Container Image: Definition, Working, Creation & Lifecycle

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November 27, 2025

This article explores the fundamentals of container images, answering common questions around what they are, how they work, and why they matter in modern containerization. It covers the key differences between Docker images and containers, the process of creating and managing images through their lifecycle, and how they are pulled, authenticated, and secured. Examples of container images, their composition, and common use cases are highlighted, along with troubleshooting guidance. Together, these sections provide a comprehensive view of container images for developers, operators, and teams using Docker and Kubernetes.

What is a container image?

A container image is an immutable image, a read-only package that contains everything needed to run an application: the app binary/executable, system libraries, and dependency files plus metadata (entrypoint, environment, and operating system userland, typically Linux). Docker images are built from a manifest (e.g., Dockerfile) and stored in a repository on a registry such as Docker Hub or a private container registry. At deployment, a container runtime (e.g., Docker, containerd) pulls the image and uses it to create a container and run containers consistently across runtime environments, including Kubernetes clusters.

Unlike a virtual machine (VM), an image doesn’t include a full kernel; it’s a compact, layer-based artifact optimized for fast deploy and repeatable workflow when you use Docker to create containers or a docker container on any host that supports the runtime.

What are container images examples?

Base / minimal images: scratch, alpine:3.20, busybox:1.36, debian:bookworm-slim, ubuntu:22.04

Distroless / hardened images: gcr.io/distroless/static, gcr.io/distroless/base-debian12, gcr.io/distroless/java17

Language runtimes: node:20-alpine, python:3.12-slim, golang:1.22-alpine, openjdk:21-jre, php:8.3-fpm, ruby:3.3

Web servers / proxies: nginx:alpine, httpd:2.4, caddy:latest, tomcat:10, traefik:v3

Databases / caches: postgres:16, mysql:8, mariadb:11, mongo:7, redis:7, memcached:1.6

Messaging / streaming: rabbitmq:3-management, confluentinc/cp-kafka:latest, eclipse-mosquitto:2

AI/ML & GPU: pytorch/pytorch:2.4-cuda12.1-cudnn8-runtime, tensorflow/tensorflow:2.16-gpu, nvidia/cuda:12.4.1-runtime-ubuntu22.04

Build / CI tools: jenkins/jenkins:lts, hashicorp/terraform:light, alpine/git, docker:27-dind

Kubernetes system images: registry.k8s.io/pause:3.10, coredns/coredns:1.11.1, etcd:3.5

Serverless bases: public.ecr.aws/lambda/python:3.12, public.ecr.aws/lambda/nodejs:20

Private application examples: mycorp/web-api:1.4.2, mycorp/batch-worker:2025.09.1

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How Container Images work?

A container image works as a read-only blueprint that packages an application with its dependencies, system libraries, and runtime settings. These images are immutable, meaning they cannot be changed once built, which guarantees consistency across environments.

Here’s how the process functions step by step:

Image composition
- A container image is built from multiple image layers, each layer representing file changes such as binaries, libraries, or configurations.
- Layers build upon a base or parent image, enabling reuse and smaller variations for similar applications.
- Official images (e.g., Docker Hub) provide trusted starting points for building custom images.
Image creation
- Developers create images using instructions (e.g., Dockerfile) that define dependencies, commands, and configuration.
- The result is a self-contained package with everything required for the container to run, independent of the host environment.
Distribution and storage
- Images can be pushed to registries such as Docker Hub, Amazon ECR, or Azure Container Registry.
- Private registries secure proprietary workloads, while public registries distribute open-source software.
- During deployment, the kubelet pulls images from the registry to the host machine.
Deployment and execution
- Container engines (Docker or any OCI-compliant runtime) launch containers from the image.
- Multiple containers can run from the same image, sharing the host kernel but remaining isolated.
- At startup, the runtime unpacks and merges layers using a union filesystem and adds a writable layer for temporary changes.
Updating and orchestration
- To update an application, build and push a new image tag—images are immutable and not modified in place.
- Orchestrators like Docker and Kubernetes automate scaling, rolling updates, and managing containers across clusters.

How are container images created?

