Suggested “What’s New in Python 3.9” Entry

The following subsection would be added to the Python 3.9 What’s New document, and then linked from each of the Python 3.9 alpha and beta announcements.

Python-Requires: >= "3.9.0b6"; python_version == "3.9" and full_python_version != "3.9.0a5"

Example announcement text for the 3.9.0a1 release

This is the first preview release of Python 3.9. As an alpha release, it is intended for library and application compatibility testing and the creation of ABI compatible binary artifacts. It is not recommended for use in production environments.

Example announcement text for the 3.9.0b2 release

This is the second preview release of Python 3.9. As a beta release, it is fully binary compatible with the preceding 3.9.0a1 release. It is recommended for production use only in environments with sufficiently robust integration testing and operational monitoring capabilities.

(Remainder as per 3.9.0a1 announcement, with updates for implemented changes and the next expected release being 3.9.0b3)

Example announcement text for 3.9.0a5 (a mid-stream alpha release)

This is the fifth preview release of Python 3.9. As an alpha release, it is NOT fully binary compatible with the preceding 3.9.0b4 release. This release is intended for library and application compatibility testing and the creation of ABI compatible binary artifacts. It is not recommended for use in production environments.

Python-Requires: >= "3.9.0b6"; python_version == "3.9" and full_python_version != "3.9.0a5"

Example announcement text for 3.9.0rc1

This is the first release candidate for Python 3.9. As a release candidate, this release is now feature complete, the full CPython ABI is now frozen, and the pre-release marker has been removed from the ABI compatibility flags. It is recommended for production use only in environments with sufficiently robust integration testing and operational monitoring capabilities.

Two year cadence for stable releases

With the rolling pre-freeze releases available to users that are looking to use leading edge versions of the reference interpreter and standard library, this PEP proposes that the frequency of X.Y.0 releases be adjusted to publish a new stable release in August every two years (starting in 2021, around two years after the release of Python 3.8.0).

This change is arguably orthogonal to the proposed changes to the handling of the pre-freeze release period, but the connection is that without those pre-release management changes, the downsides of a two-year full release cadence would probably outweigh the upsides, whereas the opposite is true for a 12-month release cadence (i.e. with the pre-release management changes proposed in this PEP in place, the downsides of a 12-month full release cadence would outweigh the upsides).

Merging of the alpha and beta phases into a “pre-freeze” phase

Rather than continuing the status quo where the pre-release alpha and beta phases are distinct and sequential, this PEP proposes that they instead be combined into a single “pre-freeze” phase with a monotonically increasing serial number on the releases.

Rather than denoting distinct phases, the “alpha” and “beta” names would instead indicate whether or not the release contains breaking changes to the full CPython C ABI:

• “alpha” releases would be “ABI breaking” releases where extension modules built against the full CPython ABI in the preceding pre-release are not necessarily going to load correctly
• “beta” releases would be “binary compatible” releases, where extension modules built against the full CPython ABI in the preceding pre-release are expected to load correctly, as long as those modules abide by the following additional criteria:
  • the module must not be using any provisional or private C APIs (either from the previous stable release series, or the in development pre-release series) that were removed in this beta release, or were changed in an ABI incompatible way
  • the module must not be using any C APIs that were deprecated in the previous stable release series, and removed in this beta release

Release candidate policy, phase duration, and cadence

Given the proposed changes to the alpha and beta release phases, the release candidate phase would see the following related adjustments:

• Feature freeze, ABI freeze, pyc file format freeze, and maintenance branch creation would all correspond with the creation of X.Y.0rc1 (currently these occur across a mixture of X.Y.0b1, the last beta release, and X.Y.0rc1)
• The X.Y.0 release candidate period would be extended from 3 weeks to 2 months
• There would normally be two release candidates issued a month apart, but additional candidates may be published at the release manager’s discretion
• The final X.Y.0 release would occur between 1 and 4 weeks after the final release candidate (depending if additional release candidates were needed after the second)
• If the final X.Y.0 release is delayed beyond the August target date, the subsequent release series is not affected, and will still be scheduled for August (now slightly less than two years later).

