Performance
Optimizing Hotel Search for Millions of Listings
November 25, 2023
9 min read
Performance
MongoDB
Redis
Search Optimization
Travel Tech
The technical challenges of building a fast, accurate hotel search engine with MongoDB, Redis caching, and smart indexing strategies.
Searching through millions of hotel listings in real-time requires careful optimization. Here's how we built a search engine that stays fast at scale.
The Scale Challenge
Our hotel search platform handles:
- 2.8 million hotel listings worldwide
- Real-time availability and pricing
- Complex filtering and sorting
- Sub-second response times
Database Architecture
We chose MongoDB for its flexibility and built:
- Optimized indexes for common search patterns
- Geospatial indexes for location-based searches
- Compound indexes for multi-criteria filtering
- Read replicas for search queries
Caching Strategy
Redis caching at multiple levels:
- Query result caching
- Hotel metadata caching
- User session caching
- Popular destination caching
Search Optimization
Key optimizations include:
- Elasticsearch for full-text search
- Query result pagination
- Lazy loading of hotel details
- CDN for static content
Performance Results
Our optimizations achieved:
- Average search time: 150ms
- 99.9% uptime
- Support for 10,000+ concurrent users
- 50% reduction in database load
Lessons Learned
Key takeaways:
- Index design is crucial for performance
- Caching strategy must be multi-layered
- Monitor and optimize based on real usage patterns
- Plan for scale from the beginning