Optimizing Microservice System with Java Spring Boot

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=30000

Query 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.