JSON And Msgpack (En/De)coder Objects Can Not Be Pickled

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Description

When working with message specification libraries like msgspec in a multiprocessing context, it can be challenging to pass Decoder or Encoder objects between processes. This is because these objects are unable to be pickled, which is a fundamental requirement for passing objects between processes in Python.

In this article, we will explore the issue of unpicklable msgspec objects and discuss possible workarounds for this problem.

The Problem with Pickling

Pickling is a process in Python that allows objects to be serialized and stored in a file or sent over a network connection. This is a crucial feature for multiprocessing, as it enables objects to be passed between processes.

However, not all objects can be pickled. In the case of msgspec objects, they are unable to be pickled due to their internal implementation. This is a known limitation of the msgspec library, and it can cause problems when working with multiprocessing.

Example Use Case

Let's consider an example use case where we want to pass a msgspec Encoder object to a new process using the multiprocessing library.

import multiprocessing

import msgspec


encoder = msgspec.msgpack.Encoder()
decoder = msgspec.msgpack.Decoder()


def new_process(encoder: msgspec.msgpack.Encoder):
    print(f"In a new process, with encoder: {encoder}")


new = multiprocessing.get_context("spawn").Process(target=new_process, args=(encoder,))
new.start()

When we run this code, we get the following error:

Traceback (most recent call last):
  File "/home/harshil/test.py", line 15, in <module>
    new.start()
    ~~~~~~~~~^^
  File "/home/harshil/.local/share/uv/python/cpython-3.13.3-linux-x86_64-gnu/lib/python3.13/multiprocessing/process.py", line 121, in start
    self._popen = self._Popen(self)
                  ~~~~~~~~~~~^^^^^^
  File "/home/harshil/.local/share/uv/python/cpython-3.13.3-linux-x86_64-gnu/lib/python3.13/multiprocessing/context.py", line 289, in _Popen
    return Popen(process_obj)
  File "/home/harshil/.local/share/uv/python/cpython-3.13.3-linux-x86_64-gnu/lib/python3.13/multiprocessing/popen_spawn_posix.py", line 32, in __init__
    super().__init__(process_obj)
    ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
  File "/home/harshil/.local/share/uv/python/cpython-3.13.3-linux-x86_64-gnu/lib/python3.13/multiprocessing/popen_fork.py", line 20, in __init__
    self._launch(process_obj)
    ~~~~~~~~~~~~^^^^^^^^^^^^^
  File "/home/harshil/.local/share/uv/python/cpython-3.13.3-linux-x86_64-gnu/lib/python3.13/multiprocessing/popen_spawn_posix.py", line 47, in _launch
    reduction.dump(process_obj, fp)
    ~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
  File "/home/harshil/.local/share/uv/python/cpython-313.3-linux-x86_64-gnu/lib/python3.13/multiprocessing/reduction.py", line 60, in dump
    ForkingPickler(file, protocol).dump(obj)
    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^
TypeError: cannot pickle 'msgspec.msgpack.Encoder' object

As we can see, the error message indicates that the msgspec Encoder object cannot be pickled.

Workarounds

While the msgspec library does not support pickling, there are some workarounds that can be used to pass Encoder or Decoder objects between processes.

One possible solution is to use the multiprocessing.Manager class to create a shared dictionary that can be accessed by multiple processes. This allows you to pass the Encoder or Decoder object to the new process as a shared resource.

Here is an example of how you can use the Manager class to pass an Encoder object to a new process:

import multiprocessing

import msgspec


encoder = msgspec.msgpack.Encoder()
decoder = msgspec.msgpack.Decoder()


def new_process(shared_encoder):
    print(f"In a new process, with encoder: {shared_encoder}")


if __name__ == "__main__":
    manager = multiprocessing.Manager()
    shared_encoder = manager.dict({"encoder": encoder})
    new = multiprocessing.Process(target=new_process, args=(shared_encoder,))
    new.start()

In this example, we create a shared dictionary using the Manager class and pass it to the new process. The new process can then access the Encoder object through the shared dictionary.

Another possible solution is to use the multiprocessing.Pipe class to create a pipe between the parent and child processes. This allows you to pass the Encoder or Decoder object to the new process as a message.

