python-peps/pep-3147.txt

PEP: 3147
Title: PYC Repository Directories
Version: $Revision$
Last-Modified: $Date$
Author: Barry Warsaw <barry@python.org>
Status: Draft
Type: Standards Track
Content-Type: text/x-rst
Created: 2009-12-16
Python-Version: 3.2
Post-History: 2010-01-30, 2010-02-25


Abstract
========

This PEP describes an extension to Python's import mechanism which
improves sharing of Python source code files among multiple installed
different versions of the Python interpreter.  It does this by
allowing more than one byte compilation file (.pyc files) to be
co-located with the Python source file (.py file).  The extension
described here can also be used to support different Python
compilation caches, such as JIT output that may be produced by an
Unladen Swallow [1]_ enabled C Python.


Background
==========

CPython compiles its source code into "byte code", and for performance
reasons, it caches this byte code on the file system whenever the
source file has changes.  This makes loading of Python modules much
faster because the compilation phase can be bypassed.  When your
source file is `foo.py`, CPython caches the byte code in a `foo.pyc`
file right next to the source.

Byte code files contain two 32-bit numbers followed by the marshaled
[2]_ code object.  The 32-bit numbers represent a magic number and a
timestamp.  The magic number changes whenever Python changes the byte
code format, e.g. by adding new byte codes to its virtual machine.
This ensures that pyc files built for previous versions of the VM
won't cause problems.  The timestamp is used to make sure that the pyc
file is not older than the py file that was used to create it.  When
either the magic number or timestamp do not match, the py file is
recompiled and a new pyc file is written.

In practice, it is well known that pyc files are not compatible across
Python major releases.  A reading of import.c [3]_ in the Python
source code proves that within recent memory, every new CPython major
release has bumped the pyc magic number.


Rationale
=========

Linux distributions such as Ubuntu [4]_ and Debian [5]_ provide more
than one Python version at the same time to their users.  For example,
Ubuntu 9.10 Karmic Koala users can install Python 2.5, 2.6, and 3.1,
with Python 2.6 being the default.

This causes a conflict for Python source files installed by the
system (including third party packages), because you cannot compile a
single Python source file for more than one Python version at a time.
Thus if your system wanted to install a `/usr/share/python/foo.py`, it
could not create a `/usr/share/python/foo.pyc` file usable across all
installed Python versions.

Furthermore, in order to ease the burden on operating system packagers
for these distributions, the distribution packages do not contain
Python version numbers [6]_; they are shared across all Python
versions installed on the system.  Putting Python version numbers in
the packages would be a maintenance nightmare, since all the packages
- *and their dependencies* - would have to be updated every time a new
Python release was added or removed from the distribution.  Because of
the sheer number of packages available, this amount of work is
infeasible.

Even C extensions can be source compatible across multiple versions of
Python.  Compiled extension modules are usually not compatible though,
and PEP 384 [7]_ has been proposed to address this by defining a
stable ABI for extension modules.

Because these distributions cannot share pyc files, elaborate
mechanisms have been developed to put the resulting pyc files in
non-shared locations while the source code is still shared.  Examples
include the symlink-based Debian regimes python-support [8]_ and
python-central [9]_.  These approaches make for much more complicated,
fragile, inscrutable, and fragmented policies for delivering Python
applications to a wide range of users.  Arguably more users get Python
from their operating system vendor than from upstream tarballs.  Thus,
solving this pyc sharing problem for CPython is a high priority for
such vendors.

This PEP proposes a solution to this problem.


Proposal
========

Python's import machinery is extended to write and search for byte
code cache files in a single directory inside every Python package
directory.  This directory will be called `__pycache__`.

Further, pyc files will contain a magic string that
differentiates the Python version they were compiled for.  This allows
multiple byte compiled cache files to co-exist for a single Python
source file.

This scheme has the added benefit of reducing the clutter in a Python
package directory.

What would this look like in practice?