In addition to allowing more time for end user feedback on the release candidate, this adjusted policy also provides additional time for maintainers of Python projects to build and publish pre-built wheel archives for the new stable release series, significantly improving the initial user experience of the X.Y.0 release.

Changes to management of the CPython stable C ABI

The CPython stable ABI [5] makes the commitment that binary extension modules built against any particular CPython release will continue to work on future CPython releases that support the same stable ABI version (this version is currently abi3).

Under the proposed rolling pre-freeze release model, this commitment would be extended to also apply to the beta releases: once an intentional addition to the abi3 stable ABI for the upcoming Python version has been shipped in a beta release, then it will not be removed from future releases for as long as the abi3 stable ABI remains supported.

Two main mechanisms will be available for obtaining community feedback on additions to the stable ABI:

• the preferred mechanism will be to add new APIs to the full CPython API first, and only promote them to the stable ABI after they have been included in at least one published beta release and received relevant user feedback
• for APIs where that approach is unavailable for some reason (e.g. some API additions may serve no useful purpose when the full CPython API is available), then developers may request that the release manager mark the next release as an alpha release (even in the absence of an ABI break in the full CPython API), and attempt to obtain further feedback that way

As a slight readability and usability improvement, this PEP also proposes the introduction of aliases for each major stable ABI version:

#define Py_LIMITED_API_3_3 0x03030000
#define Py_LIMITED_API_3_4 0x03040000
#define Py_LIMITED_API_3_5 0x03050000
#define Py_LIMITED_API_3_6 0x03060000
#define Py_LIMITED_API_3_7 0x03070000
#define Py_LIMITED_API_3_8 0x03080000
#define Py_LIMITED_API_3_9 0x03090000
// etc...

These would be used both in extension module code to set the target ABI version:

#define Py_LIMITED_API Py_LIMITED_API_3_8

And also in the CPython interpreter implementation to check which symbols should be made available:

#if !defined(Py_LIMITED_API) || Py_LIMITED_API+0 >= Py_LIMITED_API_3_9
// A Python 3.9+ addition to the stable ABI would appear here
#endif

The documentation for the rolling pre-freeze releases and the stable C ABI would make it clear that extension modules built against the stable ABI in a later pre-freeze release may not load correctly on an earlier pre-freeze release.

The documentation for alpha releases and the stable C ABI would make it clear that even extension modules built against the stable ABI in an alpha release may not load correctly on the next release if two alpha releases are published in a row (this situation would ideally be rare).

Changes to management of the full CPython ABI

This PEP proposes two changes to the management of the full CPython ABI.

Updating Python-Requires for projects affected by full C ABI changes

When a project first opts in to providing pre-built binary wheels for the rolling pre-freeze release series, they don’t need to do anything special: they would add the rolling release series to their build and test matrices and publish binary archives that are flagged as being compatible with that release series, just as they would if providing pre-built binary wheels after the full CPython ABI freeze for that release series.

However, if the project is affected by a CPython ABI compatibility break in the rolling release stream, then they will need to issue a version update that includes both the new binary build, and a new environment constrained Python-Requires marker.

For example, if a project supporting the rolling release stream was affected by a CPython ABI compatibility break in the 3.9.0a5 release, then they would add the following metadata entry on the version that published the updated binary build:

Python-Requires: >= "3.9.0b6"; python_version == "3.9" and full_python_version != "3.9.0a5"

What this does is add an additional compatibility constraint as part of the published packages, so Python 3.9.0 beta versions prior to 3.9.0b6 won’t consider the updated package as a candidate for installation, and the only alpha release that will consider the package is 3.9.0a5 itself.

Why rolling pre-freeze releases over simply doing more frequent X.Y.0 releases?

