The Event and Machine have cyclic dependency. about transitions HOT 3 CLOSED

Hello @yw94,

it is correct that Machine contains a list of events where each Event has a reference to the machine itself. The machine reference is later passed to the model wrapped into EventData. Since Event and EventData only handle references and don't initiate machines, I don't get where this leads to memory overflow. As far as I know, Python's garbage collector is smart enough to recognise the cyclic dependency and remove machines and events. I just ran a check and created roughly 25 million instances:

from transitions import Machine
import psutil
process = psutil.Process()


class Foo:

    def __init__(self):
        self.machine = Machine(
            model=self,
            states=["a", "b"],
            transitions=[{"trigger": "a_to_b", "source": "a", "dest": "b"}],
            initial="a"
        )


counter = 0

with open("memory.csv", "w") as f:
    while True:
        counter += 1
        foo = Foo()
        if counter % 50000 == 0:
            print(f"{counter},{process.memory_info().rss}", file=f)
            f.flush()

and the memory usage (in bytes) stayed roughly the same:

from transitions.

When I prevent garbage collection like this:

# ...
counter = 0
events = []
with open("memory.csv", "w") as f:
    while True:
        counter += 1
        foo = Foo()
        events.append(foo.machine.events["a_to_b"])  # <-- keep reference to an event and thus to machine
        if counter % 50000 == 0:
            print(f"{counter},{process.memory_info().rss}", file=f)
            f.flush()
# ...

the memory consumption increases rather fast:

At 1.4 million instances, the process already consume almost 12 billion bytes (12 GB).

from transitions.

@yw94: I will close this issue since I cannot verify a memory leak caused by transition with information I currently have. Feel free to comment anyway if your problem persists.

from transitions.