A container image is created through a reproducible, layered build process that packages an application and its dependencies into an images-are-read-only templates artifact:

Author build instructions
- Write a Dockerfile with instructions like FROM, COPY, RUN, ENV, and ENTRYPOINT.
- Choose trustworthy base images and dependency containers that match runtime requirements.
Assemble layers
- Each Dockerfile command creates a new container layer.
- Layers combine to form the final image.
- Optimize layer order and use multi-stage builds to reduce image size.
Build locally or in CI
- Use docker build (BuildKit) to compile OCI-compliant images.
- Test images by running a container to validate entrypoint, behavior, and health checks.
Create multi-platform artifacts
- Produce multi-architecture manifests for different OS/CPU targets.
- Allows automatic selection of the correct variant during pulls.
Tag, store, and distribute
- Tag the image and push it to a registry (Docker Hub, private registry, etc.).
- Clients pulling the image reuse local caches to speed deployment when possible.
Deploy and run
- Orchestrators start containers based on the image.
- Images remain read-only; containers add a small writable layer at runtime.
- Teams use containers for reliable, consistent releases at scale.
Lifecycle hygiene
- Rebuild images regularly to pick up upstream security patches and updates.
- Retag and redistribute updated images for different environments.

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What are the Benefits and Drawbacks?

Benefits:

Portability across environments

Faster deployment and scalability

Version control and immutability

Consistent environments for DevOps teams

Drawbacks

Security risks from unverified base images

Storage overhead for multiple layers

Complexity in managing large registries

Potential configuration drift if images aren’t rebuilt regularly

What are the Use Cases in 2025?

Core use cases

Microservices/APIs (*-slim, -alpine, nginx).

AI/ML inference (GPU images: PyTorch/TensorFlow/ONNX).

Edge/IoT (distroless/minimal, arm64, offline updates).

Workers & data/ETL (slim runtimes, pinned wheels, Redis/RabbitMQ).

Web/API gateways (Nginx/Caddy/Traefik).

Serverless (single-binary, tiny images).

Platform & ops (sidecars, operators, CLIs).

Golden bases & multi-arch (org-approved Debian/Distroless; amd64+arm64).

Security & delivery defaults

Distroless/-slim, non-root, read-only FS, drop capabilities.

Multi-stage builds; pin by digest; health/readiness probes.

SBOM + sign + provenance (SLSA/in-toto) stored as OCI referrers.

Containers Vs. Docker images: What is the difference?

Aspect	Containers	Docker Image
Nature	Running instance created from an image, executing one or more processes	Immutable package with binary/executable, dependencies, and OS libraries
State	Live process state plus a writable layer on top of the image	Read-only, static artifact
Mutability	Ephemeral — runtime changes are lost when the container is removed (unless stored in volumes)	Immutable — changes require a rebuild
Composition	Image layers plus a thin writable layer, attached namespaces, cgroups, and networking	Layered filesystem with app binary/executable, metadata, and dependencies
Lifecycle	Create → start → stop → remove (can be recreated any number of times from the same image)	Build → store → distribute
Analogy to VMs	Similar to a running VM instance providing an active workload	Similar to VM templates but lighter — no kernel included
Operational Role	Executed workload or replica that actually runs the application	Blueprint that defines how to create a container
Configuration	Uses image defaults but can be overridden at runtime (CLI flags, orchestrator manifests, env)	Defines default behavior in build (entrypoint, env)

What are the types of Container images?

#	Type	Examples
1	Base / minimal	scratch, busybox:1.36, alpine:3.20, debian:bookworm-slim, ubuntu:22.04
2	Distroless / hardened	gcr.io/distroless/static, gcr.io/distroless/base-debian12, cgr.dev/chainguard/static
3	Language runtimes	node:20-alpine, python:3.12-slim, golang:1.22-alpine, openjdk:21-jre, dotnet/runtime:8.0
4	Application & services	nginx:alpine, httpd:2.4, caddy:latest, tomcat:10, traefik:v3
5	Databases & caches	postgres:16, mysql:8, mariadb:11, mongo:7, redis:7, memcached:1.6
6	Builder vs runtime (multi-stage)	Builder: golang:1.22, node:20; Runtime: distroless/static, alpine:3.20
7	Debug / diagnostic	alpine:latest (with tools), busybox:glibc, ubuntu:tools variants
8	Vendor OS bases	registry.access.redhat.com/ubi9-minimal, debian, alpine, wolfi
9	From-scratch	scratch (single static binary; no userspace)
10	Multi-arch manifests (packaging)	Tags that include amd64, arm64, etc. under one reference

How are container images managed through their lifecycle?

