From Code to Cloud: A Comprehensive Guide to Python Deployment#

3.1 Installing Python#

Before you deploy code, you must have a consistent environment for local development. Ensure you have Python 3.7+ installed locally:

• On macOS, often Python 3.x can be installed via a package manager like Homebrew.
• On Windows, you can download from the official Python website or use the Windows Store.
• On Linux, most distributions come with Python pre-installed, but you may need to install the dev packages for advanced tasks.

3.2 Virtual Environments#

A virtual environment isolates your Python project’s dependencies from those of other projects and from your system-wide packages. This is essential for reproducible builds and consistent deployments.

To create a virtual environment:

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python3 -m venv venv
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source venv/bin/activate  # On Windows: venv\Scripts\activate

Inside the activated virtual environment, installing packages via pip ensures dependencies stay local to your project:

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pip install flask

3.3 Requirements File#

Maintain a requirements.txt file to list your project’s dependencies:

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flask==2.1.1
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requests==2.27.1
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gunicorn==20.1.0

Keeping this file up to date ensures you can replicate the environment quickly. Don’t forget to pin specific versions to avoid unexpected breakages when a library updates.

4.1 Project Structure#

Organizing your Python project can significantly simplify deployment. A common layout for a Flask project might look like this:

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my_flask_app/
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    app/
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        __init__.py
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        routes.py
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    tests/
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        test_routes.py
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    requirements.txt
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    setup.py
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    run.py

• app/ contains your core application logic.
• tests/ contains unit tests.
• requirements.txt pins dependencies.
• setup.py can be used to package your application if you plan to distribute it.
• run.py might be a script to start or initialize the application.

4.2 Setup Scripts#

If your project is distributed or installed in multiple environments, a setup script can be helpful:

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from setuptools import setup, find_packages
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setup(
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    name='my_flask_app',
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    version='0.1.0',
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    packages=find_packages(),
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    install_requires=[
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        'Flask>=2.0.0',
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        'requests>=2.20.0'
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    ],
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)

With this, you or others can simply do:

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pip install .

to install your package locally. This approach is especially beneficial when deploying to servers or using Docker, because you can run a single install step that gathers all dependencies.

5.1 Basic Deployment Steps#

If you have access to a VPS on DigitalOcean, Linode, or any other hosting provider, a typical Python deployment flow might be:

1. SSH into your server.
2. Install Python 3 and Git.
3. Clone your repository:

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   git clone https://github.com/your-username/your-repo.git
   

4. Create and activate a virtual environment on the server.
5. Install dependencies:

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   pip install -r requirements.txt
   

6. Run your application in a robust manner, for example using Gunicorn and a process manager like Supervisor or systemd.

5.2 Using Gunicorn and Nginx#

Gunicorn is a Python WSGI HTTP Server that is often paired with Nginx for production deployments.

• Gunicorn handles running your Python app and concurrency.
• Nginx is a stable, high-performance web server that routes traffic to Gunicorn and can handle static files.

Example Gunicorn command:

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gunicorn app:app --bind 0.0.0.0:8000 --workers 4

Here:

• app:app references the Python module (app.py) and the Flask application instance (app).
• --bind 0.0.0.0:8000 tells Gunicorn to listen on port 8000.
• --workers 4 allows up to four worker processes to handle requests concurrently.

Nginx can be configured as a reverse proxy. A typical /etc/nginx/sites-available/my_flask_app:

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server {
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    listen 80;
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    server_name example.com;
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    location / {
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        proxy_pass         http://127.0.0.1:8000;
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        proxy_redirect     off;
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        proxy_set_header   Host $host;
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        proxy_set_header   X-Real-IP $remote_addr;
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    }
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}

Enable the site by creating a symlink to sites-enabled and reloading Nginx:

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sudo ln -s /etc/nginx/sites-available/my_flask_app /etc/nginx/sites-enabled/
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sudo systemctl reload nginx

This basic setup is often enough for small to medium projects, although you’ll likely add a process manager like Supervisor or systemd to automatically launch Gunicorn on boot.

6.1 Why Docker?#

Containers provide an isolated, consistent environment for your application, solving the classic “works on my machine” issue. With Docker, you package your code, dependencies, and runtime configuration into an image that can run anywhere Docker is supported.

6.2 Dockerfile Basics#

A simple Dockerfile for a Flask project:

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# Use an official Python runtime as a parent image
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FROM python:3.9-slim
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# Set the working directory
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WORKDIR /usr/src/app
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# Copy the requirements file
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COPY requirements.txt ./
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# Install any needed packages
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RUN pip install --no-cache-dir -r requirements.txt
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# Copy the rest of the application code
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COPY . .
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# Expose the port the app runs on
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EXPOSE 8000
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# Define the command to run the application
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CMD ["gunicorn", "--bind", "0.0.0.0:8000", "app:app"]

Commands explained:

1. FROM python:3.9-slim picks a lightweight Python 3.9 base image.
2. WORKDIR changes the container’s working directory to /usr/src/app.
3. COPY requirements.txt . helps with Docker’s layer caching so you don’t reinstall dependencies on each build if only code changes.
4. RUN pip install installs dependencies.
5. COPY . . copies the rest of your code.
6. EXPOSE 8000 is documentation for container port usage.
7. CMD [...] is the default container entrypoint.

