High-Throughput Transaction and Data Layer Optimization
Stabilized and optimized the platform’s transaction processing layer to support high-frequency, low-latency operations while maintaining data integrity across millions of marketplace interactions.
Situation
The platform required consistent handling of large volumes of concurrent orders, inventory updates, and user interactions. The relational database layer became a bottleneck under peak load, impacting latency and user experience.
Solution
Re-architected the data layer to support high-throughput workloads through schema optimization, caching strategies, and workload segmentation across transactional paths.
OUTCOMES
Challenges
Latency
- •Slow order processing
- •Inventory update delays
Database
- •Relational bottlenecks
- •High contention paths
- •Inefficient indexing patterns
Solutions
MySQL Schema Optimization
Optimized MySQL schema design for transactional integrity and query performance.
- Refined schema structure for transactional consistency
- Reduced query execution complexity
- Increased reliability of write operations
Redis Cache Layer
Redis caching layer for real-time inventory and session data.
- Accelerated access to inventory datasets
- Reduced database dependency for sessions
- Improved response latency under load
- Enabled real-time synchronization patterns
Read/Write Segmentation
Segmented read/write workloads to reduce contention on critical database paths.
- Isolated read-heavy traffic flows
- Enabled scalable query routing strategies
Marketplace Cache Strategy
Implemented caching strategies for frequently accessed marketplace data.
- Cached frequently requested marketplace objects
- Reduced repeated database fetch overhead
- Improved user-facing response times
- Stabilized performance during spikes
Index/Query Tuning
Tuned database indexing and query patterns for high-frequency operations.
- Optimized indexes for transaction workloads
- Reduced query execution latency