Challenge
The client needed a system to handle:
- High-frequency data updates : Real-time driver location changes every few seconds
- Instant driver matching : Quickly find the nearest driver from dynamic data
- Scalability : Handle thousands of ride requests and millions of location updates
- Analytics : Generate reports without impacting real-time operations
Solution
We implemented a multi-database architecture with real-time data streaming and instant communication protocols:
Optimized Data Storage:
- Transaction Layer (Redis/MemSQL) : Stores active rides and location data for instant driver matching.
- Reporting Layer (PostgreSQL/Redshift) : Handles completed rides and analytics without affecting live data.
- Persistent Layer (DynamoDB/MongoDB) : Manages static data (user profiles, driver details).
Real-Time Processing with Kafka:
- Kafka streams live driver location data.
- Nearest driver found instantly through in-memory transaction layer queries.
Instant Communication via MQTT:
- Enables real-time trip updates, ride statuses, and notifications between drivers, passengers, and backend systems.
Independent Reporting System:
- Analytics include demand peaks, ride completion rates, surge pricing trends, and driver availability.
- Reporting runs independently, ensuring uninterrupted live operations.