Deployment Standards

This document outlines the deployment standards and practices for all Bayat projects. Following these guidelines ensures consistent, reliable, and secure deployments across all environments and project types.

Deployment Principles
Environments
Deployment Process
Versioning
Artifacts and Registries
Configuration Management
Deployment Automation
Deployment Strategies
Rollback Procedures
Release Notes
Post-Deployment Verification
Environment-Specific Guidelines
Security Considerations
Maintenance Windows
Monitoring and Alerting

Deployment Principles

All deployments at Bayat should adhere to the following core principles:

Automation: Automate deployment processes to ensure consistency and reduce human error
Repeatability: Deployment processes should produce the same results when repeated
Traceability: Track what was deployed, when, by whom, and with what configuration
Security: Include security checks and validations at each stage
Testability: Verify deployments against defined acceptance criteria
Isolation: Changes to one environment should not affect others
Rollback capability: Every deployment should have a defined rollback plan
Minimal downtime: Strive for zero-downtime deployments where possible

Environments

Standard Environments

Every project must have at least the following environments:

Environment	Purpose	Access	Deployment Frequency	Data Sensitivity
Development	Feature development and integration	Developers	Continuous	Sanitized/Fake
Testing/QA	Formal testing and validation	QA, Developers	After dev approval	Sanitized/Fake
Staging	Pre-production verification	Limited team members	After QA approval	Production-like sanitized
Production	Live customer-facing environment	Highly restricted	Scheduled releases	Real data

Environment Parity

Maintain high parity between environments:

Use the same operating systems, versions, and configurations
Use the same deployment mechanisms across all environments
Scale differences should be in resource allocation, not architecture
Document any necessary differences between environments

Deployment Process

Deployment Pipeline

All projects must implement a deployment pipeline with the following stages:

Build: Compile code, run static analysis, create deployment artifacts
Test: Run automated tests (unit, integration, etc.)
Scan: Perform security scanning, dependency vulnerability checks
Publish: Push artifacts to registries (container, package, etc.)
Deploy: Release to target environment
Verify: Run post-deployment checks and smoke tests
Monitor: Track system health and metrics after deployment

Approval Gates

Define approval requirements for each environment:

Development: Automated checks only
Testing/QA: Lead developer approval
Staging: QA approval and product owner sign-off
Production: Management approval and change management process

Required Checks

Every deployment must pass the following checks:

All automated tests pass
Security scan shows no critical or high vulnerabilities
Infrastructure validation tests pass
Required approvals have been obtained
Release documentation is complete

Versioning

Version Scheme

Use Semantic Versioning (SemVer) for all deployable artifacts:

MAJOR.MINOR.PATCH (e.g., 2.3.1)
Increment MAJOR version for incompatible API changes
Increment MINOR version for backward-compatible new features
Increment PATCH version for backward-compatible bug fixes

Version Tracking

Tag all releases in the version control system with the version number
Store the current version information in a dedicated location in each environment
Include version information in logs and monitoring data
Make the version visible in the application's admin interface or API

Artifacts and Registries

Artifact Types

Standardize on the following artifact types:

Docker containers for applications
NPM/PyPI packages for libraries
Helm charts for Kubernetes deployments
Terraform modules for infrastructure
OS-specific packages (RPM, DEB) for system components

Registry Requirements

All artifact registries must:

Require authentication for uploads and restricted downloads
Support artifact signing and verification
Maintain artifact retention policies
Be backed up regularly
Support versioning and immutability

Standardized Registries

Use the following registries:

Container images: Harbor or AWS ECR
NPM packages: GitHub Packages or JFrog Artifactory
Python packages: JFrog Artifactory
Helm charts: Harbor or JFrog Artifactory
Infrastructure modules: GitLab or GitHub

Configuration Management

Configuration Sources

Manage configuration using the following hierarchy:

Default values: Hard-coded in application
Configuration files: Version-controlled with the application
Environment variables: For environment-specific settings
Configuration service: For dynamic runtime configuration

Secrets Management

Handle sensitive information appropriately:

Never commit secrets to version control
Use a dedicated secrets management service (HashiCorp Vault, AWS Secrets Manager)
Encrypt secrets at rest and in transit
Rotate secrets regularly
Use different secrets for each environment

Configuration Validation

Validate configurations before deployment:

Verify required configuration is present
Check types and formats
Validate connections to external services
Test with the configuration before promoting to next environment

Deployment Automation

CI/CD Tools

Standardize on the following CI/CD tools:

GitHub Actions or GitLab CI for pipeline orchestration
Terraform or AWS CloudFormation for infrastructure as code
Ansible or Puppet for configuration management
ArgoCD or Flux for GitOps-based deployments

Pipeline As Code

Store pipeline configurations as code:

Keep pipeline definitions in the same repository as application code
Version pipeline changes alongside application changes
Review pipeline changes through the same process as code changes
Test pipeline changes in development environments first

Automated Testing

Include the following tests in deployment pipelines:

Unit tests
Integration tests
End-to-end tests
Performance tests (for staging/production)
Security scans
Infrastructure validation tests

Deployment Strategies

Choose the appropriate deployment strategy based on application requirements:

Blue/Green Deployment

For applications requiring minimal downtime with higher resource usage:

Deploy new version (green) alongside existing version (blue)
Test the green environment
Switch traffic from blue to green
Verify operation
Decommission blue environment

Canary Deployment

For gradual rollouts with early feedback:

Deploy new version to a small subset of infrastructure (e.g., 5%)
Route a percentage of traffic to the new version
Monitor for issues
Gradually increase traffic percentage
Proceed to full deployment or rollback based on monitoring

Rolling Deployment

For resource-efficient deployments:

Deploy new version to a subset of instances/pods
Verify proper operation
Continue deploying to more instances in batches
Complete when all instances are updated

Feature Flags

Use feature flags for controlled feature releases:

Deploy code with new features behind disabled flags
Enable features selectively for specific users or environments
Roll out features gradually by increasing the percentage of users
Monitor feature performance and issues
Enable for all users or roll back as needed

Rollback Procedures

Rollback Triggers

Define clear criteria for initiating a rollback:

Error rates exceed defined thresholds
Response times exceed SLA limits
Critical functionality is unavailable
Security vulnerability is discovered
Stakeholder decision based on business impact

Rollback Process

Document and test rollback procedures for each application:

Determine the need for rollback based on triggers
Follow application-specific rollback procedure:
- Revert to previous version
- Restore database if necessary
- Reset configuration as needed
Verify application functionality after rollback
Communicate rollback to stakeholders
Document the rollback incident

Rollback Testing

Regularly test rollback procedures:

Include rollback tests in deployment pipelines
Simulate failures and practice recovery
Time rollback operations to ensure they meet SLA requirements

Release Notes

Release Documentation

Create comprehensive release notes for each production deployment:

Summary: Brief overview of the release
Version: Clear version identifier
Date: Deployment date
Features: New features and enhancements
Bug fixes: Issues resolved
Known issues: Outstanding problems
Dependencies: External system dependencies
Configuration changes: Any new or modified configuration
Migration steps: Required actions for users or administrators
Rollback plan: Specific rollback instructions for this release

Distribution Channels

Make release notes available through:

Internal documentation system for all environments
Customer-facing documentation for production releases
Email notifications to stakeholders
Release management system (Jira, Azure DevOps, etc.)

Post-Deployment Verification

Smoke Tests

Run automated smoke tests immediately after deployment:

Verify basic functionality
Check critical paths and workflows
Validate integrations with external systems
Confirm metrics and logging are working

Acceptance Criteria

Define and verify acceptance criteria for each deployment:

Functional requirements are met
Non-functional requirements (performance, security) are satisfied
No regression in existing functionality
Documentation is complete and accurate

Progressive Exposure

For complex applications, use progressive exposure:

Deploy to internal users first
Expand to beta/early adopters
Gradually increase to full user base
Monitor each expansion phase

Environment-Specific Guidelines

Kubernetes

For Kubernetes deployments:

Use Helm charts for application packaging
Implement namespace isolation between environments
Apply resource quotas and limits
Use network policies to restrict traffic
Store Kubernetes manifests in version control
Follow GitOps practices using ArgoCD or Flux

Example Deployment Configuration:

# Helm values.yaml for a standard web application
replicaCount: 3

image:
  repository: bayat/myapp
  tag: 1.2.3
  pullPolicy: IfNotPresent

resources:
  limits:
    cpu: 500m
    memory: 512Mi
  requests:
    cpu: 200m
    memory: 256Mi

autoscaling:
  enabled: true
  minReplicas: 2
  maxReplicas: 10
  targetCPUUtilizationPercentage: 80

ingress:
  enabled: true
  annotations:
    kubernetes.io/ingress.class: nginx
    cert-manager.io/cluster-issuer: letsencrypt-prod
  hosts:
    - host: myapp.bayat.io
      paths:
        - path: /
          pathType: Prefix

securityContext:
  runAsUser: 1000
  runAsGroup: 3000
  fsGroup: 2000
  readOnlyRootFilesystem: true

Serverless

For serverless deployments:

Use infrastructure as code (AWS SAM, Serverless Framework)
Implement separate AWS accounts for each environment
Set appropriate resource limits and concurrency controls
Use API Gateway for routing and authorization
Configure monitoring and alerting
Version Lambda functions and API configurations

Example Serverless Configuration:

# serverless.yml for a typical serverless application
service: bayat-service

provider:
  name: aws
  runtime: nodejs14.x
  region: us-west-2
  stage: ${opt:stage, 'dev'}
  environment:
    STAGE: ${self:provider.stage}
    LOG_LEVEL: ${self:custom.logLevels.${self:provider.stage}}
  iamRoleStatements:
    - Effect: Allow
      Action:
        - dynamodb:Query
        - dynamodb:GetItem
      Resource: !GetAtt MyTable.Arn

custom:
  logLevels:
    dev: DEBUG
    test: INFO
    staging: INFO
    prod: WARN

functions:
  api:
    handler: src/handlers/api.handler
    events:
      - http:
          path: /users
          method: get
          authorizer:
            type: COGNITO_USER_POOLS
            authorizerId: !Ref ApiGatewayAuthorizer
      - http:
          path: /users/{id}
          method: get

resources:
  Resources:
    MyTable:
      Type: AWS::DynamoDB::Table
      Properties:
        BillingMode: PAY_PER_REQUEST
        KeySchema:
          - AttributeName: id
            KeyType: HASH
        AttributeDefinitions:
          - AttributeName: id
            AttributeType: S

Traditional Hosting

For traditional VM/server deployments:

Use infrastructure as code (Terraform, CloudFormation)
Implement configuration management (Ansible, Puppet)
Create standard machine images (AMIs, Vagrant boxes)
Apply host-based security measures
Set up proper backup and recovery procedures
Document manual recovery steps

Example Terraform Configuration:

# main.tf for a typical web server
provider "aws" {
  region = var.region
}

module "vpc" {
  source = "./modules/vpc"
  environment = var.environment
}

module "security_groups" {
  source = "./modules/security_groups"
  vpc_id = module.vpc.vpc_id
  environment = var.environment
}

resource "aws_instance" "web" {
  count = var.instance_count

  ami           = var.ami_id
  instance_type = var.instance_type
  subnet_id     = module.vpc.public_subnets[count.index % length(module.vpc.public_subnets)]
  vpc_security_group_ids = [module.security_groups.web_sg_id]
  key_name      = var.key_name

  tags = {
    Name        = "web-${var.environment}-${count.index}"
    Environment = var.environment
    Project     = var.project_name
    ManagedBy   = "terraform"
  }

  root_block_device {
    volume_size = 50
    volume_type = "gp2"
    encrypted   = true
  }

  lifecycle {
    create_before_destroy = true
  }
}

resource "aws_elb" "web" {
  name = "web-${var.environment}-elb"
  subnets = module.vpc.public_subnets
  security_groups = [module.security_groups.elb_sg_id]

  listener {
    instance_port     = 80
    instance_protocol = "http"
    lb_port           = 443
    lb_protocol       = "https"
    ssl_certificate_id = var.ssl_cert_arn
  }

  health_check {
    healthy_threshold   = 2
    unhealthy_threshold = 2
    timeout             = 3
    target              = "HTTP:80/health"
    interval            = 30
  }

  instances = aws_instance.web[*].id
  cross_zone_load_balancing = true
  idle_timeout = 400

  tags = {
    Name        = "web-${var.environment}-elb"
    Environment = var.environment
    Project     = var.project_name
    ManagedBy   = "terraform"
  }
}

Security Considerations

Pre-Deployment Security

Verify security before deployment:

Run SAST (Static Application Security Testing)
Perform dependency vulnerability scanning
Review infrastructure configurations for security issues
Validate IAM/RBAC configurations
Check for secrets in code

Runtime Security

Implement runtime security measures:

Deploy web application firewalls (WAF)
Enable runtime application security protection (RASP)
Configure network security groups and access controls
Implement API rate limiting
Enable audit logging

Compliance Verification

For regulated environments:

Document compliance requirements
Include compliance checks in deployment pipelines
Generate compliance artifacts for auditing
Require security sign-off for production deployments

Maintenance Windows

Scheduled Maintenance

Define standard maintenance windows:

Production: Weekly, during lowest traffic periods (e.g., Sundays 2-5 AM)
Staging: Bi-weekly, during business hours with notification
Testing/QA: As needed with team coordination
Development: No formal window required

Maintenance Procedures

For each maintenance window:

Announce maintenance period to stakeholders
Prepare rollback plans
Execute planned changes
Verify functionality after changes
Communicate completion of maintenance
Document actions taken

Emergency Maintenance

For unscheduled urgent changes:

Assess impact and urgency
Obtain expedited approvals
Communicate to critical stakeholders
Implement changes with heightened monitoring
Document incident and follow-up actions

Monitoring and Alerting

Deployment Monitoring

Monitor deployments in progress:

Track deployment progress and status
Monitor system health during deployment
Compare performance metrics before and after deployment
Set up alerting for deployment failures

Post-Deployment Monitoring

After deployment, monitor:

Error rates and exceptions
Response times and latency
Resource utilization (CPU, memory, disk)
Business metrics (transactions, user activity)
Security events

Alerting Guidelines

Configure appropriate alerting:

Define clear thresholds based on SLAs and normal behavior
Set different alerting levels (info, warning, critical)
Assign alerts to the right teams
Implement alert aggregation to prevent alert fatigue
Document response procedures for each alert type

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