gitlab-runner kubernetes cache is ignored - caching

I have a kubernetes cluster with a gitlab-runner 10.3.0 and kubernetes executor. There is no cache_dir defined in the runner's config.toml-file. Note that this is different that a docker executor, so the volume-solutions do not apply.
In a .gitlab-ci.yml, I configured a job to use the cache:
build:
cache:
key: "${PROJECT_NAME}"
paths:
- "node_modules/"
script:
- ls node_modules/ || echo "cache not there"
- npm i
- npm build
- ...
When I run this, I see the cache being pulled and created:
Cloning repository for some-branch with git depth set to 1...
Cloning into '/group/projectname'...
Checking out d03baa31 as some-branch...
Skipping Git submodules setup
Checking cache for projectname...
Successfully extracted cache
$ docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY
//
// ...work being done here...
//
Creating cache projectname...
node_modules/: found 24278 matching files
Created cache
Job succeeded
However, when I push another commit to this branch, the ls node_modules/ still does not find the cache.
I searched the documentation and did not find any information on how to activate the cache. The gitlab-runner-pod does not have any of the supposedly cached files there as well and according to the documentation, a cache_dir in the config is not used by the kubernetes executor.
But according to this feature page, the kubernetes executor does support cache.
So how to do this?

Due to the distributed nature of Kubernetes, you will need to configure a central cache location (typically, in the form of a S3-compatible object storage like AWS S3 or Minio). The reason behind this is explained in the Gitlab runner documentation (emphasis mine):
To speed up your builds, GitLab Runner provides a cache mechanism where selected directories and/or files are saved and shared between subsequent builds.
This is working fine when builds are run on the same host, but when you start using the Runners autoscale feature, most of your builds will be running on a new (or almost new) host, which will execute each build in a new Docker container. In that case, you will not be able to take advantage of the cache feature.
To overcome this issue, together with the autoscale feature, the distributed Runners cache feature was introduced.
It uses any S3-compatible server to share the cache between used Docker hosts. When restoring and archiving the cache, GitLab Runner will query the S3 server and will download or upload the archive.
For this, you can use the [runners.cache] section in the runner configuration:
[runners.cache]
Type = "s3"
ServerAddress = "s3.amazonaws.com"
AccessKey = "AMAZON_S3_ACCESS_KEY"
SecretKey = "AMAZON_S3_SECRET_KEY"
BucketName = "runners"
BucketLocation = "eu-west-1"
Insecure = false
Path = "path/to/prefix"
Shared = false
Edit by OP: Installation instructions for Minio for gitlab-ci

Another option to implement cache with kubernetes executor is using PVC. Usually, I am creating PVC in the gitlab-runner namespace using efs.csi.aws.com driver and access mode ReadWriteMany. Then in the config.toml:
[[runners]]
executor = "kubernetes"
cache_dir = "/cache"
...
[[runners.kubernetes.volumes.pvc]]
name = "gitlab-runner-cache"
mount_path = "/cache"
...
Then in the job logs you can see
Restoring cache
Checking cache for default-non_protected...
No URL provided, cache will not be downloaded from shared cache server. Instead a local version of cache will be extracted.
Successfully extracted cache
and test it with for example
$ cat test.txt
Sat Oct 15 08:23:12 UTC 2022
Sat Oct 15 08:24:09 UTC 2022
Sat Oct 15 08:24:57 UTC 2022
Sat Oct 15 08:25:52 UTC 2022
$ echo $(date) >> test.txt
Saving cache for successful job
Creating cache default-non_protected...
test.txt: found 1 matching files and directories
No URL provided, cache will not be uploaded to shared cache server. Cache will be stored only locally.
Created cache