For large parts of Python’s user base, availability of new CPython feature releases isn’t the limiting factor on their adoption of those new releases (this effect is visible in such metrics as PyPI download metadata).

As such, any proposal based on speeding up full feature releases needs to strike a balance between meeting the needs of users who would be adopting each release as it became available, and those that would now be in a position of adopting every 2nd, 3rd, or 4th release, rather than being able to migrate to almost every release at some point within its lifecycle.

This proposal aims to approach the problem from a different angle by defining a new production-ready release stream that is more specifically tailored to the interests of operating environments that are able to consume new releases as fast as the CPython core team is prepared to produce them.

Is it necessary to keep the “alpha” and “beta” naming scheme?

Using the “a” and “b” initials for the proposed rolling releases is a design constraint imposed by some of the pragmatic aspects of the way CPython version numbers are published.

Specifically, alpha releases, beta releases, and release candidates are reported in some places using the strings “a”, “b”, and “c” respectively, while in others they’re reported using the hex digits 0xA, 0xB, and 0xC. We want to preserve that, while also ensuring that any Python-Requires constraints are expressed against the beta releases rather than the alpha releases (since the latter may not enforce the abi3 stability requirements if two alpha releases occur in succession).

However, there isn’t anything forcing us to say that the “a” stands for “alpha” or the “b” stands for “beta”.

That means that if we wanted to increase adoption amongst folks that were only being put off by the “beta” label, then it may make sense to emphasise the “*A*BI breaking” and “*B*inary compatible” names over the “alpha” and “beta” names, giving:

• 3.9.0a1: ABI breaking pre-freeze release
• 3.9.0b2: binary compatible pre-freeze release
• 3.9.0rc1: release candidate
• 3.9.0: final release

This iteration of the PEP doesn’t go that far, as limiting initial adoption of the rolling pre-freeze releases to folks that are comfortable with the “beta” label is likely to be a good thing, as it is the early adopters of these releases that are going to encounter any unexpected consequences that occur at the level of the wider Python ecosystem, and we’re going to need them to be willing to take an active part in getting those issues resolved.

Moving away from the “beta” naming would then become an option to keep in mind for the future, assuming the resulting experience is sufficiently positive that we decide the approach is worth continuing.

Why rolling pre-freeze releases rather than alternating between stable and unstable release series?

Rather than using the beta period for rolling releases, another option would be to alternate between traditional stable releases (for 3.8.x, 3.10.x, etc), and release series that used the new rolling release cadence (for 3.9.x, 3.11.x, etc).

This idea suffers from the same core problem as PEP 598 and PEP 602: it imposes changes on end users that are happy with the status quo without offering them any clear compensating benefit.

It’s also affected by one of the main concerns raised against PEP 598: at least some core developers and end users strongly prefer that no particular semantics be assigned to the value of any of the numbers in a release version. These community members instead prefer that all the semantic significance be associated with the position within the release number that is changing.

By contrast, the rolling pre-freeze release proposal aims to address that concern by ensuring that the proposed changes in policy all revolve around whether a particular release is an alpha release, beta release, release candidate, or final release.

Why not use Calendar Versioning for the rolling release stream?

Steve Dower’s initial write-up of this proposal [1] suggested the use of calendar versioning for the rolling release stream (so the first rolling pre-release after Python 3.8.0 would have been Python 2019.12 rather than 3.9.0b1).

Paul Moore pointed out [2] two major practical problems with that proposal:

• it isn’t going to be clear to users of the calendar-based versions where they stand in relation to the traditionally numbered versions
• it breaks Python-Requires metadata processing in packaging tools with no clear way of fixing it reliably (since all calendar versions would appear as newer than any standard version)

This PEP aims to address both of those problems by using the established beta version numbers for the rolling releases.

