slots-factory 0.0.2

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slot_factory

Factory functions for creating slot objects

Slots are a python construct that allows users to create an object that doesn't contain __dict__ or __weakref__ attributes. The benefit to a slots object is that it has faster attribute access and it saves on memory use, which make slots objects ideal for when you have lots of instances of a single python object.

I've never been a huge fan of the syntax though, as it requires repetitive code for definition as well as instantiation. yuck.

class SlotObject:
    __slots__ = ('x', 'y', 'z')
    def __init__(x, y, z): -> None
        self.x = x
        self.y = y
        self.z = z

    def __repr__(self):
        contents = ", ".join(
            [f"{key}={getattr(self, key)}" for key in self.__slots__]
        )
        return f"SlotsObject({contents})"

For funsies, I wanted to see if I could create a different way to instantiate these objects, with less jargon. Something like collections.namedtuple, but again without redundant definitions and with the benefits of __slots__. This repo is the results of such endeavor.

slots_factory()

The first factory function made available is slots_factory. Simply import the function, and all **kwargs are assigned as attributes to an instance of a slots object. Type definitions are handled internally by the function, so successive calls to slots_factory with the same _name and **kwargs keys will return new instances of the same type.

For example:

In [1]: from slots_factory import slots_factory

In [2]: this = slots_factory(x=1,y=2,z=3)

In [3]: this
Out[3]: SlotsObject(x=1, y=2, z=3)

In [4]: that = slots_factory(x=4,y=5,z=6)

In [5]: that
Out[5]: SlotsObject(x=4, y=5, z=6)

In [6]: fizzbuzz = slots_factory(_name="fizzbuzz", fizz="fizz", buzz="buzz")

In [7]: fizzbuzz
Out[7]: fizzbuzz(fizz=fizz, buzz=buzz)

In [8]: slots_factory.__dict__
Out[8]:
{1040: slots_factory.slots_factory.SlotsObject,
1419034624: slots_factory.slots_factory.fizzbuzz}

As we can see, we created three instances, this, that, and fizzbuzz. this and that are instances of the same type, since the function args were the same. fizzbuzz is a different type however, since its function arguments were different.

In [9]: type(this) == type(that)
Out[9]: True

In [10]: type(this) == type(fizzbuzz)
Out[10]: False

Another benefit to this SlotsObject is that, as the underlying type is a slots object, the attributes are dynamic, unlike the namedtuple.

In [11]: this.x = 4

In [12]: this
Out[12]: SlotsObject(x=4, y=2, z=3)

The type identification and attribute setting is all done in C, in attempt to make instantiation as fast as possible. Instantiation of a SlotObject is about 80% slower than the instantiation of a namedtuple (mainly because it handles type definitions internally). Attribute access is on par however, and faster than a normal object as expected.

In [13]: from collections import namedtuple

In [14]: This = namedtuple('This', ['x', 'y', 'z', 'a', 'b', 'c'])

In [15]: %timeit this = This(x=1,y=2,z=3,a=4,b=5,c=6)
444 ns ± 5.2 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

In [16]: %timeit that = slots_factory('that', x=1,y=2,z=3,a=4,b=5,c=6)
809 ns ± 2.65 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

In [17]: %timeit this.c
24.6 ns ± 0.132 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)

In [18]: %timeit that.c
25.8 ns ± 0.13 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
%time

fast_slots()

There's a second factory function, fast_slots, which is, obviously, faster. Instead of using the builtin hashing algorithm to generate an ID, it simply uses the object name and assumes that all objects named the same, are the same. Since it skips the hashing step, it builds slot instances much faster.

In [5]: %timeit that = fast_slots('that', x=1,y=2,z=3,a=4,b=5,c=6)
579 ns ± 4.64 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

Instead of relying on an internal ID mechanism, fast_slots leverages python's try/except functionality. The internal _slots_factory_setattrs method throws an exception when the object attributes are thought to be different, so when this happens fast_slots deletes its old internalized type definition and then builds a new one. As such, if you expect to be redefining the same type over and over again, it's best to use slots_factory for better overall performance. If however you're certain to be creating identical instances of the same type (with differing attribute variables of course, that is indeed allowed by fast_slots), then you'll be better of using fast_slots to do this.

from slots_factory import slots_factory, fast_slots

# use `slots_factory` like so:
this = slots_factory(x=1)
that = slots_factory(y=2)

# use `fast_slots` like so:
category = fast_slots('category', id=1, name='category 1')
category = fast_slots('category', id=2, name='category 2')

type_factory()

Finally, if we're really craving the speeds, the most efficient way to use this module is to individually define your types and then manually spin up instances of these objects. This can be done by importing the type_factory and slots_from_type functions.

from slots_factory import type_factory, slots_from_type

type_ = type_factory("SlotsObject", ['x', 'y', 'z', 'a', 'b', 'c'])
instance = slots_from_type(type_, x=1, y=2, z=3, a=4, b=5, c=6)

In [6]: %timeit instance = slots_from_type(type_, x=1, y=2, z=3, a=4, b=5, c=6)
521 ns ± 2.73 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

slots_from_type is a convenience function; you can also instantiate your own instance and then pass it through the underlying _slots_factory_setattrs function, which is what actually populates the attributes.

from slots_factory import type_factory
from slots_factory.tools.SlotsFactoryTools import _slots_factory_setattrs

my_type = type_factory("SlotsObject", ['x', 'y', 'z', 'a', 'b', 'c'])
instance = my_type()
_slots_factory_setattrs(my_type, {'x': 1, 'y': 2, 'z': 3, 'a': 4, 'b': 5, 'c': 6})

Using the type_factory is essentially equivalent to the use of a namedtuple, but without it's count and index methods, and with dynamic attributes. Also, all of that redundant typing is back... ¯\_(ツ)_/¯