Start by clarifying functional and non-functional requirements, then estimate scale (users, photos, videos, storage, bandwidth). Design the high-level architecture covering upload, storage, feed generation, and delivery, and dive into key components like media storage, feed ranking, and caching. Discuss trade-offs and bottlenecks throughout.
Pro tip: Emphasize the unique challenges of video (transcoding, adaptive streaming) and the read-heavy nature of the feed, and propose a hybrid push-pull model for feed generation to balance latency and cost.
Ask about core features (upload, feed, follow, like, comment, search), scale (DAU, media uploads), and non-functional needs (latency, availability, consistency).
Estimate storage (photos/videos per day, size, replication), bandwidth (upload/download), and QPS for reads/writes. Use round numbers and state assumptions.
Sketch components: clients, API gateway, services (user, media, feed, social graph), storage (object store, CDN, databases), and message queues.
Detail media upload/processing (transcoding, thumbnails), feed generation (fan-out on write vs read, ranking), and delivery (CDN, caching).
Discuss consistency vs availability, push vs pull for feeds, storage costs, and how to handle hot users or viral content.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went with a fan-out-on-write approach for most users and fan-out-on-read for high-follower accounts.
Start by clarifying requirements and scale, then propose a hybrid feed generation approach: push (fan-out on write) for most users and pull (fan-out on read) for celebrities, with a ranking layer that combines candidate generation and ML scoring. For consistency, discuss trade-offs between strong and eventual consistency, ultimately recommending an eventual consistency model with read-your-writes guarantees for the user's own actions.
Pro tip: Emphasize that consistency requirements differ for the user's own timeline versus others' timelines, and that ranking should be decoupled from feed assembly to allow independent scaling and experimentation.
Ask about expected DAU, read/write ratio, latency SLAs, and whether the feed is chronological or ranked. This sets the stage for design decisions.
Propose a hybrid approach: fan-out on write for active users to precompute feeds, and fan-out on read for celebrities to avoid write amplification. Use a graph store or social graph service to determine followers.
Describe a multi-stage ranking pipeline: candidate generation (e.g., from follow graph, trending), lightweight ranking (e.g., logistic regression), and heavy ranking (e.g., deep neural networks). Mention feature store and real-time signals.
Discuss consistency trade-offs: strong consistency for user's own posts (read-your-writes) and eventual consistency for others' feeds. Use versioning or timestamps to handle out-of-order updates.
Cover caching (e.g., Redis for hot feeds), sharding, and fallback mechanisms. Mention monitoring and degradation strategies (e.g., fallback to chronological feed if ranking service fails).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about object storage, transcoding pipelines for video, and pushing assets to edge nodes.
Start by clarifying requirements and scale, then propose a multi-tier architecture with edge caching, regional origins, and a global backbone. Emphasize trade-offs between latency, cost, and consistency, and discuss how you'd handle failures and hot content.
Pro tip: Quantify the impact of your design choices—e.g., 'edge caching reduces origin load by 90% and cuts latency by 200ms'—to show you think in terms of real-world metrics and business value.
Ask about traffic patterns, content types, geographic distribution, and SLAs. Confirm the scale (billions of images/videos) and any constraints like budget or regulatory requirements.
Propose a layered approach: edge CDN for static assets, regional caching layers, and origin storage (e.g., object store). Explain how requests flow from edge to origin and how you'd handle dynamic content.
Discuss solutions for hot content (e.g., pre-warming, request coalescing), cache invalidation, and consistency. Mention techniques like TTL tuning, versioned URLs, and purge APIs.
Explain how you'd use compression, adaptive bitrate streaming, and format selection (WebP, AV1). Discuss cost optimizations like tiered storage, spot instances, and traffic shaping.
Describe failover strategies (multi-CDN, origin shielding), health checks, and monitoring for cache hit ratios, latency, and error rates. Include a plan for capacity planning and incident response.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went with user-id-based sharding for the user and follow graph tables and talked through consistent hashing.
Start by clarifying the system's requirements—scale, read/write ratio, consistency needs, and latency targets—then propose a sharding strategy (e.g., hash-based on a high-cardinality key) and a replication strategy (e.g., leader-follower with quorum reads/writes). Explain how these choices address the requirements and trade-offs, and mention how you'd handle rebalancing and failure scenarios.
Pro tip: Tie your choices to concrete numbers (e.g., 'At 1M QPS, we'd need ~100 shards with 10K QPS each') and proactively discuss operational concerns like hotspot mitigation and cross-shard queries, which shows you think beyond the happy path.
Ask about data volume, query patterns, consistency requirements, latency SLAs, and geographic distribution to ground your design in real needs.
Select a high-cardinality, evenly distributed key (e.g., user ID) and decide between hash-based, range-based, or directory-based sharding, explaining trade-offs like hotspot risk vs. range query efficiency.
Propose a replication model (e.g., leader-follower or multi-leader) with a quorum-based consistency level (e.g., R+W > N) to balance latency and consistency, and specify replication factor and placement across racks/AZs.
Describe how to add/remove shards (e.g., consistent hashing, virtual nodes), rebalance data, and handle failures (e.g., leader election, read repair, anti-entropy).
Discuss monitoring, hotspot detection, cross-shard queries, and how your choices impact latency, cost, and complexity, showing awareness of real-world operations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by stating your assumptions about Instagram's scale (e.g., 2 billion monthly active users, 500 million daily active users) and then break down the problem into read-heavy and write-heavy operations. Estimate QPS for key actions like feed loads, photo uploads, and likes, then derive storage and bandwidth requirements from those numbers, using round numbers and powers of 10 for simplicity.