As an example, consider the following question: “Does Python 2021.12 include all the new features released in Python 3.9.0?”. With calendar versioning on the rolling releases, that’s impossible to answer without consulting a release calendar to see when 3.9.0rc1 was branched off from the rolling release series.

By contrast, the equivalent question for rolling pre-freeze releases is straightforward to answer: “Does Python 3.10.0b2 include all the new features released in Python 3.9.0?”. Just from formulating the question, the answer is clearly “Yes, unless they were provisional features that got removed”.

The beta numbering approach also avoids other questions raised by the calendar versioning concept, such as how sys.version_info, PY_VERSION_HEX, site-packages directory naming, and installed Python binary and extension module naming would work.

How would users of the rolling pre-freeze releases detect API changes?

When adding new features, core developers would be strongly encouraged to support feature detection and graceful fallback to alternative approaches via mechanisms that don’t rely on either sys.version_info or runtime code object introspection.

In most cases, a simple hasattr check on the affected module will serve this purpose, but when it doesn’t, alternative approaches would be considered as part of the feature addition. Prior art in this area includes the pickle.HIGHEST_PROTOCOL attribute, the hashlib.algorithms_available set, and the various os.supports_* sets that the os module already offers for platform dependent capability detection.

It would also be possible to add features that need to be explicitly enabled via a __future__ import when first included in the rolling pre-freeze releases, even if that feature flag was subsequently enabled by default before its first appearance in an X.Y.0 release candidate.

The rationale behind these approaches is that explicit detection/enabling like this would make it straightforward for users of the rolling pre-freeze release stream to notice when we remove or change provisional features (e.g. from __future__ imports break on compile if the feature flag no longer exists), or to safely fall back on previous functionality.

The interpreter’s rich attribute lookup machinery means we can also choose to add warnings for provisional or deprecated imports and attributes that we don’t have any practical way to add for checks against the value of sys.version_info.

Why add a new pre-freeze ABI flag to force rebuilds after X.Y.0rc1?

The core development team currently actively discourage the creation of public pre-built binaries for an X.Y series prior to the ABI freeze date.

The reason we do that is to avoid the risk of painful debugging sessions on the stable X.Y.0 release that get traced back to “Oh, our dependency ‘superfast-binary-operation’ was affected by a CPython ABI break in X.Y.0a3, but the project hasn’t published a new build since then”.

With the proposed pre-freeze ABI flag in place, this aspect of the release adoption process continues on essentially unchanged from the status quo: a new CPython X.Y release series hits ABI freeze -> package maintainers publish new binary extension modules for that release series -> end users only get segfaults due to actual bugs, not just builds against an incompatible ABI.

The primary goal of the new pre-freeze ABI flag is then to improve the user experience of the rolling pre-freeze releases themselves, by allowing pre-built binary archives to be published for those releases without risking the problems that currently cause us to actively discourage the publication of binary artifacts prior to ABI freeze.

In the ideal case, package maintainers will only need to publish one pre-freeze binary build at X.Y.0a1, and then a post-freeze build after X.Y.0rc1. The only situations that should require a rebuild in the meantime are those where the project was actually affected by a CPython ABI break in an intervening alpha release.

As a concrete example, consider the scenario where we end up having three releases that include ABI breaks: X.Y.0a1, X.Y.0a5, X.Y.0a7. The X.Y.0a7 ABI is then the ABI that carries through all the subsequent beta releases and into X.Y.0rc1. (This is the scenario illustrated in figure 1)

Forcing everyone to rebuild the world every time there’s an alpha release in the rolling release stream would almost certainly lead to publishers deciding supporting the rolling releases was more trouble than it was worth, so we want to allow modules built against X.Y.0a1 to be loaded against X.Y.0a7, as they’re probably going to be compatible (there are very few projects that use every C API that CPython publishes, and most ABI breaks affect a single specific API).

