This felt manageable at first but the failure handling part is where I started rambling.
Start by clarifying requirements and scale, then design a decoupled architecture with separate services for ingestion, scheduling, and delivery. Walk through the data flow from API to message queues to channel-specific workers, and discuss trade-offs in storage, failure handling, and scaling.
Pro tip: Emphasize idempotency and exactly-once semantics for notifications, as duplicates can annoy users and damage trust. Also, mention the importance of rate limiting and user preferences to avoid spamming.
Ask about expected throughput, latency requirements, delivery guarantees, and supported channels. Estimate scale (e.g., millions of notifications per day) to inform design decisions.
Propose a microservices-based system with an API gateway for ingestion, a scheduler for delayed messages, and channel-specific workers for delivery. Use message queues (e.g., Kafka) to decouple components.
Describe the flow: API receives request, validates, persists to a database (e.g., Cassandra for scalability), and publishes to a queue. For scheduled messages, store in a timing database (e.g., Redis sorted sets) and poll. Workers consume from queues and send via providers.
Discuss retries with exponential backoff, dead-letter queues for poison messages, and idempotency keys to prevent duplicates. Ensure at-least-once delivery and monitor for failures.
Scale horizontally by adding more workers and partitioning queues. Use sharding for databases and caching for user preferences. Consider rate limiting and backpressure to handle spikes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Picked a project I thought I knew cold and then got absolutely grilled on why we chose a particular communication pattern between two services.
Choose a project you know deeply and can diagram clearly, then walk through it top-down: start with the high-level context and requirements, sketch the architecture (clients, services, data stores, queues), and trace one or two critical request flows end-to-end. Emphasize the trade-offs behind key decisions (e.g., SQL vs NoSQL, sync vs async, caching) and proactively flag areas where you'd expect follow-up questions.
Pro tip: Pick a project where you personally owned a critical component and can speak to failures, scaling limits, and what you'd change today — interviewers at DoorDash care more about your reasoning under constraints than a perfect diagram.
Briefly state the project's goal, scale (users, QPS, data volume), and key constraints (latency, consistency, compliance). This frames every later decision.
Sketch clients, API gateway/load balancer, core services, databases, caches, and message queues. Label protocols (HTTP/gRPC, Kafka, WebSockets) and note synchronous vs asynchronous paths.
Walk through one or two end-to-end flows (e.g., write path and read path) showing how data moves, where it's transformed, and how failures are handled (retries, idempotency, fallbacks).
For each major component, justify the choice: why this database, why this communication pattern, why this partitioning/sharding strategy. Mention alternatives considered and why they were rejected.
Cover how the system handles growth, outages, and data consistency. Share one concrete incident or bottleneck and what you changed as a result.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.