Using Slot Classes in DocTable Schemas

Allowing for defaulted parameters in slot classes using decorators so they can be used as doctable schemas.

Posted by Devin J. Cornell on Jun 15, 2021

Motivation: I thought it would be convenient to use slot-based classes to represent database rows because of their lower memory usage and faster variable access compared to dictionaries. Typically results from sqlalchemy select queries are provided as named tuples, but my work in allowing DocTable schemas to be defined in terms of dataclasses also allows users to use these classes to represent queried rows as objects (without ORM). There is overhead in the construction of these classes (more for time than memory usage), but it can be valuable because we can add additional methods to interact with the queried row objects (of course, this behavior can be disabled). Ideally I could use slots to further reduce memory overhead while maintaining the ability to write custom methods, but the problem is that creating slot-based classes with defaulted parameters like those needed to construct database schemas is not allowed.

For example, the definition of the following class will result in the exception "ValueError: 'a' in slots conflicts with class variable."

class Test1:
    __slots__ = ['a']
    a: int = 5

Typically the dataclasses I use to define doctable schemas look like the code block below. Note that the id column (typical of database schemas) is listed first and given a default parameter to indicate that it is the primary key and automatically incremented. Thus, all member variables after this are required to have defaulted parameters (which are usually doctable.Col() objects) and normally adding __slots__ to this class would then not be allowed.

class MyRow(doctable.DocTableRow):
    id: int = doctable.IDCol()
    payload: int = doctable.Col()

My solution was to create a decorator that would convert the provided class to a dataclass so that it would add __init__ (among other dunder methods) with defaulted parameter values. Next, I remove defaulted values from the class definition, and add them as __slots__. Finally, I create a new class which inherits from this modified class as well as DocTableRow to create a slot class with defaulted parameters (in the constructor) and include methods that doctable schemas require.

This is an example of how the decorator (which I called doctable.row) can be used to generate a doctable schema:

class MyRow:
    __slots__ = []
    id: int = doctable.IDCol()
    payload: int = doctable.Col()

Note that we must include __slots__ = [] so that the end class (which inherits from the original MyRow) will be a slot-based class (all inheriting classes must include this in the original definition). The decorator will issue an error if this is not included, but it can be disabled (along with all the advantages of slot classes) using the syntax @doctable.row(require_slots=False). We can also pass typical @dataclasses.dataclass parameters such as init=False or repr=False through the row decorator parameters. Now we can just include the @doctable.row as a decorator instead of using @dataclasses.dataclass and inheriting from DocTableRow.


I ran some benchmarks to test memory usage between my solution with slots. The x-axis shows the number of objects I created. Each object has 10 integer member variabilles (arbitrarily). The y-axis shows memory usage in GB.

benchmark results

On average, with objects of this size, slot classes use about 1/2 the memory of a regular dict-based class.