Docker for the Absolute Beginner - Hands On - DevOps

Explore docker basics by running your first container, pulling images from Docker Hub, and using Alpine and Nginx to understand base images, web servers, and container workflows.

Explore docker image and container management by listing images, freeing space with docker rmi after removing containers using an image, and cleaning up with docker system prune.

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Learn how Docker handles input and terminals with interactive mode (-i) and pseudo terminal (-t). Use -i, -t, or -it strategically for prompts, piping data, and colored output inside containers.

Master port mapping in Docker by exposing container ports to the Docker host, enabling access via the host IP on mapped ports and supporting multiple apps on distinct ports.

Persist data in Docker by mapping the host directory /opt/data to /var/lib/mysql, so data survives container removal; use -v or --mount, with the latter clarifying the mount type.

Use docker inspect to get container details in json, including state, mounts, network settings, beyond docker ps, and view logs with docker logs by container id or name.

Learn to create your own docker image for a Flask web app, using a Dockerfile, base Ubuntu, environment variables, and entry point, with layered caching and pushing to Docker Hub.

Build a custom Docker image from Ubuntu by installing Python 3 Flask, copying app source code into the image, and setting the environment variable, then run with port mapping.

Identify four problems with the traditional docker builder—re-downloading packages, secrets leaking in image metadata, architecture lock-in, and sequential stages—and anticipate how docker fixes them.

Docker init acts as a template scaffolder that generates a ready-to-edit Dockerfile, .dockerignore, compose.yaml, and readme.docker.md for new projects, applying multi-stage builds, slim images, and non-root user best practices.

Explore how docker run, commands, and entry points determine what starts in a container, contrasting cmd and entrypoint and using --entrypoint for runtime overrides.

Configure a dockerized Python app with environment variables. Use docker run -e to set colors and docker inspect to view container environment variables for different containers.

Learn to orchestrate multi-service applications with Docker Compose, using YAML configuration to connect Redis, PostgresQL, and microservices via Docker networks on a single engine.

Demonstrates running the full application stack with docker compose, defining five services (vote, result, worker, redis, db), mapping ports 8080 and 8081, and building images for launch.

Learn how the Docker engine runs applications in isolated containers through the daemon, REST API, and CLI, with three handoffs to container D and run C implementing the OCI runtime.

Explore how docker uses namespaces to isolate process IDs, network, IPC, and mounts, giving each container its own PID namespace and the sense of its own system.

Discover how Docker uses cgroups to restrict cpu and memory for containers, using the dash dash CPUs option and the dash dash memory flag to cap resources.

Learn how docker stores data on the local file system under /var/lib/docker with image and container layers, writable layers, and volumes, using copy-on-write and mounts.

Explore how Docker images are stored in registries, including Docker Hub and private registries, and learn to push, pull, and manage images with Docker login and tag commands.

Run linux containers on macOS with Docker Desktop, which uses Apple's virtualization to host a linux VM on intel and apple silicon. Learn multi-arch support, Rosetta 2 translation, and licensing.

Explore container orchestration as a solution to run containers across multiple hosts in production. Learn how it enables health checks, scaling, load balancing, and shared storage.

Learn Docker Swarm basics, forming a multi-host cluster with a manager and workers, initializing with swarm init, joining workers, and using Docker service to deploy replicas across nodes.

Explore Kubernetes concepts with kubectl to deploy deployments, manage thousands of replicas, and perform rolling upgrades and rollbacks, while understanding nodes, master, etcd, API server, scheduler, and runtimes.