Related

Azure DevOps pipeline task downloads cache from cloud on private agent

In azure devops we have a pipeline which runs on several private hosted agents on different mac machines. We defined a cache task to prevent re-downloading dependencies:
- task: Cache#2
displayName: 'Cache gradle distribution'
inputs:
key: 'gradleCache'
path: 'gradle-6.8'
cacheHitVar: GRADLE_CACHE_HIT
- script: |
curl -s https://downloads.gradle-dn.com/distributions/gradle-6.8-bin.zip --output gradle-6.8-bin.zip
unzip -q gradle-6.8-bin.zip # a folder named gradle-6-8 will be created
condition: eq(variables.GRADLE_CACHE_HIT, 'false')
displayName: 'Download and install gradle'
- script: |
export PATH=`pwd`/gradle-6.8/bin:$PATH
gradle -v
displayName: 'Gradle version'
At the second run the cache is active, but the cache-files are downloaded from the web what misses the point of a cache. Is it possible to achive a "local" cache?
It is the default behavior that the cached files are downloaded from the azure devops server on the second run of your pipeline when cache task report a "cache hit".
On the first run, a cache will be created from the files in the folder you specified in path field(ie. gradle-6.8) of the cache task, and uploaded to azure devops server.
On the second run, when cache task reports a "cache hit". The cached files will be downloaded from the azure devops server to path gradle-6.8 specified in cache task.
Using cache task is not the best solution for your scenarios. Since you are using private hosted agents, you can use below workarounds to totally skip re-downloading gradle tool.
1, You can pre-install gradle-6.8 to the mac machines where your private hosted agents are hosted. So that there is no need to download and install gradle in your pipeline.
2, You can download the gradle-6.8 to a different place other than in the $(System.DefaultWorkingDirectory) on private hosted agents, which could be auto cleaned in the future pipeline run.
For example in windows machines: On the first run of your pipeline. You can save the gradle-6.8 to D:\custom\folder\forPipelinetool instead of C:\Agent\_work\1\s (ie. $(System.DefaultWorkingDirectory)).
Then gradle-6.8 will be saved in a different place on the agent machines. And there will be not need to download gradle-6.8 in the following run. You can then disable the script task which downloads the gradle-6.8.

Is there a way to work with multiple workspaces in parallel?

In a Continuous Integration setup (Jenkins) I'd like to deploy (terraform apply) changes of the same Terraform configuration code using multiple workspaces at the same time using the same copy of the repository - i.e. the very same directory with .tf files.
Stages are executed in parallel (on same node/agent) and each of them consists of the same sequence of operations (terraform workspace select and then terraform apply).
It doesn't seem supported as the attempts fail with lock errors, e.g.
# terraform workspace select "workspace1"
# terraform apply [...] -input=false -auto-approve
Switched to workspace "workspace1".
Acquiring state lock. This may take a few moments...
Error: Error locking state: Error acquiring the state lock: ConditionalCheckFailedException: The conditional request failed
Lock Info:
ID: 52973611-a892-deac-985b-5aa28172fdaf
Path: my-project/env:/workspace5/state
Operation: OperationTypeApply
Who: #87ddd2118473
Version: 0.13.5
Created: 2021-01-11 15:20:06.635256155 +0000 UTC
Info:
So it looks like lock for workspace5 gets in the way for applying for workspace1.
Is there any way out?
I use:
Terraform 0.13.5
s3 as the backend
DynamoDB for locks
Just tested this locally so not sure if it will work or a CI system.
Rather than using terraform workspace select xxxx use the environment variable set for workspaces. So in one session do:
export TF_WORKSPACE=one-environment
terraform apply -input=false -auto-approve
Then in parralel you can do:
export TF_WORKSPACE=another-environment
terraform apply -input=false -auto-approve
This seems to work and doesnt throw any locking errors.
One idea I have (which seems to work) is to copy the whole codebase to a separate directory. I.e. each workspace to be applied to each own. And run terraform workspace select and terraform apply from separate copies.
It works. But it's lame (inefficient).
The only way I see you able to achieve what you want is by defining a lock resource in Jenkins and then essentially locking that job/step so it will only every execute it one at a time, or by ensuring that the jobs are ran on separate nodes/agents.

Gitlab CI : how to cache node_modules from a prebuilt image?