Let's say we have a Python package named `alpha` which contains a
sub-package name `beta`.  The source directory layout might look like
this::

    alpha/
        __init__.py
        one.py
        two.py
        beta/
            __init__.py
            three.py
            four.py

After byte compiling this package with Python 3.2, you would see the
following layout::

    alpha/
        __pycache__/
            __init__.cpython-32.pyc
            one.cpython-32.pyc
            two.cpython-32.pyc
        __init__.py
        one.py
        two.py
        beta/
            __pycache__/
                __init__.cpython-32.pyc
                three.cpython-32.pyc
                four.cpython-32.pyc
            __init__.py
            three.py
            four.py

Let's say that two new versions of Python are installed, one is Python
3.3 and another is Unladen Swallow.  After byte compilation, the file
system would look like this::

    alpha/
        __pycache__/
            __init__.cpython-32.pyc
            __init__.cpython-33.pyc
            __init__.unladen-10.pyc
            one.cpython-32.pyc
            one.cpython-33.pyc
            one.unladen-10.pyc
            two.cpython-32.pyc
            two.cpython-33.pyc
            two.unladen-10.pyc
        __init__.py
        one.py
        two.py
        beta/
            __pycache__/
                __init__.cpython-32.pyc
                __init__.cpython-33.pyc
                __init__.unladen-10.pyc
                three.cpython-32.pyc
                three.cpython-33.pyc
                three.unladen-10.pyc
                four.cpython-32.pyc
                four.cpython-33.pyc
                four.unladen-10.pyc
            __init__.py
            three.py
            four.py

As you can see, as long as the Python version identifier string is
unique, any number of pyc files can co-exist.  These identifier
strings are described in more detail below.

A nice property of this layout is that the `__pycache__` directories
can generally be ignored, such that a normal directory listing would
show something like this::

    alpha/
        __pycache__/
        __init__.py
        one.py
        two.py
        beta/
            __pycache__/
            __init__.py
            three.py
            four.py

This is much less cluttered than even today's Python.


Python behavior
===============

When Python searches for a module to import (say `foo`), it may find
one of several situations.  As per current Python rules, the term
"matching pyc" means that the magic number matches the current
interpreter's magic number, and the source file is not newer than the
`pyc` file.


Case 1: The first import
------------------------

When Python is asked to import module `foo`, it searches for a
`foo.py` file (or `foo` package, but that's not important for this
discussion) along its `sys.path`.  When Python locates the `foo.py`
file it will look for a `__pycache__` directory in the directory where
it found the `foo.py`.  If the `__pycache__` directory is missing,
Python will create it.  Then it will parse and byte compile the
`foo.py` file and save the byte code in `__pycache__/foo.<magic>.pyc`,
where <magic> is defined by the Python implementation, but will be a
human readable string such as `cpython-32`.


Case 2: The second import
-------------------------

When Python is asked to import module `foo` a second time (in a
different process of course), it will again search for the `foo.py`
file along its `sys.path`.  When Python locates the `foo.py` file, it
looks for a matching `__pycache__/foo.<magic>.pyc` and finding this,
it reads the byte code and continues as usual.


Case 3: __pycache__/foo.<magic>.pyc with no source
---------------------------------------------------

It's possible that the `foo.py` file somehow got removed, while
leaving the cached pyc file still on the file system.  If the
`__pycache__/foo.<magic>.pyc` file exists, but the `foo.py` file used
to create it does not, Python will raise an `ImportError` when asked
to import foo.  In other words, by default, Python will not support
importing a module unless the source file exists.

Python users who want to deploy sourceless imports are instructed to
create a custom importer that supports this behavior.  Options include
importing pycs from a zip file, or locating pyc files where the py
source file would have existed.  (See the Open Issues section for more
discussion.)


