Concept

Docker is an open-source platform that enables developers to build, ship, and run applications inside lightweight, portable containers. It uses containerization technology to package applications with all dependencies, ensuring they work seamlessly across different environments.

Examples

Example of running a Docker container:

This command downloads a small Docker image and runs a container that prints a “Hello World” message to the console.

Use Cases

• Development and testing: Run isolated environments for consistent testing.
• Continuous Integration and Deployment (CI/CD): Automate the build and deployment process.
• Cloud portability: Build once, run anywhere across cloud providers.

Problems

• Learning curve for beginners unfamiliar with containerization.
• Managing persistent storage in containers can be challenging.
• Security risks if images are not properly scanned.

Best Practices

• Use official Docker images whenever possible.
• Keep Dockerfiles simple and readable.
• Scan images for vulnerabilities using tools like Docker Scan.
• Limit container permissions and avoid running as root.

Real-World Scenarios

Docker is widely used in real-world applications. For instance:

• Netflix uses Docker to deploy microservices at scale.
• Airbnb leverages Docker for consistent development and production environments.

Questions & Answers

1. What is Docker? Docker is a containerization platform that allows applications to run in isolated environments.
2. Why use Docker instead of virtual machines? Docker containers are lightweight, faster to start, and consume fewer resources compared to VMs.
3. What command is used to run a container? docker run [image_name].

Concept

The Docker architecture is based on a client-server model. It allows users to interact with Docker using a command-line interface (CLI) or a REST API. The Docker Daemon runs in the background, handling all operations like creating, running, and managing containers.

Key features of Docker architecture include:

• Lightweight containerization using shared operating systems.
• A central Docker daemon that orchestrates the creation and management of containers.
• Support for multiple platforms, ensuring portability across environments.

Key Components

Docker architecture consists of the following primary components:

• Docker Client: The command-line interface used by developers to interact with the Docker engine.
• Docker Daemon: The background process responsible for managing containers, images, and networks.
• Docker Images: Pre-built templates used to create containers. Images are read-only and can be layered.
• Docker Containers: Runtime instances of images that run applications in an isolated environment.
• Docker Registry: A repository for storing and sharing Docker images (e.g., Docker Hub).
• Docker Networking: Provides communication between containers and the outside world.

Examples

Example of a typical interaction in Docker:

            # Pulling an image from Docker Hub
            docker pull nginx
            
            # Running a container from the image
            docker run -d -p 8080:80 nginx
            
            # Checking running containers
            docker ps
                        

This demonstrates pulling an NGINX image, running it as a container, and exposing it on port 8080.

Use Cases

• Automating application deployment with consistent environments.
• Isolating microservices using containerized components.
• Creating reproducible development setups for teams.
• Deploying scalable cloud-based architectures with container orchestration tools.

Problems

• Performance issues on older operating systems or hardware due to the Docker Daemon.
• Complexity in debugging interconnected microservices.
• Challenges in setting up secure container networking in multi-cloud environments.

Best Practices

• Use lightweight base images to minimize container size.
• Leverage multi-stage builds to separate build and runtime dependencies.
• Secure the Docker socket and limit its exposure.
• Use tools like docker-compose for managing multi-container applications.

Real-World Scenarios

In production environments, companies utilize Docker to streamline workflows:

• Spotify: Uses Docker for fast deployment of microservices.
• eBay: Uses Docker containers for scalability and isolation of multiple services.

Questions & Answers

1. What is the role of the Docker Daemon?

   The Docker Daemon is the background process that manages Docker objects like containers, images, and networks.

2. How do the Docker Client and Daemon communicate?

   They communicate via REST APIs over a socket or network.

3. What command is used to list running containers?

   The docker ps command lists all running containers.

Concept

Docker images are templates used to create Docker containers. Images are read-only layers that include the application, libraries, dependencies, and configuration files needed for an application to run. Each image is built from a base image using a Dockerfile.

Examples

Basic operations with Docker images:

            # Pulling an image from Docker Hub
            docker pull nginx
            
            # Listing all images on your system
            docker images
            
            # Building a custom image from a Dockerfile
            docker build -t my_custom_image .
            
            # Removing an image
            docker rmi nginx
                        

Example Dockerfile for a Python application:

            # Base image
            FROM python:3.9-slim
            
            # Set working directory
            WORKDIR /app
            
            # Copy files
            COPY requirements.txt .
            
            # Install dependencies
            RUN pip install -r requirements.txt
            
            # Copy application code
            COPY . .
            
            # Run the application
            CMD ["python", "app.py"]
                        

Use Cases

• Standardizing application environments for development, testing, and production.
• Versioning application images for rollback and updates.
• Sharing pre-configured application environments across teams using registries.

Problems & Solutions

Best Practices

• Use multi-stage builds to reduce image size and keep build dependencies separate from runtime.
• Always use official or trusted base images.
• Regularly scan images for vulnerabilities.
• Document the Dockerfile with comments for better readability and maintenance.

Real-World Scenarios

Examples of Docker image usage in the industry:

• Google Cloud: Uses pre-configured Docker images to deploy scalable applications.
• Microservices: Teams build lightweight Docker images for each service, ensuring fast startup and consistency.
• AI/ML Workflows: Pre-built images with TensorFlow or PyTorch allow researchers to set up environments quickly.

Questions & Answers

1. What is the purpose of a Dockerfile?

   A Dockerfile is a script that automates the creation of Docker images by defining the base image, dependencies, and instructions for building the image.

2. How can you reduce the size of a Docker image?

   Use lightweight base images like alpine, remove unnecessary files, and leverage multi-stage builds.

3. What command is used to list images on your system?

   docker images displays all images stored on your system.

Concept

Docker containers are lightweight, portable, and self-contained environments where applications run. They are created from Docker images and share the host operating system kernel, making them more efficient than virtual machines.

Containers can be started, stopped, restarted, and removed without affecting the underlying image, allowing for flexible application management.

Examples

Basic commands to manage Docker containers:

            # Run a new container
            docker run -d --name my_container nginx
            
            # List all running containers
            docker ps
            
            # Stop a running container
            docker stop my_container
            
            # Restart a container
            docker restart my_container
            
            # Remove a container
            docker rm my_container
                        

Interactive container usage example:

            # Run an interactive Bash session in a container
            docker run -it ubuntu bash
                        

Use Cases

• Running isolated environments for development and testing.
• Deploying scalable microservices in production environments.
• Testing new software versions without affecting the host system.

Problems & Solutions

Best Practices

• Name your containers descriptively using the --name flag.
• Use the --rm flag for temporary containers to avoid unnecessary cleanup.
• Regularly prune unused containers using docker container prune.
• Monitor container performance with docker stats.

Real-World Scenarios

Containers are used in real-world scenarios to achieve scalability and reliability:

• Netflix: Uses containers to scale microservices dynamically based on demand.
• Spotify: Manages thousands of containers to host music streaming services globally.
• GitLab: Runs CI/CD pipelines in isolated Docker containers to ensure clean environments.

Questions & Answers

1. What command lists all running containers?

   docker ps lists all running containers.

2. How do you remove a stopped container?

   Use docker rm [container_name] to remove a stopped container.

3. How do you allocate specific resources to a container?

   Use flags like --memory and --cpus when running the container (e.g., docker run --memory=256m --cpus=0.5 nginx).

