Accelerating Chalk Resolvers & Migrating to Inline Expressions

A conceptual playbook for making feature pipelines fast: the why, the strategies, and — most importantly — the limits, so you can tell when a piece of logic will never accelerate and restructure (or leave) it instead of burning time.

When to Use

• Optimizing the latency of an online (or offline) feature query
• Reviewing a new or existing @online / @offline Python resolver for performance
• Deciding between a Python resolver and an inline feature expression
• Triaging which un-accelerated resolvers are worth fixing
• Recognizing code that won't statically accelerate, and choosing how to restructure around it
• Moving blocking network I/O off the Python worker path
• Preserving output parity when changing how a model-feeding feature is computed

1. The core idea: where your code runs