Tag Archives: devops

Integrating AWS Device Farm with your CI/CD pipeline to run cross-browser Selenium tests

Post Syndicated from Mahesh Biradar original https://aws.amazon.com/blogs/devops/integrating-aws-device-farm-with-ci-cd-pipeline-to-run-cross-browser-selenium-tests/

Continuously building, testing, and deploying your web application helps you release new features sooner and with fewer bugs. In this blog, you will create a continuous integration and continuous delivery (CI/CD) pipeline for a web app using AWS CodeStar services and AWS Device Farm’s desktop browser testing service.  AWS CodeStar is a suite of services that help you quickly develop and build your web apps on AWS.

AWS Device Farm’s desktop browser testing service helps developers improve the quality of their web apps by executing their Selenium tests on different desktop browsers hosted in AWS. For each test executed on the service, Device Farm generates action logs, web driver logs, video recordings, to help you quickly identify issues with your app. The service offers pay-as-you-go pricing so you only pay for the time your tests are executing on the browsers with no upfront commitments or additional costs. Furthermore, Device Farm provides a default concurrency of 50 Selenium sessions so you can run your tests in parallel and speed up the execution of your test suites.

After you’ve completed the steps in this blog, you’ll have a working pipeline that will build your web app on every code commit and test it on different versions of desktop browsers hosted in AWS.

Solution overview

In this solution, you use AWS CodeStar to create a sample web application and a CI/CD pipeline. The following diagram illustrates our solution architecture.

Figure 1: Deployment pipeline architecture

Figure 1: Deployment pipeline architecture


Before you deploy the solution, complete the following prerequisite steps:

  1. On the AWS Management Console, search for AWS CodeStar.
  2. Choose Getting Started.
  3. Choose Create Project.
  4. Choose a sample project.

For this post, we choose a Python web app deployed on Amazon Elastic Compute Cloud (Amazon EC2) servers.

  1. For Templates, select Python (Django).
  2. Choose Next.
Figure 2: Choose a template in CodeStar

Figure 2: Choose a template in CodeStar

  1. For Project name, enter Demo CICD Selenium.
  2. Leave the remaining settings at their default.

You can choose from a list of existing key pairs. If you don’t have a key pair, you can create one.

  1. Choose Next.
  2. Choose Create project.
 Figure 3: Verify the project configuration

Figure 3: Verify the project configuration

AWS CodeStar creates project resources for you as listed in the following table.

Service Resource Created
AWS CodePipeline project demo-cicd-selen-Pipeline
AWS CodeCommit repository Demo-CICD-Selenium
AWS CodeBuild project demo-cicd-selen
AWS CodeDeploy application demo-cicd-selen
AWS CodeDeploy deployment group demo-cicd-selen-Env
Amazon EC2 server Tag: Environment = demo-cicd-selen-WebApp
IAM role AWSCodeStarServiceRole, CodeStarWorker*

Your EC2 instance needs to have access to run AWS Device Farm to run the Selenium test scripts. You can use service roles to achieve that.

  1. Attach policy AWSDeviceFarmFullAccess to the IAM role CodeStarWorker-demo-cicd-selen-WebApp.

You’re now ready to create an AWS Cloud9 environment.

Check the Pipelines tab of your AWS CodeStar project; it should show success.

  1. On the IDE tab, under Cloud 9 environments, choose Create environment.
  2. For Environment name, enter Demo-CICD-Selenium.
  3. Choose Create environment.
  4. Wait for the environment to be complete and then choose Open IDE.
  5. In the IDE, follow the instructions to set up your Git and make sure it’s up to date with your repo.

The following screenshot shows the verification that the environment is set up.

Figure 4: Verify AWS Cloud9 setup

Figure 4: Verify AWS Cloud9 setup

You can now verify the deployment.

  1. On the Amazon EC2 console, choose Instances.
  2. Choose demo-cicd-selen-WebApp.
  3. Locate its public IP and choose open address.
Figure 5: Locating IP address of the instance

Figure 5: Locating IP address of the instance


A webpage should open. If it doesn’t, check your VPN and firewall settings.

Now that you have a working pipeline, let’s move on to creating a Device Farm project for browser testing.

Creating a Device Farm project

To create your browser testing project, complete the following steps:

  1. On the Device Farm console, choose Desktop browser testing project.
  2. Choose Create a new project.
  3. For Project name, enter Demo cicd selenium.
  4. Choose Create project.
Figure 6: Creating AWS Device Farm project

Figure 6: Creating AWS Device Farm project


Note down the project ARN; we use this in the Selenium script for the remote web driver.

Figure 7: Project ARN for AWS Device Farm project

Figure 7: Project ARN for AWS Device Farm project


Testing the solution

This solution uses the following script to run browser testing. We call this script in the validate service lifecycle hook of the CodeDeploy project.

  1. Open your AWS Cloud9 IDE (which you made as a prerequisite).
  2. Create a folder tests under the project root directory.
  3. Add the sample Selenium script browser-test-sel.py under tests folder with the following content (replace <sample_url> with the url of your web application refer pre-requisite step 18):
import boto3
from selenium import webdriver
from selenium.webdriver.common.keys import Keys

devicefarm_client = boto3.client("devicefarm", region_name="us-west-2")
testgrid_url_response = devicefarm_client.create_test_grid_url(
    projectArn="arn:aws:devicefarm:us-west-2:<your project ARN>",

driver = webdriver.Remote(testgrid_url_response["url"],
    if driver.find_element_by_id("Layer_1"):
        print("graphics generated in full screen")
    assert driver.find_element_by_id("Layer_1")
    driver.set_window_position(0, 0) and driver.set_window_size(1000, 400)
    tower = driver.find_element_by_id("tower1")
    if tower.is_displayed():
        print("graphics generated after resizing")
        print("graphics not generated at this window size")
       # this is where you can fail the script with error if you expect the graphics to load. And pipeline will terminate
except Exception as e:

This script launches a website (in Firefox) created by you in the prerequisite step using the AWS CodeStar template and verifies if a graphic element is loaded.

The following screenshot shows a full-screen application with graphics loaded.

Figure 8: Full screen application with graphics loaded

Figure 8: Full screen application with graphics loaded


The following screenshot shows the resized window with no graphics loaded.

Figure 9: Resized window application with No graphics loaded

Figure 9: Resized window application with No graphics loaded

  1. Create the file validate_service in the scripts folder under the root directory with the following content:
if [ "$DEPLOYMENT_GROUP_NAME" == "demo-cicd-selen-Env" ]
cd /home/ec2-user
source environment/bin/activate
python tests/browser-test-sel.py

This script is part of CodeDeploy scripts and determines whether to stop the pipeline or continue based on the output from the browser testing script from the preceding step.

  1. Modify the file appspec.yml under the root directory, add the tests files and ValidateService hook , the file should look like following:
version: 0.0
os: linux
 - source: /ec2django/
   destination: /home/ec2-user/ec2django
 - source: /helloworld/
   destination: /home/ec2-user/helloworld
 - source: /manage.py
   destination: /home/ec2-user
 - source: /supervisord.conf
   destination: /home/ec2-user
 - source: /requirements.txt
   destination: /home/ec2-user
 - source: /requirements/
   destination: /home/ec2-user/requirements
 - source: /tests/
   destination: /home/ec2-user/tests

  - object: /home/ec2-user/manage.py
    owner: ec2-user
    mode: 644
      - file
  - object: /home/ec2-user/supervisord.conf
    owner: ec2-user
    mode: 644
      - file
    - location: scripts/install_dependencies
      timeout: 300
      runas: root
    - location: scripts/codestar_remote_access
      timeout: 300
      runas: root
    - location: scripts/start_server
      timeout: 300
      runas: root

    - location: scripts/stop_server
      timeout: 300
      runas: root

    - location: scripts/validate_service
      timeout: 600
      runas: root

This file is used by AWS CodeDeploy service to perform the deployment and validation steps.

  1. Modify the artifacts section in the buildspec.yml file. The section should look like the following:
    - 'template.yml'
    - 'ec2django/**/*'
    - 'helloworld/**/*'
    - 'scripts/**/*'
    - 'tests/**/*'
    - 'appspec.yml'
    - 'manage.py'
    - 'requirements/**/*'
    - 'requirements.txt'
    - 'supervisord.conf'
    - 'template-configuration.json'

This file is used by AWS CodeBuild service to package the code

  1. Modify the file Common.txt in the requirements folder under the root directory, the file should look like the following:
# dependencies common to all environments 
boto3 >= 1.10.44

  1. Save All the changes, your folder structure should look like the following:
── README.md
├── appspec.yml*
├── buildspec.yml*
├── db.sqlite3
├── ec2django
├── helloworld
├── manage.py
├── requirements
│   ├── common.txt*
│   ├── dev.txt
│   ├── prod.txt
├── requirements.txt
├── scripts
│   ├── codestar_remote_access
│   ├── install_dependencies
│   ├── start_server
│   ├── stop_server
│   └── validate_service**
├── supervisord.conf
├── template-configuration.json
├── template.yml
├── tests
│   └── browser-test-sel.py**

**newly added files
*modified file

Running the tests

The Device Farm desktop browsing project is now integrated with your pipeline. All you need to do now is commit the code, and CodePipeline takes care of the rest.

  1. On Cloud9 terminal, go to project root directory.
  2. Run git add . to stage all changed files for commit.
  3. Run git commit -m “<commit message>” to commit the changes.
  4. Run git push to push the changes to repository, this should trigger the Pipeline.
  5. Check the Pipelines tab of your AWS CodeStar project; it should show success.
  6. Go to AWS Device Farm console and click on Desktop browser testing projects.
  7. Click on your Project Demo cicd selenium.

You can verify the running of your Selenium test cases using the recorded run steps shown on the Device Farm console, the video of the run, and the logs, all of which can be downloaded on the Device Farm console, or using the AWS SDK and AWS Command Line Interface (AWS CLI).

The following screenshot shows the project run details on the console.

Figure 10: Viewing AWS Device Farm project run details

Figure 10: Viewing AWS Device Farm project run details


To Test the Selenium script locally you can run the following commands.

1. Create a Python virtual environment for your Django project. This virtual environment allows you to isolate this project and install any packages you need without affecting the system Python installation. At the terminal, go to project root directory and type the following command:

$ python3 -m venv ./venv

2. Activate the virtual environment:

$ source ./venv/bin/activate

3. Install development Python dependencies for this project:

$ pip install -r requirements/dev.txt

4. Run Selenium Script:

$ python tests/browser-test-sel.py

Testing the failure scenario (optional)

To test the failure scenario, you can modify the sample script browser-test-sel.py at the else statement.

The following code shows the lines to change:

print("graphics was not generated at this form size")
# this is where you can fail the script with error if you expect the graphics to load. And pipeline will terminate

The following is the updated code:

# this is where you can fail the script with error if you expect the graphics to load. And pipeline will terminate

Commit the change, and the pipeline should fail and stop the deployment.


Integrating Device Farm with CI/CD pipelines allows you to control deployment based on browser testing results. Failing the Selenium test on validation failures can stop and roll back the deployment, and a successful testing can continue the pipeline to deploy the solution to the final stage. Device Farm offers a one-stop solution for testing your native and web applications on desktop browsers and real mobile devices.

Mahesh Biradar is a Solutions Architect at AWS. He is a DevOps enthusiast and enjoys helping customers implement cost-effective architectures that scale..

Gaining operational insights with AIOps using Amazon DevOps Guru

Post Syndicated from Nikunj Vaidya original https://aws.amazon.com/blogs/devops/gaining-operational-insights-with-aiops-using-amazon-devops-guru/

Amazon DevOps Guru offers a fully managed AIOps platform service that enables developers and operators to improve application availability and resolve operational issues faster. It minimizes manual effort by leveraging machine learning (ML) powered recommendations. Its ML models take advantage of AWS’s expertise in operating highly available applications for the world’s largest e-commerce business for over 20 years. DevOps Guru automatically detects operational issues, predicts impending resource exhaustion, details likely causes, and recommends remediation actions.

This post walks you through how to enable DevOps Guru for your account in a typical serverless environment and observe the insights and recommendations generated for various activities. These insights are generated for operational events that could pose a risk to your application availability. DevOps Guru uses AWS CloudFormation stacks as the application boundary to detect resource relationships and co-relate with deployment events.

Solution overview

The goal of this post is to demonstrate the insights generated for anomalies detected by DevOps Guru from DevOps operations. If you don’t have a test environment and want to build out infrastructure to test the generation of insights, then you can follow along through this post. However, if you have a test or production environment (preferably spawned from CloudFormation stacks), you can skip the first section and jump directly to section, Enabling DevOps Guru and injecting traffic.

The solution includes the following steps:

1. Deploy a serverless infrastructure.

2. Enable DevOps Guru and inject traffic.

3. Review the generated DevOps Guru insights.

4. Inject another failure to generate a new insight.

As depicted in the following diagram, we use a CloudFormation stack to create a serverless infrastructure, comprising of Amazon API Gateway, AWS Lambda, and Amazon DynamoDB, and inject HTTP requests at a high rate towards the API published to list records.

Serverless infrastructure monitored by DevOps Guru

When DevOps Guru is enabled to monitor your resources within the account, it uses a combination of vended Amazon CloudWatch metrics and specific patterns from its ML models to detect anomalies. When an anomaly is detected, it generates an insight with the recommendations.

The generation of insights is dependent upon several factors. Although this post provides a canned environment to reproduce insights, the results may vary depending upon traffic pattern, timings of traffic injection, and so on.


To complete this tutorial, you should have access to an AWS Cloud9 environment and the AWS Command Line Interface (AWS CLI).

Deploying a serverless infrastructure

To deploy your serverless infrastructure, you complete the following high-level steps:

1.   Create your IDE environment.

2.   Launch the CloudFormation template to deploy the serverless infrastructure.

3.   Populate the DynamoDB table.


1. Creating your IDE environment

We recommend using AWS Cloud9 to create an environment to get access to the AWS CLI from a bash terminal. AWS Cloud9 is a browser-based IDE that provides a development environment in the cloud. While creating the new environment, ensure you choose Linux2 as the operating system. Alternatively, you can use your bash terminal in your favorite IDE and configure your AWS credentials in your terminal.

When access is available, run the following command to confirm that you can see the Amazon Simple Storage Service (Amazon S3) buckets in your account:

aws s3 ls

Install the following prerequisite packages and ensure you have Python3 installed:

sudo yum install jq -y

export AWS_REGION=$(curl -s \ | jq -r '.region')

sudo pip3 install requests

Clone the git repository to download the required CloudFormation templates:

git clone https://github.com/aws-samples/amazon-devopsguru-samples
cd amazon-devopsguru-samples/generate-devopsguru-insights/

2. Launching the CloudFormation template to deploy your serverless infrastructure

To deploy your infrastructure, complete the following steps:

  • Run the CloudFormation template using the following command:
aws cloudformation create-stack --stack-name myServerless-Stack \
--template-body file:///$PWD/cfn-shops-monitoroper-code.yaml \

The AWS CloudFormation deployment creates an API Gateway, a DynamoDB table, and a Lambda function with sample code.

  • When it’s complete, go to the Outputs tab of the stack on the AWS CloudFormation console.
  • Record the links for the two APIs: one of them to list the table contents and other one to populate the contents.

3. Populating the DynamoDB table

Run the following commands (simply copy-paste) to populate the DynamoDB table. The below three commands will identify the name of the DynamoDB table from the CloudFormation stack and populate the name in the populate-shops-dynamodb-table.json file.

dynamoDBTableName=$(aws cloudformation list-stack-resources \
--stack-name myServerless-Stack | \
jq '.StackResourceSummaries[]|select(.ResourceType == "AWS::DynamoDB::Table").PhysicalResourceId' | tr -d '"')
sudo sed -i s/"<YOUR-DYNAMODB-TABLE-NAME>"/$dynamoDBTableName/g \
aws dynamodb batch-write-item \
--request-items file://populate-shops-dynamodb-table.json

The command gives the following output:

"UnprocessedItems": {}

This populates the DynamoDB table with a few sample records, which you can verify by accessing the ListRestApiEndpointMonitorOper API URL published on the Outputs tab of the CloudFormation stack. The following screenshot shows the output.

Screenshot showing the API output

Enabling DevOps Guru and injecting traffic

In this section, you complete the following high-level steps:

1.   Enable DevOps Guru for the CloudFormation stack.

2.   Wait for the serverless stack to complete.

3.   Update the stack.

4.   Inject HTTP requests into your API.


1. Enabling DevOps Guru for the CloudFormation stack

To enable DevOps Guru for CloudFormation, complete the following steps:

  • Run the CloudFormation template to enable DevOps Guru for this CloudFormation stack:
aws cloudformation create-stack \
--stack-name EnableDevOpsGuruForServerlessCfnStack \
--template-body file:///$PWD/EnableDevOpsGuruForServerlessCfnStack.yaml \
--parameters ParameterKey=CfnStackNames,ParameterValue=myServerless-Stack \
--region ${AWS_REGION}
  • When the stack is created, navigate to the Amazon DevOps Guru console.
  • Choose Settings.
  • Under CloudFormation stacks, locate myServerless-Stack.

If you don’t see it, your CloudFormation stack has not been successfully deployed. You may remove and redeploy the EnableDevOpsGuruForServerlessCfnStack stack.

Optionally, you can configure Amazon Simple Notification Service (Amazon SNS) topics or enable AWS Systems Manager integration to create OpsItem entries for every insight created by DevOps Guru. If you need to deploy as a stack set across multiple accounts and Regions, see Easily configure Amazon DevOps Guru across multiple accounts and Regions using AWS CloudFormation StackSets.

2. Waiting for baselining of resources

This is a necessary step to allow DevOps Guru to complete baselining the resources and benchmark the normal behavior. For our serverless stack with 3 resources, we recommend waiting for 2 hours before carrying out next steps. When enabled in a production environment, depending upon the number of resources selected to monitor, it can take up to 24 hours for it to complete baselining.

Note: Unlike many monitoring tools, DevOps Guru does not expect the dashboard to be continuously monitored and thus under normal system health, the dashboard would simply show zero’ed counters for the ongoing insights. It is only when an anomaly is detected, it will raise an alert and display an insight on the dashboard.

3. Updating the CloudFormation stack

When enough time has elapsed, we will make a configuration change to simulate a typical operational event. As shown below, update the CloudFormation template to change the read capacity for the DynamoDB table from 5 to 1.

CloudFormation showing read capacity settings to modify

Run the following command to deploy the updated CloudFormation template:

aws cloudformation update-stack --stack-name myServerless-Stack \
--template-body file:///$PWD/cfn-shops-monitoroper-code.yaml \

4. Injecting HTTP requests into your API

We now inject ingress traffic in the form of HTTP requests towards the ListRestApiEndpointMonitorOper API, either manually or using the Python script provided in the current directory (sendAPIRequest.py). Due to reduced read capacity, the traffic will oversubscribe the dynamodb tables, thus inducing an anomaly. However, before triggering the script, populate the url parameter with the correct API link for the  ListRestApiEndpointMonitorOper API, listed in the CloudFormation stack’s output tab.

After the script is saved, trigger the script using the following command:

python sendAPIRequest.py

Make sure you’re getting an output of status 200 with the records that we fed into the DynamoDB table (see the following screenshot). You may have to launch multiple tabs (preferably 4) of the terminal to run the script to inject a high rate of traffic.

Terminal showing script executing and injecting traffic to API

After approximately 10 minutes of the script running in a loop, an operational insight is generated in DevOps Guru.


Reviewing DevOps Guru insights

While these operations are being run, DevOps Guru monitors for anomalies, logs insights that provide details about the metrics indicating an anomaly, and prints actionable recommendations to mitigate the anomaly. In this section, we review some of these insights.

Under normal conditions, DevOps Guru dashboard will show the ongoing insights counter to be zero. It monitors a high number of metrics behind the scenes and offloads the operator from manually monitoring any counters or graphs. It raises an alert in the form of an insight, only when anomaly occurs.

The following screenshot shows an ongoing reactive insight for the specific CloudFormation stack. When you choose the insight, you see further details. The number under the Total resources analyzed last hour may vary, so for this post, you can ignore this number.

DevOps Guru dashboard showing an ongoing reactive insight

The insight is divided into four sections: Insight overview, Aggregated metrics, Relevant events, and Recommendations. Let’s take a closer look into these sections.

The following screenshot shows the Aggregated metrics section, where it shows metrics for all the resources that it detected anomalies in (DynamoDB, Lambda, and API Gateway). Note that depending upon your traffic pattern, lambda settings, baseline traffic, the list of metrics may vary. In the example below, the timeline shows that an anomaly for DynamoDB started first and was followed by API Gateway and Lambda. This helps us understand the cause and symptoms, and prioritize the right anomaly investigation.

The listing of metrics inside an Insight

Initially, you may see only two metrics listed, however, over time, it populates more metrics that showed anomalies. You can see the anomaly for DynamoDB started earlier than the anomalies for API Gateway and Lambda, thus indicating them as after effects. In addition to the information in the preceding screenshot, you may see Duration p90 and IntegrationLatency p90 (for Lambda and API Gateway respectively, due to increased backend latency) metrics also listed.

Now we check the Relevant events section, which lists potential triggers for the issue. The events listed here depend on the set of operations carried out on this CloudFormation stack’s resources in this Region. This makes it easy for the operator to be reminded of a change that may have caused this issue. The dots (representing events) that are near the Insight start portion of timeline are of particular interest.

Related Events shown inside the Insight

If you need to delve into any of these events, just click of any of these points, and it provides more details as shown below screenshot.

Delving into details of the related event listed in Insight

You can choose the link for an event to view specific details about the operational event (configuration change via CloudFormation update stack operation).

Now we move to the Recommendations section, which provides prescribed guidance for mitigating this anomaly. As seen in the following screenshot, it recommends to roll back the configuration change related to the read capacity for the DynamoDB table. It also lists specific metrics and the event as part of the recommendation for reference.

