mementos

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A quick way to make Python classes automatically memoize (a.k.a. cache) their instances based on the arguments with which they are instantiated (i.e. args to their __init__).

It's a simple way to avoid repetitively creating expensive-to-create objects, and to make sure objects that have a natural 'identity' are created only once. If you want to be fancy, mementos implements the Multiton <https://en.wikipedia.org/wiki/Multiton_pattern>_ software pattern.

Usage

Say you have a class Thing that requires expensive computation to create, or that should be created only once. Easy peasy::

from mementos import mementos

class Thing(mementos):

    def __init__(self, name):
        self.name = name

    ...

Then Thing objects will be memoized::

t1 = Thing("one")
t2 = Thing("one")
assert t1 is t2    # same instantiation args => same object

Under the Hood

When you define a class class Thing(mementos), it looks like you're subclassing the mementos class. Not really. mementos is a metaclass, not a superclass. The full expression is equivalent to class Thing(with_metaclass(MementoMetaclass, object)), where with_metaclass and MementoMetaclass are also provided by the mementos module.

Metaclasses are not normal superclasses; instead they define how a class is constructed. In effect, they define the mysterious __new__ method that most classes don't bother defining. In this case, mementos says in effect, "hey, look in the cache for this object before you create another one."

If you like, you can use the longer invocation with the full with_metaclass spec, but it's not necessary unless you define your own memoizing functions. More on that below.

Python 2 vs. Python 3

Python 2 and 3 have different forms for specifying metaclasses. In Python 2::

from mementos import MementoMetaclass

class Thing(object):

    __metaclass__ = MementoMetaclass  # now I'm memoized!

    ...

Whereas Python 3 uses::

class Thing3(object, metaclass=MementoMetaclass):

    ...

mementos supports either of these. But Python 2 and Python 3 don't recognize each other's syntax for metaclass specification, so straightforward code for one won't even compile for the other. The with_metaclass() function shown above is the way to go for cross-version compatibility. It's very similar to that found in the six cross-version compatibility module.

Careful with Call Signatures

MementoMetaclass caches on call signature, which can vary greatly in Python, even for logically identical calls. This is especially true if kwargs are used. E.g. def func(a, b=2): pass can be called func(1), func(1, 2), func(a=1), func(1, b=2), or func(a=2, b=2). All of these resolve to the same logical call--and this is just for two parameters! If there is more than one keyword, they can be arbitrarily ordered, creating many logically identical permutations.

So if you instantiate an object once, then again with a logically identical call but using a different calling structure/signature, the object won't be created and cached just once--it will be created and cached multiple times.::

o1 = Thing("lovely")
o2 = Thing(name="lovely")
assert o1 is not o2     # because the call signature is different

This may degrade performance, and can also create errors, if you're counting on mementos to create just one object. So don't do that. Use a consistent calling style, and it won't be a problem.

In most cases, this isn't an issue, because objects tend to be instantiated with a limited number of parameters, and you can take care that you instantiate them with parallel call signatures. Since this works 99% of the time and has a simple implementation, it's worth the price of this inelegance.

Partial Signatures

If you want only part of the initialization-time call signature (i.e. arguments to __init__) to define an object's identity/cache key, there are two approaches. One is to use MementoMetaclass and design __init__ without superfluous attributes, then create one or more secondary methods to add/set useful-but-not-essential data. E.g.::

class OtherThing(with_metaclass(MementoMetaclass, object)):

    def __init__(self, name):
        self.name = name
        self.color = None   # unset for now
        self.weight = None

    def set(self, color=None, weight=None):
        self.color = color or self.color
        self.weight = weight or self.weight
        return self

ot1 = OtherThing("one").set(color='blue')
ot2 = OtherThing("one").set(weight='light')
assert ot1 is ot2
assert ot1.color == ot2.color == 'blue'
assert ot1.weight == ot2.weight == 'light'

Or you can just define your own memoizing metaclass, using the factory function described below.

