Suppose I have the following multiprocessing structure:

```
import multiprocessing as mp
def worker(working_queue, output_queue):
    while True:
        if working_queue.empty() == True:
            break 
        else:
            picked = working_queue.get()
            res_item = "Number " + str(picked)
            output_queue.put(res_item)
    return

if __name__ == '__main__':
    static_input = xrange(100)    
    working_q = mp.Queue()
    output_q = mp.Queue()
    results_bank = []
    for i in static_input:
        working_q.put(i)
    processes = [mp.Process(target=worker,args=(working_q, output_q)) for i in range(2)]
    for proc in processes:
        proc.start()
    for proc in processes:
        proc.join()
    results_bank = []
    while True:
       if output_q.empty() == True:
           break
       results_bank.append(output_q.get_nowait())
    if len(results_bank) == len(static_input):
        print "Good run"
    else:
        print "Bad run"

```

My question: How would I 'batch' write my results to a single file while the working\_queue is still 'working' (or at least, not finished)?

Note: My actual data structure is not sensitive to unordered results relative to inputs (despite my example using integers).

Also, I think that batch/set writing from the output queue is best practice rather than from the growing results bank object. However, I am open to solutions relying on either approach. I am new to multiprocessing so unsure of best practice or most efficient solution(s) to this question.