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Differences between PyPy and CPython
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This page documents the few differences and incompatibilities between
the PyPy Python interpreter and CPython.  Some of these differences
are "by design", since we think that there are cases in which the
behaviour of CPython is buggy, and we do not want to copy bugs.

Differences that are not listed here should be considered bugs of
PyPy.

Differences related to garbage collection strategies
----------------------------------------------------

Most of the garbage collectors used or implemented by PyPy are not based on
reference counting, so the objects are not freed instantly when they are no
longer reachable.  The most obvious effect of this is that files are not
promptly closed when they go out of scope.  For files that are opened for
writing, data can be left sitting in their output buffers for a while, making
the on-disk file appear empty or truncated.

Fixing this is essentially not possible without forcing a
reference-counting approach to garbage collection.  The effect that you
get in CPython has clearly been described as a side-effect of the
implementation and not a language design decision: programs relying on
this are basically bogus.  It would anyway be insane to try to enforce
CPython's behavior in a language spec, given that it has no chance to be
adopted by Jython or IronPython (or any other port of Python to Java or
.NET, like PyPy itself).

There are a few extra implications for the difference in the GC.  Most
notably, if an object has a __del__, the __del__ is never called more
than once in PyPy; but CPython will call the same __del__ several times
if the object is resurrected and dies again.  The __del__ methods are
called in "the right" order if they are on objects pointing to each
other, as in CPython, but unlike CPython, if there is a dead cycle of
objects referencing each other, their __del__ methods are called anyway;
CPython would instead put them into the list ``garbage`` of the ``gc``
module.  More information is available on the blog `[1]`__ `[2]`__.

.. __: http://morepypy.blogspot.com/2008/02/python-finalizers-semantics-part-1.html
.. __: http://morepypy.blogspot.com/2008/02/python-finalizers-semantics-part-2.html

The built-in function ``id()`` returns numbers that are not addresses
for most of PyPy's garbage collectors.
This is most visible in the default repr: a typical PyPy object can
pretend to be located ``at 0x00000009``.  This is just its ``id()``, not
its real address (because the physical address can change).

Note that if you have a long chain of objects, each with a reference to
the next one, and each with a __del__, PyPy's GC will perform badly.  On
the bright side, in most other cases, benchmarks have shown that PyPy's
GCs perform much better than CPython's.


Subclasses of built-in types
----------------------------

Officially, CPython has no rule at all for when exactly
overriden method of subclasses of built-in types get
implicitly called or not.  As an approximation, these methods
are never called by other built-in methods of the same object.
For example, an overridden ``__getitem__()`` in a subclass of
``dict`` will not be called by e.g. the built-in ``get()``
method.

The above is true both in CPython and in PyPy.  Differences
can occur about whether a built-in function or method will
call an overridden method of *another* object than ``self``.
In PyPy, they are generally always called, whereas not in
CPython.  For example, in PyPy, ``dict1.update(dict2)``
considers that ``dict2`` is just a general mapping object, and
will thus call overridden ``keys()``  and ``__getitem__()``
methods on it.  So the following code prints ``42`` on PyPy
but ``foo`` on CPython::

    >>>> class D(dict):
    ....     def __getitem__(self, key):
    ....         return 42
    ....
    >>>>
    >>>> d1 = {}
    >>>> d2 = D(a='foo')
    >>>> d1.update(d2)
    >>>> print d1['a']
    42


Ignored exceptions
-----------------------

In many corner cases, CPython can silently swallow exceptions.
The precise list of when this occurs is rather long, even
though most cases are very uncommon.  The most well-known
places are custom rich comparison methods (like \_\_eq\_\_);
dictionary lookup; calls to some built-in functions like
isinstance().

Unless this behavior is clearly present by design and
documented as such (as e.g. for hasattr()), in most cases PyPy
lets the exception propagate instead.