Recommendations provided inside the Insight

Going back to the metric section of the insight, when we select Graphed anomalies, it shows you the graphs of all related resources. Below screenshot shows a snippet showing anomaly for DynamoDB ReadThrottleEvents metrics. As seen in the below screenshot of the graph pattern, the read operations on the table are exceeding the provisioned throughput of read capacity. This clearly indicates an anomaly.

Graphed anomalies in DevOps Guru

Let’s navigate to the DynamoDB table and check our table configuration. Checking the table properties, we notice that our read capacity is reduced to 1. This is our root cause that led to this anomaly.

Checking the DynamoDB table capacity settings

If we change it to 5, we fix this anomaly. Alternatively, if the traffic is stopped, the anomaly moves to a Resolved state.

The ongoing reactive insight takes a few minutes after resolution to move to a Closed state.

Note: When the insight is active, you may see more metrics get populated over time as we detect further anomalies. When carrying out the preceding tests, if you don’t see all the metrics as listed in the screenshots, you may have to wait longer.


Injecting another failure to generate a new DevOps Guru insight

Let’s create a new failure and generate an insight for that.

1.   After you close the insight from the previous section, trigger the HTTP traffic generation loop from the AWS Cloud9 terminal.

We modify the Lambda functions’s resource-based policy by removing the permissions for API Gateway to access this function.

2.   On the Lambda console, open the function ScanFunctionMonitorOper.

3.   On the Permissions tab, access the policy.

Accessing the permissions tab for the Lambda


4.   Save a copy of the policy offline as a backup before making any changes.

5.   Note down the “Sid” values for the “AWS:SourceArn” that ends with prod/*/ and prod/*/*.

Checking the Resource-based policy for the Lambda

6.   Run the following command to remove the “Sid” JSON statements in your Cloud9 terminal:

aws lambda remove-permission --function-name ScanFunctionMonitorOper \
--statement-id <Sid-value-ending-with-prod/*/>

7.   Run the same command for the second Sid value:

aws lambda remove-permission --function-name ScanFunctionMonitorOper \
--statement-id <Sid-value-ending-with-prod/*/*>

You should see several 5XX errors, as in the following screenshot.

Terminal output now showing 500 errors for the script output

After less than 8 minutes, you should see a new ongoing reactive insight on the DevOps Guru dashboard.

Let’s take a closer look at the insight. The following screenshot shows the anomalous metric 5XXError Average of API Gateway and its duration. (This insight shows as closed because I had already restored permissions.)

Insight showing 5XX errors for API-Gateway and link to OpsItem

If you have configured to enable creating OpsItem in Systems Manager, you would see the link to OpsItem ID created in the insight, as shown above. This is an optional configuration, which will enable you to track the insights in the form of open tickets (OpsItems) in Systems Manager OpsCenter.

The recommendations provide guidance based upon the related events and anomalous metrics.

After the insight has been generated, reviewed, and verified, restore the permissions by running the following command:

aws lambda add-permission --function-name ScanFunctionMonitorOper  \
--statement-id APIGatewayProdPerm --action lambda:InvokeFunction \
--principal apigateway.amazonaws.com

If needed, you can insert the condition to point to the API Gateway ARN to allow only specific API Gateways to access the Lambda function.


Cleaning up

After you walk through this post, you should clean up and un-provision the resources to avoid incurring any further charges.

1.   To un-provision the CloudFormation stacks, on the AWS CloudFormation console, choose Stacks.

2.   Select each stack (EnableDevOpsGuruForServerlessCfnStack and myServerless-Stack) and choose Delete.

3.   Check to confirm that the DynamoDB table created with the stacks is cleaned up. If not, delete the table manually.

4.   Un-provision the AWS Cloud9 environment.



This post reviewed how DevOps Guru can continuously monitor the resources in your AWS account in a typical production environment. When it detects an anomaly, it generates an insight, which includes the vended CloudWatch metrics that breached the threshold, the CloudFormation stack in which the resource existed, relevant events that could be potential triggers, and actionable recommendations to mitigate the condition.

DevOps Guru generates insights that are relevant to you based upon the pre-trained machine-learning models, removing the undifferentiated heavy lifting of manually monitoring several events, metrics, and trends.

I hope this post was useful to you and that you would consider DevOps Guru for your production needs.


CDK Corner – January 2021

Post Syndicated from Christian Weber original https://aws.amazon.com/blogs/devops/cdk-corner-february-2021/

Social: Events in the Community

CDK Day is coming up on April 30th! This is your chance to meet and engage with the CDK Community! Last year’s event included an incredible amount of content, whether it was learning the origin story of CDK, learning how CDK is used in a Large Enterprise, there were many great sessions, as well as Eric Johnson cosplaying as the official CDK Mascot.

Do you have a story to share about using CDK, about something funny/crazy/interesting/cool/another adjective? The CFPs are now open — the community wants to hear your stories; so go ahead and submit here!

Updates: Changes made across CDK

In January, the CDK Community and the AWS CDK team were together hard at work, bringing in new changes, features, or, as NetaNir likes to call them, many new “goodies” to the CDK!

AWS Construct Library and Core

The CDK Team announced General Availability of the EKS Module in CDK with PR#12640. Moving a CDK Module from Experimental to Stable requires substantial effort from both the CDK Community and Team — the appreciation for everyone that contributed to this effort cannot be understated. Take a look at the project milestone to explore some of the work that contributed to releasing the EKS constrcut to GA. Great job everyone!

External assets are now supported from PR#12259. With this change, you can now setup cdk-assets.json with Files, Archives, or even Docker Images built by external utilities. This is great if your CDK Application relies on assets from other sources, such as an internal pipeline, or if you want to pull the latest Docker Image built from some external utility.

CDK will now alert you if your stack hits the maximum number of CloudFormation Resources. If you’re deploying complex CDK Stacks, you’ll know that sometimes you will hit this cap which seems to only happen when you’ve walked away from your computer to make a coffee while your stack is deploying, only to come back with a latte and a command line full of exceptions. This wonderful quality-of-life change was merged in PR#12193.

AWS CodeBuild

AWS CodeBuild in CDK can now be configured with Standard 5.0 Runtime Environments, which now supports many new runtime environments, including support for Python 3.9 which means, for example, CodeBuild now natively understands the union operator in Python dictionaries you’ve been using to combine dictionaries in your project.


There is now support for m6gd and r6gd Graviton EC2 Instances from CDK with PR#12302. Graviton Instances are a great way to utilize ARM Archicture at a lower cost.

Support for new io2 and and gp3 EBS Volumes were announced at re:Invent, followed up with a community contribution from leandrodamascena in PR#12074

AWS ElasticSearch

A big cost savings feature to support ElasticSearch UltraWarm nodes in CDK, now gives CDK users the opportunity to store data in S3 instead of an SSD with ElasticSearch, which can substantially reduce storage costs.


Securing S3 Buckets is a standard practice, and CDK has tightened its security on S3 Buckets by limiting the PutObject permission of Bucket.grantWrite() to just s3:PutObject instead of s3:PutObject*. This subtle change means that only the first permission is added to the IAM Principal, instead of any other IAM permission prefixed with PutObject (Such as s3:PutObjectAcl). You still have the flexibility to make this permission add-on if needed, though.

AWS StepFunctions

A member of the CDK Community, ayush987goyal, submitted PR#12436 for StepFunctions-Tasks. This feature now lets users specify the family and revision of a taskDefinitionFamily inside EcsRunTask, thanks to their effort. This modifies previous behavior of the construct where a user could only deploy the latest revision of a Task by supplying the ARN of the Task.

CloudFormation and new L1 Resources

As CDK synthesizes CloudFormation Templates, it’s important that CDK stays up to date with the CloudFormation Resource Specification these updates to our collection of L1 Constructs. Now that they’re here, the community and team can begin implementing beautiful L2 Constructs for these L1s. Interested in contributing an L2 from these L1s? Take a look at our CONTRIBUTING doc to get up and running.

In January the team introduced several updates of the CloudFormation Resource Spec to CDK, bringing support for a whole slew of new Resources, Attribute Updates and Property Changes. These updates, among others, include new resource types for CloudFormation Modules, SageMaker Pipelines, AWS Config Saved Queries, AWS DataSync, AWS Service Catalog App Registry, AWS QuickSight, Virtual Clusters for EMR Containers for Amazon Elastic MapReduce, support for DNSSEC in Route53, and support for ECR Public Repositories.

My favorite of all these is ECR Public Repositories. Public Repositories support was just recently announced, in December at AWS re:Invent. Now you can deploy and manage a public repository with CDK as an L1 Construct. So, if you have an exciting Container Image that you’ve been wanting to share with the world with your own Public Repository, set it all up with CDK!

To be in the know on updates to the CDK, and updates to CDK’s CloudFormation Resource Spec, update your repository notification settings to watch for new CDK Releases , and browse the cfnspec CHANGELOG.

Learning: Level up your CDK Knowledge

AWS has released a new training module for the CDK. This free 7 module course teaches users the fundamental concepts of the CDK, from explaining its core benefits, to defining the common language and terms, to tips for troubleshooting CDK Projects. This is a great course for developers, or related stakeholders who may be considering whether or not to adopt CDK in their team or organization.

Community Acknowledgements: Thanks for your hard work

We love highlighting Pull Requests from our community of CDK users. This month’s spotlight goes to Jacob-Doetsch, who submitted a fix when deploying Bastion Hosts backed by ARM Architecture. As ARM based architecture increases in usage across AWS, identifying and resolving these types of bugs helps CDK maintain the ability to help Developers continue moving quickly. Great job Jacob!

And finally, to round out the CDK Corner, a round of applause to the following users who merged their first Pull Request to CDK in January! The CDK Community appreciates your hard work and effort!

Improving the CPU and latency performance of Amazon applications using AWS CodeGuru Profiler

Post Syndicated from Neha Gupta original https://aws.amazon.com/blogs/devops/improving-the-cpu-and-latency-performance-of-amazon-applications-using-aws-codeguru-profiler/

Amazon CodeGuru Profiler is a developer tool powered by machine learning (ML) that helps identify an application’s most expensive lines of code and provides intelligent recommendations to optimize it. You can identify application performance issues and troubleshoot latency and CPU utilization issues in your application.

You can use CodeGuru Profiler to optimize performance for any application running on AWS Lambda, Amazon Elastic Compute Cloud (Amazon EC2), Amazon Elastic Container Service (Amazon ECS), AWS Fargate, or AWS Elastic Beanstalk, and on premises.

This post gives a high-level overview of how CodeGuru Profiler has reduced CPU usage and latency by approximately 50% and saved around $100,000 a year for a particular Amazon retail service.

Technical and business value of CodeGuru Profiler

CodeGuru Profiler is easy and simple to use, just turn it on and start using it. You can keep it running in the background and you can just look into the CodeGuru Profiler findings and implement the relevant changes.

It’s fairly low cost and unlike traditional tools that take up lot of CPU and RAM, running CodeGuru Profiler has less than 1% impact on total CPU usage overhead to applications and typically uses no more than 100 MB of memory.

You can run it in a pre-production environment to test changes to ensure no impact occurs on your application’s key metrics.

It automatically detects performance anomalies in the application stack traces that start consuming more CPU or show increased latency. It also provides visualizations and recommendations on how to fix performance issues and the estimated cost of running inefficient code. Detecting the anomalies early prevents escalating the issue in production. This helps you prioritize remediation by giving you enough time to fix the issue before it impacts your service’s availability and your customers’ experience.

How we used CodeGuru Profiler at Amazon

Amazon has on-boarded many of its applications to CodeGuru Profiler, which has resulted in an annual savings of millions of dollars and latency improvements. In this post, we discuss how we used CodeGuru Profiler on an Amazon Prime service. A simple code change resulted in saving around $100,000 for the year.

Opportunity to improve

After a change to one of our data sources that caused its payload size to increase, we expected a slight increase to our service latency, but what we saw was higher than expected. Because CodeGuru Profiler is easy to integrate, we were able to quickly make and deploy the changes needed to get it running on our production environment.

After loading up the profile in Amazon CodeGuru Profiler, it was immediately apparent from the visualization that a very large portion of the service’s CPU time was being taken up by Jackson deserialization (37%, across the two call sites). It was also interesting that most of the blocking calls in the program (in blue) was happening in the jackson.databind method _createAndCacheValueDeserializer.

Flame graphs represent the relative amount of time that the CPU spends at each point in the call graph. The wider it is, the more CPU usage it corresponds to.

The following flame graph is from before the performance improvements were implemented.

The Flame Graph before the deployment

Looking at the source for _createAndCacheValueDeserializer confirmed that there was a synchronized block. From within it, _createAndCache2 was called, which actually did the adding to the cache. Adding to the cache was guarded by a boolean condition which had a comment that indicated that caching would only be enabled for custom serializers if @JsonCachable was set.


Checking the documentation for @JsonCachable confirmed that this annotation looked like the correct solution for this performance issue. After we deployed a quick change to add @JsonCachable to our four custom deserializers, we observed that no visible time was spent in _createAndCacheValueDeserializer.


Adding a one-line annotation in four different places made the code run twice as fast. Because it was holding a lock while it recreated the same deserializers for every call, this was allowing only one of the four CPU cores to be used and therefore causing latency and inefficiency. Reusing the deserializers avoided repeated work and saved us lot of resources.

After the CodeGuru Profiler recommendations were implemented, the amount of CPU spent in Jackson reduced from 37% to 5% across the two call paths, and there was no visible blocking. With the removal of the blocking, we could run higher load on our hosts and reduce the fleet size, saving approximately $100,000 a year in Amazon EC2 costs, thereby resulting in overall savings.

The following flame graph shows performance after the deployment.

The Flame Graph after the deployment


The following graph shows that CPU usage reduced by almost 50%. The blue line shows the CPU usage the week before we implemented CodeGuru Profiler recommendations, and green shows the dropped usage after deploying. We could later safely scale down the fleet to reduce costs, while still having better performance than prior to the change.

Average Fleet CPU Utilization


The following graph shows the server latency, which also dropped by almost 50%. The latency dropped from 100 milliseconds to 50 milliseconds as depicted in the initial portion of the graph. The orange line depicts p99, green p99.9, and blue p50 (mean latency).

Server Latency



With a few lines of changed code and a half-hour investigation, we removed the bottleneck which led to lower utilization of resources and  thus we were able to decrease the fleet size. We have seen many similar cases, and in one instance, a change of literally six characters of inefficient code, reduced CPU usage from 99% to 5%.

Across Amazon, CodeGuru Profiler has been used internally among various teams and resulted in millions of dollars of savings and performance optimization. You can use CodeGuru Profiler for quick insights into performance issues of your application. The more efficient the code and application is, the less costly it is to run. You can find potential savings for any application running in production and significantly reduce infrastructure costs using CodeGuru Profiler. Reducing fleet size, latency, and CPU usage is a major win.



About the Authors

Neha Gupta

Neha Gupta is a Solutions Architect at AWS and have 16 years of experience as a Database architect/ DBA. Apart from work, she’s outdoorsy and loves to dance.

Ian Clark

Ian is a Senior Software engineer with the Last Mile organization at Amazon. In his spare time, he enjoys exploring the Vancouver area with his family.

Mitigate data leakage through the use of AppStream 2.0 and end-to-end auditing

Post Syndicated from Chaim Landau original https://aws.amazon.com/blogs/security/mitigate-data-leakage-through-the-use-of-appstream-2-0-and-end-to-end-auditing/

Customers want to use AWS services to operate on their most sensitive data, but they want to make sure that only the right people have access to that data. Even when the right people are accessing data, customers want to account for what actions those users took while accessing the data.

In this post, we show you how you can use Amazon AppStream 2.0 to grant isolated access to sensitive data and decrease your attack surface. In addition, we show you how to achieve end-to-end auditing, which is designed to provide full traceability of all activities around your data.

To demonstrate this idea, we built a sample solution that provides a data scientist with access to an Amazon SageMaker Studio notebook using AppStream 2.0. The solution deploys a new Amazon Virtual Private Cloud (Amazon VPC) with isolated subnets, where the SageMaker notebook and AppStream 2.0 instances are set up.

Why AppStream 2.0?

AppStream 2.0 is a fully-managed, non-persistent application and desktop streaming service that provides access to desktop applications from anywhere by using an HTML5-compatible desktop browser.

Each time you launch an AppStream 2.0 session, a freshly-built, pre-provisioned instance is provided, using a prebuilt image. As soon as you close your session and the disconnect timeout period is reached, the instance is terminated. This allows you to carefully control the user experience and helps to ensure a consistent, secure environment each time. AppStream 2.0 also lets you enforce restrictions on user sessions, such as disabling the clipboard, file transfers, or printing.

Furthermore, AppStream 2.0 uses AWS Identity and Access Management (IAM) roles to grant fine-grained access to other AWS services such as Amazon Simple Storage Service (Amazon S3), Amazon Redshift, Amazon SageMaker, and other AWS services. This gives you both control over the access as well as an accounting, via Amazon CloudTrail, of what actions were taken and when.

These features make AppStream 2.0 uniquely suitable for environments that require high security and isolation.

Why SageMaker?

Developers and data scientists use SageMaker to build, train, and deploy machine learning models quickly. SageMaker does most of the work of each step of the machine learning process to help users develop high-quality models. SageMaker access from within AppStream 2.0 provides your data scientists and analysts with a suite of common and familiar data-science packages to use against isolated data.

Solution architecture overview

This solution allows a data scientist to work with a data set while connected to an isolated environment that doesn’t have an outbound path to the internet.

First, you build an Amazon VPC with isolated subnets and with no internet gateways attached. This ensures that any instances stood up in the environment don’t have access to the internet. To provide the resources inside the isolated subnets with a path to commercial AWS services such as Amazon S3, SageMaker, AWS System Manager you build VPC endpoints and attach them to the VPC, as shown in Figure 1.

Figure 1: Network Diagram

Figure 1: Network Diagram

You then build an AppStream 2.0 stack and fleet, and attach a security group and IAM role to the fleet. The purpose of the IAM role is to provide the AppStream 2.0 instances with access to downstream AWS services such as Amazon S3 and SageMaker. The IAM role design follows the least privilege model, to ensure that only the access required for each task is granted.

During the building of the stack, you will enable AppStream 2.0 Home Folders. This feature builds an S3 bucket where users can store files from inside their AppStream 2.0 session. The bucket is designed with a dedicated prefix for each user, where only they have access. We use this prefix to store the user’s pre-signed SagaMaker URLs, ensuring that no one user can access another users SageMaker Notebook.

You then deploy a SageMaker notebook for the data scientist to use to access and analyze the isolated data.

To confirm that the user ID on the AppStream 2.0 session hasn’t been spoofed, you create an AWS Lambda function that compares the user ID of the data scientist against the AppStream 2.0 session ID. If the user ID and session ID match, this indicates that the user ID hasn’t been impersonated.

Once the session has been validated, the Lambda function generates a pre-signed SageMaker URL that gives the data scientist access to the notebook.

Finally, you enable AppStream 2.0 usage reports to ensure that you have end-to-end auditing of your environment.

To help you easily deploy this solution into your environment, we’ve built an AWS Cloud Development Kit (AWS CDK) application and stacks, using Python. To deploy this solution, you can go to the Solution deployment section in this blog post.

Note: this solution was built with all resources being in a single AWS Region. The support of multi Region is possible but isn’t part of this blog post.

Solution requirements

Before you build a solution, you must know your security requirements. The solution in this post assumes a set of standard security requirements that you typically find in an enterprise environment:

  • User authentication is provided by a Security Assertion Markup Language (SAML) identity provider (IdP).
  • IAM roles are used to access AWS services such as Amazon S3 and SageMaker.
  • AWS IAM access keys and secret keys are prohibited.
  • IAM policies follow the least privilege model so that only the required access is granted.
  • Windows clipboard, file transfer, and printing to local devices is prohibited.
  • Auditing and traceability of all activities is required.

Note: before you will be able to integrate SAML with AppStream 2.0, you will need to follow the AppStream 2.0 Integration with SAML 2.0 guide. There are quite a few steps and it will take some time to set up. SAML authentication is optional, however. If you just want to prototype the solution and see how it works, you can do that without enabling SAML integration.

Solution components

This solution uses the following technologies:

  • Amazon VPC – provides an isolated network where the solution will be deployed.
  • VPC endpoints – provide access from the isolated network to commercial AWS services such as Amazon S3 and SageMaker.
  • AWS Systems Manager – stores parameters such as S3 bucket names.
  • AppStream 2.0 – provides hardened instances to run the solution on.
  • AppStream 2.0 home folders – store users’ session information.
  • Amazon S3 – stores application scripts and pre-signed SageMaker URLs.
  • SageMaker notebook – provides data scientists with tools to access the data.
  • AWS Lambda – runs scripts to validate the data scientist’s session, and generates pre-signed URLs for the SageMaker notebook.
  • AWS CDK – deploys the solution.
  • PowerShell – processes scripts on AppStream 2.0 Microsoft Windows instances.

Solution high-level design and process flow

The following figure is a high-level depiction of the solution and its process flow.

