The integration of containerization technologies into Continuous Integration and Continuous Deployment (CI/CD) pipelines represents a fundamental shift in modern software engineering. By leveraging GitLab to build, test, and push Docker images, organizations can ensure that the environment used for development is identical to the environment used in production. This parity eliminates the "it works on my machine" phenomenon, providing a predictable and scalable path for application deployment. Within the GitLab ecosystem, which is available through GitLab.com, GitLab Self-Managed, and GitLab Dedicated, users across all tiers—Free, Premium, and Ultimate—can access these capabilities to automate the lifecycle of containerized applications. The complexity of this task arises from the inherent nature of CI/CD jobs: because GitLab CI jobs typically run inside Docker containers themselves, a secondary layer of orchestration is required to manage the creation of new images. This creates a nested environment challenge that engineers must solve using specialized runners, service containers, or daemon-less alternatives.

Infrastructure and Runner Configuration for Containerized Builds

To execute Docker commands within a GitLab CI/CD pipeline, the underlying execution environment, known as the GitLab Runner, must be specifically configured to support these operations. The runner acts as the agent that picks up jobs from the GitLab server and executes the instructions defined in the .gitlab-ci.yml file.

The method of execution significantly impacts the security posture and complexity of the runner's setup. There are two primary ways to enable Docker commands within these jobs: the shell executor and the Docker executor using privileged mode.

The Shell Executor Approach

The shell executor is a configuration where the GitLab Runner executes commands directly on the host machine's operating system shell. In this scenario, the gitlab-runner user is responsible for executing the Docker commands.

The operational requirement for this method is that the host server where the GitLab Runner is installed must have the Docker Engine installed and running. Furthermore, the gitlab-runner user must be granted explicit permission to interact with the Docker daemon. This is typically achieved by adding the user to the docker group on the host system.

The registration process for a shell executor involves specific terminal commands. A practitioner would utilize the registration command as follows:

bash sudo gitlab-runner register -n \ --url "https://gitlab.com/" \ --registration-token REGISTRATION_TOKEN \ --executor shell \ --description "My Runner"

The impact of choosing a shell executor is a reduction in isolation. Since the commands run directly on the host, there is a higher risk of side effects on the host system compared to isolated containerized jobs, but it simplifies the requirement for nested containerization.

The Docker Executor and Privileged Mode

When using the Docker executor—where each job runs inside its own container—a structural conflict occurs. To run docker build inside a container, that container must have access to a Docker daemon. Traditionally, this is solved by running the container in privileged mode.

Privileged mode grants the container nearly all the capabilities of the host machine's kernel, allowing it to interact with the host's hardware and manage other containers. While this enables the "Docker-in-Docker" (DinD) workflow, it introduces significant security considerations, as a compromised container in privileged mode could potentially gain control over the entire host runner.

Advanced Containerization Strategies: DinD, Podman, and Kaniko

Because of the security implications of privileged mode, several alternative strategies have emerged to handle image construction within the GitLab CI environment.

Docker-in-Docker (DinD)

The standard technique to circumvent the lack of a local daemon in a containerized job is Docker-in-Docker (DinD). In this architecture, the docker command-line interface (CLI) is separated from the dockerd daemon. The CLI acts as a client that sends instructions to a server. In a standard environment, dockerd runs on the same machine. In GitLab CI, however, the docker:dind image is used to run a separate container that acts as the daemon.

To implement this, the .gitlab-ci.yml must be configured to run the docker:dind image as a service. By assigning an alias to the service, the CLI can communicate with the daemon over the network.

A typical configuration for a DinD build looks like this:

yaml dind-build: services: - name: docker:dind alias: dockerdaemon variables: DOCKER_HOST: tcp://dockerdaemon:2375/ script: - docker build -t my-image .

In this setup, the DOCKER_HOST environment variable is critical; it instructs the Docker CLI to route its requests to the dockerdaemon alias on port 2375 rather than looking for a local Unix socket. This allows the build process to function even though the job is itself running inside a container.

Podman: The Daemon-less Alternative

Podman offers a fundamentally different architectural approach to containerization. Unlike Docker, Podman does not rely on a central daemon. The Podman CLI performs the heavy lifting itself, executing container operations directly. This makes it an ideal candidate for GitLab CI/CD because it avoids the complexities and security risks of running a daemon inside a container.

The transition from Docker to Podman is often seamless because Podman supports the same command-line options. A GitLab CI job can be configured to use the quay.io/podman/stable image to build and push images to the GitLab Container Registry.

The following examples demonstrate the two ways to implement Podman.

Direct Podman Implementation

This method uses the Podman CLI directly for all registry interactions and image builds.

```yaml
stages:
- build

podman-build:
stage: build
image:
name: quay.io/podman/stable
script:
- podman login -u "$CIREGISTRYUSER" -p "$CIREGISTRYPASSWORD" "$CIREGISTRY"
- podman build -t "$CIREGISTRYIMAGE:podman" .
- podman push "$CIREGISTRY_IMAGE:podman"
```

Podman as a Docker Replacement

For teams that have complex scripts already written for the Docker CLI, Podman can be "masked" as Docker by creating a symbolic link. This allows the existing docker commands to function while utilizing the daemon-less Podman engine underneath.

```yaml
stages:
- build

podman-build:
stage: build
image:
name: quay.io/podman/stable
script:
- ln -s /usr/bin/podman /usr/bin/docker
- docker login -u "$CIREGISTRYUSER" -p "$CIREGISTRYPASSWORD" "$CIREGISTRY"
- docker build -t "$CIREGISTRYIMAGE:podman" .
- docker push "$CIREGISTRY_IMAGE:podman"
```

The impact of using Podman is a simplified CI configuration that does not require the services keyword or privileged mode, leading to a more secure and streamlined pipeline. Additionally, GitLab has increasingly recommended the use of Kaniko for building images, which also provides a daemon-less way to build images within a container.

