I am using the below command to deploy lambda. It always create a new folder(.template and 1 more) in s3 bucket when there is change in my lamda project files. I want to overwrite the folder, so at any point in time, I will be having only 1 folder. How to do that?
sam deploy --no-fail-on-empty-changeset --s3-bucket bucketName --capabilities CAPABILITY_IAM --stack-name stackName --parameter-overrides "ParameterKey=Stage,ParameterValue=staging"
The easiest way to keep deployment artefact buckets "tidy" is to add a Bucket Lifecycle Rule that expires (deletes) objects after an arbitrary number of days.
You can create the rule in the S3 Console (Management > Lifecyle rules). You can safely expire all the objects, including the "current" version. Artefacts are read only at deploy-time and will be re-created with the next sam deploy.
Related
I'm developing an application with micronaut using SAM CLI to deploy it on AWS Lambda. As I was including dependencies and developing new features, the function packages got bigger an bigger (now they are around 250MB). This makes deployment take a while.
On top of that every time I edit template.yaml and then run sam build && sam deploy to try a new configuration on S3, RDS, etc... I have to wait for gradle to build the function again (even though it's unchanged since the last deployment) and upload the whole package to S3.
As I'm trying to configure this application with many trials and errors on SAM, waiting for this process to complete just to get an error because of some misconfiguration is getting quite counterproductive.
Also my SAM s3 bcuket is at 10GB size after just a single day of work. This may get expensive on the long run.
Is there a way to avoid those gradle rebuilds and reuploads when teh function code is unchanged?
If you are only updating the template.yml file, you could copy the new version to ./.aws-sam/build folder and then run sam deploy
$ cp template.yml ./.aws-sam/build/template.yml
$ sam deploy
If you are editing a lambda you could try to update the function code by itself (after you create it in the template and deploy of course). That can be done via the AWS CLI update-function-code command:
rm index.zip
cd lambda
zip –X –r ../index.zip *
cd ..
aws lambda update-function-code --function-name MyLambdaFunction --zip-file fileb://index.zip
more info can be found here:
Alexa Blogs - Publishing Your Skill Code to Lambda via the Command Line Interface
AWS CLI Command Reference - lambda - update-function-code
my SAM s3 bcuket is at 10GB size
Heh. Yea start deleting stuff. Maybe you can write a script using aws s3?
I'm attempting to setup CodePipeline to manage the deployment of a very simple Lambda function.
I'm completely stuck on a problem with the deployment step, and cannot figure out what could be wrong.
When the pipeline attempts to run the CodeDeploy action, it fails with the error...
BundleType must be either YAML or JSON
This is my appspec...
version: 0.0
Resources:
- my-function:
Type: AWS::Lambda::Function
Properties:
Name: "my-function"
My pipeline doesn't have a build step, as it's just a simple js file, with no dependencies, so no build is required.
I've tried adding an action to deploy to S3, and I can confirm that the zip file that's being sent to s3 contains the appspec.yml and index.js and that these are both in the root.
Most of the examples I've seen use a buildspec, but I'm not sure why I would need this, or what it would even do if I had one.
There is nothing wrong with your setup, it is a shortcoming of the services that you cannot use CodeDeploy in a CodePipeline action to Deploy a Lambda function.
The reason is because CodeDeploy expects a JSON or YAML appspec file for the Lambda deployment, but currently CodePipeline supports ZIP as a bundle type so the error is thrown.
To workaround, customers deploy Lambda in a CodePipeline is via CloudFormation deploy action (SAM to be exact). Please see this tutorial on this recommended approach:
https://docs.aws.amazon.com/lambda/latest/dg/build-pipeline.html
It looks like it's impossible to get Codedeploy to work in a CodePipeline project with a CodeBuild.
First I set up a Pipeline with 3 stages: Source, Build and Deploy, the first 2 stages work perfectly but the 3th (CodeDeploy) throws this error:
CodeBuild pushes the output artifacts to s3 in a .zip file, which is not supported by CodeDeploy.
