[Python] Time complexity vs. DB cost/I/O: Whicdfgdfgould a backend engineer prioritize?...

From a general standpoint, if we were to only consider these two, which is more important for a backend engineer to focus on: the time complexity of the application code or the cost and I/O of database operations?

My initial thought was that time complexity should be the more critical factor to consider. This is because algorithmic efficiency is a fundamental concept in computer science that directly impacts the application's processing speed and CPU usage. I expected that focusing on a lower time complexity (e.g., O(n log n) instead of O(n²)) would always be the primary goal for performance optimization.

However, I am now unsure if this is always the case in a real-world backend environment. I realize that no matter how efficient an algorithm is, its performance can be bottlenecked by slow database I/O or high query costs. This has made me question my initial assumption, and I'm no longer certain which of the two generally holds higher priority.

From a general standpoint, if we were to only consider these two, which is more important for a backend engineer to focus on: the time complexity of the application code or the cost and I/O of database operations?

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