Parallel map operation that returns a list of results.

Express a concurrent map operation that yields a list of processed items.

When you need to parallelize a function over an iterable and collect all results as a list, using a thread or process pool with chunking for efficiency.

Used in concurrent.futures based parallel processing, data pipelines, batch jobs, and any scenario where CPU‑ or I/O‑bound tasks can be batched.

[result for result in executor.map(task_func, iterable, chunksize=100)]

[result for result in executor.map(func, iterable, chunksize=N)]

task_func: callable accepting a single argument, iterable: iterable of items, chunksize: int ≥ 1, result: any object returned by task_func

• concurrent.futures.ThreadPoolExecutor
• concurrent.futures.ProcessPoolExecutor
• map
• chunksize
• tqdm progress bar

• list(executor.map(task_func
• iterable
• chunksize=100)) (more concise)
• using itertools.chain to flatten chunked results
• adding tqdm.tqdm for progress visualization

1. Forgetting to shut down the executor, causing resource leaks – cause: missing executor.shutdown() or context manager; consequence: hanging processes/threads. 2. Using a non‑picklable function with ProcessPoolExecutor – cause: function references local state; consequence: PicklingError. 3. Setting chunksize too low for large iterables – cause: excessive task submission overhead; consequence: reduced throughput. 4. Assuming result order matches completion order when using unordered variants – cause: confusion with executor.map vs. map_async; consequence: mismatched results.

list(executor.map(...)) – more concise but less explicit about result collection; list comprehension without executor – sequential map, no parallelism; tqdm.tqdm(executor.map(...)) – adds a progress bar; multiprocessing.Pool.map – alternative pooling interface with different chunking semantics.

Coming from JavaScript: may expect Promise.all‑style immediate error propagation – in Python, exceptions from task_func are raised only when the result is retrieved; → wrap task logic in try/except or inspect results after map.

• list comprehension
• executor.map
• parallel map
• map‑reduce pattern

1. Avoid when tasks are trivial or I/O‑light, as process/thread overhead outweighs gains; 2. Large chunksize reduces submission overhead but can cause load imbalance if task durations vary widely; 3. Results are returned in the same order as the input iterable, regardless of completion order, which may hide stragglers.

Enables scalable parallel execution of independent tasks, turning CPU‑ or I/O‑bound loops into throughput‑oriented batch jobs without manual thread management.

Think of a factory conveyor belt where each worker grabs a batch of items, processes them, and puts the finished batch back in order – the list comprehension gathers each finished batch as it arrives.

A chunksize of 100 is a good default for moderately sized iterables; tune based on task granularity and worker count for optimal performance.

Consider migrating to asyncio.gather for I/O‑bound async workloads, or to Dask/distributed for larger‑scale, out‑of‑core parallelism.