How arturobp3/distributed_grpc_system_k8s works
<explanation>
1. Identify the Project Type and Purpose
- This is a microservices proof‑of‑concept focusing on two Python gRPC services (servicio_a and servicio_b) packaged in Docker and deployed via Kubernetes (Minikube).
- servicio_b implements a basic greeting service; servicio_a acts as a gRPC client to servicio_b, adds user interaction, and exposes its own gRPC endpoint for testing.
2. Analyze the File Structure
- Top level
• README.md – project overview & run instructions
• k8s/ – Kubernetes deployment & service manifests for both microservices
• protos/ – canonical .proto definitions for base and personalized greetings
- servicio_b/
• Dockerfile, server.py, saludo_base.proto → exposes SayHello RPC on port 50052
- servicio_a/
• Dockerfile, server.py, client.py, saludo_personalizado.proto & imported base stubs → listens on port 50051 and forwards calls to servicio_b
- venv/ – isolated Python environment
3. Readme Insights
- Sequence: generate stubs from .proto → build Docker images → start Minikube → kubectl apply manifests → expose servicio-a for local client testing.
- No external databases, purely in‑cluster gRPC calls.
4. Main Components & Interactions
a. Components
• Client (local Python script or external tester)
• servicio_a Pod (gRPC server+client)
• servicio_b Pod (gRPC server)
• Kubernetes Service objects (ClusterIP)
• Docker images & Minikube cluster
b. Relationships
1. Client → gRPC call to servicio_a (port 50051 exposed via k8s Service)
2. servicio_a → gRPC call to servicio_b (port 50052 via k8s Service)
3. servicio_b processes and returns greeting
4. servicio_a returns final greeting to client
c. Architectural Patterns
• Microservices (single‑responsibility services)
• RPC communication (gRPC over HTTP/2)
• Containerization & orchestration (Docker + Kubernetes)
d. Technologies & Frameworks
• Python 3.10+, grpcio, protobuf
• Docker (per‑service images)
• Kubernetes manifests (Deployment, Service)
• Minikube for local cluster
5. How to Draw the Diagram
- Layer 1: Client/User (on left)
- Layer 2: Kubernetes Cluster boundary (a big box)
• Inside:
– Pod: servicio_a (shape: rounded rectangle)
· gRPC Server (port 50051)
· gRPC Client stub to servicio_b
– Service: svc‑servicio-a (ClusterIP)
– Pod: servicio_b (shape: rounded rectangle)
· gRPC Server (port 50052)
– Service: svc‑servicio-b (ClusterIP)
- Layer 3: External Tools (below cluster)
• Docker images registry (implicit)
• Minikube
- Data flows:
1. Arrow from Client → svc‑servicio-a (port 50051)
2. Arrow from servicio_a pod → svc‑servicio-b (port 50052)
3. Return arrows for gRPC responses
6. Tailoring for This Microservices Project
- Emphasize clear separation of concerns: service A vs. service B
- Show generated proto code as part of build process (optional side box)
- Indicate Docker build step and k8s apply step in a “CI/CD” or “Deployment” swimlane
7. Diagramming Guidelines
- Use distinct shapes/colors:
• Pods: blue
• Services: green
• Client: yellow
• Tools/Infra: gray
- Label every element with name and port (e.g., “servicio-b:50052”)
- Draw directional arrows for request/response flows; annotate arrows with gRPC method names (e.g., SayHello)
- Group related items (pods + services) within the Kubernetes cluster boundary
- Optionally add icons or small text notes for Docker images and Minikube
By breaking the system into these layers and components—and using clear labels, shapes, and arrows—you’ll produce a precise, accurate architecture diagram that reflects how the Python gRPC microservices interact, how they’re containerized, and how they’re orchestrated in Kubernetes.
</explanation>