Gitlab Api Download Job Artifacts ^HOT^

I ran into the same issue, and I am using Premium. I can see the artifacts were created, but the error message displayed is This job could not start because it could not retrieve the needed artifacts.

I currently have a project that would produce artifacts with an expires_in=1h limit. I recently noticed that my disk quota was almost full; attributing almost 7GB to artifacts. This is unexpected as my artifacts are only around 10M.

Gitlab Api Download Job Artifacts

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Context:I have a large repository, which produces a lot of artifacts during the build. Obviously this takes time, so I'd like to build on a beefy multi-core machine. If the build passes, I want to test in parallel across many other (smaller) machines. These test-machines are hooked up to many different kinds of equipment. Equipment that I don't want to bother the beefy machine with.

Lately, I've been working a lot with GitLab pipeline files and I have now a bit more complicated setup than usual. In my case, the middle step is included from another project as a template. I had a nasty bug that from the first step artifacts were never arriving at the last step. However as per Gitlab's docs that should be working by default.

I have an gitlab-runner (as shell) which fails to upload to "coordinator" as I suspect is the gitlab server. It's a java project. Any seen this error before, or have suggestions on how to fix? Updated both gitlab server + runner to last version, but still same error.

Caching of dependencies and build artifacts can be accomplished with the cacheconfiguration. Thecaching documentation contains alloptions for caching dependencies and build artifacts across many differentworkflows. Artifacts from a job can be defined by providing paths and anoptional expiry time.

This command returns a list of directories on the given path and their sizes. I repeated using this command and replaced the path with the largest directory from the previous run. After a couple of runs I ended up in /var/opt/gitlab/gitlab-rails/shared/ and noticed that the artifacts directory was using over 100GB of storage.

Artifacts in GitLab are products by CI-Jobs. They contain logs of failed tests or completed builds like a whole React application ready to serve. These artifacts can be downloaded via the GitLab UI and are passed to the next CI-Job in your pipeline.

We usually set the expiration date for artifacts so they get removed after a couple of days, but somehow not all of them got removed. After some research I learned that each artifact gets its own expiration-date. So when no expiration-date was given at the time of creating the artifact, then the artifact will never be removed. Even setting a global default expiration date had no effect on already existing artifacts.

After searching in the GitLab Documentation I was not able to find any option to delete old artifacts, so I started writing my own small Python Script to do so! The script can be found as a gist on github!

For those who are not familiar with the OCI Artifacts concept, the OCI Artifacts specification is a way to extend the OCI Registry specification to support storing and retrieving arbitrary content, you can learn more about OCI Artifacts concept, here. OCI Artifacts are important because today's moden software requires storing more than just container images in OCI registries such as the following artifacts would be great use-case examples of that:

Distributing software artifacts as OCI Artifacts served by OCI registries offers a standardized, secured, and efficient way to consume and reuse content within the container ecosystem, making it easier to integrate, distribute, and manage them across different environments and tools.

Depending upon whether you want to trigger the pipeline for other branches than the default one, you can have a workflow field in your .gitlab-ci.yml file. You can also tweak the condition as per the requirement of your project.

Every stage in gitlab-ci.yml is assigned a dynamic ID whenever it is executed by a runner. This ID is also referred to as job ID. It is accessible by predefined CICD variable $CI_JOB_ID. We will need the job ID of the stage responsible for generating the artifacts when we publish the artifacts as a release.

Remember the job ID we talked about? Under artifacts field, the paths determine which executable files needs to be added to the job artifact. We use reports:dotenv for generate_executables.env file which contains the job ID required by the release stage.

The most important field in the second stage is assets. It is here we will use the job ID we have stored in the environment variable during the last stage. As you can see in the url field we've used the variable as ${GE_JOB_ID}, to dynamically get the right URL of the generated artifacts.

GitLab CI allows you to add variables to .gitlab-ci.yml that are set in the build environment. The variables are stored in the git repository and are meant to store non-sensitive project configuration, for example:

.gitlab-ci.yml allows you to specify an unlimited number of jobs. Each job must have a unique name, which is not one of the Keywords mentioned above. A job is defined by a list of parameters that define the build behavior.

