Microservices¶

Comprehensive guide to Studio Platform's microservices architecture, including service design, communication patterns, and deployment strategies.

🔌 Microservices Overview¶

Microservices Architecture¶

Studio Platform is built on a microservices architecture that breaks down the monolithic application into smaller, independent services. Each service is responsible for a specific business capability and can be developed, deployed, and scaled independently.

graph TD
    A[API Gateway] --> B[Frontend Service]
    A --> C[Backend Service]
    A --> D[AI Service]
    A --> E[Document Service]
    A --> F[Identity Service]
    A --> G[Policy Service]

    C --> H[PostgreSQL]
    C --> I[Neo4j]
    C --> J[Redis]

    D --> K[Vector Store]
    D --> L[AI Models]

    E --> M[MinIO Storage]
    E --> N[Processing Queue]

    F --> O[User Database]
    G --> P[Policy Database]

    Q[Service Mesh] --> A
    Q --> B
    Q --> C
    Q --> D
    Q --> E
    Q --> F
    Q --> G

Service Characteristics¶

Core Principles¶

Service Autonomy: - Independent Deployment - Services can be deployed independently - Independent Scaling - Services can be scaled independently - Independent Development - Teams can develop services independently - Independent Technology - Services can use different technologies

Service Boundaries: - Business Capability - Each service maps to a business capability - Data Ownership - Each service owns its data - API Contract - Services communicate through well-defined APIs - Event-Driven - Services communicate through events

Service Benefits¶

Development Benefits: - Team Autonomy - Teams can work independently - Technology Diversity - Teams can choose appropriate technologies - Faster Development - Smaller codebase, faster development - Easier Testing - Smaller services easier to test

Operational Benefits: - Scalability - Scale individual services as needed - Reliability - Failure isolation between services - Deployment - Deploy services independently - Maintenance - Update services independently

🏗️ Service Design¶

Service Categories¶

Frontend Services¶

Frontend Service:

graph TD
    A[Frontend Service] --> B[Web Application]
    A --> C[Mobile Application]
    A --> D[Desktop Application]

    B --> E[React Components]
    B --> F[UI Library]
    B --> G[State Management]

    C --> H[React Native]
    C --> I[Native Components]

    D --> J[Electron]
    D --> K[Desktop Components]

Responsibilities: - User Interface - Web, mobile, and desktop interfaces - Client-Side Logic - Frontend business logic - User Experience - UX optimization - Authentication - Client-side authentication - API Communication - Backend API integration

Technology Stack: - Framework - Next.js 13+ with App Router - Language - TypeScript - Styling - Tailwind CSS - UI Components - Radix UI - State Management - React Query, Zustand

Backend Services¶

Backend Service:

graph TD
    A[Backend Service] --> B[API Controllers]
    A --> C[Business Logic]
    A --> D[Data Access]
    A --> E[External APIs]

    B --> F[User Controller]
    B --> G[Project Controller]
    B --> H[Evidence Controller]
    B --> I[Compliance Controller]

    C --> J[User Service]
    C --> K[Project Service]
    C --> L[Evidence Service]
    C --> M[Compliance Service]

    D --> N[PostgreSQL]
    D --> O[Neo4j]
    D --> P[Redis]

    E --> Q[External APIs]
    E --> R[Third-Party Services]

Responsibilities: - API Management - RESTful API endpoints - Business Logic - Core business logic implementation - Data Management - Database operations - Authentication - User authentication - Authorization - Access control

Technology Stack: - Runtime - Node.js 18+ - Framework - Express.js - Language - TypeScript - Database - PostgreSQL, Neo4j, Redis - ORM - Prisma

AI Services¶

AI Service:

graph TD
    A[AI Service] --> B[AI Controllers]
    A --> C[AI Services]
    A --> D[AI Models]
    A --> E[Vector Store]

    B --> F[Chat Controller]
    B --> G[Analysis Controller]
    B --> H[Generation Controller]

    C --> I[Chat Service]
    C --> J[Analysis Service]
    C --> K[Generation Service]

    D --> L[Google Gemini]
    D --> M[OpenAI]
    D --> N[Custom Models]