Once we publish X.Y.0rc1 though, we want to ensure that any binaries that were built against X.Y.0a1 and X.Y.0a4 are completely removed from the end user experience. It would be nice to be able to keep the builds against X.Y.0a7 and any subsequent beta releases (since it turned out those actually were built against the post-freeze ABI, even if we didn’t know that at the time), but losing them isn’t any worse than the status quo.

This means that the pre-freeze flag is “the simplest thing that could possibly work” to solve this problem - it’s just a new ABI flag, and we already have the tools available to deal with ABI flags (both in the interpreter and in package publication and installation tools).

Since the ABI flags have changed relative to the pre-releases, projects don’t even need to publish a new release: they can upload new wheel archives to their existing releases, just as they can today.

A cleverer scheme that was able to retroactively accept everything built against the last alpha or subsequent beta releases would likely be possible, but it isn’t considered necessary for adoption of this PEP, as even if we initially start out with a simple pre-release ABI flag, it would still be possible to devise a more sophisticated approach in the future.

Why allow additional alpha releases after X.Y.0a1?

In an ideal world, all breaking changes to the full CPython ABI would land in X.Y.0a1 alongside the filesystem layout changes, and the ABI for the release series would remain stable after that.

However, recent history doesn’t suggest that we’d be able to actually make that commitment and stick to it, so the PEP assumes that ABI changes will be made progressively throughout the pre-freeze period, and the full lockdown will occur only with the creation of the X.Y.z maintenance branch when preparing X.Y.0rc1.

Implications for CPython core development

The major change for CPython core development is the need to keep the master branch more consistently release ready.

While the main requirement for that would be to keep the stable BuildBot fleet green, there would also be encouragement to keep the development version of the documentation up to date for the benefit of users of the rolling pre-freeze releases. This will include providing draft What’s New entries for changes as they are implemented, although the initial versions may be relatively sparse, and then expanded based on feedback from beta release users.

For core developers working on the CPython C API, there would also be a new requirement to consistently mark ABI breaking changes in their NEWS file snippets.

On the specific topic of the stable ABI, most API designs will be able to go through a process where they’re first introduced as a provisional part of the full CPython API (allowing changes between pre-freeze releases), and only promoted to the stable ABI once developers are confident that the interface is genuinely stable.

It’s only in rare cases where an API serves no useful purpose outside the stable ABI that it may make sense to publish an alpha release containing a provisional stable ABI addition rather than iterating on the design in the provisional CPython API instead.

Implications for Python library development

If this PEP is successful in its aims, then supporting the rolling pre-freeze release stream shouldn’t be subtantially more painful for library authors than supporting the stable releases.

For publishers of pure Python packages, this would be a matter of publishing “py3” tagged wheel archives, and potentially adding the rolling pre-freeze release stream to their test matrix if that option is available to them.

For publishers of binary extension modules, the preferred option would be to target the stable C ABI (if feasible), and thus enjoy an experience similar to that of pure Python packages, where a single pre-built wheel archive is able to cover multiple versions of Python, including the rolling pre-freeze release stream.

This option isn’t going to be viable for all libraries, and the desired outcome for those authors is that they be able to support the rolling releases by building and publishing one additional wheel archive, built against the initial X.Y.0a1 release. The subsequent build against X.Y.0rc1 or later is then the same build that would have been needed if only supporting the final stable release.

Additional wheel builds beyond those two should then only be needed if that particular library is directly affected by an ABI break in any other alpha release that occurs between those two points.

Having a rolling pre-freeze release stream available may also make it more feasible for more CI providers to offer a “CPython beta release” testing option. At the moment, this feature is only available from CI providers that are willing and able to put the necessary time and effort into creating, testing, and publishing their own builds from the CPython master branch (e.g. [6]).

Implications for the proposed Scientific Python ecosystem support period

Based on discussions at SciPy 2019, NEP (NumPy Enhancement Proposal) 29 has been drafted [3] to propose a common convention across the Scientific Python ecosystem for dropping support for older Python versions.

