Monitoring MongoDB Changes with Spring Boot and Notifying a Kafka Topic

Real Life! :D

Recently, a challenge landed on my desk that made me rethink some architectural choices. The problem? We needed to monitor changes in MongoDB collections triggered by different sources and notify other services efficiently—without creating tight coupling between them.

At first, it seemed like a simple problem. “Just listen to the database, right?” But when you have multiple services modifying data, different update triggers, and the need to propagate changes without creating unnecessary dependencies, things get messy fast.

🚀 🚀 🚀 🚀 🚀 The solution that i always have in my pocket! 🚀 🚀 🚀 🚀 🚀

🔍 How Does It Work?

Spring Data MongoDB provides a feature called Change Streams, allowing us to listen to database events without manual polling. We’ll set up a Listener to detect changes in an Address entity and notify a Kafka topic.

So.. Lets code!!!! :D

🚀 Implementation

1️⃣ Dependencies in pom.xml

Add the required dependencies for Spring Boot, Spring Data MongoDB, and Kafka:

2️⃣ Creating the Address Entity

This entity will be stored in MongoDB and monitored by the Listener:

3️⃣ Implementing the MongoDB Change Listener

Now, let’s create a Change Stream Listener to detect changes in the addresses collection:

4️⃣ Configuring Kafka

In the application.yml, configure the Kafka connection:

📌 How It Works?

1. The AddressChangeListener starts a Change Stream in MongoDB to monitor the addresses collection.

2. When a document is updated, it captures the modified fields.

3. The event is then published to a Kafka topic, which can be consumed by other services.

🎯 Benefits

✅ High Performance: Change Streams avoid unnecessary database polling.

✅ Low Latency: Updates are detected almost in real-time.

✅ Scalability: Kafka ensures reliable event delivery.

✅ Reactive Architecture: Enables asynchronous communication between microservices.

📢 Conclusion

Integrating MongoDB Change Streams with Spring Boot and Kafka allows for efficient and scalable event-driven pipelines. This approach is ideal for event-driven systems, ensuring that database changes are instantly propagated to other services.

If you’re looking to elevate your architecture, this combination is a great starting point! 🚀