    E --> O[ChromaDB]
    E --> P[Pinecone]
    E --> Q[Weaviate]

Responsibilities: - AI Assistant - Conversational AI interface - Policy Generation - Automated policy creation - Evidence Analysis - Document analysis - Compliance Insights - AI-powered insights - Natural Language Processing - Text processing

Technology Stack: - Runtime - Python 3.11+ - Framework - FastAPI - AI Models - Google Gemini, OpenAI - Vector Database - ChromaDB - Task Queue - Celery

Supporting Services¶

Identity Service:

graph TD
    A[Identity Service] --> B[Authentication]
    A --> C[User Management]
    A --> D[Session Management]
    A --> E[OAuth Integration]

    B --> F[Login]
    B --> G[Logout]
    B --> H[Token Management]

    C --> I[User CRUD]
    C --> J[Profile Management]
    C --> K[Role Management]

    D --> L[Session Store]
    D --> M[Session Validation]

    E --> N[Google OAuth]
    E --> O[Microsoft OAuth]
    E --> P[SAML]

Policy Service:

graph TD
    A[Policy Service] --> B[Authorization]
    A --> C[Policy Engine]
    A --> D[Access Control]
    A --> E[Policy Management]

    B --> F[RBAC]
    B --> G[ABAC]
    B --> H[Policy Evaluation]

    C --> I[Rule Engine]
    C --> J[Decision Engine]
    C --> K[Policy Updates]

    D --> L[Permission Check]
    D --> M[Resource Access]
    D --> N[Role Validation]

🔗 Service Communication¶

Communication Patterns¶

Synchronous Communication¶

REST API Communication:

sequenceDiagram
    participant C as Client
    participant G as API Gateway
    participant S as Service
    participant D as Database

    C->>G: HTTP Request
    G->>S: Forward Request
    S->>D: Database Query
    D-->>S: Query Result
    S-->>G: Response
    G-->>C: HTTP Response

API Gateway Pattern: - Single Entry Point - Single entry point for all services - Routing - Request routing to appropriate services - Load Balancing - Load balancing across service instances - Authentication - Centralized authentication - Rate Limiting - API rate limiting and throttling

Asynchronous Communication¶

Event-Driven Communication:

sequenceDiagram
    participant S1 as Service 1
    participant E as Event Bus
    participant S2 as Service 2
    participant S3 as Service 3

    S1->>E: Publish Event
    E->>S2: Event Notification
    E->>S3: Event Notification
    S2->>S2: Process Event
    S3->>S3: Process Event

Message Queue Pattern: - Event Publishing - Services publish events to message queue - Event Subscription - Services subscribe to relevant events - Event Processing - Services process events asynchronously - Error Handling - Error handling and retry logic - Event Persistence - Event persistence and replay

Hybrid Communication¶

Mixed Communication:

graph TD
    A[Client] --> B[API Gateway]
    B --> C[Service A]
    B --> D[Service B]
    B --> E[Event Bus]

    C --> F[Database]
    D --> G[Database]

    E --> H[Service C]
    E --> I[Service D]

    H --> J[Database]
    I --> K[Database]

Communication Rules: - Synchronous - For immediate response requirements - Asynchronous - For background processing and notifications - Event-Driven - For loose coupling and scalability - API-First - For external integrations

Service Mesh¶

Service Mesh Architecture¶

Istio Service Mesh:

graph TD
    A[Ingress Gateway] --> B[Service Mesh]
    B --> C[Frontend Service]
    B --> D[Backend Service]
    B --> E[AI Service]
    B --> F[Document Service]

    B --> G[Sidecar Proxy]
    G --> H[Frontend Service]
    G --> I[Backend Service]
    G --> J[AI Service]
    G --> K[Document Service]

    L[Control Plane] --> M[Configuration]
    L --> N[Security]
    L --> O[Traffic Management]
    L --> P[Observability]

Service Mesh Benefits: - Traffic Management - Traffic routing and load balancing - Security - mTLS, authentication, authorization - Observability - Metrics, logging, tracing - Reliability - Circuit breakers, retries, timeouts - Policy Enforcement - Policy enforcement at mesh level