The name directive allows you to define the name of the created artifacts archive. That way, you can have a unique name for every archive which could be useful when you'd like to download the archive from GitSwarm. The artifacts:name variable can make use of any of the predefined variables. The default name is artifacts, which becomes artifacts.zip when downloaded.

artifacts:expire_in is used to delete uploaded artifacts after the specified time. By default, artifacts are stored on GitSwarm forever. expire_in allows you to specify how long artifacts should live before they expire, counting from the time they are uploaded and stored on GitSwarm.

To use this feature, define dependencies in context of the job and pass a list of all previous builds from which the artifacts should be downloaded. You can only define builds from stages that are executed before the current one. An error will be shown if you define builds from the current stage or next ones. Defining an empty array will skip downloading any artifacts for that job.

In the following example, we define two jobs with artifacts, build:osx and build:linux. When the test:osx is executed, the artifacts from build:osx will be downloaded and extracted in the context of the build. The same happens for test:linux and artifacts from build:linux.

The solution I implemented is the last one: I have a gitlab-ci jobs that build a docker image with all the tool chains I need. This image is updated daily using a scheduled pipelines. The other jobs use this image.

In all my projects that were using more than 5 GB, 99% of the usage came from job artifacts. I believe most of the cases are like this. The first thing I did was to set new job artifacts to expire in a week, the default is 30 days. Existing job artifacts are not affected by this setting.

If your job artifacts created in a month are much less than 5 GB in total yet still exceed the quota, it is likely caused by very old artifacts which have no expiry. In that case, reducing the default expiry may not be relevant, those old artifacts should be removed instead.

Now add the changes, commit them and push them to the remote repository on gitlab. A pipeline will be triggered with respect to your commit. And if everything goes well our mission will be accomplished.

GitLab can use MinIO as its object storage backend to store large files such as artifacts, Docker images, and Git LFS files. Given the right underlying hardware, MinIO provides the performance and scale to support any modern workload, including GitLab. We previously wrote about MinIO and GitHub Enterprise, and provided a tutorial that showed you how to work with GitHub Actions and GitHub Packages using MinIO.

To use MinIO as the object storage backend for GitLab, you need to configure GitLab to use the MinIO endpoint as the object storage URL. This can be done in the GitLab configuration file or through the GitLab user interface. Once this is done, GitLab will store artifacts, Docker images, and Git LFS files in MinIO instead of the local file system.

Artifacts: GitLab lets you manage artifacts and store them on MinIO, freeing local resources and increasing developer efficiency. When jobs are run, they may output artifacts such as a binary or tar.gz files which are stored locally on disk. You can save these job artifacts in MinIO to enable replication of data and store them on multiple disks in case of a failure.

It is not uncommon that some of our codes carry with themparticularly large storage requirements for not just our applicationbut also typically the dependencies. This evolving guide is focusedon providing tips and best practices to store desired job resultsin a repeatable manner.Though we will provide a brief overview of GitLabartifacts,caching, and thedifferences between them.We highly recommend reviewingthe upstream documentation if you have yet to use either of thesetools.

Properly leveraging dependencies can help improve job performance byavoiding the process of uploading/download large file size in addition tothe greater benefit that comes from avoiding unexpected test conditions whenmultiple artifacts exists of the same files.

The needs functionalitythat enabled the creation of directed acyclic graphs functionssimilarly. All artifacts are managed automatically based upon thejobs that have been specified as needs: [...].If desired you can disableartifact downloadsusing a different mechanism.

Undoubtedly the biggest issue teams will face when using artifacts is the sizerequirements. Artifacts can be limited in terms of acceptable sizeby the server administrators and there is not shortage ofprojects that will require multiple gigabytes of space fortheir completely built application/library.

To compound the problem GitLab does not compress the artifacts beforeuploading them to the server. Instead only ZIP is relied upon. Thisis done for a number of technical reasons and can be found mentionedon an upstream issue. 2351a5e196