Concept

Docker networking allows containers to communicate with each other, the host system, and external networks. Docker provides several network drivers to manage communication, including:

• Bridge Network: The default network for standalone containers on a single host.
• Host Network: Shares the host’s network stack with the container.
• Overlay Network: Enables communication across multiple Docker hosts.
• None Network: Disables networking for a container.

Examples

Commands to manage Docker networking:

            # List all Docker networks
            docker network ls
            
            # Create a custom bridge network
            docker network create my_bridge
            
            # Run a container on the custom network
            docker run --network my_bridge -d nginx
            
            # Inspect a network
            docker network inspect my_bridge
            
            # Remove a network
            docker network rm my_bridge
                        

Using an overlay network for multi-host communication:

            # Initialize Docker Swarm
            docker swarm init
            
            # Create an overlay network
            docker network create -d overlay my_overlay
            
            # Deploy services to the overlay network
            docker service create --network my_overlay nginx
                        

Use Cases

• Connecting microservices in a Dockerized application.
• Enabling communication between containers across multiple Docker hosts.
• Creating isolated environments for testing and development.

Problems & Solutions

Best Practices

• Use custom networks instead of the default bridge network for better isolation and control.
• Regularly inspect and clean up unused networks with docker network prune.
• Leverage overlay networks for scalable multi-host communication.
• Document network configurations for clarity and maintainability.

Real-World Scenarios

Docker networking in action:

• Microservices Architecture: Networks connect microservices running in separate containers, enabling efficient communication.
• Hybrid Cloud Deployment: Overlay networks are used to link containers across on-premises and cloud-hosted servers.
• CI/CD Pipelines: Containers in a test environment communicate through isolated Docker networks.

Questions & Answers

1. What is the default network type in Docker?

   The bridge network is the default network for standalone containers.

2. How do you create a custom Docker network?

   Use the docker network create command, specifying the network name and driver.

3. What is the purpose of an overlay network?

   An overlay network enables communication between containers across multiple Docker hosts.

Concept

Docker Compose is a tool for defining and managing multi-container Docker applications. It uses a YAML configuration file (docker-compose.yml) to define services, networks, and volumes, enabling developers to manage multiple containers with simple commands.

Key features include:

• Defining multi-container applications in a single file.
• Managing services, networks, and volumes.
• Scaling services with a single command.

Examples

Example docker-compose.yml file:

            version: '3.8'
            services:
              web:
                image: nginx
                ports:
                  - "8080:80"
                networks:
                  - app_network
              app:
                build: .
                depends_on:
                  - db
                networks:
                  - app_network
              db:
                image: postgres:latest
                environment:
                  POSTGRES_USER: user
                  POSTGRES_PASSWORD: password
                networks:
                  - app_network
            networks:
              app_network:
                driver: bridge
                        

Commands to manage the Docker Compose application:

            # Start all services
            docker-compose up -d
            
            # Stop all services
            docker-compose down
            
            # Scale a specific service
            docker-compose up --scale web=3
            
            # Check logs of a service
            docker-compose logs web
                        

Use Cases

• Managing microservices in development and testing environments.
• Setting up isolated environments for integration testing.
• Streamlining multi-container deployments for small projects.

Problems & Solutions

Best Practices

• Use descriptive names for services and networks.
• Store sensitive information in environment files (.env).
• Leverage the volumes property for data persistence.
• Regularly update the Compose version for new features and fixes.

Real-World Scenarios

Docker Compose in action:

• Local Development: Developers use Compose to replicate production environments locally.
• Testing: QA teams set up isolated environments for automated tests using Compose.
• Hackathons: Compose simplifies multi-container application setup during coding competitions.

Questions & Answers

1. What is the purpose of Docker Compose?

   Docker Compose simplifies the management of multi-container applications by defining services, networks, and volumes in a single YAML file.

2. How do you scale a service in Docker Compose?

   Use the docker-compose up --scale [service]=[number] command to scale the service.

3. How can you validate a Compose file?

   Use the docker-compose config command to validate the syntax and structure of the file.

Concept

Docker Swarm is a container orchestration tool built into Docker that enables the management of a cluster of Docker nodes. It simplifies deploying, scaling, and managing containerized applications across multiple hosts.

Key features of Docker Swarm include:

• Load balancing and service discovery.
• High availability through replication.
• Scaling services up or down with simple commands.
• Built-in encryption for secure communication between nodes.

Examples

Commands to initialize and manage a Docker Swarm:

            # Initialize a Swarm
            docker swarm init
            
            # Add a worker node to the Swarm
            docker swarm join --token [TOKEN] [MANAGER_IP]:2377
            
            # Create a service in the Swarm
            docker service create --name web_service -p 8080:80 nginx
            
            # List all services in the Swarm
            docker service ls
            
            # Scale a service to 3 replicas
            docker service scale web_service=3
            
            # Remove a service
            docker service rm web_service
                        

Use Cases

• Deploying scalable web applications across multiple servers.
• Ensuring high availability for critical services using replication.
• Managing containerized workloads in a secure and distributed environment.

Problems & Solutions

Best Practices

• Use multiple manager nodes for fault tolerance.
• Encrypt Swarm traffic to enhance security.
• Monitor resource usage to prevent overloading nodes.
• Regularly backup Swarm configurations and data.

Real-World Scenarios

Docker Swarm in action:

• Web Hosting: Swarm is used to deploy and scale web hosting environments dynamically.
• CI/CD Pipelines: Containers orchestrated by Swarm streamline build, test, and deployment workflows.
• IoT Applications: Swarm helps manage containerized IoT workloads across distributed edge devices.

Questions & Answers

1. What is the role of a Swarm manager node?

   Manager nodes handle orchestration tasks such as scheduling services and managing worker nodes.

2. How do you scale a service in Swarm?

   Use the docker service scale [service_name]=[replica_count] command to scale a service.

3. What command lists all services in a Swarm?

   The docker service ls command lists all services in the Swarm.

Concept

Docker security focuses on ensuring the confidentiality, integrity, and availability of containerized applications. By default, Docker provides isolation for containers, but misconfigurations or insecure practices can introduce vulnerabilities.

Key Docker security aspects include:

• Container isolation using namespaces and cgroups.
• Image security through vulnerability scanning and signature verification.
• Network security using secure communication protocols.

Examples

Enhancing Docker security with best practices:

            # Scan an image for vulnerabilities
            docker scan [image_name]
            
            # Limit container permissions
            docker run --read-only --cap-drop ALL --cap-add NET_BIND_SERVICE nginx
            
            # Run containers as non-root users
            docker run --user 1001:1001 nginx
                        

Securing Docker networks:

            # Create an encrypted overlay network
            docker network create -d overlay --opt encrypted my_secure_network
                        

Use Cases

• Deploying containers with minimal privileges to reduce the attack surface.
• Scanning images in CI/CD pipelines for vulnerabilities.
• Implementing network isolation for multi-tenant environments.

Problems & Solutions

Best Practices

• Use the principle of least privilege by dropping unnecessary capabilities.
• Enable content trust to ensure only signed images are used.
• Isolate sensitive data in environment variables and avoid hardcoding them.
• Regularly update Docker and base images to patch security vulnerabilities.
• Monitor containers using tools like sysdig or falco for runtime security.

