Building reliable distributed systems with event-driven patterns

Distributed systems have become a cornerstone of modern software architecture, promising scalability, resilience, and team autonomy. Yet many organizations find themselves building what are essentially distributed monoliths - systems that suffer from all the complexities of distribution while reaping few of its benefits. In our previous discussion about the microservices trap, we explored how premature adoption of microservices can lead to unnecessary complexity. Today, let’s delve deeper into a critical aspect that often undermines the very benefits these architectures promise: synchronous communication between services.

The allure and pitfalls of synchronous communication

Consider a typical e-commerce platform built with microservices. When a customer completes a purchase, the system orchestrates a complex dance of services: checking inventory, processing payments, updating order status, notifying warehouses, and sending confirmations. In many implementations, this orchestration takes the form of a chain of synchronous HTTP calls, each service waiting for the next to complete its task before proceeding.

This approach feels natural to developers, mirroring the way we think about the process: “First do this, then do that.” It provides a clear, step-by-step flow that’s easy to understand and debug. However, this apparent simplicity masks serious architectural problems that become increasingly painful as systems grow.

Imagine a chain of five services where each service takes an average of 100 milliseconds to respond. In the best case, with no network latency or retries, the entire operation takes 500 milliseconds to complete. Add realistic network conditions, occasional slowdowns, and retry attempts, and your seemingly simple operation can easily stretch into seconds. More critically, this delay isn’t just an inconvenience - it’s a fundamental limitation on your system’s throughput and reliability.

But latency is just the beginning. The real problem lies in how synchronous communication creates tight coupling between services, undermining key benefits of distributed architecture. When Service A must wait for Service B, which must wait for Service C, you’ve created a fragile chain where any weak link can bring down the entire operation. This pattern effectively negates one of the primary advantages of distributed systems: fault isolation.

Embracing asynchronous communication

The alternative to this tightly coupled approach is event-driven architecture, where services communicate through events published to a message broker. Instead of services directly calling each other, they publish events announcing state changes or significant occurrences. Other services subscribe to events they’re interested in and react accordingly.

Let’s explore how this transforms our e-commerce example:

// Traditional synchronous approach
public class OrderService {
    public OrderResult processOrder(Order order) {
        // This method can't complete if any service is down
        InventoryResult stock = inventoryService.checkStock(order.getItems());
        PaymentResult payment = paymentService.processPayment(order.getPayment());
        OrderStatus status = orderRepository.save(order);
        warehouseService.createShipment(order);
        notificationService.sendConfirmation(order);
        
        return new OrderResult(status, payment, stock);
    }
}

// Event-driven approach
public class OrderService {
    public OrderResult processOrder(Order order) {
        // Core business logic only
        OrderStatus status = orderRepository.save(order);
        
        // Publish event for others to react to
        OrderCreatedEvent event = new OrderCreatedEvent(
            order.getId(),
            order.getUserId(),
            order.getItems(),
            ZonedDateTime.now()
        );
        
        eventBus.publish("orders.created", event);
        
        return new OrderResult(status);
    }
}

This shift to event-driven architecture fundamentally changes how our system handles failures and scales under load. When the warehouse service is temporarily unavailable, it doesn’t prevent orders from being accepted and processed. When the notification service is experiencing high latency, it doesn’t slow down the core ordering process. Each service operates independently, processing events at its own pace.

The benefits extend beyond resilience. Event-driven architectures naturally support system evolution. Need to add analytics? Create a new service that subscribes to relevant events. Want to implement a new fulfillment process? Subscribe to order events and implement the new logic without touching existing services. This flexibility is particularly valuable as business requirements evolve and systems grow more complex.

However, this architectural style isn’t without its challenges. Events introduce eventual consistency - there’s no guarantee that all system components will immediately reflect the same state. This requires careful thought about how to handle scenarios where events arrive out of order or multiple times. Developers must shift their thinking from synchronous, procedural flows to asynchronous, reactive patterns.

