Learning from bidict

Working on bidict has taken me to some of the most interesting and unexpected places I’ve gotten to visit in many years of programming. (When I started this project 15+ years ago, I’d never heard of things like higher-kinded types. Thanks to bidict, I not only learned about them, I got to share a practical example with Guido where they would be beneficial for Python.)

The problem space that bidict inhabits is abundant with beautiful symmetries, delightful surprises, and rich opportunities to come up with elegant solutions.

You can check out bidict’s source to see for yourself. I’ve sought to optimize the code not just for correctness and performance, but also for clarity, maintainability, and to make for an enjoyable read.

See below for more, and feel free to let me know what you think. I hope reading bidict’s code brings you some of the joy that bidict has brought me.

Code structure

bidicts come in mutable, immutable, and ordered variants, implementing Python’s various relevant collections interfaces as appropriate.

Factoring the code to maximize reuse, modularity, and adherence to SOLID design principles (while not missing any chances for specialized optimizations) has been one of the most fun parts of working on bidict.

To see how this is done, check out the code starting with __init__.py, and then follow the path suggested in the “code review nav” comments at the top of the file:

Data structures are amazing

Data structures are one of the most fascinating and important building blocks of programming and computer science.

It’s all too easy to lose sight of the magic when having to implement them for computer science courses or job interview questions. Part of this is because many of the most interesting real-world details get left out, and you miss all the value that comes from ongoing, direct practical application.

Bidict shows how fundamental data structures can be implemented in Python for real-world usage, with practical concerns at top of mind.

OrderedBidict's design

A regular bidict encapsulates two regular dicts, keeping them in sync to preserve the bidirectional mapping invariants. How should we extend this to implement OrderedBidict?

From BidictBase, OrderedBidictBase inherits the use of two regular dicts to store the contents of the forward and inverse items.

To store the _ordering_ of the items, we use a doubly-linked list (much like OrderedDict), allowing us to e.g. move any item to the front of the bidict in constant time.

Interestingly, the nodes of the linked list encode only the ordering of the items; the nodes themselves contain no key or value data. An additional backing mapping associates the key/value data with the nodes, providing the final piece of the puzzle.

And since the implementation needs to not only look up nodes by key/value, but also key/value by node, we use a bidict for this internally. Bidicts all the way down!

Python syntax hacks

bidict used to support a specialized form of Python’s slice syntax for getting and setting keys by value:

element_by_symbol = bidict(H='hydrogen')
# [normal] syntax for the forward mapping lookup:
element_by_symbol['H']  # ==> 'hydrogen'
# [:slice] syntax for the inverse lookup (no longer supported):
element_by_symbol[:'hydrogen']  # ==> 'H'

See this code for how this was implemented.

It’s super cool when you find a way to bend Python’s syntax to support new use cases like this that stll feel like they fit well into the language, especially given that Python (wisely) limits how much you can customize its syntax.

Property-based testing is incredible

When your automated tests run, are they only checking the test cases that you happened to think of when writing your tests? How do you know you aren’t missing some important edge cases?

With property-based testing, you describe the _types_ of the test case inputs that your APIs accept, along with the properties that should hold for all valid inputs. Rather than having to think of your test case inputs manually and hard-code them into your test suite, they get generated for you dynamically, in much greater quantity and diversity than you would typically come up with by hand. This dramatically increases test coverage and confidence that your code is correct with much less actual test code.

Bidict never would have survived so many refactorings with so few bugs if it weren’t for property-based testing, enabled by the amazing Hypothesis library.

Check out bidict’s property-based tests to see this in action.

Python surprises

  • What should happen when checking equality of several ordered mappings that contain the same items but in a different order?

    First let’s see how collections.OrderedDict works. The results may surprise you:

    >>> from collections import OrderedDict
    >>> x = OrderedDict({1: 1, 2: 2})
    >>> y = {1: 1, 2: 2}
    >>> z = OrderedDict({2: 2, 1: 1})
    >>> x == y
    >>> y == z
    >>> x == z  # !!!

    So collections.OrderedDict violates the transitive property of equality. This can lead to some even more unusual behavior than the above. As an example, let’s see what would happen if bidict.frozenbidict.__eq__() behaved this way:

    >>> class BadFrozenBidict(BidictBase):
    ...     __hash__ = frozenbidict.__hash__
    ...     def __eq__(self, other):  # (deliberately simplified)
    ...         # Override to be order-sensitive, like collections.OrderedDict:
    ...         return all(i == j for (i, j) in zip(self.items(), other.items()))
    >>> x = BadFrozenBidict({1: 1, 2: 2})
    >>> y = frozenbidict({1: 1, 2: 2})
    >>> z = BadFrozenBidict({2: 2, 1: 1})
    >>> x == y
    >>> y == z
    >>> x == z  # !!!
    >>> set1 = {x, y, z}
    >>> len(set1)
    >>> set2 = {y, x, z}
    >>> len(set2)  # !!!

    According to Raymond Hettinger, the Python core developer who built Python’s collections foundation, collections.OrderedDict's __eq__() implementation should have been order-insensitive. Making it order-sensitive violates the transitive property of equality as well as the Liskov substitution principle. It’s too late now to change this for collections.OrderedDict.

    But at least it’s not too late to learn from this. Hence OrderedBidict()'s __eq__() is order-insensitive, even for ordered bidicts. For an order-sensitive equality check, bidict provides the separate equals_order_sensitive() method, thanks to Raymond’s advice.

