My first instinct was to just say 'keep a counter somewhere' which, yeah, not great.
Start by clarifying the goal and constraints, then propose a scalable, fair, and fraud-resistant system that identifies the 5 trillionth query in real-time while handling edge cases. Focus on the user experience, technical feasibility, and Google's brand values.
Pro tip: Emphasize the importance of defining 'query' precisely (e.g., excluding bots, internal searches) and consider the psychological impact of such a contest—make it inclusive and avoid incentivizing spam.
Ask questions to understand the scope: Is this a global contest? What defines a 'query'? Should it be real-time? What's the budget and legal constraints?
Propose a distributed counter that increments with each valid query, and a mechanism to flag the exact 5 trillionth query. Ensure atomicity and consistency across data centers.
Implement bot detection, rate limiting, and verification to prevent manipulation. Consider using a random selection among queries near the milestone if exact identification is too costly.
Decide on a prize that aligns with Google's brand (e.g., donation, tech gadgets, a meet-and-greet). Design how to notify the winner and handle privacy concerns.
Address potential issues: multiple queries at the same millisecond, system failures, and how to handle if the winner is a bot or ineligible. Consider a fallback plan.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the product solution and its core user flows, then translate those into a high-level system architecture that can handle Google-scale query volume. Focus on key components, data flow, and trade-offs, explicitly tying design decisions to Google's infrastructure and scale requirements.
Pro tip: Anchor your design in Google's existing infrastructure (e.g., Spanner, Bigtable, Borg) and quantify scale assumptions (e.g., billions of queries per day) to show you understand real-world constraints. Also, proactively discuss trade-offs like latency vs. consistency and cost vs. performance, as these are critical at Google's scale.
Restate the product solution and identify functional and non-functional requirements, including expected query volume, latency targets, and availability needs. Ask clarifying questions to ensure alignment with Google's scale.
Sketch the major components (e.g., load balancers, API gateways, microservices, data stores, caches) and how they interact. Emphasize horizontal scalability and fault tolerance.
Choose 1-2 components (e.g., query processing, data storage) and explain their design in detail, including data models, partitioning, replication, and consistency models. Justify choices with Google's infrastructure.
Discuss trade-offs such as latency vs. consistency, cost vs. performance, and how you would mitigate bottlenecks (e.g., caching, sharding, asynchronous processing).
Define key metrics (e.g., QPS, p99 latency, error rates) and how you would monitor and alert on them. Explain how you would iterate based on data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.