What is Docker?

Posted on 27th November 2024

Devops & Infrastructure, Open Source, and What Is

Docker is a platform for packaging applications and their dependencies into containers so they can run consistently across laptops, CI, and production servers. Instead of shipping works on my machine assumptions, you ship a reproducible runtime unit.

For teams managing frequent releases, Docker helps standardize build and deploy workflows, reduce environment drift, and speed up rollback when needed.

What Is a Docker Container?

A container is an isolated process that shares the host OS kernel while keeping application dependencies separated from other workloads.

Compared to a traditional virtual machine:

Containers start in seconds (or less)
Images are generally smaller than VM images
Multiple containers can share the same host efficiently
You package app + runtime + dependencies as one artifact

Docker popularized this model with a strong developer workflow and image ecosystem.

Docker Architecture in Practice

A typical Docker stack includes:

Dockerfile
- Defines how to build an image
- Declares base image, dependencies, copy steps, and startup command
Image
- Immutable template built from layers
- Versioned and stored in a registry
Container
- Running instance of an image
- Adds a thin writable layer for runtime changes
Registry
- Stores and distributes images (for example Docker Hub or private registries)
Runtime + Engine
- Creates and manages containers
- Handles networking, storage mounts, and process lifecycle

Images vs Containers (Simple Mental Model)

Image: Build artifact (immutable)
Container: Running process created from an image (mutable at runtime)

You can run many containers from one image, which is why consistent image versioning is critical for repeatable deployments.

A Minimal, Production-Oriented Dockerfile

FROM node:20-alpine AS base
WORKDIR /app

COPY package*.json ./
RUN npm ci --omit=dev

COPY . .
ENV NODE_ENV=production
EXPOSE 3000
CMD ["node", "server.js"]

For larger apps, use multi-stage builds to keep final images small and reduce attack surface.

Multi-Stage Build Pattern (Recommended)

FROM node:20-alpine AS build
WORKDIR /app
COPY package*.json ./
RUN npm ci
COPY . .
RUN npm run build

FROM node:20-alpine AS runtime
WORKDIR /app
COPY package*.json ./
RUN npm ci --omit=dev
COPY --from=build /app/dist ./dist
USER node
CMD [\"node\", \"dist/server.js\"]

This keeps compilers and build-time dependencies out of the runtime image.

Networking and Storage Basics

Two concepts matter most in production:

Networking

Containers can communicate over user-defined Docker networks
You map host ports to container ports (-p 80:3000)
Service discovery is easier with orchestrators and DNS-aware network naming

Storage

Container writable layers are ephemeral
Use volumes for persistent data
Keep stateful services (databases) carefully separated from stateless app containers

Security and Operational Best Practices

Use minimal base images to reduce vulnerabilities.
Pin image tags (or digests) for predictable deployments.
Run as non-root whenever possible.
Scan images in CI before release.
Avoid baking secrets into images; inject at runtime.
Keep images rebuildable from source with deterministic pipelines.

Docker in CI/CD Workflows

In modern pipelines, Docker usually follows this sequence:

Build image from commit SHA
Run tests against that image
Push image to registry
Deploy immutable image tag to staging and production

This approach reduces ambiguity because the artifact you tested is the same artifact you deploy.

If you are standardizing release workflows, these related guides are useful:

Docker vs Orchestration Tools

Docker packages and runs containers, but orchestration tools solve broader production problems:

Scheduling containers across many hosts
Health checks and auto-restarts
Scaling and service discovery
Rolling updates and policy controls

For small workloads, Docker Compose can be enough. For larger distributed systems, Kubernetes (or managed container platforms) is usually a better fit.

Production Readiness Checklist

Before deploying containerized services widely, verify that your platform can answer these questions:

Can we rebuild every image deterministically?
Do we have clear image provenance and tag strategy?
Are secrets injected securely at runtime?
Can we roll back quickly to a known-good image?
Do we collect logs/metrics outside the container filesystem?
Have we defined CPU and memory limits?

If several answers are \not yet,\ solve those gaps before scaling container adoption across business-critical services.

Common Mistakes to Avoid

Using latest tags everywhere
Keeping large build tools in runtime images
Writing logs to local container filesystem only
Treating containers as persistent pets instead of replaceable units
Mixing app and database state in the same lifecycle assumptions

Where Docker Delivers the Most Value

Docker is usually most impactful in teams that have:

Multiple environments (local, CI, staging, production) with drift issues
Several developers onboarding frequently
Cross-functional ownership between app and platform teams
A need for faster, lower-risk release rollouts

In those scenarios, a containerized artifact plus a repeatable deployment pipeline creates clearer handoffs and better incident recovery patterns.

Frequently Asked Questions

Is Docker the same as a virtual machine?

No. Containers share the host kernel, while VMs run full guest operating systems. Containers are generally lighter and faster to start.

Do I need Kubernetes to use Docker?

No. Many teams start with Docker + Compose. Kubernetes becomes valuable as complexity, scale, and multi-service coordination increase.

Are Docker containers secure by default?

They can be secure, but defaults are not enough by themselves. You still need image scanning, least privilege, secrets hygiene, and patching.

Should I store data inside containers?

For persistent production data, use volumes or external managed services. Containers should be easy to replace without data loss.

Final Takeaway

Docker is most effective when treated as part of a full delivery system, not just a packaging tool. Strong image standards, automated validation, and predictable rollout/rollback practices turn containers into a reliability advantage rather than extra operational complexity. As teams mature, these foundations make migration to orchestration and multi-service deployments significantly smoother.

Need help standardizing Docker deployments across environments? Start with one service, lock down your image process, and scale from a repeatable pipeline.