Case 4: legacy pyc files
------------------------

Python will ignore all legacy pyc files.  In other words, if a
`foo.pyc` file exists next to the `foo.py` file, it will be ignored in
all cases, including sourceless deployments.  Python users wishing to
support this use case can create a custom importer.


Flow chart
==========

Here is a flow chart describing how modules are loaded:

.. image:: pep-3147-1.png
   :scale: 75


Magic identifiers
=================

pyc files inside of the `__pycache__` directories contain a magic
identifier in their file names.  These are mnemonic tags for the
actual magic numbers used by the importer.  For example, for Python
3.2, we could use the hexlified [10]_ magic number as a unique
identifier::

    >>> from binascii import hexlify
    >>> from imp import get_magic
    >>> 'foo.{}.pyc'.format(hexlify(get_magic()).decode('ascii'))
    'foo.580c0d0a.pyc'

This isn't particularly human friendly though.  Instead, this PEP
proposes to add a mapping between internal magic numbers and a
user-friendly tag.  Newer versions of Python can add to this mapping
so that all later Pythons know the mapping between tags and magic
numbers.  By convention, the tag will contain the Python
implementation name and version nickname, where the nickname is
generally the major version number and minor version number.  Magic
numbers should not change between Python micro releases, but some
other convention can be used for changes in magic number between
pre-release development versions.

For example, CPython 3.2 would have a magic identifier tag of
`cpython-32` and write pyc files like this: `foo.cpython-32.pyc`.
When the `-O` flag is used, it would write `foo.cpython-32.pyo`.  For
backports of this feature to Python 2, when the `-U` flag is used, a
file such as `foo.cpython-27u.pyc` can be written.


Alternative Python implementations
==================================

Alternative Python implementations such as Jython [11]_, IronPython
[12]_, PyPy [13]_, Pynie [14]_, and Unladen Swallow can also use the
`__pycache__` directory to store whatever compilation artifacts make
sense for their platforms.  For example, Jython could store the class
file for the module in `__pycache__/foo.jython-32.class`.


Implementation strategy
=======================

This feature is targeted for Python 3.2, solving the problem for those
and all future versions.  It may be back-ported to Python 2.7.
Vendors are free to backport the changes to earlier distributions as
they see fit.


Effects on existing code
========================

Adoption of this PEP will affect existing code and idioms, both inside
Python and outside.  This section enumerates some of these effects.


__file__
---------

in Python 3, when you import a module, its `__file__` attribute points
to its source `py` file (in Python 2, it points to the `pyc` file).  A
package's `__file__` points to the `py` file for its `__init__.py`.
E.g.::

    >>> import foo
    >>> foo.__file__
    'foo.py'
    # baz is a package
    >>> import baz
    >>> baz.__file__
    'baz/__init__.py'

The implementation of this PEP would have to ensure that the same
directory level is returned from `__file__` as it currently does so
that the common idiom above continues to work.

As part of this PEP, we will add an `__cached__` attribute to modules,
which will always point to the actual `pyc` file that was read or
written.  When the environment variable `$PYTHONDONTWRITEBYTECODE` is
set, or the `-B` option is given, or if the source lives on a
read-only filesystem, then the `__cached__` attribute will point to
the location that the `pyc` file *would* have been written to if it
didn't exist.  This location of course includes the `__pycache__`
subdirectory in its path.

For alternative Python implementations which do not support `pyc`
files, the `__cached__` attribute may point to whatever information
makes sense.  E.g. on Jython, this might be the `.class` file for the
module: `__pycache__/foo.jython-32.class`.  Some implementations may
use multiple compiled files to create the module, in which case
`__cached__` may be a tuple.  The exact contents of `__cached__` are
Python implementation specific.

Alternative implementations for which this scheme does not make sense
should set the `__cached__` attribute to `None`.


File extension checks
---------------------

There exists some code which checks for files ending in `.pyc` and
simply chops off the last character to find the matching `.py` file.
This code will obviously fail once this PEP is implemented.