Figure 2: Solution process flow

Figure 2: Solution process flow

The process flow—illustrated in Figure 2—is:

  1. A data scientist clicks on an AppStream 2.0 federated or a streaming URL.
    1. If it’s a federated URL, the data scientist authenticates using their corporate credentials, as well as MFA if required.
    1. If it’s a streaming URL, no further authentication is required.
  2. The data scientist is presented with a PowerShell application that’s been made available to them.
  3. After starting the application, it starts the PowerShell script on an AppStream 2.0 instance.
  4. The script then:
    1. Downloads a second PowerShell script from an S3 bucket.
    2. Collects local AppStream 2.0 environment variables:
      1. AppStream_UserName
      2. AppStream_Session_ID
      3. AppStream_Resource_Name
    3. Stores the variables in the session.json file and copies the file to the home folder of the session on Amazon S3.
  5. The PUT event of the JSON file into the Amazon S3 bucket triggers an AWS Lambda function that performs the following:
    1. Reads the session.json file from the user’s home folder on Amazon S3.
    2. Performs a describe action against the AppStream 2.0 API to ensure that the session ID and the user ID match. This helps to prevent the user from manipulating the local environment variable to pretend to be someone else (spoofing), and potentially gain access to unauthorized data.
    3. If the session ID and user ID match, a pre-signed SageMaker URL is generated and stored in session_url.txt, and copied to the user’s home folder on Amazon S3.
    4. If the session ID and user ID do not match, the Lambda function ends without generating a pre-signed URL.
  6. When the PowerShell script detects the session_url.txt file, it opens the URL, giving the user access to their SageMaker notebook.

Code structure

To help you deploy this solution in your environment, we’ve built a set of code that you can use. The code is mostly written in Python and for the AWS CDK framework, and with an AWS CDK application and some PowerShell scripts.

Note: We have chosen the default settings on many of the AWS resources our code deploys. Before deploying the code, you should conduct a thorough code review to ensure the resources you are deploying meet your organization’s requirements.

AWS CDK application – ./app.py

To make this application modular and portable, we’ve structured it in separate AWS CDK nested stacks:

  • vpc-stack – deploys a VPC with two isolated subnets, along with three VPC endpoints.
  • s3-stack – deploys an S3 bucket, copies the AppStream 2.0 PowerShell scripts, and stores the bucket name in an SSM parameter.
  • appstream-service-roles-stack – deploys AppStream 2.0 service roles.
  • appstream-stack – deploys the AppStream 2.0 stack and fleet, along with the required IAM roles and security groups.
  • appstream-start-fleet-stack – builds a custom resource that starts the AppStream 2.0 fleet.
  • notebook-stack – deploys a SageMaker notebook, along with IAM roles, security groups, and an AWS Key Management Service (AWS KMS) encryption key.
  • saml-stack – deploys a SAML role as a placeholder for SAML authentication.

PowerShell scripts

The solution uses the following PowerShell scripts inside the AppStream 2.0 instances:

  • sagemaker-notebook-launcher.ps1 – This script is part of the AppStream 2.0 image and downloads the sagemaker-notebook.ps1 script.
  • sagemaker-notebook.ps1 – starts the process of validating the session and generating the SageMaker pre-signed URL.

Note: Having the second script reside on Amazon S3 provides flexibility. You can modify this script without having to create a new AppStream 2.0 image.

Deployment Prerequisites

To deploy this solution, your deployment environment must meet the following prerequisites:

Note: We used AWS Cloud9 with Amazon Linux 2 to test this solution, as it comes preinstalled with most of the prerequisites for deploying this solution.

Deploy the solution

Now that you know the design and components, you’re ready to deploy the solution.

Note: In our demo solution, we deploy two stream.standard.small AppStream 2.0 instances, using Windows Server 2019. This gives you a reasonable example to work from. In your own environment you might need more instances, a different instance type, or a different version of Windows. Likewise, we deploy a single SageMaker notebook instance of type ml.t3.medium. To change the AppStream 2.0 and SageMaker instance types, you will need to modify the stacks/data_sandbox_appstream.py and stacks/data_sandbox_notebook.py respectively.

Step 1: AppStream 2.0 image

An AppStream 2.0 image contains applications that you can stream to your users. It’s what allows you to curate the user experience by preconfiguring the settings of the applications you stream to your users.

To build an AppStream 2.0 image:

  1. Build an image following the Create a Custom AppStream 2.0 Image by Using the AppStream 2.0 Console tutorial.

    Note: In Step 1: Install Applications on the Image Builder in this tutorial, you will be asked to choose an Instance family. For this example, we chose General Purpose. If you choose a different Instance family, you will need to make sure the appstream_instance_type specified under Step 2: Code modification is of the same family.

    In Step 6: Finish Creating Your Image in this tutorial, you will be asked to provide a unique image name. Note down the image name as you will need it in Step 2 of this blog post.

  2. Copy notebook-launcher.ps1 to a location on the image. We recommend that you copy it to C:\AppStream.
  3. In Step 2—Create an AppStream 2.0 Application Catalog—of the tutorial, use C:\Windows\System32\Windowspowershell\v1.0\powershell.exe as the application, and the path to notebook-launcher.ps1 as the launch parameter.

Note: While testing your application during the image building process, the PowerShell script will fail because the underlying infrastructure is not present. You can ignore that failure during the image building process.

Step 2: Code modification

Next, you must modify some of the code to fit your environment.

Make the following changes in the cdk.json file:

  • vpc_cidr – Supply your preferred CIDR range to be used for the VPC.

    Note: VPC CIDR ranges are your private IP space and thus can consist of any valid RFC 1918 range. However, if the VPC you are planning on using for AppStream 2.0 needs to connect to other parts of your private network (on premise or other VPCs), you need to choose a range that does not conflict or overlap with the rest of your infrastructure.

  • appstream_Image_name – Enter the image name you chose when you built the Appstream 2.0 image in Step 1.a.
  • appstream_environment_name – The environment name is strictly cosmetic and drives the naming of your AppStream 2.0 stack and fleet.
  • appstream_instance_type – Enter the AppStream 2.0 instance type. The instance type must be part of the same instance family you used in Step 1 of the To build an AppStream 2.0 image section. For a list of AppStream 2.0 instances, visit https://aws.amazon.com/appstream2/pricing/.
  • appstream_fleet_type – Enter the fleet type. Allowed values are ALWAYS_ON or ON_DEMAND.
  • Idp_name – If you have integrated SAML with this solution, you will need to enter the IdP name you chose when creating the SAML provider in the IAM Console.

Step 3: Deploy the AWS CDK application

The CDK application deploys the CDK stacks.

The stacks include:

  • VPC with isolated subnets
  • VPC Endpoints for S3, SageMaker, and Systems Manager
  • S3 bucket
  • AppStream 2.0 stack and fleet
  • Two AppStream 2.0 stream.standard.small instances
  • A single SageMaker ml.t2.medium notebook

Run the following commands to deploy the AWS CDK application:

  1. Install the AWS CDK Toolkit.
    npm install -g aws-cdk

  2. Create and activate a virtual environment.
    python -m venv .datasandbox-env
    source .datasandbox-env/bin/activate

  3. Change directory to the root folder of the code repository.
  4. Install the required packages.
    pip install -r requirements.txt

  5. If you haven’t used AWS CDK in your account yet, run:
    cdk bootstrap

  6. Deploy the AWS CDK stack.
    cdk deploy DataSandbox

Step 4: Test the solution

After the stack has successfully deployed, allow approximately 25 minutes for the AppStream 2.0 fleet to reach a running state. Testing will fail if the fleet isn’t running.

Without SAML

If you haven’t added SAML authentication, use the following steps to test the solution.

  1. In the AWS Management Console, go to AppStream 2.0 and then to Stacks.
  2. Select the stack, and then select Action.
  3. Select Create streaming URL.
  4. Enter any user name and select Get URL.
  5. Enter the URL in another tab of your browser and test your application.


If you are using SAML authentication, you will have a federated login URL that you need to visit.

If everything is working, your SageMaker notebook will be launched as shown in Figure 3.

Figure 3: SageMaker Notebook

Figure 3: SageMaker Notebook

Note: if you receive a web browser timeout, verify that the SageMaker notebook instance “Data-Sandbox-Notebook” is currently in InService status.


Auditing for this solution is provided through AWS CloudTrail and AppStream 2.0 Usage Reports. Though CloudTrail is enabled by default, to collect and store the CloudTrail logs, you must create a trail for your AWS account.

The following logs will be available for you to use, to provide auditing.

Connecting the dots

To get an accurate idea of your users’ activity, you have to correlate some logs from different services. First, you collect the login information from CloudTrail. This gives you the user ID of the user who logged in. You then collect the Amazon S3 put from CloudTrail, which gives you the IP address of the AppStream 2.0 instance. And finally, you collect the AppStream 2.0 usage report which gives you the IP address of the AppStream 2.0 instance, plus the user ID. This allows you to connect the user ID to the activity on Amazon S3. For auditing & controlling exploration activities with SageMaker, please visit this GitHub repository.

Though the logs are automatically being collected, what we have shown you here is a manual way of sifting through those logs. For a more robust solution on querying and analyzing CloudTrail logs, visit Querying AWS CloudTrail Logs.

Costs of this Solution

The cost for running this solution will depend on a number of factors like the instance size, the amount of data you store, and how many hours you use the solution. AppStream 2.0 is charged per instance hour and there is one instance in this example solution. You can see details on the AppStream 2.0 pricing page. VPC endpoints are charged by the hour and by how much data passes through them. There are three VPC endpoints in this solution (S3, System Manager, and SageMaker). VPC endpoint pricing is described on the Privatelink pricing page. SageMaker Notebooks are charged based on the number of instance hours and the instance type. There is one SageMaker instance in this solution, which may be eligible for free tier pricing. See the SageMaker pricing page for more details. Amazon S3 storage pricing depends on how much data you store, what kind of storage you use, and how much data transfers in and out of S3. The use in this solution may be eligible for free tier pricing. You can see details on the S3 pricing page.

Before deploying this solution, make sure to calculate your cost using the AWS Pricing Calculator, and the AppStream 2.0 pricing calculator.


Congratulations! You have deployed a solution that provides your users with access to sensitive and isolated data in a secure manner using AppStream 2.0. You have also implemented a mechanism that is designed to prevent user impersonation, and enabled end-to-end auditing of all user activities.

To learn about how Amazon is using AppStream 2.0, visit the blog post How Amazon uses AppStream 2.0 to provide data scientists and analysts with access to sensitive data.

If you have feedback about this post, submit comments in the Comments section below.

Want more AWS Security how-to content, news, and feature announcements? Follow us on Twitter.


Chaim Landau

As a Senior Cloud Architect at AWS, Chaim works with large enterprise customers, helping them create innovative solutions to address their cloud challenges. Chaim is passionate about his work, enjoys the creativity that goes into building solutions in the cloud, and derives pleasure from passing on his knowledge. In his spare time, he enjoys outdoor activities, spending time in nature, and immersing himself in his books.


JD Braun

As a Data and Machine Learning Engineer, JD helps organizations design and implement modern data architectures to deliver value to their internal and external customers. In his free time, he enjoys exploring Minneapolis with his fiancée and black lab.

Using AWS DevOps Tools to model and provision AWS Glue workflows

Post Syndicated from Nuatu Tseggai original https://aws.amazon.com/blogs/devops/provision-codepipeline-glue-workflows/

This post provides a step-by-step guide on how to model and provision AWS Glue workflows utilizing a DevOps principle known as infrastructure as code (IaC) that emphasizes the use of templates, source control, and automation. The cloud resources in this solution are defined within AWS CloudFormation templates and provisioned with automation features provided by AWS CodePipeline and AWS CodeBuild. These AWS DevOps tools are flexible, interchangeable, and well suited for automating the deployment of AWS Glue workflows into different environments such as dev, test, and production, which typically reside in separate AWS accounts and Regions.

AWS Glue workflows allow you to manage dependencies between multiple components that interoperate within an end-to-end ETL data pipeline by grouping together a set of related jobs, crawlers, and triggers into one logical run unit. Many customers using AWS Glue workflows start by defining the pipeline using the AWS Management Console and then move on to monitoring and troubleshooting using either the console, AWS APIs, or the AWS Command Line Interface (AWS CLI).

Solution overview

The solution uses COVID-19 datasets. For more information on these datasets, see the public data lake for analysis of COVID-19 data, which contains a centralized repository of freely available and up-to-date curated datasets made available by the AWS Data Lake team.

Because the primary focus of this solution showcases how to model and provision AWS Glue workflows using AWS CloudFormation and CodePipeline, we don’t spend much time describing intricate transform capabilities that can be performed in AWS Glue jobs. As shown in the Python scripts, the business logic is optimized for readability and extensibility so you can easily home in on the functions that aggregate data based on monthly and quarterly time periods.

The ETL pipeline reads the source COVID-19 datasets directly and writes only the aggregated data to your S3 bucket.

The solution exposes the datasets in the following tables:

Table Name Description Dataset location Provider
countrycode Lookup table for country codes s3://covid19-lake/static-datasets/csv/countrycode/ Rearc
countypopulation Lookup table for the population of each county s3://covid19-lake/static-datasets/csv/CountyPopulation/ Rearc
state_abv Lookup table for US state abbreviations s3://covid19-lake/static-datasets/json/state-abv/ Rearc
rearc_covid_19_nyt_data_in_usa_us_counties Data on COVID-19 cases at US county level s3://covid19-lake/rearc-covid-19-nyt-data-in-usa/csv/us-counties/ Rearc
rearc_covid_19_nyt_data_in_usa_us_states Data on COVID-19 cases at US state level s3://covid19-lake/rearc-covid-19-nyt-data-in-usa/csv/us-states/ Rearc
rearc_covid_19_testing_data_states_daily Data on COVID-19 cases at US state level s3://covid19-lake/rearc-covid-19-testing-data/csv/states_daily/ Rearc
rearc_covid_19_testing_data_us_daily US total test daily trend s3://covid19-lake/rearc-covid-19-testing-data/csv/us_daily/ Rearc
rearc_covid_19_testing_data_us_total_latest US total tests s3://covid19-lake/rearc-covid-19-testing-data/csv/us-total-latest/ Rearc
rearc_covid_19_world_cases_deaths_testing World total tests s3://covid19-lake/rearc-covid-19-world-cases-deaths-testing/ Rearc
rearc_usa_hospital_beds Hospital beds and their utilization in the US s3://covid19-lake/rearc-usa-hospital-beds/ Rearc
world_cases_deaths_aggregates Monthly and quarterly aggregate of the world s3://<your-S3-bucket-name>/covid19/world-cases-deaths-aggregates/ Aggregate


This post assumes you have the following:

  • Access to an AWS account
  • The AWS CLI (optional)
  • Permissions to create a CloudFormation stack
  • Permissions to create AWS resources, such as AWS Identity and Access Management (IAM) roles, Amazon Simple Storage Service (Amazon S3) buckets, and various other resources
  • General familiarity with AWS Glue resources (triggers, crawlers, and jobs)


The CloudFormation template glue-workflow-stack.yml defines all the AWS Glue resources shown in the following diagram.

architecture diagram showing ETL process

Figure: AWS Glue workflow architecture diagram

Modeling the AWS Glue workflow using AWS CloudFormation

Let’s start by exploring the template used to model the AWS Glue workflow: glue-workflow-stack.yml

We focus on two resources in the following snippet:

  • AWS::Glue::Workflow
  • AWS::Glue::Trigger

From a logical perspective, a workflow contains one or more triggers that are responsible for invoking crawlers and jobs. Building a workflow starts with defining the crawlers and jobs as resources within the template and then associating it with triggers.

Defining the workflow

This is where the definition of the workflow starts. In the following snippet, we specify the type as AWS::Glue::Workflow and the property Name as a reference to the parameter GlueWorkflowName.

    Type: String
    Description: Glue workflow that tracks all triggers, jobs, crawlers as a single entity
    Default: Covid_19

    Type: AWS::Glue::Workflow
      Description: Glue workflow that tracks specified triggers, jobs, and crawlers as a single entity
      Name: !Ref GlueWorkflowName

Defining the triggers

This is where we define each trigger and associate it with the workflow. In the following snippet, we specify the property WorkflowName on each trigger as a reference to the logical ID Covid19Workflow.

These triggers allow us to create a chain of dependent jobs and crawlers as specified by the properties Actions and Predicate.

The trigger t_Start utilizes a type of SCHEDULED, which means that it starts at a defined time (in our case, one time a day at 8:00 AM UTC). Every time it runs, it starts the job with the logical ID Covid19WorkflowStarted.

The trigger t_GroupA utilizes a type of CONDITIONAL, which means that it starts when the resources specified within the property Predicate have reached a specific state (when the list of Conditions specified equals SUCCEEDED). Every time t_GroupA runs, it starts the crawlers with the logical ID’s CountyPopulation and Countrycode, per the Actions property containing a list of actions.

    Type: AWS::Glue::Trigger
      Name: t_Start
      Type: SCHEDULED
      Schedule: cron(0 8 * * ? *) # Runs once a day at 8 AM UTC
      StartOnCreation: true
      WorkflowName: !Ref GlueWorkflowName
        - JobName: !Ref Covid19WorkflowStarted

    Type: AWS::Glue::Trigger
      Name: t_GroupA
      StartOnCreation: true
      WorkflowName: !Ref GlueWorkflowName
        - CrawlerName: !Ref CountyPopulation
        - CrawlerName: !Ref Countrycode
          - JobName: !Ref Covid19WorkflowStarted
            LogicalOperator: EQUALS
            State: SUCCEEDED

Provisioning the AWS Glue workflow using CodePipeline

Now let’s explore the template used to provision the CodePipeline resources: codepipeline-stack.yml

This template defines an S3 bucket that is used as the source action for the pipeline. Any time source code is uploaded to a specified bucket, AWS CloudTrail logs the event, which is detected by an Amazon CloudWatch Events rule configured to start running the pipeline in CodePipeline. The pipeline orchestrates CodeBuild to get the source code and provision the workflow.

For more information on any of the available source actions that you can use with CodePipeline, such as Amazon S3, AWS CodeCommit, Amazon Elastic Container Registry (Amazon ECR), GitHub, GitHub Enterprise Server, GitHub Enterprise Cloud, or Bitbucket, see Start a pipeline execution in CodePipeline.

We start by deploying the stack that sets up the CodePipeline resources. This stack can be deployed in any Region where CodePipeline and AWS Glue are available. For more information, see AWS Regional Services.

Cloning the GitHub repo

Clone the GitHub repo with the following command:

$ git clone https://github.com/aws-samples/provision-codepipeline-glue-workflows.git

Deploying the CodePipeline stack

Deploy the CodePipeline stack with the following command:

$ aws cloudformation deploy \
--stack-name codepipeline-covid19 \
--template-file cloudformation/codepipeline-stack.yml \
--capabilities CAPABILITY_NAMED_IAM \
--no-fail-on-empty-changeset \
--region <AWS_REGION>

When the deployment is complete, you can view the pipeline that was provisioned on the CodePipeline console.

CodePipeline console showing the deploy pipeline in failed state

Figure: CodePipeline console

The preceding screenshot shows that the pipeline failed. This is because we haven’t uploaded the source code yet.

In the following steps, we zip and upload the source code, which triggers another (successful) run of the pipeline.

Zipping the source code

Zip the source code containing Glue scripts, CloudFormation templates, and Buildspecs file with the following command:

$ zip -r source.zip . -x images/\* *.history* *.git* *.DS_Store*

You can omit *.DS_Store* from the preceding command if you are not a Mac user.

Uploading the source code

Upload the source code with the following command:

$ aws s3 cp source.zip s3://covid19-codepipeline-source-<AWS_ACCOUNT_ID>-<AWS_REGION>

Make sure to provide your account ID and Region in the preceding command. For example, if your AWS account ID is 111111111111 and you’re using Region us-west-2, use the following command:

$ aws s3 cp source.zip s3://covid19-codepipeline-source-111111111111-us-west-2

Now that the source code has been uploaded, view the pipeline again to see it in action.

CodePipeline console showing the deploy pipeline in success state

Figure: CodePipeline console displaying stage “Deploy” in-progress

Choose Details within the Deploy stage to see the build logs.

CodeBuild console displaying build logs

Figure: CodeBuild console displaying build logs

To modify any of the commands that run within the Deploy stage, feel free to modify: deploy-glue-workflow-stack.yml

Try uploading the source code a few more times. Each time it’s uploaded, CodePipeline starts and runs another deploy of the workflow stack. If nothing has changed in the source code, AWS CloudFormation automatically determines that the stack is already up to date. If something has changed in the source code, AWS CloudFormation automatically determines that the stack needs to be updated and proceeds to run the change set.

Viewing the provisioned workflow, triggers, jobs, and crawlers

To view your workflows on the AWS Glue console, in the navigation pane, under ETL, choose Workflows.

Glue console showing workflows

Figure: Navigate to Workflows

To view your triggers, in the navigation pane, under ETL, choose Triggers.

Glue console showing triggers

Figure: Navigate to Triggers

To view your crawlers, under Data Catalog, choose Crawlers.

Glue console showing crawlers

Figure: Navigate to Crawlers

To view your jobs, under ETL, choose Jobs.

Glue console showing jobs

Figure: Navigate to Jobs

Running the workflow

The workflow runs automatically at 8:00 AM UTC. To start the workflow manually, you can use either the AWS CLI or the AWS Glue console.

To start the workflow with the AWS CLI, enter the following command:

$ aws glue start-workflow-run --name Covid_19 --region <AWS_REGION>

To start the workflow on the AWS Glue console, on the Workflows page, select your workflow and choose Run on the Actions menu.

Glue console run workflow

Figure: AWS Glue console start workflow run

To view the run details of the workflow, choose the workflow on the AWS Glue console and choose View run details on the History tab.

Glue console view run details of a workflow

Figure: View run details

The following screenshot shows a visual representation of the workflow as a graph with your run details.

Glue console showing visual representation of the workflow as a graph.

Figure: AWS Glue console displaying details of successful workflow run

Cleaning up

To avoid additional charges, delete the stack created by the CloudFormation template and the contents of the buckets you created.

1. Delete the contents of the covid19-dataset bucket with the following command:

$ aws s3 rm s3://covid19-dataset-<AWS_ACCOUNT_ID>-<AWS_REGION> --recursive

2. Delete your workflow stack with the following command:

$ aws cloudformation delete-stack --stack-name glue-covid19 --region <AWS_REGION>

To delete the contents of the covid19-codepipeline-source bucket, it’s simplest to use the Amazon S3 console because it makes it easy to delete multiple versions of the object at once.

