Start by clarifying requirements and constraints, then outline a high-level architecture with parallel fan-out and a merge layer. Discuss failure handling strategies (timeouts, fallbacks, partial results) and trade-offs between consistency, latency, and complexity. Conclude with how you'd monitor and evolve the system.
Pro tip: Emphasize that partial failures should be handled gracefully by returning what's available with metadata about missing sources, and that per-source timeouts should be configurable and adaptive. Also, mention the importance of idempotency and caching to reduce downstream load.
Ask about expected latency, consistency requirements, client behavior on partial data, and downstream SLAs. This shapes the design and trade-offs.
Propose an API gateway or bootstrapper service that calls downstream sources in parallel using async I/O or futures. Aggregate results into a single response, possibly with a schema that includes per-source status.
Implement per-source timeouts with circuit breakers and fallbacks (e.g., cached data, defaults). Decide on a failure policy: fail-fast, best-effort, or partial success with error details.
Define how to merge results (e.g., JSON merge, field-level merging) and handle conflicts. Consider versioning and eventual consistency if sources are updated asynchronously.
Mention metrics (latency, error rates per source), logging, and tracing. Discuss scaling the bootstrapper horizontally and adding caching or pre-computation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went straight to a read-through cache with a short TTL and the interviewer asked what happens if a feature flag flips but the client is still serving a stale cached value.
Start by clarifying the bootstrap process and its performance characteristics, then propose a caching strategy that balances latency, consistency, and cost. Discuss tradeoffs such as staleness, cache invalidation complexity, and memory overhead, and tie them to DoorDash's scale and reliability needs.
Pro tip: Emphasize that caching is not just about speed but also about resilience: a well-designed cache can serve as a fallback during bootstrap source outages, but you must carefully manage TTLs and invalidation to avoid serving stale data that could break downstream systems.
Ask questions to understand what data is being bootstrapped, how often it changes, and the impact of stale data on the system.
Propose a caching layer (e.g., in-memory, distributed cache) and define TTL, eviction policies, and refresh mechanisms based on data volatility.
Discuss tradeoffs like increased complexity, potential staleness, cache invalidation challenges, and resource consumption.
Suggest mitigations such as versioned cache keys, background refresh, circuit breakers, and monitoring to detect stale or missing data.
Relate the solution to DoorDash's requirements for low latency, high availability, and cost efficiency, and propose metrics to evaluate success.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the current API contract and how bootstrap sources are consumed, then propose a versioning and extensibility strategy that allows new sources to be added without breaking existing clients. Emphasize backward compatibility, additive changes, and a deprecation policy, while balancing trade-offs between flexibility and complexity.
Pro tip: Mention that you would design the API to treat bootstrap sources as a pluggable, versioned resource with a stable core schema, and use feature flags or capability discovery so clients can opt-in to new sources without forced upgrades.
Ask questions to understand the current API contract, client usage patterns, and how bootstrap sources are added today. Identify what 'breaking' means for existing clients (e.g., schema changes, endpoint removal).
Propose an additive-only evolution strategy: new sources are added as new fields or endpoints, never modifying existing ones. Use versioning (e.g., /v1/, /v2/) or content negotiation to manage breaking changes if absolutely necessary.
Define a clear deprecation policy with timelines, communication plans, and tooling to help clients migrate. Provide dual-running periods where old and new coexist.
Add monitoring for client usage of deprecated features and contract tests to catch unintended breaking changes. Use canary releases and feature flags to roll out new sources safely.
Acknowledge trade-offs between strict backward compatibility (slower innovation) and rapid iteration (client breakage). Mention alternatives like GraphQL or gRPC for schema evolution, but justify your choice based on context.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.