Python interpreters

Versions of Python

By a version of Python we usually mean the variant of Python language and standard library interface as used by a specific version of CPython, the reference implementation of Python.

Python versions are determined from the two first version components. The major version is incremented when major incompatible changes are introduced in the language, as was the case in Python 3. Along with minor version changes, the new releases introduce new features and remove deprecated APIs. The Python documentation generally indicates when a particular API was added or deprecated, and when it is planned to be removed.

Practically speaking, this means that a program written purely for Python 2 is unlikely to work on Python 3, and requires major changes to achieve compatibility. On the other hand, a program written for Python 3.7 is very likely to work with Python 3.8, and reasonably likely to support Python 3.6 as well. If that is not the case, minor changes are usually sufficient to fix that.

For example, Python 3.7 introduced a new importlib.resources module. If your program uses it, it will not work on Python 3.6 without a backwards compatibility code.

Python 3.8 removed the deprecated platform.linux_distribution() function. If your program used it, it will not work on Python 3.8 without changes. However, it was deprecated since Python 3.5, so if you were targetting 3.7, you should not have been using it in the first place.

Gentoo supports building packages against Python 2.7 and a shifting window of 3-4 versions of Python 3. They are provided as slots of dev-lang/python.

Life cycle of a Python implementation

Every Python implementation (understood as a potential target) in Gentoo follows roughly the following life cycle:

  1. The interpreter is added to ~arch for initial testing. At this point, packages can not declare support for the implementation yet.

    New CPython releases enter this stage when upstream releases the first alpha version. Since the feature set is not stable yet, it is premature to declare the package compatibility with these versions.

  2. The new Python target is added. It is initially stable-masked, so only ~arch users can use it. At this point, packages start being tested against the new target and its support starts being declared in PYTHON_COMPAT.

    CPython releases enter this stage when the first beta release is made. These versions are not fully stable yet either and package regressions do happen between beta releases but they are stable enough for initial testing.

  3. When ready, the new interpreter is stabilized. The target is not yet available for stable users, though.

    CPython releases enter this stage roughly 30 days after the first stable release (i.e. X.Y.0 final) is made. This is also the stage where PyPy3 releases are in Gentoo for the time being.

  4. The stable-mask for the target is removed. For this to happen, the inconsistencies in stable graph need to be addressed first via stabilizing newer versions of packages.

    CPython releases enter this stage after the interpreter is marked stable on all architectures and all the packages needed to clear the stable depenendency graph are stable as well.

  5. Over time, developers are repeatedly asked to push testing packages for the new target forward and stabilize new versions supporting it. Eventually, the final push for updates happens and packages not supporting the new target start being removed.

  6. If applicable, the new target becomes the default. The developers are required to test new packages against it. The support for old target is slowly being discontinued.

    Currently, we try to plan the switch to the next CPython version around June — July every year, to make these events more predictable to Gentoo users.

  7. Eventually, the target becomes replaced by the next one. When it nears end of life, the final packages requiring it are masked for removal and the target flags are disabled.

    We generally aim to preserve support for old targets for as long as they are found still needed by Gentoo users. However, as more upstream packages remove support for older versions of Python, the cost of preserving the support becomes too great.

  8. The compatibility declarations are cleaned up from PYTHON_COMPAT, and obsolete ebuild and eclass code is cleaned up.

  9. Finally, the interpreter is removed when it becomes no longer feasible to maintain it (usually because of the cost of fixing vulnerabilities or build failures).

Stability guarantees of Python implementations

The language and standard library API of every Python version is expected to be stable since the first beta release of the matching CPython version. However, historically there were cases of breaking changes prior to a final release (e.g. the revert of enum changes in Python 3.10), as well as across minor releases (e.g. urlsplit() URL sanitization / security fix).

The ABI of every CPython version is considered stable across bugfix releases since the first RC. This includes the public ABI of libpython, C extensions and compiled Python modules. Prior to the first RC, breaking changes to either may still happen. Gentoo currently does not account for these changes to the high cost of using slot operators, and therefore users using ~arch CPython may have to occasionally rebuild Python packages manually.

Additionally, modern versions of CPython declare so-called ‘stable ABI’ that remains forward compatible across Python versions. This permits upstreams to release wheels that can be used with multiple CPython versions (contrary to the usual case of building wheels separately for each version). However, this does not affect Gentoo packaging at the moment.

PyPy does not hold specific ABI stability guarantees. Gentoo packages use subslots to declare the current ABI version, and the eclasses use slot operators in dependencies to enforce rebuilds whenever the ABI version changes. Fortunately, lately this has only occurred whenever Gentoo switched to the next PyPy branch (i.e. the one corresponding to the next Python language version).

