I've had enough ideas bouncing around in my head that I had to get them written up :)
So I'm proposing to produce an informational PEP to describe what a "good" C API looks like and act as guidance as we implement new APIs or change existing ones.
This is a rough, incomplete first draft that nonetheless I think is enough to trigger useful discussions. It's a brain dump, but I've already dumped most of this before.
They're in the text below, but I'll repeat here:
our current API and to help drive discussions of any new API or API changes
I don't have any particular desire to own the entire doc, so if anyone wants to become a co-author I'm very open to that. However, I do have strong opinions on this topic after a number of years working with *excellent* API designs, designers and processes. If you want to propose a _totally_ different vision from this, please consider writing an alternative rather than trying to co-opt this one :)
(Doc in approximate Markdown, automatically wrapped to 72 cols for email and I haven't checked if that broke stuff. Sorry if it did)
CPython C API Design Guidelines
This document is intended to be a set of guiding principles for development of the current CPython C API. Future additions and enhancements to the CPython C API should follow, or at least be influenced by, the principles described here. At a minimum, any new or modified C APIs should be able to be categorised according to the terminology defined here, even if exceptions have to be made.
# Things this document is NOT
This document is NOT a design of a completely new API (though a hypothetical new API should follow this design).
This document is NOT documentation of the current API (though the current API should come to resemble it over time).
This document is NOT a set of binding rules in the same sense as PEP 7 and PEP 8 (though designs should be tested against it and exceptions should be rare).
This document is NOT permission to make backwards-incompatible modifications to the current API (though backwards-incompatible modifications should still be made where warranted).
A common understanding of certain terms is necessary to talking about the CPython C API. This section has two goals: to clarify existing common terminology, and to introduce new terminology. Terms are presented in a logical order, rather than alphabetically.
## Existing terms
**Application**: Any independent program that can be launched directly. Compare and contrast with *extension*. CPython is normally considered an application.
**Extension**: A program that integrates into an application, and cannot be launched directly but must be loaded by that application. Python modules, native or otherwise, are considered extenions. When embedded into another application, CPython is considered an extension.
**Native extension**: A subset of all extensions that are compiled to the same language as the application they integrate with. When embedded into an application that is written in C or uses C-compatible conventions, CPython is considered a native extension.
**API**: Application Programming Interface. The set of interactions defined by an application to allow extensions to extend, control, and interact with the first. Typically refers to OOP objects and functions in the abstract. CPython has one API that applies for all scenarios in all contexts, though each scenario will likely only use a subset of this API.
**ABI**: Application Binary Interface. The implementation of an API such that its interactions can be realized by a digital computer. Typically includes memory layouts and binary representations, and is a function of the build tools used to compile CPython. CPython has different ABIs in different contexts, and a different ABI for native extensions compared to extensions.
**Stdlib**: Standard library. Components that build upon the Python language in order to provide useful building blocks and pre-written functionality for users.
## New terms
These terms are introduced briefly here and described in much greater detail below.
**API ring**: One subset of an API for the purpose of extension compatibility. Extensions to CPython care about rings. Extensions choose to target a particular ring to trade off between deeper integration and tighter coupling. Targeting one ring includes access to all rings outside of that one. Rings are orthogonal to layers.
**API layer**: One subset of an API for the purpose of application and internal compatibility. Applications that embed CPython, and the CPython implementation itself, cares about layers. Applications choose to adopt or implement a particular layer, implicitly including all lower layers. Layers are orthogonal to rings.
# Quick Overview
For context as you continue reading, these are the API **rings** provided by CPython:
These are the API **layers** provided by CPython:
(Reminder that this document does not reflect the current state of CPython, but is both aspirational and defining terms for the purposes of discussion. This is not a proposal to remove anything from the standard distribution!)
# API Rings
CPython provides three API rings, listed here from outermost to innermost:
An extension that targets the Python ring does not have access to the CPython or Internal rings. Likewise, an extension that targets the CPython ring does not have access to the Internal ring, but does use the Python ring.
When CPython is an extension of another application, that application can also select which ring to target.
The expectation is that all Python implementations can provide an equivalent Python ring, CPython officially supports extensions using the CPython ring when targeting CPython, and the Internal ring is available but unsupported.
## Python API ring
The Python ring provides functionality that should be equivalent across all Python implementations - in essence, the Python language itself defines this ring.
The C implementation of the Python API allows native code to interact with Python objects as if it were written in Python. The Python API supports duck-typing and should correctly handle the substitution of alternative types.
For a concrete example,
PyObject_GetItem is part of the Python ring
PyDict_GetItem is in the CPython ring.
Compatibility requirements for the Python API match the language version. Specifically, code relying on the Python API should only break or change behaviour if the equivalent code written in Python would also break or change behaviour.
For CPython, including
Python.h should only provide access to the
Python ring. Accessing any other rings should produce a compile error.
## CPython API ring
The CPython ring provides functionality that is specific to CPython. Extensions that opt in to the CPython ring are tied directly to CPython, but have access to functions that are specific to CPython.
Functions in the CPython ring may require the caller to be using C or be able to provide C structures allocated in memory.
In general, most applications that embed CPython will use the CPython ring. Also, native extensions in the Optional stdlib layer
For a concrete example, the
PyCapsule type belongs in the CPython ring
(that is, other implementations are not required to provide this
particular way to smuggle C pointers through Python objects).
As a second concrete example,
PyType_FromSpec belongs in the CPython
ring. (The equivalent in the Python ring would be to call the
object, while the equivalent in the internal ring would be to define a
Compatibility requirements for the CPython API match the CPython major.minor version. Specifically, code relying on the CPython API should only break or change behaviour if the major.minor version changes.
For CPython, as well as
Python.h, also include
obtain access to APIs in the CPython ring.
## Internal API ring
The Internal ring provides functionality that is used to implement CPython. Extensions that opt in to the Internal ring may need to rebuild for every CPython build.
In general, most of the Required stdlib layer will use the Internal ring.
For CPython, as well as
Python.h, also include
to obtain access to APIs in the Internal ring.
# API Layers
CPython provides four API layers, listed here from top to bottom:
An application embedding Python targets one layer and all those below it, which affects the functionality available in Python.
Higher layers may depend on the APIs provided by lower layers, but not the other way around. In general, layers should aim to maximise interaction with the next layer down and avoid skipping it, but this is not a strict requirement.
Lower layers are required to maintain backwards compatibility more strictly than the layers above them.
Components within a layer that depend on other components within that layer must be treated as a single component for determining whether it may be included or omitted.
Standard Python distributions (that is, anything that may be launched
python command) will depend upon most components in the
Optional stdlib layer, and hence will require _everything_ from the
Required stdlib layer and below. Only embedders and potentially
deployment tools will use reduced layers.
(Reminder: this document does not present the current state of CPython.)
## Core layer
This layer is the core language and evaluation engine. By adopting this layer, an application can provide platform-independent Python execution. However, it may require providing implementations of a number of callbacks in order to be functional (e.g. for dynamic memory allocation).
Examples of current components that fit into the core layer:
Important but potentially non-obvious implications of relying only on the core layer:
adaptation layer, but there is no way to avoid it here. So any user of the core API will need to provide allocators and deallocators. The CPython Platform adaptation layer provides the "default" implementations, but if an embedder does not want to use these then targeting the Core layer will omit them.
layer, which leaves
sys.stdout (among others) without a
default implementation. An application that wants to support these
without adding more layers needs to provide its own implementations
current platform requires the Platform adaptation layer, while arbitrary encoding and decoding requires the Optional stdlib layer.
Platform adaptation layer provides the support for frozen, bytecode and natively-encoded source imports, while the Optional stdlib layer is required for arbitrary encodings in source files
## Platform adaptation layer
This layer provides the CPython implementation of platform-specific adapters to support the core layer.
Important but potentially non-obvious implications of relying only on the platform adaptation layer:
of codecs are in the optional stdlib layer. To fully separate these layers, an implementation of the current file system encoding would be required in the Platform adaptation layer. (But arbitrarily encoding/decoding the _contents_ of a file may require higher layers.)
imports that can be fully satisfied without this are provided here (e.g. native extension modules, precompiled bytecode, frozen modules, natively encoded source files)
## Required stdlib layer
This layer provides common APIs for interactions between other modules. All components in the Optional stdilib layer may assume that if _they_ are present, everything in this layer is also present.
## Optional stdlib layer
This layer provides modules that fundamentally stand alone. None of the lower levels may depend on these components being present, and components in this layer should explicitly declare dependencies on others in the same layer.
This layer is valuable for embedders and distributors that want to omit
certain functionality. For example, omitting
socket should be possible
when that functionality is not required, as it is in the Optional stdlib
layer, and omitting it should only affect those components in the
Optional stdlib layer that have explicitly required it.
Components in the Optional stdlib layer may be independently versioned.