[f.result() for f in concurrent.futures.as_completed(futures)]

Concurrency & Async

Meaning

Collects the return values of submitted Future objects in the order they complete rather than the order they were submitted. It solves the problem of needing to wait for all tasks to finish before processing any results, which wastes time when some tasks finish much earlier than others. Reach for this when you have multiple concurrent tasks with variable durations and want to process each result as soon as it becomes available.

Primary Function

Concurrency

Communicative Purpose

Enables processing of concurrent task results in completion order rather than submission order, minimizing idle wait time.

Pattern

[future.result() for future in concurrent.futures.as_completed(futures)]

Core Structure

[... for ... in concurrent.futures.as_completed(...)]

Função primária

Concurrency

Propósito comunicativo

Enables processing of concurrent task results in completion order rather than submission order, minimizing idle wait time.

Situações de gatilho

Concurrent I/O: waiting for multiple HTTP responses with different latencies; Parallel computation: collecting results from thread pool tasks where execution times vary widely; Data pipelines: processing items as soon as any worker finishes rather than blocking on the slowest

Contextos

concurrent.futures, ThreadPoolExecutor, ProcessPoolExecutor, parallel data processing, concurrent I/O orchestration

Padrão

[future.result() for future in concurrent.futures.as_completed(futures)]

Estrutura central

[... for ... in concurrent.futures.as_completed(...)]

Slots de substituição

future: Future object returned by executor.submit(), futures: iterable collection of Future objects

Colocados típicos

ThreadPoolExecutor
ProcessPoolExecutor
executor.submit()
executor.map()
concurrent.futures.wait
Future

Substituições comuns

executor.map(func
iterable) — simpler API but returns results in submission order
asyncio.gather(*coros) — async equivalent for coroutine-based concurrency
[f.result() for f in futures] — gets results in submission order
blocks sequentially on each

Erros comuns

Forgetting to call .result() and collecting Future objects instead of their values — downstream code expecting actual data will fail. Not handling exceptions raised inside futures — .result() re-raises the worker exception, crashing the entire comprehension. Passing a single Future instead of an iterable to as_completed() — causes TypeError since as_completed expects a collection.

Similar / contraste

executor.map() — returns results in submission order with simpler API; asyncio.gather() — coroutine-based equivalent that awaits all at once; concurrent.futures.wait() — blocks until a condition is met rather than iterating over completions

Interferências

Coming from JavaScript: expecting Promise.all()-like behavior where all results resolve together — as_completed yields results one at a time as each finishes. Coming from asyncio: may try to use await inside the comprehension — as_completed from concurrent.futures is for thread/process Futures, not coroutines; use asyncio.as_completed instead.

Família do chunk

concurrent.futures.as_completed
executor.submit
executor.map
concurrent.futures.wait
Future.result

Nuance

Do NOT use when submission order matters and you need results aligned with input order — use executor.map() instead. as_completed() returns a one-shot iterator; wrap in list() if you need to iterate the completions multiple times. The iteration order is non-deterministic across runs since it depends on which worker finishes first.

Efeito pragmático

Reduces total wall-clock time for heterogeneous workloads by processing fast results immediately instead of blocking on the slowest task.

Dica de memória

Like a restaurant kitchen window — dishes come out as each cook finishes, not in the order they were ordered.

Nota

as_completed() accepts an optional timeout parameter in seconds; futures not done by the timeout are silently skipped during iteration.

Upgrade path

asyncio.as_completed() for coroutine-based concurrency with native async/await

Frequência: MediumFormulaicidade: Semi-fixedTipo de construção: list comprehensionPrioridade de aquisição: Active recallPrioridade de output: OutputTag de espaçamento: Medium-term

Examples

import concurrent.futures import urllib.request urls = ["https://example.com/api/users", "https://example.com/api/posts", "https://example.com/api/comments"] with concurrent.futures.ThreadPoolExecutor(max_workers=3) as executor: futures = [executor.submit(urllib.request.urlopen, url) for url in urls] results = [future.result() for future in concurrent.futures.as_completed(futures)] for response in results: print(f"Got response with status {response.status}")

import concurrent.futures def compute_square(n): return n * n numbers = [1, 2, 3, 4, 5] with concurrent.futures.ProcessPoolExecutor() as executor: futures = [executor.submit(compute_square, n) for n in numbers] results = [future.result() for future in concurrent.futures.as_completed(futures)] print(f"Squares: {results}")

import concurrent.futures import os def get_file_size(filepath): return os.path.getsize(filepath) files = ["report.csv", "data.json", "config.yaml"] with concurrent.futures.ThreadPoolExecutor(max_workers=3) as executor: futures = [executor.submit(get_file_size, f) for f in files] sizes = [future.result() for future in concurrent.futures.as_completed(futures)] print(f"Total bytes across files: {sum(sizes)}")