Harish Kumar

Scaling Spring Boot to Millions of RPS

Published on 10 July 2025 · High-Performance, System-Design, Java

Handling millions of requests per second (RPS) in a **Java Spring Boot** environment requires a fundamental shift from traditional application design to a highly distributed, reactive, and resilient microservices architecture. The key is to **distribute load, avoid blocking I/O, and serve data from the fastest possible layer.**

Here is a breakdown of how to achieve this scale using the Java/Spring ecosystem, covering key strategies for high-throughput systems.

1. 🌐 Load Balancer

The load balancer is the entry point that evenly distributes traffic across your horizontally scaled Spring Boot application instances.

2. 📈 Horizontal Scaling

This is the single most critical factor: instead of buying one massive server (vertical scaling), deploy hundreds or thousands of smaller, identical instances.

3. 💾 Caching Layer

Caching is the primary mechanism for mitigating the load on your database and application servers, serving the majority of requests from memory.

Example Caching Layer (Conceptual)


// /service/ProductService.java
@Service
@EnableCaching
public class ProductService {
    // ... repository dependency

    // This data is fetched from the database only on a cache miss.
    @Cacheable(value = "products", key = "#id")
    public ProductEntity findProductById(Long id) {
        log.info("Fetching product {} from DB (Cache Miss)", id);
        return productRepository.findById(id)
                   .orElseThrow(() -> new NotFoundException("Product not found"));
    }

    // Updates the DB and refreshes the cache entry
    @CachePut(value = "products", key = "#product.id")
    @Transactional
    public ProductEntity updateProduct(ProductEntity product) {
        // ... update logic
        return productRepository.save(product);
    }
}

4. 🖼️ CDN for Static Content

Content Delivery Networks (CDNs) handle static assets and can cache API responses.

5. 📧 Async Processing (Queues)

Any operation that is slow, involves external services, or doesn't need an immediate response from the client should be **decoupled** using a message queue.

Example Asynchronous Producer (Kafka)


// /service/OrderService.java
@Service
public class OrderService {
    private final KafkaTemplate<String, String> kafkaTemplate;
    // ... constructor injection

    public OrderDto placeOrder(OrderRequestDto request) {
        // 1. Save the order to the database (fast operation)
        OrderEntity savedOrder = orderRepository.save(request.toEntity());

        // 2. Publish the processing task to the queue immediately (non-blocking)
        String orderEvent = savedOrder.getId().toString();
        kafkaTemplate.send("order-processing-topic", orderEvent);

        // 3. Return a successful 202 Accepted response to the client
        return new OrderDto(savedOrder.getId(), "ACCEPTED");
    }
}

6. 💿 Database Sharding

When a single database server can no longer handle the write load or total data volume, you must implement **sharding** (horizontal partitioning).

7. 🚫 Rate Limiting

Rate limiting is a protective layer to prevent system overload, resource exhaustion, and abuse from malicious or buggy clients.

8. 📐 Lightweight Payloads

The size of your request and response bodies directly impacts network latency and processing load.