Container image lifecycle management spans governance from build to retirement, ensuring repeatable, secure deployments while recognizing that images are read-only templates and not running workloads.
Here is the lifecycle of container image management:

Plan and standards — Define base image policy, naming, tagging, retention; choose what’s baked vs set at deploy time.

Build and attest — Versioned Dockerfiles, reproducible builds, SBOMs, signatures, provenance.

Scan and approve — Vulnerability/policy scans gate promotion to higher environments.

Tag, version, store — Push to registries with semantic tags and immutable digests; keep multi-arch variants.

Distribute and replicate — Mirror across regions/registries; use CDN/registry sync for proximity and resilience.

Pull, cache, launch — Orchestrators pull by digest; reuse cached layers; start containers from the image.

Configure and run — Keep secrets/env/mounts/resources outside the image; treat the write layer as ephemeral.

Update and rollback — Publish new tag/digest; blue-green or canary; rollback by pinning the prior digest.

Monitor and audit — Track provenance and deployments by digest; alert on drift from approved images.

Patch and rebuild — Rebase on patched parents; rebuild to pick up dependency fixes (no image mutation).

Retire and garbage-collect — Deprecate old tags, quarantine risky images, run registry GC per policy.

Compliance and backup — Enforce content trust and license compliance; keep audit logs; snapshot registries for DR.

How are container images pulled and authenticated in Kubernetes?

Here is how container images are pulled and authenticated in Kubernetes:

Below is the streamlined flow and guidance.

Resolution and policy
- Kubernetes parses name:tag or name@sha256:digest and applies imagePullPolicy (Always, IfNotPresent, Never).
- The kubelet requests the CRI runtime (containerd/CRI-O) to pull the image if it is not cached.
Pull flow
- The runtime fetches the manifest and layers from the registry.
- The runtime unpacks layers and prepares the image for container startup.
- Images must be fetched before the container runs the image entrypoint/command.
Authentication order
- Anonymous access for public images (if allowed).
- imagePullSecrets: use kubernetes.io/dockerconfigjson secret referenced in spec.imagePullSecrets.
- ServiceAccount: Pods inherit pull secrets from the assigned ServiceAccount.
- Credential providers: exec/cloud plugins mint short-lived tokens (ECR, GCR/Artifact Registry, ACR).
Cloud registries
- ECR: IRSA/instance roles or kubelet credential provider.
- GCR / Artifact Registry: Workload Identity or keyless/OIDC.
- ACR: Managed Identity or service principal.
Best practices
- Pin images by digest @sha256:… to guarantee the tested version.
- Scope secrets per namespace/registry; avoid long-lived cluster-wide credentials.
- Prefer short-lived federated authentication (OIDC/IAM).
- Enforce TLS with trusted CAs; restrict unnecessary egress.
Common failure points
- ImagePullBackOff / ErrImagePull due to incorrect secret type/key, missing secret in Pod namespace, wrong registry URL, or expired cloud token.
- Stale cached images when using IfNotPresent; resolve by pushing new digest/tag or using Always policy.

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How do you troubleshoot container image pull errors?

Here is how you troubleshoot container image pull errors:

Read the exact error/events — kubectl describe pod <name> for ErrImagePull/ImagePullBackOff; kubectl get events -n <ns> --sort-by=.lastTimestamp.

Validate the image reference — Confirm registry/repo/name:tag or @sha256:<digest>; test from node: crictl pull <ref> or ctr i pull <ref>; prefer digest pinning.

