Power of Big Data: MLOps for business.
Welcome to the next instalment of the “Power of Big Data” series. The entire series aims to make readers aware of how much Big Data is needed and how…
Read morePlease dive in the second part of a blog series based on a project delivered for one of our clients. If you miss the first part, please check it here.
In several of the CI/CD jobs in our project, we create container images and upload them to GitLab's Registry. Building container images in GitLab's CI/CD requires infrastructure preparation, or we have to use a tool other than Docker. If we want to build images using Docker, then we must give the Docker client access to the Docker daemon socket, which is not recommended for security reasons. If we want to use shared GitLab.com runners, access to the daemon socket is not possible.
We can solve the problem in several ways:
buildah
tool to build the container image inside the container and the skopeo
program to upload the image to the GitLab Registry. Both programs are part of the https://github.com/containers project and do not require a privileged daemon norroot
access (unlike Docker).kaniko
. Kaniko is a tool dedicated to building container images in Kubernetes and in containers. Like buildah
, it doesn't require special permissions. An example of its use is described in the official GitLab documentation.Our team has already had experience with Kaniko in other projects, so we made use of it in this project.
The definition of a job using kaniko in the .gitlab-ci.yml
looks like this:
build-base-image:
stage: prepare
image:
name: gcr.io/kaniko-project/executor:debug
entrypoint: [""]
script:
- echo
"{\"auths\":{\"$CI_REGISTRY\":{\"username\":\"$CI_REGISTRY_USER\",\"password\":\"$CI_REGISTRY_PASSWORD\"}}}" > /kaniko/.docker/config.json
- /kaniko/executor --context $CI_PROJECT_DIR --dockerfile
$CI_PROJECT_DIR/Dockerfile --destination
$CI_REGISTRY_IMAGE:$CI_COMMIT_TAG
As you can guess, after studying the above code fragment, Kaniko builds a container image and immediately uploads it to the indicated image registry.
GitLab has an integrated container image registry (Registry). A one-time password is created for each job, which allows you to use Registry without having to manually create a dedicated account. GitLab passes the login and temporary password to the Runner, which then sets them in the environment variables of the job process.
This is the end result of a successful job output that creates a container image using Kaniko and uploads it to the Registry built into GitLab:
In our project, some jobs take a very long time, longer than the lifetime of a one-time Registry password. Limiting the duration of such a one-time password is necessary from the security point of view.
Uploading the image to the Registry fails and ends with the following error:
We solved this problem in a traditional way:
After these modifications, we no longer had problems uploading container images to the Registry, even if the CI/CD job took 3 hours.
If you use paid GitLab-as-a-service plans, you can use a certain number of minutes for the CI pipeline, i.e. shared Runners. Each of the paid plans has a different limit. Using shared Runners is very convenient because we don't have to worry about maintaining our own Runners and thus save time and money. The limit may be sufficient in some projects, but in our project we quickly reached the monthly limit.
You will see this type of message when you use the available time of shared Runners in a given month:
We solved this problem by setting up a dedicated Kubernetes cluster for Runners. We allow GitLab to decide which Runner to use (shared or our own). Thanks to this, the load is distributed to both types of Runners, we reduce expenses and shorten the time of pipeline execution.
Instructions for using Kubernetes cluster as the platform for Runners are described in the Problem 2 section.
If the runner or container we use in our CI/CD has locales that do not support UTF-8, and the user making commits to the repository that has characters that are not ASCII in the name or surname, then the CI/CD job may end with the following error:
FAILURE: Build failed with an exception.
* What went wrong:
Could not set the value of environment variable 'GITLAB_USER_NAME':
could not convert string to current locale
There is even an issue with this in this GitLab project.
A workaround is also provided there. You can change the value of the GITLAB_USER_NAME
variable so that it doesn't contain non-ASCII characters. It can be assigned, for example, to the value of the variable containing the user's login (assuming that the login consists only of ASCII characters).
To the before_script
section in .gitlab-ci.yml
, add:
# Workaround for "Could not set the value of environment variable
'GITLAB_USER_NAME': could not convert string to current locale" problem.
# https://gitlab.com/gitlab-org/gitlab-foss/issues/38698
- export GITLAB_USER_NAME=$(echo $GITLAB_USER_LOGIN)
Follow our profile on Linkedin and stay up to date for the next part!
Welcome to the next instalment of the “Power of Big Data” series. The entire series aims to make readers aware of how much Big Data is needed and how…
Read moreBig Data Technology Warsaw Summit 2020 is fast approaching. This will be 6th edition of the conference that is jointly organised by Evention and…
Read moreFrom 0 to MLOps with Snowflake ❄️ In the first part of the blogpost, we presented our kedro-snowflake plugin that enables you to run your Kedro…
Read moreData Studio is a reporting tool that comes along with other Google Cloud Platform products to bring out a simple yet reliable BI platform. There are…
Read moreData Mesh as an answer In more complex Data Lakes, I usually meet the following problems in organizations that make data usage very inefficient: Teams…
Read moreFlink is an open-source stream processing framework that supports both batch processing and data streaming programs. Streaming happens as data flows…
Read moreTogether, we will select the best Big Data solutions for your organization and build a project that will have a real impact on your organization.
What did you find most impressive about GetInData?