Which Multiprocessing.Pool Method Should You Use: Apply, Apply_Async, or Map?

Multiprocessing.Pool: Understanding Apply, Apply_Async, and Map

Multiprocessing in Python allows concurrent execution of tasks in separate processes. The multiprocessing.Pool offers various methods to run functions with different configurations: apply, apply_async, and map. This article clarifies the distinctions between these methods through examples.

Apply vs. Apply_Async

Similar to Python's built-in apply, Pool.apply executes a function in a separate process and blocks until completion. This is suitable if you need immediate access to the result.

In contrast, Pool.apply_async calls a function asynchronously, returning an AsyncResult object. To obtain the actual result, you must call get() on the AsyncResult, which will block until the function completes. This method is advantageous when you want to avoid blocking the main process while waiting for results.

Map

Pool.map applies the specified function to multiple arguments, preserving the order of the arguments and results. Unlike apply_async, map blocks until all results are available. This method is ideal when you need the results in a specific order or want to combine them into a single result.

Summary Table

Method	Execution	Blocking	Result Order
apply	In a separate process	Yes	N/A
apply_async	Asynchronously	No	Not guaranteed
map	In separate processes	Yes	Preserved

Use Cases

• Use Pool.apply when you want to run a function in a separate process and block until the result is available.
• Use Pool.apply_async when you want to run a function asynchronously without blocking the main process.
• Use Pool.map when you want to apply the same function to multiple arguments and receive the results in the same order as the arguments.

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