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Docker Architecture and Components

Overview

Describe the Docker client–daemon path and the roles of dockerd, containerd, and the OCI runtime so you can debug “where did my request fail?”

You talk to the Docker CLI; it calls the Docker Engine API on dockerd. The daemon uses containerd and runc to create containers on the host kernel.

This is a core tutorial in Module 1 · Container Fundamentals of the REBASH Academy Docker for Cloud & DevOps Engineers series — written for Cloud, DevOps, Platform, and SRE engineers.

Prerequisites

Learning Objectives

By the end of this tutorial, you will be able to:

  • Trace CLI → dockerd → containerd → runc
  • Distinguish image, container, and registry
  • Know what docker context selects
  • Relate namespaces/cgroups to isolation

Architecture

This topic’s control points and relationships are shown below.

Docker architecture

Theory

What

Docker’s architecture separates the CLI, the Engine (dockerd), containerd, and an OCI runtime such as runc, all sitting on the host kernel. Images, containers, networks, and volumes are API objects the daemon manages. Registries store images remotely.

Why

Troubleshooting “Docker is broken” means knowing which layer failed: CLI talking to the wrong context, daemon down, pull failing in containerd, or runtime/kernel limits. Production platforms (and Kubernetes) reuse pieces of this stack — especially containerd and OCI — so the mental model transfers.

How it works

You type docker commands; the CLI calls the Engine API (local Unix socket or TCP). dockerd orchestrates networks, volumes, and higher-level UX. It delegates image pull and container lifecycle to containerd, which invokes runc to start the process in namespaces/cgroups. An image is immutable layers plus config; a container adds a writable layer and process state; a registry (Docker Hub, GitHub Container Registry, Amazon Elastic Container Registry) is the remote store.

Component Role
Docker CLI User interface (docker)
dockerd Engine API, networks, volumes
containerd Image pull, container lifecycle
runc OCI runtime — starts the process
Host kernel Namespaces, cgroups, filesystems

Key concepts

  • Client–daemon — CLI is not the container runtime
  • Contexts — CLI can point at remote engines
  • Rootful vs rootless — privilege model of the daemon
  • Shim / supervisors — keep containers reparented correctly after daemon restarts

Common pitfalls

  • Exposing the Docker socket without understanding it is root-equivalent
  • Debugging inside the CLI when dockerd is the failing component
  • Assuming Desktop networking equals Linux Engine networking
  • Confusing image ID, digest, and tag

Hands-on Lab

Create a workspace for this tutorial.

mkdir -p ~/rebash-docker/module-01-arch && cd ~/rebash-docker/module-01-arch

Focus: map client/daemon/images/containers with inspect output

Step 1 – Explore architecture objects

docker pull nginx:alpine
docker create --name rebash-arch nginx:alpine
docker inspect rebash-arch --format 'image={{.Image}}'
docker image inspect nginx:alpine --format 'id={{.Id}}'
docker system df

Step 2 – Cleanup create-only container

docker rm rebash-arch
docker ps -a --filter name=rebash-arch

Final step – Cleanup note

docker rm -f rebash-arch 2>/dev/null || true
# Keep ~/rebash-docker/ for later tutorials

Validation

  • Lab commands run under ~/rebash-docker/module-01-arch/
  • You can explain each Theory section in your own words
  • You used modern tooling where it applies to this topic
  • You can describe one production failure mode for this topic

Code Walkthrough

Production practice for Docker Architecture and Components always combines:

  1. Inspect before you change (status, plan, logs, dry-run)
  2. Prefer reversible, documented changes (Git, IaC, drop-ins, version pins)
  3. Capture evidence (command output, pipeline logs) for handovers
  4. Prefer current tools and APIs over legacy shortcuts
  5. Least privilege — escalate credentials only when required

Keep runbooks short enough to follow under pressure. Automate checks; keep humans for judgement.

Security Considerations

  • Treat credentials and tokens for docker as privileged — never commit them
  • Prefer short-lived auth (OIDC, roles, SSO) over long-lived keys
  • Validate blast radius before apply/deploy/delete operations
  • Restrict who can approve production changes
  • Collect audit logs; limit who can read sensitive traces

Common Mistakes

Exposing the Docker socket without understanding it is root-equivalent

Validate assumptions against the Theory section and official docs before changing production.

Debugging inside the CLI when dockerd is the failing component

Lab shortcuts (open security groups, admin roles, skip approvals) must not ship unchanged.

Changing production without a rollback path

Always know how to revert (previous artefact, prior release, state rollback, DNS failback).

Best Practices

  • Encode Docker Architecture and Components changes as code and review them in pull requests
  • Pin versions (images, modules, actions, provider plugins)
  • Separate environments with clear promotion gates
  • Alert on symptoms with runbooks attached
  • Destroy lab resources; tag everything with owner and expiry where possible

Troubleshooting

Symptom Likely cause Fix
Auth / permission denied Wrong identity, policy, or scope Check caller identity, roles, and least-privilege policies
Timeout / no route Network, DNS, security group, or endpoint Trace path, DNS, and allow-lists before retrying
Drift / unexpected plan Manual change or wrong state/workspace Reconcile desired vs actual; avoid click-ops on managed resources
Pipeline/job red Flaky step, cache, or missing secret Read failing step logs; bisect recent workflow/config changes
Cost spike Idle load balancer, NAT, oversized compute Inventory billable resources; stop/delete labs promptly

Summary

Docker Architecture and Components is essential for Cloud and DevOps engineers working with docker. Practise the lab until the inspection and change path is muscle memory, then continue the track.

Interview Questions

  1. Role of dockerd versus the CLI?
  2. What storage driver concerns matter on Linux?
  3. How do containerd/runc fit the stack?
  4. Why does architecture knowledge help troubleshooting?
  5. What is the difference between create and run?

Sample answer — question 2

Use docker info and inspect to see driver/runtime details.

Sample answer — question 4

Limit who can talk to the daemon socket.

References