Alternative Python implementations

CPython is the reference and most commonly used Python implementation. However, there are other interpreters that aim to maintain reasonable compatibility with it.

PyPy is an implementation of Python built using in-house RPython language, using a Just-in-Time compiler to achieve better performance (generally in long-running programs running a lot of Python code). It maintains quite good compatibility with CPython, except when programs rely on its implementation details or GC behavior.

PyPy upstream provides PyPy variants compatible with Python 2.7 and one version of Python 3. Gentoo supports building packages against PyPy3. PyPy2.7 is provided as dev-python/pypy, while PyPy3 is provided as dev-python/pypy3.

Jython is an implementation of Python written in Java. Besides being a stand-alone Python interpreter, it supports bidirectional interaction between Python and Java libraries.

Jython development is very slow paced, and it is currently bound to Python 2.7. Gentoo does not provide Jython anymore.

IronPython is an implementation of Python for the .NET framework. Alike Jython, it supports bidirectional interaction between Python and .NET Framework. It is currently bound to Python 2.7. It is not packaged in Gentoo.

Brython is an implementation of Python 3 for client-side web programming (in JavaScript). It provides a subset of Python 3 standard library combined with access to DOM objects.

MicroPython is an implementation of Python 3 aimed for microcontrollers and embedded environments. It aims to maintain some compatibility with CPython while providing stripped down standard library and additional modules to interface with hardware. It is packaged as dev-lang/micropython.

Tauthon is a fork of Python 2.7 that aims to backport new language features and standard library modules while preserving backwards compatibility with existing code. It is not packaged in Gentoo.

Support for multiple implementations

The support for simultaneously using multiple Python implementations is implemented primarily through USE flags. The packages installing or using Python files define either PYTHON_TARGETS or PYTHON_SINGLE_TARGET flags that permit user to choose which implementations are used.

Modules and extensions are installed separately for each interpreter, in its specific site-packages directory. This means that a package can run using a specific target correctly only if all its dependencies were also installed for the same implementation. This is enforced via USE dependencies.

Additionally, dev-lang/python-exec provides a mechanism for installing multiple variants of each Python script simultaneously. This is necessary to support scripts that differ between Python versions (particularly between Python 2 and Python 3) but it is also used to prevent scripts from being called via unsupported interpreter (i.e. one that does not have its accompanying modules or dependencies installed).

This also implies that all installed Python scripts must have their shebangs adjusted to use a specific Python interpreter (not python nor python3 but e.g. python3.7), and all other executables must also be modified to call specific version of Python directly.

Backports

A common method of improving compatibility with older versions of Python is to backport new standard library modules or features. Packages doing that are generally called backports.

Ideally, backports copy the code from the standard library with minimal changes, and provide a matching API. In some cases, new versions of backports are released as the standard library changes, and their usability extends from providing a missing module to extending older version of the module. For example, the dev-python/funcsigs package originally backported function signatures from Python 3.3 to older versions, and afterwards was updated to backport new features from Python 3.6, becoming useful to versions 3.3 through 3.5.

Sometimes, the opposite happens. dev-python/mock started as a stand-alone package, and was integrated into the standard library as unittest.mock later on. Afterwards, the external package became a backport of the standard library module.

In some cases backports effectively replace external packages. Once lzma module has been added to the standard library, its backport dev-python/backports-lzma has effectively replaced the competing LZMA packages.

Individual backports differ by the level of compatibility with the standard library provided, and therefore on the amount of additional code needed in your program. The exact kind of dependencies used depends on that.

dev-python/ipaddress is a drop-in backport of the ipaddress module from Python 3.3. It is using the same module name, so a code written to use this module will work out-of-the-box on Python 2.7 if the package is installed. As a side note, since Python always prefers built-in modules over external packages, there is no point in enabling Python 3 in this package as the installed module would never be used. Appropriately, you should depend on this package only for the Python versions needing it.

dev-python/mock is a compatible backport of the unittest.mock module. It can’t use the same name as the standard library module, therefore the packages need to use it conditionally, e.g.:

try:
    from unittest.mock import Mock
except ImportError:  # py<3.3
    from mock import Mock

or:

import sys
if sys.hexversion >= 0x03030000:
    from unittest.mock import Mock
else:
    from mock import Mock

However, the actual API remains compatible, so the programs do not need more compatibility code than that. In some cases, upstreams fail (or even refuse) to use the external mock package conditionally — in that case, you either need to depend on this package unconditionally, or patch it.

dev-python/trollius aimed to provide a backport of asyncio for Python 2. Since the asyncio framework relies on new Python syntax, the backport cannot be API compatible and requires using a different syntax than native asyncio code.