
def fill():
while len(pending) < limit:
try:
job_id, value = next(jobs)
except StopIteration:
return
future = executor.submit(run_job, value)
pending[future] = job_id
fill()
while pending:
done, _ = wait(pending, return_when=FIRST_COMPLETED)
for future in done:
job_id = pending.pop(future)
try:
yield job_id, future.result(), None
except Exception as exc:
yield job_id, None, exc
fill()Here each yielded tuple contains a job ID, either a result or None, and either an exception or None. If None is a legitimate result or failure representation in your application, use a distinct result type instead. Call it inside the executor’s context manager so pending work is still governed by the runner’s chosen shutdown behavior.
Close the pool and understand cancellation
An executor context manager shuts down the pool on exit and waits for pending work to finish. This is convenient for a batch whose caller should not leave while submitted work remains outstanding:
with ThreadPoolExecutor() as executor:
# submit jobs and collect outcomes
...
# The context manager waits for work to finish.Cancellation does not forcibly stop a callable that is already running. Future.cancel() succeeds only before execution begins. Likewise, shutdown(cancel_futures=True) cancels futures that have not started, but does not cancel running calls. Choose shutdown behavior with that distinction in mind; if jobs need cooperative stopping, the job code needs its own cancellation mechanism.
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Make a process-pool runner portable
Process pools require extra care compared with the thread-pool example. Keep worker functions at module scope, pass picklable arguments, and protect process-launching code in a portable script with the standard main guard:
from concurrent.futures import ProcessPoolExecutor
def cpu_job(value):
return value * value
def main():
with ProcessPoolExecutor() as executor:
print(list(executor.map(cpu_job, [2, 3, 4])))
if __name__ == "__main__":
main()- The worker callable and values sent to it must be picklable.
- The subprocess must be able to import the main module.
- Do not call executor or future methods from a callable running in a process pool; doing so can deadlock.
The Python 3.13 documentation notes that the multiprocessing default start method changes away from fork in Python 3.14. If an application depends on fork, request the appropriate multiprocessing context explicitly rather than relying on the default. Verify version-specific behavior against the Python version the application actually runs.
Where this runner stops being the right abstraction
This design handles local concurrent execution: submit work, identify it, collect outcomes, and close the pool. It does not provide a persistent queue, durable retries, scheduling, or distributed orchestration. If jobs must survive process or machine restarts, run across multiple hosts, or be scheduled independently of a calling process, those are separate system requirements rather than features of this small executor-based runner.
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