Real-World Scenarios

How organizations leverage Docker security:

• Banking Applications: Containers run with strict privilege controls and are regularly scanned for vulnerabilities.
• Healthcare Systems: Docker is used with encrypted networks to securely transfer patient data.
• Cloud Providers: Implement runtime security monitoring to detect anomalous behavior in containers.

Questions & Answers

1. How do you scan a Docker image for vulnerabilities?

   Use the docker scan [image_name] command to scan an image for vulnerabilities.

2. What is the purpose of the --cap-drop flag?

   The --cap-drop flag removes unnecessary Linux capabilities from a container to reduce the attack surface.

3. Why should containers be run as non-root users?

   Running containers as non-root users minimizes the impact of potential security breaches by limiting permissions.

Concept

Docker simplifies CI/CD pipelines by providing consistent, portable, and isolated environments. It enables seamless transitions between development, testing, and production stages, ensuring compatibility and reducing integration issues.

Key benefits of using Docker in CI/CD pipelines include:

• Environment consistency across all stages of development.
• Faster builds with pre-built Docker images.
• Isolation for running multiple pipelines simultaneously.
• Portability across different CI/CD tools and platforms.

Examples

Basic CI/CD pipeline with Docker:

            # Sample CI/CD pipeline YAML for GitLab
            stages:
              - build
              - test
              - deploy
            
            build:
              stage: build
              script:
                - docker build -t my_app:latest .
            
            test:
              stage: test
              script:
                - docker run --rm my_app:latest pytest
            
            deploy:
              stage: deploy
              script:
                - docker tag my_app:latest my_repo/my_app:latest
                - docker push my_repo/my_app:latest
                        

Docker in Jenkins pipeline:

            pipeline {
                agent any
                stages {
                    stage('Build') {
                        steps {
                            sh 'docker build -t my_app .'
                        }
                    }
                    stage('Test') {
                        steps {
                            sh 'docker run --rm my_app pytest'
                        }
                    }
                    stage('Deploy') {
                        steps {
                            sh 'docker push my_repo/my_app:latest'
                        }
                    }
                }
            }
                        

Use Cases

• Building and testing microservices in isolated containers.
• Deploying updates seamlessly with containerized applications.
• Running multiple CI/CD pipelines concurrently without conflicts.

Problems & Solutions

Best Practices

• Use lightweight base images to minimize build times.
• Implement tagging strategies for Docker images (e.g., v1.0, latest).
• Scan images for vulnerabilities before deploying to production.
• Automate cleanup of old images and containers to save resources.
• Store sensitive information securely using secrets management tools.

Real-World Scenarios

How Docker enhances CI/CD pipelines:

• E-commerce platforms: Automate testing and deployment of microservices for faster delivery cycles.
• AI/ML projects: Containerize training environments to ensure consistency across different hardware setups.
• DevOps teams: Use Dockerized CI/CD tools like Jenkins and GitLab CI for efficient pipeline management.

Questions & Answers

1. How does Docker improve CI/CD pipelines?

   Docker ensures consistent environments, isolates builds, and speeds up deployments, reducing integration issues.

2. What is the role of Docker in testing?

   Docker isolates test environments, allowing developers to run tests in containers without affecting the host system.

3. How do you optimize Docker builds in CI/CD pipelines?

   Use multi-stage builds, leverage layer caching, and select lightweight base images to optimize build times.

Concept

Multi-stage builds in Docker allow you to create optimized images by using multiple stages in a single Dockerfile. This approach helps reduce image size by discarding intermediate stages and including only the final required artifacts.

Key benefits include:

• Smaller final image size by excluding build dependencies.
• Improved security by reducing the attack surface.
• Cleaner and more maintainable Dockerfile.

Examples

Example Dockerfile using multi-stage builds:

            # Stage 1: Build
            FROM node:16 as build
            WORKDIR /app
            COPY package.json yarn.lock ./
            RUN yarn install
            COPY . .
            RUN yarn build
            
            # Stage 2: Production
            FROM nginx:alpine
            COPY --from=build /app/build /usr/share/nginx/html
            EXPOSE 80
            CMD ["nginx", "-g", "daemon off;"]
                        

Explanation:

• The first stage uses the Node.js image to build the application.
• The second stage uses a lightweight NGINX image and copies only the built artifacts.

Use Cases

• Building and deploying React, Angular, or Vue applications with minimized production images.
• Compiling and packaging applications in one stage and deploying them in another.
• Creating lightweight images for microservices in production.

Problems & Solutions

Best Practices

• Use lightweight base images like alpine in the final stage.
• Name stages meaningfully for better understanding and maintenance.
• Always clean up unnecessary files in each stage to reduce the image size.
• Leverage --target during development to test specific stages.

Real-World Scenarios

Multi-stage builds in action:

• Frontend Applications: Building React or Angular apps and serving them via NGINX with a minimal production image.
• Java Applications: Building a JAR file in a Maven container and deploying it in a lightweight OpenJDK image.
• Microservices: Deploying optimized microservice containers with only runtime dependencies.

Questions & Answers

1. What is the purpose of multi-stage builds?

   Multi-stage builds allow the creation of optimized Docker images by separating build and runtime stages, reducing the final image size.

2. How do you copy files between stages in a multi-stage build?

   Use the COPY --from=[stage_name] command to copy files from a specific stage to another.

3. How can you debug a specific stage in a multi-stage build?

   Use the --target flag to build and inspect a specific stage (e.g., docker build --target build .).

Concept

Docker plugins and extensions allow users to enhance Docker’s functionality by integrating custom modules or third-party tools. Plugins are used to add capabilities such as logging, storage, networking, and monitoring.

Key features of Docker plugins and extensions:

• Extensibility to meet custom requirements.
• Support for third-party integrations like monitoring and security tools.
• Ease of installation and management via the Docker CLI.

Examples

Installing and managing Docker plugins:

            # List all available plugins
            docker plugin ls
            
            # Install a Docker plugin (e.g., Portworx storage plugin)
            docker plugin install portworx/pxd:latest
            
            # Enable a plugin
            docker plugin enable portworx/pxd
            
            # Disable a plugin
            docker plugin disable portworx/pxd
            
            # Remove a plugin
            docker plugin rm portworx/pxd
                        

Example of extending Docker functionality with a monitoring extension:

            # Using Prometheus for Docker monitoring
            docker run -d --name prometheus -p 9090:9090 prom/prometheus
                        

Use Cases

• Adding logging capabilities to monitor container activity.
• Integrating custom storage backends for persistent data management.
• Enhancing network configurations for multi-host setups.
• Implementing security plugins to scan for vulnerabilities and enforce policies.

Problems & Solutions

Best Practices

• Use only trusted and verified plugins to ensure security.
• Regularly update plugins to benefit from the latest features and fixes.
• Disable unused plugins to reduce resource consumption.
• Document all installed plugins for better manageability.

Real-World Scenarios

Docker plugins in action:

• Storage Management: Using the Portworx plugin to manage persistent storage in containerized environments.
• Logging: Using Fluentd or Logspout plugins to collect and forward logs from Docker containers.
• Networking: Leveraging Weave Net or Calico plugins for advanced networking configurations.

Questions & Answers

1. What are Docker plugins?

   Docker plugins are modules that extend Docker’s core functionality, such as adding custom storage, networking, or logging capabilities.

