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In summary, the most interesting aspect of this transcript is the nuanced CAP theorem application, specifically prioritizing strong consistency for ticket booking while maintaining high availability for search, and the practical solution of using distributed locks and virtual waiting queues to handle massive traffic surges during high-demand events.
- Follow a structured interview framework: requirements, core entities/APIs, high-level design, and deep dives.
- Prioritize strong consistency for ticket booking to prevent double-booking, while ensuring high availability for search and event viewing.
- Implement a two-phase booking process (reserve then confirm) with a 10-minute expiration window.
- Use a distributed lock (e.g., Redis with TTL) instead of database timestamps or cron jobs to reliably manage ticket reservation expiration.
- Optimize search performance by integrating ElasticSearch with an inverted index and change data capture (CDC) for data synchronization.
- Handle massive traffic surges for popular events using a virtual waiting queue to protect backend services and improve user experience.
- Leverage caching (Redis, CDN, or query caching) to reduce read load on the primary database, given a high read-to-write ratio.
- Focus interview math/estimations on decisions that directly impact design choices rather than generic back-of-the-envelope calculations.
Notes this inspired
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- ElasticSearchGrowing
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