Introduction
In this article, I will share real-world experience on optimizing microservice system performance using Java Spring Boot, based on projects like BCSS, CIC and other systems.
1. Database Optimization
Connection Pooling
Use HikariCP with appropriate configuration to optimize connection pool. This is one of the most important factors in microservice system optimization.
spring.datasource.hikari.maximum-pool-size=20
spring.datasource.hikari.minimum-idle=5
spring.datasource.hikari.connection-timeout=30000Query Optimization
- Use appropriate indexing
- Avoid N+1 query problem with @EntityGraph or JOIN FETCH
- Use pagination for large datasets
2. Message Queue Optimization
Kafka Configuration
- Tuning batch size and linger time
- Use compression (gzip, snappy)
- Appropriate partition strategy
ActiveMQ/RabbitMQ
- Prefetch count optimization
- Message TTL and dead letter queue
3. Caching Strategy
Redis Caching
- Multi-level caching (L1: local, L2: Redis)
- Cache invalidation strategy
- Appropriate TTL with business logic
4. Monitoring and Observability
Use ELK Stack, Prometheus, Grafana to monitor:
- API response time
- Database query performance
- Message queue throughput
- System resource usage
Conclusion
Optimizing microservice systems is a continuous process, requiring monitoring and adjustment based on actual metrics. If you need support with microservice system optimization, please contact for detailed consultation.