  • See nan as a Key.

  • See Equivalent but distinct Hashables.

Better memory usage through __slots__

Using __slots__ speeds up attribute access, and can dramatically reduce memory usage in CPython when creating many instances of the same class.

As an example, the Node class used internally (in the linked list that backs OrderedBidictBase) uses slots for better performance at scale, since there are as many node instances kept in memory as there are items in every ordered bidict in memory. See: _orderedbase.py

Note that extra care must be taken when using slots with pickling and weakrefs; see the code for more.

Better memory usage through weakref

A bidict and its inverse use weakref to avoid creating a reference cycle. As a result, when you drop your last reference to a bidict, its memory is reclaimed immediately in CPython rather than having to wait for the next garbage collection. See: _base.py

As another example, the Node class used internally by OrderedBidictBase uses weakrefs to avoid creating reference cycles in the doubly-linked lists used to encode the ordering of inserted items. See: _orderedbase.py

Using descriptors for managed attributes

To abstract the details of creating and dereferencing the weakrefs that OrderedBidictBase's aforementioned doubly-linked list nodes use to refer to their neighbor nodes, a WeakAttr descriptor is used to manage access to these attributes automatically. See: _orderedbase.py

The implicit __class__ reference

Anytime you have to reference the exact class of an instance (and not a potential subclass) from within a method body, you can use the implicit, lexically-scoped __class__ reference rather than hard-coding the current class’s name. See: https://docs.python.org/3/reference/datamodel.html#executing-the-class-body

Subclassing namedtuple classes

To get the performance benefits, intrinsic sortability, etc. of NamedTuple (or namedtuple()) while customizing behavior, API, etc., you can subclass.

See the OnDup class in _dup.py for an example.

Here’s another example:

>>> from collections import namedtuple
>>> from itertools import count

>>> class Node(namedtuple('_Node', 'cost tiebreaker data parent depth')):
...     """Represent nodes in a graph traversal. Suitable for use with e.g. heapq."""
...     __slots__ = ()
...     _counter = count()  # break ties between equal-cost nodes, avoid comparing data
...     # Give call sites a cleaner API for creating new Nodes
...     def __new__(cls, cost, data, parent=None):
...         tiebreaker = next(cls._counter)
...         depth = parent.depth + 1 if parent else 0
...         return super().__new__(cls, cost, tiebreaker, data, parent, depth)
...     def __repr__(self):
...         return 'Node(cost={cost}, data={data!r})'.format(**self._asdict())

>>> start = Node(cost=0, data='foo')
>>> child = Node(cost=5, data='bar', parent=start)
>>> child
Node(cost=5, data='bar')
>>> child.parent
Node(cost=0, data='foo')
>>> child.depth

namedtuple-style dynamic class generation

See the implementation of namedbidict (it was since removed due to low usage).

API Design

How to deeply integrate with Python’s collections and other built-in APIs?

  • Beyond implementing collections.abc.Mapping, bidicts implement additional APIs that dict and OrderedDict implement (e.g. setdefault(), popitem(), etc.).

    • When creating a new API, making it familiar, memorable, and intuitive is hugely important to a good user experience.

  • Thanks to Hashable’s implementing abc.ABCMeta.__subclasshook__(), any class that implements the required methods of the Hashable interface (namely, __hash__()) makes it a virtual subclass already, no need to explicitly extend. I.e. As long as Foo implements a __hash__() method, issubclass(Foo, Hashable) will always be True, no need to explicitly subclass via class Foo(Hashable): ...

  • How to make your own open ABC like Hashable?

    • Override __subclasshook__() to check for the interface you require.

    • Interesting consequence of the __subclasshook__() design: the “subclass” relation becomes intransitive. e.g. object is a subclass of Hashable, list is a subclass of object, but list is not a subclass of Hashable.

  • What if you needed to derive from a second metaclass? Be careful to avoid “TypeError: metaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its bases”. See the great write-up in https://blog.ionelmc.ro/2015/02/09/understanding-python-metaclasses/.

  • collections.abc.Mapping and collections.abc.MutableMapping don’t implement __subclasshook__(), so you must either explicitly subclass them (in which case you inherit their concrete method implementations) or use abc.ABCMeta.register() (to register as a virtual subclass without inheriting any of the implementation).

  • Notice that Python provides collections.abc.Reversible but no collections.abc.Ordered or collections.abc.OrderedMapping. See: https://bugs.python.org/issue28912

  • See the Zen of Python for how to make APIs Pythonic.

    The following Zen of Python guidelines have been particularly influential for bidict: - “Errors should never pass silently. Unless explicitly silenced. - “In the face of ambiguity, refuse the temptation to guess.” - “Readability counts.” - “There should be one – and preferably only one – obvious way to do it.”

Python’s data model


  • CPython vs. PyPy (and other Python implementations)

  • Python 2 vs. Python 3

    • As affects bidict, mostly dict API changes, but also functions like zip(), map(), filter(), etc.

    • __ne__() fixed in Python 3

    • Borrowing methods from other classes:

      In Python 2, must grab the .im_func / __func__ attribute off the borrowed method to avoid getting TypeError: unbound method ...() must be called with ... instance as first argument

Other interesting stuff in the standard library


See the Thanks page for some of the fantastic tools for software verification, performance, code quality, etc. that bidict has provided a great opportunity to learn and use.