Kafka Integration

@KafkaListener(topics = "orders", groupId = "order-group")
public void consume(String message) {
  System.out.println(message);
}

import org.springframework.kafka.annotation.KafkaListener;
import org.springframework.kafka.core.KafkaTemplate;
import org.springframework.web.bind.annotation.*;
import org.springframework.stereotype.Service;

@RestController
@RequestMapping("/kafka")
class KafkaController {

  private final KafkaProducer producer;

  public KafkaController(KafkaProducer producer) {
    this.producer = producer;
  }

  @GetMapping("/send")
  public String sendMessage(@RequestParam String msg) {
    producer.sendMessage(msg);
    return "Message sent to Kafka";
  }
}

@Service
class KafkaProducer {

  private final KafkaTemplate<String, String> kafkaTemplate;

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

  public void sendMessage(String msg) {
    kafkaTemplate.send("orders", msg);
  }
}

@Service
class KafkaConsumer {

  @KafkaListener(topics = "orders", groupId = "order-group")
  public void consume(String message) {
    System.out.println("Received: " + message);
  }
}

// application.properties
// spring.kafka.bootstrap-servers=localhost:9092
// spring.kafka.consumer.group-id=order-group

// Output:
// /kafka/send -> Message sent to Kafka
// Consumer prints received message

• 1 Distributed event streaming platform.
• 2 Used for real-time data pipelines.
• 3 Supports publish-subscribe model.
• 4 Highly scalable and fault-tolerant.

• 1 Producer – sends messages
• 2 Consumer – reads messages
• 3 Topic – message channel
• 4 Broker – Kafka server

• 1 Producer sends message to topic.
• 2 Kafka stores message.
• 3 Consumer reads message.
• 4 Processing happens asynchronously.

• 1 High throughput messaging.
• 2 Scalable architecture.
• 3 Decouples microservices.
• 4 Real-time processing.

• 1 Used in microservices communication.
• 2 Used in real-time data pipelines.
• 3 Used in event-driven systems.
• 4 Used in logging and analytics systems.
• 5 SaaS products use Kafka Integration in Spring Boot in services, dashboards, background jobs, and API workflows.
• 6 ERP and banking systems apply Kafka Integration in Spring Boot with validation, logging, review, and rollback plans.
• 7 E-commerce and healthcare platforms use Kafka Integration in Spring Boot carefully because reliability and data correctness matter.

• 1 A Java program first evaluates the surrounding context, then applies the Kafka Integration in Spring Boot rules to the current data.
• 2 The important mental model is input, transformation, result, and failure path.
• 3 In production, the same flow usually sits inside a larger layer such as a controller, service, repository, job, or UI component.

• 1 Choose the simplest implementation first, then measure real workloads.
• 2 Watch for repeated work inside loops, unnecessary allocations, and slow I/O in hot paths.
• 3 Prefer clear data structures and stable APIs before micro-optimizing syntax.

• 1 Treat external input as untrusted until it is validated.
• 2 Avoid hardcoded secrets and never print sensitive values in examples or logs.
• 3 Use established libraries for authentication, encryption, parsing, and database access.

• 1 Not configuring Kafka brokers correctly.
• 2 Ignoring consumer group design.
• 3 Not handling message failures.
• 4 Overloading single topic.
• 5 Skipping the small working example before adding framework code.
• 6 Ignoring null, empty, duplicate, and boundary inputs.
• 7 Mixing business logic, input handling, and output formatting in one place.
• 8 Using broad error handling that hides the real failure.
• 9 Forgetting to test the behavior after refactoring.
• 10 Adding clever code that future maintainers will struggle to read.

• 1 Use separate topics for different events.
• 2 Design proper consumer groups.
• 3 Enable retry and error handling.
• 4 Monitor Kafka lag and throughput.
• 5 Start with clear requirements and one minimal working example.
• 6 Use meaningful names that explain business intent.
• 7 Keep examples small enough to debug line by line.
• 8 Validate input at every trust boundary.
• 9 Handle errors explicitly and preserve useful context.
• 10 Prefer simple control flow over deeply nested logic.
• 11 Separate domain logic from I/O and framework code.
• 12 Write tests for normal, boundary, and failure cases.
• 13 Review security assumptions before production use.
• 14 Measure performance before optimizing.
• 15 Document non-obvious decisions close to the code or in project notes.
• 16 Use official documentation when behavior is version-specific.
• 17 Keep dependencies current and remove unused code.
• 18 Avoid hardcoded secrets, credentials, and environment-specific paths.
• 19 Log operational events without exposing sensitive data.
• 20 Design examples so learners can safely modify and rerun them.

• 1 Beginner: rewrite the example with different names and values.
• 2 Intermediate: add validation and handle one expected failure case.
• 3 Advanced: place Kafka Integration in Spring Boot inside a small service-style design with tests.

• 1 Build a small Java console feature that demonstrates Kafka Integration in Spring Boot.
• 2 Accept input, process it with the concept, print a clear result, and handle invalid input.
• 3 Add a README note explaining the design choice and two edge cases you tested.

• 1 If the program does not compile, check spelling, imports, braces, and file/class names first.
• 2 If output is unexpected, print intermediate values and verify each branch of the logic.
• 3 If the design feels complex, reduce it to the smallest working example and add pieces back one at a time.

• 1 Practice Kafka Integration in Spring Boot with a second example from a business domain such as inventory, payroll, banking, or e-commerce.
• 2 Review related Java topics that cover data flow, error handling, testing, and clean design.
• 3 Compare your solution with official documentation and simplify anything you cannot explain clearly.