This is the main prompt and it ballooned fast.
Start by clarifying requirements and scale, then design a high-level architecture that integrates with Instagram's existing infrastructure. Focus on real-time bidding, data consistency, and scalability, and discuss trade-offs between consistency and availability.
Pro tip: Leverage Instagram's existing social graph and notification systems to drive engagement, but be mindful of rate limits and API constraints. Propose a separate microservice for auctions to avoid impacting core Instagram services.
Ask about scale (number of users, auctions), consistency needs, and integration with Instagram (e.g., authentication, posting). Define functional and non-functional requirements.
Outline components: API gateway, auction service, bid service, database, real-time notification service. Explain how sellers create auctions via Instagram posts and how buyers bid.
Design schemas for auctions, bids, and users. Consider using a relational database for transactions and a NoSQL store for scalability. Discuss indexing for fast bid queries.
Implement WebSocket or long polling for real-time updates. Use a message queue to handle bid events and ensure ordered processing. Discuss concurrency control to prevent race conditions.
Address scaling with sharding, caching, and CDN. Discuss trade-offs: strong vs. eventual consistency, latency vs. accuracy, and cost implications.
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 requirements and constraints, such as expected bid volume, latency needs, and consistency guarantees. Then, propose a concurrency control mechanism (e.g., optimistic locking, serializable transactions) and a deterministic tie-breaking rule (e.g., earliest timestamp, highest bid amount). Finally, discuss trade-offs and potential edge cases.
Pro tip: Mention that tie-breaking should be deterministic and fair, and consider using a monotonic sequence number or timestamp to avoid ambiguity. Also, highlight the importance of idempotency and auditability in bid processing.
Ask about expected concurrency levels, latency requirements, and consistency needs (e.g., strong vs. eventual). This shows you understand the problem context.
Propose a mechanism like optimistic locking (version numbers), pessimistic locking, or serializable transactions. Explain how it prevents race conditions.
Specify a deterministic rule, such as earliest timestamp, highest bid amount, or a combination. Ensure it's fair and unambiguous.
Discuss handling of clock skew, network delays, and duplicate bids. Mention idempotency keys and audit logs.
Compare options in terms of performance, complexity, and consistency. Justify your choice based on the requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: scale (e.g., millions of concurrent users), latency needs, bid update frequency, and client constraints. Then compare WebSockets, SSE, and polling against those requirements, and propose a hybrid architecture (e.g., WebSockets for real-time bidding, SSE for one-way updates, polling as fallback) with clear trade-offs.
Pro tip: Acknowledge that at Meta's scale, pure polling is a non-starter due to server load, but a well-designed long-polling fallback is still necessary for restrictive networks. Show you understand that the choice isn't binary—you can use different transports for different client capabilities and update types.
Ask about scale (concurrent users, bids per second), latency tolerance, bid update frequency, client platforms, and network conditions. This ensures your solution is grounded in real constraints.
Compare WebSockets (full-duplex, low latency, but stateful and complex), SSE (one-way, simpler, auto-reconnect, but limited to text and HTTP/1.1 connection limits), and polling (simple, stateless, but high overhead and latency).
Recommend WebSockets as the primary transport for real-time bid fan-out, with SSE for one-way updates (e.g., auction status) and long-polling as a fallback for restrictive networks. Explain how you'd handle connection management and scaling.
Discuss horizontal scaling with a pub/sub layer (e.g., Redis, Kafka), connection draining, heartbeats, reconnection logic, and backpressure. Mention how to handle millions of concurrent connections.
Conclude with a clear recommendation based on the requirements, highlighting why WebSockets (with fallbacks) best meet the latency and scale needs, and note any potential drawbacks.
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Pretty standard scoping question but I used it to set up the rest of my design.
Start by clarifying the system's purpose and expected scale, then explicitly state your prioritized NFRs with clear trade-offs. Use a structured framework to discuss latency, consistency, and availability, tying each to business impact and technical mechanisms.
Pro tip: Anchor your NFR priorities in user-perceived impact and business metrics (e.g., revenue per millisecond), and proactively mention how you'd validate them with SLOs and load testing.
Ask about the system's core functionality, expected traffic patterns, and peak load multipliers to ground your NFR choices.
State your prioritized order (e.g., availability > latency > consistency) and justify it based on user experience and business goals.
Explain how you'd achieve each NFR (e.g., caching, replication, rate limiting) and the trade-offs involved (e.g., consistency vs. latency).
Describe strategies for handling spikes: autoscaling, load shedding, graceful degradation, and queueing.
Mention how you'd set SLOs, monitor them, and use load testing to ensure the system meets NFRs under stress.
