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Places semaphore and admission boundaries around scarce work without retaining unlimited tasks.
Connects virtual environments, pyproject metadata, version constraints, lock artifacts, Python versions, and CI verification.
Produces a pinned, reviewable Python artifact once and promotes the same image across environments.
Chooses atomic database expressions, row locks, and constraints from the invariant being protected.
Separates single scheduler ownership from task-level overlap and idempotency controls.
Connects pool limits to process topology, transaction duration, database capacity, and queueing evidence.
Uses expected versions and conditional writes so delayed or retried Python tasks cannot commit stale decisions.
Streams bounded chunks with explicit resource lifetime, cancellation, late-failure, and proxy behavior.
Verifies commit callbacks without confusing callback registration with durable external delivery.
Overrides FastAPI boundaries for focused tests and restores the global override mapping reliably.
Uses parametrization for one behavior across meaningful cases while keeping failures and intent easy to read.
Handles multipart uploads with bounded size, untrusted metadata, content inspection, controlled storage, scanning, and cleanup.
Keeps FastAPI dependency resolution at transport and composition boundaries while application services use explicit dependencies.
Trusts proxy-derived scheme, host, and client data only from known hops and configures externally visible paths correctly.
Uses documented thread-safe scheduling APIs and keeps event-loop objects confined.
Uses a password-specific algorithm, measurable work settings, and gradual rehashing without blocking service capacity.
Reuses bounded redis-py pools for the application lifetime and closes async resources deliberately.
Coordinates readiness, signals, request drain, lifespan cleanup, and a bounded termination deadline.
Separates stale checkout protection, connection aging, pool waits, statement timeouts, and uncertain transactions.
Treats cancellation as cooperative control flow with reliable resource cleanup and propagation.
Uses FastAPI lifespan to create and close process-owned resources around request serving.
Uses Pydantic models as explicit transport contracts without making them persistence or domain models automatically.
Keeps Celery messages small, safe to deserialize, and compatible across rolling deployments.
Aligns pytest-asyncio discovery, loop lifetime, and async fixture ownership without custom loop competition.