Start by clarifying requirements and scale, then propose a layered data model with inheritance and overrides. Walk through real-time evaluation with caching and conflict resolution, and finish with a safe deployment strategy using canary releases and rollback.
Pro tip: Emphasize idempotency and auditability in rule changes; Netflix values systems that can be safely rolled back and audited for compliance.
Ask about expected QPS, number of publishers, rule complexity, and latency SLAs. Confirm whether rules are evaluated per request or precomputed.
Propose a hierarchical model: global defaults, publisher-level overrides, and campaign-specific rules. Use a tree or DAG to represent inheritance and avoid cycles.
Specify precedence rules (e.g., most specific wins, or explicit priority). Handle overlapping conditions with deterministic resolution and logging.
Design an evaluation engine that compiles rules into an efficient structure (e.g., decision tree or bitset). Use caching for hot rules and invalidate on updates.
Implement versioned rule sets, canary deployment, and automated rollback on error metrics. Ensure atomic updates and audit trails.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.