Database Indexing
Create indexes on frequently queried columns: origin, destination, departure_date, airline, price. Use composite indexes for common query patterns. Monitor query performance and adjust indexes based on actual usage.
- Index origin and destination airports
- Composite index on (origin, destination, date)
- Index price for sorting operations
- Use partial indexes for filtered queries
Query Optimization
Optimize SQL queries by selecting only required columns, using LIMIT clauses, and avoiding N+1 queries. Use database query analyzers to identify slow queries. Consider using materialized views for complex aggregations.
- Select only required columns (avoid SELECT *)
- Use pagination for large result sets
- Implement query result caching
- Use EXPLAIN to analyze query plans
Redis Caching
Cache search results in Redis with smart key strategies. Cache popular routes longer than less common ones. Implement cache warming for peak travel periods. Use Redis for session management and rate limiting.
- Cache search results (key: origin-dest-date)
- TTL: 5 minutes for popular routes, 15 minutes for others
- Cache airline and airport data (24 hours)
- Use Redis for session storage
Search Result Ranking
Implement intelligent ranking algorithms. Consider factors like price, duration, departure time, airline preference, and user history. Use machine learning for personalized rankings.
- Rank by price, duration, and departure time
- Consider user preferences and history
- Boost direct flights over connections
- Personalize results based on past bookings
Conclusion
Optimizing flight search requires a combination of database indexing, query optimization, intelligent caching, and smart ranking algorithms. Monitor performance metrics continuously and iterate based on real-world usage patterns.