A couple of years ago, I wrote a blog post on tips on how to write memory-efficient loops in Python which became quite popular. The positive response encouraged me to put in writing a second part, where I delve into additional memory optimization methods.
When writing python code, loops are usually not the one place where we must be mindful of memory usage. In data-related projects and object-oriented code development, it is crucial to make sure that that our classes are also memory efficient. Often, we invest a big period of time designing and writing complex and complicated classes, only to find that they perform poorly in testing or production as a result of the massive amount of knowledge they should carry.
By following the techniques and approaches discussed within the article, you may create classes that optimize memory usage and improve overall performance. This blog post explores three techniques and advisable approaches for creating memory-efficient Python classes.
Using Python’s __slots__ dunder, you may explicitly define the attributes that a category can ever possess. This generally helps optimize the memory usage of our classes by avoiding the creation of a dynamic dictionary for attribute storage.
By default, Python classes store their instance attributes in a personal dictionary (__dict__). This dictionary allows for a variety of flexibility, as you may add, modify, or delete the category attributes at runtime. Nevertheless, this flexibility often comes at the associated fee of memory overhead. Each instance of the category has a dictionary that stores attribute names and values as key-values pairs. When using __slots__, Python reserves only a hard and fast amount of space for the required attributes directly in each instance, as a substitute of using the default dictionary.
Here’s an example of a Python class that uses __slots__ to extend memory efficiency: