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利用 Apache Kafka 实现分布式事务的完整指南

本文聚焦 Kafka 原生能力，从「事务语义 → 代码 → 运维 → 故障场景」逐层展开，给出可在生产环境直接落地的全套方案。

一、Kafka 分布式事务的 3 个核心语义

二、架构全景图

┌─────────────────────────────────────────────────────────────┐
│  Producer (订单服务)                                         │
│  1. beginTransaction()                                       │
│  2. insert into order_tbl …                                  │
│  3. send("stock-deduct", orderId)                            │
│  4. commitTransaction()   ─┐                                 │
└────────────────────────────┼─────────────────────────────┐   ││ 两阶段提交                   │   │
┌────────────────────────────┼─────────────────────────────┘   │
│  Broker                                                    │   │
│  • Transaction Coordinator (TC)                            │   │
│  • __transaction_state 日志 (3 副本)                       │   │
│  • 写入分区队列                                           │   │
└────────────────────────────┼─────────────────────────────┐   ││ 仅投递 committed 消息        │   │
┌────────────────────────────┼─────────────────────────────┘   │
│  Consumer (库存服务)                                       │
│  5. poll() → read_committed                               │
│  6. update stock_tbl set qty = qty - ? where id = ?        │
│  7. ack()                                                  │
└─────────────────────────────────────────────────────────────┘

三、Producer 端完整配置与代码

1. 通用 Producer 参数

bootstrap.servers=kafka:9092
enable.idempotence=true               # 幂等发送
transactional.id=order-service-tx-1   # 全局唯一
acks=all
max.in.flight.requests.per.connection=5
transaction.timeout.ms=30000          # 小于 broker 的 max.transaction.timeout.ms

2. Spring Boot 双事务（Kafka + JDBC）

@Configuration
public class KafkaChainedTxConfig {@Beanpublic ProducerFactory<String, String> producerFactory() {Map<String, Object> props = new HashMap<>();props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "kafka:9092");props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class);props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, JsonSerializer.class);props.put(ProducerConfig.ENABLE_IDEMPOTENCE_CONFIG, true);props.put(ProducerConfig.TRANSACTIONAL_ID_CONFIG, "order-service-tx");DefaultKafkaProducerFactory<String, String> pf =new DefaultKafkaProducerFactory<>(props);pf.setTransactionIdPrefix("order-tx-");          // 支持并发事务return pf;}@Beanpublic KafkaTransactionManager<?, ?> kafkaTransactionManager(ProducerFactory<?, ?> pf) {return new KafkaTransactionManager<>(pf);}@Bean("chainedTxManager")public ChainedTransactionManager chainedTxManager(KafkaTransactionManager<?, ?> ktm,DataSourceTransactionManager dstm) {return new ChainedTransactionManager(ktm, dstm);}
}

3. Service 层

@Service
public class OrderService {private final OrderRepository repo;private final KafkaTemplate<String, OrderEvent> kafka;@Transactional("chainedTxManager")public void createOrder(CreateOrderCommand cmd) {// 1. 本地事务Order order = repo.save(new Order(cmd));// 2. 发送事务消息OrderEvent event = new OrderEvent(order.getId(), cmd.getSkuId(), cmd.getQty());kafka.send("stock-deduct", order.getId().toString(), event);// 3. 若 DB 回滚，Kafka 事务也回滚；反之亦然}
}

四、Consumer 端：幂等 + 重试 + 死信队列

1. 消费者配置

bootstrap.servers=kafka:9092
group.id=stock-service
isolation.level=read_committed
enable.auto.commit=false
max.poll.records=100

2. 监听器（批量 + 幂等）

@Component
public class StockConsumer {private final StockRepository stockRepo;@KafkaListener(topics = "stock-deduct",containerFactory = "batchFactory")public void listen(List<ConsumerRecord<String, OrderEvent>> records,Acknowledgment ack) {for (var r : records) {try {consumeOne(r.value());} catch (DuplicateKeyException ex) {// 幂等冲突，跳过} catch (DataIntegrityViolationException ex) {// 库存不足，记录告警并手动 ack，不再重试} catch (Exception ex) {// 其他异常：抛出让 SeekToCurrentErrorHandler 重试throw ex;}}ack.acknowledge();}@Transactionalpublic void consumeOne(OrderEvent e) {int affected = stockRepo.deductQty(e.getSkuId(), e.getQty(), e.getOrderId());if (affected == 0) {throw new IllegalStateException("库存扣减失败");}}
}

3. 重试与死信队列（Spring Kafka）

@Bean
public ConcurrentKafkaListenerContainerFactory<?, ?> batchFactory(ConsumerFactory<String, OrderEvent> cf) {ConcurrentKafkaListenerContainerFactory<String, OrderEvent> factory =new ConcurrentKafkaListenerContainerFactory<>();factory.setConsumerFactory(cf);factory.setBatchListener(true);// 最多重试 3 次后发送到 DLQDefaultErrorHandler handler =new DefaultErrorHandler(new DeadLetterPublishingRecoverer(kafkaTemplate(), (r, e) -> new TopicPartition("stock-deduct.DLT", r.partition())),new FixedBackOff(1000L, 2));factory.setCommonErrorHandler(handler);return factory;
}

五、事务超时 & 死锁排查

六、完整监控体系

1. JMX 指标

   • Producer：record-send-rate, transaction-duration-avg
   • Broker：transaction-coordinator-metrics → transactional-id-count
   • Consumer：records-lag-max, commit-latency-avg

2. Prometheus + Grafana

   - pattern: kafka.producer<type=producer-metrics, client-id=(.+)><>(transaction-duration-avg)name: kafka_producer_transaction_duration_avglabels:client_id: "$1"
   

3. 告警规则示例

   - alert: KafkaTransactionStuckexpr: kafka_producer_transaction_duration_avg > 20for: 1mannotations:summary: "事务长时间未完成"
   

七、故障演练清单

八、小结

至此，从配置、代码到监控、故障演练 的 Kafka 分布式事务闭环已完整落地。