Monitoring and debugging take on new dimensions in event-driven systems. Instead of following a simple request-response chain, we need to track events as they flow through multiple services over time. This requires sophisticated logging and correlation strategies. Every event should carry a correlation ID that links together all the actions triggered by an initial request:

public class OrderProcessor {
    private void handleOrderCreated(OrderCreatedEvent event) {
        String correlationId = event.getCorrelationId();
        logger.info("Processing order {} for correlation {}", 
                   event.getOrderId(), correlationId);
        
        try {
            // Process the order
            inventoryService.reserveStock(event.getItems(), correlationId);
            
            // Publish a subsequent event
            OrderProcessedEvent processed = new OrderProcessedEvent(
                event.getOrderId(),
                correlationId,
                ProcessingStatus.COMPLETED
            );
            eventBus.publish("orders.processed", processed);
            
        } catch (Exception e) {
            logger.error("Failed to process order {} (correlation: {})",
                        event.getOrderId(), correlationId, e);
            
            // Publish failure event for other services to react
            OrderFailedEvent failed = new OrderFailedEvent(
                event.getOrderId(),
                correlationId,
                e.getMessage()
            );
            eventBus.publish("orders.failed", failed);
        }
    }
}

The complexity of distributed debugging is offset by the benefits of better failure isolation and system resilience. When issues occur, they tend to be contained within specific services rather than cascading through the entire system. This containment makes problems easier to isolate and fix, even if the initial diagnosis requires more sophisticated tooling.

Testing event-driven systems requires a different mindset. Beyond the usual unit and integration tests, we need to verify how our services handle various event scenarios. This includes late events, duplicate events, and out-of-order events. More importantly, we need to test how services behave when other parts of the system are unavailable or slow:

@Test
public void shouldHandleLateInventoryUpdates() {
    // Given an order has been created
    OrderCreatedEvent orderEvent = createTestOrder();
    processor.handleOrderCreated(orderEvent);
    
    // When inventory update arrives after a delay
    Thread.sleep(TimeUnit.SECONDS.toMillis(5));
    InventoryUpdatedEvent inventoryEvent = new InventoryUpdatedEvent(
        orderEvent.getOrderId(),
        orderEvent.getCorrelationId(),
        InventoryStatus.IN_STOCK
    );
    processor.handleInventoryUpdated(inventoryEvent);
    
    // Then the order should still be processed correctly
    verify(orderRepository).updateStatus(
        orderEvent.getOrderId(), 
        OrderStatus.READY_FOR_FULFILLMENT
    );
}

These tests are more complex than traditional request-response tests, but they help ensure our system remains reliable under real-world conditions.

The evolution to event-driven architecture often raises questions about data consistency and transaction management. Without the ability to wrap multiple operations in a single transaction, we need new patterns for maintaining data consistency. The saga pattern becomes particularly important, allowing us to coordinate multiple steps while maintaining the ability to roll back if something goes wrong.

Consider a scenario where an order needs to update both inventory and customer credit. Instead of a single transaction, we implement a sequence of events with compensating transactions for failures:

public class OrderSaga {
    public void start(Order order) {
        // Start the saga with initial event
        OrderStartedEvent startEvent = new OrderStartedEvent(order);
        eventBus.publish("order.started", startEvent);
    }
    
    @EventListener("inventory.reserved")
    public void onInventoryReserved(InventoryReservedEvent event) {
        // Progress to next step
        CustomerCreditCheckEvent creditEvent = new CustomerCreditCheckEvent(
            event.getOrderId(),
            event.getCustomerId(),
            event.getAmount()
        );
        eventBus.publish("credit.check", creditEvent);
    }
    
    @EventListener("credit.check.failed")
    public void onCreditCheckFailed(CreditCheckFailedEvent event) {
        // Compensating transaction to release inventory
        ReleaseInventoryEvent releaseEvent = new ReleaseInventoryEvent(
            event.getOrderId()
        );
        eventBus.publish("inventory.release", releaseEvent);
    }
}

This pattern maintains system consistency while preserving the benefits of loose coupling and fault isolation.

The transition to event-driven architecture represents more than just a technical change - it’s a fundamental shift in how we think about system design. Instead of orchestrating precise sequences of operations, we create systems that react to state changes and handle uncertainty gracefully. This approach aligns better with the realities of distributed computing, where network partitions, service outages, and variable latencies are facts of life rather than exceptional conditions.

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Building reliable distributed systems is a journey, not a destination. If your team is wrestling with the challenges of service coordination, looking to improve system reliability, or planning a transition to event-driven architecture, we’d love to help. Our experience helping organizations navigate these architectural transitions has taught us that success lies in careful planning and incremental adaptation. Reach out to us through our contact form or send us an email at contact [at] asyncsource.com to discuss how we can help your team build more resilient systems.