Pick a focused question that fits your time, stack, and interview goal.
How much time do you have?
Show one-drill sessions you can finish now.
12 results across 1 active filter
Page 1 of 1
Explains per-worker memory and copy-on-write savings without preloading process-unsafe resources.
Produces a pinned, reviewable Python artifact once and promotes the same image across environments.
Trusts proxy-derived scheme, host, and client data only from known hops and configures externally visible paths correctly.
Coordinates readiness, signals, request drain, lifespan cleanup, and a bounded termination deadline.
Uses FastAPI lifespan to create and close process-owned resources around request serving.
Treats Python web workers as measured processes with separate memory, lifecycle, and failure boundaries.
Separates process survival, traffic readiness, and slow initialization without turning shared dependency failure into a restart storm.
Separates Python allocation growth from process RSS, native memory, fragmentation, caches, and workload peaks.
Uses representative traffic and tail latency to find the real bottleneck and a safe operating range.
Chooses evidence based on whether latency comes from CPU execution, waiting, contention, or an external dependency.
Distinguishes the synchronous WSGI request contract from ASGI's asynchronous, multi-event application contract.
Turns Django's production settings, server, storage, security, and operational dependencies into a repeatable release check.