A Dockerfile defines one reproducible image; Compose defines how multiple containers, networks, volumes, health checks, and environment values run together for a development or test stack.
A Dockerfile defines one reproducible image; Compose defines how multiple containers, networks, volumes, health checks, and environment values run together for a development or test stack.
| Question | Practical answer |
|---|---|
| When is it useful? | A notes app uses a multi-stage Dockerfile for the web image and Compose to run it beside a database with a named volume and health-dependent startup. |
| What should you do? | Containerize a two-service sample, add health checks, rebuild after a code change, and verify that database data survives container recreation. |
| How do you know it worked? | A clean machine can start the stack from documented commands, services become healthy, secrets are not baked into images, and stop/restart is predictable. |
| Common failure | Do not use Compose startup order as readiness; add real health checks and application retry behavior. |
flowchart LR
A[Question] --> B[Dockerfile and Compose practice project]
B --> C[Small example]
C --> D[Evidence]
The important idea is not to stop at a definition: connect the concept to a small example and observable evidence.
A notes app uses a multi-stage Dockerfile for the web image and Compose to run it beside a database with a named volume and health-dependent startup.
Before acting, write the success signal. Change one condition at a time, observe the result, and record assumptions. For Dockerfile and Compose practice project, this separates what you know from what you are merely guessing.
Goal: Containerize a two-service sample, add health checks, rebuild after a code change, and verify that database data survives container recreation.
Expected result: A clean machine can start the stack from documented commands, services become healthy, secrets are not baked into images, and stop/restart is predictable.
Do not use Compose startup order as readiness; add real health checks and application retry behavior.
When the result differs from your prediction, do not change many things at once. Check inputs, versions, environment, permissions, and logs, then repeat from the smallest example.
Use the linked resource or repository at the end of the page when you need a full implementation. Check current versions before applying commands to a real project.
Automate testing and deployment with a GitHub Actions workflow.
See DevOps as one feedback loop, then practise the tools in the order they become useful.
DevOps is a feedback-oriented way of delivering and operating software, not a job title or toolchain. Product, development, security, and operations share responsibility from planning through production learning.