Interview question

What is Apache Kafka and how does Spring Boot integrate with it? Apache Kafka क्या है और Spring Boot इससे कैसे integrate करता है?

Answer

Apache Kafka is a distributed event streaming platform for building real-time data pipelines. Spring Boot integrates via Spring Cloud Stream or spring-kafka, enabling producer/consumer patterns for high-throughput, fault-tolerant message processing.

// Maven dependency
// spring-kafka or spring-cloud-stream-kafka

// Kafka Producer
@Service
public class KafkaProducer {
    @Autowired
    private KafkaTemplate<String, String> kafkaTemplate;
    
    public void sendMessage(String topic, String message) {
        kafkaTemplate.send(topic, message)
            .addCallback(
                result -> System.out.println('Sent: ' + message),
                ex -> System.err.println('Error: ' + ex.getMessage())
            );
    }
}

// Kafka Consumer
@Service
public class KafkaConsumer {
    @KafkaListener(topics = 'orders', groupId = 'order-service')
    public void consumeMessage(String message) {
        System.out.println('Received: ' + message);
        // Process message
    }
}

// With Spring Cloud Stream
@Configuration
public class KafkaStreamConfig {
    @Bean
    public Function<String, String> process() {
        return input -> {
            System.out.println('Processing: ' + input);
            return input.toUpperCase();
        };
    }
}

// Kafka concepts:
// Topic - category of events
// Partition - parallel processing unit
// Consumer Group - multiple consumers for same topic
// Offset - position in partition
// Replication - fault tolerance

// Key features:
// 1. High throughput - millions of messages/sec
// 2. Durability - persisted to disk
// 3. Scalability - add brokers dynamically
// 4. Stream processing - Kafka Streams API
// 5. Exactly-once semantics - no duplicate processing

Apache Kafka:

Kafka Producer:
kafkaTemplate.send(topic, message);

Kafka Consumer:
@KafkaListener(topics = 'name', groupId = 'id')
public void consume(String msg) { }

Key Concepts:
1. Topic - event categories
2. Partition - parallelism
3. Consumer Group - multiple consumers
4. Offset - message position
5. Replication - fault tolerance

Features:
- High throughput
- Persistent storage
- Distributed
- Stream processing
- Exactly-once delivery

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