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spring-boot-saga-pattern

giuseppe-trisciuoglio/developer-kit

Distributed transaction patterns for Spring Boot microservices using Saga Pattern with choreography or orchestration.

What is spring-boot-saga-pattern?

Implements the Saga Pattern to coordinate distributed transactions across microservices, replacing two-phase commit with sequences of local transactions and compensating actions. Use when building eventual-consistency workflows, handling transaction rollback across services, or coordinating complex business processes with Kafka, RabbitMQ, or Axon Framework.

  • Design transaction flows with compensating transaction mappings
  • Choose between choreography (event-driven) and orchestration (centralized coordinator) approaches
  • Implement local ACID transactions in each service with idempotent event publishing
  • Create compensating transactions for rollback and failure recovery
  • Configure message brokers (Kafka/RabbitMQ) with exactly-once semantics and idempotent consumers
  • Build saga orchestrators to manage complex workflows and state

How to install spring-boot-saga-pattern

npx skills add https://github.com/giuseppe-trisciuoglio/developer-kit --skill spring-boot-saga-pattern
Prerequisites
  • Spring Boot 2.x or 3.x project
  • Message broker: Kafka or RabbitMQ configured and running
  • Database for persisting saga state and local transactions
  • Understanding of microservices architecture and eventual consistency concepts
Claude Code
Cursor
Windsurf
Cline

How to use spring-boot-saga-pattern

  1. 1.Map your transaction flow and design compensating transactions for each forward step
  2. 2.Choose choreography (event-driven with Spring Cloud Stream) or orchestration (Axon Framework/Eventuate) based on complexity
  3. 3.Implement local @Transactional methods in each service that publish events after commit
  4. 4.Create idempotent compensating transaction methods for each service
  5. 5.Configure Kafka/RabbitMQ with transactional IDs and exactly-once semantics
  6. 6.For orchestration: build a SagaOrchestrator that listens for failures and triggers compensation
  7. 7.For choreography: implement @KafkaListener event handlers that chain steps and handle failures
  8. 8.Add metrics and monitoring to track saga status, compensation execution, and failure rates

Use cases

Good for
  • Coordinating order processing across payment, inventory, and shipment services with automatic refunds on failure
  • Implementing distributed refund workflows that trigger compensation across multiple services
  • Building eventual-consistency workflows where strong consistency is not required
  • Handling partial failures in multi-step business processes with automatic rollback
  • Managing complex microservice orchestrations with centralized saga coordinators
Who it's for
  • Microservices architects designing distributed transaction systems
  • Backend engineers building Spring Boot microservices
  • Teams implementing eventual-consistency patterns
  • Developers replacing two-phase commit with scalable alternatives

spring-boot-saga-pattern FAQ

What is the difference between choreography and orchestration sagas?

Choreography uses event-driven communication where each service listens for events and publishes new ones (decoupled but harder to track). Orchestration uses a centralized coordinator that explicitly commands each service (easier to monitor but creates a single point of coordination).

How do I ensure compensating transactions are idempotent?

Use database constraints (unique keys), deduplication tables, or idempotency keys in your compensation logic. Test that running compensation multiple times produces the same result without side effects.

What happens if a compensating transaction fails?

Implement dead-letter queues for failed compensation messages, set up alerts for stuck sagas, and use circuit breakers. Saga state must be persisted so you can manually intervene or retry.

Does Saga Pattern guarantee strong consistency?

No, sagas provide eventual consistency. The system will eventually reach a consistent state through compensation, but intermediate states may be inconsistent. Use sagas when eventual consistency is acceptable.

Should I use Axon Framework or plain Kafka/RabbitMQ?

Use plain Kafka/RabbitMQ for simple choreography sagas. Use Axon Framework or Eventuate for complex orchestrations, especially in brownfield systems or when you need built-in saga management and recovery.

Full instructions (SKILL.md)

Source of truth, from giuseppe-trisciuoglio/developer-kit.


name: spring-boot-saga-pattern description: Provides distributed transaction patterns using the Saga Pattern for Spring Boot microservices. Use when implementing distributed transactions across services, handling compensating transactions, ensuring eventual consistency, or building choreography or orchestration-based sagas with Kafka, RabbitMQ, or Axon Framework. allowed-tools: Read, Write, Edit, Bash, Glob, Grep

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

ApproachUse CaseStack
ChoreographyGreenfield, few participantsSpring Cloud Stream + Kafka/RabbitMQ
OrchestrationComplex workflows, brownfieldAxon Framework, Eventuate Tram, Camunda

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

    @KafkaListener(topics = "payment.failed", groupId = "order-service")
    public void handleCompensation(PaymentFailedEvent event) {
        orderRepository.findByOrderId(event.orderId())
            .ifPresent(order -> {
                order.setStatus(CANCELLED);
                orderRepository.save(order);
            });
    }
}

Orchestration-Based Saga with Axon Framework

// Command
@Aggregate
public class OrderAggregate {
    @AggregateIdentifier
    private String orderId;

    @CommandHandler
    public OrderAggregate(CreateOrderCommand cmd) {
        apply(new OrderCreatedEvent(cmd.orderId(), cmd.items()));
    }

    @EventSourcingHandler
    public void on(OrderCreatedEvent event) {
        this.orderId = event.orderId();
    }

    @CommandHandler
    public void handle(CancelOrderCommand cmd) {
        apply(new OrderCancelledEvent(cmd.orderId(), cmd.reason()));
    }
}

References