When to Use Kafka: A Short, Detailed Guide for Developers

Ever wondered how apps handle billions of requests : like orders or clicks without crashing? Meet Apache Kafka, the super-fast train station powering big systems! In this blog series, we will do deep dive into Kafka and why it’s a rockstar for Spring Boot apps, and when to use it (or stick with simpler tools like REST APIs ).

What Is Apache Kafka?

Apache Kafka is an open-source distributed event streaming platform.

In simpler words:

• It’s like a giant messaging system for your apps.
• It lets applications publish (send) and subscribe (receive) data streams in real time.
• It’s designed to handle massive amounts of data , millions of events per second.

Think of Kafka as a central nervous system for your organization’s data: everything flows through it, and multiple systems can listen and act on those events simultaneously.

Core Concepts

• Producer → An app that sends messages (e.g., “new order placed”).
• Consumer → An app that listens to messages (e.g., “payment service processes order”).
• Topic → A channel where messages are published.
• Broker → A Kafka server that stores and serves the messages.

When Kafka is Awesome (Use It!)

Kafka is like a superhero for specific situations in your Spring Boot app. Here’s when it’s the perfect tool:

1. High-Volume Data (Lots of Messages)
   If your app handles thousands or millions of messages (like user clicks, orders, or logs), Kafka can process them super fast. It’s built to handle massive data streams without slowing down.
   Example: An e-commerce app gets 10,000 orders during a sale. Your Spring Boot app sends orders to a Kafka topic, and multiple services (inventory, payments, analytics) process them in parallel without crashing.
2. Event-Driven Systems (Real-Time Action)
   If you want your app to react to events instantly (like notifying users or updating dashboards), Kafka’s topics let multiple services listen for events in real time.
   Example: When a user places an order, your Spring Boot app sends a message to the “orders” topic. The payment service, email service, and analytics service all listen (using @KafkaListener) and act immediately.
3. Microservices Communication (Decoupling)
   In a microservices setup, where you have multiple Spring Boot apps (e.g., one for orders, one for payments), Kafka lets them talk without directly calling each other’s REST APIs. This makes your system more flexible and reliable.
   Example: Your order service sends a message to a Kafka topic, and the payment service picks it up later, even if one service is temporarily down.
4. Asynchronous Processing (Speedy Responses)
   If your app needs to feel fast for users, Kafka lets you send messages and move on without waiting for processing. This is great for tasks like sending emails or updating databases.
   Example: Your Spring Boot app sends an “order placed” message to Kafka and tells the user “Order received!” instantly. A consumer processes the order in the background.
5. Reliability and Fault Tolerance
   Kafka stores messages until they’re processed, so nothing gets lost if a service crashes. It also supports retries (@RetryableTopic) and dead letter topics (@DltHandler) for handling errors.
   Example: If your payment processor is down, Kafka holds the order messages safely until it’s back online.
6. Data Streaming and Analytics
   If you need to analyze data in real time (e.g., tracking user behavior or sales trends), Kafka can stream messages to analytics tools.
   Example: Your app sends user clicks to a Kafka topic, and a consumer feeds them into a dashboard for live analytics.

When Kafka is Overkill (Skip It!)

Kafka is powerful, but it’s not always the best choice. Sometimes, simpler tools like REST APIs, a database, or a message queue like RabbitMQ are better. Here’s when Kafka might be too much:

1. Small, Simple Apps (Low Traffic)
   If your Spring Boot app handles just a few requests per day (e.g., a small internal tool), Kafka’s setup is like building a huge train station for a tiny toy shop. A REST API or database is easier.
   Example: If your app just lets users save a few records to a database, a simple @RestController and JPA repository are enough.
2. Synchronous Needs (Immediate Responses)
   If your app needs an immediate response (e.g., “Is this user’s payment valid?”), REST APIs are better because they give instant answers. Kafka is asynchronous, so it’s not ideal for real-time request-response.
   Example: For a login system checking credentials, a REST API call to a user service is simpler than using Kafka.
3. Simple Workflows (No Complex Communication)
   If your app does everything in one place (e.g., takes an order and saves it to a database), Kafka’s messaging system adds unnecessary complexity.
   Example: A to-do list app that stores tasks in a database doesn’t need Kafka, just use Spring Data JPA.
4. Limited Resources (Setup and Maintenance)
   Kafka requires servers (brokers, KRaft, etc.) and configuration, which can be a hassle for small teams or simple projects. Setting up Kafka in a cloud like AWS (with EC2 instances) or even locally takes time and money.
   Example: If you’re a solo developer building a prototype, a simple REST API or database is faster to set up than Kafka.
5. Low Fault Tolerance Needs
   If losing a few messages isn’t a big deal (e.g., non-critical logs), Kafka’s robust storage and retry features might be more than you need.
   Example: If your app logs user actions for debugging and it’s okay to miss a few, a simple logging library is enough.

Here’s a quick table comparing Kafka to REST APIs, databases, and RabbitMQ for common use case scenarios.

So that’s all about Kafka, when to use it and when not to. In next blog we will do deep dive into internals of kafka.

Thanks for reading till here, hope you have got a understanding of use case when kafka should be used. This is based on my learnings and experience. Please feel free to share your feedbacks if any. See you in next blog. Till then keep learning, keep upskilling!