Client: Major Hotel Bookings Aggregator, UK

Challenge: The client's existing travel search engine, which managed over 200 million daily queries, was hindered by its SQL Server-based architecture. This system struggled with scalability, both vertically and horizontally, making it cost-inefficient to handle increased traffic or expand hotel data due to rising infrastructure costs.

Approach: DeepArt Labs conducted an in-depth analysis of the client’s system usage and data flow. To address scalability and efficiency issues, we implemented a cutting-edge in-memory data grid (IMDG) platform. This platform was designed to handle all data relevant to hotel availability searches, significantly reducing reliance on the persistent database.

Solution:

• Custom Cache Layer: Developed a custom cache layer using IMDG to hold essential data in memory, enabling faster data retrieval.
• Data Loading Mechanism: Implemented a swift mechanism for loading data from databases into the memory grid.
• Message Queue Pipeline: Supported incremental intraday data updates to keep the search results current and accurate.

Results:

• Increased Query Capacity: Post-implementation, the system could handle 50% more traffic, managing over 300 million queries daily.
• Reduced Costs and Response Times: Infrastructure costs were reduced by 80%, and query response times improved, with 95% of queries returning results in under a second.
• Business Growth: The enhanced system not only supported existing customer demands but also allowed for more frequent queries, leading to increased revenue and new business opportunities.

Conclusion: By transitioning to an in-memory processing solution, DeepArt Labs helped the client overcome significant technical and business challenges, setting a new standard in travel search engine performance and scalability.

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