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Shows how task interleaving across await points can violate a multi-step invariant.
Separates caller time bounds, cancellation propagation, and ownership of protected underlying work.
Builds a cooperative scheduling model for tasks, callbacks, readiness, and I/O polling.
Chooses a concurrency model from workload, libraries, isolation, runtime build, and operational cost.
Finds synchronous I/O or CPU work that prevents the event loop from scheduling other tasks.
Combines asyncio debug evidence, task inspection, warnings, profiling, and capacity signals.
Defines fail-fast, all-results, and partial-success behavior for concurrent asyncio operations.
Places semaphore and admission boundaries around scarce work without retaining unlimited tasks.
Uses documented thread-safe scheduling APIs and keeps event-loop objects confined.
Treats cancellation as cooperative control flow with reliable resource cleanup and propagation.
Designs backpressure, worker ownership, completion tracking, failure, and shutdown around asyncio queues.
Coordinates admission stop, task cancellation or draining, resource closure, and bounded termination.
Explains serialization, import safety, start methods, worker failure, and data-transfer cost.
Explains task references, exceptions, caller lifetime, shutdown, and durable-work boundaries.
Explains conventional CPython thread execution and the optional free-threaded runtime boundary.
Explains awaitable work, scheduled ownership, and eventual-result integration in asyncio.
Separates overlapping I/O work from simultaneous execution across processing resources.
Uses task-aware context for tracing while keeping business inputs explicit and correctly reset.
Chooses task synchronization by protected state, notification predicates, and limited capacity.
Compares structured task ownership with ordered result aggregation and different failure behavior.
Compares convenient context-propagating thread offload with explicit executor selection and ownership.