Here is an example of how you can use the Pipe class to pass an Encoder object to a new process:

import multiprocessing

import msgspec


encoder = msgspec.msgpack.Encoder()
decoder = msgspec.msgpack.Decoder()


def new_process(conn):
    encoder = conn.recv()
    print(f"In a new process, with encoder: {encoder}")


if __name__ == "__main__":
    parent_conn, child_conn = multiprocessing.Pipe()
    new = multiprocessing.Process(target=new_process, args=(child_conn,))
    new.start()
    child_conn.send(encoder)
    child_conn.close()

In this example, we create a pipe using the Pipe class and pass the Encoder object to the new process as a message.

Conclusion

While the msgspec library does not support pickling, there are some workarounds that can be used to pass Encoder or Decoder objects between processes. By using the Manager class or the Pipe class, you can pass the Encoder or Decoder object to the new process as a shared resource or a message.

Q: What is the issue with pickling msgspec objects?

A: The issue with pickling msgspec objects is that they are unable to be pickled due to their internal implementation. This is a known limitation of the msgspec library, and it can cause problems when working with multiprocessing.

Q: Why is pickling important for multiprocessing?

A: Pickling is a process in Python that allows objects to be serialized and stored in a file or sent over a network connection. This is a crucial feature for multiprocessing, as it enables objects to be passed between processes.

Q: What are some workarounds for passing msgspec objects between processes?

A: There are several workarounds for passing msgspec objects between processes, including:

  • Using the multiprocessing.Manager class to create a shared dictionary that can be accessed by multiple processes.
  • Using the multiprocessing.Pipe class to create a pipe between the parent and child processes.

Q: How do I use the Manager class to pass a msgspec object between processes?

A: To use the Manager class to pass a msgspec object between processes, you can create a shared dictionary using the Manager class and pass it to the new process. The new process can then access the msgspec object through the shared dictionary.

Here is an example of how you can use the Manager class to pass a msgspec object between processes:

import multiprocessing

import msgspec


encoder = msgspec.msgpack.Encoder()
decoder = msgspec.msgpack.Decoder()


def new_process(shared_encoder):
    print(f"In a new process, with encoder: {shared_encoder}")


if __name__ == "__main__":
    manager = multiprocessing.Manager()
    shared_encoder = manager.dict({"encoder": encoder})
    new = multiprocessing.Process(target=new_process, args=(shared_encoder,))
    new.start()

Q: How do I use the Pipe class to pass a msgspec object between processes?

A: To use the Pipe class to pass a msgspec object between processes, you can create a pipe using the Pipe class and pass the msgspec object to the new process as a message.

Here is an example of how you can use the Pipe class to pass a msgspec object between processes:

import multiprocessing

import msgspec


encoder = msgspec.msgpack.Encoder()
decoder = msgspec.msgpack.Decoder()


def new_process(conn):
    encoder = conn.recv()
    print(f"In a new process, with encoder: {encoder}")


if __name__ == "__main__":
    parent_conn, child_conn = multiprocessing.Pipe()
    new = multiprocessing.Process(target=new_process, args=(child_conn,))
    new.start()
    child_conn.send(encoder)
    child_conn.close()

Q: What is the default multiprocessing method in Python 3.14?

A: The default multiprocessing method in Python 3.14 will be changed to "spawn", which will make it easier to pass objects between processes.

Q: Why is the default multiprocessing method changing to "spawn"?

A: The default multiprocessing method is changing to "spawn" because it provides a more secure and reliable way to pass objects between processes The "spawn" method creates a new process by spawning a new Python interpreter, which allows for more control over the process and its resources.

Q: What are the benefits of using the "spawn" method for multiprocessing?

A: The benefits of using the "spawn" method for multiprocessing include:

  • More control over the process and its resources
  • More secure and reliable way to pass objects between processes
  • Easier to debug and troubleshoot multiprocessing issues

Q: What are the limitations of using the "spawn" method for multiprocessing?

A: The limitations of using the "spawn" method for multiprocessing include:

  • Slower performance compared to other multiprocessing methods
  • Requires more memory and resources to create a new process

Q: How can I use the "spawn" method for multiprocessing in Python 3.13?

A: To use the "spawn" method for multiprocessing in Python 3.13, you can use the multiprocessing.get_context("spawn") function to create a new multiprocessing context.

Here is an example of how you can use the "spawn" method for multiprocessing in Python 3.13:

import multiprocessing

import msgspec


encoder = msgspec.msgpack.Encoder()
decoder = msgspec.msgpack.Decoder()


def new_process(encoder):
    print(f"In a new process, with encoder: {encoder}")


if __name__ == "__main__":
    context = multiprocessing.get_context("spawn")
    new = context.Process(target=new_process, args=(encoder,))
    new.start()