The situation is this:
I'm running Cypress tests in a Gitlab CI (launched by vue-cli). To speed up the execution, I built a Docker image that contains the necessary dependencies.
How can I cache node_modules from the prebuilt image to use it in the test job ?
Currently I'm using an awful (but working) solution:
testsE2e:
image: path/to/prebuiltImg
stage: tests
script:
- ln -s /node_modules/ /builds/path/to/prebuiltImg/node_modules
- yarn test:e2e
- yarn test:e2e:report
But I think there must be a cleaner way using the Gitlab CI cache.
I've been testing:
cacheE2eDeps:
image: path/to/prebuiltImg
stage: dependencies
cache:
key: e2eDeps
paths:
- node_modules/
script:
- find / -name node_modules # check that node_modules files are there
- echo "Caching e2e test dependencies"
testsE2e:
image: path/to/prebuiltImg
stage: tests
cache:
key: e2eDeps
script:
- yarn test:e2e
- yarn test:e2e:report
But the job cacheE2eDeps displays a "WARNING: node_modules/: no matching files" error.
How can I do this successfully? The Gitlab documentation doesn't really talk about caching from a prebuilt image...
The Dockerfile used to build the image :
FROM cypress/browsers:node13.8.0-chrome81-ff75
COPY . .
RUN yarn install
There is not documentation for caching data from prebuilt images, because it’s simply not done. The dependencies are already available in the image so why cache them in the first place? It would only lead to an unnecessary data duplication.
Also, you seem to operate under the impression that cache should be used to share data between jobs, but it’s primary use case is sharing data between different runs of the same job. Sharing data between jobs should be done using artifacts.
In your case you can use cache instead of prebuilt image, like so:
variables:
CYPRESS_CACHE_FOLDER: "$CI_PROJECT_DIR/cache/Cypress"
testsE2e:
image: cypress/browsers:node13.8.0-chrome81-ff75
stage: tests
cache:
key: "e2eDeps"
paths:
- node_modules/
- cache/Cypress/
script:
- yarn install
- yarn test:e2e
- yarn test:e2e:report
The first time the above job is run, it’ll install dependencies from scratch, but the next time it’ll fetch them from the runner cache. The caveat is that unless all runners that run this job share cache, each time you run it on a new runner it’ll install the dependencies from scratch.
Here’s the documentation about using yarn with GitLab CI.
Edit:
To elaborate on using cache vs artifacts - artifacts are meant for both storing job output (eg. to manually download it later) and for passing results of one job to another one from a subsequent stage, while cache is meant to speed up job execution by preserving files that the job needs to download from the internet. See GitLab documentation for details.
Contents of node_modules directory obviously fit into the second category.

How to have a "cache per package.json" file in GitLab CI?

I have a Vue web application that is built, tested and deployed using GitLab CI.
GitLab CI has a "Cache" feature where specific products of a Job can be cached so that future runs of the Job in the same Pipeline can be avoided and the cached products be used instead.
I'd like to improve my workflow's performance by caching the node_modules directory so it can be shared across Pipelines.
GitLab Docs suggests using ${CI_COMMIT_REF_SLUG} as the cache key to achieve this. However, this means "caching per-branch" and I would like to improve on that.
I would like to have a cache "per package.json". That is, only if there is a change in the contents of package.json will the cache key change and npm install will be run.
I was thinking of using a hash of the contents of the package.json file as the cache key. Is this possible with GitLab CI? If so, how?
This is now possible as of Gilab Runner v12.5
cache:
key:
files:
- Gemfile.lock
- package-lock.json // or yarn.lock
paths:
- vendor/ruby
- node_modules
It means cache key will be a SHA checksum computed from the most recent commits (up to two, if two files are listed) that changed the given files. Whenever one of these files changes, a new cache key is computed and a new cache is created. Any future job runs using the same Gemfile.lock and package.json with cache:key:files will use the new cache, instead of rebuilding the dependencies.
More info: https://docs.gitlab.com/ee/ci/yaml/#cachekeyfiles
Also make sure to use always --frozen-lockfile flag in your CI jobs. (or npm ci) Regular npm install or yarn install / yarn commands generate new lock files and you wouldn't usually notice it until you install packages again. Thus makes your build artifacts and caches inconsistent.
For that behavior use only:changes parameter with a static cache name.
Ex:
install:
image: node:latest
script:
- npm install
cache:
untracked: true
key: npm #static name, can use any branch, any commit, etc..
paths:
- node_modules
only: #Only execute this job when theres a change in package.json
changes:
- package.json
If you need read this to set cache properly in runners:
https://docs.gitlab.com/runner/configuration/autoscale.html#distributed-runners-caching https://docs.gitlab.com/ee/ci/caching/

