Design and Optimization of Ctrip Ticket Booking Transaction System for Flash‑Sale Events

Background In the post‑pandemic era, the travel industry has rapidly recovered, leading to frequent high‑traffic flash‑sale promotions. The ticket reservation transaction system must handle billions of requests while guaranteeing stable, fast, and accurate booking experiences for domestic and overseas users.

Flash‑Sale Case Analysis Typical flash‑sale scenarios (e.g., Double 11, 618, train ticket rushes, concert tickets) share three characteristics: massive concurrent traffic, strict timing, and the need for reliable fulfillment. Ctrip’s ticket system distinguishes itself with strong consistency and complex multi‑dimensional purchase limits.

System Architecture Design and Evolution The system’s goals are stability (steady operation), accuracy (strong data consistency), and speed (smooth user experience). To achieve these under high‑concurrency, targeted upgrades are applied.

Stability : Ensure uninterrupted sales flow.

Accuracy : Maintain transactional consistency.

Speed : Provide rapid confirmation and booking.

3.1 System Stability Challenges and Solutions

Redis overload & cache hotspots Horizontal scaling alone cannot eliminate hotspot keys or large‑key issues. The solution is automatic hotspot detection and multi‑level caching, which identifies hot keys (e.g., >10 accesses per second) and builds local caches to offload Redis.

Cache big‑key problem Big keys cause request blocking, high memory usage, and network congestion. Mitigation includes trimming cache objects, compressing them, splitting large keys, and establishing a regular big‑key scanning and remediation process.

Database overload High‑traffic flash sales cause cache‑miss storms that hammer the DB. The strategy replaces cache‑delete with cache‑overwrite, aggregates change messages, and processes updates asynchronously to reduce DB pressure.

Supplier system instability Suppliers may become slow or rate‑limited under peak load, affecting order flow. Mitigations include a buffering queue (peak‑shaving), automatic disabling of affected suppliers, and periodic retry mechanisms.

Traffic control strategy Multi‑layer throttling is applied: SOA‑level interface limits, custom product‑level limits, and dynamic hotspot detection similar to Redis hot‑key detection. Sliding‑window algorithms split one‑second traffic into ten 100 ms windows to evenly distribute load.

3.2 Write‑Data Consistency Challenges and Solutions Inventory deduction under flash sales creates row‑level lock bottlenecks in MySQL. The solution is asynchronous inventory deduction: preload stock to Redis, deduct in Redis during the sale, and asynchronously update the DB via message queues, ensuring “no oversell” while tolerating brief eventual consistency.

3.3 Achieving High‑Availability Sustainability

3.3.1 Architecture health governance Quantify system health across runtime metrics, architectural complexity, and engineering quality. Regular health checks guide continuous improvement.

3.3.2 Large‑event and holiday guarantee system Pre‑emptive capacity planning, stress testing, and emergency runbooks ensure the system remains highly available during major promotions and holidays.

Conclusion The article summarizes how Ctrip’s ticket reservation system tackles flash‑sale challenges through read‑hotspot mitigation, write‑bottleneck alleviation, strong transactional guarantees, and layered traffic control, while maintaining continuous health governance and dedicated high‑availability plans to sustain stable operation under extreme load.