3. Navigate to the S3 bucket named covid19-codepipeline-source-<AWS_ACCOUNT_ID>- <AWS_REGION>.

4. Choose List versions.

5. Select all the files to delete.

6. Choose Delete and follow the prompts to permanently delete all the objects.

S3 console delete all object versions

Figure: AWS S3 console delete all object versions

7. Delete the contents of the covid19-codepipeline-artifacts bucket:

$ aws s3 rm s3://covid19-codepipeline-artifacts-<AWS_ACCOUNT_ID>-<AWS-REGION> --recursive

8. Delete the contents of the covid19-cloudtrail-logs bucket:

$ aws s3 rm s3://covid19-cloudtrail-logs-<AWS_ACCOUNT_ID>-<AWS-REGION> --recursive

9. Delete the pipeline stack:

$ aws cloudformation delete-stack --stack-name codepipeline-covid19 --region <AWS-REGION>


In this post, we stepped through how to use AWS DevOps tooling to model and provision an AWS Glue workflow that orchestrates an end-to-end ETL pipeline on a real-world dataset.

You can download the source code and template from this Github repository and adapt it as you see fit for your data pipeline use cases. Feel free to leave comments letting us know about the architectures you build for your environment. To learn more about building ETL pipelines with AWS Glue, see the AWS Glue Developer Guide and the AWS Data Analytics learning path.

About the Authors

Nuatu Tseggai

Nuatu Tseggai is a Cloud Infrastructure Architect at Amazon Web Services. He enjoys working with customers to design and build event-driven distributed systems that span multiple services.

Suvojit Dasgupta

Suvojit Dasgupta is a Sr. Customer Data Architect at Amazon Web Services. He works with customers to design and build complex data solutions on AWS.

Building a Jenkins Pipeline with AWS SAM

Post Syndicated from Eric Johnson original https://aws.amazon.com/blogs/compute/building-a-jenkins-pipeline-with-aws-sam/

This post is courtesy of Tarun Kumar Mall, SDE at AWS.

This post shows how to set up a multi-stage pipeline on a Jenkins host for a serverless application, using the AWS Serverless Application Model (AWS SAM).


This tutorial uses Jenkins Pipeline plugin. A commit to the main branch of the repository starts and deploys the application, using the AWS SAM CLI. This tutorial deploys a small serverless API application called HelloWorldApi.

The pipeline consists of stages to build and deploy the application. Jenkins first ensures that the build environment is set up and installs any necessary tools. Next, Jenkins prepares the build artifacts. It promotes the artifacts to the next stage, where they are deployed to a beta environment using the AWS SAM CLI. Integration tests are run after deployment. If the tests pass, the application is deployed to the production environment.

CICD workflow diagram

CICD workflow diagram

The following prerequisites are required:

Setting up the backend application and development stack

Using AWS CloudFormation to define the infrastructure, you can create multiple environments or stacks from the same infrastructure definition. A “dev stack” is a copy of production infrastructure deployed to a developer account for testing purposes.

As serverless services use a pay-for-value model, it can be cost effective to use a high-fidelity copy of your production stack. Dev stacks are created by each developer as needed and deleted without having any negative impact on production.

For complex applications, it may not be feasible for every developer to have their own stack. However, for this tutorial, setting up the dev stack first for testing is recommended. Setting up a dev stack takes you through a manual process of how a stack is created. Later, this process is used to automate the setup using Jenkins.

To create a dev stack:

  1. Clone backend application repository https://github.com/aws-samples/aws-sam-jenkins-pipeline-tutorial
    git clone https://github.com/aws-samples/aws-sam-jenkins-pipeline-tutorial.git
  2. Build the application and run the guided deploy command:
    cd aws-sam-jenkins-pipeline-tutorial
    sam build
    sam deploy --guided

    AWS SAM guided deploy output

    AWS SAM guided deploy output

This sets up a development stack and saves the settings in the samconfig.toml file with configuration environment specific to a user. This also triggers a deployment.

  1. After deployment, make a small code change. For example, in the file hello-world/app.js change the message Hello world to Hello world from user <your name>.
  2. Deploy the updated code:
    sam build
    sam deploy -–config-env <your_username>

With this command, each developer can create their own configuration environment. They can use this for deploying to their development stack and testing changes before pushing changes to the repository.

Once deployment finishes, the API endpoint is displayed in the console output. You can use this endpoint to make GET requests and test the API manually.

Deployment output

Deployment output

To update and run the integration test:

  1. Open the hello-world/tests/integ/test-integ-api.js file.
  2. Update the assert statement in line 32 to include <your name> from the previous step:
    it("verifies if response contains my username", async () => {
      assert.include(apiResponse.data.message, "<your name>");
  3. Open package.json and add the line in bold:
      "scripts": {
        "test": "mocha tests/unit/",
        "integ-test": "mocha tests/integ/"
  4. From the terminal, run the following commands:
    cd hello-world
    npm install
    AWS_REGION=us-west-2 STACK_NAME=sam-app-user1-dev-stack npm run integ-test
    If you are using Microsoft Windows, instead run:
    cd hello-world
    npm install
    set AWS_REGION=us-west-2
    set STACK_NAME=sam-app-user1-dev-stack
    npm run integ-test

    Test results

    Test results

You have deployed a fully configured development stack with working integration tests. To push the code to GitHub:

  1. Create a new repository in GitHub.
    1. From the GitHub account homepage, choose New.
    2. Enter a repository name and choose Create Repository.
    3. Copy the repository URL.
  2. From the root directory of the AWS SAM project, run:
    git init
    git commit -am “first commit”
    git remote add origin <your-repository-url>
    git push -u origin main

Creating an IAM user for Jenkins

To create an IAM user for the Jenkins deployment:

  1. Sign in to the AWS Management Console and navigate to IAM.
  2. Select Users from side navigation and choose Add user.
  3. Enter the User name as sam-jenkins-demo-credentials and grant Programmatic access to this user.
  4. On the next page, select Attach existing policies directly and choose Create Policy.
  5. Select the JSON tab and enter the following policy. Replace <YOUR_ACCOUNT_ID> with your AWS account ID:
        "Version": "2012-10-17",
        "Statement": [
                "Sid": "CloudFormationTemplate",
                "Effect": "Allow",
                "Action": [
                "Resource": [
                "Sid": "CloudFormationStack",
                "Effect": "Allow",
                "Action": [
                "Resource": [
                "Sid": "S3",
                "Effect": "Allow",
                "Action": [
                "Resource": [
                "Sid": "Lambda",
                "Effect": "Allow",
                "Action": [
                "Resource": [
                "Sid": "IAM",
                "Effect": "Allow",
                "Action": [
                "Resource": [
                "Sid": "APIGateway",
                "Effect": "Allow",
                "Action": [
                "Resource": [
  6. Choose Review Policy and add a policy name on the next page.
  7. Choose Create Policy button.
  8. Return to the previous tab to continue creating the IAM user. Choose Refresh and search for the policy name you created. Select the policy.
  9. Choose Next Tags and then Review.
  10. Choose Create user and save the Access key ID and Secret access key.

Configuring Jenkins

To configure AWS credentials in Jenkins:

  1. On the Jenkins dashboard, go to Manage Jenkins > Manage Plugins in the Available tab. Search for the Pipeline: AWS Steps plugin and choose Install without restart.
  2. Navigate to Manage Jenkins > Manage Credentials > Jenkins (global) > Global Credentials > Add Credentials.
  3. Select Kind as AWS credentials and use the ID sam-jenkins-demo-credentials.
  4. Enter the access key ID and secret access key and choose OK.

    Jenkins credential configuration

    Jenkins credential configuration

  5. Create Amazon S3 buckets for each Region in the pipeline. S3 bucket names must be unique within a partition:
    aws s3 mb s3://sam-jenkins-demo-us-west-2-<your_name> --region us-west-2
    aws s3 mb s3://sam-jenkins-demo-us-east-1-<your_name> --region us-east-1
  6. Create a file named Jenkinsfile at the root of the project and add:
    pipeline {
      agent any
      stages {
        stage('Install sam-cli') {
          steps {
            sh 'python3 -m venv venv && venv/bin/pip install aws-sam-cli'
            stash includes: '**/venv/**/*', name: 'venv'
        stage('Build') {
          steps {
            unstash 'venv'
            sh 'venv/bin/sam build'
            stash includes: '**/.aws-sam/**/*', name: 'aws-sam'
        stage('beta') {
          environment {
            STACK_NAME = 'sam-app-beta-stage'
            S3_BUCKET = 'sam-jenkins-demo-us-west-2-user1'
          steps {
            withAWS(credentials: 'sam-jenkins-demo-credentials', region: 'us-west-2') {
              unstash 'venv'
              unstash 'aws-sam'
              sh 'venv/bin/sam deploy --stack-name $STACK_NAME -t template.yaml --s3-bucket $S3_BUCKET --capabilities CAPABILITY_IAM'
              dir ('hello-world') {
                sh 'npm ci'
                sh 'npm run integ-test'
        stage('prod') {
          environment {
            STACK_NAME = 'sam-app-prod-stage'
            S3_BUCKET = 'sam-jenkins-demo-us-east-1-user1'
          steps {
            withAWS(credentials: 'sam-jenkins-demo-credentials', region: 'us-east-1') {
              unstash 'venv'
              unstash 'aws-sam'
              sh 'venv/bin/sam deploy --stack-name $STACK_NAME -t template.yaml --s3-bucket $S3_BUCKET --capabilities CAPABILITY_IAM'
  7. Commit and push the code to the GitHub repository by running following commands:
    git commit -am “Adding Jenkins pipeline config.”
    git push origin -u main

Next, create a Jenkins Pipeline project:

  1. From the Jenkins dashboard, choose New Item, select Pipeline, and enter the project name sam-jenkins-demo-pipeline.

    Jenkins Pipeline creation wizard

    Jenkins Pipeline creation wizard

  2. Under Build Triggers, select Poll SCM and enter * * * * *. This polls the repository for changes every minute.

    Jenkins build triggers configuration

    Jenkins build triggers configuration

  3. Under the Pipeline section, select Definition as Pipeline script from SCM.
    • Select GIT under SCM and enter the repository URL.
    • Set Branches to build to */main.
    • Set the Script Path to Jenkinsfile.

      Jenkins pipeline configuration

      Jenkins pipeline configuration

  4. Save the project.

After the build finishes, you see the pipeline:

Jenkins pipeline stages

Jenkins pipeline stages

Review the logs for the beta stage to check that the integration test is completed successfully.

Jenkins stage logs

Jenkins stage logs


This tutorial uses a Jenkins Pipeline to add an automated CI/CD pipeline to an AWS SAM-generated example application. Jenkins automatically builds, tests, and deploys the changes after each commit to the repository.

Using Jenkins, developers can gain the benefits of continuous integration and continuous deployment of serverless applications to the AWS Cloud with minimal configuration.

For more information, see the Jenkins Pipeline and AWS Serverless Application Model documentation.

We want to hear your feedback! Is this approach useful for your organization? Do you want to see another implementation? Contact us on Twitter @edjgeek or via comments!

Building end-to-end AWS DevSecOps CI/CD pipeline with open source SCA, SAST and DAST tools

Post Syndicated from Srinivas Manepalli original https://aws.amazon.com/blogs/devops/building-end-to-end-aws-devsecops-ci-cd-pipeline-with-open-source-sca-sast-and-dast-tools/

DevOps is a combination of cultural philosophies, practices, and tools that combine software development with information technology operations. These combined practices enable companies to deliver new application features and improved services to customers at a higher velocity. DevSecOps takes this a step further, integrating security into DevOps. With DevSecOps, you can deliver secure and compliant application changes rapidly while running operations consistently with automation.

Having a complete DevSecOps pipeline is critical to building a successful software factory, which includes continuous integration (CI), continuous delivery and deployment (CD), continuous testing, continuous logging and monitoring, auditing and governance, and operations. Identifying the vulnerabilities during the initial stages of the software development process can significantly help reduce the overall cost of developing application changes, but doing it in an automated fashion can accelerate the delivery of these changes as well.

To identify security vulnerabilities at various stages, organizations can integrate various tools and services (cloud and third-party) into their DevSecOps pipelines. Integrating various tools and aggregating the vulnerability findings can be a challenge to do from scratch. AWS has the services and tools necessary to accelerate this objective and provides the flexibility to build DevSecOps pipelines with easy integrations of AWS cloud native and third-party tools. AWS also provides services to aggregate security findings.

In this post, we provide a DevSecOps pipeline reference architecture on AWS that covers the afore-mentioned practices, including SCA (Software Composite Analysis), SAST (Static Application Security Testing), DAST (Dynamic Application Security Testing), and aggregation of vulnerability findings into a single pane of glass. Additionally, this post addresses the concepts of security of the pipeline and security in the pipeline.

You can deploy this pipeline in either the AWS GovCloud Region (US) or standard AWS Regions. As of this writing, all listed AWS services are available in AWS GovCloud (US) and authorized for FedRAMP High workloads within the Region, with the exception of AWS CodePipeline and AWS Security Hub, which are in the Region and currently under the JAB Review to be authorized shortly for FedRAMP High as well.

Services and tools

In this section, we discuss the various AWS services and third-party tools used in this solution.

CI/CD services

For CI/CD, we use the following AWS services:

  • AWS CodeBuild – A fully managed continuous integration service that compiles source code, runs tests, and produces software packages that are ready to deploy.
  • AWS CodeCommit – A fully managed source control service that hosts secure Git-based repositories.
  • AWS CodeDeploy – A fully managed deployment service that automates software deployments to a variety of compute services such as Amazon Elastic Compute Cloud (Amazon EC2), AWS Fargate, AWS Lambda, and your on-premises servers.
  • AWS CodePipeline – A fully managed continuous delivery service that helps you automate your release pipelines for fast and reliable application and infrastructure updates.
  • AWS Lambda – A service that lets you run code without provisioning or managing servers. You pay only for the compute time you consume.
  • Amazon Simple Notification Service – Amazon SNS is a fully managed messaging service for both application-to-application (A2A) and application-to-person (A2P) communication.
  • Amazon Simple Storage Service – Amazon S3 is storage for the internet. You can use Amazon S3 to store and retrieve any amount of data at any time, from anywhere on the web.
  • AWS Systems Manager Parameter Store – Parameter Store gives you visibility and control of your infrastructure on AWS.

Continuous testing tools

The following are open-source scanning tools that are integrated in the pipeline for the purposes of this post, but you could integrate other tools that meet your specific requirements. You can use the static code review tool Amazon CodeGuru for static analysis, but at the time of this writing, it’s not yet available in GovCloud and currently supports Java and Python (available in preview).

  • OWASP Dependency-Check – A Software Composition Analysis (SCA) tool that attempts to detect publicly disclosed vulnerabilities contained within a project’s dependencies.
  • SonarQube (SAST) – Catches bugs and vulnerabilities in your app, with thousands of automated Static Code Analysis rules.
  • PHPStan (SAST) – Focuses on finding errors in your code without actually running it. It catches whole classes of bugs even before you write tests for the code.
  • OWASP Zap (DAST) – Helps you automatically find security vulnerabilities in your web applications while you’re developing and testing your applications.

Continuous logging and monitoring services

The following are AWS services for continuous logging and monitoring:

Auditing and governance services

The following are AWS auditing and governance services:

  • AWS CloudTrail – Enables governance, compliance, operational auditing, and risk auditing of your AWS account.
  • AWS Identity and Access Management – Enables you to manage access to AWS services and resources securely. With IAM, you can create and manage AWS users and groups, and use permissions to allow and deny their access to AWS resources.
  • AWS Config – Allows you to assess, audit, and evaluate the configurations of your AWS resources.

Operations services

The following are AWS operations services:

  • AWS Security Hub – Gives you a comprehensive view of your security alerts and security posture across your AWS accounts. This post uses Security Hub to aggregate all the vulnerability findings as a single pane of glass.
  • AWS CloudFormation – Gives you an easy way to model a collection of related AWS and third-party resources, provision them quickly and consistently, and manage them throughout their lifecycles, by treating infrastructure as code.
  • AWS Systems Manager Parameter Store – Provides secure, hierarchical storage for configuration data management and secrets management. You can store data such as passwords, database strings, Amazon Machine Image (AMI) IDs, and license codes as parameter values.
  • AWS Elastic Beanstalk – An easy-to-use service for deploying and scaling web applications and services developed with Java, .NET, PHP, Node.js, Python, Ruby, Go, and Docker on familiar servers such as Apache, Nginx, Passenger, and IIS. This post uses Elastic Beanstalk to deploy LAMP stack with WordPress and Amazon Aurora MySQL. Although we use Elastic Beanstalk for this post, you could configure the pipeline to deploy to various other environments on AWS or elsewhere as needed.

Pipeline architecture

The following diagram shows the architecture of the solution.

AWS DevSecOps CICD pipeline architecture

AWS DevSecOps CICD pipeline architecture


The main steps are as follows:

  1. When a user commits the code to a CodeCommit repository, a CloudWatch event is generated which, triggers CodePipeline.
  2. CodeBuild packages the build and uploads the artifacts to an S3 bucket. CodeBuild retrieves the authentication information (for example, scanning tool tokens) from Parameter Store to initiate the scanning. As a best practice, it is recommended to utilize Artifact repositories like AWS CodeArtifact to store the artifacts, instead of S3. For simplicity of the workshop, we will continue to use S3.
  3. CodeBuild scans the code with an SCA tool (OWASP Dependency-Check) and SAST tool (SonarQube or PHPStan; in the provided CloudFormation template, you can pick one of these tools during the deployment, but CodeBuild is fully enabled for a bring your own tool approach).
  4. If there are any vulnerabilities either from SCA analysis or SAST analysis, CodeBuild invokes the Lambda function. The function parses the results into AWS Security Finding Format (ASFF) and posts it to Security Hub. Security Hub helps aggregate and view all the vulnerability findings in one place as a single pane of glass. The Lambda function also uploads the scanning results to an S3 bucket.
  5. If there are no vulnerabilities, CodeDeploy deploys the code to the staging Elastic Beanstalk environment.
  6. After the deployment succeeds, CodeBuild triggers the DAST scanning with the OWASP ZAP tool (again, this is fully enabled for a bring your own tool approach).
  7. If there are any vulnerabilities, CodeBuild invokes the Lambda function, which parses the results into ASFF and posts it to Security Hub. The function also uploads the scanning results to an S3 bucket (similar to step 4).
  8. If there are no vulnerabilities, the approval stage is triggered, and an email is sent to the approver for action.
  9. After approval, CodeDeploy deploys the code to the production Elastic Beanstalk environment.
  10. During the pipeline run, CloudWatch Events captures the build state changes and sends email notifications to subscribed users through SNS notifications.
  11. CloudTrail tracks the API calls and send notifications on critical events on the pipeline and CodeBuild projects, such as UpdatePipeline, DeletePipeline, CreateProject, and DeleteProject, for auditing purposes.
  12. AWS Config tracks all the configuration changes of AWS services. The following AWS Config rules are added in this pipeline as security best practices:
  13. CODEBUILD_PROJECT_ENVVAR_AWSCRED_CHECK – Checks whether the project contains environment variables AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY. The rule is NON_COMPLIANT when the project environment variables contains plaintext credentials.
  14. CLOUD_TRAIL_LOG_FILE_VALIDATION_ENABLED – Checks whether CloudTrail creates a signed digest file with logs. AWS recommends that the file validation be enabled on all trails. The rule is noncompliant if the validation is not enabled.

Security of the pipeline is implemented by using IAM roles and S3 bucket policies to restrict access to pipeline resources. Pipeline data at rest and in transit is protected using encryption and SSL secure transport. We use Parameter Store to store sensitive information such as API tokens and passwords. To be fully compliant with frameworks such as FedRAMP, other things may be required, such as MFA.

Security in the pipeline is implemented by performing the SCA, SAST and DAST security checks. Alternatively, the pipeline can utilize IAST (Interactive Application Security Testing) techniques that would combine SAST and DAST stages.

As a best practice, encryption should be enabled for the code and artifacts, whether at rest or transit.

In the next section, we explain how to deploy and run the pipeline CloudFormation template used for this example. Refer to the provided service links to learn more about each of the services in the pipeline. If utilizing CloudFormation templates to deploy infrastructure using pipelines, we recommend using linting tools like cfn-nag to scan CloudFormation templates for security vulnerabilities.


Before getting started, make sure you have the following prerequisites:

Deploying the pipeline

To deploy the pipeline, complete the following steps: Download the CloudFormation template and pipeline code from GitHub repo.

  1. Log in to your AWS account if you have not done so already.
  2. On the CloudFormation console, choose Create Stack.
  3. Choose the CloudFormation pipeline template.
  4. Choose Next.
  5. Provide the stack parameters:
    • Under Code, provide code details, such as repository name and the branch to trigger the pipeline.
    • Under SAST, choose the SAST tool (SonarQube or PHPStan) for code analysis, enter the API token and the SAST tool URL. You can skip SonarQube details if using PHPStan as the SAST tool.
    • Under DAST, choose the DAST tool (OWASP Zap) for dynamic testing and enter the API token, DAST tool URL, and the application URL to run the scan.
    • Under Lambda functions, enter the Lambda function S3 bucket name, filename, and the handler name.
    • Under STG Elastic Beanstalk Environment and PRD Elastic Beanstalk Environment, enter the Elastic Beanstalk environment and application details for staging and production to which this pipeline deploys the application code.
    • Under General, enter the email addresses to receive notifications for approvals and pipeline status changes.

CF Deploymenet - Passing parameter values

CloudFormation deployment - Passing parameter values

CloudFormation template deployment

After the pipeline is deployed, confirm the subscription by choosing the provided link in the email to receive the notifications.

The provided CloudFormation template in this post is formatted for AWS GovCloud. If you’re setting this up in a standard Region, you have to adjust the partition name in the CloudFormation template. For example, change ARN values from arn:aws-us-gov to arn:aws.