6.3 Building and Running#

Build your image and run a container:

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docker build -t my_flask_app .
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docker run -p 8000:8000 my_flask_app

Your application is now available at http://localhost:8000 on your host machine. With Docker, the environment which runs your app is always the same—reducing deployment issues on different OSes or server configurations.

6.4 Docker Compose for Multi-Service Apps#

If you have a microservices architecture or need multiple services (e.g., a database, a cache server), Docker Compose simplifies local and production setups.

An example docker-compose.yml:

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version: '3'
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services:
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  web:
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    build: .
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    ports:
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      - "8000:8000"
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    environment:
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      - ENV=production
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    depends_on:
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      - redis
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  redis:
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    image: "redis:6.2"

This example sets up a web service from your Dockerfile and a Redis container for caching. With docker-compose up --build, everything starts together.

7.1 A Quick Comparison#

Popular cloud providers offer a variety of managed services for Python deployments:

7.2 AWS Elastic Beanstalk#

AWS Elastic Beanstalk (EB) streamlines deploying applications without having to manage underlying servers. For Python:

1. You package your code and dependencies into a ZIP file (or directly from Git with the EB CLI).
2. EB provisions and auto-scales the resources (EC2, Load Balancers, etc.).
3. Automatically sets up environment variables, logs, monitoring, etc.

Basic steps:

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pip install awsebcli
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eb init -p python-3.9 my-flask-application
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eb create my-env
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eb open

EB then handles provisioning. You can configure environment variables or scaling rules via the AWS console or the EB CLI.

7.3 Google Cloud Run#

Google Cloud Run allows you to run containers without managing servers. You build a Docker image and deploy it to Cloud Run:

1. Push your Docker image to Google Container Registry (GCR).
2. Deploy via console or gcloud CLI:

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   gcloud run deploy --image gcr.io/<PROJECT-ID>/<IMAGE> --platform managed
   

3. Cloud Run automatically scales down your containers to zero if there’s no traffic, potentially saving costs for low-traffic services.

7.4 Azure App Service#

Deploying Python applications on Azure App Service:

1. Create a new Web App on the Azure Portal, specifying Python as your runtime.
2. Use the Azure CLI or GitHub Actions to deploy your code.
3. Monitor logs in the Azure Portal and scale up/down instances depending on traffic or CPU usage.

7.5 Heroku#

Heroku is a well-known platform as a service (PaaS) that pioneered simple Git-based deployment:

1. Create a Procfile that states how to run your app:

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   web: gunicorn app:app
   

2. Make sure you have a requirements.txt file.
3. Commit your code to Git, then:

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   heroku create
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   git push heroku main
   

4. Heroku automatically detects your Python app, creates a container, and deploys it.

Heroku is ideal for rapid prototyping or smaller-scale apps due to its approachable interface. However, large-scale enterprise apps may need more customizable solutions from AWS, GCP, or Azure.

8.1 Why CI/CD?#

CI/CD pipelines automate the process of building, testing, and deploying your code. By incorporating automated testing and checks, you prevent broken code from reaching production and reduce manual overhead.

8.2 Popular CI/CD Tools#

• GitHub Actions: Integrated with GitHub, easy to get started with.
• GitLab CI: High integration with GitLab repos, powerful pipeline definitions in .gitlab-ci.yml.
• Jenkins: Open-source, self-hosted solution with countless plugins.
• CircleCI: SaaS-based, popular for easy configuration.
• Azure DevOps: Seamless integration with Azure services.

8.3 Sample GitHub Actions Workflow#

Below is an example .github/workflows/ci.yml that:

1. Runs Python tests.
2. Builds a Docker image.
3. Pushes the Docker image to a registry on the main branch.

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name: CI
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on:
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  push:
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    branches: [ "main" ]
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jobs:
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  build-and-test:
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    runs-on: ubuntu-latest
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    steps:
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      - name: Checkout code
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        uses: actions/checkout@v2
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      - name: Set up Python
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        uses: actions/setup-python@v2
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        with:
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          python-version: '3.9'
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      - name: Install dependencies
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        run: |
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          pip install -r requirements.txt
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      - name: Run tests
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        run: |
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          pytest --color=yes
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      - name: Build Docker Image
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        run: |
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          docker build -t my_flask_app:latest .
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      - name: Log in to Docker Hub
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        run: echo "${{ secrets.DOCKERHUB_TOKEN }}" | docker login -u ${{ secrets.DOCKERHUB_USERNAME }} --password-stdin
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      - name: Push Docker Image
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        run: |
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          docker tag my_flask_app:latest my_dockerhub_username/my_flask_app:latest
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          docker push my_dockerhub_username/my_flask_app:latest

By integrating tests and Docker image building in your CI workflow, you ensure your code is always tested and ready for deployment.

