Talks - Arthur Pastel: Demystifying AsyncIO: Building Your Own Event Loop in Python

AsyncIO enables concurrent operations by allowing other tasks to run while waiting for I/O operations, without blocking execution

The event loop is the core component that:

• Schedules and manages callbacks
• Handles task execution timing
• Coordinates I/O operations through selectors
• Maps file descriptors to callbacks

Key AsyncIO components:

• Futures - Objects representing eventual results
• Tasks - Link between coroutines and futures
• Coroutines - Functions that can be suspended/resumed
• Selectors - Handle multiple I/O operations efficiently

Task scheduling behaviors:

• Tasks don’t start executing until explicitly awaited
• Only run until first await statement when created
• Can be canceled if registered in event loop
• Can be scheduled for future execution with call_later()

Testing async code considerations:

• Results may not be deterministic due to parallel execution
• Pytest fixtures available for event loop handling
• Need to handle task cancellation properly
• Important to manage proper task cleanup

Performance aspects:

• Alternative implementations like uvloop can be 2-4x faster
• GIL prevents true parallel execution of CPU-bound tasks
• I/O operations can run concurrently
• Event loop implementation choice impacts application performance

Error handling:

• Task cancellation requires proper exception handling
• Need to handle cleanup of scheduled tasks during shutdown
• Important to gracefully close connections and tasks

Best practices:

• Use async context managers when possible
• Leverage async iterators for lazy fetching
• Consider task scheduling timing carefully
• Properly await tasks for completion