Handle Millions of Concurrent Requests

 

How to Handle Millions of Concurrent Requests in Spring

You can’t serve millions of users from a single Spring Boot app. You need to design the whole system for scalability, fault tolerance, and speed. Here’s a clear breakdown:

1. Application Layer

  • Spring WebFlux (with Netty)

    • Use this instead of Spring MVC (Tomcat/Jetty).

    • WebFlux is non-blocking and event-driven, so one thread can handle thousands of requests.

    • Best choice for high concurrency APIs (chat apps, streaming, IoT, real-time dashboards).

  • Spring MVC (with Tomcat/Jetty)

    • Uses thread-per-request model.

    • Easier for CRUD apps, but won’t scale well for millions of concurrent users.

👉 Use WebFlux if scaling is your priority.

2. Scalability & Deployment

  • Kubernetes / Docker

    • Run many small Spring Boot app instances in containers.

    • Scale up/down automatically based on traffic.

  • Load Balancer (NGINX / AWS ALB / GCP Load Balancer)

    • Distributes requests across app instances.

    • Prevents one instance from being overloaded.

👉 Horizontal scaling (more instances) is how you handle millions.

3. Data Layer

  • Database (PostgreSQL / MySQL / Cassandra / MongoDB)

    • Use primary DB for writes.

    • Use read replicas for queries.

    • Shard data if dataset is huge.

  • Connection Pool (HikariCP)

    • Manages DB connections efficiently.

    • Avoids opening/closing DB connections per request.

  • Caching (Redis / Memcached / Caffeine)

    • Store frequently accessed data in memory.

    • Reduces load on DB.

    • Example: Cache user profiles, product catalog, sessions.

4. Async & Messaging

  • Message Broker (Kafka / RabbitMQ / Pulsar)

    • Offload heavy tasks (emails, payments, notifications) from the main request.

    • Handles traffic spikes without slowing down the API.

👉 User request = quick response → background job does the heavy work.

5. Network Optimizations

  • API Gateway (Spring Cloud Gateway / Kong / NGINX)

    • Central entry point for APIs.

    • Provides rate limiting, request routing, security.

  • CDN (Cloudflare / AWS CloudFront / Akamai)

    • Deliver static files (images, JS, CSS) from nearest location to the user.

    • Reduces latency.

6. Reliability & Monitoring

  • Resilience4j (Circuit Breakers, Rate Limiting)

    • Prevents cascading failures when DB or external APIs slow down.

  • Monitoring (Micrometer + Prometheus + Grafana)

    • Track requests, failures, latency, DB usage, queue size.

  • Logging (ELK Stack or OpenSearch)

    • Centralize logs for debugging under heavy load.


⚡ Suggested Tech Stack for Millions of Requests

Layer

Technology

Why

Framework

Spring Boot + WebFlux

Non-blocking, scalable APIs

Container & Orchestration

Docker + Kubernetes

Horizontal scaling & deployment

Load Balancer

NGINX / AWS ALB / GCP LB

Distribute traffic evenly

Database

PostgreSQL/MySQL (with replicas) or Cassandra (for massive scale)

Reliable data storage

Connection Pool

HikariCP

Efficient DB connections

Cache

Redis / Memcached

Fast in-memory access, reduce DB load

Message Queue

Kafka / RabbitMQ

Async tasks, event-driven

API Gateway

Spring Cloud Gateway / Kong

Routing, rate limiting, security

Monitoring

Micrometer + Prometheus + Grafana

Metrics and alerts

Logging

ELK Stack / OpenSearch

Centralized logs

CDN

Cloudflare / CloudFront

Fast static content delivery


In short:

  • Use Spring WebFlux for APIs.

  • Deploy on Kubernetes with auto-scaling.

  • Use Redis for caching and Kafka for async processing.

  • Store data in PostgreSQL/MySQL with replicas (or Cassandra if extremely large).

  • Protect with API Gateway + Resilience4j.

  • Monitor everything with Prometheus/Grafana.

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