Event-Driven Payroll Processing with Kafka and Spring Boot

Payroll processing is mission-critical. A failed payment or incorrect calculation affects real people's livelihoods. At WageNest, we built an event-driven architecture using Apache Kafka that made our system more reliable and resilient.

Why Event-Driven?

Traditional request-response patterns don't work well for payroll:

• Payment processing takes time (bank APIs, validation, reconciliation)
• Failures need retry logic with compensation
• Multiple systems need to react to payment events
• Audit trails must be complete and ordered

Event-driven architecture solved these problems.

Architecture Overview

Our Kafka-based system handles:

1. Payment initiation events - When payroll is submitted
2. Validation events - Checking employee data, amounts, accounts
3. Processing events - Interacting with banking APIs
4. Status update events - Success, failure, pending
5. Reconciliation events - Matching payments to confirmations
6. Notification events - Alerting employees and admins

Implementation with Spring Boot

Producer Setup

@Service
public class PayrollEventProducer {
    private final KafkaTemplate<String, PayrollEvent> kafkaTemplate;
    
    public void publishPaymentInitiated(PayrollTransaction transaction) {
        PayrollEvent event = PayrollEvent.builder()
            .eventType("PAYMENT_INITIATED")
            .transactionId(transaction.getId())
            .tenantId(transaction.getTenantId())
            .timestamp(Instant.now())
            .payload(transaction)
            .build();
            
        kafkaTemplate.send("payroll-events", event.getTenantId(), event);
    }
}

Consumer with Error Handling

@Service
public class PaymentProcessor {
    @KafkaListener(topics = "payroll-events", groupId = "payment-processor")
    public void processPayment(PayrollEvent event) {
        try {
            // Process payment with banking API
            PaymentResult result = bankingService.processPayment(event);
            
            // Publish success event
            publishPaymentSuccess(event, result);
        } catch (RetryableException e) {
            // Kafka will retry based on configuration
            throw e;
        } catch (Exception e) {
            // Publish failure event for compensation
            publishPaymentFailure(event, e);
        }
    }
}

Fault Tolerance Strategies

1. Retry Mechanisms

Configured exponential backoff:

spring:
  kafka:
    consumer:
      properties:
        retry.backoff.ms: 1000
        max.poll.interval.ms: 300000

2. Dead Letter Queues

Failed events go to DLQ for manual review:

@Bean
public DefaultErrorHandler errorHandler() {
    return new DefaultErrorHandler(
        new DeadLetterPublishingRecoverer(kafkaTemplate),
        new FixedBackOff(1000L, 3L)
    );
}

3. Compensation Logic

When a payment fails mid-process:

• Reverse any partial transactions
• Update employee records
• Notify all parties
• Log for audit trail

Results

After implementing Kafka:

• Payment reconciliation issues: Reduced by 40%
• System reliability: 99.9% uptime
• Failed payment recovery: 95% automatic retry success
• Audit trail completeness: 100%

Best Practices

1. Idempotent consumers - Events might be delivered twice
2. Ordered processing - Use partition keys wisely
3. Schema evolution - Plan for message format changes
4. Monitoring - Track lag, throughput, errors
5. Testing - Use embedded Kafka for integration tests

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Interested in event-driven architecture for financial systems? Let's connect!