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Systems Design and Scalability Questions

Focuses on designing scalable distributed systems and marketplace architectures. Topics include core marketplace components such as search and discovery, real time availability, booking and reservation flows, payment processing, and host to guest matching and how those systems interact. Expect to identify scalability bottlenecks, propose caching strategies, database optimization including sharding and replication, horizontal scaling approaches, and reason about consistency versus availability trade offs. Also cover real time synchronization strategies, handling race conditions such as double booking, event driven designs and message based architectures, and considerations for monitoring and operational resilience.

HardTechnical
29 practiced
How would you shard and replicate data for a high-scale marketplace where listings, bookings, and availability checks have very different access patterns? Explain your partition key choices, how you would avoid hotspots on popular listings or destinations, and how you would handle resharding or replica lag.
MediumSystem Design
33 practiced
Design the search and discovery subsystem for a marketplace with tens of millions of listings and traffic spikes during weekends. The system must support keyword search, filters, sorting by relevance or price, and location-based queries. Walk through the services, indexing strategy, caching layers, and how you would keep results fresh as listings change.
EasyTechnical
41 practiced
You're designing a marketplace reservation system where users can browse listings, hold inventory, and book a time slot. In which parts of the flow would you prefer strong consistency, and where would eventual consistency be acceptable? Explain the trade-offs in terms of correctness, user experience, and scalability.
MediumTechnical
33 practiced
An existing monolithic booking service is hitting latency and error-rate spikes under peak traffic. The hot paths are search results, availability checks, and booking submissions. How would you analyze the bottlenecks and decide whether to scale horizontally, add read replicas, split services, or change the data model first?
MediumTechnical
30 practiced
Suppose search traffic is healthy, but conversion is dropping because guests are seeing irrelevant or unavailable listings in the top results. What would you investigate in the host-to-guest matching and ranking pipeline, and what changes would you make to improve relevance without hurting freshness or latency?

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