Running the pipeline

To trigger the pipeline, commit changes to your application repository files. That generates a CloudWatch event and triggers the pipeline. CodeBuild scans the code and if there are any vulnerabilities, it invokes the Lambda function to parse and post the results to Security Hub.

When posting the vulnerability finding information to Security Hub, we need to provide a vulnerability severity level. Based on the provided severity value, Security Hub assigns the label as follows. Adjust the severity levels in your code based on your organization’s requirements.

  • 1–39 – LOW
  • 40– 69 – MEDIUM
  • 70–89 – HIGH
  • 90–100 – CRITICAL

The following screenshot shows the progression of your pipeline.

CodePipeline stages

CodePipeline stages

SCA and SAST scanning

In our architecture, CodeBuild trigger the SCA and SAST scanning in parallel. In this section, we discuss scanning with OWASP Dependency-Check, SonarQube, and PHPStan. 

Scanning with OWASP Dependency-Check (SCA)

The following is the code snippet from the Lambda function, where the SCA analysis results are parsed and posted to Security Hub. Based on the results, the equivalent Security Hub severity level (normalized_severity) is assigned.

Lambda code snippet for OWASP Dependency-check

Lambda code snippet for OWASP Dependency-check

You can see the results in Security Hub, as in the following screenshot.

SecurityHub report from OWASP Dependency-check scanning

SecurityHub report from OWASP Dependency-check scanning

Scanning with SonarQube (SAST)

The following is the code snippet from the Lambda function, where the SonarQube code analysis results are parsed and posted to Security Hub. Based on SonarQube results, the equivalent Security Hub severity level (normalized_severity) is assigned.

Lambda code snippet for SonarQube

Lambda code snippet for SonarQube

The following screenshot shows the results in Security Hub.

SecurityHub report from SonarQube scanning

SecurityHub report from SonarQube scanning

Scanning with PHPStan (SAST)

The following is the code snippet from the Lambda function, where the PHPStan code analysis results are parsed and posted to Security Hub.

Lambda code snippet for PHPStan

Lambda code snippet for PHPStan

The following screenshot shows the results in Security Hub.

SecurityHub report from PHPStan scanning

SecurityHub report from PHPStan scanning

DAST scanning

In our architecture, CodeBuild triggers DAST scanning and the DAST tool.

If there are no vulnerabilities in the SAST scan, the pipeline proceeds to the manual approval stage and an email is sent to the approver. The approver can review and approve or reject the deployment. If approved, the pipeline moves to next stage and deploys the application to the provided Elastic Beanstalk environment.

Scanning with OWASP Zap

After deployment is successful, CodeBuild initiates the DAST scanning. When scanning is complete, if there are any vulnerabilities, it invokes the Lambda function similar to SAST analysis. The function parses and posts the results to Security Hub. The following is the code snippet of the Lambda function.

Lambda code snippet for OWASP-Zap

Lambda code snippet for OWASP-Zap

The following screenshot shows the results in Security Hub.

SecurityHub report from OWASP-Zap scanning

SecurityHub report from OWASP-Zap scanning

Aggregation of vulnerability findings in Security Hub provides opportunities to automate the remediation. For example, based on the vulnerability finding, you can trigger a Lambda function to take the needed remediation action. This also reduces the burden on operations and security teams because they can now address the vulnerabilities from a single pane of glass instead of logging into multiple tool dashboards.


In this post, I presented a DevSecOps pipeline that includes CI/CD, continuous testing, continuous logging and monitoring, auditing and governance, and operations. I demonstrated how to integrate various open-source scanning tools, such as SonarQube, PHPStan, and OWASP Zap for SAST and DAST analysis. I explained how to aggregate vulnerability findings in Security Hub as a single pane of glass. This post also talked about how to implement security of the pipeline and in the pipeline using AWS cloud native services. Finally, I provided the DevSecOps pipeline as code using AWS CloudFormation. For additional information on AWS DevOps services and to get started, see AWS DevOps and DevOps Blog.


Srinivas Manepalli is a DevSecOps Solutions Architect in the U.S. Fed SI SA team at Amazon Web Services (AWS). He is passionate about helping customers, building and architecting DevSecOps and highly available software systems. Outside of work, he enjoys spending time with family, nature and good food.

Field Notes: How FactSet Uses ‘microAccounts’ to Reduce Developer Friction and Maintain Security at Scale

Post Syndicated from Tarik Makota original https://aws.amazon.com/blogs/architecture/field-notes-how-factset-uses-microaccounts-to-reduce-developer-friction-and-maintain-security-at-scale/

This is post was co-written by FactSet’s Cloud Infrastructure team, Gaurav Jain, Nathan Goodman, Geoff Wang, Daniel Cordes, Sunu Joseph and AWS Solution Architects, Amit Borulkar and Tarik Makota.

FactSet considers developer self-service and DevOps essential for realizing cloud benefits.  As part of their cloud adoption journey, they wanted developers to have a frictionless infrastructure provisioning experience while maintaining standardization and security of their cloud environment.  To achieve their objectives, they use what they refer to as a ‘microAccounts approach’. In their microAccount approach, each AWS account is allocated for one project and is owned by a single team.

In this blog, we describe how FactSet manages 1000+ AWS accounts at scale using the microAccounts approach. First, we cover the core concepts of their approach. Then we outline how they manage access and permissions. Finally, we show how they manage their networking implementation and how they use automation to manage their AWS Cloud infrastructure.

How FactSet started with AWS

They started their cloud adoption journey with what they now call a ‘macroAccounts’ approach. In the early days they would set up a handful of AWS accounts. These macroAccounts were then shared across several different application teams and projects.   They have hundreds of application teams along with thousands of developers and they quickly experienced the challenges of a macroAccounts approach. These include the following:

  1. AWS Identity and Access Management (IAM) policies and resource tagging were complex to design in order to maintain least privilege. For example, if a developer desired the ability to start/stop Amazon EC2 instances, they would need to ensure that they are limited to starting/stopping only their own instances.  This complexity kept increasing as developers wanted to automate their workflows using constructs such as AWS Lambda functions, and containers.
  2. They had difficulty in properly attributing cloud costs across departments.  More importantly they kept going back and forth on: how do we establish accountability and transparency around spends by groups, projects, or teams?
  3. It was difficult to track and manage impact of infrastructure change to FactSet applications. For example, how is maintenance off underlying security group or IAM policy affecting FactSet applications?
  4. Significant effort was required in managing service quotas and limits across various applications being under single AWS account.

FactSet’s solution – microAccounts

Recognizing the issues, they decided to take a different approach to AWS account management. Instead of creating a few shared macro-accounts, they decided to create one AWS account per project (microAccounts) with clearly defined ownership and product allocation.  An analogy might be that macro-accounts were like leaving the main door of a house open but locking individual closets and rooms to limit access. This is opposed to safeguarding the entry to the house but largely leaving individual closets and rooms open for the tenant to manage.

Benefits of microAccounts

They have been operating their AWS Cloud infrastructure using microAccounts for about two years now. Benefits of the microAccount approach include:

1.      Access & Permissions: By associating an account with a project they simplified which services are allowed, which resources that development team can access, and are able to ensure that those permissions cascade properly to underlying resources.  The following diagram shows their microAccount strategy.


Tagging versus microAccount strategy

Figure 1 – Tagging versus microAccount strategy

2.      Service Quotas & Limits: Given most service quotas are account specific, microAccounts allow their developers to plan limits based on their application needs.  In a shared account configuration, there was no mechanism to limit separate teams from using up a larger portion of the service quota, leaving other teams with less.  These limits extend beyond infrastructure provisioning to run time tasks like Lambda concurrency, API throttling limits on parameter store and more.

3.      AWS Service Permissions: microAccounts allowed FactSet to easily implement least privilege across services. By using IAM service control policies (SCPs) they limit what AWS services an account can access.  They start with a default set of services and based on business need we can grant a specific account access to other non-common services without having to worry about those services creeping into other use cases.  For example, they disable storage gateway by default, but can allow access for a specific account if needed.

4.      Blast Radius Containment:  microAccounts provides the ability to create safety boundaries. This is in the event of any stability and security issues, they stay isolated within that specific application (AWS account) and they don’t affect operations of other applications.

5.     Cost Attributions:  Clearly defined account ownership provides a simple and straightforward way to attribute costs to a specific team, project, or product.  They don’t have to enforce the tagging individual resources for cost purposes. AWS account acts like an application resource group so all resources in the account are implicitly tagged.

6.      Account Notifications & Operations:  Single threaded account ownership allows FactSet to automatically relay any required notification to right developers.  Moreover, given that account ownership is fundamental in defining who is allowed access to the account, there is a high level of confidence in the validity of this mapping as opposed to relying on just tagging.

7.      Account Standards & Extensions: we manage microAccounts through a CI/CD pipeline which allows us to standardize and extend without interruptions.  For example, all their microAccounts are provisioned with a standard AWS Key Management Service (AWS KMS) key, an AWS Backup Vault & policy, private Amazon Route 53 zone, AWS Systems Manager Parameter Store with network information for Terraform or AWS CloudFormation templates.

8.      Developer Experience: microAccount automation and guardrails allow developers to get started quickly instead of spending time debugging things like correct SCP/IAM permissions and more. Developers tend to work across multiple applications and their experience has improved as they have a standard set of expectations for their AWS environment. This is particularly useful as they move from application to application.

Access and permissions for microAccounts

FactSet creates every AWS account with a standard set of IAM roles and permissions. Furthermore, each account has its own SCP which defines the list of services allowed in the account.  Based on application needs, they can extend the permissions.  Interactive roles are mapped to an ActiveDirectory (AD) group, and membership of the AD group is managed by the development teams themselves.  Standard roles are:

  • DevOps Role – Interactive role used to provision and manage infrastructure.
  • Developer Role – Interactive role used to read/write data (and some infrastructure)
  • ReadOnly Role – Interactive role with read-only access to the account.  This can be granted to account supervisors, product developers, and other similar roles.
  • Support Roles – Interactive roles for certain admin teams to assist account owners if needed
  • ServiceExecutionRole – Role that can be attached to entities such as Lambda functions, CodeBuild, EC2 instances, and has similar permissions to a developer role.
IAM Role Privileges

Figure 2 – IAM Role Privileges

Networking for microAccounts

  • FactSet leverages AWS Resource Access Manager (RAM) to share appropriate subnets with each account.  Each microAccount provisioned has access to subnets by sing AWS Shared VPCs.  They create a single VPC per business unit per environment (Dev, Prod, UAT, and Shared Services) in each region.  RAM enabled them to easily and securely share AWS resources with any AWS account within their AWS Organization.  When an account is created they allocate appropriate subnets to that account.
  • They use AWS Transit Gateway to manage inter-VPC routing and communication across multiple VPCs in a region.  They didn’t want to limit our ability to scale up quickly.  AWS Transit Gateway is a single place to land their AWS Direct Connect circuits in each Region.  It provides them with a consolidated place to manage routing tables that propagated to each VPC when they are attached.


VPC Sharing for microAccounts

Figure 3 – VPC Sharing for microAccounts

Automation & Config Management for microAccounts

To create frictionless self-service cloud infrastructure early on, FactSet realized that automation is a must.  Their infrastructure automation uses source-control as a source of truth for defining each microAccount. This helps them ensure repeatable and standardized account provisioning process, as well as flexibility to adjust specific settings and permissions on per account needs.

Account provisioning flow

Figure 4 – Account provisioning flow

By default, their accounts are only enabled in a small set of Regions.  They control it via the following policy block.  If they add new Region(s), they would implement that change in source-control and automated enforcement checks would add it to SCP.

    "Sid": "DenyOtherRegions",
    "Effect": "Deny",
    "Action": "*",
    "Resource": "*",
    "Condition": {
        "StringNotEquals": {
            "aws:RequestedRegion": ["us-east-1","eu-west-2"]
        "ForAllValues:StringNotLike": {
            "aws:PrincipalArn": [

Lessons Learned

During their journey to adopt microAccounts, FactSet came across some new challenges that are worth highlighting:

  1. IAM role creation: Their DevOps Role can create new IAM roles within the account.  To ensure that newly created role complies with least-privilege principles, they attach a standard permission boundary which limits its permissions to not extend beyond DevOps level.
  2. Account Deletion: While AWS provides APIs for account creation, currently there is no API to delete or rename an account.  This is not an issue since only a small percentage of accounts had to be deleted because of a cancelled project for example.
  3. Account Creation / Service Activation: Although automation is used to provision accounts it can still take time for all services in account to be fully activated.  Some services like Amazon EC2 have asynchronous processes to be activated in a new account.
  4. Account Email, Root Password, and MFA: Upon account creation, they don’t set up a root password or MFA.  That is only setup on the primary (master) account.  Given each account requires a unique email address, they leverage Amazon Simple Email Service (Amazon SES) to create a new email address with cloud administrator team as the recipients.  When they need to log in as root (very unusual), they go through the process of password reset before logging in.
  5. Service Control Policies: There were two primary challenges related to SCPs:
    • SCP is a property in the primary (master) account that is attached to a child microAccount.  However, they also wanted to manage SCP like any other account config and store it in source-control along with other account configuration.  This required IAM role used by our automation to have special permissions to be able to create/attach/detach SCPs in the primary (master) account.
    • There is a hard limit of 1000 SCPs in the primary (master) account.  If you have a SCP per account, this would limit you to 1000 microAccounts.  They solved this by re-using SCPs across accounts with same policies.  Content of a policy is hashed to create a unique SCP identifier, and accounts with same hashes are attached to same SCP.
  6. Sharing data (typically S3) across microAccounts: they leverage a concept of “trusted-accounts” to allow other accounts access to an account’s resources including S3 and KMS keys.
  7. It may feel like an anti-pattern to have resources with static costs like Application Load Balancers (ALB) and KMS for individual projects as opposed to a shared pool.  The list of resources with a base cost is small as most of the services are largely priced based on usage.  For FactSet, resource isolation is a key benefit of microAccounts, and therefore outweighs some of these added costs.
  8. Central Inventory & Logging: With 100s of accounts, it is worth investing in a more centralized inventory and AWS CloudTrail logs collection system.
  9. Costs, Reserved Instances (RI), and Savings Plans: FactSet found AWS Cost Explorer at the level of your primary (master) account to be a great tool for cost-transparency.  They leverage AWS Cost Explorer’s API to import that data into their internal cost transparency tools.  RIs and Savings Plans are managed centrally and leverage automatic sharing between accounts within the same master (primary) organization.


The microAccounts approach provides FactSet with the agility to operate according to specific needs of different teams and projects in the enterprise. They are currently deploying in twelve AWS Regions with automated AWS account provisioning happening in minutes and drift checks executing multiple times throughout the day. This frees up their developers to focus on solving business problems to maximize the benefits of cloud computing, so that their business can innovate and accelerate their clients’ digital transformations.

Their experience operating regulated infrastructure in the cloud demonstrated that microAccounts are pivotal for managing cloud at scale. With microAccounts they were able to accelerate projects onboarded to cloud by 5X, reduce number of IAM permission tickets by 10X, and experienced 3X fewer stability issues. We hope that this blog post provided useful insights to help determine if the microAccount strategy is a good fit for you.

In their own words, FactSet creates flexible, open data and software solutions for tens of thousands of investment professionals around the world, which provides instant access to financial data and analytics that investors use to make crucial decisions. At FactSet, we are always working to improve the value that our products provide.

Recommended Reading:

Defining an AWS Multi-Account Strategy for telecommunications companies

Why should I set up a multi-account AWS environment?

Field Notes provides hands-on technical guidance from AWS Solutions Architects, consultants, and technical account managers, based on their experiences in the field solving real-world business problems for customers.


Developing enterprise application patterns with the AWS CDK

Post Syndicated from Krishnakumar Rengarajan original https://aws.amazon.com/blogs/devops/developing-application-patterns-cdk/

Enterprises often need to standardize their infrastructure as code (IaC) for governance, compliance, and quality control reasons. You also need to manage and centrally publish updates to your IaC libraries. In this post, we demonstrate how to use the AWS Cloud Development Kit (AWS CDK) to define patterns for IaC and publish them for consumption in controlled releases using AWS CodeArtifact.

AWS CDK is an open-source software development framework to model and provision cloud application resources in programming languages such as TypeScript, JavaScript, Python, Java, and C#/.Net. The basic building blocks of AWS CDK are called constructs, which map to one or more AWS resources, and can be composed of other constructs. Constructs allow high-level abstractions to be defined as patterns. You can synthesize constructs into AWS CloudFormation templates and deploy them into an AWS account.

AWS CodeArtifact is a fully managed service for managing the lifecycle of software artifacts. You can use CodeArtifact to securely store, publish, and share software artifacts. Software artifacts are stored in repositories, which are aggregated into a domain. A CodeArtifact domain allows organizational policies to be applied across multiple repositories. You can use CodeArtifact with common build tools and package managers such as NuGet, Maven, Gradle, npm, yarn, pip, and twine.

Solution overview

In this solution, we complete the following steps:

  1. Create two AWS CDK pattern constructs in Typescript: one for traditional three-tier web applications and a second for serverless web applications.
  2. Publish the pattern constructs to CodeArtifact as npm packages. npm is the package manager for Node.js.
  3. Consume the pattern construct npm packages from CodeArtifact and use them to provision the AWS infrastructure.

We provide more information about the pattern constructs in the following sections. The source code mentioned in this blog is available in GitHub.

Note: The code provided in this blog post is for demonstration purposes only. You must ensure that it meets your security and production readiness requirements.

Traditional three-tier web application construct

The first pattern construct is for a traditional three-tier web application running on Amazon Elastic Compute Cloud (Amazon EC2), with AWS resources consisting of Application Load Balancer, an Autoscaling group and EC2 launch configuration, an Amazon Relational Database Service (Amazon RDS) or Amazon Aurora database, and AWS Secrets Manager. The following diagram illustrates this architecture.


Traditional stack architecture

Serverless web application construct

The second pattern construct is for a serverless application with AWS resources in AWS Lambda, Amazon API Gateway, and Amazon DynamoDB.

Serverless application architecture

Publishing and consuming pattern constructs

Both constructs are written in Typescript and published to CodeArtifact as npm packages. A semantic versioning scheme is used to version the construct packages. After a package gets published to CodeArtifact, teams can consume them for deploying AWS resources. The following diagram illustrates this architecture.

Pattern constructs


Before getting started, complete the following steps:

  1. Clone the code from the GitHub repository for the traditional and serverless web application constructs:
    git clone https://github.com/aws-samples/aws-cdk-developing-application-patterns-blog.git
    cd aws-cdk-developing-application-patterns-blog
  2. Configure AWS Identity and Access Management (IAM) permissions by attaching IAM policies to the user, group, or role implementing this solution. The following policy files are in the iam folder in the root of the cloned repo:
    • BlogPublishArtifacts.json – The IAM policy to configure CodeArtifact and publish packages to it.
    • BlogConsumeTraditional.json – The IAM policy to consume the traditional three-tier web application construct from CodeArtifact and deploy it to an AWS account.
    • PublishArtifacts.json – The IAM policy to consume the serverless construct from CodeArtifact and deploy it to an AWS account.

Configuring CodeArtifact

In this step, we configure CodeArtifact for publishing the pattern constructs as npm packages. The following AWS resources are created:

  • A CodeArtifact domain named blog-domain
  • Two CodeArtifact repositories:
    • blog-npm-store – For configuring the upstream NPM repository.
    • blog-repository – For publishing custom packages.

Deploy the CodeArtifact resources with the following code:

cd prerequisites/
rm -rf package-lock.json node_modules
npm install
cdk deploy --require-approval never
cd ..

Log in to the blog-repository. This step is needed for publishing and consuming the npm packages. See the following code:

aws codeartifact login \
     --tool npm \
     --domain blog-domain \
     --domain-owner $(aws sts get-caller-identity --output text --query 'Account') \
     --repository blog-repository

Publishing the pattern constructs

  1. Change the directory to the serverless construct:
    cd serverless
  2. Install the required npm packages:
    rm package-lock.json && rm -rf node_modules
    npm install
  3. Build the npm project:
    npm run build
  4. Publish the construct npm package to the CodeArtifact repository:
    npm publish

    Follow the previously mentioned steps for building and publishing a traditional (classic Load Balancer plus Amazon EC2) web app by running these commands in the traditional directory.

    If the publishing is successful, you see messages like the following screenshots. The following screenshot shows the traditional infrastructure.

    Successful publishing of Traditional construct package to CodeArtifact

    The following screenshot shows the message for the serverless infrastructure.

    Successful publishing of Serverless construct package to CodeArtifact

    We just published version 1.0.1 of both the traditional and serverless web app constructs. To release a new version, we can simply update the version attribute in the package.json file in the traditional or serverless folder and repeat the last two steps.

    The following code snippet is for the traditional construct:

        "name": "traditional-infrastructure",
        "main": "lib/index.js",
        "files": [
        "types": "lib/index.d.ts",
        "version": "1.0.1",

    The following code snippet is for the serverless construct:

        "name": "serverless-infrastructure",
        "main": "lib/index.js",
        "files": [
        "types": "lib/index.d.ts",
        "version": "1.0.1",

Consuming the pattern constructs from CodeArtifact

In this step, we demonstrate how the pattern constructs published in the previous steps can be consumed and used to provision AWS infrastructure.

