https://kitfucoda.medium.com/actor-models-in-pygame-lessons-in-serialization-and-state-isolation-979c5af57cc5
My attempt to build a prototype recreating the Matrix rain effect last week forced a hard look at my mini Pygame framework. The implementation yielded a mixed result that I’d previously overlooked—not due to the Actor model itself, but a misjudgment in where the "Physical" meets the "Semantic." Extracting a portable framework through refactoring was the goal, yet I was still facing inefficiencies in state management and event dispatch lookups.
In a search for order, I decided to overengineer the project for the sake of learning. My plan was to split the application into three independent processes: the state actor, the display loop, and the event dispatcher. If a little isolation is good, then I figured more processes in parallel must mean faster execution. I wanted to bypass the Global Interpreter Lock and find true independence for each component.
Then I hit the waterfall. The code eventually ran, but the realization of a "Transparency Tax" came an hour later. Instead of hitting a 60 FPS target, my frame rate plummeted into the 30s. Between the serialization overhead of sending callables through cross-process queues and the blocking nature of Pygame’s clock, the Actor process was left with little room for concurrency. The added complexity of message passing became the very bottleneck I sought to avoid.
I failed the mission to cleanly separate components into independent processes, but the expedition pushed me far enough from my comfort zone that the insights were worth the crash. If experience is a painting, the trail of footsteps is exactly how lines are formed in an image. I’m moving back to a single-process model for the next revision, keeping the clean Actor API but losing the performance tax.
#Python #Pygame #SoftwareArchitecture #ActorModel #Concurrency #KitFuCoda #Fediverse