Registry Authentication and Image Management

Building an image is only half of the workflow; the image must be successfully stored in a container registry to be useful for deployment. GitLab provides a built-in Container Registry that can be used with Free, Premium, or Ultimate tiers.

Authentication Procedures

Before any build or push command can be executed, the runner must authenticate with the registry. This is typically handled using the built-in environment variables provided by GitLab CI/CD:

• $CI_REGISTRY_USER: The username for the registry.
• $CI_REGISTRY_PASSWORD: The password/token for the registry.
• $CI_REGISTRY: The address of the GitLab Container Registry.
• $CI_REGISTRY_IMAGE: The base image name for the project.

To ensure that multiple jobs in a pipeline can access the registry without repeating authentication logic, the docker login command should be placed in the before_script section of the .gitlab-ci.yml file.

Optimization and Best Practices

To ensure efficiency and reliability in the pipeline, several technical best practices should be implemented:

• Use --pull: When executing docker build --pull, the builder attempts to fetch the latest versions of the base images. While this adds a small amount of time to the job, it ensures that the build is not using stale or vulnerable base layers.
• Explicit docker pull: When working with multiple runners, images might be cached locally on some runners but not others. Performing an explicit docker pull before a docker run command ensures that the most recently built version of an image is used, preventing execution errors caused by stale local images.
• Unique Tagging: Using the Git SHA (e.g., $CI_COMMIT_SHA) as the image tag ensures that every single job produces a unique, immutable image. This prevents the "stale image" problem where a deployment might accidentally use an old version of the software.

Multistage Dockerfiles for Streamlined Pipelines

A significant advancement in container orchestration is the use of multistage Dockerfiles. Historically, GitLab CI pipelines were often split into multiple stages: a "build" stage to compile the application, and a "package" stage to create the Docker image. This often required passing large build artifacts between jobs using GitLab CI cache or artifacts, adding significant complexity to the pipeline.

Multistage builds consolidate this entire process into a single docker build command. By using multiple FROM instructions in a single Dockerfile, the builder can use one stage for heavy compilation and a second, much smaller stage for the final runtime environment.

Implementation of Multistage Builds

In a multistage Dockerfile, the first stage (e.g., builder) contains all the tools needed to compile the code (compilers, package managers, source code). The second stage starts from a minimal base image (like an alpine version) and only copies the necessary compiled binaries or production files from the first stage.

Consider the following implementation for a Node.js application:

```dockerfile

Stage 1: The Builder

FROM node:8 as builder
WORKDIR /usr/src/app
COPY package.json .
RUN npm install
COPY ./src /usr/src/app/
RUN npm run build

Stage 2: The Final Runtime

FROM node:8-alpine
WORKDIR /usr/src/app
COPY package.json .
RUN npm install --production

Copying specific files from the 'builder' stage

COPY --from=builder /usr/src/app/dist /usr/src/app/dist
COPY --from=builder /usr/src/app/server.js /usr/src/app/server.js
CMD ["node", "server.js"]
```

The impact of this methodology is twofold. First, it drastically reduces the size of the final production image by excluding build-time dependencies (like npm, gcc, or header files). Second, it simplifies the GitLab CI configuration, as the entire build-and-package logic is encapsulated within the Dockerfile itself, allowing the .gitlab-ci.yml to be reduced to a single, clean build step.

Analysis of Containerized CI/CD Architectures

The evolution of building Docker images within GitLab CI/CD reflects a broader trend in DevOps toward abstraction and security. The transition from heavy, privileged "Docker-in-Docker" setups to lighter, daemon-less approaches like Podman and Kaniko demonstrates a clear movement toward "least privilege" security models. By removing the dependency on a central daemon, engineers can run containerized builds in environments that are inherently more secure and easier to scale across distributed runner clusters.

Furthermore, the adoption of multistage Dockerfiles has fundamentally changed how CI pipelines are designed. The shift from "pipeline-driven builds" (where the CI orchestrator manages the build steps) to "Dockerfile-driven builds" (where the container engine manages the build steps) reduces the surface area for pipeline failure. When the build logic resides in the Dockerfile, it becomes portable and independent of the specific CI/CD tool being used. This decoupling is essential for organizations aiming for multi-cloud or hybrid-cloud strategies, as the containerization logic remains consistent whether the job is running on GitLab, GitHub Actions, or a local developer machine.

Ultimately, successful containerized workflows in GitLab require a strategic choice between the simplicity of the shell executor, the robustness of DinD, the security of Podman, and the efficiency of multistage builds. An expert implementation balances these factors to create a pipeline that is not only fast and automated but also secure and highly reproducible.

Sources

1. GitLab Documentation: Use Docker to build Docker images
2. PythonSpeed: Building Docker images on GitLab CI
3. GitLab Documentation: Build and push container images
4. Snorre.io: Building Docker images with GitLab CI