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

AsyncIO enables massively parallel I/O processing while maintaining single-threaded execution

Key components of an event loop include:

• Selectors for monitoring file descriptors
• Callback queues for executing scheduled tasks
• Futures to represent results of async operations
• Tasks to wrap and manage coroutines

The event loop operates in “ticks” - atomic steps that:

• Check for ready I/O operations
• Execute scheduled callbacks
• Manage timed operations
• Process completed futures

File descriptors are fundamental to async I/O:

• Each process starts with stdin(0), stdout(1), stderr(2)
• New file descriptors added for network sockets, files, devices
• Selectors monitor multiple file descriptors efficiently

Performance considerations:

• UV loop (using libuv) is 2-4x faster than default event loop
• Selector-based implementations preferred on common x86_64 Linux
• Async I/O more efficient than threading for I/O-bound operations

Core async patterns:

• call_soon() for immediate callback scheduling
• call_later() for delayed execution
• gather() for concurrent future waiting
• Coroutines for structured async code
• Context managers and iterators for async resource management

With GIL removal in future Python versions:

• Multiple event loops per process may be possible
• Better thread integration expected
• Memory management simplification likely