  1. From the root of the GitHub package, change the directory to the examples directory containing code for consuming traditional or serverless constructs.To consume the traditional construct, use the following code:
    cd examples/traditional

    To consume the serverless construct, use the following code:

    cd examples/serverless
  2. Open the package.json file in either directory and note that the packages and versions we consume are listed in the dependencies section, along with their version.
    The following code shows the traditional web app construct dependencies:

    "dependencies": {
        "@aws-cdk/core": "1.30.0",
        "traditional-infrastructure": "1.0.1",
        "aws-cdk": "1.47.0"

    The following code shows the serverless web app construct dependencies:

    "dependencies": {
        "@aws-cdk/core": "1.30.0",
        "serverless-infrastructure": "1.0.1",
        "aws-cdk": "1.47.0"
  3. Install the pattern artifact npm package along with the dependencies:
    rm package-lock.json && rm -rf node_modules
    npm install
  4. As an optional step, if you need to override the default Lambda function code, build the npm project. The following commands build the Lambda function source code:
    cd ../override-serverless
    npm run build
    cd -
  5. Bootstrap the project with the following code:
    cdk bootstrap

    This step is applicable for serverless applications only. It creates the Amazon Simple Storage Service (Amazon S3) staging bucket where the Lambda function code and artifacts are stored.

  6. Deploy the construct:
    cdk deploy --require-approval never

    If the deployment is successful, you see messages similar to the following screenshots. The following screenshot shows the traditional stack output, with the URL of the Load Balancer endpoint.

    Traditional CloudFormation stack outputs

    The following screenshot shows the serverless stack output, with the URL of the API Gateway endpoint.

    Serverless CloudFormation stack outputs

    You can test the endpoint for both constructs using a web browser or the following curl command:

    curl <endpoint output>

    The traditional web app endpoint returns a response similar to the following:

    [{"app": "traditional", "id": 1605186496, "purpose": "blog"}]

    The serverless stack returns two outputs. Use the output named ServerlessStack-v1.Api. See the following code:


  7. Optionally, upgrade to a new version of pattern construct.
    Let’s assume that a new version of the serverless construct, version 1.0.2, has been published, and we want to upgrade our AWS infrastructure to this version. To do this, edit the package.json file and change the traditional-infrastructure or serverless-infrastructure package version in the dependencies section to 1.0.2. See the following code example:

    "dependencies": {
        "@aws-cdk/core": "1.30.0",
        "serverless-infrastructure": "1.0.2",
        "aws-cdk": "1.47.0"

    To update the serverless-infrastructure package to 1.0.2, run the following command:

    npm update

    Then redeploy the CloudFormation stack:

    cdk deploy --require-approval never

Cleaning up

To avoid incurring future charges, clean up the resources you created.

  1. Delete all AWS resources that were created using the pattern constructs. We can use the AWS CDK toolkit to clean up all the resources:
    cdk destroy --force

    For more information about the AWS CDK toolkit, see Toolkit reference. Alternatively, delete the stack on the AWS CloudFormation console.

  2. Delete the CodeArtifact resources by deleting the CloudFormation stack that was deployed via AWS CDK:
    cd prerequisites
    cdk destroy –force


In this post, we demonstrated how to publish AWS CDK pattern constructs to CodeArtifact as npm packages. We also showed how teams can consume the published pattern constructs and use them to provision their AWS infrastructure.

This mechanism allows your infrastructure for AWS services to be provisioned from the configuration that has been vetted for quality control and security and governance checks. It also provides control over when new versions of the pattern constructs are released, and when the teams consuming the constructs can upgrade to the newly released versions.

About the Authors

Usman Umar


Usman Umar is a Sr. Applications Architect at AWS Professional Services. He is passionate about developing innovative ways to solve hard technical problems for the customers. In his free time, he likes going on biking trails, doing car modifications, and spending time with his family.



Krishnakumar Rengarajan


Krishnakumar Rengarajan is a DevOps Consultant with AWS Professional Services. He enjoys working with customers and focuses on building and delivering automated solutions that enables customers on their AWS cloud journeys.

Deploying CIS Level 1 hardened AMIs with Amazon EC2 Image Builder

Post Syndicated from Joseph Keating original https://aws.amazon.com/blogs/devops/deploying-cis-level-1-hardened-amis-with-amazon-ec2-image-builder/

The NFL, an AWS Professional Services partner, is collaborating with NFL’s Player Health and Safety team to build the Digital Athlete Program. The Digital Athlete Program is working to drive progress in the prevention, diagnosis, and treatment of injuries; enhance medical protocols; and further improve the way football is taught and played. The NFL, in conjunction with AWS Professional Services, delivered an Amazon EC2 Image Builder pipeline for automating the production of Amazon Machine Images (AMIs). Following similar practices from the Digital Athlete Program, this post demonstrates how to deploy an automated Image Builder pipeline.

“AWS Professional Services faced unique environment constraints, but was able to deliver a modular pipeline solution leveraging EC2 Image Builder. The framework serves as a foundation to create hardened images for future use cases. The team also provided documentation and knowledge transfer sessions to ensure our team was set up to successfully manage the solution.”

—Joseph Steinke, Director, Data Solutions Architect, National Football League

A common scenario AWS customers face is how to build processes that configure secure AWS resources that can be leveraged throughout the organization. You need to move fast in the cloud without compromising security best practices. Amazon Elastic Compute Cloud (Amazon EC2) allows you to deploy virtual machines in the AWS Cloud. EC2 AMIs provide the configuration utilized to launch an EC2 instance. You can use AMIs for several use cases, such as configuring applications, applying security policies, and configuring development environments. Developers and system administrators can deploy configuration AMIs to bring up EC2 resources that require little-to-no setup. Often times, multiple patterns are adopted for building and deploying AMIs. Because of this, you need the ability to create a centralized, automated pattern that can output secure, customizable AMIs.

In this post, we demonstrate how to create an automated process that builds and deploys Center for Internet Security (CIS) Level 1 hardened AMIs. The pattern that we deploy includes Image Builder, a CIS Level 1 hardened AMI, an application running on EC2 instances, and Amazon Inspector for security analysis. You deploy the AMI configured with the Image Builder pipeline to an application stack. The application stack consists of EC2 instances running Nginx. Lastly, we show you how to re-hydrate your application stack with a new AMI utilizing AWS CloudFormation and Amazon EC2 launch templates. You use Amazon Inspector to scan the EC2 instances launched from the Image Builder-generated AMI against the CIS Level 1 Benchmark.

After going through this exercise, you should understand how to build, manage, and deploy AMIs to an application stack. The infrastructure deployed with this pipeline includes a basic web application, but you can use this pattern to fit many needs. After running through this post, you should feel comfortable using this pattern to configure an AMI pipeline for your organization.

The project we create in this post addresses the following use case: you need a process for building and deploying CIS Level 1 hardened AMIs to an application stack running on Amazon EC2. In addition to demonstrating how to deploy the AMI pipeline, we also illustrate how to refresh a running application stack with a new AMI. You learn how to deploy this configuration with the AWS Command Line Interface (AWS CLI) and AWS CloudFormation.

AWS services used
Image Builder allows you to develop an automated workflow for creating AMIs to fit your organization’s needs. You can streamline the creation and distribution of secure images, automate your patching process, and define security and application configuration into custom AWS AMIs. In this post, you use the following AWS services to implement this solution:

  • AWS CloudFormation – AWS CloudFormation allows you to use domain-specific languages or simple text files to model and provision, in an automated and secure manner, all the resources needed for your applications across all Regions and accounts. You can deploy AWS resources in a safe, repeatable manner, and automate the provisioning of infrastructure.
  • AWS KMSAmazon Key Management Service (AWS KMS) is a fully managed service for creating and managing cryptographic keys. These keys are natively integrated with most AWS services. You use a KMS key in this post to encrypt resources.
  • Amazon S3Amazon Simple Storage Service (Amazon S3) is an object storage service utilized for storing and encrypting data. We use Amazon S3 to store our configuration files.
  • AWS Auto ScalingAWS Auto Scaling allows you to build scaling plans that automate how groups of different resources respond to changes in demand. You can optimize availability, costs, or a balance of both. We use Auto Scaling to manage Nginx on Amazon EC2.
  • Launch templatesLaunch templates contain configurations such as AMI ID, instance type, and security group. Launch templates enable you to store launch parameters so that they don’t have to be specified every time instances are launched.
  • Amazon Inspector – This automated security assessment service improves the security and compliance of applications deployed on AWS. Amazon Inspector automatically assesses applications for exposures, vulnerabilities, and deviations from best practices.

Architecture overview
We use Ansible as a configuration management component alongside Image Builder. The CIS Ansible Playbook applies a Level 1 set of rules to the local host of which the AMI is provisioned on. For more information about the Ansible Playbook, see the GitHub repo. Image Builder offers AMIs with Security Technical Implementation Guides (STIG) levels low-high as part of its pipeline build.

The following diagram depicts the phases of the Image Builder pipeline for building a Nginx web server. The numbers 1–6 represent the order of when each phase runs in the build process:

  1. Source
  2. Build components
  3. Validate
  4. Test
  5. Distribute
  6. AMI

Figure: Shows the EC2 Image Builder steps

The workflow includes the following steps:

  1. Deploy the CloudFormation templates.
  2. The template creates an Image Builder pipeline.
  3. AWS Systems Manager completes the AMI build process.
  4. Amazon EC2 starts an instance to build the AMI.
  5. Systems Manager starts a test instance build after the first build is successful.
  6. The AMI starts provisioning.
  7. The Amazon Inspector CIS benchmark starts.

CloudFormation templates
You deploy the following CloudFormation templates. These CloudFormation templates have a great deal of configurations. They deploy the following resources:

  • vpc.yml – Contains all the core networking configuration. It deploys the VPC, two private subnets, two public subnets, and the route tables. The private subnets utilize a NAT gateway to communicate to the internet. The public subnets have full outbound access to the IGW.
  • kms.yml – Contains the AWS KMS configuration that we use for encrypting resources. The KMS key policy is also configured in this template.
  • s3-iam-config.yml – Contains the launch configuration and autoscaling groups for the initial Nginx launch. For updates and patching to Nginx, we use Image Builder to build those changes.
  • infrastructure-ssm-params.yml – Contains the Systems Manager parameter store configuration. The parameters are populated by using outputs from other CloudFormation templates.
  • nginx-config.yml – Contains the configuration for Nginx. Additionally, this template contains the network load balancer, target groups, security groups, and EC2 instance AWS Identity and Access Management (IAM) roles.
  • nginx-image-builder.yml – Contains the configuration for the Image Builder pipeline that we use to build AMIs.

To follow the steps to provision the pipeline deployment, you must have the following prerequisites:

Deploying the CloudFormation templates
To deploy your templates, complete the following steps:

1. Clone the source code repository found in the following location:

git clone https://github.com/aws-samples/deploy-cis-level-1-hardened-ami-with-ec2-image-builder-pipeline.git

You now use the AWS CLI to deploy the CloudFormation templates. Make sure to leave the CloudFormation template names as we have written in this post.

2. Deploy the VPC CloudFormation template:

aws cloudformation create-stack \
--stack-name vpc-config \
--template-body file://Templates/vpc.yml \
--parameters file://Parameters/vpc-params.json  \
--capabilities CAPABILITY_IAM \
--region us-east-1

The output should look like the following code:


    "StackId": "arn:aws:cloudformation:us-east-1:123456789012:stack/vpc-config/7faaab30-247f-11eb-8712-0e65b6fb18f9"


3. Open the Parameters/kms-params.json file and update the UserARN parameter with your account ID:

      "ParameterKey": "KeyName",
      "ParameterValue": "DemoKey"
    "ParameterKey": "UserARN",
    "ParameterValue": "arn:aws:iam::<input_your_account_id>:root"


4. Deploy the KMS key CloudFormation template:

aws cloudformation create-stack \
--stack-name kms-config \
--template-body file://Templates/kms.yml \
--parameters file://Parameters/kms-params.json \
--capabilities CAPABILITY_IAM \
--region us-east-1

The output should look like the following:

"StackId": "arn:aws:cloudformation:us-east-1:123456789012:stack/kms-config/f65aca80-08ff-11eb-8795-12275bc6e1ef"


5. Open the Parameters/s3-iam-config.json file and update the DemoConfigS3BucketName parameter to a unique name of your choosing:

    "ParameterKey" : "Environment",
    "ParameterValue" : "dev"
    "ParameterKey": "NetworkStackName",
    "ParameterValue" : "vpc-config"
    "ParameterKey" : "KMSStackName",
    "ParameterValue" : "kms-config"
    "ParameterKey": "DemoConfigS3BucketName",
    "ParameterValue" : "<input_your_unique_bucket_name>"
    "ParameterKey" : "EC2InstanceRoleName",
    "ParameterValue" : "EC2InstanceRole"


6. Deploy the IAM role configuration template:

aws cloudformation create-stack \
--stack-name s3-iam-config \
--template-body file://Templates/s3-iam-config.yml \
--parameters file://Parameters/s3-iam-config.json \
--capabilities CAPABILITY_NAMED_IAM \
--region us-east-1

The output should look like the following:

"StackId": "arn:aws:cloudformation:us-east-1:123456789012:stack/s3-iam-config/9be9f990-0909-11eb-811c-0a78092beb51"


Configuring IAM roles and policies

This solution uses a couple of service-linked roles. Let’s generate these roles using the AWS CLI.


1. Run the following commands:

aws iam create-service-linked-role --aws-service-name autoscaling.amazonaws.com
aws iam create-service-linked-role --aws-service-name imagebuilder.amazonaws.com

If you see a message similar to following code, it means that you already have the service-linked role created in your account and you can move on to the next step:

An error occurred (InvalidInput) when calling the CreateServiceLinkedRole operation: Service role name AWSServiceRoleForImageBuilder has been taken in this account, please try a different suffix.

Now that you have generated the IAM roles used in this post, you add them to the KMS key policy. This allows the roles to encrypt and decrypt the KMS key.


2. Open the Parameters/kms-params.json file:

      "ParameterKey": "KeyName",
      "ParameterValue": "DemoKey"
    "ParameterKey": "UserARN",
    "ParameterValue": "arn:aws:iam::12345678910:root"


3. Add the following values as a comma-separated list to the UserARN parameter key:



When finished, the file should look similar to the following:

      "ParameterKey": "KeyName",
      "ParameterValue": "DemoKey"
    "ParameterKey": "UserARN",
    "ParameterValue": "arn:aws:iam::123456789012:role/aws-service-role/autoscaling.amazonaws.com/AWSServiceRoleForAutoScaling,arn:aws:iam::<input_your_aws_account_id>:role/NginxS3PutLambdaRole,arn:aws:iam::123456789012:role/aws-service-role/imagebuilder.amazonaws.com/AWSServiceRoleForImageBuilder,arn:aws:iam::12345678910:role/EC2InstanceRole,arn:aws:iam::12345678910:role/EC2ImageBuilderRole,arn:aws:iam::12345678910:root"

Updating the CloudFormation stack

Now that the AWS KMS parameter file has been updated, you update the AWS KMS CloudFormation stack.

1. Run the following command to update the kms-config stack:

aws cloudformation update-stack \
--stack-name kms-config \
--template-body file://Templates/kms.yml \
--parameters file://Parameters/kms-params.json \
--capabilities CAPABILITY_IAM \
--region us-east-1


The output should look like the following:

"StackId": "arn:aws:cloudformation:us-east-1:123456789012:stack/kms-config/6e84b750-0905-11eb-b543-0e4dccb471bf"


2. Open the AnsibleConfig/component-nginx.yml file and update the <input_s3_bucket_name> value with the bucket name you generated from the s3-iam-config stack:

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: MIT-0
name: 'Ansible Playbook Execution on Amazon Linux 2'
description: 'This is a sample component that demonstrates how to download and execute an Ansible playbook against Amazon Linux 2.'
schemaVersion: 1.0
  - s3bucket:
      type: string
      value: <input_s3_bucket_name>
  - name: build
      - name: InstallAnsible
        action: ExecuteBash
           - sudo amazon-linux-extras install -y ansible2
      - name: CreateDirectory
        action: ExecuteBash
            - sudo mkdir -p /ansibleloc/roles
      - name: DownloadLinuxCis
        action: S3Download
          - source: 's3://{{ s3bucket }}/components/linux-cis.zip'
            destination: '/ansibleloc/linux-cis.zip'
      - name: UzipLinuxCis
        action: ExecuteBash
            - unzip /ansibleloc/linux-cis.zip -d /ansibleloc/roles
            - echo "unzip linux-cis file"
      - name: DownloadCisPlaybook
        action: S3Download
          - source: 's3://{{ s3bucket }}/components/cis_playbook.yml'
            destination: '/ansibleloc/cis_playbook.yml'
      - name: InvokeCisAnsible
        action: ExecuteBinary
          path: ansible-playbook
            - '{{ build.DownloadCisPlaybook.inputs[0].destination }}'
            - '--tags=level1'
      - name: DeleteCisPlaybook
        action: ExecuteBash
            - rm '{{ build.DownloadCisPlaybook.inputs[0].destination }}'
      - name: DownloadNginx
        action: S3Download
          - source: s3://{{ s3bucket }}/components/nginx.zip'
            destination: '/ansibleloc/nginx.zip'
      - name: UzipNginx
        action: ExecuteBash
            - unzip /ansibleloc/nginx.zip -d /ansibleloc/roles
            - echo "unzip Nginx file"
      - name: DownloadNginxPlaybook
        action: S3Download
          - source: 's3://{{ s3bucket }}/components/nginx_playbook.yml'
            destination: '/ansibleloc/nginx_playbook.yml'
      - name: InvokeNginxAnsible
        action: ExecuteBinary
          path: ansible-playbook
            - '{{ build.DownloadNginxPlaybook.inputs[0].destination }}'
      - name: DeleteNginxPlaybook
        action: ExecuteBash
            - rm '{{ build.DownloadNginxPlaybook.inputs[0].destination }}'

  - name: validate
      - name: ValidateDebug
        action: ExecuteBash
            - sudo echo "ValidateDebug section"

  - name: test
      - name: TestDebug
        action: ExecuteBash
            - sudo echo "TestDebug section"
      - name: Download_Inspector_Test
        action: S3Download
          - source: 's3://ec2imagebuilder-managed-resources-us-east-1-prod/components/inspector-test-linux/1.0.1/InspectorTest'
            destination: '/workdir/InspectorTest'
      - name: Set_Executable_Permissions
        action: ExecuteBash
            - sudo chmod +x /workdir/InspectorTest
      - name: ExecuteInspectorAssessment
        action: ExecuteBinary
          path: '/workdir/InspectorTest'


Adding files to your S3 buckets

Now you assume a role you generated from one of the previous CloudFormation stacks. This allows you to add files to the encrypted S3 bucket.

1. Run the following command and make sure to update the role to use your AWS account ID number:

aws sts assume-role --role-arn "arn:aws:iam::<input_your_aws_account_id>:role/EC2ImageBuilderRole" --role-session-name AWSCLI-Session

You see an output similar to the following:

    "Credentials": {
        "AccessKeyId": "<AWS_ACCESS_KEY_ID>",
        "SecretAccessKey": "<AWS_SECRET_ACCESS_KEY_ID>",
        "SessionToken": "<AWS_SESSION_TOKEN>",
        "Expiration": "2020-11-20T02:54:17Z"
    "AssumedRoleUser": {
        "AssumedRoleId": "ACPATGCCLSNJCNSJCEWZ:AWSCLI-Session",
        "Arn": "arn:aws:sts::123456789012:assumed-role/EC2ImageBuilderRole/AWSCLI-Session"

You now assume the EC2ImageBuilderRole IAM role from the command line. This role allows you to create objects in the S3 bucket generated from the s3-iam-config stack. Because this bucket is encrypted with AWS KMS, any user or IAM role requires specific permissions to decrypt the key. You have already accounted for this in a previous step by adding the EC2ImageBuilderRole IAM role to the KMS key policy.


2. Create the following environment variable to use the EC2ImageBuilderRole role. Update the values with the output from the previous step:

export AWS_ACCESS_KEY_ID=AccessKeyId
export AWS_SECRET_ACCESS_KEY=SecretAccessKey
export AWS_SESSION_TOKEN=SessionToken


3. Check to make sure that you have actually assumed the role EC2ImageBuilderRole:

aws sts get-caller-identity

You should see an output similar to the following:

    "Account": "123456789012",
    "Arn": "arn:aws:sts::123456789012:assumed-role/EC2ImageBuilderRole/AWSCLI-Session"


4. Create a folder inside of the encrypted S3 bucket generated in the s3-iam-config stack:

aws s3api put-object --bucket <input_your_bucket_name> --key components


5. Zip the configuration files that you use in the Image Builder pipeline process:

zip -r linux-cis.zip LinuxCis/
zip -r nginx.zip Nginx/


6. Upload the configuration files to S3 bucket. Update the bucket name with the S3 bucket name you generated in the s3-iam-config stack.

aws s3 cp linux-cis.zip s3://<input_your_bucket_name>/components/

aws s3 cp nginx.zip s3://<input_your_bucket_name>/components/

aws s3 cp AnsibleConfig/cis_playbook.yml s3://<input_your_bucket_name>/components/

aws s3 cp AnsibleConfig/nginx_playbook.yml s3://<input_your_bucket_name>/components/

aws s3 cp AnsibleConfig/component-nginx.yml s3://<input_your_bucket_name>/components/

Deploying your pipeline

You’re now ready to deploy your pipeline.

1. Switch back to the original IAM user profile you used before assuming the EC2ImageBuilderRole. For instructions, see How do I assume an IAM role using the AWS CLI?


2. Open the Parameters/nginx-image-builder-params.json file and update the ImageBuilderBucketName parameter with the S3 bucket name generated in the s3-iam-config stack:

    "ParameterKey": "Environment",
    "ParameterValue": "dev"
    "ParameterKey": "ImageBuilderBucketName",
    "ParameterValue": "<input_your_bucket_name>"
    "ParameterKey": "NetworkStackName",
    "ParameterValue": "vpc-config"
    "ParameterKey": "KMSStackName",
    "ParameterValue": "kms-config"
    "ParameterKey": "S3ConfigStackName",
    "ParameterValue": "s3-iam-config"


3. Deploy the nginx-image-builder.yml template:

aws cloudformation create-stack \
--stack-name cis-image-builder \
--template-body file://Templates/nginx-image-builder.yml \
--parameters file://Parameters/nginx-image-builder-params.json \
--capabilities CAPABILITY_NAMED_IAM \
--region us-east-1

The template takes around 35 minutes to complete. Deploying this template starts the Image Builder pipeline.


