Design of an In-Memory Cached Search Architecture for Scalable Hospitality Data Platforms

The client faces limitations in their existing database-centric system, decelerating growth due to architectural constraints. With approximately 99% of queries being read-only and handling around 200 million daily queries, the system struggles to scale horizontally. Additional infrastructure incurs minimal performance gains, leading to increasing costs and system bottlenecks that hinder business expansion and the ability to support more clients and hotel data efficiently.

A large hospitality booking aggregator seeking to enhance search performance and scalability for hotel availability data.

• Reduce persistent database (SQL or equivalent) usage and cut infrastructure costs by at least 80%.
• Achieve near-linear horizontal scalability for the hotel availability search engine.
• Enhance system efficiency to approximately 70% in in-memory processing.
• Increase query handling capacity to support 50% greater traffic, exceeding 300 million queries daily.
• Reduce average query response times by 60%, with over 95% served below one second.
• Enable the platform to support new business opportunities through scalable architecture, facilitating expansion and feature integration.
• Improve user experience, leading to increased customer query frequency and associated revenue growth.

• An in-memory caching layer to store comprehensive hotel availability data, enabling rapid read-only query responses.
• A customized, high-speed data loading mechanism to populate the cache from the primary relational database systems.
• A message queue pipeline supporting incremental intra-day data updates to keep cache data current.
• Support for efficient, high-volume read-only queries, optimized for both performance and resource utilization.
• Monitoring and management tools for cache health, data freshness, and query performance metrics.

• Primary relational database systems for baseline data loads
• Real-time message queues for incremental updates
• Monitoring and analytics tools for system performance tracking

• Horizontal scalability to support increasing query volume with minimal cost increase
• System's in-memory processing efficiency around 70%
• Query response times below 1 second for over 95% of requests
• System reliability, data consistency, and high availability
• Cost reduction by approximately 80% in infrastructure

Implementation of an in-memory cached hotel availability search system is expected to dramatically lower database load and infrastructure costs by at least 80%, while enabling nearly linear horizontal scalability. This will allow the platform to handle over 300 million queries daily with a 60% reduction in average response times, leading to improved customer satisfaction, increased query frequencies, and higher revenue. The scalable architecture will also open new avenues for business expansion and innovative feature development.