Auth & secrets — Secret type kubernetes.io/dockerconfigjson with key .dockerconfigjson; secret in the Pod’s namespace; referenced via imagePullSecrets or ServiceAccount; renew short-lived ECR/GAR/ACR tokens and fix clock skew.

Registry endpoint & TLS — Host/region/path correct; curl -I https://<registry>/v2/ (expect 200/401); install private CA on nodes if intercepting/MITM.

Network & DNS — Node resolves/reaches registry (nslookup, dig, curl); egress/proxies/firewalls allow TCP/443; air-gapped mirror reachable and synced.

Image policy & cache — IfNotPresent can reuse stale layers; bump tag/digest or set Always; clear corrupt cache: crictl rmi <ref> / ctr i rm <ref>.

Platform/manifest — Manifest list must include node os/arch (e.g., linux/arm64); otherwise “no matching manifest.”

Rate limits & quotas — Public registries throttle anonymous pulls; authenticate or mirror; check org quotas (pulls/min, bandwidth, repos).

OCI/media type — Older runtimes may reject layer compression (e.g., zstd); update containerd/CRI-O or rebuild with compatible formats.

Large images/timeouts — Slim via multi-stage builds and smaller bases; leverage layer caching; only then consider increasing kubelet/runtime timeouts.

Kubelet/runtime health — Inspect node logs; verify credential provider plugins (ECR/GAR/ACR) installed and executable.

Access control — Ensure the repo grants pull to the identity (robot account/IAM role/service principal).

Reproduce outside K8s — Pull from the node; if it works, recheck Pod SA, imagePullSecrets, and namespace wiring.

How do you secure container images?

Securing container images requires controlling their origin, contents, and distribution to prevent vulnerabilities and supply chain risks. Key practices include:

Start with trusted sources
- Use official images or vendor-verified bases; avoid unverified public repositories.
- Apply organization-approved base images to reduce hidden risks.
Minimize attack surface
- Build small, purpose-specific images; remove unnecessary packages, tools, and shells.
- Favor distroless or minimal bases over general-purpose ones.
Scan for vulnerabilities
- Run automated CVE scans on every build and before release.
- Continuously rescan stored images, as new vulnerabilities are published daily.
Apply immutability and provenance
- Treat images as immutable—do not modify in place.
- Sign images and verify signatures at deploy time (e.g., cosign, Notary).
- Maintain an SBOM (software bill of materials) for visibility into dependencies.
Control distribution
- Push only to secured private registries with TLS and access controls.
- Restrict pull permissions to authorized teams; enable auditing and logging.
Use digest pinning
- Deploy images by digest (sha256:…) instead of mutable tags like latest.
- Ensures the executable and binaries match what was tested.
Keep images updated
- Rebuild when upstream dependency containers or base images publish patches.
- Retire and garbage-collect outdated images to limit attack exposure.
Integrate into orchestration security
- Enforce policies in Kubernetes or other platforms to block unsigned, unscanned, or untrusted images.
- Combine with runtime protections to detect drift or tampering within a container.

FAQs

Q1. What is Container?

A container is a standard package that includes an application and all its dependencies so it can run the same way on any system. It starts as an isolated process from a container image, shares the host operating system kernel, and launches in seconds.

Q2. How is a container image different from a container?

An image is the blueprint (stored artifact); a container is a running instance of that image with a thin writable layer and runtime configuration.

Q3. What is a Dockerfile?

A Dockerfile is a declarative text file that defines the exact steps to build a container image (base image, file copies, commands, environment, entrypoint). It enables deterministic, automated builds—including multi-stage builds for smaller, more secure images.

Q4. What is container image security?

Container image security refers to the practices and tools used to ensure that container images are safe, trusted, and free from vulnerabilities. It includes scanning images for known CVEs, verifying signatures, enforcing secure base images, and preventing the use of tampered or untrusted images before they are deployed.

Dhanush VM

Dhanush V M is a seasoned technology leader with over a decade of expertise spanning DevOps, performance engineering, cloud deployments, and solution architecture. As a Solution Architect at CleanStart, he leads key architectural initiatives, drives modern DevOps practices, and delivers customer-centric solutions that strengthen software supply chain security.