I'm using gitlab CI for deployment.
I'm running into a problem when the review branch is deleted.
stop_review:
variables:
GIT_STRATEGY: none
stage: cleanup
script:
- echo "$AWS_REGION"
- echo "Stopping review branch"
- serverless config credentials --provider aws --key ${AWS_ACCESS_KEY_ID} --secret ${AWS_SECRET_ACCESS_KEY}
- echo "$CI_COMMIT_REF_NAME"
- serverless remove --stage=$CI_COMMIT_REF_NAME --verbose
only:
- branches
except:
- master
environment:
name: review/$CI_COMMIT_REF_NAME
action: stop
when: manual
error is This command can only be run in a Serverless service directory. Make sure to reference a valid config file in the current working directory if you're using a custom config file
I have tried different GIT_STRATEGY, can some point me in right direction?
In order to run serverless remove, you'll need to have the serverless.yml file available, which means the actual repository will need to be cloned. (or that file needs to get to GitLab in some way).
It's required to have a serverless.yml configuration file available when you run serverless remove because the Serverless Framework allows users to provision infrastructure using not only the framework's YML configuration but also additional resources (like CloudFormation in AWS) which may or may not live outside of the specified app or stage CF Stack entirely.
In fact, you can also provision infrastructure into other providers as well (AWS, GCP, Azure, OpenWhisk, or actually any combination of these).
So it's not sufficient to simply identify the stage name when running sls remove, you'll need the full serverless.yml template.
Related
We develop a project all RestApi services is a Lambda function, We use serverless framework for AWS Lambda deploying. Project is mono-repo.
It's folder structure like this
./app (this is shared folder and a layer on AWS )
- /Models
- /Helper
- ...
./services
- /ServiceGroup
- /service1
- service.handler
- config.yml
- /service2
- service.handler
- config.yml
- /AnotherServiceGroup
- /service3
- service.handler
- config.yml
- /service4
- service.handler
- config.yml
- serverless.yml (this yml configuration file is including all services configuration files)
We use custom own gitlab server. And this project CI folder run this code for lambda deploy
serverless deploy
This command deploy all services but we want deploy only changed services. How to customize CI for that?
If all of your services share the same serverless.yml file, they live in the same cloudformation stack, so what you're asking for doesn't work.
You can use serverless deploy function to update just one function, but that skips cloudformation, so new resources won't be created and your stack will drift. You should use it during development, but not for production deployments.
I strongly recommend splitting your stacks. You can still use a mono-repo, and then you can conditionally deploy services depending on which files changed for a given commit.
My team uses AWS for our infrastructure, across 3 different AWS accounts. We'll call them simply sandbox, staging, and production.
I recently set up Terraform against our AWS infrastructure, and its hierarchy maps against our accounts, then by either application, or AWS service itself. The repo structure looks something like this:
staging
iam
groups
main.tf
users
main.tf
s3
main.tf
sandbox
iam
...
production
applications
gitlab
main.tf
route53
main.tf
...
We're using separate configurations per AWS service (e.g., IAM or S3) or application (e.g., GitLab) so we don't end up with huge .tf files per account that would take a long time to apply updates for any one change. Ideally, we'd like to move away from the service-based configuration approach and move towards more application-based configurations, but the problem at hand remains the same either way.
This approach has been working fine when applying updates manually from the command line, but I'd love to move it to GitLab CI/CD to better automate our workflow, and that's where things have broken down.
In my existing setup, if I make an single change to, say, staging/s3/main.tf, GitLab doesn't seem to have a good way out of the box to only run terraform plan or terraform apply for that specific configuration.
If I instead moved everything into a single main.tf file for an entire AWS account (or multiple files but tied to a single state file), I could simply have GitLab trigger a job to do plan or apply to just that configuration. It might take 15 minutes to run based on the number of AWS resources we have in each account, but it's a potential option I suppose.
It seems like my issue might be ultimately related to how GitLab handles "monorepos" than how Terraform handles its workflow (after all, Terraform will happily plan/apply my changes if I simply tell it what has changed), although I'd also be interested in hearing about how people structure their Terraform environments given -- or in order to avoid entirely -- these limitations.
Has anyone solved an issue like this in their environment?
The nice thing about Terraform is that it's idempotent so you can just apply even if nothing has changed and it will be a no-op action anyway.
If for some reason you really only want to run a plan/apply on a specific directory when things change then you can achieve this by using only.changes so that Gitlab will only run the job if the specified files have changed.
So if you have your existing structure then it's as simple as doing something like this:
stages:
- terraform plan
- terraform apply
.terraform_template:
image: hashicorp/terraform:latest
before_script:
- LOCATION=$(echo ${CI_JOB_NAME} | cut -d":" -f2)
- cd ${LOCATION}
- terraform init
.terraform_plan_template:
stage: terraform plan
extends: .terraform_template
script:
- terraform plan -input=false -refresh=true -module-depth=-1 .