9.1 Environment Variables#

Environment variables are a common way to provide configuration in 12-factor apps. Rather than hardcoding secrets in your Python code, you can store them in environment variables:

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export DATABASE_URL=postgres://user:pass@host:5432/db

Use os.environ.get("DATABASE_URL") in Python to retrieve the value:

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import os
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DATABASE_URL = os.environ.get("DATABASE_URL")

9.2 Secret Management in Cloud#

Cloud platforms often have dedicated tools for secret management:

• AWS: AWS Secrets Manager or Parameter Store.
• GCP: Secret Manager.
• Azure: Key Vault.

These services integrate well with other cloud components, securely rotate credentials, and often have role-based access controls.

9.3 .env Files#

In local development, you might store environment variables in a .env file managed by a library like python-dotenv. However, storing secrets in a plain text file is risky. Make sure you exclude such files from version control with your .gitignore.

10.1 Horizontal Scaling#

For Python applications, horizontal scaling typically involves running multiple instances (workers) of your app behind a load balancer. Cloud providers offer auto-scaling features that spin up or down instances based on CPU, memory usage, or custom metrics.

10.2 Vertical Scaling#

Sometimes, it’s easier to add more CPU or RAM to a single instance if your application can’t yet handle concurrency across multiple workers. However, vertical scaling has physical limits, and eventually you’ll need a horizontally scalable architecture.

10.3 Caching Layers#

Caching is vital for handling large-scale traffic:

• Use Redis or Memcached to store frequently accessed data.
• Cache entire pages or partial templates if your content is relatively static.
• Django’s caching framework and Flask caching extensions can greatly reduce database queries.

10.4 Database Scalability#

Relational databases can become bottlenecks as traffic grows. Consider:

• Read replicas for offloading read queries.
• Sharding for extremely large datasets.
• Switching to NoSQL solutions if your data model suits it.

11.1 Logs#

Proper logs help you understand issues and track performance:

• Use Python’s built-in logging module.
• Aggregate logs in a cloud-based logging solution (AWS CloudWatch, GCP Cloud Logging, or self-hosted ELK stack).
• Make logs easily searchable with structured outputs (JSON).

11.2 Metrics and Health Checks#

Tools like Prometheus, Graphite, or Datadog can gather metrics such as request per second (RPS), error rate, CPU usage, and memory consumption. Implement health-check endpoints in your Python app so that load balancers know if the service is healthy.

11.3 Alerts#

Set thresholds for your metrics so that you get alerts (via Slack, email, or SMS) if something goes out of range. Quick notifications reduce downtime and let you fix problems before users are heavily impacted.

12.1 Zero-Downtime Deployments#

A zero-downtime deployment strategy ensures that your application remains available during updates:

• Use blue-green deployments, where new versions of the app are deployed in parallel and traffic is switched over once ready.
• Leverage rolling updates if using container orchestration like Kubernetes.

12.2 Using Kubernetes#

For highly complex, large-scale systems, Kubernetes orchestrates containers across multiple servers:

1. You define your application in YAML files called “manifests.”
2. Kubernetes manages scaling and rolling updates automatically.
3. Popular managed solutions include Amazon EKS, Google GKE, and Azure AKS.

A simple Kubernetes deployment might look like this:

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apiVersion: apps/v1
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kind: Deployment
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metadata:
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  name: flask-deployment
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spec:
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  replicas: 3
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  selector:
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    matchLabels:
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      app: flask-app
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  template:
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    metadata:
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      labels:
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        app: flask-app
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    spec:
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      containers:
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      - name: flask-container
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        image: my_dockerhub_username/my_flask_app:latest
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        ports:
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        - containerPort: 8000

You then expose this deployment via a Kubernetes Service that routes external traffic to the pods.

12.3 Security Best Practices#

• Always keep security patches up to date for your OS or Docker base image.
• Use HTTPS for encrypted traffic.
• Filter user input to prevent injection attacks, especially in web frameworks.
• Employ role-based access control (RBAC) for cloud infrastructure and Kubernetes.
• Container scanning tools (like Trivy) can detect vulnerabilities in your images.

12.4 Blue-Green vs. Rolling Deployments#

Different deployment strategies have different trade-offs:

• Blue-Green: Create an entire new environment (green) while the old one (blue) is live, then switch traffic. Simplifies rollback but can cost more temporarily, as you run two environments in parallel.
• Rolling: Incrementally update instances. Potentially less resource overhead, but you need robust health checks to ensure partial upgrades don’t cause downtime.