Monitoring the pipeline

You can get more details about the pipeline on the AWS Management Console.

1. On the Image Builder console, choose Image pipelines to see the status of the pipeline.

Figure: Shows the EC2 Image Builder Pipeline status


2. Choose the pipeline (for this post, cis-image-builder-LinuxCis-Pipeline)

On the pipeline details page, you can view more information and make updates to its configuration.

Figure: Shows the Image Builder Pipeline metadata

At this point, the Image Builder pipeline has started running the automation document in Systems Manager. Here you can monitor the progress of the AMI build.


3. On the Systems Manager console, choose Automation.


4. Choose the execution ID of the arn:aws:ssm:us-east-1:123456789012:document/ImageBuilderBuildImageDocument document.

Figure: Shows the Image Builder Pipeline Systems Manager Automation steps


5. Choose the step ID to see what is happening in each step.

At this point, the Image Builder pipeline is bringing up an Amazon Linux 2 EC2 instance. From there, we run Ansible playbooks that configure the security and application settings. The automation is pulling its configuration from the S3 bucket you deployed in a previous step. When the Ansible run is complete, the instance stops and an AMI is generated from this instance. When this is complete, a cleanup is initiated that ends the EC2 instance. The final result is a CIS Level 1 hardened Amazon Linux 2 AMI running Nginx.


Updating parameters

When the stack is complete, you retrieve some new parameter values.

1. On the Systems Manager console, choose Automation.


2. Choose the execution ID of the arn:aws:ssm:us-east-1:123456789012:document/ImageBuilderBuildImageDocument document.


3. Choose step 21.

The following screenshot shows the output of this step.

Figure: Shows step of EC2 Image Builder Pipeline


4. Open the Parameters/nginx-config.json file and update the AmiId parameter with the AMI ID generated from the previous step:

    "ParameterKey" : "Environment",
    "ParameterValue" : "dev"
    "ParameterKey": "NetworkStackName",
    "ParameterValue" : "vpc-config"
    "ParameterKey" : "S3ConfigStackName",
    "ParameterValue" : "s3-iam-config"
    "ParameterKey": "AmiId",
    "ParameterValue" : "<input_the_cis_hardened_ami_id>"
    "ParameterKey": "ApplicationName",
    "ParameterValue" : "Nginx"
    "ParameterKey": "NLBName",
    "ParameterValue" : "DemoALB"
    "ParameterKey": "TargetGroupName",
    "ParameterValue" : "DemoTG"


5. Deploy the nginx-config.yml template:

aws cloudformation create-stack \
--stack-name nginx-config \
--template-body file://Templates/nginx-config.yml \
--parameters file://Parameters/nginx-config.json \
--capabilities CAPABILITY_NAMED_IAM \
--region us-east-1

The output should look like the following:

    "StackId": "arn:aws:cloudformation:us-east-1:123456789012:stack/nginx-config/fb2b0f30-24f6-11eb-ad7c-0a3238f55eb3"


6. Deploy the infrastructure-ssm-params.yml template:

aws cloudformation create-stack \
--stack-name ssm-params-config \
--template-body file://Templates/infrastructure-ssm-params.yml \
--parameters file://Parameters/infrastructure-ssm-parameters.json \
--capabilities CAPABILITY_NAMED_IAM \
--region us-east-1


Verifying Nginx is running

Let’s verify that our Nginx service is up and running properly. You use Session Manager to connect to a testing instance.

1. On the Amazon EC2 console, choose Instances.

You should see three instances, as in the following screenshot.

Figure: Shows the Nginx EC2 instances

You can connect to either one of the Nginx instances.


2. Select the testing instance.


3. On the Actions menu, choose Connect.


4. Choose Session Manager.


5. Choose Connect.

A terminal on the EC2 instance opens, similar to the following screenshot.

Figure: Shows the Session Manager terminal

6. Run the following command to ensure that Nginx is running properly:

curl localhost:8080

You should see an output similar to the following screenshot.

Figure: Shows Nginx output from terminal

Reviewing resources and configurations

Now that you have deployed the core services that for the solution, take some time to review the services that you have just deployed.


IAM roles

This project creates several IAM roles that are used to manage AWS resources. For example, EC2ImageBuilderRole is used to configure new AMIs with the Image Builder pipeline. This role contains only the permissions required to manage the Image Builder process. Adopting this pattern enforces the practice of least privilege. Additionally, many of the IAM polices attached to the IAM roles are restricted down to specific AWS resources. Let’s look at a couple of examples of managing IAM permissions with this project.


The following policy restricts Amazon S3 access to a specific S3 bucket. This makes sure that the role this policy is attached to can only access this specific S3 bucket. If this role needs to access any additional S3 buckets, the resource has to be explicitly added.

  - PolicyName: GrantS3Read
        - Sid: GrantS3Read
          Effect: Allow
            - s3:List*
            - s3:Get*
            - s3:Put*
          Resource: !Sub 'arn:aws:s3:::${S3Bucket}*'

Let’s look at the EC2ImageBuilderRole. A common scenario that occurs is when you need to assume a role locally in order to perform an action on a resource. In this case, because you’re using AWS KMS to encrypt the S3 bucket, you need to assume a role that has access to decrypt the KMS key so that artifacts can be uploaded to the S3 bucket. In the following AssumeRolePolicyDocument, we allow Amazon EC2 and Systems Manager services to be assumed by this role. Additionally, we allow IAM users to assume this role as well.

  Version: 2012-10-17
    - Effect: Allow
          - ec2.amazonaws.com
          - ssm.amazonaws.com
          - imagebuilder.amazonaws.com
        AWS: !Sub 'arn:aws:iam::${AWS::AccountId}:root'
        - sts:AssumeRole

The principle !Sub 'arn:aws:iam::${AWS::AccountId}:root allows for any IAM user in this account to assume this role locally. Normally, this role should be scoped down to specific IAM users or roles. For the purpose of this post, we grant permissions to all users of the account.


Nginx configuration

The AMI built from the Image Builder pipeline contains all of the application and security configurations required to run Nginx as a web application. When an instance is launched from this AMI, no additional configuration is required.

We use Amazon EC2 launch templates to configure the application stack. The launch templates contain information such as the AMI ID, instance type, and security group. When a new AMI is provisioned, you simply update the launch template CloudFormation parameter with the new AMI and update the CloudFormation stack. From here, you can start an Auto Scaling group instance refresh to update the application stack to use the new AMI. The Auto Scaling group is updated with instances running on the updated AMI by bringing down one instance at a time and replacing it.


Amazon Inspector configuration

Amazon Inspector is an automated security assessment service that helps improve the security and compliance of applications deployed on AWS. With Amazon Inspector, assessments are generated for exposure, vulnerabilities, and deviations from best practices.

After performing an assessment, Amazon Inspector produces a detailed list of security findings prioritized by level of severity. These findings can be reviewed directly or as part of detailed assessment reports that are available via the Amazon Inspector console or API. We can use Amazon Inspector to assess our security posture against the CIS Level 1 standard that we use our Image Builder pipeline to provision. Let’s look at how we configure Amazon Inspector.

A resource group defines a set of tags that, when queried, identify the AWS resources that make up the assessment target. Any EC2 instance that is launched with the tag specified in the resource group is in scope for Amazon Inspector assessment runs. The following code shows our configuration:

  Type: "AWS::Inspector::ResourceGroup"
      - Key: "ResourceGroup"
        Value: "Nginx"

  Type: AWS::Inspector::AssessmentTarget
    AssessmentTargetName : "NginxAssessmentTarget"
    ResourceGroupArn : !Ref ResourceGroup

In the following code, we specify the tag set in the resource group, which makes sure that when an instance is launched from this AMI, it’s under the scope of Amazon Inspector:

  Type: AWS::ImageBuilder::Image
    ImageRecipeArn: !Ref Recipe
    InfrastructureConfigurationArn: !Ref Infrastructure
    DistributionConfigurationArn: !Ref Distribution
      ImageTestsEnabled: false
      TimeoutMinutes: 60
      ResourceGroup: 'Nginx'


Building and deploying a new image with Amazon Inspector tests enabled

For this final portion of this post, we build and deploy a new AMI with an Amazon Inspector evaluation.

1. In your text editor, open Templates/nginx-image-builder.yml and update the pipeline and IBImage resource property ImageTestsEnabled to true.

The updated configuration should look like the following:

  Type: AWS::ImageBuilder::Image
    ImageRecipeArn: !Ref Recipe
    InfrastructureConfigurationArn: !Ref Infrastructure
    DistributionConfigurationArn: !Ref Distribution
      ImageTestsEnabled: true
      TimeoutMinutes: 60
      ResourceGroup: 'Nginx'


2. Update the stack with the new configuration:

aws cloudformation update-stack \
--stack-name cis-image-builder \
--template-body file://Templates/nginx-image-builder.yml \
--parameters file://Parameters/nginx-image-builder-params.json \
--capabilities CAPABILITY_NAMED_IAM \
--region us-east-1

This starts a new AMI build with an Amazon Inspector evaluation. The process can take up to 2 hours to complete.

3. On the Amazon Inspector console, choose Assessment Runs.

Figure: Shows Amazon Inspector Assessment Run

4. Under Reports, choose Download report.

5. For Select report type, select Findings report.

6. For Select report format, select PDF.

7. Choose Generate report.

The following screenshot shows the findings report from the Amazon Inspector run.

This report generates an assessment against the CIS Level 1 standard. Any policies that don’t comply with the CIS Level 1 standard are explicitly called out in this report.

Section 3.1 lists any failed policies.


Figure: Shows Inspector findings

These failures are detailed later in the report, along with suggestions for remediation.

In section 4.1, locate the entry 1.3.2 Ensure filesystem integrity is regularly checked. This section shows the details of a failure from the Amazon Inspector findings report. You can also see suggestions on how to remediate the issue. Under Recommendation, the findings report suggests a specific command that you can use to remediate the issue.


Figure: Shows Inspector findings issue

You can use the Image Builder pipeline to simply update the Ansible playbooks with this setting, then run the Image Builder pipeline to build a new AMI, deploy the new AMI to an EC2 Instance, and run the Amazon Inspector report to ensure that the issue has been resolved. Finally, we can see the specific instances that have been assessed that have this issue.

Organizations often customize security settings based off of a given use case. Your organization may choose CIS Level 1 as a standard but elect to not apply all the recommendations. For example, you might choose to not use the FirewallD service on your Linux instances, because you feel that using Amazon EC2 security groups gives you enough networking security in place that you don’t need an additional firewall. Disabling FirewallD causes a high severity failure in the Amazon Inspector report. This is expected and can be ignored when evaluating the report.


In this post, we showed you how to use Image Builder to automate the creation of AMIs. Additionally, we also showed you how to use the AWS CLI to deploy CloudFormation stacks. Finally, we walked through how to evaluate resources against CIS Level 1 Standard using Amazon Inspector.


About the Authors


Joe Keating is a Modernization Architect in Professional Services at Amazon Web Services. He works with AWS customers to design and implement a variety of solutions in the AWS Cloud. Joe enjoys cooking with a glass or two of wine and achieving mediocrity on the golf course.




Virginia Chu is a Sr. Cloud Infrastructure Architect in Professional Services at Amazon Web Services. She works with enterprise-scale customers around the globe to design and implement a variety of solutions in the AWS Cloud.


Easily configure Amazon DevOps Guru across multiple accounts and Regions using AWS CloudFormation StackSets

Post Syndicated from Nikunj Vaidya original https://aws.amazon.com/blogs/devops/configure-devops-guru-multiple-accounts-regions-using-cfn-stacksets/

As applications become increasingly distributed and complex, operators need more automated practices to maintain application availability and reduce the time and effort spent on detecting, debugging, and resolving operational issues.

Enter Amazon DevOps Guru (preview).

Amazon DevOps Guru is a machine learning (ML) powered service that gives you a simpler way to improve an application’s availability and reduce expensive downtime. Without involving any complex configuration setup, DevOps Guru automatically ingests operational data in your AWS Cloud. When DevOps Guru identifies a critical issue, it automatically alerts you with a summary of related anomalies, the likely root cause, and context on when and where the issue occurred. DevOps Guru also, when possible, provides prescriptive recommendations on how to remediate the issue.

Using Amazon DevOps Guru is easy and doesn’t require you to have any ML expertise. To get started, you need to configure DevOps Guru and specify which AWS resources to analyze. If your applications are distributed across multiple AWS accounts and AWS Regions, you need to configure DevOps Guru for each account-Region combination. Though this may sound complex, it’s in fact very simple to do so using AWS CloudFormation StackSets. This post walks you through the steps to configure DevOps Guru across multiple AWS accounts or organizational units, using AWS CloudFormation StackSets.


Solution overview

The goal of this post is to provide you with sample templates to facilitate onboarding Amazon DevOps Guru across multiple AWS accounts. Instead of logging into each account and enabling DevOps Guru, you use AWS CloudFormation StackSets from the primary account to enable DevOps Guru across multiple accounts in a single AWS CloudFormation operation. When it’s enabled, DevOps Guru monitors your associated resources and provides you with detailed insights for anomalous behavior along with intelligent recommendations to mitigate and incorporate preventive measures.

We consider various options in this post for enabling Amazon DevOps Guru across multiple accounts and Regions:

  • All resources across multiple accounts and Regions
  • Resources from specific CloudFormation stacks across multiple accounts and Regions
  • For All resources in an organizational unit

In the following diagram, we launch the AWS CloudFormation StackSet from a primary account to enable Amazon DevOps Guru across two AWS accounts and carry out operations to generate insights. The StackSet uses a single CloudFormation template to configure DevOps Guru, and deploys it across multiple accounts and regions, as specified in the command.

Figure: Shows enabling of DevOps Guru using CloudFormation StackSets

Figure: Shows enabling of DevOps Guru using CloudFormation StackSets

When Amazon DevOps Guru is enabled to monitor your resources within the account, it uses a combination of vended Amazon CloudWatch metrics, AWS CloudTrail logs, and specific patterns from its ML models to detect an anomaly. When the anomaly is detected, it generates an insight with the recommendations.

Figure: Shows DevOps Guru generating Insights based upon ingested metrics

Figure: Shows DevOps Guru monitoring the resources and generating insights for anomalies detected



To complete this post, you should have the following prerequisites:

  • Two AWS accounts. For this post, we use the account numbers 111111111111 (primary account) and 222222222222. We will carry out the CloudFormation operations and monitoring of the stacks from this primary account.
  • To use organizations instead of individual accounts, identify the organization unit (OU) ID that contains at least one AWS account.
  • Access to a bash environment, either using an AWS Cloud9 environment or your local terminal with the AWS Command Line Interface (AWS CLI) installed.
  • AWS Identity and Access Management (IAM) roles for AWS CloudFormation StackSets.
  • Knowledge of CloudFormation StackSets


(a) Using an AWS Cloud9 environment or AWS CLI terminal
We recommend using AWS Cloud9 to create an environment to get access to the AWS CLI from a bash terminal. Make sure you select Linux2 as the operating system for the AWS Cloud9 environment.

Alternatively, you may use your bash terminal in your favorite IDE and configure your AWS credentials in your terminal.

(b) Creating IAM roles

If you are using Organizations for account management, you would not need to create the IAM roles manually and instead use Organization based trusted access and SLRs. You may skip the sections (b), (c) and (d). If not using Organizations, please read further.

Before you can deploy AWS CloudFormation StackSets, you must have the following IAM roles:

  • AWSCloudFormationStackSetAdministrationRole
  • AWSCloudFormationStackSetExecutionRole

The IAM role AWSCloudFormationStackSetAdministrationRole should be created in the primary account whereas AWSCloudFormationStackSetExecutionRole role should be created in all the accounts where you would like to run the StackSets.

If you’re already using AWS CloudFormation StackSets, you should already have these roles in place. If not, complete the following steps to provision these roles.

(c) Creating the AWSCloudFormationStackSetAdministrationRole role
To create the AWSCloudFormationStackSetAdministrationRole role, sign in to your primary AWS account and go to the AWS Cloud9 terminal.

Execute the following command to download the file:

curl -O https://s3.amazonaws.com/cloudformation-stackset-sample-templates-us-east-1/AWSCloudFormationStackSetAdministrationRole.yml

Execute the following command to create the stack:

aws cloudformation create-stack \
--stack-name AdminRole \
--template-body file:///$PWD/AWSCloudFormationStackSetAdministrationRole.yml \
--capabilities CAPABILITY_NAMED_IAM \
--region us-east-1

(d) Creating the AWSCloudFormationStackSetExecutionRole role
You now create the role AWSCloudFormationStackSetExecutionRole in the primary account and other target accounts where you want to enable DevOps Guru. For this post, we create it for our two accounts and two Regions (us-east-1 and us-east-2).

Execute the following command to download the file:

curl -O https://s3.amazonaws.com/cloudformation-stackset-sample-templates-us-east-1/AWSCloudFormationStackSetExecutionRole.yml

Execute the following command to create the stack:

aws cloudformation create-stack \
--stack-name ExecutionRole \
--template-body file:///$PWD/AWSCloudFormationStackSetExecutionRole.yml \
--parameters ParameterKey=AdministratorAccountId,ParameterValue=111111111111 \
--capabilities CAPABILITY_NAMED_IAM \
--region us-east-1

Now that the roles are provisioned, you can use AWS CloudFormation StackSets in the next section.


Running AWS CloudFormation StackSets to enable DevOps Guru

With the required IAM roles in place, now you can deploy the stack sets to enable DevOps Guru across multiple accounts.

As a first step, go to your bash terminal and clone the GitHub repository to access the CloudFormation templates:

git clone https://github.com/aws-samples/amazon-devopsguru-samples
cd amazon-devopsguru-samples/enable-devopsguru-stacksets


(a) Configuring Amazon SNS topics for DevOps Guru to send notifications for operational insights

If you want to receive notifications for operational insights generated by Amazon DevOps Guru, you need to configure an Amazon Simple Notification Service (Amazon SNS) topic across multiple accounts. If you have already configured SNS topics and want to use them, identify the topic name and directly skip to the step to enable DevOps Guru.

Note for Central notification target: You may prefer to configure an SNS Topic in the central AWS account so that all Insight notifications are sent to a single target. In such a case, you would need to modify the central account SNS topic policy to allow other accounts to send notifications.

To create your stack set, enter the following command (provide an email for receiving insights):

aws cloudformation create-stack-set \
--stack-set-name CreateDevOpsGuruTopic \
--template-body file:///$PWD/CreateSNSTopic.yml \
--parameters ParameterKey=EmailAddress,ParameterValue=<[email protected]> \
--region us-east-1

Instantiate AWS CloudFormation StackSets instances across multiple accounts and multiple Regions (provide your account numbers and Regions as needed):

aws cloudformation create-stack-instances \
--stack-set-name CreateDevOpsGuruTopic \
--accounts '["111111111111","222222222222"]' \
--regions '["us-east-1","us-east-2"]' \
--operation-preferences FailureToleranceCount=0,MaxConcurrentCount=1

After running this command, the SNS topic devops-guru is created across both the accounts. Go to the email address specified and confirm the subscription by clicking the Confirm subscription link in each of the emails that you receive. Your SNS topic is now fully configured for DevOps Guru to use.

Figure: Shows creation of SNS topic to receive insights from DevOps Guru

Figure: Shows creation of SNS topic to receive insights from DevOps Guru


(b) Enabling DevOps Guru

Let us first examine the CloudFormation template format to enable DevOps Guru and configure it to send notifications over SNS topics. See the following code snippet:

    Type: AWS::DevOpsGuru::ResourceCollection
          StackNames: *

    Type: AWS::DevOpsGuru::NotificationChannel
          TopicArn: arn:aws:sns:us-east-1:111111111111:SnsTopic


When the StackNames property is fed with a value of *, it enables DevOps Guru for all CloudFormation stacks. However, you can enable DevOps Guru for only specific CloudFormation stacks by providing the desired stack names as shown in the following code:


    Type: AWS::DevOpsGuru::ResourceCollection
          - StackA
          - StackB


For the CloudFormation template in this post, we provide the names of the stacks using the parameter inputs. To enable the AWS CLI to accept a list of inputs, we need to configure the input type as CommaDelimitedList, instead of a base string. We also provide the parameter SnsTopicName, which the template substitutes into the TopicArn property.

See the following code:

AWSTemplateFormatVersion: 2010-09-09
Description: Enable Amazon DevOps Guru

    Type: CommaDelimitedList
    Description: Comma separated names of the CloudFormation Stacks for DevOps Guru to analyze.
    Default: "*"

    Type: String
    Description: Name of SNS Topic

    Type: AWS::DevOpsGuru::ResourceCollection
          StackNames: !Ref CfnStackNames

    Type: AWS::DevOpsGuru::NotificationChannel
          TopicArn: !Sub arn:aws:sns:${AWS::Region}:${AWS::AccountId}:${SnsTopicName}


Now that we reviewed the CloudFormation syntax, we will use this template to implement the solution. For this post, we will consider three use cases for enabling Amazon DevOps Guru:

(i) For all resources across multiple accounts and Regions

(ii) For all resources from specific CloudFormation stacks across multiple accounts and Regions

(iii) For all resources in an organization

Let us review each of the above points in detail.

(i) Enabling DevOps Guru for all resources across multiple accounts and Regions

Note: Carry out the following steps in your primary AWS account.

You can use the CloudFormation template (EnableDevOpsGuruForAccount.yml) from the current directory, create a stack set, and then instantiate AWS CloudFormation StackSets instances across desired accounts and Regions.

