How I Optimized a Spring Boot Application to Handle 1 Million Requests Per Second

Discover how I optimized a Spring Boot application to handle 1 million requests per second. Learn proven strategies for performance tuning, database optimization, and scaling with Kubernetes and WebFlux.

In today’s digital age, scalability isn’t just a buzzword—it’s a necessity. High-traffic applications demand performance, reliability, and the ability to handle millions of requests seamlessly. Recently, I worked on optimizing a Spring Boot application to handle 1 million requests per second, and I want to share the journey with you.

This blog outlines the strategies, tools, and lessons learned during this challenging but rewarding experience.

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How I Optimized a Spring Boot Application to Handle 1 Million Requests Per Second

Step 1: Identifying the Bottlenecks

Before diving into optimization, it’s crucial to understand where the bottlenecks are. I used profiling and monitoring tools like:

• JProfiler: To analyze CPU and memory usage.
• New Relic: For end-to-end application monitoring.
• Spring Boot Actuator: To gather insights into HTTP requests, thread pools, and system metrics.

Key Findings:

1. High latency in some REST endpoints.
2. Slow database queries due to inefficient indexing and N+1 query issues.
3. High thread contention during peak loads.

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Step 2: Moving to Reactive Programming

The first major shift was adopting Spring WebFlux for critical endpoints. Unlike the traditional blocking model in Spring MVC, WebFlux uses a non-blocking reactive model, which allows the application to process more concurrent requests with fewer threads.

Impact:

• Reduced thread usage.
• Increased concurrency without requiring additional hardware resources.

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Step 3: Database Optimization

The database was one of the biggest performance bottlenecks. Here’s how I tackled it:

1. Optimized Queries:
   • Refactored inefficient queries and avoided expensive JOINs.
   • Used Hibernate’s @BatchSize and @Fetch annotations to solve N+1 problems.
2. Added Indexes:
   • Proper indexing reduced query execution time drastically.
3. Caching:
   • Implemented Redis for frequently accessed data.
   • Used Spring Cache Abstraction for seamless integration.
4. Connection Pool Tuning:
   • Configured HikariCP, the default connection pool in Spring Boot, to handle high concurrent traffic.
   • Increased the maximum pool size and fine-tuned timeouts for optimal performance.

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Step 4: Tuning Thread Pools and Connection Limits

The default thread pool configurations in Tomcat and Netty weren’t sufficient to handle the scale. I optimized these configurations as follows:

• For blocking tasks (like I/O-heavy operations):
  Configured the spring.task.execution.pool for asynchronous processing.
• For WebFlux:
  Tuned Netty’s connection limits and worker threads to ensure smooth processing under load.

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Step 5: Leveraging CDN and Load Balancers

To reduce the load on the backend and enhance response times:

• CDN Integration: I used Cloudflare to cache static resources like images, CSS, and JavaScript files, offloading those requests from the application.
• Load Balancing: Configured an NGINX reverse proxy along with AWS Application Load Balancer (ALB) to distribute traffic across multiple application instances.

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Step 6: Optimizing Serialization and Compression

Serialization was another area of improvement. By switching to Kryo serialization, I reduced the overhead of data transfer.

Additionally:

• Enabled GZIP compression for HTTP responses, reducing the size of payloads sent over the network.

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Step 7: Scaling Horizontally with Kubernetes

The application was containerized using Docker and deployed on a Kubernetes cluster. Key optimizations included:

• Horizontal Pod Autoscaling: Added rules to spin up additional pods during traffic spikes.
• Istio Service Mesh: Implemented for traffic shaping and resilience, ensuring high availability during load testing.

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Step 8: HTTP/2 and Keep-Alive

Upgrading to HTTP/2 brought significant performance gains:

• Multiplexing allowed multiple requests over a single connection, reducing latency.
• Persistent connections (Keep-Alive) reduced the overhead of establishing new connections.

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Step 9: Stress Testing

No optimization effort is complete without rigorous testing. I used the following tools to simulate real-world traffic:

• Apache JMeter: For load testing and analyzing response times under heavy load.
• Gatling: For high-performance stress testing.

Through iterative testing and optimization cycles, I gradually improved the system’s capacity and resilience.

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The Results

After implementing these strategies, the Spring Boot application achieved the following:

• 1 million requests per second consistently under simulated traffic.
• Average response time: 30ms.
• No downtime or degradation during peak loads.

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Key Takeaways

1. Measure Before Optimizing: Always start with profiling tools to identify the real bottlenecks.
2. Small Wins Add Up: There’s no single “magic fix.” Scalability is about incremental improvements across the stack.
3. Invest in Tools and Automation: Modern tools like Kubernetes, Redis, and WebFlux make scaling easier — if used effectively.
4. Test Relentlessly: Stress test your application regularly to ensure it can handle unexpected traffic spikes.

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Final Thoughts

Scaling a Spring Boot application to handle millions of requests is challenging but absolutely achievable with the right approach. By focusing on both backend optimizations and infrastructure improvements, you can build a highly scalable and resilient system.

Have you faced similar scalability challenges? Share your experiences in the comments below — I’d love to hear how you solved them!

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#SpringBoot #Java #PerformanceOptimization #Scaling #TechLeadership