Jobs in the pipeline exist to facilitate a particular task within the Continuous Integration (CI) and Continuous Deployment (CD) process. The order of jobs in the GitLab CI/CD pipeline is specified in the ‘stages’ section of the .gitlab-ci.yml file. Here’s a list of the jobs in this pipeline and a brief summary of each job’s purpose:
image: hadolint/hadolint:latest-debian
stage: quality
before_script:
- cd $CI_PROJECT_DIR
script:
- hadolint --ignore DL3008 Dockerfile This job evaluates the Dockerfile using Hadolint, a Dockerfile
linter that has rules defined as per Docker’s best practices. The job
utilizes a debian-based image of Hadolint. Before running the main
script, Hadolint changes the current directory to the project directory.
Hadolint then evaluates the Dockerfile and ignores any rules defined
with the --ignore flag, in this case, rule DL3008.
stage: chatgtp
image:
name: $CONTAINER_CLIENT_IMAGE
artifacts:
expire_in: 1 month
paths:
- $CI_PROJECT_DIR/chatgpt_analysis*
before_script:
- apt update && apt install curl git jq ca-certificates pandoc openssh-client -y --no-upgrade --no-install-recommends --no-install-suggests
script:
...The ‘chatgpt_analysis’ job generates an in-depth analysis report
using OpenAI’s GPT model. The job starts by installing the required
packages (curl, git, jq,
ca-certificates, pandoc, and
openssh-client) onto the debian-based image
($CONTAINER_CLIENT_IMAGE). It then runs a series of script
commands, such as:
.gitlab-ci.yml and other CI/CD
files in the projectThe final analysis is stored in a ‘.md’ file and a ‘.html’ file,
named ’chatgpt_analysis*_’ – followed by the date
$(date +%Y%m%d). The ‘.html’ file is then transferred to a
server using the ‘scp’ command. All generated files, as specified by the
path ($CI_PROJECT_DIR/chatgpt_analysis*) in the
artifacts: section, are stored as artifacts for 1
month.
stage: Docker-hub-build
image: docker:19.03.8-dind
tags:
- arm
artifacts:
expire_in: 2 hours
paths:
- $CI_PROJECT_DIR
timeout: 3 hours
before_script:
- docker login -u "$DOCKER_HUB_USER" -p "$DOCKER_HUB_TOKEN" $DOCKER_HUB_REGISTRY
script:
- cd $CI_PROJECT_DIR
- apk add --no-cache curl
- export SQUID_VERSION=$(curl -s http://www.squid-cache.org/Versions/v6/ | egrep -m 1 -oh squid-.*.tar.gz | cut -d '"' -f1)
- docker build -f Dockerfile --build-arg SQUID_VERSION=$SQUID_VERSION --pull -t $CONTAINER_BUILD_NOPROD_NAME_ARM .
- docker push $CONTAINER_BUILD_NOPROD_NAME_ARMUpon successful execution of the previous jobs, the ‘docker-hub-build-arm’ job builds an ARM version of the Squid Docker image. After logging into Docker Hub, it fetches the Squid version, builds the Docker image with Squid, and pushes the built Docker image to Docker Hub.
stage: Docker-hub-test
extends: .services-arm
tags:
- arm
artifacts:
script:
- apt update && apt install -y curl --no-upgrade --no-install-recommends --no-install-suggests
- export https_proxy=http://$CONTAINER_TEST_NAME:3128 && curl -k https://www.google.fr
variables:
HOSTNAME: squidpipeline
needs: ["docker-hub-build-arm"]In the ‘docker-hub-test-arm’ job, the built ARM Docker image is
tested whether it is functioning as expected. With
extends: .services-arm, the job extends the shared
configuration of ARM services. The job installs ‘curl’, sets up the
proxy environment, and runs ‘curl’ to fetch the google website. This
validates that the Squid HTTP proxy server is functioning correctly. The
job depends on the ‘docker-hub-build-arm’ job, which needs to succeed
first before this job can be run.
stage: Docker-hub-pushtag
image: docker:19.03.8-dind
tags:
- arm
artifacts:
before_script:
- docker login -u "$DOCKER_HUB_USER" -p "$DOCKER_HUB_TOKEN" $DOCKER_HUB_REGISTRY
script:
...In the ‘docker-hub-pushtag-arm’ job, Docker tags are pushed to Docker Hub after testing the images. The Docker operations take place on an ARM Docker (19.03.8) image. The command fetches the Squid version, and tags the image with it before pushing both tags to Docker Hub.
The ‘Docker-hub-build’, ‘docker-hub-test’, ‘docker-hub-pushtag’ jobs function similar to their ARM counterpart jobs, but on AMD64 architecture.
Every job has a set of variables that help in accomplishing the task the job is meant to do. Some of the crucial ones are: - $CI_JOB_NAME: This contains the name of the job as defined in .gitlab-ci.yml. - $CI_COMMIT_REF_SLUG: Slug of the commit reference name. - $CI_BUILDS_DIR: This contains the path to the directory where builds are stored. - $CI_PROJECT_NAME: The project name the pipeline belongs to. - $CI_COMMIT_BRANCH: This variable holds the branch name that triggers the pipeline. - $CONTAINER_CLIENT_IMAGE: The image name used in the job.
Job-specific variables include:
The script section uses these variables and other defined constants to run the specific task.
The keyword needs: ['job_name'] defines jobs
dependencies in a pipeline, which means that a job will only run once
the listed jobs complete successfully. In this pipeline, the
dependencies among the jobs are:
No jobs depend directly on the hadolint and chatgpt_analysis job.
Each job generates a specific output, and in some cases, they also create artifacts:
The latest commit (1392658 Update chatgpt Jobs content) in the pipeline seems to be an update to the way the ‘chatgpt_analysis’ job performs its tasks. While it’s difficult to deduce the exact changes without looking at prior versions, updating the job content typically entails refining the tasks the job should perform, adding more steps, or enhancing the existing ones. This could potentially improve the analysis report generated by this job, making it more robust or comprehensive.
This update would affect every subsequent pipeline run as it changes the way the ‘chatgpt_analysis’ job executes. However, it won’t affect other jobs directly unless there’s a dependency on the ‘chatgpt_analysis’ job. The update’s success will be measured through the quality of the generated reports.
Project: https://gitlab.com/fredbcode-images/squid
Pipeline: https://gitlab.com/fredbcode-images/squid/-/pipelines/1530382379
Docker images: https://hub.docker.com/r/fredbcode