Since python 3.10 dataclass instances can easily enable a memory optimization.

By default, when we define a class in Python, it uses a dict to store its attributes and methods.

This means that every instance of the class has an extensible dictionary to store its data, this flexibility comes at the cost of memory usage.

When we use slots, Python creates a tuple to store the attributes of the class instead of a dictionary, reducing memory usage.

Non compliant Code Example

@dataclass
class MyDataclass:
    a: int
    b: int
    c: int

Compliant Solution

@dataclass(slots=True)
class MySlottedDataclass:
    a: int
    b: int
    c: int

Relevance Analysis

Configuration

  • Host: HP ProBook 450 G7

  • Measurement: VJoule tool

Context

Two approaches were benchmarked: - Non-compliant: Using logging with .format() - Compliant: Using logging with %s and kwargs

Impact Analysis

result

Conclusion

Extract from https://wiki.python.org/moin/UsingSlots

The slots declaration allows us to explicitly declare data members, causes Python to reserve space for them in memory, and prevents the creation of dict and weakref attributes. It also prevents the creation of any variables that aren’t declared in slots.

Why Use slots? The short answer is slots are more efficient in terms of memory space and speed of access, and a bit safer than the default Python method of data access.

Using slots can be an effective way to optimize memory usage in Python classes and improve the performance of memory-intensive applications. It is a minor remediation cost to add slots in class definitions and can have significant benefits in terms of memory efficiency.

References