concurrent.futures.ThreadPoolExecutor.map(): timeout not working

Question

import concurrent.futures
import time 

def process_one(i):
    try:                                                                             
        print("dealing with {}".format(i))                                           
        time.sleep(50)
        print("{} Done.".format(i))                                                  
    except Exception as e:                                                           
        print(e)

def process_many():
    with concurrent.futures.ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor: 
        executor.map(process_one,
                range(100),                                                          
                timeout=3)                                                           


if __name__ == '__main__':                                                           
    MAX_WORKERS = 10
    try:
        process_many()
    except Exception as e:                                                           
        print(e)      

The docs say:

The returned iterator raises a concurrent.futures.TimeoutError if __next__() is called and the result isn’t available after timeout seconds from the original call to Executor.map()

But here the script didn't raise any exception and kept waiting. Any suggestions?

Answer 1

As the docs specify, the timeout error will only be raised if you're calling the __next__() method on the map. To call this method, you could for example convert the output to a list:

from concurrent import futures
import threading
import time


def task(n):
    print("Launching task {}".format(n))
    time.sleep(n)
    print('{}: done with {}'.format(threading.current_thread().name, n))
    return n / 10


with futures.ThreadPoolExecutor(max_workers=5) as ex:
    results = ex.map(task, range(1, 6), timeout=3)
    print('main: starting')
    try:
        # without this conversion to a list, the timeout error is not raised
        real_results = list(results) 
    except futures._base.TimeoutError:
        print("TIMEOUT")

Output:

Launching task 1
Launching task 2
Launching task 3
Launching task 4
Launching task 5
ThreadPoolExecutor-9_0: done with 1
ThreadPoolExecutor-9_1: done with 2
TIMEOUT
ThreadPoolExecutor-9_2: done with 3
ThreadPoolExecutor-9_3: done with 4
ThreadPoolExecutor-9_4: done with 5

Here, the n-th task sleeps for n seconds, so the timeout is raised after task 2 is completed.

---

EDIT: If you want to terminate the tasks that didn't complete, you could try the answers in this question (they don't use ThreadPoolExecutor.map() though), or you could just ignore the returned values from the other tasks and let them finish:

from concurrent import futures
import threading
import time


def task(n):
    print("Launching task {}".format(n))
    time.sleep(n)
    print('{}: done with {}'.format(threading.current_thread().name, n))
    return n


with futures.ThreadPoolExecutor(max_workers=5) as ex:
    results = ex.map(task, range(1, 6), timeout=3)
    outputs = []
    try:
        for i in results:
            outputs.append(i)
    except futures._base.TimeoutError:
        print("TIMEOUT")
    print(outputs)

Output:

Launching task 1
Launching task 2
Launching task 3
Launching task 4
Launching task 5
ThreadPoolExecutor-5_0: done with 1
ThreadPoolExecutor-5_1: done with 2
TIMEOUT
[1, 2]
ThreadPoolExecutor-5_2: done with 3
ThreadPoolExecutor-5_3: done with 4
ThreadPoolExecutor-5_4: done with 5