What Giulio Franco says is true for multithreading vs. multiprocessing in general. However, Python* has an added issue: There’s a Global Interpreter Lock that prevents two threads in the same process from running Python code at the same time. This means that if you have 8 cores, and change your code to use 8 threads, … Read more
I could not use the codes posted so far because the codes using “multiprocessing.Pool” do not work with lambda expressions and the codes not using “multiprocessing.Pool” spawn as many processes as there are work items. I adapted the code s.t. it spawns a predefined amount of workers and only iterates through the input list if … Read more
I wouldn’t call concurrent.futures more “advanced” – it’s a simpler interface that works very much the same regardless of whether you use multiple threads or multiple processes as the underlying parallelization gimmick. So, like virtually all instances of “simpler interface”, much the same trade-offs are involved: it has a shallower learning curve, in large part … Read more
On Windows the subprocesses will import (i.e. execute) the main module at start. You need to insert an if __name__ == ‘__main__’: guard in the main module to avoid creating subprocesses recursively. Modified testMain.py: import parallelTestModule if __name__ == ‘__main__’: extractor = parallelTestModule.ParallelExtractor() extractor.runInParallel(numProcesses=2, numThreads=4)
There are two key differences between imap/imap_unordered and map/map_async: The way they consume the iterable you pass to them. The way they return the result back to you. map consumes your iterable by converting the iterable to a list (assuming it isn’t a list already), breaking it into chunks, and sending those chunks to the … Read more
TL;DR Making the Right Choice: We have walked through the most popular forms of concurrency. But the question remains – when should choose which one? It really depends on the use cases. From my experience (and reading), I tend to follow this pseudo code: if io_bound: if io_very_slow: print(“Use Asyncio”) else: print(“Use Threads”) else: print(“Multi … Read more
I just now wrote a log handler of my own that just feeds everything to the parent process via a pipe. I’ve only been testing it for ten minutes but it seems to work pretty well. (Note: This is hardcoded to RotatingFileHandler, which is my own use case.) Update: @javier now maintains this approach as … Read more
Here is a list of what can be pickled. In particular, functions are only picklable if they are defined at the top-level of a module. This piece of code: import multiprocessing as mp class Foo(): @staticmethod def work(self): pass if __name__ == ‘__main__’: pool = mp.Pool() foo = Foo() pool.apply_async(foo.work) pool.close() pool.join() yields an error … Read more
Back in the old days of Python, to call a function with arbitrary arguments, you would use apply: apply(f,args,kwargs) apply still exists in Python2.7 though not in Python3, and is generally not used anymore. Nowadays, f(*args,**kwargs) is preferred. The multiprocessing.Pool modules tries to provide a similar interface. Pool.apply is like Python apply, except that the … Read more
is there a variant of pool.map which support multiple arguments? Python 3.3 includes pool.starmap() method: #!/usr/bin/env python3 from functools import partial from itertools import repeat from multiprocessing import Pool, freeze_support def func(a, b): return a + b def main(): a_args = [1,2,3] second_arg = 1 with Pool() as pool: L = pool.starmap(func, [(1, 1), (2, … Read more