Deployment Architecture
Overview
The MOOD MNKY deployment architecture is designed for reliability, scalability, and developer productivity. Our infrastructure follows cloud-native principles and leverages modern deployment techniques to ensure consistent and efficient delivery of our applications.Our deployment architecture is built on containerization, infrastructure as code, and automated CI/CD pipelines to ensure reliable and consistent deployments.
Infrastructure Stack
Compute
Docker containers orchestrated with Kubernetes for scalable and reliable deployments
Database
Supabase for relational database, authentication, and storage
Content Delivery
CloudFlare for CDN, DNS, and edge functions
Environments
We maintain separate environments to support our development process:- Development
- Staging
- Production
Deployment Architecture
Our deployment architecture follows a modern containerized approach:Container Strategy
Base Images
Base Images
We use a set of standardized base images to ensure consistency across services:
Application Images
Application Images
Each application has its own optimized Dockerfile tailored to its specific needs:
Container Optimization
Container Optimization
We implement several optimizations to minimize container size and improve security:
- Multi-stage builds to reduce final image size
- Layer caching for faster build times
- Non-root users for improved security
- Dependency scanning for vulnerabilities
- Image signing for authenticity verification
- Minimal base images to reduce attack surface
Kubernetes Configuration
We use Kubernetes to orchestrate our containerized applications:- Namespace Structure
- Deployment Configuration
- Service Configuration
CI/CD Pipeline
Our continuous integration and delivery pipeline is implemented using GitHub Actions:1
Source Control
All code is maintained in our GitHub repository, with dedicated branches for features, releases, and hotfixes.
2
Continuous Integration
Every commit triggers automated tests, linting, and builds.
3
Container Building
Successful builds are packaged into Docker containers and pushed to our registry.
4
Deployment
Containers are deployed to the appropriate environment based on the branch.
Database Deployments
Our database migrations are managed using Supabase migrations:Migration Process
Migration Process
Database changes follow a structured process:
- Development: Migrations are created locally and tested in development
- Version Control: Migration files are committed to the repository
- CI Verification: Migrations are tested against a test database
- Staging Apply: Migrations are applied to the staging environment
- Production Apply: After verification, migrations are applied to production
Migration Safety
Migration Safety
We follow several practices to ensure safe database migrations:
- Backwards-compatible schema changes when possible
- Multi-stage migrations for complex changes
- Pre-migration backups for critical environments
- Read-only transition periods during major changes
- Rollback plans for each migration
Migration Strategies
Migration Strategies
Different types of schema changes require different approaches:Adding Tables/Columns: Safe operation that can be performed without downtimeModifying Columns: Requires careful planning for existing dataRemoving Tables/Columns: Multi-stage process with code changes first
Secrets Management
We use a combination of approaches to manage secrets and configuration:Kubernetes Secrets
Sensitive information is stored as Kubernetes secrets and mounted into containers at runtime.
Environment Configuration
Non-sensitive configuration is stored as ConfigMaps and environment variables.
Monitoring and Observability
Our observability stack ensures we can monitor application health and performance:Logging
Structured logs from all services are centralized in our logging platform for analysis.
Metrics
Application and infrastructure metrics provide insights into system behavior and performance.
Tracing
Distributed tracing helps us understand request flows across our microservices architecture.
Alerting
Proactive alerting notifies the team of potential issues before they impact users.
Dashboards
Custom dashboards visualize system health and business metrics for different stakeholders.
Error Tracking
Automated error collection and analysis helps us identify and fix issues quickly.
Deployment Strategies
We employ different deployment strategies depending on the nature of the change:Blue-Green Deployments
Blue-Green Deployments
For significant updates, we use blue-green deployments to minimize risk:
- Deploy the new version (green) alongside the current version (blue)
- Gradually redirect traffic to the green environment
- Monitor for issues and quickly rollback if needed by routing traffic back to blue
- Once stable, decommission the blue environment
Canary Releases
Canary Releases
For testing new features with a subset of users:
- Deploy the new version to a small subset of servers
- Route a percentage of traffic to the new version
- Gradually increase the percentage as confidence grows
- Complete the rollout when the feature is proven stable
Rolling Updates
Rolling Updates
For routine changes and updates:
- Deploy the new version to a subset of servers
- Wait for the new instances to become healthy
- Gradually replace old instances with new ones
- Continue until all instances are updated
Feature Flags
We use feature flags to safely deploy new features and control their availability:Disaster Recovery
Our disaster recovery plan ensures we can recover from infrastructure failures:1
Backup Strategy
Regular automated backups of all critical data, including databases and persistent storage.
2
Recovery Testing
Periodic testing of recovery procedures to ensure they work as expected.
3
Geographic Redundancy
Critical systems are replicated across multiple geographic regions.
4
Incident Response
Documented procedures for responding to different types of infrastructure incidents.
Security Practices
Our deployment architecture includes several security measures:Network Isolation
Services are organized in isolated network segments with controlled communication paths.
Access Controls
Least privilege principle applied to all systems and services.
Vulnerability Scanning
Regular scanning of containers and dependencies for known vulnerabilities.
Data Encryption
Data encrypted both at rest and in transit throughout the infrastructure.
Security Monitoring
Continuous monitoring for suspicious activities and security events.
Compliance
Adherence to industry standards and best practices for security.
Resources
Kubernetes Documentation
Official Kubernetes documentation
Supabase Documentation
Official Supabase documentation
Internal Runbooks
Detailed operational procedures
For questions about our deployment architecture, please contact the MOOD MNKY DevOps team.