🗄️ Data Management¶

Data Ownership¶

Service Data Ownership¶

Data Ownership Principles: - Service Ownership - Each service owns its data - Data Isolation - Services have isolated data stores - Data Sharing - Services share data through APIs - Data Consistency - Eventual consistency across services - Data Privacy - Services respect data privacy

Data Ownership Examples:

graph TD
    A[Frontend Service] --> B[User Session Data]
    C[Backend Service] --> D[User Data]
    C --> E[Project Data]
    C --> F[Evidence Data]
    G[AI Service] --> H[AI Model Data]
    I[Document Service] --> J[File Metadata]
    K[Identity Service] --> L[Authentication Data]
    M[Policy Service] --> N[Policy Data]

Data Consistency¶

Eventual Consistency:

sequenceDiagram
    participant S1 as Service 1
    participant E as Event Bus
    participant S2 as Service 2
    participant D1 as Database 1
    participant D2 as Database 2

    S1->>D1: Update Data
    S1->>E: Publish Event
    E->>S2: Event Notification
    S2->>D2: Update Data
    D2-->>S2: Confirmation
    S2-->>E: Acknowledgment
    E-->>S1: Event Acknowledgment

Consistency Patterns: - Eventual Consistency - Accept temporary inconsistency - Event Sourcing - Store events as source of truth - CQRS - Separate read and write models - Saga Pattern - Distributed transaction management - Eventual Consistency - Eventually consistent state

Data Synchronization¶

Data Sync Patterns¶

Event-Driven Sync:

graph TD
    A[Service A] --> B[Event Bus]
    B --> C[Service B]
    B --> D[Service C]

    A --> E[Database A]
    C --> F[Database B]
    D --> G[Database C]

    A -.-> H[Data Sync]
    C -.-> I[Data Sync]
    D -.-> J[Data Sync]

Sync Strategies: - Event-Driven - Events drive data synchronization - Polling - Periodic data polling - Streaming - Real-time data streaming - Batch Processing - Batch data processing - Change Data Capture - Database change capture

🔧 Service Deployment¶

Container Deployment¶

Docker Configuration¶

Dockerfile Example:

# Backend Service Dockerfile
FROM node:18-alpine AS builder

WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production

COPY . .
RUN npm run build

FROM node:18-alpine AS runtime

RUN addgroup -g 1001 -S nodejs && \
    adduser -S nodejs -u 1001

WORKDIR /app
COPY --from=builder /app/node_modules ./node_modules
COPY --from=builder /app/dist ./dist

USER nodejs

EXPOSE 4000

HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
  CMD curl -f http://localhost:4000/api/health || exit 1

CMD ["npm", "start"]

Multi-Stage Build: - Builder Stage - Build application - Runtime Stage - Production runtime - Security - Non-root user - Health Check - Health check endpoint - Optimization - Image optimization

Docker Compose¶

Development Compose:

# docker-compose.dev.yml
version: '3.8'

services:
  backend:
    build:
      context: ./backend
      dockerfile: Dockerfile
      target: development
    ports:
      - "4000:4000"
    volumes:
      - ./backend:/app
      - /app/node_modules
    environment:
      - NODE_ENV=development
      - WATCHPACK_POLLING=true
    depends_on:
      - postgres
      - redis
      - neo4j

  ai-service:
    build:
      context: ./ai-service
      dockerfile: Dockerfile
      target: development
    ports:
      - "5000:5000"
    volumes:
      - ./ai-service:/app
      - /app/venv
    environment:
      - PYTHONPATH=/app
      - GOOGLE_API_KEY=${GOOGLE_API_KEY}
    depends_on:
      - chroma

  postgres:
    image: pgvector/pgvector:pg15
    environment:
      POSTGRES_USER: studio
      POSTGRES_PASSWORD: dev
      POSTGRES_DB: studio_dev
    volumes:
      - postgres_data:/var/lib/postgresql/data
    ports:
      - "5432:5432"

Kubernetes Deployment¶

Kubernetes Configuration¶

Deployment Configuration:

# backend-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: backend-service
  labels:
    app: backend-service
spec:
  replicas: 3
  selector:
    matchLabels:
      app: backend-service
  template:
    metadata:
      labels:
        app: backend-service
    spec:
      containers:
      - name: backend
        image: studio/backend:latest
        ports:
        - containerPort: 4000
        env:
        - name: NODE_ENV
          value: "production"
        - name: DATABASE_URL
          valueFrom:
            secretKeyRef:
              name: database-secret
              key: url
        resources:
          requests:
            cpu: 500m
            memory: 1Gi
          limits:
            cpu: 1000m
            memory: 2Gi
        livenessProbe:
          httpGet:
            path: /api/health
            port: 4000
          initialDelaySeconds: 30
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /api/ready
            port: 4000
          initialDelaySeconds: 5
          periodSeconds: 5

Service Configuration:

# backend-service.yaml
apiVersion: v1
kind: Service
metadata:
  name: backend-service
spec:
  selector:
    app: backend-service
  ports:
  - protocol: TCP
    port: 4000
    targetPort: 4000
  type: ClusterIP

🔍 Service Discovery¶

Service Registration¶

Service Registry¶

Consul Service Registry:

# consul.yml
consul:
  datacenter: dc1
  data_dir: /opt/consul
  server: true
  bootstrap_expect: 3
  ui:
    enabled: true
  connect:
    enabled: true
  services:
    - name: backend-service
      id: backend-service-1
      address: 192.168.1.10
      port: 4000
      tags: [backend, api]
      checks:
        - http: http://192.168.1.10:4000/api/health
          interval: 10s
          timeout: 5s

Service Registration:

// Service registration
import Consul from 'consul';

class ServiceRegistry {
  private consul: Consul;

  constructor() {
    this.consul = new Consul({
      host: process.env.CONSUL_HOST,
      port: process.env.CONSUL_PORT,
    });
  }

  async registerService(serviceName: string, serviceId: string, address: string, port: number) {
    try {
      await this.consul.agent.service.register({
        name: serviceName,
        id: serviceId,
        address: address,
        port: port,
        tags: [serviceName],
        check: {
          http: `http://${address}:${port}/api/health`,
          interval: '10s',
          timeout: '5s',
        },
      });

      console.log(`Service ${serviceName} registered successfully`);
    } catch (error) {
      console.error('Failed to register service:', error);
    }
  }

  async deregisterService(serviceId: string) {
    try {
      await this.consul.agent.service.deregister(serviceId);
      console.log(`Service ${serviceId} deregistered successfully`);
    } catch (error) {
      console.error('Failed to deregister service:', error);
    }
  }

  async discoverService(serviceName: string) {
    try {
      const services = await this.consul.agent.health.service({
        service: serviceName,
        passing: true,
      });

      return services.map(service => ({
        id: service.Service.ID,
        address: service.Service.Address,
        port: service.Service.Port,
        tags: service.Service.Tags,
      }));
    } catch (error) {
      console.error('Failed to discover service:', error);
      return [];
    }
  }
}

Load Balancing¶

Load Balancing Strategies¶

Round Robin Load Balancing:

# nginx.conf
upstream backend {
  least_conn;
  server backend-1:4000 max_fails=3 fail_timeout=30s;
  server backend-2:4000 max_fails=3 fail_timeout=30s;
  server backend-3:4000 max_fails=3 fail_timeout=30s;
}

server {
  listen 80;
  server_name api.studio.com;

  location /api/ {
    proxy_pass http://backend;
    proxy_set_header Host $host;
    proxy_set_header X-Real-IP $remote_addr;
    proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
    proxy_set_header X-Forwarded-Proto $scheme;

    health_check_interval 5s;
    health_check_timeout 3s;
    health_check_fail_timeout 30s;
  }
}

Load Balancing Algorithms: - Round Robin - Requests distributed evenly - Least Connections - Requests sent to least busy server - IP Hash - Requests sent based on client IP - Weighted Round Robin - Weighted distribution - Random - Random server selection

🔧 Service Monitoring¶

Health Checks¶

Health Check Implementation¶

Health Check Endpoint:

// Health check service
export class HealthCheckService {
  private dependencies: Map<string, () => Promise<boolean>> = new Map();

  constructor() {
    this.dependencies.set('database', this.checkDatabase);
    this.dependencies.set('redis', this.checkRedis);
    this.dependencies.set('neo4j', this.checkNeo4j);
    this.dependencies.set('external_apis', this.checkExternalApis);
  }

  async checkHealth(): Promise<HealthStatus> {
    const checks = new Map<string, Promise<boolean>>();

    for (const [name, check] of this.dependencies) {
      checks.set(name, check());
    }

    const results = await Promise.allSettled(checks);
    const status = results.every(result => result.status === 'fulfilled');

    return {
      status: status ? 'healthy' : 'unhealthy',
      timestamp: new Date().toISOString(),
      checks: Array.from(checks.entries()).map(([name, promise]) => ({
        name,
        status: promise.status === 'fulfilled' ? 'healthy' : 'unhealthy',
        duration: 0, // TODO: measure duration
      })),
    };
  }

  private async checkDatabase(): Promise<boolean> {
    try {
      await DatabaseService.query('SELECT 1');
      return true;
    } catch (error) {
      console.error('Database health check failed:', error);
      return false;
    }
  }

  private async checkRedis(): Promise<boolean> {
    try {
      await RedisService.ping();
      return true;
    } catch (error) {
      console.error('Redis health check failed:', error);
      return false;
    }
  }

  private async checkNeo4j(): Promise<boolean> {
    try {
      await Neo4jService.query('RETURN 1');
      return true;
    } catch (error) {
      console.error('Neo4j health check failed:', error);
      return false;
    }
  }

  private async checkExternalApis(): Promise<boolean> {
    try {
      // Check external API connectivity
      const response = await fetch('https://api.google.com');
      return response.ok;
    } catch (error) {
      console.error('External API health check failed:', error);
      return false;
    }
  }
}

Metrics Collection¶

Prometheus Metrics¶

Metrics Exporter:

// Metrics service
import { register, collectDefaultMetrics, Counter, Histogram, Gauge } from 'prom-client';

// Create metrics
const httpRequestDuration = new Histogram({
  name: 'http_request_duration_seconds',
  help: 'Duration of HTTP requests in seconds',
  labelNames: ['method', 'route', 'status_code'],
  buckets: [0.1, 0.5, 1, 2, 5, 10],
});

const httpRequestTotal = new Counter({
  name: 'http_requests_total',
  help: 'Total number of HTTP requests',
  labelNames: ['method', 'route', 'status_code'],
});

const activeConnections = new Gauge({
  name: 'active_connections',
  help: 'Number of active connections',
  labelNames: ['service'],
});

export class MetricsService {
  static recordHttpRequest(method: string, route: string, statusCode: number, duration: number) {
    httpRequestDuration
      .labels(method, route, statusCode.toString())
      .observe(duration);

    httpRequestTotal
      .labels(method, route, statusCode.toString())
      .inc();
  }

  static incrementActiveConnections(service: string) {
    activeConnections.labels(service).inc();
  }

  static decrementActiveConnections(service: string) {
    activeConnections.labels(service).dec();
  }

  static getMetrics() {
    return collectDefaultMetrics();
  }
}

✅ Microservices Best Practices¶

Design Best Practices¶

Service Design¶

• Single Responsibility - Each service has a single responsibility
• Bounded Context - Well-defined service boundaries
• API-First - Design APIs first
• Event-Driven - Use events for communication
• Observability - Make services observable

Communication Best Practices¶

• Synchronous - For immediate response requirements
• Asynchronous - For background processing
• Event-Driven - For loose coupling
• API Gateway - Single entry point
• Service Mesh - For service-to-service communication

Common Microservices Mistakes¶

❌ Avoid These Mistakes: - Not defining clear service boundaries - Not implementing proper error handling - Not considering service dependencies - Not implementing proper monitoring - Not designing for failure

✅ Follow These Best Practices: - Define clear service boundaries - Implement comprehensive error handling - Consider service dependencies carefully - Implement comprehensive monitoring - Design for failure and recovery

!!! tip Start Small Start with a few core services and gradually add more as needed. Don't create too many services initially.

!!! note Service Boundaries Define clear service boundaries and data ownership. Avoid tight coupling between services.

!!! question Need Help? Check our Microservices Support for microservices assistance, or join our developer community.