PEP 630 – Isolating Extension Modules

Author:: Petr Viktorin <encukou at gmail.com>
Discussions-To:: Capi-SIG list
Status:: Final
Type:: Informational
Created:: 25-Aug-2020
Post-History:: 16-Jul-2020

Table of Contents

Important

This PEP is a historical document. The up-to-date, canonical documentation can now be found at Isolating Extension Modules HOWTO.

See PEP 1 for how to propose changes.

Abstract

Traditionally, state belonging to Python extension modules was kept in C static variables, which have process-wide scope. This document describes problems of such per-process state and efforts to make per-module state—a better default—possible and easy to use.

The document also describes how to switch to per-module state where possible. This transition involves allocating space for that state, potentially switching from static types to heap types, and—perhaps most importantly—accessing per-module state from code.

About This Document

As an informational PEP, this document does not introduce any changes; those should be done in their own PEPs (or issues, if small enough). Rather, it covers the motivation behind an effort that spans multiple releases, and instructs early adopters on how to use the finished features.

Once support is reasonably complete, this content can be moved to Python’s documentation as a HOWTO. Meanwhile, in the spirit of documentation-driven development, gaps identified in this PEP can show where to focus the effort, and it can be updated as new features are implemented.

Whenever this PEP mentions extension modules, the advice also applies to built-in modules.

Note

This PEP contains generic advice. When following it, always take into account the specifics of your project.

For example, while much of this advice applies to the C parts of Python’s standard library, the PEP does not factor in stdlib specifics (unusual backward compatibility issues, access to private API, etc.).

PEPs related to this effort are:

PEP 384 – Defining a Stable ABI, which added a C API for creating heap types
PEP 489 – Multi-phase extension module initialization
PEP 573 – Module State Access from C Extension Methods

This document is concerned with Python’s public C API, which is not offered by all implementations of Python. However, nothing in this PEP is specific to CPython.

As with any Informational PEP, this text does not necessarily represent a Python community consensus or recommendation.

Motivation

An interpreter is the context in which Python code runs. It contains configuration (e.g. the import path) and runtime state (e.g. the set of imported modules).

Python supports running multiple interpreters in one process. There are two cases to think about—users may run interpreters:

in sequence, with several Py_InitializeEx/Py_FinalizeEx cycles, and
in parallel, managing “sub-interpreters” using Py_NewInterpreter/Py_EndInterpreter.

Both cases (and combinations of them) would be most useful when embedding Python within a library. Libraries generally shouldn’t make assumptions about the application that uses them, which includes assuming a process-wide “main Python interpreter”.

Currently, CPython doesn’t handle this use case well. Many extension modules (and even some stdlib modules) use per-process global state, because C static variables are extremely easy to use. Thus, data that should be specific to an interpreter ends up being shared between interpreters. Unless the extension developer is careful, it is very easy to introduce edge cases that lead to crashes when a module is loaded in more than one interpreter in the same process.

Unfortunately, per-interpreter state is not easy to achieve—extension authors tend to not keep multiple interpreters in mind when developing, and it is currently cumbersome to test the behavior.

Rationale for Per-module State

Instead of focusing on per-interpreter state, Python’s C API is evolving to better support the more granular per-module state. By default, C-level data will be attached to a module object. Each interpreter will then create its own module object, keeping the data separate. For testing the isolation, multiple module objects corresponding to a single extension can even be loaded in a single interpreter.

Per-module state provides an easy way to think about lifetime and resource ownership: the extension module will initialize when a module object is created, and clean up when it’s freed. In this regard, a module is just like any other PyObject *; there are no “on interpreter shutdown” hooks to think—or forget—about.

Goal: Easy-to-Use Module State

It is currently cumbersome or impossible to do everything the C API offers while keeping modules isolated. Enabled by PEP 384, changes in PEP 489 and PEP 573 (and future planned ones) aim to first make it possible to build modules this way, and then to make it easy to write new modules this way and to convert old ones, so that it can become a natural default.

Even if per-module state becomes the default, there will be use cases for different levels of encapsulation: per-process, per-interpreter, per-thread or per-task state. The goal is to treat these as exceptional cases: they should be possible, but extension authors will need to think more carefully about them.

