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PEP: 412
Title: Key-Sharing Dictionary
Version: $Revision$
Last-Modified: $Date$
Author: Mark Shannon <>
Status: Final
Type: Standards Track
Content-Type: text/x-rst
Created: 08-Feb-2012
Python-Version: 3.3 or 3.4
Post-History: 08-Feb-2012
This PEP proposes a change in the implementation of the builtin
dictionary type ``dict``. The new implementation allows dictionaries
which are used as attribute dictionaries (the ``__dict__`` attribute
of an object) to share keys with other attribute dictionaries of
instances of the same class.
The current dictionary implementation uses more memory than is
necessary when used as a container for object attributes as the keys
are replicated for each instance rather than being shared across many
instances of the same class. Despite this, the current dictionary
implementation is finely tuned and performs very well as a
general-purpose mapping object.
By separating the keys (and hashes) from the values it is possible to
share the keys between multiple dictionaries and improve memory use.
By ensuring that keys are separated from the values only when
beneficial, it is possible to retain the high-performance of the
current dictionary implementation when used as a general-purpose
mapping object.
The new dictionary behaves in the same way as the old implementation.
It fully conforms to the Python API, the C API and the ABI.
Memory Usage
Reduction in memory use is directly related to the number of
dictionaries with shared keys in existence at any time. These
dictionaries are typically half the size of the current dictionary
Benchmarking shows that memory use is reduced by 10% to 20% for
object-oriented programs with no significant change in memory use for
other programs.
The performance of the new implementation is dominated by memory
locality effects. When keys are not shared (for example in module
dictionaries and dictionary explicitly created by ``dict()`` or
``{}``) then performance is unchanged (within a percent or two) from
the current implementation.
For the shared keys case, the new implementation tends to separate
keys from values, but reduces total memory usage. This will improve
performance in many cases as the effects of reduced memory usage
outweigh the loss of locality, but some programs may show a small slow
Benchmarking shows no significant change of speed for most benchmarks.
Object-oriented benchmarks show small speed ups when they create large
numbers of objects of the same class (the gcbench benchmark shows a
10% speed up; this is likely to be an upper limit).
Both the old and new dictionaries consist of a fixed-sized dict struct
and a re-sizeable table. In the new dictionary the table can be
further split into a keys table and values array. The keys table
holds the keys and hashes and (for non-split tables) the values as
well. It differs only from the original implementation in that it
contains a number of fields that were previously in the dict struct.
If a table is split the values in the keys table are ignored, instead
the values are held in a separate array.
Split-Table dictionaries
When dictionaries are created to fill the __dict__ slot of an object,
they are created in split form. The keys table is cached in the type,
potentially allowing all attribute dictionaries of instances of one
class to share keys. In the event of the keys of these dictionaries
starting to diverge, individual dictionaries will lazily convert to
the combined-table form. This ensures good memory use in the common
case, and correctness in all cases.
When resizing a split dictionary it is converted to a combined table.
If resizing is as a result of storing an instance attribute, and there
is only instance of a class, then the dictionary will be re-split
immediately. Since most OO code will set attributes in the __init__
method, all attributes will be set before a second instance is created
and no more resizing will be necessary as all further instance
dictionaries will have the correct size. For more complex use
patterns, it is impossible to know what is the best approach, so the
implementation allows extra insertions up to the point of a resize
when it reverts to the combined table (non-shared keys).
A deletion from a split dictionary does not change the keys table, it
simply removes the value from the values array.
Combined-Table dictionaries
Explicit dictionaries (``dict()`` or ``{}``), module dictionaries and
most other dictionaries are created as combined-table dictionaries. A
combined-table dictionary never becomes a split-table dictionary.
Combined tables are laid out in much the same way as the tables in the
old dictionary, resulting in very similar performance.
The new dictionary implementation is available at [1]_.
Pros and Cons
Significant memory savings for object-oriented applications. Small
improvement to speed for programs which create lots of similar
Change to data structures: Third party modules which meddle with the
internals of the dictionary implementation will break.
Changes to repr() output and iteration order: For most cases, this
will be unchanged. However, for some split-table dictionaries the
iteration order will change.
Neither of these cons should be a problem. Modules which meddle with
the internals of the dictionary implementation are already broken and
should be fixed to use the API. The iteration order of dictionaries
was never defined and has always been arbitrary; it is different for
Jython and PyPy.
Alternative Implementation
An alternative implementation for split tables, which could save even
more memory, is to store an index in the value field of the keys table
(instead of ignoring the value field). This index would explicitly
state where in the value array to look. The value array would then
only require 1 field for each usable slot in the key table, rather
than each slot in the key table.
This "indexed" version would reduce the size of value array by about
one third. The keys table would need an extra "values_size" field,
increasing the size of combined dicts by one word. The extra
indirection adds more complexity to the code, potentially reducing
performance a little.
The "indexed" version will not be included in this implementation, but
should be considered deferred rather than rejected, pending further
.. [1] Reference Implementation:
This document has been placed in the public domain.
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