How to Handle Large Traffic in Spring Boot Applications: Real-World Examples & Best Practices

🚀 Introduction

Spring Boot has become the go-to framework for building modern Java applications. But as your app grows, one of the biggest challenges is handling large traffic without compromising performance.

In this blog, I’ll share real-world strategies we implemented in a Spring Boot microservices application during a festive sale, where traffic surged by 3x. You’ll see practical examples, code snippets, and the performance improvements we achieved.

⚡ The Challenge: Sudden Traffic Surge

We had a payment microservice built with Spring Boot + MySQL + REST APIs.

• 3x spike in concurrent users
• Database CPU usage hitting 90%
• API response time increasing from 400ms → 1.8s
• Risk of timeouts and failed payments

🔑 Strategy #1: Caching with Redis

The biggest bottleneck was repetitive DB queries for user details and payment validation.

✅ Solution: Introduced Spring Cache with Redis.

@Cacheable(value = "userCache", key = "#userId")
public User getUserDetails(String userId) {
    return userRepository.findById(userId).orElseThrow();
}

    

✔ Reduced 40% DB queries instantly.
✔ Redis cluster ensured high availability under load.

🔑 Strategy #2: Optimize Database Connections (HikariCP)

Spring Boot uses HikariCP as the default connection pool, but defaults aren’t always optimal.

✅ Tuned connection pool size:

spring.datasource.hikari.maximum-pool-size: 30
spring.datasource.hikari.minimum-idle: 10
spring.datasource.hikari.idle-timeout: 30000
spring.datasource.hikari.max-lifetime: 1800000

    

✔ Ensured stable DB connections.
✔ Avoided connection exhaustion during peak traffic.

🔑 Strategy #3: Asynchronous Processing with RabbitMQ

Tasks like sending confirmation emails and logging audit events were blocking API responses.

✅ Solution: Offloaded to RabbitMQ + @Async processing.

@Async
public void sendNotification(String orderId) {
    rabbitTemplate.convertAndSend("notificationQueue", orderId);
}

    

✔ Freed up API threads.
✔ Reduced average response time by 500ms.

🔑 Strategy #4: Horizontal Scaling with Kubernetes

Vertical scaling (bigger servers) wasn’t enough.

✅ Solution: Deployed multiple Spring Boot instances on Kubernetes with auto-scaling.

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: payment-service-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: payment-service
  minReplicas: 3
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70

    

✔ Pods scaled up automatically when CPU > 70%.
✔ Handled millions of requests/day seamlessly.

🔑 Strategy #5: Monitoring with Prometheus + Grafana

Without monitoring, optimization is guesswork.

✅ Added Prometheus metrics with Spring Actuator:

management.endpoints.web.exposure.include: health,info,prometheus

    

✔ Tracked latency, error rates, and DB performance.
✔ Grafana dashboards gave real-time visibility during peak load.

📊 The Results

• API latency dropped from 1.8s → 400ms
• Database CPU usage reduced by 35%
• Application handled 3x traffic without downtime
• Improved customer checkout success rate

✅ Best Practices for Handling Large Traffic in Spring Boot

• Use caching (Redis/Hazelcast) to cut DB load
• Tune HikariCP for optimal DB connections
• Offload heavy tasks using async processing & queues
• Leverage Kubernetes auto-scaling for horizontal growth
• Always monitor metrics to detect bottlenecks early

🏁 Conclusion

Scaling Spring Boot applications for large traffic isn’t about a single fix—it’s about combining caching, async processing, database tuning, horizontal scaling, and monitoring.

Note : If you’re preparing for high-traffic events (sales, launches, campaigns), start implementing these strategies before the surge hits.