How to deploy with Gitlab-Ci to EC2 using AWS CodeDeploy/CodePipeline/S3

I've been working on a SlackBot project based in Scala using Gradle and have been looking into ways to leverage Gitlab-CI for the purpose of deploying to AWS EC2.
I am able to fully build and test my application with Gitlab-CI.
How can I perform a deployment from Gitlab-CI to Amazon EC2 Using CodeDeploy and CodePipeline?
Answer to follow as a Guide to do this.
I have created a set of sample files to go with the Guide provided below.
These files are available at the following link: https://gitlab.com/autronix/gitlabci-ec2-deployment-samples-guide/
Scope
This guide assumes the following
Gitlab EE hosted project - may work on private CE/EE instances (not tested)
Gitlab as the GIT versioning repository
Gitlab-CI as the Continuous Integration Engine
Existing AWS account
AWS EC2 as the target production or staging system for the deployment
AWS EC2 Instance running Amazon Linux AMI
AWS S3 as the storage facility for deployment files
AWS CodeDeploy as the Deployment engine for the project
AWS CodePipeline as the Pipeline for deployment
The provided .gitlab-ci.yml sample is based on a Java/Scala + Gradle project.
The script is provided as a generic example and will need to be adapted to your specific needs when implementing Continuous Delivery through this method.
The guide will assume that the user has basic knowledge about AWS services and how to perform the necessary tasks.
Note: The guide provided in this sample uses the AWS console to perform tasks. While there are likely CLI equivalent for the tasks performed here, these will not be covered throughout the guide.
Motivation
The motivation for creating these scripts and deployment guide came from the lack of availability of a proper tutorial showing how to implement Continuous Delivery using Gitlab and AWS EC2.
Gitlab introduced their freely available CI engine by partnering with Digital Ocean, which enables user repositories to benefit from good quality CI for free.
One of the main advantages of using Gitlab is that they provide built-in Continuous Integration containers for running through the various steps and validate a build.
Unfortunately, Gitblab nor AWS provide an integration that would allow to perform Continuous Deliver following passing builds.
This Guide and Scripts (https://gitlab.com/autronix/gitlabci-ec2-deployment-samples-guide/) provide a simplified version of the steps that I've undertaken in order to have a successful CI and CD using both Gitlab and AWS EC2 that can help anyone else get started with this type of implementation.
Setting up the environment on AWS
The first step in ensuring a successful Continuous Delivery process is to set up the necessary objects on AWS in order to allow the deployment process to succeed.
AWS IAM User
The initial requirement will be to set up an IAM user:
https://console.aws.amazon.com/iam/home#users
Create a user
Attach the following permissions:
CodePipelineFullAccess
AmazonEC2FullAccess
AmazonS3FullAccess
AWSCodeDeployFullAccess
Inline Policy:
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"autoscaling:*",
"codedeploy:*",
"ec2:*",
"elasticloadbalancing:*",
"iam:AddRoleToInstanceProfile",
"iam:CreateInstanceProfile",
"iam:CreateRole",
"iam:DeleteInstanceProfile",
"iam:DeleteRole",
"iam:DeleteRolePolicy",
"iam:GetInstanceProfile",
"iam:GetRole",
"iam:GetRolePolicy",
"iam:ListInstanceProfilesForRole",
"iam:ListRolePolicies",
"iam:ListRoles",
"iam:PassRole",
"iam:PutRolePolicy",
"iam:RemoveRoleFromInstanceProfile",
"s3:*"
],
"Resource": "*"
}
]
}
Generate security credentials
Note: The policies listed above are very broad in scope. You may adjust to your requirements by creating custom policies that limit access only to certain resources.
Note: Please keep these credentials in a safe location. You will need them in a later step.
AWS EC2 instance & Role
Instance Role for CodeDeploy
https://console.aws.amazon.com/iam/home#roles
Create a new Role that will be assigned to your EC2 Instance in order to access S3,
Set the name according to your naming conventions (ie. MyDeploymentAppRole)
Select Amazon EC2 in order to allow EC2 instances to run other AWS services
Attache the following policies:
AmazonEC2FullAccess
AmazonS3FullAccess
AWSCodeDeployRole
