Top 5 Code-Level Techniques to Handle High Traffic in Spring Boot: Part 1

When your app goes viral or hits a major user milestone, there’s one thing you absolutely can’t afford: your APIs crashing.

Whether you’re building an e-commerce backend, a social platform, or a microservices-based system with Spring Boot, designing for peak load isn’t just a best practice — it’s essential.

The good news? You don’t need a massive budget or complex infrastructure to start preparing. Often, it begins with smart choices in your codebase.

In this two-part blog series, we’ll explore practical strategies to make your Spring Boot APIs resilient and performant under heavy traffic.

🧠 So What Is Peak Load and Why It Matters

Peak load is when your application receives an unusually high number of requests — like during sales, promotions, or trending events. If your app isn’t ready, users might see:

• ⛔️ 500 Internal Server Errors

• 🐢 Slow responses

• 🔄 Timeouts

🧱 The Core Strategy: Absorb, Redirect, and Recover

Think of your API system like a dam:

Absorb sudden spikes, redirect excess load, and recover quickly from overload.

Let’s break down the key components using the Spring Framework.

1. 🔌 Connection Pooling with Spring Boot

Every time your Spring application needs to interact with a database—whether it’s saving user data, retrieving product information, or running a report—it must establish a connection, perform the operation, and then close the connection. Creating and tearing down these connections repeatedly under high load introduces latency and exhausts database and system resources.

Connection pooling solves this by maintaining a set of pre-established connections that are reused across requests. There are a lot of popular connection pooling frameworks like Apache Commons DBCP, HikariCP, C3P0. With Spring Boot and HikariCP, the pool is initialized when the application starts, creating a ready-to-use pool of connections. When a request comes in, Spring borrows an available connection from the pool, performs the operation, and returns the connection to the pool instead of closing it. This greatly reduces overhead, lowers latency, and prevents the database from becoming a bottleneck during peak traffic.

Example with hikariCp:

spring:
  datasource:
    url: jdbc:mysql://localhost:3306/mydb
    username: root
    password: secret
    hikari:
      maximum-pool-size: 20 # Max number of connections in the pool
      minimum-idle: 5 #Min number of idle (ready) connections
      connection-timeout: 30000 #the maximum amount of time (in milliseconds) that a client (your Spring Boot application) will wait to get a connection from the pool.
      idle-timeout: 600000 #the maximum amount of time (in milliseconds) that a connection is allowed to sit idle in the pool before being closed.

➡️ Match pool size to the number of concurrent DB connections your app can handle efficiently.

2. 🚦 Rate Limiting to Control Abuse

If users or bots hit your API too often, they can bring your server down.

✅ Solution: Add Rate Limiting or Throttling

🚦 What is Rate Limiting?

Rate Limiting controls how many requests a client (user, IP, token, etc.) can make to your API within a specific time window.

🧠 Why Use Rate Limiting?

• Protects your app from abuse or misuse (e.g., brute-force attacks or API scraping).
• Keeps your backend and database healthy under high load.
• Ensures fair use across all users.

🔧 Example

„A user can call the /login API 5 times per minute.“

If the user exceeds that, they get a 429 Too Many Requests error.

🔁 What is Throttling?

Throttling is closely related to rate limiting. But while rate limiting blocks requests beyond a threshold, throttling may slow them down or queue them instead.

📌 Difference in a Nutshell

Use libraries like Bucket4j or resilience4j to implement rate limits per IP or user.

Example with resilience4j:

@RateLimiter(name = "productDetailRateLimiter")
public String fetchData() {
    return "Success!";
}

In application.yml:

resilience4j:
  ratelimiter:
    instances:
      productDetailRateLimiter:
        limitForPeriod: 100       # Allow 100 requests (customers)
        limitRefreshPeriod: 1s    # every 1 second
        timeoutDuration: 0s       # if full, immediately say "no"

      checkoutRateLimiter:
        limitForPeriod: 10        # Allow only 10 requests (customers)
        limitRefreshPeriod: 5s    # every 5 seconds (checkout is resource intensive)
        timeoutDuration: 2s       # if full, wait up to 2 seconds

3. 🗃️ Add Caching for Frequently Requested Data

APIs that serve the same data repeatedly — like product lists, configurations, or top-rated items — should avoid hitting the database every time. Caching helps improve response times and reduce load.

In Spring Boot, you can use Caffeine for fast in-memory (local) caching or Redis for distributed caching. Combining both gives you the best of both worlds:

• 🧠 Caffeine: Blazing-fast in-process memory cache
• 🌐 Redis: Shared cache across app instances (useful in cloud or clustered environments)

✅ Solution: Use Spring Cache with Caffeine + Redis

Step 1: Add Dependencies

In pom.xml:

<!-- Spring Cache Abstraction -->
<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-cache</artifactId>
</dependency>

<!-- Caffeine -->
<dependency>
    <groupId>com.github.ben-manes.caffeine</groupId>
    <artifactId>caffeine</artifactId>
</dependency>

<!-- Redis -->
<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-data-redis</artifactId>
</dependency>

Step 2: Enable Caching

@SpringBootApplication
@EnableCaching
public class Application {
    public static void main(String[] args) {
        SpringApplication.run(Application.class, args);
    }
}

Step 3: Use @Cacheable to Cache Methods

@Cacheable(cacheNames = "products")
public List<Product> getAllProducts() {
    return productRepository.findAll();
}

Step 4: Configure Cache in application.yml

spring:
  cache:
    type: redis

  redis:
    host: localhost
    port: 6379

  caffeine:
    spec: maximumSize=500,expireAfterWrite=5m

Step 5: Combine Caffeine (L1) + Redis (L2)

