SpringBoot Performance Optimization: Monitoring, Profiling, and Tuning Strategies

SpringBoot has become the leading Java framework, and as service load increases, performance optimization becomes essential.

First, expose metrics using SpringBoot Actuator and integrate with Prometheus by adding Maven dependencies and configuring application.properties to enable endpoints, then access http://localhost:8080/actuator/prometheus for metrics.

<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-actuator</artifactId>
</dependency>
<dependency>
    <groupId>io.micrometer</groupId>
    <artifactId>micrometer-registry-prometheus</artifactId>
</dependency>
<dependency>
    <groupId>io.micrometer</groupId>
    <artifactId>micrometer-core</artifactId>
</dependency>

management.endpoint.metrics.enabled=true
management.endpoints.web.exposure.include=*
management.endpoint.prometheus.enabled=true
management.metrics.export.prometheus.enabled=true

Inject MeterRegistry to record custom business metrics, as shown in the example controller that increments a counter.

@Autowired
MeterRegistry registry;

@GetMapping("/test")
@ResponseBody
public String test() {
    registry.counter("test", "from", "127.0.0.1", "method", "test").increment();
    return "ok";
}

Use Prometheus together with Grafana for visualization and Alertmanager for alerts.

Generate Java flame graphs with async‑profiler by running the application with the -agentpath option and opening the resulting profile.svg .

java -agentpath:/root/build/libasyncProfiler.so=start,svg,file=profile.svg -jar spring-petclinic-2.3.1.BUILD-SNAPSHOT.jar

Apply SkyWalking by adding its Java agent jar to the startup command to collect distributed tracing data.

java -javaagent:/opt/skywalking-agent/skywalking-agent.jar -Dskywalking.agent.service_name=the-demo-name -jar /opt/test-service/spring-boot-demo.jar --spring.profiles.active=dev

Overall optimization ideas include monitoring, profiling, HTTP tuning (CDN, cache‑control, gzip, keep‑alive), Tomcat parameter tuning, or replacing Tomcat with Undertow via Maven exclusions.

<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-web</artifactId>
    <exclusions>
        <exclusion>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-tomcat</artifactId>
        </exclusion>
    </exclusions>
</dependency>
<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-undertow</artifactId>
</dependency>

Layer‑wise improvements cover Controller (use @ResponseBody, limit result size), Service (design patterns, distributed transactions vs. BASE), and DAO (avoid unnecessary queries, consider sharding).

Finally, the article summarizes the discussed tools and techniques, emphasizing that SpringBoot already uses high‑performance components such as HikariCP, Lettuce, and Caffeine, and that further tuning can yield significant latency reductions.