GitLab Executors

4.3. Docker Executor

Description: Runs jobs inside Docker containers.

Best For: Ensuring a consistent environment across jobs, easy dependency management.

Why? Docker is one of the most commonly used executors because it provides a consistent and isolated environment for running CI/CD jobs. Docker images serve as snapshots of environments, including everything needed to run code. This consistency ensures that jobs behave predictably across different stages of the pipeline, reducing inconsistencies and debugging efforts. Docker executors also simplify dependency management, as all necessary libraries and configurations are encapsulated within the Docker image.

Example Configuration

# Define the stages
stages:
  - build
  - test
  - security
  - deploy

# Use a Docker image for the jobs
#  Specifies the Docker image to use for the jobs. This ensures that all jobs run in a consistent Python environment.
image: python:3.8

Advantages

• Consistency: Docker images ensure that the same environment is used across all stages of the pipeline, reducing inconsistencies and “it works on my machine” issues.
• Isolation: Each job runs in an isolated container, which means that dependencies and configurations do not interfere with each other.
• Reusability: Docker images can be reused across different projects and pipelines, making it easier to maintain and update environments.

Example with Data Analysis Project: By using the Docker executor, we can ensure that our calculator application runs the same way in CI/CD as it does on local machines and production servers. This reduces the risk of errors caused by environmental differences, making our deployments more reliable.

Disadvantages

• Security: If not properly configured, Docker containers can be exploited to gain elevated privileges on the host machine.
• Performance: Running Docker containers requires additional resources (CPU, memory, and disk I/O), which can impact the performance of the CI/CD pipeline, especially on shared runners