Note: The policies listed above are very broad in scope. You may adjust to your requirements by creating custom policies that limit access only to certain resources.
Launch Instance
https://console.aws.amazon.com/ec2/v2/home
Click on Launch Instance and follow these steps:
Select Amazon Linux AMI 2016.03.3 (HVM), SSD Volume Type
Select the required instance type (t2.micro by default)
Next
Select IAM Role to be MyDeploymentAppRole (based on the name created in the previous section)
Next
Select Appropriate Storage
Next
Tag your instance with an appropriate name (ie. MyApp-Production-Instance)
add additional tags as required
Next
Configure Security group as necessary
Next
Review and Launch your instance
You will be provided with the possibility to either generate or use SSH keys. Please select the appropriate applicable method.
Setting up instance environment
Install CodeDeploy Agent
Log into your newly created EC2 instance and follow the instructions:
http://docs.aws.amazon.com/codedeploy/latest/userguide/how-to-run-agent-install.html
CodeDeploy important paths:
CodeDeploy Deployment base directory: /opt/codedeploy-agent/deployment-root/
CodeDeploy Log file: /var/log/aws/codedeploy-agent/codedeploy-agent.log
Tip: run tail -f /var/log/aws/codedeploy-agent/codedeploy-agent.log to keep track of the deployment in real time.
Install your project prerequisites
If your project has any prerequisites to run, make sure that you install those before running the deployment, otherwise your startup script may fail.
AWS S3 repository
https://console.aws.amazon.com/s3/home
In this step, you will need to create an S3 bucket that will be holding your deployment files.
Simply follow these steps:
Choose Create Bucket
Select a bucket name (ie. my-app-codepipeline-deployment)
Select a region
In the console for your bucket select Properties
Expand the Versioning menu
choose Enable Versioning
AWS CodeDeploy
https://console.aws.amazon.com/codedeploy/home#/applications
Now that the basic elements are set, we are ready to create the Deployment application in CodeDeploy
To create a CodeDeploy deployment application follow these steps:
Select Create New Application
Choose an Application Name (ie. MyApp-Production )
Choose a Deployment Group Name (ie. MyApp-Production-Fleet)
Select the EC2 Instances that will be affected by this deployment - Search by Tags
Under Key Select Name
Under Value Select MyApp-Production-Instance
Under Service Role, Select MyDeploymentAppRole
Click on Create Application
Note: You may assign the deployment to any relevant Tag that applied to the desired instances targeted for deployment. For simplicity's sake, only the Name Tag has been used to choose the instance previously defined.
AWS CodePipeline
https://console.aws.amazon.com/codepipeline/home#/dashboard
The next step is to proceed with creating the CodePipeline, which is in charge of performing the connection between the S3 bucket and the CodeDeploy process.
To create a CodePipeline, follow these steps:
Click on Create Pipeline
Name your pipeline (ie. MyAppDeploymentPipeline )
Next
Set the Source Provider to Amazon S3
set Amazon S3 location to the address of your bucket and target deployment file (ie. s3://my-app-codepipeline-deployment/myapp.zip )
Next
Set Build Provider to None - This is already handled by Gitlab-CI as will be covered later
Next
Set Deployment Provider to AWS CodeDeploy
set Application Name to the name of your CodeDeploy Application (ie. MyApp-Production)
set Deployment Group to the name of your CodeDeploy Deployment Group (ie. MyApp-Production-Fleet )
Next
Create or Choose a Pipeline Service Role
Next
Review and click Create Pipeline
Setting up the environment on Gitlab
Now that The AWS environment has been prepared to receive the application deployment we can proceed with setting up the CI environment and settings to ensure that the code is built and deployed to an EC2 Instance using S3, CodeDeploy and the CodePipeline.
Gitlab Variables
In order for the deployment to work, we will need to set a few environment variables in the project repository.
In your Gitlab Project, navigate to the Variables area for your project and set the following variables:
AWS_DEFAULT_REGION => your AWS region
AWS_SECRET_ACCESS_KEY => your AWS user credential secret key (obtained when you generated the credentials for the user)
AWS_ACCESS_KEY_ID => your AWS user credential key ID (obtained when you generated the credentials for the user)
