Which Pool Method Should I Use in Python Multiprocessing?

Multiprocessing.Pool: Which Method Should I Use?

Multiprocessing allows Python to execute functions concurrently across multiple processes. However, choosing the appropriate method can be confusing, especially when considering Pool.apply, Pool.apply_async, and Pool.map. Let's clarify their differences and use cases:

Pool.apply vs. Pool.apply_async vs. Pool.map

1. Pool.apply:

• Calls a function in a separate process and blocks the current process until the function returns.
• Use when you need to perform a single function call and wait for the result immediately.

2. Pool.apply_async:

• Similar to Pool.apply, calls a function in a separate process, but returns immediately.
• Returns an AsyncResult object that allows you to retrieve the result later.
• Supports callback functions that are invoked when the result is available.
• Useful for asynchronous processing of multiple function calls.

3. Pool.map:

• Calls the same function on a sequence of arguments in parallel.
• Blocks until all results are obtained.
• Preserves the order of the arguments and results.
• Ideal for applying the same function to multiple data points and obtaining a list of results in the same order.

Choosing the Right Method

Use Pool.apply if:

• You need to block until a single function returns.
• You don't want to deal with asynchronous processing.

Use Pool.apply_async if:

• You want to call multiple functions asynchronously and retrieve their results later.
• You want to use callbacks to handle results.

Use Pool.map if:

• You need to apply the same function to multiple arguments concurrently.
• You want to preserve the argument-result order.

Example: Asynchronous Callback in Pool.apply_async

<code class="python">import multiprocessing as mp
import time

def foo_pool(x):
    time.sleep(2)
    return x*x

result_list = []
def log_result(result):
    result_list.append(result)

def apply_async_with_callback():
    pool = mp.Pool()
    for i in range(10):
        pool.apply_async(foo_pool, args = (i, ), callback = log_result)
    pool.close()
    pool.join()
    print(result_list)

if __name__ == '__main__':
    apply_async_with_callback()</code>

Output:

[1, 0, 4, 9, 25, 16, 49, 36, 81, 64]

Notice that the order of results may not align with the order of function calls, unlike Pool.map.

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