2. How do you list all available plugins?

   Use the docker plugin ls command to list all installed plugins.

3. What is a common issue when installing Docker plugins?

   Compatibility issues with the Docker version or missing dependencies are common challenges.

Concept

Docker monitoring and logging are critical for maintaining the performance, reliability, and security of containerized applications. Monitoring tracks resource usage (CPU, memory, disk I/O), while logging provides insights into container activities and application behavior.

Key aspects include:

• Real-time monitoring of container health and performance.
• Centralized log management for better debugging and auditing.
• Integration with third-party monitoring and logging tools.

Examples

Monitoring Docker containers using built-in commands:

            # Check resource usage of running containers
            docker stats
            
            # Inspect container logs
            docker logs [container_name]
            
            # Follow logs in real-time
            docker logs -f [container_name]
                        

Using Prometheus and Grafana for monitoring:

            # Run Prometheus for collecting metrics
            docker run -d --name prometheus -p 9090:9090 prom/prometheus
            
            # Run Grafana for visualizing metrics
            docker run -d --name grafana -p 3000:3000 grafana/grafana
                        

Use Cases

• Monitoring container health and resource consumption in production environments.
• Analyzing application logs to debug errors and optimize performance.
• Setting up alerts for container failures or resource overuse.

Problems & Solutions

Best Practices

• Regularly monitor resource usage to prevent container overloading.
• Use structured logging formats (e.g., JSON) for better analysis and indexing.
• Implement log rotation to manage disk space efficiently.
• Leverage visualization tools like Grafana to track trends and anomalies.
• Automate alerts for critical container health metrics.

Real-World Scenarios

Docker monitoring and logging in action:

• Web Hosting: Monitor container resource usage to optimize web server performance.
• DevOps Teams: Use centralized logging solutions to debug CI/CD pipeline issues.
• Financial Applications: Set up alerts for unexpected behavior in containerized banking services.

Questions & Answers

1. What command shows real-time resource usage of containers?

   The docker stats command shows real-time resource usage.

2. How do you centralize logs for multiple containers?

   Use tools like Fluentd, Logstash, or Graylog to centralize logs.

3. What tools can be used for visualizing Docker metrics?

   Tools like Grafana, Prometheus, and cAdvisor can be used for visualizing metrics.

Concept

Using Docker in production environments requires a focus on scalability, security, and performance optimization. Docker enables seamless deployment, isolation, and orchestration of applications, making it ideal for production workloads.

Key considerations for Docker in production:

• Resource management and container orchestration.
• Security hardening for containers and images.
• Monitoring and logging for real-time performance insights.

Examples

Deploying a containerized web application in production:

            # Run a container with resource limits
            docker run -d --name web_app -p 80:80 --memory=512m --cpus=1 my_web_app:latest
                        

Using Docker Compose for production environments:

            version: '3.8'
            services:
              app:
                image: my_web_app:latest
                deploy:
                  replicas: 3
                  resources:
                    limits:
                      memory: 512m
                      cpus: "1.0"
                ports:
                  - "80:80"
                logging:
                  driver: json-file
              db:
                image: postgres:latest
                deploy:
                  replicas: 1
                  restart_policy:
                    condition: on-failure
                volumes:
                  - db_data:/var/lib/postgresql/data
            volumes:
              db_data:
                        

Use Cases

• Hosting scalable web applications with load balancing and auto-scaling.
• Deploying microservices architectures with container orchestration tools like Kubernetes.
• Running batch jobs and background tasks in isolated environments.

Problems & Solutions

Best Practices

• Use a container orchestration tool like Kubernetes or Docker Swarm for large-scale deployments.
• Minimize the attack surface by running containers as non-root users.
• Leverage multi-stage builds to create lightweight images.
• Set up robust monitoring and logging with tools like Prometheus and Grafana.
• Implement automated backups for critical data stored in volumes.

Real-World Scenarios

How Docker is used in production:

• E-commerce Platforms: Handle fluctuating traffic by auto-scaling containerized microservices.
• Healthcare Applications: Ensure compliance and security with containerized, encrypted workflows.
• Cloud-Native Environments: Orchestrate large-scale applications with Kubernetes and Docker.

Questions & Answers

1. How can you limit a container’s resource usage?

   Use the --memory and --cpus flags to set resource limits when running a container.

2. What tools are recommended for monitoring Docker in production?

   Prometheus and Grafana are commonly used for monitoring Docker environments.

3. How do you manage persistent data in Docker containers?

   Use Docker volumes or external storage solutions to manage persistent data.

Concept

Kubernetes is an open-source container orchestration platform that automates the deployment, scaling, and management of containerized applications. Docker serves as the container runtime, and Kubernetes manages the containers across clusters of machines.

Key features of Kubernetes:

• Automated container orchestration for scaling and failover.
• Load balancing and service discovery.
• Self-healing containers to replace failed instances.
• Storage orchestration and configuration management.

Examples

Basic Kubernetes workflow:

            # Create a Kubernetes deployment
            kubectl create deployment web --image=nginx
            
            # Expose the deployment as a service
            kubectl expose deployment web --port=80 --type=LoadBalancer
            
            # Scale the deployment to 3 replicas
            kubectl scale deployment web --replicas=3
            
            # View the pods in the deployment
            kubectl get pods
                        

Example YAML for Kubernetes deployment:

            apiVersion: apps/v1
            kind: Deployment
            metadata:
              name: web
            spec:
              replicas: 3
              selector:
                matchLabels:
                  app: web
              template:
                metadata:
                  labels:
                    app: web
                spec:
                  containers:
                  - name: nginx
                    image: nginx:latest
                    ports:
                    - containerPort: 80
                        

Use Cases

• Running scalable, fault-tolerant microservices.
• Managing multi-container applications in production.
• Orchestrating machine learning and data processing workloads.

Problems & Solutions

Best Practices

• Use namespaces to isolate environments (e.g., development, testing, production).
• Implement resource requests and limits for containers.
• Use ConfigMaps and Secrets to manage configurations securely.
• Automate deployments with CI/CD pipelines integrated with Kubernetes.
• Regularly update and patch Kubernetes clusters to ensure security.

Real-World Scenarios

How Kubernetes is used with Docker:

• Tech Companies: Deploy and manage large-scale microservices architectures (e.g., Spotify, Airbnb).
• Financial Services: Run containerized applications with strict compliance and security requirements.
• AI Workflows: Orchestrate containerized machine learning pipelines across multiple nodes.

Questions & Answers

1. What is the role of Kubernetes in container orchestration?

   Kubernetes automates the deployment, scaling, and management of containerized applications.

2. How do you scale a Kubernetes deployment?

   Use the kubectl scale deployment [name] --replicas=[number] command to scale a deployment.

3. What is a Kubernetes Service?

   A Kubernetes Service provides stable networking and load balancing for accessing Pods.

Concept

Serverless architecture allows developers to focus on writing code without worrying about infrastructure management. Docker enhances serverless platforms by providing consistent environments for packaging and deploying serverless functions.

Key features of using Docker in serverless environments:

• Portable function packaging with Docker images.
• Consistent runtime environments for serverless functions.
• Improved local testing and debugging before deployment.