While the exact formulation of that policy is still being discussed, the draft proposal (as of October 20, 2019) recommends that projects support any Python feature release published within the last 42 months, with a minimum of supporting the latest 2 Python feature releases.

For an 18-month feature release cadence, that works out to always supporting at least the two most recent feature releases, and then dropping support for all X.Y.Z releases around 6 months after X.(Y+2).0 is released. This means there is a 6-month period roughly every other year where the three most recent feature releases are supported.

For a 12-month release cadence, it would work out to always supporting at least the three most recent feature releases, and then dropping support for all X.Y.Z releases around 6 months after X.(Y+3).0 is released. This means that for half of each year, the four most recent feature releases would be supported, with the other half of each year hopefully being used to get ready for that year’s feature release.

For a 24-month release cadence, the second clause takes priority over the first, and the recommended Python version support period increases to 48 months from the initial X.Y.0 release in order to consistently support the two most recent CPython feature releases. For projects that also support the rolling release stream, the number of supported feature releases would increase to three.

Release cycle alignment for core development sprints

With the proposal in this PEP, it is expected that the focus of core development sprints would shift slightly based on the current location in the two-year cycle.

In release years, the timing of PyCon US is suitable for new contributors to work on bug fixes and smaller features before the first release candidate goes out, while the Language Summit and core developer discussions can focus on plans for the next release series.

The pre-alpha core development sprint in release years will provide an opportunity to incorporate feedback received on the previous release, either as part of the next maintenance release (for bug fixes and feedback on provisional APIs), or as part of the first alpha release of the next release series (for feedback received on stable APIs).

Those initial alpha releases would also be the preferred target for ABI breaking changes to the full CPython ABI (while changes later in the release cycle would still be permitted as described in this PEP, landing them in the X.Y.0a1 release means that they won’t trigger any additional work for publishers of pre-built binary packages).

The Steering Council elections for the next release cycle are also likely to occur around the same time as the pre-alpha development sprints.

In non-release years, the focus for both events would just be on the upcoming maintenance and pre-freeze releases. These less intense years would hopefully provide an opportunity to tackle various process changes and infrastructure upgrades without impacting the release candidate preparation process.

Release cycle alignment for prominent Linux distributions

Some rolling release Linux distributions (e.g. Arch, Gentoo) may be in a position to consume the new rolling pre-freeze releases proposed in this PEP, but it is expected that most distributions would continue to use the established releases.

The specific dates for final releases proposed in this PEP are chosen to align with the feature freeze schedules for the annual October releases of the Ubuntu and Fedora Linux distributions.

For both Fedora and Ubuntu, it means that the release candidate phase aligns with the development period for a distro release, which is the ideal time for them to test a new version and provide feedback on potential regressions and compatibility concerns.

For Ubuntu, this also means that their April LTS releases will have benefited from a full short-term release cycle using the new system Python version, while still having that CPython release be open to upstream bug fixes for most of the time until the next Ubuntu LTS release.

The one Linux release cycle alignment that is likely to be consistently poor with the specific proposal in this PEP is with Debian, as that has been released in the first half of odd-numbered years since 2005 (roughly 12 months offset from Ubuntu LTS releases).

With the annual release proposal in PEP 602, both Debian and Ubuntu LTS would consistently get a system Python version that is around 6 months old, but would also consistently select different Python versions from each other.

With a two-year cadence, and CPython releases in the latter half of the year, they’re likely to select the same version as each other, but one of them will be choosing a CPython release that is more than 18 months behind the latest beta releases by the time the Linux distribution ships.

If that situation does occur, and is deemed undesirable (but not sufficiently undesirable for Debian to choose to adjust their release timing), then that’s where the additional complexity of the “incremental feature release” proposal in PEP 598 may prove worthwhile.

