Microservices Architecture Standards

This document outlines the standards and best practices for designing, building, and operating microservices-based applications at Bayat. Following these guidelines ensures consistent, maintainable, and scalable microservices architectures.

Microservices Principles

All microservices-based applications at Bayat should adhere to the following core principles:

Single Responsibility: Each service should focus on a specific business capability
Autonomy: Services should be developed, deployed, and scaled independently
Resilience: Services should be designed to gracefully handle failures
Decentralization: Avoid shared databases and centralized governance
Observable: Services must expose metrics, logs, and traces
Evolutionary Design: Design for change and continuous refactoring
Domain-Driven: Align services with business domains
Automation: Maximize automation for development, testing, and operations

Service Design

Service Boundaries

Define service boundaries based on:

Business capabilities and domains
Team structure and ownership
Data cohesion and access patterns
Change frequency and scalability requirements

A well-designed service should:

Encapsulate a clear business capability
Own its data and expose it only through well-defined interfaces
Be independently deployable and testable
Have a focused and maintainable codebase (< 10,000 LOC guideline)

Service Size Guidelines

Consider the following factors when determining service size:

Too Large: Multiple domains, overlapping concerns, complex interfaces, diverse data needs
Too Small: Tight coupling to other services, excessive inter-service communication, limited functionality
Just Right: Clear responsibility, manageable codebase, reasonable interface, team ownership

Service Types

Standardize on the following service patterns:

API Services: Expose functionality to clients and other services
Processing Services: Handle background and asynchronous workloads
Integration Services: Adapt and translate between external systems
Aggregation Services: Combine multiple service responses for clients
Infrastructure Services: Provide common platform capabilities

Communication Patterns

Synchronous Communication

For real-time interactions:

Use REST for simple CRUD operations and queries
Use GraphQL for complex data queries and aggregations
Use gRPC for high-performance internal service communication
Implement proper timeouts, retries, and circuit breakers
Document all synchronous APIs comprehensively

Asynchronous Communication

For event-driven and decoupled operations:

Use message queues (RabbitMQ, Amazon SQS) for task distribution
Use event streaming (Kafka, Kinesis) for event sourcing and analysis
Implement idempotent receivers to handle duplicate events
Design for at-least-once delivery semantics
Maintain event schemas and versioning

Choosing the Right Pattern

Scenario	Recommended Pattern
User-initiated actions	Synchronous REST/GraphQL
Data querying	Synchronous REST/GraphQL
Long-running operations	Asynchronous with callbacks
System events	Event streaming
Cross-service workflows	Orchestration service or choreography
High-volume data processing	Event streaming

Communication Standards

All service-to-service communication must be encrypted (TLS)
Authentication required for all service calls
Use standard headers for tracing, correlation IDs, and tenant information
Implement graceful degradation when dependent services are unavailable

Data Management

Data Ownership

Each service owns and manages its data
No direct database access from outside the service
Data is only exposed through service APIs
Consider CQRS for complex read/write patterns

Database Per Service

Implement database-per-service pattern:

Each service has its own logical database
Services should not share database instances in production
Choose the appropriate database type for each service (relational, NoSQL, time-series, etc.)
Implement proper data backup and recovery procedures

Data Consistency

For maintaining consistency across services:

Prefer eventual consistency where possible
Use the Saga pattern for distributed transactions
Implement compensating transactions for failure recovery
Consider event sourcing for complex state tracking
Document consistency guarantees for each service

Data Duplication

When duplicating data across services:

Clearly document the source of truth
Implement data synchronization mechanisms
Use event-based updates to propagate changes
Track synchronization health and alert on inconsistencies

API Design

API Standards

All service APIs must follow these standards:

Use HTTP status codes correctly
Implement consistent error response formats
Use hypermedia links when appropriate (HATEOAS)
Follow RESTful resource naming conventions
Version all APIs explicitly

API Documentation

Document all APIs using:

OpenAPI (Swagger) for REST APIs
GraphQL Schema with descriptions for GraphQL APIs
Protocol Buffers with comments for gRPC APIs
Include example requests and responses
Document error conditions and handling

API Versioning

Implement proper API versioning:

Use semantic versioning for API changes
Include version in URL path (/v1/resources)
Support at least one previous version during transition periods
Document deprecation timelines
Provide migration guides for breaking changes

API Gateways

Use API gateways for:

Request routing
Authentication and authorization
Rate limiting and throttling
Response caching
Analytics and monitoring
Cross-cutting concerns (CORS, compression)

Service Discovery

Discovery Mechanisms

Implement service discovery using:

DNS-based discovery for simplicity
Service registry (Consul, etcd) for dynamic environments
Load balancers for stable endpoints
Kubernetes service discovery when applicable

Client-Side Discovery

For client-side discovery:

Use service registries for real-time service information
Implement health-check-aware client libraries
Consider client-side load balancing for high-volume calls
Cache service information with appropriate TTLs