.terraform_apply_template:
stage: terraform apply
extends: .terraform_template
script:
- terraform apply -input=false -refresh=true -auto-approve=true .
terraform-plan:production/applications/gitlab:
extends: .terraform_plan_template
only:
refs:
- master
changes:
- production/applications/gitlab/*
- modules/gitlab/*
terraform-apply:production/applications/gitlab:
extends: .terraform_apply_template
only:
refs:
- master
changes:
- production/applications/gitlab/*
- modules/gitlab/*
I've also assumed the existence of modules that are in a shared location to indicate how this pattern can also look for changes elsewhere in the repo than just the directory you are running Terraform against.
If this isn't the case and you have a flatter structure and you're happy to have a single apply job then you can simplify this to something like:
stages:
- terraform
.terraform_template:
image: hashicorp/terraform:latest
stage: terraform
before_script:
- LOCATION=$(echo ${CI_JOB_NAME} | cut -d":" -f2)
- cd ${LOCATION}
- terraform init
script:
- terraform apply -input=false -refresh=true -auto-approve=true .
only:
refs:
- master
changes:
- ${CI_JOB_NAME}/*
production/applications/gitlab:
extends: .terraform_template
In general though this can just be avoided by allowing Terraform to run against all of the appropriate directories on every push (probably only applying on push to master or other appropriate branch) because, as mentioned, Terraform is idempotent so it won't do anything if nothing has changed. This also has the benefit that if your automation code hasn't changed but something has changed in your provider (such as someone opening up a security group) then Terraform will go put it back to how it should be the next time it is triggered.
I have a monolith github project that has multiple different applications that I'd like to integrate with an AWS Codebuild CI/CD workflow. My issue is that if I make a change to one project, I don't want to update the other. Essentially, I want to create a logical fork that deploys differently based on the files changed in a particular commit.
Basically my project repository looks like this:
- API
-node_modules
-package.json
-dist
-src
- REACTAPP
-node_modules
-package.json
-dist
-src
- scripts
- 01_install.sh
- 02_prebuild.sh
- 03_build.sh
- .ebextensions
In terms of Deployment, my API project gets deployed to elastic beanstalk and my REACTAPP gets deployed as static files to S3. I've tried a few things but decided that the only viable approach is to manually perform this deploy step within my own 03_build.sh script - because there's no way to build this dynamically within Codebuild's Deploy step (I could be wrong).
Anyway, my issue is that I essentially need to create a decision tree to determine which project gets excecuted, so if I make a change to API and push, it doesn't automatically deploy REACTAPP to S3 unnecessarliy (and vica versa).
I managed to get this working on localhost by updating environment variables at certain points in the build process and then reading them in separate steps. However this fails on Codedeploy because of permission issues i.e. I don't seem to be able to update env variables from within the CI process itself.
Explicitly, my buildconf.yml looks like this:
version: 0.2
env:
variables:
VARIABLES: 'here'
AWS_ACCESS_KEY_ID: 'XXXX'
AWS_SECRET_ACCESS_KEY: 'XXXX'
AWS_REGION: 'eu-west-1'
AWS_BUCKET: 'mybucket'
phases:
install:
commands:
- sh ./scripts/01_install.sh
pre_build:
commands:
- sh ./scripts/02_prebuild.sh
build:
commands:
- sh ./scripts/03_build.sh
I'm running my own shell scripts to perform some logic and I'm trying to pass variables between scripts: install->prebuild->build
To give one example, here's the 01_install.sh where I diff each project version to determine whether it needs to be updated (excuse any minor errors in bash):
#!/bin/bash
# STAGE 1
# _______________________________________
# API PROJECT INSTALL
# Do if API version was changed in prepush (this is just a sample and I'll likely end up storing the version & previous version within the package.json):
if [[ diff ./api/version.json ./api/old_version.json ]] > /dev/null 2>&1
## then
echo "🤖 Installing dependencies in API folder..."
cd ./api/ && npm install
## Set a variable to be used by the 02_prebuild.sh script
TEST_API="true"
export TEST_API
else
echo "No change to API"
fi
# ______________________________________
# REACTAPP PROJECT INSTALL
# Do if REACTAPP version number has changed (similar to above):
...
Then in my next stage I read these variables to determine whether I should run tests on the project 02_prebuild.sh:
#!/bin/bash
# STAGE 2
# _________________________________
# API PROJECT PRE-BUILD
# Do if install was initiated
if [[ $TEST_API == "true" ]]; then
echo "🤖 Run tests on API project..."
cd ./api/ && npm run tests
echo $TEST_API
BUILD_API="true"
export BUILD_API
else
echo "Don't test API"
fi
# ________________________________
# TODO: Complete for REACTAPP, similar to above
...
