Quick Python: Concurrent Futures
Published on
Updated on
Another way to perform concurrency in python is to use the concurrent.futures module.
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
def add(x, y):
return x + y
with ThreadPoolExecutor(max_workers=4) as executor:
future = executor.submit(add, 1, 2)
result = future.result(timeout=30) # unit: seconds
If max_workers=None then it will default to the number of processors on the machine multiplied by 5.
If timeout=None then there is no time limit applied.
You can also apply a function to a list or iterables
def double(x):
return 2 * x
with ThreadPoolExecutor() as executor:
future = executor.map(function_handle, [1, 2, 3])
result = future.result()
Instead of threads, it is also possible to spawn processes to side-step the global interpreter lock. The documentation warns that only picklable objects can be executed and returned though.
def add(x, y):
return x + y
with ProcessPoolExecutor() as executor:
future = executor.submit(add, 1, 2)
result = future.result()