python / python/cpython

`multiprocessing.managers.BaseProxy` can not be unpickled when using a custom `authkey`

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stdlib topic-multiprocessing type-bug
Dominant language
Python
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Description

Bug report

Bug description:

The problem, simplified

When using a Manager with a custom authkey, a pickled proxy object can not be unpickled:

from multiprocessing.managers import BaseManager, BaseProxy


class MyManager(BaseManager):
    pass


class MyObject:
    def __init__(self):
        self.value = 42

    def get_value(self):
        return self.value

    def set_value(self, new_value):
        self.value = new_value


class MyProxy(BaseProxy):
    _exposed_ = ("get_value", "set_value")

    def get_value(self):
        return self._callmethod("get_value")

    def set_value(self, new_value):
        return self._callmethod("set_value", (new_value,))


_my_object = MyObject()


def _get_my_object():
    return _my_object


def test_proxy_unpickle():
    MyManager.register("get_my_object", callable=_get_my_object, proxytype=MyProxy)

    manager = MyManager(
        address=("", 0),
        authkey=b"customkey", # <--- CUSTOM AUTHKEY
    )
    manager.start()

    proxy_object = manager.get_my_object()
    assert isinstance(proxy_object, MyProxy)
    assert proxy_object.get_value() == 42

    # We simulate pickling...
    rebuild_proxy, args = proxy_object.__reduce__()

    # ... and unpickling in a child process
    # Fails with: multiprocessing.context.AuthenticationError: digest sent was rejected
    rebuild_proxy(*args)

(It works when no custom authkey is passed to MyManager.)

This is because When a proxy is pickled the authkey is deliberately dropped. Accordingly, BaseProxy.__init__ uses process.current_process().authkey and fails to connect to the manager.

Actually, there is one condition where an authkey is passed:

class BaseProxy:
    def __reduce__(self):
        kwds = {}
        if get_spawning_popen() is not None:
            kwds['authkey'] = self._authkey

(So only while spawning a new process, but not at any other time later.)

This makes me wonder why the authkey parameter of BaseManager was introduced at all if it does not work in all cases but the default process.current_process().authkey does...

The documentation says:

An important feature of proxy objects is that they are picklable so they can be passed between processes.

As shown above, this is not unconditionally true. I have the feeling that the implementation can not be changed, so I would suggest to make the documentation more precise.

The context

I'm using dask_jobqueue.SLURMCluster to start worker processes on a distributed system. I want to use a multiprocessing Manager to enable communication between the main process and it's workers for progress reporting. (I already have it running for "regular" multiprocessing and I'm hesitant to switch to another means of IPC just because of this problem...)

I see two ways how I could make this work:

  • Do multiprocessing.process.current_process().authkey = b"customkey" during the initialization of the Dask worker.
  • Override BaseProxy.__reduce__ to unconditionally set kwds['authkey'] = bytes(self._authkey).

In both cases, I potentially expose authkey to the outside world... Any advice?

CPython versions tested on:

3.13

Operating systems tested on:

Linux

Linked PRs
  • gh-144311

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start with multiprocessing.managers.BaseProxy.reduce and BaseProxy.init, using the reproduction in the issue to trace how a custom authkey is handled during proxy unpickling. Review the proxy-object documentation and linked PR gh-144311; done means the documented behavior and the custom-authkey reproduction agree.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
distributed-systems
Issue type
Bug
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
35/100

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