Examples

Using Docker with AWS Lambda:

            # Build a Docker image for a Lambda function
            docker build -t lambda_function .
            
            # Test the Lambda function locally
            docker run -p 9000:8080 lambda_function
            
            # Deploy the Docker image to AWS Lambda
            aws lambda create-function \
              --function-name myFunction \
              --package-type Image \
              --code ImageUri=123456789012.dkr.ecr.us-east-1.amazonaws.com/lambda_function:latest \
              --role arn:aws:iam::123456789012:role/lambda-role
                        

Using Docker with OpenFaaS:

            # Deploy a function to OpenFaaS
            faas-cli up -f function.yml
            
            # YAML for function definition
            version: 1.0
            provider:
              name: openfaas
              gateway: http://127.0.0.1:8080
            functions:
              hello-world:
                image: my-hello-world
                handler: ./hello-world
                        

Use Cases

• Deploying serverless functions in hybrid cloud environments.
• Creating portable, consistent environments for serverless workloads.
• Building and testing serverless functions locally before deploying to the cloud.

Problems & Solutions

Best Practices

• Use lightweight base images tailored for serverless platforms.
• Test functions locally using Docker containers before deployment.
• Leverage multi-stage builds to keep images lean and efficient.
• Use managed container registries for storing and deploying images.
• Regularly update images to ensure compatibility with serverless platforms.

Real-World Scenarios

How Docker integrates with serverless architecture:

• Hybrid Cloud Environments: Use Docker to deploy serverless functions across multiple clouds.
• Development and Testing: Run serverless functions locally with Docker for easier debugging.
• CI/CD Pipelines: Package serverless functions in Docker images and deploy them as part of automated pipelines.

Questions & Answers

1. How does Docker enhance serverless development?

   Docker provides portable and consistent environments, allowing serverless functions to run locally and in the cloud.

2. What is the role of multi-stage builds in serverless Docker images?

   Multi-stage builds help minimize image size, ensuring faster startup times for serverless functions.

3. How do you deploy a Docker image to AWS Lambda?

   Push the image to a container registry (e.g., Amazon ECR) and use the aws lambda create-function command to deploy.

Concept

Docker is a cornerstone of DevOps workflows, enabling developers and operations teams to work together seamlessly. It simplifies application deployment, ensures consistency across environments, and accelerates software delivery pipelines.

Key features of Docker in DevOps:

• Containerization for environment consistency.
• Automation of build, test, and deployment workflows.
• Integration with CI/CD pipelines for faster delivery.

Examples

Basic DevOps workflow with Docker:

            # Build a Docker image in a CI pipeline
            docker build -t my_app:latest .
            
            # Run automated tests in the CI pipeline
            docker run --rm my_app:latest pytest
            
            # Push the Docker image to a registry
            docker push my_repo/my_app:latest
            
            # Deploy the Docker image using Kubernetes
            kubectl set image deployment/my-app my-app=my_repo/my_app:latest
                        

Integrating Docker with Jenkins:

            pipeline {
                agent any
                stages {
                    stage('Build') {
                        steps {
                            sh 'docker build -t my_app:latest .'
                        }
                    }
                    stage('Test') {
                        steps {
                            sh 'docker run --rm my_app:latest pytest'
                        }
                    }
                    stage('Push') {
                        steps {
                            sh 'docker push my_repo/my_app:latest'
                        }
                    }
                    stage('Deploy') {
                        steps {
                            sh 'kubectl set image deployment/my-app my-app=my_repo/my_app:latest'
                        }
                    }
                }
            }
                        

Use Cases

• Automating CI/CD pipelines for faster software delivery.
• Ensuring consistency between development, testing, and production environments.
• Deploying containerized applications in hybrid or multi-cloud environments.

Problems & Solutions

Best Practices

• Use CI/CD tools (e.g., Jenkins, GitLab CI) integrated with Docker for automated workflows.
• Store Docker images in private, secure registries.
• Set up automated tests in CI pipelines to ensure container functionality.
• Implement resource limits for containers in staging and production environments.
• Use environment-specific configurations managed by tools like Kubernetes ConfigMaps or Helm charts.

Real-World Scenarios

How Docker integrates with DevOps workflows:

• CI/CD Automation: Automates build, test, and deploy pipelines using Docker and Kubernetes.
• Hybrid Cloud Deployments: Facilitates consistent deployments across multiple cloud platforms.
• Microservices Architecture: Manages and deploys containerized microservices with Docker and orchestration tools.

Questions & Answers

1. How does Docker simplify DevOps workflows?

   Docker ensures consistent environments, accelerates CI/CD pipelines, and supports automation of build and deployment processes.

2. What is the role of Docker in CI/CD pipelines?

   Docker provides portable containers for testing, building, and deploying applications in a consistent manner.

3. How do you secure Docker images in a DevOps workflow?

   Use private registries and integrate vulnerability scanning tools into CI/CD pipelines.

Concept

Docker volumes provide a way to persist data generated and used by Docker containers. Unlike bind mounts, volumes are managed by Docker and provide better isolation and portability.

Types of Docker volumes:

• Anonymous Volumes: Automatically created volumes tied to the container.
• Named Volumes: Managed by Docker and explicitly created by users.
• Bind Mounts: Directly map host paths to container paths.

Examples

Basic volume commands:

            # Create a named volume
            docker volume create my_volume
            
            # Run a container with a volume
            docker run -d --name web -v my_volume:/var/www/html nginx
            
            # List all volumes
            docker volume ls
            
            # Inspect a volume
            docker volume inspect my_volume
            
            # Remove a volume
            docker volume rm my_volume
                        

Using bind mounts:

            docker run -d --name web -v /host/path:/container/path nginx
                        

Use Cases

• Storing persistent data for databases like MySQL or PostgreSQL.
• Sharing files between containers in a secure and isolated way.
• Keeping configuration files consistent across multiple containers.

Problems & Solutions

Best Practices

• Use named volumes for better management and portability.
• Regularly prune unused volumes to free up disk space.
• Use bind mounts cautiously for sensitive host paths.
• Set appropriate permissions for shared volumes.
• Document volume usage in docker-compose.yml files for team collaboration.

Real-World Scenarios

How Docker volumes are used:

• Databases: Persisting data for MySQL, PostgreSQL, or MongoDB containers.
• Web Servers: Storing static content like HTML, CSS, and JavaScript files.
• CI/CD Pipelines: Sharing artifacts and logs between containers during builds and tests.

Questions & Answers

1. What is the difference between bind mounts and volumes?

   Bind mounts map host paths to container paths directly, while volumes are managed by Docker and offer better portability and isolation.

2. How do you inspect the details of a Docker volume?

   Use the command docker volume inspect [volume_name] to inspect a volume.

3. How can you remove unused volumes?

   Run docker volume prune to clean up all unused volumes.

Concept

Docker simplifies the separation of production and development environments for Rails applications. By leveraging Docker Compose, environment-specific configurations, and multi-stage builds, developers can ensure consistency and efficiency across environments.

Key strategies:

• Use docker-compose.override.yml for development-specific overrides.
• Leverage multi-stage Dockerfiles to build production and development images.
• Configure environment variables securely for each environment.

