Multiprocessing.pool: When to use apply, apply_async or map?

python

multiprocessing

concurrency

async-execution

byAlex Kataev·Mar 1, 2025

Settle on apply, apply_async, or map from multiprocessing.Pool based on your data transformation requirements. Utilize apply for synchronous execution which waits for the result, apply_async for non-blocking asynchronous execution with an option for callbacks, and map for applying a function in batch over an iterable synchronously. Here's the algorithm DNA:

from multiprocessing import Pool

def work(data):
    # Add a pinch of salt to data conversions
    return data * 2

# Synchronous execution: like waiting for Friday, will halt until done
print(Pool().apply(work, (2,)))  # Output: 4

# Light-speed execution: like binge-watching Netflix, runs in the background
res = Pool().apply_async(work, (2,), callback=lambda x: print(f'Async is the new sexy: {x}'))  # Output: Async is the new sexy: 4

# One function, many data: like spreading love or rumors
print(Pool().map(work, [1, 2, 3]))  # Output: [2, 4, 6]

Task characteristics and timings often make the choice for you: instant results scream for apply, while non-blocking operations are apply_async territory. When you need to mass apply a function with an eye on result order, map saves the day.

Deep diving into the methods

Ordering your orders

map presents its results just like the order of your iterable. However, apply_async is the rebel which may play out of tune. If performance sequence is on your playlist, map or its cousin map_async are your go-to collections.

Handling diverse tasks

apply_async earns its cape by dealing with different functions and a palette of arguments. An all-rounder in a mixed workload, especially when tasks show different colors and sizes :art:

Callbacks, not just for call centers!

apply_async offers a callback option, a bell that rings when your function returns. Think of it as 24x7 virtual assistant, always available, with no idle chitchat.

Sidestepping the GIL problem

Global Interpreter Lock or GIL can stop your multi-threading party. Multiprocessing, and particularly the apply_async and map_async duo, are your bouncers, enforcing independent processes.

Concurrent Execution

Granularity grabs the gold

For tasks faster than a cheetah, using apply_async with a callback can steer clear of the management overhead juggernaut associated with map.

Navigating very large seas

With vast iterable oceans to traverse, consider imap or imap_unordered. They're a lazy sailor's dream, loading iterables only as needed and processing results as they're ready.

Saving the day with exception handling

apply_async is your firefighting friend. Use the error_callback parameter to manage fire drills in worker processes, aiding a sturdy solution for unforeseen tasks.

Harnessing CPU power

For CPU-bound tasks, apply or apply_async can crank up the horsepower of multicore processors, leading to rocketing speedups in execution.

Unleashing the full power

Task chaining in the pipeline

Think of apply_async as your conveyor belt, where callback function acts as the switch that pushes the next task box forward. It forms a long task pipeline, transforming raw data into refined results.

The balancing act

With imap or imap_unordered, perform a circus-worthy balancing trick where tasks are spread evenly among all performers - or rather, processes. Especially useful when the tasks put varying amounts of weight on the see-saw.