Understanding Task Ordering with Python's Concurrent.Futures

One of the most powerful features of modern programming languages is their ability to manage multiple threads or processes simultaneously. Python is no exception, thanks to its concurrent.futures library. Specifically, through its ThreadPoolExecutor or ProcessPoolExecutor, Python can handle multiple tasks concurrently, significantly improving the efficiency of your code.

A common question among Python developers using concurrent.futures is whether the results of these concurrent tasks come back in the same order as initially called. The answer is a conditional yes.

When using ThreadPoolExecutor or ProcessPoolExecutor, if you are utilizing the map() function, Python ensures that the results come back in the order of the original calls, regardless of the sequence in which the tasks were completed. This ordering is extremely helpful when the sequence of tasks matters in the context of your program.

Here’s a simple example:

from concurrent.futures import ThreadPoolExecutor 

def task(n):
    return n * n
    
with ThreadPoolExecutor() as executor: 
    results = executor.map(task, (1, 2, 3, 4, 5)) 

for result in results: 
    print(result)

In this code, even if task 5 completes before task 2, the results will still be printed in the order of 1, 2, 3, 4, 5.

However, there’s a caveat. If you’re submitting tasks using a different method - submit() - and waiting for them using as_completed(), the results might return in a different order than they were submitted. This is because as_completed() yields tasks as and when they complete.

In many scenarios, this completion-order returns could be advantageous, especially when each task is independent, and you want to handle the completed tasks while other tasks are still running. But, it’s essential to be aware that you won’t be maintaining the same order as the tasks were initially submitted.

In conclusion, Python’s concurrent.futures offers different techniques for multithreading or multiprocessing. Depending on your requirements, you can choose to maintain tasks order by using map() or opt to handle tasks as they finish by using submit() with as_completed().