This is where I spent most of the interview.
Start by clarifying requirements: what 'total nights booked' and 'average nightly price' mean, how date ranges are selected, and the scale of data. Then propose a high-level architecture with pre-aggregation (e.g., daily rollups) and a fast query layer (e.g., in-memory cache or columnar store) to meet the 200ms latency. Finally, discuss trade-offs between consistency, cost, and complexity.
Pro tip: Emphasize that the 200ms requirement likely necessitates pre-computed aggregates and caching, but also discuss how to handle late-arriving data and ensure the dashboard remains reasonably fresh. Mention that you'd measure and monitor p95 latency to validate the design.
Ask questions to understand data volume, update frequency, definition of 'nights booked' and 'average nightly price', and whether the date range is arbitrary or limited. Confirm the 200ms is for UI response, not just backend.
Propose a fact table of bookings with nightly granularity, and pre-aggregate daily totals per listing and host. Consider using a star schema or denormalized rollup tables for fast reads.
Select a storage solution that supports fast range queries (e.g., columnar database like Redshift, or a key-value store with pre-computed aggregates). Add a caching layer (e.g., Redis) for frequently accessed date ranges.
Ensure the query path is optimized: use indexes, partition by date, and limit the amount of data scanned. Consider materialized views or incremental updates to keep aggregates fresh.
Talk about consistency vs. latency (e.g., eventual consistency of aggregates), handling late bookings or cancellations, and how to backfill or recompute aggregates. Mention monitoring and alerting for performance regressions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pre-aggregation buys you speed but your numbers can be up to 5 minutes stale.
Start by clarifying the system's requirements and constraints, then discuss the tradeoff between data freshness and query latency, and finally propose a balanced solution using techniques like caching, asynchronous updates, or tiered storage. Emphasize that the optimal approach depends on the specific use case and business needs.
Pro tip: Demonstrate awareness of Airbnb's specific context, such as the need for real-time availability in search versus eventual consistency in analytics, and mention how you'd measure and monitor the tradeoff using metrics like cache hit rate and staleness.
Ask questions to understand the specific use case, data update frequency, acceptable staleness, and latency SLAs. Identify which parts of the system require strong consistency versus eventual consistency.
Explain that fresher data often means higher latency due to synchronous updates or cache invalidation, while lower latency may require serving stale data from caches or replicas.
Suggest techniques such as read-through/write-through caches, time-to-live (TTL) based expiration, change data capture (CDC) for asynchronous updates, and tiered storage (hot vs. cold data).
Weigh the pros and cons of each strategy against the requirements, and recommend a hybrid approach if appropriate. Discuss how to measure and monitor the chosen solution.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Read-through cache keyed on host ID plus the date range.
Start by clarifying the dashboard's requirements—read-heavy, data freshness, and scale—then propose a multi-layer caching strategy (client, CDN, application, database). Explain how each layer handles cache misses and eviction, and tie your choices to trade-offs like consistency, latency, and cost.
Pro tip: Quantify the impact: mention expected hit ratios, latency improvements, and cost savings to show you think in terms of business metrics, not just technical details.
Ask about data volume, read/write ratio, freshness tolerance, and user geography to tailor the caching strategy.
Propose caching at multiple levels: browser, CDN, application (in-memory), and database query cache, explaining the purpose of each.
Describe strategies like cache-aside, read-through, and write-through, and how to prevent stampedes with request coalescing or locks.
Select eviction policies (LRU, LFU, TTL) based on access patterns and data volatility, and justify your choice.
Explain how to invalidate or update cached data (TTL, event-driven, versioning) and handle consistency trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Basically asking whether your summary table can serve both use cases or if you need two separate tables.
Start by clarifying the access patterns and scale, then propose a denormalized, pre-aggregated data model that separates write and read paths. Use a summary table for per-host aggregates and a detail table for per-listing breakdowns, both updated incrementally via events or batch jobs. Emphasize trade-offs like consistency, storage cost, and query performance.
Pro tip: Mention that you would store pre-aggregated metrics in a wide-column store like Cassandra or a relational table with materialized views, and use change data capture (CDC) to keep them in sync. This shows you understand both data modeling and operational concerns.
Ask about query patterns, data volume, latency requirements, and consistency needs to ensure the model fits the use case.
Propose a normalized listing table for source-of-truth data and a host table for host metadata, then discuss how to derive aggregates.
Create a host_aggregates table storing pre-computed metrics (e.g., total listings, average rating) and a listing_breakdown table with per-listing details, both keyed for fast lookups.
Explain how to keep aggregates fresh using event-driven updates (e.g., Kafka streams) or scheduled batch jobs, and how to handle late-arriving data.
Acknowledge trade-offs: increased storage, potential staleness, and complexity in maintaining consistency, and suggest mitigations like versioning or TTL.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.