Were there any difficult tasks you had to solve? Problems with RPS, optimization, acceleration?

In Python projects, I often encountered performance optimization tasks, especially under high loads (RPS). For example, when processing a large number of requests in a web service, there were issues with delays and memory consumption.

To address these tasks, I used several approaches:

• Using asynchronous programming (asyncio, aiohttp) for request handling without blocking.
• Caching computation results with Redis or built-in LRU cache to reduce database load.
• Profiling code with cProfile and line_profiler to identify bottlenecks.
• Optimizing algorithms and data structures, such as replacing lists with sets for faster search.
• Using multiprocessing or distributing load across multiple service instances.

An example of simple caching with functools.lru_cache:

from functools import lru_cache

@lru_cache(maxsize=128)
def expensive_computation(x):
    # simulating a long operation
    import time
    time.sleep(2)
    return x * x

# When called again with the same argument, the result is retrieved from cache
print(expensive_computation(4))
print(expensive_computation(4))  # faster than the first call