This is a big question and I think I underestimated how much ground it covers.
Start by clarifying requirements and scale (e.g., number of users, posts, followers, latency, delivery guarantees). Then design a high-level architecture with a fan-out service, message queue, and delivery workers, discussing trade-offs between push and pull models. Finally, dive into data modeling, scalability, and reliability considerations.
Pro tip: Emphasize the trade-offs between fan-out on write vs. read, and propose a hybrid approach for celebrity users to avoid write amplification. Also, mention the importance of idempotency and deduplication to handle retries.
Ask questions to understand functional and non-functional requirements: number of users, posts per day, followers per user, latency expectations, delivery guarantees (at-least-once, exactly-once), and whether notifications are real-time or can be batched.
Propose a system with a post service that publishes events to a message queue (e.g., Kafka). A fan-out service consumes events, determines followers, and enqueues notification tasks. Delivery workers process tasks and push to devices via APNs/FCM or other channels.
Design schemas for posts, followers, and notifications. Consider using a graph database for social relationships, a wide-column store for timelines, and a queue for pending notifications. Discuss indexing and sharding strategies.
Discuss push vs. pull models, fan-out on write vs. read, and hybrid approaches for celebrities. Address partitioning, load balancing, and caching to handle high throughput and low latency.
Ensure at-least-once delivery with idempotent consumers, dead-letter queues for failures, and retry mechanisms. Mention monitoring, alerting, and metrics for system health.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I knew the hybrid answer exists but I fumbled explaining why you'd actually switch strategies at a follower threshold.
Start by defining the problem: fan-out on write (push) vs. fan-out on read (pull) for delivering posts to millions of followers. Then compare tradeoffs in latency, write/read amplification, storage, and cost, and propose a hybrid approach for celebrity accounts.
Pro tip: Mention that the hybrid approach can be tuned based on follower count and activity, and that you'd monitor metrics like delivery latency and system load to adjust thresholds dynamically.
Ask about expected read/write patterns, latency SLAs, consistency needs, and scale (number of celebrities, followers, posts per day).
Describe how posts are immediately written to all followers' feeds, highlighting low read latency but high write amplification and storage cost.
Describe how feeds are assembled on demand by fetching from followed accounts, highlighting low write cost but high read latency and potential hotspots.
Discuss latency, scalability, storage, cost, and complexity for both models, and note that pure push or pull rarely works at extreme scale.
Suggest using push for normal users and pull for celebrities, with a threshold (e.g., follower count) and caching to optimize performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements (scale, latency, delivery guarantees) and then walk through the pipeline stages: ingestion, queuing, processing, delivery, and monitoring. Emphasize how retries, idempotency, and dead-letter queues work together to ensure reliability and exactly-once semantics.
Pro tip: Mention that idempotency keys should be generated at the source and stored with a TTL, and that DLQs should be monitored and have a replay mechanism to avoid data loss.
Ask about expected volume, latency, delivery guarantees (at-least-once vs exactly-once), and failure handling expectations. This shapes the design.
Outline components: API gateway for ingestion, message queue (e.g., Kafka, SQS) for buffering, worker pool for processing, and external services for delivery (email, SMS, push).
Use exponential backoff with jitter for transient failures, and set a max retry limit. Distinguish between retryable and non-retryable errors.
Generate unique idempotency keys per notification, store them with a TTL, and check before processing to prevent duplicate deliveries.
After max retries, move messages to a DLQ. Set up alerts, dashboards, and a manual or automated replay process to recover from failures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I drew out a fan-out to per-channel workers after the main queue, each with its own retry and rate-limit logic.
Start by clarifying requirements such as scale, latency, and reliability, then propose a unified notification service that abstracts channel-specific providers. Describe the architecture with components like message queue, template engine, provider adapters, and delivery tracking, and discuss trade-offs around consistency, retries, and idempotency.
Pro tip: Emphasize idempotency and deduplication across channels to prevent duplicate notifications, and mention how you'd handle provider-specific rate limits and failures with circuit breakers and fallback strategies.
Ask about expected volume, latency SLAs, delivery guarantees, and whether users can opt out per channel. This shows you think about non-functional requirements before designing.
Propose a unified notification service that receives requests via API, validates and enriches them, then routes to channel-specific adapters. Use a message queue (e.g., Kafka, SQS) to decouple ingestion from delivery for scalability and reliability.
Explain how each channel is integrated: APNs/FCM for push, SMTP or email service (e.g., SendGrid) for email, SMS gateway (e.g., Twilio) for SMS, and in-app via WebSocket or polling. Mention provider-specific SDKs and authentication.
Discuss retries with exponential backoff, dead-letter queues, idempotency keys, and delivery status tracking. Include monitoring, logging, and alerting for failures and latency.
Talk about trade-offs like synchronous vs asynchronous delivery, cost, and complexity. Mention potential extensions like user preferences, A/B testing, and analytics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the product context and user impact, then propose a layered system that combines rate limiting, batching, and deduplication. Walk through the design from client to backend, highlighting trade-offs between real-time delivery and aggregation.
Pro tip: Emphasize user control and configurability—giving users granular preferences builds trust and reduces spam complaints. Also, mention that deduplication should consider both exact duplicates and semantically similar notifications.
Ask about the types of notifications, expected volume, latency requirements, and user expectations. This ensures the solution aligns with product goals.
Propose rate limiting per user and per notification type, batching notifications within time windows, and user-configurable preferences to control frequency.
Use a unique key (e.g., event ID) to deduplicate exact duplicates, and group similar notifications (e.g., 'X and 3 others liked your post') using aggregation logic.
Address trade-offs like delayed delivery vs. real-time, storage costs for deduplication keys, and how to scale with increasing users and notification types.
Suggest metrics (e.g., notification open rates, spam reports) and A/B testing to refine thresholds and aggregation rules over time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the system's purpose and user expectations to derive concrete latency and throughput targets, then propose a scalable architecture that addresses those targets through horizontal scaling, caching, and asynchronous processing. Emphasize trade-offs and justify your choices based on requirements and constraints.
Pro tip: Always tie latency and throughput targets to business metrics (e.g., revenue impact of 100ms delay) and propose a plan to measure and iterate, showing you think beyond just technical numbers.
Ask questions to understand the system's purpose, user base, expected load, and any existing SLAs. Identify critical user journeys and their latency sensitivity.
Propose specific, measurable targets (e.g., p99 latency < 200ms, 10k RPS) based on requirements and industry benchmarks. Justify each target with reasoning.
Outline a scalable architecture: horizontal scaling, load balancing, caching, sharding, asynchronous processing, and CDNs. Explain how each component helps meet targets.
Discuss potential bottlenecks (e.g., database, network) and trade-offs (consistency vs. latency, cost vs. performance). Propose mitigation strategies.
Describe how you would measure performance, set up alerts, and iterate on targets as the system evolves. Mention load testing and capacity planning.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.