Pro tip: Always clarify that these are rough estimates and focus on the methodology rather than exact numbers; interviewers care more about your thought process and ability to identify bottlenecks than precise calculations.
Clearly define the scale: number of users, daily active users, and average actions per user per day. Specify what you're estimating (e.g., feed reads, photo uploads, likes, comments).
Calculate queries per second for read-heavy operations (e.g., feed loads) and write-heavy operations (e.g., photo uploads). Use daily active users and average actions per user, then divide by seconds in a day (86400) and account for peak traffic (e.g., 2-3x average).
Determine average size per stored item (e.g., photo size, metadata) and multiply by daily uploads to get daily storage growth. Extend to yearly storage and consider replication and backups.
Calculate inbound bandwidth from uploads (average photo size * upload QPS) and outbound bandwidth from downloads (average photo size * feed QPS). Convert to bits per second (e.g., Mbps, Gbps) and consider peak traffic.
Verify that numbers are within reason (e.g., compare to known data points) and summarize the key figures. Mention that these are rough estimates and highlight any assumptions that could significantly impact results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Token bucket per user per endpoint was my answer.
Start by clarifying the platform's scale, traffic patterns, and abuse vectors, then propose a layered defense combining rate limiting algorithms, distributed enforcement, and adaptive abuse detection. Emphasize trade-offs between user experience, accuracy, and system complexity, and tie your choices to Meta's scale and product goals.
Pro tip: Mention that rate limiting alone is insufficient—abuse prevention requires a feedback loop with anomaly detection and rapid iteration, and you should discuss how to avoid false positives that harm legitimate users.
Ask about scale (QPS, users), abuse types (scraping, spam, DDoS, credential stuffing), and business impact. Identify what 'abuse' means for this platform and what user experience constraints exist.
Choose algorithms (token bucket, sliding window, leaky bucket) based on burst tolerance and accuracy needs. Define limits per user, IP, API key, and endpoint, and decide where to enforce (client, edge, service, or distributed store).
Use a low-latency, scalable store like Redis or a custom in-memory grid with eventual consistency. Discuss sharding, replication, and fallback mechanisms to avoid single points of failure.
Layer in anomaly detection (ML models, heuristics) to catch sophisticated abuse that static limits miss. Use signals like request patterns, device fingerprints, and reputation scores to dynamically adjust limits.
Instrument metrics (blocked requests, false positives, latency), set up alerts, and create a feedback loop to tune thresholds. Discuss how to handle false positives (e.g., CAPTCHA, manual review) and communicate with users.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Short answer: push for mobile via APNs/FCM, pull as a fallback for web.
Start by clarifying requirements (scale, latency, delivery guarantees) and then present a high-level design covering producers, notification service, queues, and delivery channels. Compare push vs. pull models across dimensions like latency, scalability, and complexity, and recommend a hybrid approach with justification.
Pro tip: At Meta's scale, a hybrid model is often necessary: use push for real-time notifications and pull for batch or less time-sensitive ones. Also, consider the impact of mobile push notifications on battery life and the need for rate limiting to prevent notification fatigue.
Ask about scale (DAU, notifications per second), latency requirements, delivery guarantees (at-least-once, exactly-once), and supported channels (push, email, SMS, in-app).
Outline components: notification producers (services triggering notifications), a notification service that processes and routes, message queues for decoupling, and delivery workers for each channel.
Discuss trade-offs: push offers low latency but can overwhelm clients and requires connection management; pull is simpler but introduces latency and polling overhead. Consider hybrid approaches.
Explain how to scale: sharding, partitioning, using Kafka for buffering, and ensuring fault tolerance with retries and dead-letter queues.
Conclude with a recommended architecture (e.g., hybrid push-pull) and justify based on requirements, mentioning potential optimizations like batching and prioritization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Mentioned distributed tracing, structured logging, and metrics dashboards.
Start by clarifying the platform's scale, critical user journeys, and SLAs, then propose a layered observability stack (metrics, logs, traces) with SLO-driven alerting, and finally outline a DR strategy with defined RTO/RPO, multi-region failover, and regular game-day testing. Emphasize trade-offs between cost, complexity, and resilience, and tie everything back to user impact and business continuity.
Pro tip: Meta values a data-driven, user-first mindset: quantify the impact of downtime (e.g., revenue per minute) and propose SLOs that directly reflect user experience, not just infrastructure health. Also, mention how you'd use observability data to continuously improve DR readiness through chaos engineering and postmortems.
Ask about the platform's scale, critical user journeys, SLAs, compliance needs, and budget. This ensures your strategy is tailored and demonstrates you think before proposing solutions.
Propose collecting metrics, logs, and traces using tools like Prometheus, ELK, and Jaeger. Define SLOs and error budgets, and set up alerting that focuses on user-facing symptoms rather than just resource utilization.
Establish RTO and RPO based on business impact. Choose a DR pattern (e.g., active-active, active-passive, pilot light) and justify it with trade-offs between cost, complexity, and recovery speed.
Describe how you'd implement failover, data replication, and backup strategies. Emphasize automation for failover and recovery to reduce human error and downtime.
Explain the importance of regular DR drills, chaos engineering, and post-incident reviews. Use observability data to identify gaps and continuously improve the strategy.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.