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Python code optimization may seem easy or hard depending on the performance target. If the target is “best effort”, carefully choosing the algorithm and applying well-known common practices is usually enough. If the target is dictated by the UX, you have to go down a few abstraction layers and hack the system sometimes. Or rewrite the underlying libraries. Or change the language, really. This post is about our experience in Python code optimizations when whatever you do is not fast enough.

To provide a fast, seamless, and highly available experience for the end-user, modern applications increasingly rely on third-party services. Due to this rising complexity, it's become important for IT employees to ensure that these services are up and running and communicating as they should. API monitoring has thus become a must-have for DevOps teams.