Async I/O Is Not Enough
For the past few months, I’ve been exploring Go. Having done quite a bit of grueling work shaving off milliseconds from Python web apps, I’ve found Go to be incredible. You can schedule dirt cheap concurrent operations — simply by adding go in front of a function call — and achieve true parallelism across cores.
Python’s asyncio tasks are also dirt cheap, which is especially useful for spawning tons of I/O operations (like DB calls for clients). However, unlike Go, they still pose an issue for web applications. When: a) latency is critical — horizontal scaling does not optimize single request duration, and b) there is an ever-growing amount of Python processing code scattered across the application, native extensions would be an overkill.
For CRUD apps, these constraints are somewhat less relevant, but they are quite typical for data science applications. Go shines in these scenarios, whereas asyncio is not a panacea. Why? Concurrency != parallelism. Single-threading by design itself. And thanks to our dear friend, the GIL.
