The AI-assisted format was genuinely interesting.
Start by clarifying requirements and constraints, then design a modular architecture with a flag store, evaluator, and bucketing service. Focus on how targeting rules and sticky bucketing work, and discuss trade-offs around consistency, performance, and update mechanisms.
Pro tip: Emphasize deterministic bucketing using a stable hash of flag key and user ID, and mention that persisting the bucketing seed or using a consistent hashing algorithm ensures stickiness across restarts. Also, highlight the importance of caching and efficient rule evaluation for low-latency queries.
Ask questions to understand expected scale, update frequency, consistency needs, and integration points. Confirm flag types, targeting rule complexity, and rollout percentage semantics.
Outline the main modules: flag configuration store, rule evaluator, bucketing service, and a client API. Discuss how flags are loaded and refreshed (e.g., polling, push, or embedded config).
Explain how AND/OR conditions are evaluated against context attributes, and how percentage rollouts use consistent hashing (e.g., MurmurHash) with a stable seed to assign users to buckets deterministically.
Describe how to ensure bucketing remains consistent across restarts: use a deterministic hash of flag key and user ID, and optionally persist the bucketing seed or user assignments if needed.
Cover performance (caching, pre-compiled rules), consistency (eventual vs strong), and operational concerns (flag updates, monitoring, fallback defaults).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was framed as an extension of the LLD question, which I wasn't expecting.
Start by clarifying requirements and constraints, then propose a high-level architecture with a fast in-memory evaluation engine and a durable control plane. Focus on data modeling, caching, and consistency trade-offs to meet latency and throughput goals, and discuss how to handle updates and failures.
Pro tip: Emphasize that the evaluation path must be completely decoupled from the control plane and served from memory with no external calls, as even a single network hop can exceed the 10ms P95 budget. Also, mention that batching should be done server-side to reduce per-request overhead.
Ask questions to understand the scale, consistency needs, and failure modes. Confirm that flags are read-heavy, updates are infrequent, and that eventual consistency is acceptable for flag changes.
Define a flag schema with targeting rules, and choose a storage solution for the control plane (e.g., a relational DB or a versioned key-value store). Consider using a compact binary format for efficient in-memory representation.
Propose a stateless service that loads all flags into memory and evaluates locally. Use a push-based mechanism (e.g., pub/sub) to propagate updates from the control plane to all instances, ensuring low-latency reads.
Discuss techniques like caching, pre-compilation of rules, and efficient data structures (e.g., bitsets, tries) to achieve sub-10ms P95. For batched evaluations, process flags in parallel and return results in a single response.
Explain how to handle stale data, network partitions, and service failures. Propose a fallback strategy (e.g., default flag values) and monitoring for latency, error rates, and flag update propagation delays.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.