When to choose asyncio vs threading in Python?
Reported in Ubisoft European engineering loops. Python concurrency interview topic for backend roles.
Interview scenario
Often asked in Ubisoft loops at European offices (London, Berlin, Amsterdam, Paris, Stockholm, Dublin, and remote EU). Prepare a clear spoken answer plus key trade-offs.
Model answer
Try answering aloud first
Cover trade-offs, structure, and a concrete example before revealing the baseline response.
How to frame this at Ubisoft: Connect your answer to measurable impact, clarity of thought, and trade-offs the team cares about. Below is a strong baseline response you can adapt with your own project examples.
Use asyncio for high-concurrency I/O workloads where tasks spend time waiting on network or disk. It uses cooperative scheduling and a single event loop, which can handle many sockets efficiently.
Use threading for blocking libraries that are not async-aware or when integrating legacy code quickly. For CPU-bound tasks, prefer multiprocessing because Python threads are constrained by the GIL for bytecode execution.
Interview-quality answers mention operational complexity too: async gives throughput but requires end-to-end async stack discipline.
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