I’ve developed a fully working POC for a scalable platform. Working instance is here - link This document outlines the design and implementation of a microservices architecture platform using AWS EKS to host a chat application. The solution covers infrastructure, orchestration, deployment, release lifecycle, testing, monitoring, and security.
Platform Choice: AWS (Amazon Web Services)
Rationale:
- AWS provides a comprehensive suite of services that support microservices architectures.
- EKS (Elastic Kubernetes Service) offers managed Kubernetes, simplifying cluster management.
- Integrated services for storage (EBS, S3), databases (RDS, Aurora), networking (VPC, Route 53), and security (IAM, KMS).
- Global presence and reliability with multiple availability zones.
Key Services:
- EKS (Elastic Kubernetes Service): Managed Kubernetes service for container orchestration.
- AuroraDB: A fully managed, highly available, and auto-scaling relational database service.
Orchestration Technology: Kubernetes
Components:
- EKS (Elastic Kubernetes Service): Managed Kubernetes service for container orchestration.
- Helm: Kubernetes package manager for managing complex deployments.
- Kustomize: Native Kubernetes configuration management tool.
- Cluster Autoscaler: Automatically adjusts the size of the Kubernetes cluster based on the needs of the application.
- AuroraDB: Managed PostgreSQL-compatible relational database with built-in high availability and auto-scaling.
Rationale:
- Kubernetes provides robust orchestration capabilities, including scaling, self-healing, and rolling updates.
- EKS simplifies Kubernetes management, providing a secure and scalable environment.
- AuroraDB offers automatic scaling, backup, and recovery features, reducing the operational burden.
Tools: Terraform
Rationale:
- Terraform allows for infrastructure as code (IaC), enabling version-controlled, repeatable deployments.
- Strong support for AWS services.
Configuration Highlights:
- Set up the AWS provider.
- Create a VPC, subnets, and an EKS cluster.
- Configure node groups for the EKS cluster.
- Set up an AuroraDB cluster with auto-scaling configuration.
Tools: Helm, Kubernetes, Github actions
Rationale:
- Helm simplifies deployment management through templated Kubernetes manifests.
- Kubernetes provides native support for deploying and managing containerized applications.
Configuration Highlights:
- Define Helm chart values for backend and frontend services.
- Include configuration for connecting to the AuroraDB instance.
- Use Helm commands to install and upgrade the application.
Phases:
- Development: Code changes are committed to the feature branch.
- Build: CI/CD pipeline builds Docker images and runs unit tests.
- Testing: Deploy to a staging environment and run integration tests.
- Approval: Manual approval for production deployment.
- Release: Deploy to the production environment using Helm.
CI/CD Pipeline:
- Use GitHub Actions or another CI/CD tool to automate the build, test, and deployment process.
- Steps include checking out code, building Docker images, pushing images to a registry, and deploying to EKS using Helm.
Types of Tests:
- Unit Tests: Test individual components of Terraform scripts using Terratest.
- Integration Tests: Validate the interaction between components after deployment.
- End-to-End Tests: Verify the entire application flow in a staging environment.
Testing Strategy:
- Implement Terratest for infrastructure testing.
- Deploy infrastructure to a test environment and run integration tests.
- Perform end-to-end tests in a staging environment to ensure system integrity.
Tools:
- Prometheus & Grafana: Monitoring and alerting.
- AWS CloudWatch: Logs and metrics collection.
- ELK Stack (Elasticsearch, Logstash, Kibana): Centralized logging.
Rationale:
- Prometheus provides powerful monitoring and alerting capabilities.
- Grafana offers flexible, customizable dashboards.
- CloudWatch and ELK Stack ensure comprehensive logging and metrics collection.
Monitoring Strategy:
- Configure Prometheus to scrape metrics from Kubernetes and application endpoints.
- Use Grafana to visualize metrics and set up alerting rules.
- Centralize logs using AWS CloudWatch and the ELK Stack to monitor application and infrastructure logs.
Tools and Practices:
- IAM (Identity and Access Management): Fine-grained access control for AWS resources.
- KMS (Key Management Service): Managed service to create and control the encryption keys.
- Secrets Manager: Securely store and manage sensitive information such as database credentials.
- Security Groups and Network ACLs: Control inbound and outbound traffic to AWS resources.
- Pod Security Policies: Enforce security standards for pods running in the Kubernetes cluster.
- AWS WAF (Web Application Firewall): Protect web applications from common web exploits.
Security Strategy:
- IAM Policies: Implement the principle of least privilege by defining IAM policies that grant the minimum required permissions.
- Encryption: Encrypt data at rest using AWS KMS and in transit using TLS.
- Secrets Management: Use AWS Secrets Manager to store and retrieve secrets securely.
- Network Security: Use Security Groups and Network ACLs to control traffic to and from EKS nodes and other AWS resources.
- Pod Security: Define and enforce pod security policies to limit container privileges and ensure compliance with security standards.
- Web Application Security: Deploy AWS WAF to protect the frontend service from common web vulnerabilities such as SQL injection and cross-site scripting (XSS).
Additional Measures:
- Regular Audits: Conduct regular security audits and vulnerability assessments.
- Logging and Monitoring: Enable detailed logging and monitoring to detect and respond to security incidents promptly.
- Incident Response: Develop and maintain an incident response plan to handle potential security breaches effectively.
The full implementation and detailed configurations are available in the following repositories:
