Comment Comment by sijokun I ran into memory issues with a high-load project so I built a compact binary encoder/decoder for Pydantic models. It cut in-RAM object size by up to 7× vs json.dumps(), and ended up saving the whole service from collapsing.GitHub: https://github.com/sijokun/PyBynticWorks with annotated Pydantic models and gives you: – .serialize() -> bytes – .deserialize(bytes) -> ModelCurious to hear whether others here have hit similar problems and how you solved it?P.S. Project was a Telegram MiniApp with 10m+ MAU, we were storing cached user objects in Redis Cluster parent sijokun 1 year ago
GitHub: https://github.com/sijokun/PyByntic
Works with annotated Pydantic models and gives you: – .serialize() -> bytes – .deserialize(bytes) -> Model
Curious to hear whether others here have hit similar problems and how you solved it?
P.S. Project was a Telegram MiniApp with 10m+ MAU, we were storing cached user objects in Redis Cluster