AWS_S3_LOCATION => the location of your deployment zip file (ie. s3://my-app-codepipeline-deployment/my_app.zip )
These variables will be accessible by the scripts executed by the Gitlab-CI containers.
Startup script
A simple startup script has been provided (https://gitlab.com/autronix/gitlabci-ec2-deployment-samples-guide/blob/master/deploy/extras/my_app.sh) to allow the deployment to perform the following tasks:
Start the application and create a PID file
Check the status of the application through the PID file
Stop the application
You may find this script under deploy/extras/my_app.sh
Creating gitlab-ci.yml
The gitlab-ci.yml file is in charge of performing the Continuous Integration tasks associated with a given commit.
It acts as a simplified group of shell scripts that are organized in stages which correspond to the different phases in your Continuous Integration steps.
For more information on the details and reference, please refer to the following two links:
http://docs.gitlab.com/ce/ci/quick_start/README.html
http://docs.gitlab.com/ce/ci/yaml/README.html
You may validate the syntax of your gitlab-ci.yml file at any time with the following tool: https://gitlab.com/ci/lint
For the purpose of deployment, we will cover only the last piece of the sample provided with this guide:
deploy-job:
# Script to run for deploying application to AWS
script:
- apt-get --quiet install --yes python-pip # AWS CLI requires python-pip, python is installed by default
- pip install -U pip # pip update
- pip install awscli # AWS CLI installation
- $G build -x test -x distTar # # Build the project with Gradle
- $G distZip # creates distribution zip for deployment
- aws s3 cp $BUNDLE_SRC $AWS_S3_LOCATION # Uploads the zipfile to S3 and expects the AWS Code Pipeline/Code Deploy to pick up
# requires previous CI stages to succeed in order to execute
when: on_success
stage: deploy
environment: production
cache:
key: "$CI_BUILD_NAME/$CI_BUILD_REF_NAME"
untracked: true
paths:
- build/
# Applies only to tags matching the regex: ie: v1.0.0-My-App-Release
only:
- /^v\d+\.\d+\.\d+-.*$/
except:
- branches
- triggers
This part represents the whole job associated with the deployment following the previous, if any, C.I. stages.
The relevant part associated with the deployment is this:
# Script to run for deploying application to AWS
script:
- apt-get --quiet install --yes python-pip # AWS CLI requires python-pip, python is installed by default
- pip install -U pip # pip update
- pip install awscli # AWS CLI installation
- $G build -x test -x distTar # # Build the project with Gradle
- $G distZip # creates distribution zip for deployment
- aws s3 cp $BUNDLE_SRC $AWS_S3_LOCATION # Uploads the zipfile to S3 and expects the AWS Code Pipeline/Code Deploy to pick up
The first step involves installing the python package management system: pip.
pip is required to install AWS CLI, which is necessary to upload the deployment file to AWS S3
In this example, we are using Gradle (defined by the environment variable $G); Gradle provides a module to automatically Zip the deployment files. Depending on the type of project you are deploying this method will be different for generating the distribution zip file my_app.zip.
The aws s3 cp $BUNDLE_SRC $AWS_S3_LOCATION command uploads the distribution zip file to the Amazon S3 location that we defined earlier. This file is then automatically detected by CodePipeline, processed and sent to CodeDeploy.
Finally, CodeDeploy performs the necessary tasks through the CodeDeploy agent as specified by the appspec.yml file.
Creating appspec.yml
The appspec.yml defines the behaviour to be followed by CodeDeploy once a deployment file has been received.
A sample file has been provided along with this guide along with sample scripts to be executed during the various phases of the deployment.
Please refer to the specification for the CodeDeploy AppSpec for more information on how to build the appspec.yml file: http://docs.aws.amazon.com/codedeploy/latest/userguide/app-spec-ref.html
Generating the Deployment ZipFile
In order for CodeDeploy to work properly, you must create a properly generated zip file of your application.
The zip file must contain:
Zip root
appspec.yml => CodeDeploy deployment instructions
deployment stage scripts
