I'm trying to find a way to get secrets from KV without creating a secret scope
OR
Create the secret scope automatically using Databricks CLI (following https://learn.microsoft.com/en-us/azure/databricks/security/secrets/secret-scopes#--create-an-azure-key-vault-backed-secret-scope-using-the-databricks-cli)
For the second option, I'm confuse on where run those command lines.
Ideally, can Databricks CLI be used to retrieve secrets instead of creating the secret scope?
If you want to use dbutils.secrets.get or Databricks CLI, then you need to have secret scope created. To create secret scope using CLI you need to run it from your personal computer, for example, that has Databricks CLI installed. Please note the comment that if you're creating a secret scope from Key Vault using CLI, then you need to provide AAD token, not the Databricks PAT. Simplest way to do that is to set environment variables and then use CLI:
export DATABRICKS_HOST=https://adb-....azuredatabricks.net
export DATABRICKS_TOKEN=$(az account get-access-token -o tsv
--query accessToken --resource 2ff814a6-3304-4ab8-85cb-cd0e6f879c1d)
databricks secrets create-scope ...
I try to run and test an AWS Lambda service written in Golang locally using SAM CLI. I have two problems:
The Lambda does not work locally if I use .zip files. When I deploy the code to AWS, it works without an issue, but if I try to run locally with .zip files, I get the following error:
A required privilege is not held by the client: 'handler' -> 'C:\Users\user\AppData\Local\Temp\tmpbvrpc0a9\bootstrap'
If I don't use .zip, then it works locally, but I still want to deploy as .zip and it is not feasible to change the template.yml every time I want to test locally
If I try to access AWS resources, I need to set the following environment variables:
AWS_ACCESS_KEY_ID
AWS_SECRET_ACCESS_KEY
AWS_SESSION_TOKEN
However, if I set these variables in template.yml and then use sam local start-api --env-vars to fill them with the credentials, then the local environment works and can access AWS resources, but when I deploy the code to the real AWS, it gives an error, since these variables are reserved. I also tried to use different names for these variables, but then the local environment does not work, and also tried to omit these from template.yml and just use the local env-vars, but environment variables must be present in template.yml and cannot be created with env-vars, can only fill existing variables with values.
How can I make local env work but still be able to deploy to AWS?
For accessing AWS resources you need to look at IAM permissions rather than using programmatic access keys, check this document out for cloudformation.
To be clear virtually nothing deployed on AWS needs those keys, it's all about applying permissions to X(lambda, ec2 etc etc) - those keys are only really needed for the aws cli and some local envs like serverless and sam
The serverless framework now supports golang, if you're new I'd say give that a go while you get up to speed with IAM/Cloudformation.
I'm trying to set up some automation for a school project. The gist of it is:
Install an EC2 instance via CloudFormation. Then
Use cfn-init to
Install a very basic Ansible configuration
Download an Ansible playbook from S3
Run said playbook to install a Redshift cluster via CloudFormation
Install some necessary packages
Install some necessary Python modules
Download a Python script that will
Connect to the Redshift database
Create a table
Use the COPY command to import data into the table
It all works up to the point of executing the script. Doing so manually works a treat, but that is because I can copy the created Redshift endpoint into the script for the database connection. The issue I have is that I don't know how to extract that output value from CloudFormation so it can be inserted it into the script for a fully automated (save the initial EC2 deployment) solution.
I see that Ansible has at least one means of doing so (cloudformation_facts, for instance), but I'm a bit foggy on how to implement it. I've looked at examples but it hasn't become any clearer. Without context I'm lost and so far all I've seen are standalone snippets.
In order to ensure an answer is listed:
I figured out the describe-stacks and describe-stack-resources sub-commands to the aws cloudformation cli command. Using these, I was able to track down the information I needed. In particular, I needed to access a role. This is the command that I used:
aws cloudformation describe-stacks --stack-name=StackName --region=us-west-2 \
--query 'Stacks[0].Outputs[?OutputKey==`RedshiftClusterEndpointAddress`].OutputValue' \
--output text
I first used the describe-stacks subcommand to get a list of my stacks. The relevant stack is the first in the list (an array) so I used Stacks[0] at the top of my query for the describe-stack-recources subcommand. I then used Outputs since I am interested in a value from the CloudFormation output list. I know the name of the key (RedshiftClusterEndpointAddress), so I used that as the parameter. I then used OutputValue to return the value of RedshiftClusterEndpointAddress.
I'm attempting to use a script which automatically creates snapshots of all EBS volumes on an AWS instance. This script is running on several other instances with no issue.
The current instance already has an AWS profile configured which is used for other purposes. My understanding is I should be able to specify the profile my script uses, but I can't get this to work.
I've added a new set of credentials to the /home/ubuntu/.aws file by adding the following under the default credentials which are already there:
[snapshot_creator]
aws_access_key_id=s;aldkas;dlkas;ldk
aws_secret_access_key=sdoij34895u98jret
In the script I have tried adding AWS_PROFILE=snapshot_creatorbut when I run it I get the error Unable to locate credentials. You can configure credentials by running "aws configure".
So, I delete my changes to /home/ubuntu/.aws and instead run aws configure --profile snapshot_creator. However after entering all information I get the error [Errno 17] File exists: '/home/ubuntu/.aws'.
So I add my changes to the .aws file again and this time in the script for every single command starting with aws ec2 I add the parameter --profile snapshot_creator, but this time when I run the script I get The config profile (snapshot_creator) could not be found.
How can I tell the script to use this profile? I don't want to change the environment variables for the instance because of the aforementioned other use of AWS CLI for other purposes.
Credentials should be stored in the file "/home/ubuntu/.aws/credentials"
I guess this error is because it couldn't create a directory. Can you delete the ".aws" file and re-run the configure command? It should create the credentials file under "/home/ubuntu/.aws/"
File exists: '/home/ubuntu/.aws'
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.