For this, I tried to set up a Lambda function between CodeBuild and CodeDeploy like this: (Source -> CodeBuild -> Invoke Lambda -> CodeDeploy), The Lambda function uploads the appspec.yml file to s3 and calls putJobSuccessResult, But I still get the same error.
BundleType must be either YAML or JSON
There is a known limitation where the deployment of a Lambda using CodePipeline, with CodeDeploy as the Deployment Provider is not supported as of yet.
This is because CodePipeline will always zip the bundle/artifact, whereas CodeDeploy expects a YAML/JSON file as the source (appspec.yaml file) for Lambda Function deployment.
In order to work around this limitation, you have two options:
Run AWS CLI commands inside your CodeBuild Stage to update/deploy your lambda function
OR
Use CodeBuild to package your lambda function Code and push the artifact to a CloudFormation stage, which will update or create your Lambda Function Resource. You should find the reference documentation at [1] useful for getting the required information about packaging your SAM application.
Ref:
[1] SAM Packaging - https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/serverless-deploying.html#serverless-sam-cli-using-package-and-deploy
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.
I have a AWS EC2 instance running with me and there is a maven project running on tomcat7. What I have tried is I am using Jenkins for the CI.So whenever the new push happens to the Git-hub Jenkins starts to build, after completion of build it will upload the war file to the AWS S3.
Where I have stuck is, I am not getting a way to deploy the war file to the AWS Ec2 instance.
I have tried to use Code Deployment where at a point it showed me that it supports only tar, tar.gz and zip is there any way out to deploy the war file to the AWS EC2 instance from the S3.
Thank you.
You can use Amazon Code Deploy which can manage deployment from a S3 bucket and can automate deployment to EC2 instance of your file/scripts.
From the Overview of a Deployment
Here's how it works:
First, you create deployable content – such as web pages, executable
files, setup scripts, and so on – on your local development machine or
similar environment, and then you add an application specification
file (AppSpec file). The AppSpec file is unique to AWS CodeDeploy; it
defines the deployment actions you want AWS CodeDeploy to execute. You
bundle your deployable content and the AppSpec file into an archive
file, and then upload it to an Amazon S3 bucket or a GitHub
repository. This archive file is called an application revision (or
simply a revision).
Next, you provide AWS CodeDeploy with
information about your deployment, such as which Amazon S3 bucket or
GitHub repository to pull the revision from and which set of instances
to deploy its contents to. AWS CodeDeploy calls a set of instances a
deployment group. A deployment group contains individually tagged
instances, Amazon EC2 instances in Auto Scaling groups, or both.
Each time you successfully upload a new application revision that you
want to deploy to the deployment group, that bundle is set as the
target revision for the deployment group. In other words, the
application revision that is currently targeted for deployment is the
target revision. This is also the revision that will be pulled for
automatic deployments.
Next, the AWS CodeDeploy agent on each
instance polls AWS CodeDeploy to determine what and when to pull the
revision from the specified Amazon S3 bucket or GitHub repository.
Finally, the AWS CodeDeploy agent on each instance pulls the target
revision from the specified Amazon S3 bucket or GitHub repository and,
using the instructions in the AppSpec file, deploys the contents to
the instance.
AWS CodeDeploy keeps a record of your deployments so
that you can get information such as deployment status, deployment
configuration parameters, instance health, and so on.
Good part is that code deploy has no additional cost, you only pay for the resources (EC2, S3) that are used in your pipeline
Assuming you have already created a S3 bucket.
Step 1: Create a IAM user / Role who have access to a s3 bucket where in you are placing the WAR file
Step 2: Write a custom script which will download WAR File from S3 to your EC2 instance.
You can also use aws cli to download contents from s3 to your local machine.
Create a startup.sh file and add these contents
aws s3 cp s3://com.yoursitename/warFile/sample.war /tmp
sudo mv /tmp/sample.war /var/lib/tomcat/webapps/ROOT.war
sudo service tomcat restart