Non-goals: Speedups and the GIL

There is some effort to speed up CPython on multi-core CPUs by making the GIL per-interpreter. While isolating interpreters helps that effort, defaulting to per-module state will be beneficial even if no speedup is achieved, as it makes supporting multiple interpreters safer by default.

Making Modules Safe with Multiple Interpreters

There are many ways to correctly support multiple interpreters in extension modules. The rest of this text describes the preferred way to write such a module, or to convert an existing one.

Note that support is a work in progress; the API for some features your module needs might not yet be ready.

A full example module is available as xxlimited.

This section assumes that “you” are an extension module author.

Isolated Module Objects

The key point to keep in mind when developing an extension module is that several module objects can be created from a single shared library. For example:

>>> import sys
>>> import binascii
>>> old_binascii = binascii
>>> del sys.modules['binascii']
>>> import binascii  # create a new module object
>>> old_binascii == binascii
False

As a rule of thumb, the two modules should be completely independent. All objects and state specific to the module should be encapsulated within the module object, not shared with other module objects, and cleaned up when the module object is deallocated. Exceptions are possible (see Managing Global State), but they will need more thought and attention to edge cases than code that follows this rule of thumb.

While some modules could do with less stringent restrictions, isolated modules make it easier to set clear expectations (and guidelines) that work across a variety of use cases.

Surprising Edge Cases

Note that isolated modules do create some surprising edge cases. Most notably, each module object will typically not share its classes and exceptions with other similar modules. Continuing from the example above, note that old_binascii.Error and binascii.Error are separate objects. In the following code, the exception is not caught:

>>> old_binascii.Error == binascii.Error
False
>>> try:
...     old_binascii.unhexlify(b'qwertyuiop')
... except binascii.Error:
...     print('boo')
...
Traceback (most recent call last):
  File "<stdin>", line 2, in <module>
binascii.Error: Non-hexadecimal digit found

This is expected. Notice that pure-Python modules behave the same way: it is a part of how Python works.

The goal is to make extension modules safe at the C level, not to make hacks behave intuitively. Mutating sys.modules “manually” counts as a hack.

Managing Global State

Sometimes, state of a Python module is not specific to that module, but to the entire process (or something else “more global” than a module). For example:

The readline module manages the terminal.
A module running on a circuit board wants to control the on-board LED.

In these cases, the Python module should provide access to the global state, rather than own it. If possible, write the module so that multiple copies of it can access the state independently (along with other libraries, whether for Python or other languages).

If that is not possible, consider explicit locking.

If it is necessary to use process-global state, the simplest way to avoid issues with multiple interpreters is to explicitly prevent a module from being loaded more than once per process—see Opt-Out: Limiting to One Module Object per Process.

Managing Per-Module State

To use per-module state, use multi-phase extension module initialization introduced in PEP 489. This signals that your module supports multiple interpreters correctly.

Set PyModuleDef.m_size to a positive number to request that many bytes of storage local to the module. Usually, this will be set to the size of some module-specific struct, which can store all of the module’s C-level state. In particular, it is where you should put pointers to classes (including exceptions, but excluding static types) and settings (e.g. csv’s field_size_limit) which the C code needs to function.

Note

Another option is to store state in the module’s __dict__, but you must avoid crashing when users modify __dict__ from Python code. This means error- and type-checking at the C level, which is easy to get wrong and hard to test sufficiently.

If the module state includes PyObject pointers, the module object must hold references to those objects and implement the module-level hooks m_traverse, m_clear and m_free. These work like tp_traverse, tp_clear and tp_free of a class. Adding them will require some work and make the code longer; this is the price for modules which can be unloaded cleanly.

An example of a module with per-module state is currently available as xxlimited; example module initialization shown at the bottom of the file.

Opt-Out: Limiting to One Module Object per Process

A non-negative PyModuleDef.m_size signals that a module supports multiple interpreters correctly. If this is not yet the case for your module, you can explicitly make your module loadable only once per process. For example:

static int loaded = 0;

static int
exec_module(PyObject* module)
{
    if (loaded) {
        PyErr_SetString(PyExc_ImportError,
                        "cannot load module more than once per process");
        return -1;
    }
    loaded = 1;
    // ... rest of initialization
}

Module State Access from Functions

Accessing the state from module-level functions is straightforward. Functions get the module object as their first argument; for extracting the state, you can use