From Seconds to Milliseconds: Fixing Python Cold Starts with SnapStart

Python Lambda functions utilizing Pydantic or similar initialization processes often suffer from slow cold starts. Lambda SnapStart for Python 3.12+ significantly improves this by caching the initialized state. With SnapStart, the heavy initialization happens once during deployment, not at every function invocation. Before SnapStart, cold starts involved code download, runtime setup, initialization, and event handling, causing delays. SnapStart changes this by creating a snapshot of the fully initialized function and restoring from it later. This process bypasses the slow initialization steps, leading to faster startup times. AWS provides `@register_before_snapshot` and `@register_after_restore` hooks to manage unique artifacts and ensure function correctness. Implementing SnapStart in CDK involves specifying the Python 3.12+ runtime, enabling SnapStart, and creating a version alias. The pre-snapshot initialization and after-restore hooks have time limits to prevent delays. Code or dependency changes require a new version to update the snapshot. SnapStart currently has limitations regarding ephemeral storage, EFS, provisioned concurrency, and containers. Ultimately, Lambda SnapStart makes serverless a more viable option for complex, model-heavy Python applications.