Data Management & Consistency Flow

Introduction to Data Management

In a secure microservices architecture, each service manages its own data to ensure autonomy, using patterns like Event Sourcing, Database per Service, and Sagas to achieve eventual consistency. An API Gateway (or edge service) routes client requests—validated with OAuth2/JWT tokens from an Auth Server—to downstream services using RBAC for authorization. Services communicate securely using mutual TLS (mTLS), store data encrypted with AES-256, and leverage Service Discovery for dynamic routing, Redis for caching, and Prometheus for monitoring. Event-driven flows and saga coordination ensure system-wide data integrity, while layered security controls protect sensitive operations across the architecture.

Data Management & Consistency Flow

This diagram illustrates distributed data management using Event Sourcing, Database per Service, and Sagas to achieve eventual consistency across microservices. Clients interact through an API Gateway, which validates JWTs from an Authentication Server and routes requests to backend services (A, B, C) based on RBAC. Each service handles its own state and persists it to a dedicated Database. Post-persistence, events are published to an Event Store, which acts as a central broker for triggering downstream reactions or saga flows. The Saga Orchestrator coordinates multi-service transactions to maintain system-wide integrity.

Supporting infrastructure includes Service Discovery for locating services, Cache for performance optimization, and Monitoring for observability. Flows are color-coded as follows:
Yellow (dashed): Client interactions and event flows
Orange-red: Authenticated request routing
Blue (dotted): Saga orchestration and mTLS flows
Green (dashed): Database/cache access
Purple: Monitoring and observability

Key Data Management Patterns

Core patterns and components for secure data management include:

• Event Sourcing: Captures state changes as events in a Kafka-based Event Store, enabling replay.
• Database per Service: Isolated databases (MongoDB, PostgreSQL, DynamoDB) with AES-256 encryption.
• Sagas: Coordinates distributed transactions via a Saga Orchestrator (e.g., Camunda).
• Authentication: OAuth2/JWT tokens issued by Keycloak, validated at the API Gateway.
• Authorization: RBAC in services for role-based data access control.
• Security: mTLS for inter-service communication, encryption for data at rest.
• Service Discovery: Consul for dynamic service routing.
• Cache: Redis for caching event aggregates or query results.
• Monitoring: Prometheus for tracking event delays and consistency.

Benefits of Data Management Patterns

• Autonomy: Database per Service enables independent scaling and technology choices.
• Security: JWT, RBAC, mTLS, and encryption protect sensitive data operations.
• Resilience: Event Sourcing allows recovery via event replay, ensuring integrity.
• Coordination: Sagas manage complex workflows without distributed locks.
• Scalability: Event-driven systems and caching handle high update volumes.
• Observability: Monitoring tracks consistency and performance metrics.

Implementation Considerations

Implementing secure data management requires careful planning:

• Authentication Setup: Deploy Keycloak for OAuth2/JWT with short-lived tokens.
• Authorization Design: Map roles to data operations in services using RBAC.
• Security Hardening: Use mTLS and AES-256 encryption for data in transit and at rest.
• Event Schema Design: Use versioned schemas in Kafka to avoid compatibility issues.
• Database Selection: Choose MongoDB for Service A, PostgreSQL for Service B, DynamoDB for Service C.
• Saga Orchestration: Implement Camunda or Kafka Streams for saga coordination.
• Service Discovery: Configure Consul with health checks and mTLS.
• Cache Strategy: Use Redis with TTLs for event aggregates and query results.
• Monitoring: Deploy Prometheus and Grafana to track event processing and consistency.
• Error Handling: Use compensating transactions in sagas and dead-letter queues for failed events.

Example Configuration: Kafka Event Store

Below is a Kafka configuration for an Event Store using a topic for service events:

# Create a Kafka topic for events
kafka-topics.sh --create \
  --topic service-events \
  --bootstrap-server kafka:9092 \
  --partitions 3 \
  --replication-factor 2 \
  --config retention.ms=604800000

# Example producer configuration (Node.js)
const { Kafka } = require('kafkajs');
const kafka = new Kafka({
  clientId: 'service-a',
  brokers: ['kafka:9092'],
  ssl: {
    ca: [fs.readFileSync('ca-cert.pem')],
    key: fs.readFileSync('client-key.pem'),
    cert: fs.readFileSync('client-cert.pem')
  }
});
const producer = kafka.producer();
await producer.connect();
await producer.send({
  topic: 'service-events',
  messages: [{ value: JSON.stringify({ type: 'OrderCreated', data: { orderId: 123 } }) }]
});
await producer.disconnect();

# Example consumer configuration (Node.js)
const consumer = kafka.consumer({ groupId: 'service-b-group' });
await consumer.connect();
await consumer.subscribe({ topic: 'service-events', fromBeginning: true });
await consumer.run({
  eachMessage: async ({ topic, partition, message }) => {
    const event = JSON.parse(message.value.toString());
    console.log(`Processing event: ${event.type}`);
  }
});
                

Example Configuration: Service A with RBAC and mTLS

Below is a Node.js Service A configuration with RBAC, mTLS, and Kafka integration:

const express = require('express');
const { Kafka } = require('kafkajs');
const jwt = require('jsonwebtoken');
const https = require('https');
const fs = require('fs');

const app = express();
const JWT_SECRET = process.env.JWT_SECRET || 'your-secret-key';
const kafka = new Kafka({
  clientId: 'service-a',
  brokers: ['kafka:9092'],
  ssl: {
    ca: [fs.readFileSync('ca-cert.pem')],
    key: fs.readFileSync('client-key.pem'),
    cert: fs.readFileSync('client-cert.pem')
  }
});
const producer = kafka.producer();

// mTLS configuration
const serverOptions = {
  key: fs.readFileSync('server-key.pem'),
  cert: fs.readFileSync('server-cert.pem'),
  ca: fs.readFileSync('ca-cert.pem'),
  requestCert: true,
  rejectUnauthorized: true
};

const checkRBAC = (requiredRole) => (req, res, next) => {
  const authHeader = req.headers.authorization;
  if (!authHeader || !authHeader.startsWith('Bearer ')) {
    return res.status(401).json({ error: 'Unauthorized' });
  }
  const token = authHeader.split(' ')[1];
  try {
    const decoded = jwt.verify(token, JWT_SECRET);
    if (!decoded.role || decoded.role !== requiredRole) {
      return res.status(403).json({ error: 'Insufficient permissions' });
    }
    req.user = decoded;
    next();
  } catch (err) {
    return res.status(403).json({ error: 'Invalid token' });
  }
};

// Endpoint to create data and publish event
app.post('/data', checkRBAC('admin'), async (req, res) => {
  const data = req.body;
  await db.save('data', data); // Save to MongoDB
  await producer.connect();
  await producer.send({
    topic: 'service-events',
    messages: [{ value: JSON.stringify({ type: 'DataCreated', data }) }]
  });
  await producer.disconnect();
  res.json(data);
});

https.createServer(serverOptions, app).listen(3000, () => {
  console.log('Service A running on port 3000 with mTLS');
});