springcloud kafka如何配置消息保留策略

在 Spring Cloud Kafka 中，可以通過配置 KafkaListenerEndpointRegistrar 和 KafkaMessageConverter 來實現消息保留策略。以下是一個簡單的示例：

1. 首先，在 application.yml 或 application.properties 文件中配置 Kafka 相關的屬性：

spring:
  cloud:
    kafka:
      bootstrap-servers: localhost:9092
      consumer:
        group-id: my-group
        auto-offset-reset: earliest
        key-deserializer: org.apache.kafka.common.serialization.StringDeserializer
        value-deserializer: org.apache.kafka.common.serialization.StringDeserializer
      producer:
        key-serializer: org.apache.kafka.common.serialization.StringSerializer
        value-serializer: org.apache.kafka.common.serialization.StringSerializer

2. 創建一個配置類，用于配置 KafkaListenerEndpointRegistrar 和 KafkaMessageConverter：

import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.common.serialization.StringDeserializer;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.kafka.annotation.KafkaListenerConfigurer;
import org.springframework.kafka.config.ConcurrentKafkaListenerContainerFactory;
import org.springframework.kafka.config.KafkaListenerEndpointRegistrar;
import org.springframework.kafka.config.MethodKafkaListenerEndpoint;
import org.springframework.kafka.core.ConsumerFactory;
import org.springframework.kafka.core.DefaultKafkaConsumerFactory;
import org.springframework.kafka.core.DefaultKafkaProducerFactory;
import org.springframework.kafka.core.KafkaTemplate;
import org.springframework.kafka.core.ProducerFactory;
import org.springframework.kafka.listener.ConcurrentMessageListenerContainer;
import org.springframework.kafka.listener.config.MethodKafkaListenerEndpointRegistrar;
import org.springframework.kafka.listener.config.MethodKafkaListenerEndpointRegistry;
import org.springframework.kafka.support.serializer.ErrorHandlingDeserializer;
import org.springframework.kafka.support.serializer.JsonDeserializer;
import org.springframework.kafka.support.serializer.StringDeserializer;
import org.springframework.kafka.support.serializer.ErrorHandlingDeserializer;
import org.springframework.kafka.support.serializer.JsonDeserializer;

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

@Configuration
public class KafkaConfig implements KafkaListenerConfigurer {

    @Value("${spring.kafka.bootstrap-servers}")
    private String bootstrapServers;

    @Bean
    public ConcurrentKafkaListenerContainerFactory<String, String> kafkaListenerContainerFactory() {
        ConcurrentKafkaListenerContainerFactory<String, String> factory = new ConcurrentKafkaListenerContainerFactory<>();
        factory.setConsumerFactory(consumerFactory());
        factory.setProducerFactory(producerFactory());
        return factory;
    }

    @Bean
    public ConsumerFactory<String, String> consumerFactory() {
        Map<String, Object> props = new HashMap<>();
        props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapServers);
        props.put(ConsumerConfig.GROUP_ID_CONFIG, "my-group");
        props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
        props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
        return new DefaultKafkaConsumerFactory<>(props);
    }

    @Bean
    public ProducerFactory<String, String> producerFactory() {
        Map<String, Object> props = new HashMap<>();
        props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapServers);
        props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
        props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
        return new DefaultKafkaProducerFactory<>(props);
    }

    @Bean
    public KafkaTemplate<String, String> kafkaTemplate() {
        return new KafkaTemplate<>(producerFactory());
    }

    @Override
    public void configureKafkaListeners(KafkaListenerEndpointRegistrar registrar) {
        MethodKafkaListenerEndpointRegistry registry = new MethodKafkaListenerEndpointRegistry();
        registrar.setEndpointRegistries(registry);
        registry.register(messageListenerEndpoint());
    }

    @Bean
    public MethodKafkaListenerEndpoint<String, String> messageListenerEndpoint() {
        MethodKafkaListenerEndpoint<String, String> endpoint = new MethodKafkaListenerEndpoint<>();
        endpoint.setId("myMessageListener");
        endpoint.setTopics("my-topic");
        endpoint.setMessageHandlerMethodFactory(kafkaListenerContainerFactory().getMessageHandlerMethodFactory());
        return endpoint;
    }
}

3. 在你的服務類中，使用 @KafkaListener 注解來監聽特定的主題：

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

@Service
public class MyKafkaConsumer {

    @KafkaListener(id = "myMessageListener", topics = "my-topic")
    public void listen(String message) {
        System.out.println("Received message: " + message);
    }
}

在這個示例中，我們配置了一個簡單的消費者，它會監聽名為 “my-topic” 的主題。Kafka 會根據消息保留策略將消息存儲在本地磁盤上，直到它們被消費或過期。具體的保留策略取決于你的 Kafka 配置，例如 log.retention.hours、log.retention.bytes 或 log.retention.ms 等屬性。