hadolint on the
Dockerfile to ensure it follows best practices and reports linting
errors.Docker-hub-build, but for ARM architecture.Docker-hub-test, but for ARM architecture.Docker-hub-pushtag, but for ARM architecture.chatgpt to analyze
the jobs and update the README file with explanations of jobs. It also
updates the README on Docker Hub.This job uses hadolint, a Dockerfile linter, to analyze
the Dockerfile and ensure it follows best practices for Dockerfiles.
before_script:
- cd $CI_PROJECT_DIR
script:
- hadolint --ignore DL3008 Dockerfile cd $CI_PROJECT_DIR changes the current working directory
to the root project directory in CI build.
hadolint --ignore DL3008 Dockerfile runs hadolint on the
Dockerfile, ignoring linting rule DL3008.
This job fetches the latest version of Squid from GitHub and updates
the README file and an environment file (variables.env)
with this version.
script:
- apt update && apt install git curl ca-certificates -y --no-upgrade --no-install-recommends --no-install-suggests
- export SQUID_VERSION=$(curl -LsXGET https://github.com/squid-cache/squid/releases/latest | grep -m 1 "Release" | cut -d " " -f4 |tr -d 'v')
- echo "SQUID_VERSION=$SQUID_VERSION" > variables.env
- echo $SQUID_VERSION
- sed -i "s/{{SQUID_VERSION}}/$SQUID_VERSION/g" README_template.md
- sed -i "s/{{DATE}}/$(date +%Y%m%d)/g" README_template.md
- cp README_template.md README.md
- git config user.email "fredbcode"
- git config user.name "fredbcode"
- git add README.md
- git commit -m "README Auto update [skip ci]" || true
- git push https://$GITLAB_TOKEN@gitlab.com/fredbcode-images/squid.git HEAD:master || trueIn the script section, it updates the package list
(apt update) and installs
git, curl, and ca-certificates.
Then it fetches the latest release of Squid from its GitHub
repository (curl -LsXGET...) and stores this version in the
SQUID_VERSION environment variable for subsequent
steps.
Next, it updates the variables.env file and the
README_template.md file with the new Squid version. It also adds the
current date to README_template.md before copying it to README.md.
Finally, it commits any changes made to README.md and pushes them to the repository using Git.
This job builds the Docker image using the Dockerfile provided in the project and the Squid version previously fetched. The name of the image includes the “build-noprod” tag and appropriate CPU architecture (AMD64 or ARM).
before_script:
- docker login -u "$DOCKER_HUB_USER" -p "$DOCKER_HUB_TOKEN" $DOCKER_HUB_REGISTRY
script:
- source variables.env
- docker build --build-arg SQUID_VERSION=$SQUID_VERSION --pull -t $CONTAINER_BUILD_NOPROD_NAME .
- docker push $CONTAINER_BUILD_NOPROD_NAMEIn the before_script section, it logs into Docker Hub
using a username and a password stored in secrets.
In the script section, it sources the
variables.env file to use the Squid version inside this
file. Then it builds the Docker image using this version with the
docker build... command, and then pushes this image to
Docker Hub.
This job tests the Docker image built in the previous step. It starts the Squid container and tries to use it as a proxy to make a web request.
before_script:
- apt update && apt install -y curl --no-upgrade --no-install-recommends --no-install-suggests
script:
- export https_proxy=http://$CONTAINER_TEST_NAME:3128 && curl -k https://www.google.frIn the before_script section, it updates the package
list (apt update) and installs curl for making
web requests.
In the script section, it sets up the Squid proxy for
curl (export https_proxy...) and makes a request to an
external server (curl -k...).
This job pushes the Docker image built and tested in the previous steps to Docker Hub. It tags the image with the Squid version and pushes these images to the registry.
before_script:
- docker login -u "$DOCKER_HUB_USER" -p "$DOCKER_HUB_TOKEN" $DOCKER_HUB_REGISTRY
script:
- source variables.env
- docker pull $CONTAINER_BUILD_NOPROD_NAME
- docker tag $CONTAINER_BUILD_NOPROD_NAME $HUB_REGISTRY_IMAGE:$SQUID_VERSION
- docker push $HUB_REGISTRY_IMAGE:$SQUID_VERSIONIn the before_script section, it logs into Docker Hub
using a username and a password stored in secrets.
