python-peps/peps/pep-0267.rst

PEP: 267
Title: Optimized Access to Module Namespaces
Author: Jeremy Hylton <jeremy@alum.mit.edu>
Status: Deferred
Type: Standards Track
Content-Type: text/x-rst
Created: 23-May-2001
Python-Version: 2.2
Post-History:


Deferral
========

While this PEP is a nice idea, no-one has yet emerged to do the work of
hashing out the differences between this PEP, :pep:`266` and :pep:`280`.
Hence, it is being deferred.


Abstract
========

This PEP proposes a new implementation of global module namespaces
and the builtin namespace that speeds name resolution.  The
implementation would use an array of object pointers for most
operations in these namespaces.  The compiler would assign indices
for global variables and module attributes at compile time.

The current implementation represents these namespaces as
dictionaries.  A global name incurs a dictionary lookup each time
it is used; a builtin name incurs two dictionary lookups, a failed
lookup in the global namespace and a second lookup in the builtin
namespace.

This implementation should speed Python code that uses
module-level functions and variables.  It should also eliminate
awkward coding styles that have evolved to speed access to these
names.

The implementation is complicated because the global and builtin
namespaces can be modified dynamically in ways that are impossible
for the compiler to detect.  (Example: A module's namespace is
modified by a script after the module is imported.)  As a result,
the implementation must maintain several auxiliary data structures
to preserve these dynamic features.


Introduction
============

This PEP proposes a new implementation of attribute access for
module objects that optimizes access to module variables known at
compile time.  The module will store these variables in an array
and provide an interface to lookup attributes using array offsets.
For globals, builtins, and attributes of imported modules, the
compiler will generate code that uses the array offsets for fast
access.

[describe the key parts of the design: dlict, compiler support,
stupid name trick workarounds, optimization of other module's
globals]

The implementation will preserve existing semantics for module
namespaces, including the ability to modify module namespaces at
runtime in ways that affect the visibility of builtin names.


DLict design
============

The namespaces are implemented using a data structure that has
sometimes gone under the name ``dlict``.  It is a dictionary that has
numbered slots for some dictionary entries.  The type must be
implemented in C to achieve acceptable performance.  The new
type-class unification work should make this fairly easy.  The
``DLict`` will presumably be a subclass of dictionary with an
alternate storage module for some keys.

A Python implementation is included here to illustrate the basic
design::

    """A dictionary-list hybrid"""

    import types

    class DLict:
        def __init__(self, names):
            assert isinstance(names, types.DictType)
            self.names = {}
            self.list = [None] * size
            self.empty = [1] * size
            self.dict = {}
            self.size = 0

        def __getitem__(self, name):
            i = self.names.get(name)
            if i is None:
                return self.dict[name]
            if self.empty[i] is not None:
                raise KeyError, name
            return self.list[i]

        def __setitem__(self, name, val):
            i = self.names.get(name)
            if i is None:
                self.dict[name] = val
            else:
                self.empty[i] = None
                self.list[i] = val
                self.size += 1

        def __delitem__(self, name):
            i = self.names.get(name)
            if i is None:
                del self.dict[name]
            else:
                if self.empty[i] is not None:
                    raise KeyError, name
                self.empty[i] = 1
                self.list[i] = None
                self.size -= 1

        def keys(self):
            if self.dict:
                return self.names.keys() + self.dict.keys()
            else:
                return self.names.keys()

        def values(self):
            if self.dict:
                return self.names.values() + self.dict.values()
            else:
                return self.names.values()

        def items(self):
            if self.dict:
                return self.names.items()
            else:
                return self.names.items() + self.dict.items()

        def __len__(self):
            return self.size + len(self.dict)

        def __cmp__(self, dlict):
            c = cmp(self.names, dlict.names)
            if c != 0:
                return c
            c = cmp(self.size, dlict.size)
            if c != 0:
                return c
            for i in range(len(self.names)):
                c = cmp(self.empty[i], dlict.empty[i])
            if c != 0:
                return c
            if self.empty[i] is None:
                c = cmp(self.list[i], dlict.empty[i])
                if c != 0:
                    return c
            return cmp(self.dict, dlict.dict)

        def clear(self):
            self.dict.clear()
            for i in range(len(self.names)):
                if self.empty[i] is None:
                    self.empty[i] = 1
                    self.list[i] = None

        def update(self):
            pass

        def load(self, index):
            """dlict-special method to support indexed access"""
            if self.empty[index] is None:
                return self.list[index]
            else:
                raise KeyError, index # XXX might want reverse mapping

        def store(self, index, val):
            """dlict-special method to support indexed access"""
            self.empty[index] = None
            self.list[index] = val

        def delete(self, index):
            """dlict-special method to support indexed access"""
            self.empty[index] = 1
            self.list[index] = None


Compiler issues
===============

The compiler currently collects the names of all global variables
in a module.  These are names bound at the module level or bound
in a class or function body that declares them to be global.

The compiler would assign indices for each global name and add the
names and indices of the globals to the module's code object.
Each code object would then be bound irrevocably to the module it
was defined in.  (Not sure if there are some subtle problems with
this.)

For attributes of imported modules, the module will store an
indirection record.  Internally, the module will store a pointer
to the defining module and the offset of the attribute in the
defining module's global variable array.  The offset would be
initialized the first time the name is looked up.


Runtime model
=============

The PythonVM will be extended with new opcodes to access globals
and module attributes via a module-level array.

A function object would need to point to the module that defined
it in order to provide access to the module-level global array.

For module attributes stored in the ``dlict`` (call them static
attributes), the get/delattr implementation would need to track
access to these attributes using the old by-name interface.  If a
static attribute is updated dynamically, e.g.::

   mod.__dict__["foo"] = 2

The implementation would need to update the array slot instead of
the backup dict.


Backwards compatibility
=======================

The ``dlict`` will need to maintain meta-information about whether a
slot is currently used or not.  It will also need to maintain a
pointer to the builtin namespace.  When a name is not currently
used in the global namespace, the lookup will have to fail over to
the builtin namespace.

In the reverse case, each module may need a special accessor
function for the builtin namespace that checks to see if a global
shadowing the builtin has been added dynamically.  This check
would only occur if there was a dynamic change to the module's
``dlict``, i.e. when a name is bound that wasn't discovered at
compile-time.

These mechanisms would have little if any cost for the common case
whether a module's global namespace is not modified in strange
ways at runtime.  They would add overhead for modules that did
unusual things with global names, but this is an uncommon practice
and probably one worth discouraging.

It may be desirable to disable dynamic additions to the global
namespace in some future version of Python.  If so, the new
implementation could provide warnings.


Related PEPs
============

:pep:`266`, Optimizing Global Variable/Attribute Access, proposes a
different mechanism for optimizing access to global variables as
well as attributes of objects.  The mechanism uses two new opcodes
``TRACK_OBJECT`` and ``UNTRACK_OBJECT`` to create a slot in the local
variables array that aliases the global or object attribute.  If
the object being aliases is rebound, the rebind operation is
responsible for updating the aliases.

The objecting tracking approach applies to a wider range of
objects than just module.  It may also have a higher runtime cost,
because each function that uses a global or object attribute must
execute extra opcodes to register its interest in an object and
unregister on exit; the cost of registration is unclear, but
presumably involves a dynamically resizable data structure to hold
a list of callbacks.

The implementation proposed here avoids the need for registration,
because it does not create aliases.  Instead it allows functions
that reference a global variable or module attribute to retain a
pointer to the location where the original binding is stored.  A
second advantage is that the initial lookup is performed once per
module rather than once per function call.


Copyright
=========

This document has been placed in the public domain.