Spring Boot Saga Pattern

Overview

Implements distributed transactions across microservices using the Saga Pattern. Replaces two-phase commit with a sequence of local transactions and compensating actions. Supports choreography (event-driven) and orchestration (centralized coordinator) approaches with Kafka, RabbitMQ, or Axon Framework.

When to Use

• Building distributed transactions across multiple microservices
• Replacing two-phase commit (2PC) with a more scalable solution
• Handling transaction rollback when a service fails
• Ensuring eventual consistency in microservices architecture
• Implementing compensating transactions for failed operations
• Coordinating complex business processes spanning multiple services

Trigger phrases: distributed transactions, saga pattern, compensating transactions, microservices transaction, eventual consistency, rollback across services, orchestration pattern, choreography pattern

Instructions

1. Design Transaction Flow

Map the sequence of operations and their compensating transactions:

Order → Payment → Inventory → Shipment
  ↓        ↓        ↓          ↓
Cancel  Refund   Release    Cancel

Validation: Verify every forward step has a corresponding compensation.

2. Choose Implementation Approach

Validation: Review team expertise and system complexity before choosing.

3. Implement Services with Local Transactions

Each service completes its local ACID transaction atomically:

@Service
@RequiredArgsConstructor
public class OrderService {
    private final OrderRepository orderRepository;
    private final KafkaTemplate<String, Object> kafka;

    @Transactional
    public Order createOrder(CreateOrderCommand cmd) {
        Order order = orderRepository.save(new Order(cmd.orderId(), cmd.items()));
        kafka.send("order.created", new OrderCreatedEvent(order.getId(), order.getItems()));
        return order;
    }
}

Validation: Test that local transaction commits before event is published.

4. Implement Compensating Transactions

Every forward operation requires an idempotent compensation:

@Service
@RequiredArgsConstructor
public class PaymentService {
    private final PaymentRepository paymentRepository;
    private final KafkaTemplate<String, Object> kafka;

    public void processPayment(PaymentRequest request) {
        Payment payment = paymentRepository.save(new Payment(request.orderId(), request.amount()));
        kafka.send("payment.processed", new PaymentProcessedEvent(payment.getId(), request.orderId()));
    }

    @Transactional
    public void refundPayment(String paymentId) {
        paymentRepository.findById(paymentId)
            .ifPresent(p -> {
                p.setStatus(REFUNDED);
                paymentRepository.save(p);
                kafka.send("payment.refunded", new PaymentRefundedEvent(paymentId));
            });
    }
}

Validation: Confirm compensation can execute safely multiple times (idempotency).

5. Set Up Message Broker

Configure Kafka with idempotent consumers:

@Configuration
@EnableKafka
public class KafkaConfig {
    @Bean
    public ConcurrentKafkaListenerContainerFactory<String, Object> kafkaListenerContainerFactory(
            ConsumerFactory<String, Object> consumerFactory) {
        ConcurrentKafkaListenerContainerFactory<String, Object> factory =
            new ConcurrentKafkaListenerContainerFactory<>();
        factory.setConsumerFactory(consumerFactory);
        factory.setCommonErrorHandler(new DefaultErrorHandler());
        return factory;
    }
}

Validation: Enable transactional ID and verify exactly-once semantics.

6. Implement Saga Orchestrator (Orchestration Only)

@Service
@RequiredArgsConstructor
public class OrderSagaOrchestrator {
    private final KafkaTemplate<String, Object> kafka;
    private final SagaStateRepository sagaStateRepo;

    public void startSaga(OrderRequest request) {
        String sagaId = UUID.randomUUID().toString();
        sagaStateRepo.save(new SagaState(sagaId, STARTED, LocalDateTime.now()));
        kafka.send("saga.order.start", new StartOrderSagaCommand(sagaId, request));
    }

    @KafkaListener(topics = "payment.failed")
    public void handlePaymentFailed(PaymentFailedEvent event) {
        kafka.send("order.compensate", new CompensateOrderCommand(event.getSagaId()));
        kafka.send("inventory.compensate", new ReleaseInventoryCommand(event.getSagaId()));
        sagaStateRepo.updateStatus(event.getSagaId(), FAILED);
    }
}

Validation: Verify saga state persists before sending commands. Check compensation triggers on each failure path.

7. Implement Event Handlers (Choreography Only)

@Service
public class OrderEventHandler {
    private final OrderService orderService;
    private final KafkaTemplate<String, Object> kafka;

    @KafkaListener(topics = "payment.processed", groupId = "order-service")
    public void onPaymentProcessed(PaymentProcessedEvent event) {
        try {
            InventoryReservedEvent result = orderService.reserveInventory(event.toInventoryRequest());
            kafka.send("inventory.reserved", result);
        } catch (InsufficientInventoryException e) {
            kafka.send("inventory.insufficient", new InsufficientInventoryEvent(event.getOrderId(), event.getPaymentId()));
        }
    }
}

Validation: Test that each event handler correctly triggers the next step or compensation.

8. Add Monitoring and Observability

@Configuration
public class SagaMetricsConfig {
    @Bean
    public MeterRegistry meterRegistry() {
        return new PrometheusMeterRegistry(PrometheusConfig.DEFAULT);
    }
}

Track: saga execution duration, compensation count, failure rate, stuck sagas.

Validation: Set up alerts for sagas exceeding expected duration.

Best Practices

Design:

• Make compensating transactions idempotent using database constraints or deduplication tables
• Use immutable events (Java records) to prevent accidental mutation
• Store saga state in persistent storage for recovery

Error Handling:

• Implement circuit breakers for inter-service calls
• Use dead-letter queues for messages exceeding retry limits
• Set appropriate timeouts per saga step (30s default, configurable)

Monitoring:

• Track saga status: PENDING, COMPLETED, COMPENSATING, FAILED
• Monitor compensation execution time
• Alert when sagas exceed SLA duration

Constraints and Warnings

• Every forward transaction MUST have a corresponding compensating transaction
• Compensating transactions MUST be idempotent to handle retry scenarios
• Saga state MUST be persisted to handle failures and recovery
• Never use synchronous communication between saga participants
• Sagas provide eventual consistency, not strong consistency
• Test all failure scenarios including partial failures
• Consider Axon Framework or Eventuate for complex orchestrations
• Ensure message brokers are highly available

Examples

Choreography-Based Saga

// Application.java
@SpringBootApplication
@EnableKafka
@EnableKafkaListeners
public class OrderApplication {
    public static void main(String[] args) {
        SpringApplication.run(OrderApplication.class, args);
    }
}

// Event Classes (immutable)
public record OrderCreatedEvent(String orderId, List<OrderItem> items) {}
public record PaymentProcessedEvent(String paymentId, String orderId) {}
public record InventoryReservedEvent(String reservationId, String orderId) {}
public record PaymentFailedEvent(String orderId, String reason) {}
public record InsufficientInventoryEvent(String orderId, String paymentId) {}

// OrderService wit