Pool workers do not complete all tasks

Question

I have a relatively simple python multiprocessing script that sets up a pool of workers that append output to a pandas dataframe by way of a custom manager. What I am finding is when I call close()/join() on the pool, not all the tasks submitted by apply_async are being completed.

Here's a simplified example that submits 1000 jobs but only half complete causing an assertion error. Have I overlooked something very simple or is this perhaps a bug?

from pandas import DataFrame
from multiprocessing.managers import BaseManager, Pool

class DataFrameResults:
    def __init__(self):
        self.results = DataFrame(columns=("A", "B")) 

    def get_count(self):
        return self.results["A"].count()

    def register_result(self, a, b):
        self.results = self.results.append([{"A": a, "B": b}], ignore_index=True)

class MyManager(BaseManager): pass

MyManager.register('DataFrameResults', DataFrameResults)

def f1(results, a, b):
    results.register_result(a, b)

def main():
    manager = MyManager()
    manager.start()
    results = manager.DataFrameResults()

    pool = Pool(processes=4)

    for (i) in range(0, 1000):
        pool.apply_async(f1, [results, i, i*i])
    pool.close()
    pool.join()

    print results.get_count()
    assert results.get_count() == 1000

if __name__ == "__main__":
    main()

Answer 1

[EDIT] The issue which you're seeing is because of this code:

self.results = self.results.append(...)

this isn't atomic. So in some cases, the thread will be interrupted after reading self.results (or while appending) but before it can assign the new frame to self.results -> this instance will be lost.

The correct solution is to wait use the results objects to get the results and then append all of them in the main thread.