Why Python Multiprocessing Manager Produce Threading Locks?

>>> import multiprocessing >>> multiprocessing.Manager().Lock() >>> type(multiprocessing.Lock())

Solution 1:

Managed objects are always proxies; the goal of the manager is to make non-multiprocessing-aware objects into multiprocessing aware.

There is no point in doing this for multiprocessing.Lock() objects; these are implemented using semaphores and are fully multiprocessing capable without assistance.

threading.Lock on the other hand is not multiprocessing aware; there are some differences between threading.Lock() objects and multiprocessing.Lock(); the latter supports a timeout when acquiring a lock, for example.

Solution 2:

It is certainly not expected since the documentation clearly states that Lock()

Create a shared threading.Lock object and return a proxy for it.

As to why it returns a threading.Lock instead of a multiprocessing object is a different story I unfortunately can't answer.

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