gitlab – Page 3 – Kev's Development Toolbox

July 30, 2018July 29, 2018

GitLab CI Runner artifacts size error: “too large archive”

As part of putting together a GitLab CI pipeline to build a Python deployment for an AWS Lambda, I ran into an issue with the size of the build dir that I’m zipping up ready for deployment:

Uploading artifacts...
./build: found 2543 matching files 
ERROR: Uploading artifacts to coordinator... too large archive id=181 responseStatus=413 Request Entity Too Large status=413 Request Entity Too Large token=rtRUzgtp
FATAL: Too large

Hmm. Ok. A quick search found this post which says there’s a setting to increase the max build artifact size – it’s under /admin/application_settings, in the Continuous Integration setting – looks like the default is 100MB, so let’s bump that up and try again:

July 29, 2018

Setting up a new shared GitLab runner

Assuming you already have a runner installed (follow steps here).

Navigate to the [your-gitlab]/admin/runners page and scroll down to “How to setup a shared Runner for a new project” section – copy the server url and registration token for the next section.

On the machine where you want to the runner to execute, run

sudo gitlab-runner register

when prompted, enter the url and token.

For info on available executors see here.

July 11, 2018

Changing a GitLab Runner from ‘Locked to a Project’ to Shared

I have a GitLab Runner assigned to a project that I’d like to share with another similar project. Currently it looks like this:

Pressing the small edit icon, I can see these options:

I want to reuse this same runner, so I unchecked the ‘Lock to current projects’ checkbox.

Now if I go to the CI/CD settings for my other project I can see it is available, so I click ‘enable for this project’

Now my Pending Job that was triggered after my first push to my repo has kicked in and is being deployed to my test Docker server. Cool.

May 13, 2018

Building and deploying Docker containers using GitLab CI Pipelines

As part of migrating this blog to Docker containers to move to a different VPS provider (here, here and here), I found myself repeating a number of steps manually, which always a good indication that there’s an opportunity to automate some or all of those steps.

Each time I made a change in the configuration or changed the content to be deployed, I found myself rebuilding the Docker image and either running locally, pushing to my test server, and eventually pushing to my prod VPS and running there.

I’m using a locally running GitLab for my version control, so to use its build pipeline features was a natural next step. I talked about setting up a GitLab runner previously here – this is what performs the work for your pipeline.

You configure your pipeline with a .gitlab-ci.yml file in the root of your repo. I defined 2 stages, build and deploy:

stages:
 - build
 - deploy

For my build stage, I have a single task which is to build my images using my docker-compose.yml:

build:
 stage: build
 script:
 - docker-compose build
 tags:
 - docker-test

For my deploy steps, I defined one for deploying to my test server, and one for deploying to my production VPS. This is the deploy to my locally running Docker server. It changes DOCKER_HOST to point to my test server, and then uses the docker-compose.yml again to bring down the running containers, and bring up the new containers with the updated images:

deploy-containers:
 stage: deploy
 script:
 - export DOCKER_HOST=tcp://192.x.x.x:2375
 - docker-compose down
 - docker-compose up -d
 tags:
 - docker-test

And one for my deploy to production. Note that this step is defined with ‘when: manual’ which tells GitLab the task is only run manually (when you click on the ‘>’ run icon):

prod-deploy-containers:
 stage: deploy
 script:
 - pwd && ls -l
 - ./docker-compose-vps-down.sh
 - ./docker-compose-vps-up.sh
 when: manual
 tags:
 - docker-prod

Here’s what the complete pipeline looks like in GitLab:

With this in place, now any changes committed to the repo result in a new image created and pushed to my test server automatically, and when I’ve completed testing the changes I can optionally deploy the changes to my prod VPS hosted server.