Visiting the Factory

The first iteration of mementos defined a single metaclass. It's since been reimplemented as a parameterized meta-metaclass. Cool, huh? That basically means that it defines a function, memento_factory() that, given a metaclass name and a function defining how cache keys are constructed, returns a corresponding metaclass. MementoMetaclass is the only metaclass that the module pre-defines, but it's easy to define your own memoizing metaclass.::

from mementos import memento_factory, with_metaclass

IdTracker = memento_factory('IdTracker',
                            lambda cls, args, kwargs: (cls, id(args[0])) )

class MyTracker(with_metaclass(IdTracker, object)):
    ...

    # object identity is the object id of first argument to __init__
    # (and there must be one, else the args[0] reference => IndexError)

The first argument to memento_factory() is the name of the metaclass being defined. The second is a callable (e.g. lambda expression or function object) that takes three arguments: a class object, an argument list, and a keyword arg dict. Note that there is no * or ** magic--args passed to the key function have already been resolved into basic data structures.

The callable must return a globally-unique, hashable key for an object. This key will be stored in the _memento_cache, which is a simple dict.

When various arguments are used as the cache key/object identity, you may use a tuple that includes the class and arguments you want to key off of. This can also help debugging, should you need to examine the _memento_cache cache directly. But in cases like the IdTracker above, it's not mandatory that you keep extra information around. The raw id(args[0]) integer value would suffice, as would a constructed string or other immutable, hashable value.

In cases where arguments are very flexible, or involve flexible data types, a high-powered hashing function such as that provided by SuperHash <http://pypi.python.org/pypi/SuperHash>_ might come in handy. E.g.::

from superhash import superhash

SuperHashMeta = memento_factory('SuperHashMeta',
                            lambda cls, args, kwargs: (cls, superhash(args)) )

For the 1% edge-cases where multiple call variations must be conclusively resolved to a unique canonical signature, that can be done on a custom basis (based on the specific args). Or in Python 2.7 and 3.x, the inspect module's getcallargs() function can be used to create a generic "call fingerprint" that can be used as a key. (See the tests for example code.)

Notes

• See CHANGES.rst for the extended Change Log.

• mementos is not to be confused with memento <http://pypi.python.org/pypi/memento>_, which does something completely different.

• mementos was originally derived from an ActiveState recipe <http://code.activestate.com/recipes/286132-memento-design-pattern-in-python/>_ by Valentino Volonghi. While the current implementation quite different and the scope much broader, the availability of that recipe was what enabled this module and the growing list of modules that depend on it. This is what open source evolution is all about. Thank you, Valentino!

• It is safe to memoize multiple classes at the same time. They will all be stored in the same cache, but their class is a part of the cache key, so the values are distinct.

• This implementation is not thread-safe, in and of itself. If you're in a multi-threaded environment, consider wrapping object instantiation in a lock.

• Automated multi-version testing managed with pytest <http://pypi.python.org/pypi/pytest>, pytest-cov <http://pypi.python.org/pypi/pytest-cov>, coverage <https://pypi.python.org/pypi/coverage/4.0b1>_ and tox <http://pypi.python.org/pypi/tox>. Continuous integration testing with Travis-CI <https://travis-ci.org/jonathaneunice/mementos>. Packaging linting with pyroma <https://pypi.python.org/pypi/pyroma>_.

• The author, Jonathan Eunice <mailto:[e-mail]>_ or @jeunice on Twitter <http://twitter.com/jeunice>_ welcomes your comments and suggestions.

Installation

To install or upgrade to the latest version::

pip install -U mementos

You may need to prefix these with sudo to authorize installation. In environments without super-user privileges, you may want to use pip's --user option, to install only for a single user, rather than system-wide. Depending on your system configuration, you may also need to use separate pip2 and pip3 programs to install for Python 2 and 3 respectively. As a fall-back for cases where the releationship between pip and the Python interpreter you want to run is unclear, you can invoke pip as a module under a specific Python executable::

python3.6 -m pip install -U mementos

Testing

To run the module tests, use one of these commands::

tox                # normal run - speed optimized
tox -e py27        # run for a specific version only (e.g. py27, py34)
tox -c toxcov.ini  # run full coverage tests