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Nikhil Amin
Nikhil Amin

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CH-06: The Echo of Events — Jai & Veeru vs. The Microservices Mayhem

#java #programming

It was a peaceful morning — until it wasn’t.

Jai and Veeru had just pushed their latest update to production when alerts started flooding in.

🚨 Service Down! 🚨
🚨 Database Overload! 🚨
🚨 API Latency Spike! 🚨

Veeru panicked. “What the hell is happening?! We just deployed a small feature!”

Jai grabbed his coffee. “Looks like we triggered a microservices meltdown…”

Their system, which relied on REST APIs for communication, was now buckling under high traffic. The database was overwhelmed with requests, leading to timeouts, bottlenecks, and service failures.

Veeru groaned. “Jai… is this how big tech companies handle millions of users?”

Jai smirked. “Not even close. It’s time to introduce you to event-driven microservices.”

The Problem: REST-Based Microservices Can’t Handle Scale

Veeru scratched his head. “But we already have a microservices architecture. Why is it failing?”

Jai explained, _“Our services communicate synchronously using REST APIs. But what happens when:

  • ✅ Traffic surges?
  • ✅ A service fails?
  • ✅ Requests pile up faster than they can be processed?”

Veeru nodded. “It creates a domino effect, slowing everything down!”_

Jai pulled up a diagram.

🛑 Current REST-Based Flow:

  • 1️⃣ User sends a request to Service A.
  • 2️⃣ Service A calls Service B synchronously.
  • 3️⃣ Service B queries the database, which is already overloaded.
  • 4️⃣ Response takes too long → User faces a timeout.

“This,” Jai said, pointing to the mess on the screen, “is why big tech companies don’t rely on REST for everything.”

Veeru sighed. “So, what’s the solution?”

Jai grinned. “We decouple services using Apache Kafka.”

The Fix: Event-Driven Microservices with Kafka

Solution: Replacing REST Calls with Kafka Events

With Kafka, instead of services waiting on each other, they communicate asynchronously using an event-driven approach.

How It Works:

  • ✅ Service A publishes an event to Kafka (instead of calling Service B directly).
  • ✅ Kafka stores and streams the event to subscribers (Service B, C, etc.).
  • ✅ Service B processes the event when it’s ready, not when forced.

Veeru’s eyes lit up. “That means no blocking and no direct dependencies!”

Jai nodded. “Exactly! Now, if a service crashes, the events stay in Kafka until it recovers. No data loss!”

Implementing Kafka in Spring Boot

Jai opened his code editor. “Let’s convert our system to event-driven microservices.”

Step 1: Add Kafka Dependencies

<dependency>
    <groupId>org.springframework.kafka</groupId>
    <artifactId>spring-kafka</artifactId>
    <version>3.0.0</version>
</dependency>
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Step 2: Create a Kafka Producer (Publishing Events)

@Service
public class OrderEventProducer {

    private final KafkaTemplate<String, String> kafkaTemplate;

    public OrderEventProducer(KafkaTemplate<String, String> kafkaTemplate) {
        this.kafkaTemplate = kafkaTemplate;
    }

    public void sendOrderEvent(String orderId) {
        kafkaTemplate.send("order-events", orderId);
    }
}
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Step 3: Create a Kafka Consumer (Processing Events)

@Service
public class OrderEventConsumer {

    @KafkaListener(topics = "order-events", groupId = "order-group")
    public void consume(String orderId) {
        System.out.println("Processing order: " + orderId);
    }
}
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Veeru whistled. “That’s it? No more synchronous API calls?”

Jai grinned. “Nope. Now our services talk to each other through Kafka. No more timeouts, database overload, or cascading failures.”

The Transformation: Before vs. After Kafka

BEFORE (REST-Based Microservices):

  • ⏳ Services block while waiting for responses.
  • ⚠️ If one service fails, the whole chain collapses.
  • 📈 High traffic = database overload = system failure.

AFTER (Event-Driven with Kafka):

  • ⚡ Services communicate asynchronously.
  • ✅ Failures don’t break the whole system.
  • 📊 System auto-scales with high traffic.

The Mystery Deepens…

Just as they were about to celebrate, another alert popped up.

🚨 Unauthorized Kafka Topic Detected: “shadow-events” 🚨

Veeru’s stomach dropped. “Jai… did we create that topic?”

Jai quickly checked the logs. Unknown events were flowing through Kafka — events that neither of them had written.

Veeru gulped. “Could someone be injecting rogue events into our system?”

Jai’s expression darkened. “Or worse… have we just uncovered a Kafka security breach?”

📖 To be continued… 🚀

💡Key Takeaways from This Chapter

✔ Microservices should be decoupled — REST isn’t scalable for high-traffic systems.
✔ Use Apache Kafka — It enables asynchronous event-driven communication.
✔ Event Streams Reduce Failures — If a service crashes, Kafka retains the event for processing later
✔ Spring Boot + Kafka = Scalable Microservices — Build resilient architectures that handle millions of users.

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