How to return values from Process- or Thread insta

Question

So I want to run a function which can either search for information on the web or directly from my own mysql database. The first process will be time-consuming, the second relatively fast.

With this in mind I create a process which starts this compound search (find_compound_view). If the process finishes relatively fast it means it's present on the database so I can render the results immediately. Otherwise, I will render "drax_retrieving_data.html".

The stupid solution I came up with was to run the function twice, once to check if the process takes a long time, the other to actually get the return values of the function. This is pretty much because I don't know how to return the values of my find_compound_view function. I've tried googling but I can't seem to find how to return the values from the class Process specifically.

   p = Process(target=find_compound_view, args=(form,))
        p.start()
        is_running = p.is_alive()
        start_time=time.time()
        while is_running:
            time.sleep(0.05)
            is_running = p.is_alive()
            if time.time() - start_time > 10 :
                print('Timer exceeded, DRAX is retrieving info!',time.time() - start_time)
                return render(request,'drax_internal_dbs/drax_retrieving_data.html')
        compound = find_compound_view(form,use_email=False)

   if compound:
      data=*****
      return  render(request, 'drax_internal_dbs/result.html',data)

Answer 1

You will need a multiprocessing.Pipe or a multiprocessing.Queue to send the results back to your parent-process. If you just do I/0, you should use a Thread instead of a Process, since it's more lightweight and most time will be spend on waiting. I'm showing you how it's done for Process and Threads in general.

---

Process with Queue

The multiprocessing queue is build on top of a pipe and access is synchronized with locks/semaphores. Queues are thread- and process-safe, meaning you can use one queue for multiple producer/consumer-processes and even multiple threads in these processes. Adding the first item on the queue will also start a feeder-thread in the calling process. The additional overhead of a multiprocessing.Queue makes using a pipe for single-producer/single-consumer scenarios preferable and more performant.

Here's how to send and retrieve a result with a multiprocessing.Queue:

from multiprocessing import Process, Queue

SENTINEL = 'SENTINEL'

def sim_busy(out_queue, x):
    for _ in range(int(x)):
        assert 1 == 1
    result = x
    out_queue.put(result)
    # If all results are enqueued, send a sentinel-value to let the parent know
    # no more results will come.
    out_queue.put(SENTINEL)


if __name__ == '__main__':

    out_queue = Queue()

    p = Process(target=sim_busy, args=(out_queue, 150e6))  # 150e6 == 150000000.0
    p.start()

    for result in iter(out_queue.get, SENTINEL):  # sentinel breaks the loop
        print(result)

The queue is passed as argument into the function, results are .put() on the queue and the parent get.()s from the queue. .get() is a blocking call, execution does not resume until something is to get (specifying timeout parameter is possible). Note the work sim_busy does here is cpu-intensive, that's when you would choose processes over threads.

---

Process & Pipe

For one-to-one connections a pipe is enough. The setup is nearly identical, just the methods are named differently and a call to Pipe() returns two connection objects. In duplex mode, both objects are read-write ends, with duplex=False (simplex) the first connection object is the read-end of the pipe, the second is the write-end. In this basic scenario we just need a simplex-pipe:

from multiprocessing import Process, Pipe

SENTINEL = 'SENTINEL'


def sim_busy(write_conn, x):
    for _ in range(int(x)):
        assert 1 == 1
    result = x
    write_conn.send(result)
    # If all results are send, send a sentinel-value to let the parent know
    # no more results will come.
    write_conn.send(SENTINEL)


if __name__ == '__main__':

    # duplex=False because we just need one-way communication in this case.
    read_conn, write_conn = Pipe(duplex=False)

    p = Process(target=sim_busy, args=(write_conn, 150e6))  # 150e6 == 150000000.0
    p.start()

    for result in iter(read_conn.recv, SENTINEL):  # sentinel breaks the loop
        print(result)

---

Thread & Queue

For use with threading, you want to switch to queue.Queue. queue.Queue is build on top of a collections.deque, adding some locks to make it thread-safe. Unlike with multiprocessing's queue and pipe, objects put on a queue.Queue won't get pickled. Since threads share the same memory address-space, serialization for memory-copying is unnecessary, only pointers are transmitted.

from threading import Thread
from queue import Queue
import time

SENTINEL = 'SENTINEL'


def sim_io(out_queue, query):
    time.sleep(1)
    result = query + '_result'
    out_queue.put(result)
    # If all results are enqueued, send a sentinel-value to let the parent know
    # no more results will come.
    out_queue.put(SENTINEL)


if __name__ == '__main__':

    out_queue = Queue()

    p = Thread(target=sim_io, args=(out_queue, 'my_query'))
    p.start()

    for result in iter(out_queue.get, SENTINEL):  # sentinel-value breaks the loop
        print(result)

---

• Read here why for result in iter(out_queue.get, SENTINEL): should be prefered over a while True...break setup, where possible.
• Read here why you should use if __name__ == '__main__': in all your scripts and especially in multiprocessing.
• More about get()-usage here.