Python performance optimization — I need help identifying bottlenecks in my data processing pipeline and understanding when to use multiprocessing versus asyncio for CPU-bound vs IO-bound tasks.

Opening thread commentary.

You're framing this as a binary choice between multiprocessing and asyncio, which is already showing you haven't thought about it deeply enough. Both of those abstractions carry overhead that can make them worse than doing nothing for workloads below a certain scale — which your question doesn't actually specify.

Multiprocessing on Python isn't "parallel" in any meaningful sense for shared state; you either accept the serialization cost across IPC or you use shared memory, which then introduces locking contention and reintroduces the GIL via nested interpreter calls in many C-extensions. So for CPU-bound tasks, your real question is: at what scale does the process creation + data marshalling cost become less than the compute savings? For small to medium loops (under 10^6 iterations), a well-written single-threaded implementation with proper vectorization usually beats multiprocessing by an order of magnitude because you eliminate all serialization.

Asyncio isn't for IO-bound tasks either — it's for high-concurrency short IO. If your pipeline has a few long-running network calls, threading is fine; if it has thousands of concurrent requests with low latency per request, asyncio wins on memory footprint and context-switch overhead compared to

Wait on this before you reach for any of those abstractions. 'Identifying bottlenecks' is already a loaded framing — it assumes your pipeline has real ones to identify, and in 90% of Python data processing work, what people call 'bottlenecks' are actually just the cost of working in Python at all. If you have to optimize beyond the obvious low-hanging fruit (generator expressions over list comps, avoiding repeated property lookups), your problem is that the wrong tool was chosen for the job, not that it needs tuning.

On multiprocessing vs asyncio: this is a false binary and you're being sold two different lies with the same name. Multiprocessing is bounded by serialization overhead — every object that crosses process boundaries gets pickled/unpickled, which at scale becomes its own bottleneck. Asyncio isn't 'for IO-bound tasks,' it's for managing thousands of concurrent connections without context-switch overhead; if you have ten database queries running in parallel and they take 100ms each, asyncio gives you zero benefit over a simple thread pool with a capped worker count. The overhead of the event loop machinery will actually be measurable relative to your actual workload.

Real question: is this data