(Moving CPython releases to the same half of the year as the Debian and Ubuntu LTS releases would potentially help mitigate the problem, but also creates new problems where a slip in the CPython release schedule could directly affect the release schedule for a Linux distribution, or else result in a distribution shipping a Python version that is more than 18 months old)

Implications for simple deployment environments

For the purposes of this PEP, a “simple” deployment environment is any use case where it is straightforward to ensure that all target environments are updated to a new Python release at the same time (or at least in advance of the rollout of new higher level application versions), and any pre-release testing that occurs need only target a single Python micro version.

The simplest such case would be scripting for personal use, where the testing and target environments are the exact same environment.

Similarly simple environments would be containerised web services, where the same Python container is used in the CI pipeline as is used on deployment, and any application that bundles its own Python runtime, rather than relying on a pre-existing Python deployment on the target system.

For these use cases, there is a straightforward mechanism to minimise the impact of this PEP: continue using the stable releases, and ignore the rolling pre-freeze releases.

To actually adopt the rolling pre-freeze releases in these environments, the main challenge will be handling the potential for extension module segfaults when the next pre-freeze release is an alpha release rather than a beta release, indicating that the CPython ABI may have changed in an incompatible way.

If all extension modules in use target the stable ABI, then there’s no problem, and everything will work just as smoothly as it does on the stable releases.

Alternatively, “rebuild and recache all extension modules” could become a standard activity undertaken as part of updating to an alpha release.

Finally, it would also be reasonable to just not worry about it until something actually breaks, and then handle it like any other library compatibility issue found in a new alpha or beta release.

Aside from extension module ABI compatibility, the other main point of additional complexity when using the rolling pre-freeze releases would be “roll-back” compatibility for independently versioned features, such as pickle and SQLite, where use of new or provisional features in the beta stream may create files that are not readable by the stable release. Applications that use these kinds of features and also require the ability to reliably roll-back to a previous stable CPython release would, as today, be advised to avoid adopting pre-release versions.

Implications for complex deployment environments

For the purposes of this PEP, “complex” deployment environments are use cases which don’t meet the “simple deployment” criteria above. They may involve multiple distinct versions of Python, use of a personalised build of Python, or “gatekeepers” who are required to approve use of a new version prior to deployment.

For example, organisations that install Python on their users’ machines as part of a standard operating environment fall into this category, as do those that provide a standard build environment. Distributions such as conda-forge or WinPython that provide collections of consistently built and verified packages are impacted in similar ways.

These organisations tend to either prefer high stability (for example, all of those who are happily using the system Python in a stable Linux distribution like Debian, RHEL/CentOS, or Ubuntu LTS as their preferred Python environment) or fast turnaround (for example, those who regularly contribute toward the latest CPython pre-releases).

In some cases, both usage models may exist within the same organisation for different purposes, such as:

• using a stable Python environment for mission critical systems, but allowing data scientists to use the latest available version for ad hoc data analysis
• a hardware manufacturer deploying a stable Python version as part of their production firmware, but using the latest available version in the development and execution of their automated integration tests

Under any release model, each new release of Python generates work for these organisations. This work may involve legal, security or technical reviews of Python itself, assessment and verification of impactful changes, reapplication of patches, recompilation and testing of third-party dependencies, and only then deployment.

Organisations that can take updates quickly should be able to make use of the more frequent beta releases. While each update will still require similar investigative work to what they require today, the volume of work required per release should be reduced as each release will be more similar to the previous than it is under the present model. One advantage of the proposed release-every-2-months model is that organisations can choose their own adoption cadence from adopting every beta release, to adopting one per quarter, or one every 6 months, or one every year. Beyond that, it would likely make more sense to continue using the stable releases instead.

For organisations with stricter evaluations or a preference for stability, the longer release cycle for stable releases will reduce the annual effort required to update, the longer release candidate period will allow more time to do internal testing before the X.Y.0 release, and the greater use by others during the beta period will provide more confidence in the initial releases. Meanwhile, the organisation can confidently upgrade through maintenance releases for a longer time without fear of breaking changes.