To set up Caffeine + Redis hybrid caching, define a custom CacheManager:

@Bean
public CacheManager cacheManager(RedisConnectionFactory redisConnectionFactory) {
    Caffeine<Object, Object> caffeine = Caffeine.newBuilder()
        .maximumSize(500)
        .expireAfterWrite(5, TimeUnit.MINUTES);

    CaffeineCacheManager caffeineCacheManager = new CaffeineCacheManager();
    caffeineCacheManager.setCaffeine(caffeine);

    RedisCacheManager redisCacheManager = RedisCacheManager.builder(redisConnectionFactory).build();

    CompositeCacheManager compositeCacheManager = new CompositeCacheManager(
        caffeineCacheManager,
        redisCacheManager
    );

    compositeCacheManager.setFallbackToNoOpCache(false);
    return compositeCacheManager;
}

👉This is ideal for applications needing fast local reads with distributed consistency.

4. ⏳ Async Processing with Queues

When your API needs to perform a heavy or time-consuming task — like sending emails, processing images, generating reports, or calling external services — doing it synchronously (i.e., within the request-response cycle) can slow things down or even cause timeouts during high traffic.

Instead, you can process these tasks asynchronously, freeing up your API to respond quickly.

✅ Solution 1: Use @Async for Fire-and-Forget Tasks

Spring Boot makes asynchronous method execution super easy with the @Async annotation.

Example:

@PostMapping("/send-email")
public ResponseEntity<String> sendEmail(@RequestBody EmailDto dto) {
    emailService.sendEmail(dto); // this is @Async
    return ResponseEntity.ok("Email scheduled");
}

• Responds fast, but if the app crashes before task completion, the work is lost (no durability)

✅ Solution 2: Using RabbitMQ for Queued Job

@PostMapping("/register")
public ResponseEntity<?> registerUser(@RequestBody User user) {
    userService.save(user);
    rabbitTemplate.convertAndSend("emailQueue", user.getEmail());
    return ResponseEntity.ok("User registered");
}

@RabbitListener(queues = "emailQueue")
public void sendEmail(String email) {
    // Send confirmation email
}

• It is durable, even after restart
• It decouples API from email logic

✅ Solution 3. Using Kafka for Logging or Events

@PostMapping("/checkout")
public ResponseEntity<?> checkout(@RequestBody Order order) {
    orderService.save(order);
    kafkaTemplate.send("order-events", new OrderEvent(order));
    return ResponseEntity.ok("Order placed");
}

• It can log events for analytics
• It is scalable under high load
• Async consumers can process downstream (e.g., inventory, invoice)

🧠 Why It Helps with High Traffic

• Reduces response time → Frees up API threads
• Avoids blocking on slow operations (email, DB writes, external APIs)
• Smooths traffic spikes via message queues
• Scales better with distributed consumers

🔄 Use Cases in High API Traffic

5. 🛑 Circuit Breakers with Resilience4j

When your API relies on external services like payment gateways, email providers, or third-party APIs, there’s always a risk that they might fail or become slow.

Under high traffic, repeated failed calls can lead to:

• Cascading failures
• Thread exhaustion
• Service-wide slowdowns

This is where the Circuit Breaker pattern shines. It helps your app fail fast, protect itself, and recover gracefully.

✅ What is a Circuit Breaker?

A circuit breaker monitors external calls and „opens the circuit“ if too many failures happen in a short time. This stops further attempts temporarily, giving the system time to recover.

• 🟢 Closed: Normal operation
• 🔴 Open: Calls are blocked immediately
• 🟡 Half-Open: Allows a few test calls to check recovery

✅ Solution: Use Resilience4j Circuit Breaker in Spring Boot

Add the dependency in pom.xml:

<dependency>
  <groupId>io.github.resilience4j</groupId>
  <artifactId>resilience4j-spring-boot2</artifactId>
</dependency>
<dependency>
  <groupId>org.springframework.boot</groupId>
  <artifactId>spring-boot-starter-aop</artifactId>
</dependency>

🔧 Example Usage

@CircuitBreaker(name = "paymentService", fallbackMethod = "fallbackPayment")
public PaymentResponse chargeCard(String userId) {
    return paymentClient.charge(userId); // external API call
}

If the call fails repeatedly, the circuit „opens“ and the fallback method is triggered:

public PaymentResponse fallbackPayment(String userId, Throwable ex) {
    return new PaymentResponse("Payment service unavailable");
}

⚙️ Configure Circuit Breaker in application.yml

resilience4j.circuitbreaker:
  instances:
    paymentService:
      registerHealthIndicator: true
      slidingWindowSize: 10 
      failureRateThreshold: 50 #If more than 5 out of 10 calls fail, open the circuit

      waitDurationInOpenState: 30s #Stay open for 30 seconds before allowing test calls again

🧠 When to Use Circuit Breakers

🏁 Conclusion

Controlling high traffic isn’t just about throwing hardware at the problem — it starts with writing efficient, resilient code. In this post, we explored essential code-level strategies to prepare your Spring Boot APIs for peak load:

• 🔌 Connection pooling to avoid DB overload
• 🚦 Rate limiting to protect endpoints from abuse
• 🗃️ Caching with Caffeine (and Redis) to serve repeated requests faster
• ⏳ Async processing to offload heavy background tasks
• 🛑 Circuit breakers to prevent cascading failures from unstable dependencies

Each of these techniques helps your application stay responsive, even when traffic spikes or dependencies slow down.

👀 What’s Next?

Code-level techniques take you far, but without the right infrastructure, you’re still at risk.

Stay tuned for Part 2: Infrastructure-Level Strategies for Handling High Traffic in Spring Boot APIs.