Examples

Multi-stage Dockerfile for Rails:

            # Base stage
            FROM ruby:3.1 AS base
            WORKDIR /app
            COPY Gemfile Gemfile.lock ./
            RUN bundle install
            
            # Development stage
            FROM base AS development
            ENV RAILS_ENV=development
            EXPOSE 3000
            CMD ["rails", "server", "-b", "0.0.0.0"]
            
            # Production stage
            FROM base AS production
            ENV RAILS_ENV=production
            RUN bundle exec rake assets:precompile
            CMD ["rails", "server", "-b", "0.0.0.0"]
                        

Docker Compose with overrides:

            # docker-compose.yml
            version: '3.8'
            services:
              web:
                build: .
                volumes:
                  - .:/app
                ports:
                  - "3000:3000"
            
            # docker-compose.override.yml (for development)
            version: '3.8'
            services:
              web:
                environment:
                  - RAILS_ENV=development
                volumes:
                  - ./tmp:/app/tmp
            
            # docker-compose.prod.yml (for production)
            version: '3.8'
            services:
              web:
                environment:
                  - RAILS_ENV=production
                ports:
                  - "80:3000"
                        

Use Cases

• Isolating development dependencies like webpack-dev-server from production.
• Precompiling assets in production for optimized performance.
• Testing environment-specific behavior using Docker Compose overrides.

Problems & Solutions

Best Practices

• Use separate Compose files for development, testing, and production environments.
• Store secrets securely using Docker secrets or external tools like Vault.
• Minimize image size for production by excluding unnecessary dependencies.
• Use logging and monitoring tools like Logstash and Grafana in production.
• Test deployment configurations in a staging environment before production.

Real-World Scenarios

How Rails developers manage environments with Docker:

• Development: Run Rails, PostgreSQL, and Redis locally using Docker Compose for seamless development.
• Production: Deploy precompiled Rails apps with NGINX for optimized performance.
• Staging: Mirror production settings for testing features before release.

Questions & Answers

1. How do you separate production and development configurations in Docker?

   Use multi-stage builds and separate Docker Compose files with environment-specific overrides.

2. What is the purpose of docker-compose.override.yml?

   It overrides the default Compose file settings, commonly used for development-specific configurations.

3. How do you secure secrets in production?

   Use Docker secrets or external secret management tools like Vault or AWS Secrets Manager.

Concept

This guide outlines the steps to set up a Rails project in Docker for development. It includes setting up a Rails application with Docker, PostgreSQL, and Redis, along with Docker Compose for simplified management.

Key components:

• Rails application container for the app runtime.
• PostgreSQL container for the database.
• Redis container for caching and background jobs.

Setup

Project file structure:

            project/
            ├── Dockerfile
            ├── docker-compose.yml
            ├── Gemfile
            ├── Gemfile.lock
            ├── config/
            └── other_rails_files/
                        

Dockerfile:

            # Base image
            FROM ruby:3.1
            
            # Install dependencies
            RUN apt-get update -qq && apt-get install -y build-essential libpq-dev nodejs yarn
            
            # Set working directory
            WORKDIR /app
            
            # Add Gemfile and Gemfile.lock
            COPY Gemfile Gemfile.lock ./
            
            # Install gems
            RUN bundle install
            
            # Copy the application code
            COPY . .
            
            # Expose the Rails port
            EXPOSE 3000
            
            # Start Rails server
            CMD ["rails", "server", "-b", "0.0.0.0"]
                        

docker-compose.yml:

            version: '3.8'
            
            services:
              app:
                build:
                  context: .
                volumes:
                  - .:/app
                ports:
                  - "3000:3000"
                environment:
                  - DATABASE_HOST=db
                  - DATABASE_USERNAME=postgres
                  - DATABASE_PASSWORD=postgres
                depends_on:
                  - db
                  - redis
            
              db:
                image: postgres:13
                environment:
                  POSTGRES_USER: postgres
                  POSTGRES_PASSWORD: postgres
                volumes:
                  - pg_data:/var/lib/postgresql/data
            
              redis:
                image: redis:6
            
            volumes:
              pg_data:
                        

Gemfile:

            source 'https://rubygems.org'
            gem 'rails', '~> 7.0.0'
            gem 'pg', '~> 1.1'
            gem 'redis', '~> 4.0'
                        

Examples

Steps to initialize and run the Rails application:

            # Step 1: Build the Docker image
            docker-compose build
            
            # Step 2: Create the database
            docker-compose run app rails db:create
            
            # Step 3: Start the development server
            docker-compose up
                        

Access the Rails application at http://localhost:3000.

Best Practices

• Use environment-specific docker-compose.override.yml files.
• Persist database data with named volumes.
• Bind mount the Rails application to reflect code changes immediately.
• Use a lightweight base image to optimize build times.

Questions & Answers

1. How do you run Rails commands with Docker?

   Use docker-compose run app [command], e.g., docker-compose run app rails console.

2. How do you persist database data?

   Use named volumes, as defined in the volumes section of docker-compose.yml.

3. How do you debug issues in the app container?

   Use docker-compose exec app bash to access the container shell.

Concept

This setup integrates React as the frontend, Rails as the backend API, PostgreSQL as the database, Redis for caching and job management, and Sidekiq for background job processing. Docker is used to orchestrate all these services seamlessly.

Key components:

• Rails API container serving the backend.
• React container serving the frontend using webpack-dev-server.
• PostgreSQL container for database operations.
• Redis container for caching and Sidekiq job management.
• Sidekiq container for processing background jobs.

Setup

Project file structure:

            project/
            ├── backend/ (Rails API)
            ├── frontend/ (React app)
            ├── Dockerfile
            ├── docker-compose.yml
            ├── Gemfile
            ├── Gemfile.lock
            ├── config/
            └── other_rails_files/
                        

Dockerfile (multi-stage for Rails):

            # Base image
            FROM ruby:3.1 AS base
            WORKDIR /app
            COPY Gemfile Gemfile.lock ./
            RUN bundle install
            
            # Production setup
            FROM base AS production
            COPY . .
            RUN bundle exec rake assets:precompile
            
            # Development setup
            FROM base AS development
            COPY . .
            EXPOSE 3000
            CMD ["rails", "server", "-b", "0.0.0.0"]
                        

Dockerfile (React):

            # Base image
            FROM node:16 AS base
            WORKDIR /app
            COPY package.json yarn.lock ./
            RUN yarn install
            
            # Development setup
            FROM base AS development
            COPY . .
            EXPOSE 3001
            CMD ["yarn", "start"]
                        

docker-compose.yml:

            version: '3.8'
            
            services:
              rails:
                build:
                  context: ./backend
                  dockerfile: Dockerfile
                volumes:
                  - ./backend:/app
                ports:
                  - "3000:3000"
                environment:
                  - DATABASE_HOST=db
                  - REDIS_URL=redis://redis:6379/1
                depends_on:
                  - db
                  - redis
            
              react:
                build:
                  context: ./frontend
                  dockerfile: Dockerfile
                volumes:
                  - ./frontend:/app
                ports:
                  - "3001:3001"
            
              db:
                image: postgres:13
                environment:
                  POSTGRES_USER: postgres
                  POSTGRES_PASSWORD: postgres
                volumes:
                  - pg_data:/var/lib/postgresql/data
            
              redis:
                image: redis:6
            
              sidekiq:
                build:
                  context: ./backend
                  dockerfile: Dockerfile
                command: bundle exec sidekiq
                depends_on:
                  - redis
                  - db
            
            volumes:
              pg_data:
                        

Examples

Steps to initialize and run the application:

            # Step 1: Build the Docker images
            docker-compose build
            
            # Step 2: Create the database
            docker-compose run rails rails db:create
            
            # Step 3: Start all services
            docker-compose up
                        

Access the Rails API at http://localhost:3000 and the React frontend at http://localhost:3001.