provided samples would be placed in the scripts directory in the zip file, would require the presence my_app.sh script to be added at the root of your application directory (ie. my_app directory in the zip)
distribution code - in our example it would be under the my_app directory
Tools such as Gradle and Maven are capable of generating distribution zip files with certain alterations to the zip generation process.
If you do not use such a tool, you may have to instruct Gitlab-CI to generate this zip file in a different manner; this method is outside of the scope of this guide.
Deploying your application to EC2
The final step in this guide is actually performing a successful deployment.
The stages of Continuous integration are defined by the rules set in the gitlab-ci.yml. The example provided with this guide will initiate a deploy for any reference matching the following regex: /^v\d+\.\d+\.\d+-.*$/.
In this case, pushing a Tag v1.0.0-My-App-Alpha-Release through git onto your remote Gitlab would initiate the deployment process. You may adjust these rules as applicable to your project requirements.
The gitlab-ci.yml example provided would perform the following jobs when detecting the Tag v1.0.0-My-App-Alpha-Release:
build job - compile the sources
test job - run the unit tests
deploy-job - compile the sources, generate the distribution zip, upload zip to Amazon S3
Once the distribution zip has been uploaded to Amazon S3, the following steps happen:
CodePipeline detects the change in the revision of the S3 zip file
CodePipeline validates the file
CodePipeline sends signal that the bundle for CodeDeploy is ready
CodeDeploy executes the deployment steps
Start - initialization of the deployment
Application Stop - Executes defined script for hook
DownloadBundle - Gets the bundle file from the S3 repository through the CodePipeline
BeforeInstall - Executes defined script for hook
Install - Copies the contents to the deployment location as defined by the files section of appspec.yml
AfterInstall - Executes defined script for hook
ApplicationStart - Executes defined script for hook
ValidateService - Executes defined script for hook
End - Signals the CodePipeline that the deployment has completed successfully
Successful deployment screenshots:
References
Gitlab-CI QuickStart: http://docs.gitlab.com/ce/ci/quick_start/README.html
Gitlab-CI .gitlab-ci.yml: http://docs.gitlab.com/ce/ci/yaml/README.html
AWS CodePipeline Walkthrough: http://docs.aws.amazon.com/codepipeline/latest/userguide/getting-started-w.html
Install or Reinstall the AWS CodeDeploy Agent: http://docs.aws.amazon.com/codedeploy/latest/userguide/how-to-run-agent-install.html
AWS CLI Getting Started - Env: http://docs.aws.amazon.com/cli/latest/userguide/cli-chap-getting-started.html#cli-environment
AppSpec Reference: http://docs.aws.amazon.com/codedeploy/latest/userguide/app-spec-ref.html
autronix's answer is awesome, although in my case I had to gave up the CodePipeline part due to the following error : The deployment failed because a specified file already exists at this location : /path/to/file. This is because I already have files at the location since I'm using an existing instance with a server running already on it.
Here is my workaround :
In the .gitlab-ci.yml here is what I changed :
deploy:
stage: deploy
script:
- curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip" # Downloading and installing awscli
- unzip awscliv2.zip
- ./aws/install
- aws deploy push --application-name App-Name --s3-location s3://app-deployment/app.zip # Adding revision to s3 bucket
- aws deploy create-deployment --application-name App-Name --s3-location bucket=app-deployment,key=app.zip,bundleType=zip --deployment-group-name App-Name-Fleet --deployment-config-name CodeDeployDefault.OneAtATime --file-exists-behavior OVERWRITE # Ordering the deployment of the new revision
when: on_success
only:
refs:
- dev
The important part is the aws deploy create-deployment line with it's flag --file-exists-behavior. There are three options available, OVERWRITE was the one I needed and I couldn't manage to set this flag with CodePipeline so I went with the cli option.
I've also changed a bit the part for the upload of the .zip. Instead of creating the .zip myself I'm using aws deploy push command which will create a .zip for me on the s3 bucket.
There is really nothing else to modify.

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