Why does this Python code with threading have race conditions?

Question

This code creates a race condition:

import threading

ITERS = 100000
x = [0]

def worker():
    for _ in range(ITERS):
        x[0] += 1  # this line creates a race condition
        # because it takes a value, increments and then writes
        # some inrcements can be done together, and lost

def main():
    x[0] = 0  # you may use `global x` instead of this list trick too
    t1 = threading.Thread(target=worker)
    t2 = threading.Thread(target=worker)
    t1.start()
    t2.start()
    t1.join()
    t2.join()

for i in range(5):
    main()
    print(f'iteration {i}. expected x = {ITERS*2}, got {x[0]}')

Output:

$ python3 test.py
iteration 0. expected x = 200000, got 200000
iteration 1. expected x = 200000, got 148115
iteration 2. expected x = 200000, got 155071
iteration 3. expected x = 200000, got 200000
iteration 4. expected x = 200000, got 200000

Python3 version:

Python 3.9.7 (default, Sep 10 2021, 14:59:43) 
[GCC 11.2.0] on linux

I thought GIL would prevent it and not allow two threads run together until they do something io-related or call a C library. At least this is what you may conclude from the docs.

Then, what does GIL actually do, and when do threads run in parallel?

See: https://stackoverflow.com/questions/1717393/is-the-operator-thread-safe-in-python and especially https://stackoverflow.com/questions/38266186/is-extending-a-python-list-e-g-l-1-guaranteed-to-be-thread-safe — juanpa.arrivillaga, Dec 27 '21 at 09:28
This shouldn't be the job for a lock? I mean why don't you use a lock when adding the number of the list? Operation on list do not guarantee atomicity. Have u tried with a lock and the same occur? Also I think using a deque should ensure more atomicity than a simple list. There was an stackoverflow answer where Alex martelli points out that you should you a combination of deque and queue when using threading — Federico Baù, Dec 27 '21 at 09:30
aaaaand https://web.archive.org/web/20201108091210/http://effbot.org/pyfaq/what-kinds-of-global-value-mutation-are-thread-safe.htm — juanpa.arrivillaga, Dec 27 '21 at 09:31
@FedericoBaù it should, of course, when you know this is going to happen. When I learned Python and tried multithreading a decade ago, it seemed that it should be like Javascript, that executes an entire function until it ends and lets the event loop go on. — culebrón, Dec 27 '21 at 09:33
@culebrón found the alex martelli opinion regarding threading https://stackoverflow.com/a/2846697/13903942 — Federico Baù, Dec 27 '21 at 09:33
@juanpa.arrivillaga oh, that's where the example code came originally. — culebrón, Dec 27 '21 at 09:36
@culebrón ahh yes but is quite different than javascript for sure. I understand the javascript background now because you use race considin and used off course for async work.normally when talking about python threading I normally read block non blocking ;) — Federico Baù, Dec 27 '21 at 09:38
Try it with Python 3.10, I think there was a recent interesting Q&A that pointed out that this doesn't happen anymore (though it's not safe to assume it doesn't). — Kelly Bundy, Dec 27 '21 at 13:22

culebrón · Accepted Answer · 2021-12-27T17:33:50.993

Reading the docs better, I think there's the answer:

The mechanism used by the CPython interpreter to assure that only one thread executes Python bytecode at a time. This simplifies the CPython implementation by making the object model (including critical built-in types such as dict) implicitly safe against concurrent access. Locking the entire interpreter makes it easier for the interpreter to be multi-threaded, at the expense of much of the parallelism afforded by multi-processor machines.

However, some extension modules, either standard or third-party, are designed so as to release the GIL when doing computationally-intensive tasks such as compression or hashing. Also, the GIL is always released when doing I/O.

I don't know the internals, but guess each line or block of this bytecode is executed alone, and other threads are waiting (which makes it slow). But some lines consist of multiple blocks, and aren't atomic.

Here's what you get if run dis.dis('x[0] += 1'):

          0 LOAD_NAME                0 (x)
          2 LOAD_CONST               0 (0)
          4 DUP_TOP_TWO
          6 BINARY_SUBSCR
          8 LOAD_CONST               1 (1)
         10 INPLACE_ADD
         12 ROT_THREE
         14 STORE_SUBSCR
         16 LOAD_CONST               2 (None)
         18 RETURN_VALUE

Some of these are executed in concurrent way, and make the race condition. So GIL only guarantees the internals of structures like list or dict won't be damaged.

use `dis.dis("x[0] += 1")` – juanpa.arrivillaga Dec 27 '21 at 09:27 — juanpa.arrivillaga, Dec 27 '21 at 09:27

Why does this Python code with threading have race conditions?

1 Answers1