Start by clarifying requirements and scale (e.g., millions of concurrent viewers, comment rate, latency, ordering guarantees). Then propose a scalable architecture using a pub/sub system for ingestion and fan-out, with a distributed log for ordering and persistence. Finally, discuss trade-offs and optimizations for late joiners and replay.
Pro tip: Emphasize the importance of backpressure and rate limiting to handle comment spikes, and consider using a hybrid approach (e.g., WebSockets for real-time, HTTP for history) to balance latency and scalability.
Ask about scale (concurrent viewers, comments per second), latency expectations, ordering guarantees (global vs per-user), and retention for replay.
Outline components: ingestion service, message queue/pub-sub (e.g., Kafka), fan-out service, and storage for history. Consider using WebSockets for real-time delivery.
Design ingestion to handle high write throughput with partitioning and load balancing. For fan-out, use a pub/sub model where each viewer subscribes to a stream, possibly with edge servers to reduce latency.
Use a distributed log (e.g., Kafka) to assign timestamps or sequence numbers. Discuss trade-offs between strict global ordering (higher latency) and eventual consistency.
Store comments in a time-series database or object storage with indexing by video ID and timestamp. For late joiners, fetch recent history via API and then switch to live stream.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging the immediate need to protect system stability, then outline a multi-layered strategy covering detection, mitigation, and long-term resilience. Emphasize trade-offs between quick fixes and sustainable solutions, and highlight how you would prioritize user experience while preventing cascading failures.
Pro tip: Show you understand that virality is a business opportunity, not just a technical problem—propose ways to capitalize on the traffic while maintaining reliability, such as dynamic scaling and caching, and mention how you'd measure success beyond just uptime.
Identify the viral event through monitoring alerts (e.g., sudden spike in requests, latency, error rates) and quickly assess the scope: which video, how much traffic, and what resources are affected.
Implement immediate mitigations to prevent outages: enable auto-scaling, increase cache TTLs, rate-limit non-critical traffic, and if necessary, degrade gracefully (e.g., serve lower-resolution video).
Scale horizontally (add more instances) and vertically (upgrade resources), optimize hot paths (e.g., CDN caching, database read replicas), and consider sharding or partitioning to distribute load.
Continuously monitor key metrics (latency, error rates, throughput) and be ready to adjust strategies in real-time, using canary deployments or feature flags to test changes safely.
After the spike subsides, conduct a post-mortem to identify bottlenecks and implement long-term improvements like better capacity planning, chaos engineering, and automated scaling policies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a pre-ingestion filter layer, rate limit per user with a token bucket in Redis, and async moderation for ML-based checks.
Start by clarifying the scale and requirements (e.g., comments per second, latency targets, moderation policies). Then propose a layered architecture: rate limiting at the edge (API gateway) using distributed counters (e.g., Redis with sliding window), and asynchronous moderation using a pipeline with ML models and human review. Emphasize trade-offs between accuracy, latency, and cost, and how to handle high throughput with sharding, caching, and backpressure.
Pro tip: Demonstrate awareness of Meta's scale by mentioning specific techniques like using a leaky bucket algorithm with local and global limits, and leveraging Kafka for decoupling moderation from the write path. Also, discuss how to handle false positives and provide user feedback without compromising the experience.
Ask about expected throughput, latency requirements, moderation policies (e.g., spam, hate speech), and consistency needs. This shows you understand the problem before jumping to solutions.
Propose a distributed rate limiter using algorithms like sliding window or token bucket, with Redis or a similar in-memory store. Discuss sharding by user ID or IP to scale horizontally, and consider local caching to reduce latency.
Outline an asynchronous pipeline: comments are written to a queue (e.g., Kafka), then processed by ML models for classification, with human review for edge cases. Ensure idempotency and handle failures with retries and dead-letter queues.
Explain how to scale components: use consistent hashing for rate limiter shards, partition Kafka topics, and autoscale moderation workers. Discuss backpressure mechanisms to protect downstream services.
Highlight trade-offs between strictness and user experience, and between real-time and batch moderation. Mention monitoring metrics (e.g., rate limit hits, moderation latency) and alerting for anomalies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.