Boosting Python performance by leveraging lru_cache functionality

In this article, we’ll explore Python’s LRU (Least Recently Used) cache, a handy tool for optimizing memory management in applications that frequently access data. By storing recently used items in memory and discarding less used ones when space is limited, LRU caching helps boost performance while reducing computational overhead.

Let’s dive into how you can easily implement and benefit from this efficient caching strategy using Python’s functools module.

1. Caching API Calls:

Imagine you have a function that retrieves data from an external API.

Making repeated calls to the same API endpoint with identical parameters can be inefficient. Here’s how @lru_cache can help:

import time
from functools import lru_cache

@lru_cache(maxsize=10)  # Cache up to 10 recent API responses
def fetch_data_from_api(url, params):
    params_key = params[0]  # Convert to tuple of sorted key-value pairs

    # Simulate an API call that returns data
    time.sleep(1)  # Simulate network latency
    return {"data": "This data is from the API"}

# Example usage
params1 = {"key": "value"}
params2 = {"key": "value"}

data1 = fetch_data_from_api("https://api.example.com/data", tuple(sorted(params1.items())))
data2 =…