Spring Boot is a powerful framework for building microservices and enterprise applications. However, as your applications scale, performance issues can emerge — slow startup times, memory bloat, high CPU usage, or sluggish response times.

This guide explores best practices to optimize performance in Spring Boot applications, covering tuning at the application, JVM, and infrastructure levels. Whether you’re deploying monoliths or microservices, these tips help make your services faster and more efficient.

Reduce Startup Time

Spring Boot applications can experience slow startup times, especially with large dependency graphs.

Best practices:

  • Enable lazy initialization: only load beans when needed.
spring.main.lazy-initialization=true
  • Profile startup with Spring Boot Actuator and /actuator/startup.
  • Avoid classpath scanning across unrelated packages.
  • Remove unused @ComponentScan directives and @Enable* annotations.

Optimize JVM Settings

Proper JVM tuning is essential for performance, especially under heavy load.

Key flags:

-Xms512m -Xmx2g -XX:+UseG1GC -XX:+TieredCompilation -XX:+UseStringDeduplication
  • Use G1GC or ZGC for large heaps and lower GC pause times.
  • Profile memory with tools like JVisualVM or JFR.
  • Use -Dspring.profiles.active=prod to avoid dev-time overhead.

Leverage HTTP and Connection Settings

Spring Boot apps often communicate over HTTP. Configure RestTemplate, WebClient, or servlet containers efficiently:

server:
tomcat:
threads:
max: 200
min-spare: 10
max-connections: 10000
accept-count: 1000

Use a connection pool like HikariCP for JDBC:

spring.datasource.hikari:
maximum-pool-size: 20
minimum-idle: 5
idle-timeout: 30000
max-lifetime: 600000

Use Efficient Caching

Caching reduces load on databases and services. Use Spring’s built-in caching abstraction:

@Cacheable(value = "products", key = "#id")
public Product getProduct(String id) {
return productRepository.findById(id).orElseThrow();
}

Use a high-performance in-memory store like Caffeine:

<dependency>
<groupId>com.github.ben-manes.caffeine</groupId>
<artifactId>caffeine</artifactId>
</dependency>

And configure it:

spring.cache.caffeine.spec=maximumSize=500,expireAfterWrite=10m

Optimize Hibernate and JPA

Data access is often the #1 bottleneck in Spring Boot applications.

Tips:

  • Use pagination for large result sets.
  • Avoid N+1 queries with proper fetch strategies.
  • Enable SQL logging in dev, disable in prod:
spring.jpa.show-sql=false
  • Tune batch sizes for inserts/updates:
spring.jpa.properties.hibernate.jdbc.batch_size=30
spring.jpa.properties.hibernate.order_inserts=true
spring.jpa.properties.hibernate.order_updates=true
  • Use indexes in your database and tune queries with EXPLAIN plans.

Enable Asynchronous Processing

Spring makes it easy to run background tasks without blocking main threads:

@EnableAsync
public class AppConfig {}

@Async
public void sendEmail(String userId) {
// send logic
}

Use a custom ThreadPoolTaskExecutor to control async behavior and avoid overloading.

Avoid Memory Leaks and Bloat

Memory leaks can destroy performance over time. Common culprits:

  • Overloaded caches
  • ThreadLocal variables not cleaned up
  • Long-lived sessions in HTTP

Use heap dumps and tools like Eclipse MAT to inspect memory usage patterns.

ThreadLocal<MyContext> context = new ThreadLocal<>();
// Be sure to call context.remove() after use

Monitor with Actuator and Metrics

Spring Boot Actuator gives insights into runtime performance:

management:
endpoints:
web:
exposure:
include: health, metrics, prometheus

Track:

  • jvm.memory.used
  • http.server.requests
  • process.cpu.usage

Integrate with Prometheus and Grafana for dashboards and alerting.

Use Native Compilation (Spring Boot 3 + GraalVM)

Spring Boot 3 supports AOT (ahead-of-time) compilation using GraalVM:

  • Native executables with lightning-fast startup
  • Minimal memory footprint

Set up with:

./mvnw -Pnative native:compile

Great for serverless or containerized apps.

Conclusion

Spring Boot is designed to be production-ready, but real-world performance requires intentional design and tuning. From JVM flags and caching to database optimization and native builds, these best practices help you deliver fast, scalable, and reliable services.

Monitoring and profiling are your best friends. Start small, measure often, and continuously evolve your performance strategy as your app grows.