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6.异常处理

来源参考的deepseek，如有侵权联系立删

1.异常分类与处理原则

异常类型	典型场景	处理建议
可恢复异常	网络抖动、数据库锁冲突	重试机制（有限次数 + 退避策略）
不可恢复异常	消息格式错误、权限不足	直接记录日志并进入死信队列
事务异常	事务超时、生产者ID冲突	终止事务并回滚操作

2.生产者异常处理

1. 同步发送捕获异常

public void sendSync(String topic, String message) {try {ListenableFuture<SendResult<String, String>> future = kafkaTemplate.send(topic, message);future.get(5, TimeUnit.SECONDS); // 阻塞等待结果} catch (InterruptedException | ExecutionException | TimeoutException e) {// 记录日志并触发补偿逻辑log.error("消息发送失败: {}", e.getMessage());throw new BusinessException("消息发送失败", e);}
}

2. 异步发送回调处理

public void sendAsync(String topic, String message) {kafkaTemplate.send(topic, message).addCallback(result -> {// 发送成功处理log.info("消息发送成功: topic={}", result.getRecordMetadata().topic());},ex -> {// 发送失败处理log.error("消息发送失败", ex);if (ex instanceof RetriableException) {// 可重试异常（如网络问题）retrySend(topic, message);} else {// 不可重试异常（如消息过大）deadLetterService.saveToDlq(topic, message);}});
}

3.消费者异常处理

1.全局异常处理器

@Configuration
public class KafkaGlobalErrorConfig {// 定义全局错误处理器（支持批量/单消息模式）@Beanpublic CommonErrorHandler globalErrorHandler(KafkaTemplate<String, Object> template) {// 重试策略：3次重试，间隔5秒DefaultErrorHandler handler = new DefaultErrorHandler(new DeadLetterPublishingRecoverer(template), // 死信队列恢复器new FixedBackOff(5000L, 3));// 指定可重试异常类型handler.addRetryableExceptions(NetworkException.class);handler.addNotRetryableExceptions(SerializationException.class);// 偏移量提交策略handler.setCommitRecovered(true);return handler;}// 容器工厂绑定全局处理器@Beanpublic ConcurrentKafkaListenerContainerFactory<String, Object> kafkaListenerContainerFactory(ConsumerFactory<String, Object> consumerFactory,CommonErrorHandler globalErrorHandler) {ConcurrentKafkaListenerContainerFactory<String, Object> factory =new ConcurrentKafkaListenerContainerFactory<>();factory.setConsumerFactory(consumerFactory);factory.setCommonErrorHandler(globalErrorHandler);return factory;}
}

2.方法级处理

import lombok.extern.slf4j.Slf4j;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.kafka.annotation.KafkaListener;
import org.springframework.kafka.listener.ConsumerAwareListenerErrorHandler;
import org.springframework.kafka.support.KafkaHeaders;
import org.springframework.kafka.support.serializer.DeserializationException;@Slf4j
@Configuration
public class KafkaExceptionConfig {/*** 自定义异常处理器*/@Beanpublic ConsumerAwareListenerErrorHandler orderErrorHandler() {return (message, exception, consumer) -> {// 业务相关错误处理（如库存不足）/*   if (exception instanceof InventoryException) {retryService.scheduleRetry(message.getPayload());}*/System.out.println("异常执行:"+exception);return null;};}/*** 注册全局异常处理器*/@Beanpublic ConsumerAwareListenerErrorHandler globalExceptionHandler() {return (message, exception, consumer) -> {log.error("捕获消费异常: topic={}, message={}",message.getHeaders().get(KafkaHeaders.RECEIVED_TOPIC),message.getPayload(),exception);// 反序列化异常特殊处理if (exception.getCause() instanceof DeserializationException) {// 跳过错消息并提交偏移量return null;}throw exception; // 其他异常继续抛出};}}

    @KafkaListener(topics = "test", groupId = "spring-group",errorHandler = "globalExceptionHandler")public void listenBatch(List<String> messages, Acknowledgment ack) {messages.forEach(msg -> System.out.println("批量消息：" + msg));//异常测试int i = 1/0;ack.acknowledge();}

3.重试yml配置

spring:kafka:listener:retry:max-attempts: 3               # 最大重试次数backoff:initial-interval: 1000     # 初始间隔（毫秒）multiplier: 2.0            # 间隔倍数exclude-exceptions:          # 不重试的异常- javax.validation.ValidationException

4.死信队列（DLQ）配置

1. 启用死信队列

spring:kafka:listener:dead-letter-publish:enable: true                  # 自动发布到死信队列dead-letter-topic: dlq-${topic} # 死信队列命名规则

2. 手动发送到DLQ

@KafkaListener(topics = "payments")
public void handlePayment(PaymentEvent event, Acknowledgment ack) {try {paymentService.process(event);ack.acknowledge();} catch (InvalidPaymentException ex) {// 手动发送到DLQkafkaTemplate.send("dlq-payments", event);ack.acknowledge(); // 避免重复消费}
}

5.事务场景异常处理

1. 声明式事务

@Transactional
public void processWithTransaction(Order order) {// 数据库操作orderRepository.save(order);// Kafka事务消息kafkaTemplate.send("orders", order.toEvent());// 其他业务...
}

2. 事务异常回滚

@Bean
public KafkaTransactionManager<String, Object> transactionManager(ProducerFactory<String, Object> pf) {return new KafkaTransactionManager<>(pf);
}@Transactional(rollbackFor = {KafkaException.class, SQLException.class})
public void transactionalProcess() {// 数据库与Kafka操作
}

6.监控与告警

1. Actuator 健康检查

management:endpoints:web:exposure:include: health,kafkahealth:kafka:enabled: true

2. Prometheus 指标

@Bean
public MicrometerConsumerListener<K, V> consumerMetrics() {return new MicrometerConsumerListener<>("kafka.consumer");
}@Bean
public MicrometerProducerListener<K, V> producerMetrics() {return new MicrometerProducerListener<>("kafka.producer");
}

7.完整异常处理流程

1. 捕获异常 → 2. 分类判断 → 3. 重试/记录/DLQ → 4. 提交Offset → 5. 监控告警

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8.最佳实践总结

• 分层处理：全局处理器兜底 + 方法级精细控制
• 幂等消费：确保消息重复消费时的数据安全性
• 监控覆盖：跟踪重试次数、DLQ堆积等关键指标
• 事务隔离：@Transactional + read_committed 保证数据一致性