Best Practices

• Use environment-specific Compose files for production and development.
• Persist database data with named volumes.
• Use proper logging for Sidekiq and Rails to monitor background jobs.
• Integrate frontend and backend APIs with CORS properly configured in development.

Questions & Answers

1. How do you run Rails console in Docker?

   Use docker-compose run rails rails console.

2. How do you persist Redis and PostgreSQL data?

   Use named volumes as configured in docker-compose.yml.

3. How do you debug issues in the Rails or React containers?

   Use docker-compose exec rails bash or docker-compose exec react bash to access the respective containers.

Concept

This setup integrates a React frontend, Rails backend, PostgreSQL database, Redis for caching, Sidekiq for background jobs, and Nginx for reverse proxy and serving static files, all orchestrated with Docker Compose for a production environment.

Key Components:

• Rails backend serving APIs and job processing via Sidekiq.
• React frontend served as static files through Nginx.
• PostgreSQL database for persistent storage.
• Redis for caching and Sidekiq job management.
• Nginx for reverse proxy and static file serving.

Setup

Directory structure:

            project/
            ├── backend/ (Rails API)
            │   ├── Dockerfile
            │   ├── Gemfile
            │   ├── Gemfile.lock
            │   ├── config/
            │   │   ├── database.yml
            │   │   ├── sidekiq.yml
            │   │   └── puma.rb
            ├── frontend/ (React App)
            │   ├── Dockerfile
            │   ├── package.json
            │   ├── yarn.lock
            │   ├── public/
            │   ├── src/
            ├── nginx/
            │   └── nginx.conf
            ├── docker-compose.yml
            └── .env
                        

Instructions

1. Build Docker Images

            docker-compose build
                        

2. Create and Migrate the Database

            docker-compose run backend rails db:create db:migrate
                        

3. Start All Services

            docker-compose up -d
                        

4. Access the Application

• API (Rails): http://localhost/api
• Frontend (React): http://localhost

Files

Backend (Rails)

Dockerfile

            FROM ruby:3.1
            WORKDIR /app
            COPY Gemfile Gemfile.lock ./
            RUN bundle install
            COPY . .
            RUN bundle exec rake assets:precompile
            EXPOSE 3000
            CMD ["rails", "server", "-b", "0.0.0.0"]
                        

database.yml

            default: &default
              adapter: postgresql
              encoding: unicode
              username: postgres
              password: postgres
              host: db
            
            development:
              <<: *default
              database: myapp_development
            
            production:
              <<: *default
              database: myapp_production
                        

sidekiq.yml

            :concurrency: 5
            :queues:
              - default
                        

Frontend (React)

Dockerfile

            FROM node:16
            WORKDIR /app
            COPY package.json yarn.lock ./
            RUN yarn install
            COPY . .
            RUN yarn build
            CMD ["yarn", "serve"]
                        

Nginx

nginx.conf

            server {
                listen 80;
            
                location /api {
                    proxy_pass http://backend:3000;
                    proxy_set_header Host $host;
                    proxy_set_header X-Real-IP $remote_addr;
                }
            
                location / {
                    root /usr/share/nginx/html;
                    index index.html;
                    try_files $uri /index.html;
                }
            }
                        

Docker Compose

            version: '3.8'
            
            services:
              backend:
                build:
                  context: ./backend
                environment:
                  - DATABASE_HOST=db
                  - DATABASE_USERNAME=postgres
                  - DATABASE_PASSWORD=postgres
                  - REDIS_URL=redis://redis:6379/1
                volumes:
                  - ./backend:/app
                depends_on:
                  - db
                  - redis
            
              frontend:
                build:
                  context: ./frontend
                volumes:
                  - ./frontend:/app
                depends_on:
                  - backend
            
              nginx:
                image: nginx:stable-alpine
                ports:
                  - "80:80"
                volumes:
                  - ./nginx/nginx.conf:/etc/nginx/conf.d/default.conf
                depends_on:
                  - backend
                  - frontend
            
              db:
                image: postgres:13
                environment:
                  POSTGRES_USER: postgres
                  POSTGRES_PASSWORD: postgres
                volumes:
                  - pg_data:/var/lib/postgresql/data
            
              redis:
                image: redis:6
            
              sidekiq:
                build:
                  context: ./backend
                command: bundle exec sidekiq
                depends_on:
                  - redis
                  - db
            
            volumes:
              pg_data:
                        

Questions & Answers

1. How do you secure the production setup?

   Configure Nginx with HTTPS using SSL certificates and manage secrets with tools like Vault.

2. What if a service fails?

   Check logs with docker-compose logs [service] and restart with docker-compose restart [service].

Concept

This setup supports both development and production environments:

• Development: React served dynamically via `yarn start` and proxied by Nginx.
• Production: React precompiled into static files and served directly by Nginx.

Nginx Configurations

Development (`nginx.dev.conf`)

            server {
                listen 80;
            
                # Proxy API calls to Rails backend
                location /api {
                    proxy_pass http://backend:3000;
                    proxy_set_header Host $host;
                    proxy_set_header X-Real-IP $remote_addr;
                }
            
                # Proxy React development server
                location / {
                    proxy_pass http://frontend:3001;
                    proxy_set_header Host $host;
                    proxy_set_header X-Real-IP $remote_addr;
                    proxy_set_header Upgrade $http_upgrade;
                    proxy_set_header Connection "upgrade";
                }
            }
                        

Production (`nginx.prod.conf`)

            server {
                listen 80;
            
                # Proxy API calls to Rails backend
                location /api {
                    proxy_pass http://backend:3000;
                    proxy_set_header Host $host;
                    proxy_set_header X-Real-IP $remote_addr;
                }
            
                # Serve precompiled React static files
                location / {
                    root /usr/share/nginx/html;
                    index index.html;
                    try_files $uri /index.html;
                }
            }
                        

Docker Compose

            version: '3.8'
            
            services:
              backend:
                build:
                  context: ./backend
                environment:
                  - DATABASE_HOST=db
                  - DATABASE_USERNAME=postgres
                  - DATABASE_PASSWORD=postgres
                  - REDIS_URL=redis://redis:6379/1
                volumes:
                  - ./backend:/app
                depends_on:
                  - db
                  - redis
            
              frontend:
                build:
                  context: ./frontend
                volumes:
                  - ./frontend:/app
                ports:
                  - "3001:3001" # React dev server
                command: yarn start
                depends_on:
                  - backend
            
              nginx:
                image: nginx:stable-alpine
                ports:
                  - "80:80"
                volumes:
                  - ./nginx/nginx.dev.conf:/etc/nginx/conf.d/default.conf # Development config
                  # Uncomment below for production
                  # - ./nginx/nginx.prod.conf:/etc/nginx/conf.d/default.conf
                depends_on:
                  - backend
                  - frontend
            
              db:
                image: postgres:13
                environment:
                  POSTGRES_USER: postgres
                  POSTGRES_PASSWORD: postgres
                volumes:
                  - pg_data:/var/lib/postgresql/data
            
              redis:
                image: redis:6
            
              sidekiq:
                build:
                  context: ./backend
                command: bundle exec sidekiq
                depends_on:
                  - redis
                  - db
            
            volumes:
              pg_data:
                        