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Rushed through this more than I wanted to.
Start by clarifying the auction system's core requirements—types of auctions, bidding rules, and scale expectations—then propose a high-level API design with key endpoints and a data model that supports those operations. Structure your answer around entities, relationships, and trade-offs, and be ready to dive deeper into any area the interviewer probes.
Pro tip: Explicitly call out trade-offs (e.g., consistency vs. availability, normalized vs. denormalized schemas) and tie them to Meta's scale and real-time needs; this shows you think like a senior engineer, not just a coder.
Ask about auction types (English, Dutch, sealed-bid), expected scale (users, items, bids per second), and key constraints like real-time updates, consistency, and latency. This ensures your design targets the right problem.
Identify main entities: User, Item, Auction, Bid, and possibly Category, Watchlist, and Transaction. Describe their attributes and relationships (e.g., one auction has many bids, one user places many bids).
Outline RESTful or RPC-style endpoints for creating auctions, placing bids, retrieving auction details, and listing bids. Include request/response schemas and consider real-time updates via WebSockets or long polling.
Choose a database (SQL vs. NoSQL) based on access patterns and consistency needs. Define tables/collections, indexes, and how to handle high write throughput for bids (e.g., sharding, caching).
Discuss how to scale (horizontal scaling, caching, message queues), handle concurrency (optimistic locking, atomic operations), and ensure consistency (e.g., bid ordering, preventing race conditions).
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Didn't have a crisp answer ready for this.
Start by clarifying the auction's goals and constraints (e.g., ad auctions, fairness objectives, scale). Then systematically cover anti-abuse mechanisms (fraud detection, bid manipulation prevention) and fairness mechanisms (allocation fairness, transparency), discussing trade-offs and monitoring. Conclude with how you'd measure success and iterate.
Pro tip: Emphasize that fairness and anti-abuse are not one-time features but require continuous monitoring, adversarial testing, and adaptation to new attack vectors. Also, tie mechanisms to business metrics like long-term revenue and user trust.
Ask questions to understand the auction type (e.g., ad auction, NFT), scale, stakeholders, and what 'fairness' means in this context (e.g., equal opportunity, proportional representation). Identify potential abuse vectors and regulatory constraints.
Propose layered defenses: real-time fraud detection (e.g., anomaly detection on bids), rate limiting, identity verification, and economic disincentives (e.g., penalties for shill bidding). Include post-hoc auditing and machine learning models to adapt to new threats.
Implement fairness constraints in allocation (e.g., randomized throttling, fairness-aware pacing), transparency in rules and outcomes, and bias detection. Consider trade-offs between fairness and efficiency/revenue.
Describe how these mechanisms integrate into the auction pipeline (e.g., pre-bid filtering, real-time scoring, post-auction analysis). Set up dashboards and alerts for key metrics (e.g., fraud rate, fairness metrics) and a feedback loop for continuous improvement.
Discuss trade-offs: false positives vs. false negatives in fraud detection, fairness vs. revenue, latency vs. accuracy. Propose A/B testing and simulation to measure impact, and a process for updating mechanisms as adversaries adapt.
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Start by clarifying requirements and scale (e.g., number of concurrent auctions, read/write ratio, latency SLOs). Then propose a high-level architecture that partitions auctions by auction ID or user ID, using sharding and replication to distribute load, and discuss trade-offs like consistency vs. availability. Finally, dive into specific components (e.g., bidding service, auction state store, notification system) and how they scale.
Pro tip: Emphasize that auctions are naturally partitionable by auction ID, but be mindful of hot auctions (e.g., popular items) that can cause hotspots; propose techniques like consistent hashing with virtual nodes or dynamic splitting to mitigate. Also, discuss the importance of idempotency and exactly-once processing for bids to avoid double-bidding.
Ask questions to understand the expected number of concurrent auctions, bid rate, read/write ratio, latency requirements, and consistency needs. This ensures your design targets the right constraints.
Propose partitioning auctions by auction ID (or a composite key) using consistent hashing to distribute load across shards. Discuss replication for fault tolerance and read scalability.
Choose a data store that supports high write throughput and low latency (e.g., Cassandra, DynamoDB) for auction state and bids. Consider using an in-memory cache for hot auctions and a durable log for bid events.
Address hotspots by dynamically splitting hot auctions across multiple shards or using a dedicated service for high-traffic auctions. Discuss techniques like request coalescing, rate limiting, and queueing.
Explain how to ensure correctness (e.g., using optimistic concurrency or distributed locks) while maintaining availability. Discuss trade-offs between consistency models (strong vs. eventual) and their impact on user experience.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.