In my final script I use the BUILD_API variable to build to the dist folder, then I deploy that to either Elastic Beanstalk (for API) or S3 (for REACTAPP).
When I run this locally it works, however when I run it on Codebuild I get a permissions failure presumably because my bash scripts cannot export ENV_VAR. I'm wondering either if anyone knows how to update ENV_VARIABLES from within the build process itself, or if anyone has a better approach to achieve my goals (conditional/ variable build process on Codebuild)
EDIT:
So an approach that I've managed to get working is instead of using Env variables, I'm creating new files with specific names using fs then reading the contents of the file to make logical decisions. I can access these files from each of the bash scripts so it works pretty elegantly with some automatic cleanup.
I won't edit the original question as it's still an issue and I'd like to know how/ if other people solved this. I'm still playing around with how to actually use the eb deploy and s3 cli commands within the build scripts as codebuild does not seem to come with the eb cli installed and my .ebextensions file does not seem to be honoured.
Source control repos like Github can be configured to send a post event to an API endpoint when you push to a branch. You can consume this post request in lambda through API Gateway. This event data includes which files were modified with the commit. The lambda function can then process this event to figure out what to deploy. If you’re struggling with deploying to your servers from the codebuild container, you might want to try posting an artifact to s3 with an installable package and then have your server grab it from there.
Right now, I deploy my (Spring Boot) application to EC2 instance like:
Build JAR file on local machine
Deploy/Upload JAR via scp command (Ubuntu) from my local machine
I would like to automate that process, but:
without using Jenkins + Rundeck CI/CD tools
without using AWS CodeDeploy service since that does not support GitLab
Question: Is it possible to perform 2 simple steps (that are now done manualy - building and deploying via scp) with GitLab CI/CD tools and if so, can you present simple steps to do it.
Thanks!
You need to create a .gitlab-ci.yml file in your repository with CI jobs defined to do the two tasks you've defined.
Here's an example to get you started.
stages:
- build
- deploy
build:
stage: build
image: gradle:jdk
script:
- gradle build
artifacts:
paths:
- my_app.jar
deploy:
stage: deploy
image: ubuntu:latest
script:
- apt-get update
- apt-get -y install openssh-client
- scp my_app.jar target.server:/my_app.jar
In this example, the build job run a gradle container and uses gradle to build the app. GitLab CI artifacts are used to capture the built jar (my_app.jar), which will be passed on to the deploy job.
The deploy job runs an ubuntu container, installs openssh-client (for scp), then executes scp to open my_app.jar (passed from the build job) to the target server.
You have to fill in the actual details of building and copying your app. For secrets like SSH keys, set project level CI/CD variables that will be passed in to your CI jobs.
Create shell file with the following contents.
#!/bin/bash
# Copy JAR file to EC2 via SCP with PEM in home directory (usually /home/ec2-user)
scp -i user_key.pem file.txt ec2-user#my.ec2.id.amazonaws.com:/home/ec2-user
#SSH to EC2 Instnace
ssh -T -i "bastion_keypair.pem" ec2-user#y.ec2.id.amazonaws.com /bin/bash <<-'END2'
#The following commands will be executed automatically by bash.
#Consdier this as remote shell script.
killall java
java -jar ~/myJar.jar server ~/config.yml &>/dev/null &
echo 'done'
#Once completed, the shell will exit.
END2
In 2020, this should be easier with GitLab 13.0 (May 2020), using an older feature Auto DevOps (introduced in GitLab 11.0, June 2018)
Auto DevOps provides pre-defined CI/CD configuration allowing you to automatically detect, build, test, deploy, and monitor your applications.
Leveraging CI/CD best practices and tools, Auto DevOps aims to simplify the setup and execution of a mature and modern software development lifecycle.
Overview
But now (May 2020):
Auto Deploy to ECS
Until now, there hasn’t been a simple way to deploy to Amazon Web Services. As a result, Gitlab users had to spend a lot of time figuring out their own configuration.
In Gitlab 13.0, Auto DevOps has been extended to support deployment to AWS!
Gitlab users who are deploying to AWS Elastic Container Service (ECS) can now take advantage of Auto DevOps, even if they are not using Kubernetes. Auto DevOps simplifies and accelerates delivery and cloud deployment with a complete delivery pipeline out of the box. Simply commit code and Gitlab does the rest! With the elimination of the complexities, teams can focus on the innovative aspects of software creation!
In order to enable this workflow, users need to:
define AWS typed environment variables: ‘AWS_ACCESS_KEY_ID’ ‘AWS_ACCOUNT_ID’ and ‘AWS_REGION’, and
enable Auto DevOps.
Then, your ECS deployment will be automatically built for you with a complete, automatic, delivery pipeline.
See documentation and issue
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.