Create a parallel consumer for Kafka in a Java Spring Boot application,

To create a parallel consumer for Apache Kafka in a Java Spring Boot application, you can utilize the Spring Kafka library and leverage the ConcurrentKafkaListenerContainerFactory provided by Spring Kafka. Here's an example:

1. Add Dependencies: Start by adding the necessary dependencies to your pom.xml file.

<dependencies>
    <!-- Other dependencies -->
    <dependency>
        <groupId>org.springframework.kafka</groupId>
        <artifactId>spring-kafka</artifactId>
    </dependency>
</dependencies>

2. Configure Kafka Consumer: Create a configuration class, e.g., KafkaConsumerConfig.java, to configure the Kafka consumer properties.

import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.kafka.annotation.EnableKafka;
import org.springframework.kafka.config.ConcurrentKafkaListenerContainerFactory;
import org.springframework.kafka.core.ConsumerFactory;
import org.springframework.kafka.core.DefaultKafkaConsumerFactory;

import java.util.HashMap;
import java.util.Map;

@Configuration
@EnableKafka
public class KafkaConsumerConfig {

    // Configure Kafka consumer properties
    @Bean
    public ConsumerFactory<String, String> consumerFactory() {
        Map<String, Object> props = new HashMap<>();
        props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "your-kafka-bootstrap-servers");
        props.put(ConsumerConfig.GROUP_ID_CONFIG, "your-consumer-group-id");
        props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringDeserializer");
        props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringDeserializer");
        // Additional consumer properties

        return new DefaultKafkaConsumerFactory<>(props);
    }

    // Configure the listener container factory for parallel consumption
    @Bean
    public ConcurrentKafkaListenerContainerFactory<String, String> kafkaListenerContainerFactory() {
        ConcurrentKafkaListenerContainerFactory<String, String> factory = new ConcurrentKafkaListenerContainerFactory<>();
        factory.setConsumerFactory(consumerFactory());
        factory.setConcurrency(3); // Set the number of consumer threads

        return factory;
    }
}

In the above configuration, you set up the necessary Kafka consumer properties such as bootstrap servers, group ID, and deserializers. Then, you configure the ConcurrentKafkaListenerContainerFactory to control the concurrency level of the consumer. In this example, factory.setConcurrency(3) indicates that three consumer threads will be created to process Kafka messages in parallel.

3. Create Kafka Consumer: Create a Kafka consumer component that listens to Kafka topics and processes messages.

import org.springframework.kafka.annotation.KafkaListener;
import org.springframework.stereotype.Component;

@Component
public class KafkaConsumer {

    @KafkaListener(topics = "your-topic-name")
    public void consume(String message) {
        // Process the Kafka message
        System.out.println("Received message: " + message);
    }
}

In this example, the KafkaConsumer component listens to the specified Kafka topic using the @KafkaListener annotation. The consume method processes the received messages. You can customize the method to perform your desired business logic.

4. Run the Application: Run your Spring Boot application, and the Kafka consumer will start listening to the Kafka topic with the specified concurrency level. Messages from the topic will be processed in parallel by the consumer threads.

By configuring the ConcurrentKafkaListenerContainerFactory with the desired concurrency level, you can achieve parallel consumption of Kafka messages in a Java Spring Boot application. Adjust the concurrency value based on your application's requirements and the capacity of your Kafka cluster.

Keep Coding….