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In Python’s concurrent.futures, use Executor.map() when the same function should run on each input and you want results in input order. For individually submitted tasks, keep the returned futures in submission order and call result() on them in that order. Use as_completed() only when you want to handle tasks as they finish; by itself, it returns completion order, not submission order.
Use Executor.map() for ordered results from the same function
Executor.map() runs calls concurrently but yields each result in the order of the input iterables. A slow early task can hold up delivery of later results, even if those tasks have already finished. For a uniform operation over a sequence, this is usually the simplest way to keep each output aligned with its input.
from concurrent.futures import ThreadPoolExecutor
def work(item):
return process(item)
with ThreadPoolExecutor() as executor:
results = list(executor.map(work, items))The result at each position corresponds to the input at that position. See the Python 3.13 concurrent.futures documentation.
Keep futures in submission order when using submit()
Use submit() when you need to configure individual calls or submit tasks separately. It returns a Future for each call. Store those futures in a list as you submit them, then retrieve their results by iterating over that same list:
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from concurrent.futures import ThreadPoolExecutor
with ThreadPoolExecutor() as executor:
futures = [executor.submit(work, item) for item in items]
results = [future.result() for future in futures]Future.result() waits if its task is still running, returns the task’s value when it finishes, and raises that task’s exception when the result is retrieved. Because retrieval follows the list order, the results stay aligned with submission order. The trade-off is that a slow earlier task can make the caller wait even when later futures are already complete. See the Python 3.13 concurrent.futures documentation.
Handle tasks as they finish and still build ordered output
as_completed() yields futures in completion order. If you need to process results as soon as they are available but also need a final list in submission order, associate each future with its original index and store each result in that indexed slot.
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from concurrent.futures import ThreadPoolExecutor, as_completed
with ThreadPoolExecutor() as executor:
futures = {
executor.submit(work, item): index
for index, item in enumerate(items)
}
results = [None] * len(items)
for future in as_completed(futures):
index = futures[future]
results[index] = future.result()The loop handles futures as they complete; assigning by index restores the original order in results. If a task raises an exception, calling its result() raises that exception at that point in the loop. See the Python 3.13 concurrent.futures documentation.
Choose the pattern that matches how you consume results
| Need | Pattern | Ordering behavior |
|---|---|---|
| Apply one function to input iterables and collect results in input order | executor.map(fn, inputs) |
Yields results in input order |
| Submit customized or separate calls and retrieve results in submission order | Keep futures in a list; call result() in list order |
Retrieves results in submission order; may wait behind an earlier slow task |
| Act on each result as soon as its task finishes, then produce ordered output | as_completed() plus future-to-index mapping |
Processes in completion order and places values into indexed output slots |
Exceptions, timeouts, and Python version details
With Executor.map(), an exception from a task is raised when the corresponding value is retrieved from the iterator. In the Python 3.13 documentation, the map timeout is measured from the original call to Executor.map(); requesting a result that is not ready within that time raises TimeoutError. These behaviors mean result iteration should account for task failures and timeouts rather than assuming every call succeeds. See the Python 3.13 documentation.
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Python 3.14 documents a buffersize parameter for Executor.map(), which limits the number of submitted tasks whose results have not yet been yielded. The same documentation notes that chunksize has no effect with ThreadPoolExecutor; it is not a thread-pool batching control. Check the documentation for your Python version before relying on newer arguments: Python 3.14 concurrent.futures documentation.
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