Instructions

For Development

1. Build Docker images: docker-compose build
2. Start all services: docker-compose up
3. Access the application:
• React: http://localhost
• API: http://localhost/api

For Production

1. Build Docker images: docker-compose build
2. Switch Nginx to production config: Update the `nginx` volume in docker-compose.yml:

            - ./nginx/nginx.prod.conf:/etc/nginx/conf.d/default.conf
                            

3. Precompile assets for React and Rails:

            # Rails
            docker-compose run backend rails assets:precompile
            # React
            docker-compose run frontend yarn build
                            

4. Start all services: docker-compose up
5. Access the application:
• React: http://localhost
• API: http://localhost/api

Questions & Answers

1. How do I debug a service?

   Use docker-compose logs [service] to view logs for a specific service.

2. How do I handle WebSocket connections?

   Ensure the Nginx development configuration includes proxy_set_header Upgrade and proxy_set_header Connection "upgrade" for WebSocket support.

3. How do I secure the production setup?

   Use HTTPS with SSL certificates configured in the Nginx production configuration.

Docker Basics

# Dockerfile
            FROM python:3.9
            WORKDIR /app
            COPY requirements.txt .
            RUN pip install -r requirements.txt
            COPY . .
            CMD ["python", "app.py"]

docker build -t python-app .
            docker run -p 5000:5000 python-app

docker run -d --name redis-server -p 6379:6379 redis

Docker Compose

version: '3.8'
            
            services:
              app:
                build: .
                ports:
                  - "3000:3000"
                depends_on:
                  - db
                  - redis
            
              db:
                image: postgres:13
                environment:
                  POSTGRES_USER: postgres
                  POSTGRES_PASSWORD: postgres
            
              redis:
                image: redis:6

docker-compose up

docker-compose up --scale app=3

Deployment

docker-compose -f docker-compose.prod.yml up

version: '3.8'
            
            services:
              app:
                image: myapp:latest
                ports:
                  - "80:80"
                environment:
                  RAILS_ENV: production
                depends_on:
                  - db
            
              db:
                image: postgres:13
                environment:
                  POSTGRES_USER: postgres
                  POSTGRES_PASSWORD: postgres

docker logs myapp

docker run --log-driver=syslog myapp

Advanced Docker

            Docker:
              - Startup Time: Seconds
              - Size: MBs
              - Resource Usage: Low
            
            Virtual Machine:
              - Startup Time: Minutes
              - Size: GBs
              - Resource Usage: High

# Create a network
            docker network create my_bridge
            
            # Run two containers on the same network
            docker run --network my_bridge --name app1 nginx
            docker run --network my_bridge --name app2 redis

Docker Compose

# .env
            DB_USER=postgres
            DB_PASSWORD=securepassword

version: '3.8'
            
            services:
              db:
                image: postgres:13
                environment:
                  POSTGRES_USER: ${DB_USER}
                  POSTGRES_PASSWORD: ${DB_PASSWORD}

services:
              db:
                image: postgres:13
                healthcheck:
                  test: ["CMD", "pg_isready", "-U", "postgres"]
                  interval: 10s
                  timeout: 5s
                  retries: 5

Deployment

# Deploy a service
            docker service create --name web --replicas 3 -p 80:80 nginx
            
            # Update the service with zero downtime
            docker service update --image nginx:latest web

docker run --memory="256m" --cpus="1" nginx

Optimization

# Dockerfile
            # Build stage
            FROM node:16 as build
            WORKDIR /app
            COPY package.json yarn.lock ./
            RUN yarn install
            COPY . .
            RUN yarn build
            
            # Production stage
            FROM nginx:alpine
            COPY --from=build /app/build /usr/share/nginx/html

Security

docker scan myimage:latest

Debugging

docker logs web
            docker exec -it web bash
            cat /var/log/nginx/error.log

Real-World Scenarios

docker-compose run app rails db:migrate

# entrypoint.sh
            #!/bin/bash
            set -e
            bundle exec rails db:migrate
            exec "$@"

name: Docker CI/CD
            
            on:
              push:
                branches:
                  - main
            
            jobs:
              build:
                runs-on: ubuntu-latest
                steps:
                  - uses: actions/checkout@v2
                  - name: Build Docker image
                    run: docker build -t myapp:latest .
                  - name: Push to Docker Hub
                    run: echo "${{ secrets.DOCKER_PASSWORD }}" | docker login -u "${{ secrets.DOCKER_USERNAME }}" --password-stdin && docker push myapp:latest

Docker Hub Deployment

docker pull username/myapp:latest

name: Docker Hub CI/CD
            
            on:
              push:
                branches:
                  - main
            
            jobs:
              build-and-push:
                runs-on: ubuntu-latest
                steps:
                  - uses: actions/checkout@v2
                  - name: Log in to Docker Hub
                    run: echo "${{ secrets.DOCKER_PASSWORD }}" | docker login -u "${{ secrets.DOCKER_USERNAME }}" --password-stdin
                  - name: Build and push Docker image
                    run: |
                      docker build -t username/myapp:latest .
                      docker push username/myapp:latest

Amazon ECR Deployment

image: docker:latest
            
            services:
              - docker:dind
            
            variables:
              AWS_DEFAULT_REGION: us-east-1
              ECR_REGISTRY: .dkr.ecr.us-east-1.amazonaws.com
              ECR_REPOSITORY: myapp
            
            stages:
              - build
              - deploy
            
            build:
              stage: build
              script:
                - aws ecr get-login-password --region $AWS_DEFAULT_REGION | docker login --username AWS --password-stdin $ECR_REGISTRY
                - docker build -t $ECR_REPOSITORY:latest .
                - docker tag $ECR_REPOSITORY:latest $ECR_REGISTRY/$ECR_REPOSITORY:latest
                - docker push $ECR_REGISTRY/$ECR_REPOSITORY:latest

CI/CD Integration

Places to Use Docker

Actual Use Cases

Common Points in Docker Usage

Advanced Use Cases

Industry-Specific Applications

Integration with Other Technologies

Docker Architecture

# List running containers
            docker ps
            
            # Pull an image from Docker Hub
            docker pull nginx

Docker Networking

# Create a custom bridge network
            docker network create my_bridge
            
            # Run containers in the custom network
            docker run --network my_bridge --name app1 nginx
            docker run --network my_bridge --name app2 redis

Resource Management

# Run an Nginx container with resource limits
            docker run --name nginx \
              --memory="256m" \
              --cpus="1" \
              nginx

Best Practices

# Stage 1: Build
            FROM node:16 AS build
            WORKDIR /app
            COPY package.json yarn.lock ./
            RUN yarn install
            COPY . .
            RUN yarn build
            
            # Stage 2: Run
            FROM nginx:alpine
            COPY --from=build /app/build /usr/share/nginx/html

Container Failures

Networking

Volumes Management

Performance Monitoring

Debugging Tools

Cleanup Commands