Server-Side Discovery

For server-side discovery:

Use load balancers or API gateways
Configure proper health checks
Implement proper failover mechanisms
Document discovery endpoints

Deployment and Scaling

Containerization

Package services as containers:

Use Docker for containerization
Create minimal container images
Avoid storing sensitive data in containers
Scan container images for vulnerabilities
Follow container security best practices

Container Orchestration

Use Kubernetes for container orchestration:

Deploy each service as a separate Kubernetes deployment
Use namespaces for environment isolation
Implement proper resource requests and limits
Use horizontal pod autoscaling based on metrics
Configure appropriate liveness and readiness probes

Scaling Policies

Define scaling policies for each service:

Identify scaling metrics (CPU, memory, requests per second, queue depth)
Set appropriate minimum and maximum instance counts
Configure scaling thresholds based on performance testing
Document scaling behaviors and limitations
Test scaling under load

Example Kubernetes HPA configuration:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: order-service
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: order-service
  minReplicas: 3
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Pods
    pods:
      metric:
        name: requests_per_second
      target:
        type: AverageValue
        averageValue: 500

Monitoring and Observability

Metrics

Each service must expose the following metrics:

Request count, latency, and error rates
Resource utilization (CPU, memory, disk, network)
Business-specific metrics
Dependencies health and performance
Queue depths and processing rates

Use Prometheus or similar systems for metrics collection and alerting.

Logging

Implement standardized logging:

Use structured logging formats (JSON)
Include correlation IDs in all logs
Log at appropriate levels (ERROR, WARN, INFO, DEBUG)
Centralize logs with Elasticsearch, Splunk, or similar
Implement log retention policies

Log entry example:

{
  "timestamp": "2023-03-12T15:22:31.123Z",
  "level": "INFO",
  "service": "order-service",
  "instance": "order-service-7d9f6b5b9c-2jkvn",
  "traceId": "4c0f8c2b-cb4e-11ed-afa1-0242ac120002",
  "userId": "user-123",
  "message": "Order created successfully",
  "orderId": "order-456",
  "orderValue": 129.99
}

Distributed Tracing

Implement distributed tracing:

Use OpenTelemetry for instrumentation
Propagate trace context across service boundaries
Sample traces based on environment and traffic volume
Collect traces in Jaeger, Zipkin, or similar systems
Analyze traces for performance optimization

Health Checks

Implement comprehensive health checks:

Liveness: Basic check if service is running
Readiness: Check if service can handle requests
Dependency: Check status of critical dependencies
Business logic: Verify critical functionalities
Deep health: End-to-end verification of key flows

Example health check implementation:

@Controller('health')
export class HealthController {
  constructor(
    private db: DatabaseService,
    private messageQueue: MessageQueueService,
  ) {}

  @Get('liveness')
  async liveness() {
    return { status: 'UP' };
  }

  @Get('readiness')
  async readiness() {
    const dbStatus = await this.db.ping();
    const mqStatus = await this.messageQueue.ping();
    
    return {
      status: dbStatus.healthy && mqStatus.healthy ? 'UP' : 'DOWN',
      details: {
        database: dbStatus,
        messageQueue: mqStatus,
      }
    };
  }
}

Security

Authentication and Authorization

Implement robust authentication and authorization:

Use OAuth 2.0 or OpenID Connect for authentication
Implement role-based access control (RBAC)
Use short-lived tokens and proper token validation
Implement proper API key management for service-to-service communication
Audit all authentication and authorization decisions

Network Security

Secure the network layer:

Encrypt all service-to-service communication (mutual TLS)
Implement network policies to restrict traffic between services
Use API gateways for external traffic
Scan for network vulnerabilities regularly
Document network topology and security controls

Secure Coding

Follow secure coding practices:

Validate all inputs
Protect against common vulnerabilities (OWASP Top 10)
Use secure dependencies and keep them updated
Perform regular security scans of codebase and containers
Implement proper error handling to avoid information disclosure

Secrets Management

Manage secrets securely:

Use dedicated secrets management solutions (HashiCorp Vault, AWS Secrets Manager)
Never store secrets in code or configuration files
Rotate secrets regularly
Audit secret access
Implement least privilege for secret access

Testing

Unit Testing

Test individual service components:

Aim for high test coverage (>80%)
Mock external dependencies
Focus on business logic and error handling
Automate unit tests in CI/CD pipeline

Integration Testing

Test service integrations:

Test API contracts
Verify database interactions
Test messaging patterns
Use test containers for dependencies

Component Testing

Test services in isolation:

Deploy single service with test dependencies
Verify all endpoints and functionality
Test scaling and resource utilization
Verify metrics and logging

End-to-End Testing

Test complete service interactions:

Deploy realistic service topology
Test critical user journeys
Verify distributed transactions
Test failure scenarios and recovery

Performance Testing

Verify service performance:

Establish performance baselines
Test maximum throughput
Measure response times under load
Identify bottlenecks
Verify scaling behavior

DevOps Practices

CI/CD Pipeline

Implement comprehensive CI/CD:

Automated builds for every commit
Unit and integration tests for every build
Vulnerability scanning
Static code analysis
Automated deployments to development environments
Controlled promotions to higher environments

Example CI/CD Pipeline:

# GitLab CI/CD pipeline example
stages:
  - build
  - test
  - scan
  - deploy-dev
  - integration-test
  - deploy-staging
  - performance-test
  - deploy-prod

build:
  stage: build
  script:
    - docker build -t ${SERVICE_NAME}:${CI_COMMIT_SHA} .
    - docker push ${SERVICE_NAME}:${CI_COMMIT_SHA}

unit-test:
  stage: test
  script:
    - npm run test:unit

integration-test:
  stage: test
  script:
    - npm run test:integration

security-scan:
  stage: scan
  script:
    - trivy image ${SERVICE_NAME}:${CI_COMMIT_SHA}
    - sonarqube-scanner

deploy-dev:
  stage: deploy-dev
  script:
    - helm upgrade --install ${SERVICE_NAME} ./charts/${SERVICE_NAME} --set image.tag=${CI_COMMIT_SHA} -n development
  environment:
    name: development

# Additional stages for higher environments

Infrastructure as Code

Manage infrastructure through code:

Use Terraform, CloudFormation, or similar for infrastructure provisioning
Use Helm charts for Kubernetes deployments
Version control all infrastructure code
Review infrastructure changes
Test infrastructure changes in non-production environments

Feature Flags

Implement feature flags for controlled releases:

Use centralized feature flag management
Deploy code with features disabled by default
Enable features gradually
Monitor feature impact
Support easy rollback through feature disablement

Service Templates

Starter Templates

Provide standardized service templates for common patterns:

REST API service
Event processor service
GraphQL service
Background worker service
Integration service

Each template should include:

Basic service structure
Standardized logging, metrics, and tracing
Health checks
Documentation templates
CI/CD configuration
Deployment manifests

Example Service Structure:

service-name/
├── src/
│   ├── api/              # API controllers and routes
│   ├── core/             # Core business logic
│   ├── config/           # Service configuration
│   ├── models/           # Data models
│   ├── repositories/     # Data access
│   ├── services/         # Business services
│   └── utils/            # Utilities
├── test/
│   ├── unit/             # Unit tests
│   ├── integration/      # Integration tests
│   └── e2e/              # End-to-end tests
├── Dockerfile            # Container definition
├── charts/               # Helm charts for deployment
│   └── service-name/
├── .gitlab-ci.yml        # CI/CD configuration
├── package.json          # Dependencies
└── README.md             # Documentation

Shared Libraries

Develop and maintain shared libraries for common functionalities:

Authentication and authorization
Logging and observability
Resilience patterns (circuit breakers, retries)
Common data models
API clients

Governance

Service Catalog

Maintain a central service catalog:

Document all services and their responsibilities
Track service owners and dependencies a
Monitor service health and SLAs
Review service metrics and quality

Example Service Registry Entry:

name: order-service
description: Manages order creation and processing
team: commerce
owner: commerce-team@bayat.io
repository: https://git.bayat.io/commerce/order-service
api-documentation: https://docs.bayat.io/apis/order-service
dependencies:
  - user-service
  - product-service
  - payment-service
sla:
  availability: 99.95%
  latency_p95: 300ms
technologies:
  language: Node.js
  framework: NestJS
  database: MongoDB
  messaging: RabbitMQ

Architecture Review

Establish architecture review process:

Review new service proposals
Evaluate changes to service boundaries
Assess technology choices
Verify compliance with standards
Provide design feedback and guidance

Standards Evolution

Continuously improve standards:

Gather feedback from development teams
Review and update standards quarterly
Communicate changes clearly
Provide migration paths for existing services
Run architecture town halls for knowledge sharing

Migration Strategies

Monolith to Microservices

Guidelines for decomposing monoliths:

Start with domains: Identify bounded contexts in the monolith
Strangler pattern: Gradually replace monolith functionality
Data separation: Extract service-specific data incrementally
API facade: Create API layer over the monolith
Prioritize value: Begin with high-value or problematic areas

Implementation Approach

Recommended implementation sequence:

Create the new service with its own database
Implement data synchronization from monolith to service
Redirect reads to the new service
Redirect writes to the new service
Migrate historical data
Remove functionality from the monolith

Common Challenges

Address common migration challenges:

Shared data: Use data replication or views initially
Transactions: Implement sagas for distributed transactions
Authentication: Create unified authentication service
Deployment: Use blue/green deployments for seamless transition
Testing: Create comprehensive test suite before migration

Version History

Version	Date	Description
1.0	2025-03-20	Initial version

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