Kubernetes vs Flask — A Clear Comparison

Learning Flask — Daily Schedule:
7:00 AM — Wake up, exercise (30 min)
8:00 AM — Review the day's learning goals
9:00 AM — Deep study (2 hours)
   - Week 1: Flask setup, routes, request/response
   - Week 2: Jinja2 templates & forms
   - Week 3: REST APIs & JSON
   - Week 4: SQLAlchemy & deployment
12:00 PM — Lunch break
1:00 PM — Build a mini-project (2 hours)
2:00 PM — Debug, read docs, fix errors
5:00 PM — Review notes, plan tomorrow
6:00 PM — Exercise, rest
8:00 PM — Read Flask docs / blog posts (30 min)
10:00 PM — Wind down

Week 1 Review:
□ Can I install Flask and run the dev server?
□ Do I understand routes and HTTP methods?
□ Can I return HTML and JSON responses?

Week 2 Review:
□ Can I render templates with Jinja2?
□ Can I handle form submissions?
□ Do I understand sessions?

Week 3 Review:
□ Can I build a REST endpoint?
□ Do I understand status codes (200, 404, 500)?
□ Can I test my API with curl or Postman?

Week 4 Review:
□ Have I connected a database with SQLAlchemy?
□ Have I deployed my app to a server?
□ Is my code on GitHub with a README?

Learning Kubernetes — Daily Schedule:
7:00 AM — Wake up, exercise (30 min)
8:00 AM — Review the day's learning goals
9:00 AM — Deep study (2 hours)
   - Week 5: Docker images & containers
   - Week 6: Pods, Deployments, kubectl
   - Week 7: Services & Ingress
   - Week 8: ConfigMaps, Secrets, rolling updates
12:00 PM — Lunch break
1:00 PM — Hands-on practice (2 hours)
2:00 PM — Read docs, debug cluster issues
5:00 PM — Review notes, plan tomorrow
6:00 PM — Exercise, rest
8:00 PM — Read Kubernetes docs / blog posts (30 min)
10:00 PM — Wind down

Week 5 Review:
□ Can I build a Docker image for a Flask app?
□ Do I understand containers vs virtual machines?
□ Can I push an image to a registry?

Week 6 Review:
□ Can I create a Pod and a Deployment?
□ Do I understand kubectl get/apply/describe?
□ Can I scale replicas up and down?

Week 7 Review:
□ Can I expose a Deployment with a Service?
□ Do I understand ClusterIP vs NodePort vs LoadBalancer?
□ Can I set up a basic Ingress?

Week 8 Review:
□ Can I pass config via ConfigMaps and Secrets?
□ Do I understand rolling updates and rollbacks?
□ Have I deployed a full app to a cluster?

# app.py — a minimal Flask app
from flask import Flask

app = Flask(__name__)

@app.route("/")
def home():
    return {"message": "Hello from Flask!"}

@app.route("/health")
def health():
    return {"status": "ok"}

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=5000)

# deployment.yaml — a minimal Kubernetes Deployment
apiVersion: apps/v1
kind: Deployment
metadata:
  name: flask-app
spec:
  replicas: 3
  selector:
    matchLabels:
      app: flask-app
  template:
    metadata:
      labels:
        app: flask-app
    spec:
      containers:
        - name: flask-app
          image: myregistry/flask-app:1.0
          ports:
            - containerPort: 5000
---
apiVersion: v1
kind: Service
metadata:
  name: flask-app
spec:
  selector:
    app: flask-app
  ports:
    - port: 80
      targetPort: 5000
  type: LoadBalancer

# Dockerfile — containerize a Flask app
FROM python:3.12-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .

EXPOSE 5000

CMD ["python", "app.py"]

Production workflow checklist:

Build phase:
□ Flask app runs locally
□ requirements.txt is complete
□ Health check endpoint exists

Container phase:
□ Dockerfile builds successfully
□ Image runs locally
□ Image pushed to a registry

Kubernetes phase:
□ Deployment YAML written
□ Service YAML written
□ Replicas set (start with 2-3)
□ Resource limits configured
□ Liveness / readiness probes set

Scale phase:
□ Horizontal Pod Autoscaler considered
□ Logs and metrics configured
□ Rolling update strategy set

Scaling a Flask app on Kubernetes:

1. Build and push the image:
   docker build -t myregistry/flask-app:1.0 .
   docker push myregistry/flask-app:1.0

2. Apply the Deployment:
   kubectl apply -f deployment.yaml

3. Check pods:
   kubectl get pods

4. Scale manually:
   kubectl scale deployment flask-app --replicas=5

5. Set up auto-scaling:
   kubectl autoscale deployment flask-app \
     --cpu-percent=70 \
     --min=2 --max=10

6. Expose the app:
   kubectl get service flask-app