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Deployment & Orchestration

Managing the deployment, scaling, and lifecycle of the various system components requires robust orchestration, especially at scale.

Containerization

  • Technology: Docker
  • Purpose: Package each service component (Django app, Dispatcher, Celery workers with scraping dependencies, Parsing service) along with its dependencies into standardized, portable container images.
  • Benefits: Ensures consistency across development, testing, and production environments. Simplifies dependency management. Enables easier scaling and deployment.

Container Orchestration

  • Technology: Kubernetes (K8s) - potentially using managed services like AWS EKS, Google GKE, or Azure AKS.
  • Purpose: Automate the deployment, scaling, management, and networking of containerized applications.
  • Key Benefits for this System:
    • Automated Scaling: Use Horizontal Pod Autoscaler (HPA) to automatically scale the number of Celery worker pods based on metrics like message queue depth (requires custom metrics adapter like KEDA) or CPU/memory usage.
    • Deployment Strategies: Perform rolling updates or canary deployments to release new code versions with minimal downtime.
    • Self-Healing: Automatically restarts containers/pods that fail health checks.
    • Resource Management: Define CPU and memory requests/limits for containers to ensure efficient resource allocation and prevent noisy neighbor problems.
    • Service Discovery & Load Balancing: Manages internal communication between services (e.g., workers connecting to databases or queues).
    • Configuration & Secrets Management: Securely manage database credentials, API keys, and other sensitive configuration.

Infrastructure as Code (IaC)

  • Technologies: Terraform, Pulumi, AWS CloudFormation, Azure Resource Manager, Google Cloud Deployment Manager.
  • Purpose: Define and manage all cloud infrastructure resources (Kubernetes cluster, managed databases, message queues, S3 buckets, IAM roles, monitoring setup) using declarative configuration files stored in version control.
  • Benefits:
    • Reproducibility: Easily create identical environments (dev, staging, prod).
    • Automation: Automate infrastructure provisioning and updates.
    • Version Control: Track changes to infrastructure over time.
    • Disaster Recovery: Faster recreation of infrastructure if needed.

CI/CD Pipelines

graph TD
    A[Push Code to Git] --> B(CI Server Triggered);
    B --> C(Run Tests);
    C -- Pass --> D(Build Docker Image);
    D --> E(Push Image to Registry);
    E --> F(CD Server Triggered);
    F --> G(Deploy to Kubernetes);

    C -- Fail --> H(Notify Developer);
  • Technologies: GitHub Actions, GitLab CI, Jenkins, CircleCI.
  • Purpose: Automate the process of building container images, running tests (unit, integration), and deploying updated application code and infrastructure changes.
  • Typical Workflow:
    1. Code pushed to Git repository.
    2. CI pipeline triggers: runs tests, performs static analysis.
    3. If tests pass, build new Docker images.
    4. Push images to a container registry (e.g., Docker Hub, ECR, GCR, ACR).
    5. CD pipeline triggers: applies Kubernetes deployment updates, potentially runs IaC tool for infrastructure changes.

Role of Managed Cloud Services

  • Purpose: Leverage cloud provider services to reduce operational overhead for common infrastructure components.
  • Examples:
    • Message Queues: AWS SQS, Google Pub/Sub, Azure Service Bus (handle scaling, availability, durability).
    • Databases: AWS RDS/Aurora, Google Cloud SQL, Azure SQL DB (handle patching, backups, scaling, high availability).
    • Object Storage: AWS S3, Google Cloud Storage, Azure Blob Storage (highly scalable and durable storage).
    • Container Registry: AWS ECR, Google GCR, Azure ACR (store Docker images).
    • Kubernetes: AWS EKS, Google GKE, Azure AKS (manage the K8s control plane).

By combining these orchestration tools and practices, the system can be deployed, scaled, and managed effectively and reliably.