To support this use case, we'll add two new methods to the `imp`
package [15]_:

 * `imp.source_from_cache(py_path)` -> `pyc_path`
 * `imp.cache_from_source(pyc_path)` -> `py_path`

Alternative implementations are free to override these functions to
return reasonable values based on their own support for this PEP.


PEP 302 loaders
---------------

PEP 302 [16]_ defined loaders have a `.get_filename()` method which
points to the `__file__` for a module.  As part of this PEP, we will
extend this API, to include a new method `.get_paths()` which will
return a 2-tuple containing the path to the source file and the path
to where the matching `pyc` file is (or would be).


Backports
---------

For versions of Python earlier than 3.2 (and possibly 2.7), it is
possible to backport this PEP.  However, in Python 3.2 (and possibly
2.7), this behavior will be turned on by default, and in fact, it will
replace the old behavior.  Backports will need to support the old
layout by default.  We suggest supporting PEP 3147 through the use of
an environment variable called `$PYTHONCACHEDIR` or the command line
switch `-Xcachedir` to enable the feature.


Alternatives
============

PEP 304
-------

There is some overlap between the goals of this PEP and PEP 304 [17]_,
which has been withdrawn.  However PEP 304 would allow a user to
create a shadow file system hierarchy in which to store `pyc` files.
This concept of a shadow hierarchy for `pyc` files could be used to
satisfy the aims of this PEP.  Although the PEP 304 does not indicate
why it was withdrawn, shadow directories have a number of problems.
The location of the shadow `pyc` files would not be easily discovered
and would depend on the proper and consistent use of the
`$PYTHONBYTECODE` environment variable both by the system and by end
users.  There are also global implications, meaning that while the
system might want to shadow `pyc` files, users might not want to, but
the PEP defines only an all-or-nothing approach.

As an example of the problem, a common (though fragile) Python idiom
for locating data files is to do something like this::

    from os import dirname, join
    import foo.bar
    data_file = join(dirname(foo.bar.__file__), 'my.dat')

This would be problematic since `foo.bar.__file__` will give the
location of the `pyc` file in the shadow directory, and it may not be
possible to find the `my.dat` file relative to the source directory
from there.


Fat byte compilation files
--------------------------

An earlier version of this PEP described "fat" Python byte code files.
These files would contain the equivalent of multiple `pyc` files in a
single `pyf` file, with a lookup table keyed off the appropriate magic
number.  This was an extensible file format so that the first 5
parallel Python implementations could be supported fairly efficiently,
but with extension lookup tables available to scale `pyf` byte code
objects as large as necessary.

The fat byte compilation files were fairly complex, and inherently
introduced difficult race conditions, so the current simplification of
using directories was suggested.  The same problem applies to using
zip files as the fat pyc file format.


Multiple file extensions
------------------------

The PEP author also considered an approach where multiple thin byte
compiled files lived in the same place, but used different file
extensions to designate the Python version.  E.g. foo.pyc25,
foo.pyc26, foo.pyc31 etc.  This was rejected because of the clutter
involved in writing so many different files.  The multiple extension
approach makes it more difficult (and an ongoing task) to update any
tools that are dependent on the file extension.


Reference implementation
========================

A pure-Python reference implementation will be written using
importlib [18]_, which may need some modifications to its API and
abstract base classes.  Once the semantics are agreed upon and the
implementation details are settled, we'll port this to the C
implementation in `import.c`.  We will have extensive tests that
guarantee that the pure-Python implementation and the built-in
implementation remain in sync.


Open issues
===========

Byte code only packages
-----------------------

Some users of Python distribute packages containing only the byte code
files (pyc).  The use cases for this are to make it more difficult for
end-users to view the source code, and to reduce maintenance burdens
when end users casually edit the source files.

This PEP currently promote no default support for bytecode-only
packages.  The primary motivator for this are that we can reduce stat
calls if the importer only looks for .py files, making Python start-up
and import faster.

The question is how to balance the requirements of bytecode-only users
with the more universally beneficial faster start up times for
requiring source files?  Should all Python users pay the extra stat
call penalty in the general case for a minority use case by default?
Evidence shows that the extra stats can be fairly costly to start up
time.

There are several ways out of this.  Should we decide that it's
important enough to support bytecode-only packages, the semantics
would be as follows:

* If there is a traditional, non-magic-tagged .pyc file in the
  location where a .py file should be found, it will satisfy the
  import.
* The `__file__` attribute of the module will point to the .pyc file.
* The `__cached__` attribute of the module will point to the .pyc file
  too.
* The existence of a matching `__pycached__/foo.<magic>.pyc` file
  without the source py file will *not* satisfy the import.   This
  means that if the source file is removed, the pyc file will be
  ignored (unlike in today's implementation).

Other ways to satisfy the bytecode-only packagers requirements would
have less impact on the general Python user population, and include:

* Add a `-X` switch and/or environment variable to enable
  the bytecode-only search algorithm.
* Let those who want more protection against casual py hackers package
  their code in a zip file, which is supported today.  Sub-options
  include supporting pyc-only imports only in zip files, or still
  requiring the py file for zip imports.
* Provide a custom importer supporting bytecode-only packages, which
  would have to be enabled explicitly by the application.  Either
  Python would provide such a custom importer or it would be left to
  third parties to implement.
* Add a marker to a package's `__init__.py` file to enable
  bytecode-only imports for everything else in the package.
* Leave it to third-party tools such as py2exe [20]_ to build an
  ecosystem and standards around source-less distributions.


__cached__ vs. __compiled__
----------------------------

Guido says: "I still prefer __compiled__ over __cached__ but I don't
feel strong about it."

Barry likes `__cached__` because it the more general term seems to fit
in better with future possible use cases such as JIT output from
Unladen Swallow.


References
==========

.. [1] PEP 3146

.. [2] The marshal module:
   http://www.python.org/doc/current/library/marshal.html

.. [3] import.c:
   http://svn.python.org/view/python/branches/py3k/Python/import.c?view=markup

.. [4] Ubuntu: <http://www.ubuntu.com>

.. [5] Debian: <http://www.debian.org>

.. [6] Debian Python Policy:
   http://www.debian.org/doc/packaging-manuals/python-policy/

.. [7] PEP 384

.. [8] python-support:
   http://wiki.debian.org/DebianPythonFAQ#Whatispython-support.3F

.. [9] python-central:
   http://wiki.debian.org/DebianPythonFAQ#Whatispython-central.3F

.. [10] binascii.hexlify():
   http://www.python.org/doc/current/library/binascii.html#binascii.hexlify

.. [11] Jython: http://www.jython.org/

.. [12] IronPython: http://ironpython.net/

.. [13] PyPy: http://codespeak.net/pypy/dist/pypy/doc/

.. [14] Pynie: http://code.google.com/p/pynie/

.. [15] imp: http://www.python.org/doc/current/library/imp.html

.. [16] PEP 302

.. [17] PEP 304

.. [18] importlib: http://docs.python.org/3.1/library/importlib.html

.. [19] http://mail.python.org/pipermail/python-dev/2010-March/098042.html

.. [20] py2exe: http://www.py2exe.org/

ACKNOWLEDGMENTS
===============

Barry Warsaw's original idea was for fat Python byte code files.
Martin von Loewis reviewed an early draft of the PEP and suggested the
simplification to store traditional `pyc` and `pyo` files in a
directory.  Many other people reviewed early versions of this PEP and
provided useful feedback including but not limited to:

* David Malcolm
* Josselin Mouette
* Matthias Klose
* Michael Hudson
* Michael Vogt
* Piotr Ożarowski
* Scott Kitterman
* Toshio Kuratomi


Copyright
=========

This document has been placed in the public domain.



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