Start by clarifying functional and non-functional requirements, then design a high-level architecture with separate services for search, booking, and inventory sync. Focus on real-time availability and booking consistency across chains, discussing trade-offs between consistency, latency, and scalability.
Pro tip: Emphasize idempotency and distributed transactions to handle double bookings, and discuss how to handle partial failures when some chains are down. This shows maturity in building reliable distributed systems.
Ask about scale (users, hotels, searches per second), consistency needs (real-time vs eventual), and supported chains. Define core features: search, book, cancel, and view reservations.
Sketch components: API gateway, search service, booking service, inventory service, and external chain adapters. Use a message queue for async updates and a cache for hot data.
Explain how to aggregate results from multiple chains with low latency: parallel requests, timeouts, fallbacks, and caching. Discuss indexing strategies for fast availability checks.
Detail the booking flow: reserve inventory, process payment, confirm with chain. Use idempotency keys, distributed transactions (e.g., saga pattern), and compensating actions for failures.
Discuss scaling reads vs writes, sharding by hotel/chain, and trade-offs between strong consistency (for bookings) and eventual consistency (for search). Mention monitoring and rate limiting.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the core of the whole discussion.
Start by clarifying the constraints: search volume, latency requirements, freshness needs, and cost of upstream calls. Then compare the three options—real-time calls, caching, and pre-allocation—across dimensions like latency, consistency, cost, and complexity, and recommend a hybrid approach based on the specific scenario.
Pro tip: Emphasize that the right answer depends on the business context—e.g., for a high-traffic travel platform, a hybrid of caching with short TTL and pre-allocated blocks for high-demand hotels often balances freshness and performance. Show you can quantify trade-offs with rough numbers (e.g., QPS, cache hit rate, cost per call).
Ask about expected search volume, acceptable latency, data freshness requirements, and budget constraints. This ensures your analysis is grounded in real needs.
Discuss pros (always fresh, no stale data) and cons (high latency, rate limits, cost, dependency on upstream availability). Mention when this is appropriate (low volume, high freshness).
Explain caching strategies (TTL, write-through, refresh-ahead) and trade-offs: reduced latency and cost vs. potential staleness and cache invalidation complexity. Note that TTL should align with how often inventory changes.
Describe pre-allocating inventory blocks: guarantees availability and fast response, but risks over/under-allocation, wasted inventory, and reconciliation complexity. Suitable for high-demand, predictable inventory.
Propose a combination: e.g., cache with short TTL for most searches, pre-allocate for peak or premium inventory, and fall back to real-time calls for cache misses or critical freshness. Justify based on the constraints from step 1.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the most interesting sub-question of the whole session.
Acknowledge that both options have trade-offs, but the best answer depends on the specific SLOs and business context. Propose a hybrid approach that uses a fast fallback (e.g., live API call with a tight timeout) and a circuit breaker to prevent cascading failures, while also considering asynchronous refill and request queuing with bounded wait times. Emphasize that tail latency is critical for user experience, so blocking indefinitely is rarely acceptable.
Pro tip: Show that you think in terms of percentiles (p99, p99.9) and not just averages, and mention that you'd instrument both paths to measure the actual tail latency impact before deciding. This demonstrates a data-driven approach and maturity.
Ask about the expected request rate, the size of the room pool, the acceptable latency SLOs, and the cost of a live API call (e.g., rate limits, latency).
For live API call: adds latency and potential failures but avoids blocking. For blocking: may cause timeouts and increased tail latency, but avoids external dependency.
Suggest a fallback to live API with a short timeout (e.g., 100ms) and a circuit breaker to fail fast if the API is slow or down. Also consider asynchronous refill of the pool to reduce the chance of exhaustion.
Explain that live API calls can increase p99 latency due to network variability, while blocking can cause queue buildup and timeouts, also increasing tail latency. A hybrid approach bounds the worst-case latency.
Emphasize the need to measure both approaches in production, monitor p99/p99.9 latencies, and adjust thresholds based on data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Partitioning by hotel ID plus date felt natural and I said so.
Start by clarifying the requirements: read/write patterns, consistency needs, and scale. Then propose a cache invalidation strategy that balances consistency and performance, such as event-driven invalidation with TTL as a fallback. Finally, describe a partitioning scheme that shards data by hotel, region, and date to distribute load and enable efficient queries.
Pro tip: Mention that you would monitor cache hit rates and invalidation latency, and be prepared to adjust the strategy based on real-world metrics. Also, consider using a write-through cache for critical availability data to ensure consistency.
Ask about read/write ratio, consistency requirements, and scale (number of hotels, regions, dates). This informs the choice of caching and partitioning strategy.
Propose an invalidation strategy: e.g., event-driven invalidation when availability changes, combined with a TTL to handle missed events. Discuss trade-offs between consistency and latency.
Decide between write-through, write-behind, or refresh-ahead based on access patterns. For high-read availability, consider proactive refresh for popular hotels/dates.
Describe partitioning across hotels, regions, and dates. For example, shard by hotel ID to keep all dates for a hotel together, or by region for geo-distribution. Use date-based partitioning for efficient range queries.
Discuss how to handle hot keys (e.g., popular hotels) and cache failures (e.g., fallback to database, circuit breakers). Mention monitoring and auto-scaling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.