python multiprocessing apply_async only uses one process

Question

I have a script that includes opening a file from a list and then doing something to the text within that file. I\'m using python multiprocessing and Pool to try to parallel

Answer 1

Maybe in this case you should use map_async:

import os
from multiprocessing import Pool

results = []
def testFunc(file):
    message =  ("Working in Process #%d" % (os.getpid()))
    #This is just an illustration of some logic. This is not what I'm actually doing.
    for line in file:
        if 'dog' in line:
            results.append(line)
    return message

if __name__=="__main__":
    print("saddsf")
    p = Pool(processes=2)
    files = ['/path/to/file1.txt', '/path/to/file2.txt']
    results = p.map_async(testFunc, files)
    print(results.get())

Answer 2

• apply_async farms out one task to the pool. You would need to call apply_async many times to exercise more processors.
• Don't allow both processes to try to write to the same list, results. Since the pool workers are separate processes, the two won't be writing to the same list. One way to work around this is to use an ouput Queue. You could set it up yourself, or use apply_async's callback to setup the Queue for you. apply_async will call the callback once the function completes.
• You could use map_async instead of apply_async, but then you'd get a list of lists, which you'd then have to flatten.

So, perhaps try instead something like:

import os
import multiprocessing as mp

results = []   

def testFunc(file):
    result = []
    print "Working in Process #%d" % (os.getpid())
    # This is just an illustration of some logic. This is not what I'm
    # actually doing.
    with open(file, 'r') as f:
        for line in f:
            if 'dog' in line:
                result.append(line)
    return result


def collect_results(result):
    results.extend(result)

if __name__ == "__main__":
    p = mp.Pool(processes=2)
    files = ['/path/to/file1.txt', '/path/to/file2.txt']
    for f in files:
        p.apply_async(testFunc, args=(f, ), callback=collect_results)
    p.close()
    p.join()
    print(results)