In the script section, it sources the
variables.env file to use the Squid version inside this
file. It then pulls the Docker image from Docker Hub and names (tags) it
with the Squid version. Finally, it pushes these tagged images to Docker
Hub.
Similar to Docker-hub-build, but the image is built for
ARM architecture.
Similar to Docker-hub-test, but the test is run on the
ARM architecture image.
Similar to Docker-hub-pushtag, but the ARM architecture
image is pushed to Docker Hub.
This stage includes several jobs from CI templates like security static application testing (SAST). It analyzes the code for possible security vulnerabilities.
This job uses chatgpt to analyze the jobs and update the
README file with explanations of jobs. It also updates the README on
Docker Hub.
script:
....
- RESPONSE=$(curl -X POST https://api.openai.com/v1/chat/completions -H "Authorization:Bearer $CHATGPT_API_KEY" -H "Content-Type:application/json" -d "$JSON_CONTENT")
....In the script section, it sends a POST request to the
OpenAI API with the key and content as JSON. It gets the API’s response
about the analysis of the job.
GIT_CLONE_PATH: Path to clone Git repositories.CONTAINER_CLIENT_IMAGE: The base Docker image for the
Docker container.chatgpt_analysis*: The results of the
chatgpt analysis (artifacts).variables.env: Environment file containing variables
like SQUID_VERSION..gitlab-ci.yml, gitlabci/*: YAML files
containing GitLab CI/CD pipeline’s configuration details.README.md: The readme file for the project.DOCKER_HUB_USER, DOCKER_HUB_TOKEN,
DOCKER_HUB_REGISTRY: Environment variables used to connect
and push images to Docker Hub.$HUB_REGISTRY_IMAGE: Docker Hub registry image
name.$CI_PROJECT_DIR, $CI_PROJECT_NAME,
$CI_COMMIT_BRANCH, $CI_PIPELINE_URL,
$CHATGPT_API_KEY, $SSH_NOSTROMO_KEY,
$GITLAB_TOKEN: Variables provided by GitLab CI/CD for
project path, project name, commit branch, pipeline URL, the
chatgpt API key, the SSH key, and the GitLab token
respectively.The jobs are connected and dependent on each other as follows:
Get-version for Squid version.Docker-hub-build as it tests the built image.Docker-hub-test and Get-version. It pushes the
image to the registry after it is built and tested successfully.get-version for Squid version.Docker-hub-build-arm as it tests the built image.Docker-hub-test-arm and get-version. It pushes
the image to the registry after it is built and tested
successfully.Get-version,
Docker-hub-test, and Docker-hub-test-arm
because it uses their results.Quality: Linting errors from hadolint if
any.Get-version: The latest Squid version saved in
variables.env. README.md is also updated and
committed to the repository with the latest version of Squid.Docker-hub-build: The Docker image built for the latest
Squid version.Docker-hub-test: The result of the web request made
using the Squid proxy. If the request is successful, the Docker image is
deemed to have passed the test.Docker-hub-pushtag: The Docker image pushed to Docker
Hub, tagged with the latest Squid version.Docker-hub-build-arm: Similar to
Docker-hub-build but for the ARM architecture.Docker-hub-test-arm: Similar to
Docker-hub-test but for the ARM architecture.Docker-hub-pushtag-arm: Similar to
Docker-hub-pushtag but for the ARM architecture.test: Results of various tests like SAST.Docs: The chatgpt analysis result of the
jobs, and the updated README in Docker Hub. The analysis result is also
stored as a Markdown file as an artifact.In this commit, the README file was updated (likely with a new Squid version) and the commit was made with the message “README Auto update [skip ci]”. The “[skip ci]” in the commit message tells GitLab CI/CD to skip running pipelines for this commit. The purpose of this is probably to avoid unnecessary pipeline runs for README updates. The impact of this commit is minimal on the pipeline as it only affects the README file and does not trigger a new pipeline run.