The following command creates the stack set:

aws cloudformation create-stack-set \
--stack-set-name EnableDevOpsGuruForAccount \
--template-body file:///$PWD/EnableDevOpsGuruForAccount.yml \
--parameters ParameterKey=CfnStackNames,ParameterValue=* \
ParameterKey=SnsTopicName,ParameterValue=devops-guru \
--region us-east-1

The following command instantiates AWS CloudFormation StackSets instances across multiple accounts and Regions:

aws cloudformation create-stack-instances \
--stack-set-name EnableDevOpsGuruForAccount \
--accounts '["111111111111","222222222222"]' \
--regions '["us-east-1","us-east-2"]' \
--operation-preferences FailureToleranceCount=0,MaxConcurrentCount=1


The following screenshot of the AWS CloudFormation console in the primary account running StackSet, shows the stack set deployed in both accounts.

Figure: Screenshot for deployed StackSet and Stack instances

Figure: Screenshot for deployed StackSet and Stack instances


The following screenshot of the Amazon DevOps Guru console shows DevOps Guru is enabled to monitor all CloudFormation stacks.

Figure: Screenshot of DevOps Guru dashboard showing DevOps Guru enabled for all CloudFormation stacks

Figure: Screenshot of DevOps Guru dashboard showing DevOps Guru enabled for all CloudFormation stacks


(ii) Enabling DevOps Guru for specific CloudFormation stacks for individual accounts

Note: Carry out the following steps in your primary AWS account

In this use case, we want to enable Amazon DevOps Guru only for specific CloudFormation stacks for individual accounts. We use the AWS CloudFormation StackSets override parameters feature to rerun the stack set with specific values for CloudFormation stack names as parameter inputs. For more information, see Override parameters on stack instances.

If you haven’t created the stack instances for individual accounts, use the create-stack-instances AWS CLI command and pass the parameter overrides. If you have already created stack instances, update the existing stack instances using update-stack-instances and pass the parameter overrides. Replace the required account number, Regions, and stack names as needed.

In account 111111111111, create instances with the parameter override with the following command, where CloudFormation stacks STACK-NAME-1 and STACK-NAME-2 belong to this account in us-east-1 Region:

aws cloudformation create-stack-instances \
--stack-set-name  EnableDevOpsGuruForAccount \
--accounts '["111111111111"]' \
--parameter-overrides ParameterKey=CfnStackNames,ParameterValue=\"<STACK-NAME-1>,<STACK-NAME-2>\" \
--regions '["us-east-1"]' \
--operation-preferences FailureToleranceCount=0,MaxConcurrentCount=1

Update the instances with the following command:

aws cloudformation update-stack-instances \
--stack-set-name EnableDevOpsGuruForAccount \
--accounts '["111111111111"]' \
--parameter-overrides ParameterKey=CfnStackNames,ParameterValue=\"<STACK-NAME-1>,<STACK-NAME-2>\" \
--regions '["us-east-1"]' \
--operation-preferences FailureToleranceCount=0,MaxConcurrentCount=1


In account 222222222222, create instances with the parameter override with the following command, where CloudFormation stacks STACK-NAME-A and STACK-NAME-B belong to this account in the us-east-1 Region:

aws cloudformation create-stack-instances \
--stack-set-name  EnableDevOpsGuruForAccount \
--accounts '["222222222222"]' \
--parameter-overrides ParameterKey=CfnStackNames,ParameterValue=\"<STACK-NAME-A>,<STACK-NAME-B>\" \
--regions '["us-east-1"]' \
--operation-preferences FailureToleranceCount=0,MaxConcurrentCount=1

Update the instances with the following command:

aws cloudformation update-stack-instances \
--stack-set-name EnableDevOpsGuruForAccount \
--accounts '["222222222222"]' \
--parameter-overrides ParameterKey=CfnStackNames,ParameterValue=\"<STACK-NAME-A>,<STACK-NAME-B>\" \
--regions '["us-east-1"]' \
--operation-preferences FailureToleranceCount=0,MaxConcurrentCount=1


The following screenshot of the DevOps Guru console shows that DevOps Guru is enabled for only two CloudFormation stacks.

Figure: Screenshot of DevOps Guru dashboard showing DevOps Guru enabled for only two CloudFormation stacks

Figure: Screenshot of DevOps Guru dashboard with DevOps Guru enabled for two CloudFormation stacks


(iii) Enabling DevOps Guru for all resources in an organization

Note: Carry out the following steps in your primary AWS account

If you’re using AWS Organizations, you can take advantage of the AWS CloudFormation StackSets feature support for Organizations. This way, you don’t need to add or remove stacks when you add or remove accounts from the organization. For more information, see New: Use AWS CloudFormation StackSets for Multiple Accounts in an AWS Organization.

The following example shows the command line using multiple Regions to demonstrate the use. Update the OU as needed. If you need to use additional Regions, you may have to create an SNS topic in those Regions too.

To create a stack set for an OU and across multiple Regions, enter the following command:

aws cloudformation create-stack-set \
--stack-set-name EnableDevOpsGuruForAccount \
--template-body file:///$PWD/EnableDevOpsGuruForAccount.yml \
--parameters ParameterKey=CfnStackNames,ParameterValue=* \
ParameterKey=SnsTopicName,ParameterValue=devops-guru \
--region us-east-1 \
--permission-model SERVICE_MANAGED \
--auto-deployment Enabled=true,RetainStacksOnAccountRemoval=true

Instantiate AWS CloudFormation StackSets instances for an OU and across multiple Regions with the following command:

aws cloudformation create-stack-instances \
--stack-set-name  EnableDevOpsGuruForAccount \
--deployment-targets OrganizationalUnitIds='["<organizational-unit-id>"]' \
--regions '["us-east-1","us-east-2"]' \
--operation-preferences FailureToleranceCount=0,MaxConcurrentCount=1

In this way, you can run CloudFormation StackSets to enable and configure DevOps Guru across multiple accounts, Regions, with simple and easy steps.


Reviewing DevOps Guru insights

Amazon DevOps Guru monitors for anomalies in the resources in the CloudFormation stacks that are enabled for monitoring. The following screenshot shows the initial dashboard.

Figure: Screenshot of DevOps Guru dashboard

Figure: Screenshot of DevOps Guru dashboard

On enabling DevOps Guru, it may take up to 24 hours to analyze the resources and baseline the normal behavior. When it detects an anomaly, it highlights the impacted CloudFormation stack, logs insights that provide details about the metrics indicating an anomaly, and prints actionable recommendations to mitigate the anomaly.

Figure: Screenshot of DevOps Guru dashboard showing ongoing reactive insight

Figure: Screenshot of DevOps Guru dashboard showing ongoing reactive insight

The following screenshot shows an example of an insight (which now has been resolved) that was generated for the increased latency for an ELB. The insight provides various sections in which it provides details about the metrics, the graphed anomaly along with the time duration, potential related events, and recommendations to mitigate and implement preventive measures.

Figure: Screenshot for an Insight generated about ELB Latency

Figure: Screenshot for an Insight generated about ELB Latency


Cleaning up

When you’re finished walking through this post, you should clean up or un-provision the resources to avoid incurring any further charges.

  1. On the AWS CloudFormation StackSets console, choose the stack set to delete.
  2. On the Actions menu, choose Delete stacks from StackSets.
  3. After you delete the stacks from individual accounts, delete the stack set by choosing Delete StackSet.
  4. Un-provision the environment for AWS Cloud9.



This post reviewed how to enable Amazon DevOps Guru using AWS CloudFormation StackSets across multiple AWS accounts or organizations to monitor the resources in existing CloudFormation stacks. Upon detecting an anomaly, DevOps Guru generates an insight that includes the vended CloudWatch metric, the CloudFormation stack in which the resource existed, and actionable recommendations.

We hope this post was useful to you to onboard DevOps Guru and that you try using it for your production needs.


About the Authors

Author's profile photo


Nikunj Vaidya is a Sr. Solutions Architect with Amazon Web Services, focusing in the area of DevOps services. He builds technical content for the field enablement and offers technical guidance to the customers on AWS DevOps solutions and services that would streamline the application development process, accelerate application delivery, and enable maintaining a high bar of software quality.





Nuatu Tseggai is a Cloud Infrastructure Architect at Amazon Web Services. He enjoys working with customers to design and build event-driven distributed systems that span multiple services.


Raising code quality for Python applications using Amazon CodeGuru

Post Syndicated from Ran Fu original https://aws.amazon.com/blogs/devops/raising-code-quality-for-python-applications-using-amazon-codeguru/

We are pleased to announce the launch of Python support for Amazon CodeGuru, a service for automated code reviews and application performance recommendations. CodeGuru is powered by program analysis and machine learning, and trained on best practices and hard-learned lessons across millions of code reviews and thousands of applications profiled on open-source projects and internally at Amazon.

Amazon CodeGuru has two services:

  • Amazon CodeGuru Reviewer – Helps you improve source code quality by detecting hard-to-find defects during application development and recommending how to remediate them.
  • Amazon CodeGuru Profiler – Helps you find the most expensive lines of code, helps reduce your infrastructure cost, and fine-tunes your application performance.

The launch of Python support extends CodeGuru beyond its original Java support. Python is a widely used language for various use cases, including web app development and DevOps. Python’s growth in data analysis and machine learning areas is driven by its rich frameworks and libraries. In this post, we discuss how to use CodeGuru Reviewer and Profiler to improve your code quality for Python applications.

CodeGuru Reviewer for Python

CodeGuru Reviewer now allows you to analyze your Python code through pull requests and full repository analysis. For more information, see Automating code reviews and application profiling with Amazon CodeGuru. We analyzed large code corpuses and Python documentation to source hard-to-find coding issues and trained our detectors to provide best practice recommendations. We expect such recommendations to benefit beginners as well as expert Python programmers.

CodeGuru Reviewer generates recommendations in the following categories:

  • AWS SDK APIs best practices
  • Data structures and control flow, including exception handling
  • Resource leaks
  • Secure coding practices to protect from potential shell injections

In the following sections, we provide real-world examples of bugs that can be detected in each of the categories:

AWS SDK API best practices

AWS has hundreds of services and thousands of APIs. Developers can now benefit from CodeGuru Reviewer recommendations related to AWS APIs. AWS recommendations in CodeGuru Reviewer cover a wide range of scenarios such as detecting outdated or deprecated APIs, warning about API misuse, authentication and exception scenarios, and efficient API alternatives.

Consider the pagination trait, implemented by over 1,000 APIs from more than 150 AWS services. The trait is commonly used when the response object is too large to return in a single response. To get the complete set of results, iterated calls to the API are required, until the last page is reached. If developers were not aware of this, they would write the code as the following (this example is patterned after actual code):

def sync_ddb_table(source_ddb, destination_ddb):
    response = source_ddb.scan(TableName=“table1”)
    for item in response['Items']:
        destination_ddb.put_item(TableName=“table2”, Item=item)

Here the scan API is used to read items from one Amazon DynamoDB table and the put_item API to save them to another DynamoDB table. The scan API implements the Pagination trait. However, the developer missed iterating on the results beyond the first scan, leading to only partial copying of data.

The following screenshot shows what CodeGuru Reviewer recommends:

The following screenshot shows CodeGuru Reviewer recommends on the need for pagination

The developer fixed the code based on this recommendation and added complete handling of paginated results by checking the LastEvaluatedKey value in the response object of the paginated API scan as follows:

def sync_ddb_table(source_ddb, destination_ddb):
    response = source_ddb.scan(TableName==“table1”)
    for item in response['Items']:
        destination_ddb.put_item(TableName=“table2”, Item=item)
    # Keeps scanning util LastEvaluatedKey is null
    while "LastEvaluatedKey" in response:
        response = source_ddb.scan(
        for item in response['Items']:
            destination_ddb.put_item(TableName=“table2”, Item=item)

CodeGuru Reviewer recommendation is rich and offers multiple options for implementing Paginated scan. We can also initialize the ExclusiveStartKey value to None and iteratively update it based on the LastEvaluatedKey value obtained from the scan response object in a loop. This fix below conforms to the usage mentioned in the official documentation.

def sync_ddb_table(source_ddb, destination_ddb):
    table = source_ddb.Table(“table1”)
    scan_kwargs = {
    done = False
    start_key = None
    while not done:
        if start_key:
            scan_kwargs['ExclusiveStartKey'] = start_key
        response = table.scan(**scan_kwargs)
        for item in response['Items']:
            destination_ddb.put_item(TableName=“table2”, Item=item)
        start_key = response.get('LastEvaluatedKey', None)
        done = start_key is None

Data structures and control flow

Python’s coding style is different from other languages. For code that does not conform to Python idioms, CodeGuru Reviewer provides a variety of suggestions for efficient and correct handling of data structures and control flow in the Python 3 standard library:

  • Using DefaultDict for compact handling of missing dictionary keys over using the setDefault() API or dealing with KeyError exception
  • Using a subprocess module over outdated APIs for subprocess handling
  • Detecting improper exception handling such as catching and passing generic exceptions that can hide latent issues.
  • Detecting simultaneous iteration and modification to loops that might lead to unexpected bugs because the iterator expression is only evaluated one time and does not account for subsequent index changes.

The following code is a specific example that can confuse novice developers.

def list_sns(region, creds, sns_topics=[]):
    sns = boto_session('sns', creds, region)
    response = sns.list_topics()
    for topic_arn in response["Topics"]:
    return sns_topics
def process():
    for region, creds in jobs["auth_config"]:
        arns = list_sns(region, creds)

The process() method iterates over different AWS Regions and collects Regional ARNs by calling the list_sns() method. The developer might expect that each call to list_sns() with a Region parameter returns only the corresponding Regional ARNs. However, the preceding code actually leaks the ARNs from prior calls to subsequent Regions. This happens due to an idiosyncrasy of Python relating to the use of mutable objects as default argument values. Python default value are created exactly one time, and if that object is mutated, subsequent references to the object refer to the mutated value instead of re-initialization.

The following screenshot shows what CodeGuru Reviewer recommends:

The following screenshot shows CodeGuru Reviewer recommends about initializing a value for mutable objects

The developer accepted the recommendation and issued the below fix.

def list_sns(region, creds, sns_topics=None):
    sns = boto_session('sns', creds, region)
    response = sns.list_topics()
    if sns_topics is None: 
        sns_topics = [] 
    for topic_arn in response["Topics"]:
    return sns_topics

Resource leaks

A Pythonic practice for resource handling is using Context Managers. Our analysis shows that resource leaks are rampant in Python code where a developer may open external files or windows and forget to close them eventually. A resource leak can slow down or crash your system. Even if a resource is closed, using Context Managers is Pythonic. For example, CodeGuru Reviewer detects resource leaks in the following code:

def read_lines(file):
    lines = []
    f = open(file, ‘r’)
    for line in f:
    return lines

The following screenshot shows that CodeGuru Reviewer recommends that the developer either use the ContextLib with statement or use a try-finally block to explicitly close a resource.

The following screenshot shows CodeGuru Reviewer recommend about fixing the potential resource leak

The developer accepted the recommendation and fixed the code as shown below.

def read_lines(file):
    lines = []
    with open(file, ‘r’) as f: 
        for line in f:
    return lines

Secure coding practices

Python is often used for scripting. An integral part of such scripts is the use of subprocesses. As of this writing, CodeGuru Reviewer makes a limited, but important set of recommendations to make sure that your use of eval functions or subprocesses is secure from potential shell injections. It issues a warning if it detects that the command used in eval or subprocess scenarios might be influenced by external factors. For example, see the following code:

def execute(cmd):
        retcode = subprocess.call(cmd, shell=True)
    except OSError as e:

The following screenshot shows the CodeGuru Reviewer recommendation:

The following screenshot shows CodeGuru Reviewer recommends about potential shell injection vulnerability

The developer accepted this recommendation and made the following fix.

def execute(cmd):
        retcode = subprocess.call(shlex.quote(cmd), shell=True)
    except OSError as e:

As shown in the preceding recommendations, not only are the code issues detected, but a detailed recommendation is also provided on how to fix the issues, along with a link to the Python official documentation. You can provide feedback on recommendations in the CodeGuru Reviewer console or by commenting on the code in a pull request. This feedback helps improve the performance of Reviewer so that the recommendations you see get better over time.

Now let’s take a look at CodeGuru Profiler.

CodeGuru Profiler for Python

Amazon CodeGuru Profiler analyzes your application’s performance characteristics and provides interactive visualizations to show you where your application spends its time. These visualizations a. k. a. flame graphs are a powerful tool to help you troubleshoot which code methods have high latency or are over utilizing your CPU.

Thanks to the new Python agent, you can now use CodeGuru Profiler on your Python applications to investigate performance issues.

The following list summarizes the supported versions as of this writing.

  • AWS Lambda functions: Python3.8, Python3.7, Python3.6
  • Other environments: Python3.9, Python3.8, Python3.7, Python3.6

Onboarding your Python application

For this post, let’s assume you have a Python application running on Amazon Elastic Compute Cloud (Amazon EC2) hosts that you want to profile. To onboard your Python application, complete the following steps:

1. Create a new profiling group in CodeGuru Profiler console called ProfilingGroupForMyApplication. Give access to your Amazon EC2 execution role to submit to this profiling group. See the documentation for details about how to create a Profiling Group.

2. Install the codeguru_profiler_agent module:

pip3 install codeguru_profiler_agent

3. Start the profiler in your application.

An easy way to profile your application is to start your script through the codeguru_profiler_agent module. If you have an app.py script, use the following code:

python -m codeguru_profiler_agent -p ProfilingGroupForMyApplication app.py

Alternatively, you can start the agent manually inside the code. This must be done only one time, preferably in your startup code:

from codeguru_profiler_agent import Profiler

if __name__ == "__main__":
     start_application()    # your code in there....

Onboarding your Python Lambda function

Onboarding for an AWS Lambda function is quite similar.

  1. Create a profiling group called ProfilingGroupForMyLambdaFunction, this time we select “Lambda” for the compute platform. Give access to your Lambda function role to submit to this profiling group. See the documentation for details about how to create a Profiling Group.
  2. Include the codeguru_profiler_agent module in your Lambda function code.
  3. Add the with_lambda_profiler decorator to your handler function:
from codeguru_profiler_agent import with_lambda_profiler

def handler_function(event, context):
      # Your code here

Alternatively, you can profile an existing Lambda function without updating the source code by adding a layer and changing the configuration. For more information, see Profiling your applications that run on AWS Lambda.

Profiling a Lambda function helps you see what is slowing down your code so you can reduce the duration, which reduces the cost and improves latency. You need to have continuous traffic on your function in order to produce a usable profile.

Viewing your profile

After running your profile for some time, you can view it on the CodeGuru console.

Screenshot of Flame graph visualization by CodeGuru Profiler

Each frame in the flame graph shows how much that function contributes to latency. In this example, an outbound call that crosses the network is taking most of the duration in the Lambda function, caching its result would improve the latency.

For more information, see Investigating performance issues with Amazon CodeGuru Profiler.

Supportability for CodeGuru Profiler is documented here.

If you don’t have an application to try CodeGuru Profiler on, you can use the demo application in the following GitHub repo.


This post introduced how to leverage CodeGuru Reviewer to identify hard-to-find code defects in various issue categories and how to onboard your Python applications or Lambda function in CodeGuru Profiler for CPU profiling. Combining both services can help you improve code quality for Python applications. CodeGuru is now available for you to try. For more pricing information, please see Amazon CodeGuru pricing.


About the Authors

Neela Sawant is a Senior Applied Scientist in the Amazon CodeGuru team. Her background is building AI-powered solutions to customer problems in a variety of domains such as software, multimedia, and retail. When she isn’t working, you’ll find her exploring the world anew with her toddler and hacking away at AI for social good.



Pierre Marieu is a Software Development Engineer in the Amazon CodeGuru Profiler team in London. He loves building tools that help the day-to-day life of other software engineers. Previously, he worked at Amadeus IT, building software for the travel industry.




Ran Fu is a Senior Product Manager in the Amazon CodeGuru team. He has a deep customer empathy, and love exploring who are the customers, what are their needs, and why those needs matter. Besides work, you may find him snowboarding in Keystone or Vail, Colorado.


New- Amazon DevOps Guru Helps Identify Application Errors and Fixes

Post Syndicated from Harunobu Kameda original https://aws.amazon.com/blogs/aws/amazon-devops-guru-machine-learning-powered-service-identifies-application-errors-and-fixes/

Today, we are announcing Amazon DevOps Guru, a fully managed operations service that makes it easy for developers and operators to improve application availability by automatically detecting operational issues and recommending fixes. DevOps Guru applies machine learning informed by years of operational excellence from Amazon.com and Amazon Web Services (AWS) to automatically collect and analyze data such as application metrics, logs, and events to identify behavior that deviates from normal operational patterns.

Once a behavior is identified as an operational problem or risk, DevOps Guru alerts developers and operators to the details of the problem so they can quickly understand the scope of the problem and possible causes. DevOps Guru provides intelligent recommendations for fixing problems, saving you time resolving them. With DevOps Guru, there is no hardware or software to deploy, and you only pay for the data analyzed; there is no upfront cost or commitment.

Distributed/Complex Architecture and Operational Excellence
As applications become more distributed and complex, operators need more automated practices to maintain application availability and reduce the time and effort spent on detecting, debugging, and resolving operational issues. Application downtime, for example, as caused by misconfiguration, unbalanced container clusters, or resource depletion, can result in significant revenue loss to an enterprise.

In many cases, companies must invest developer time in deploying and managing multiple monitoring tools, such as metrics, logs, traces, and events, and storing them in various locations for analysis. Developers or operators also spend time developing and maintaining custom alarms to alert them to issues such as sudden spikes in load balancer errors or unusual drops in application request rates. When a problem occurs, operators receive multiple alerts related to the same issue and spend time combining alerts to prioritize those that need immediate attention.

How DevOps Guru Works
The DevOps Guru machine learning models leverages AWS expertise in running highly available applications for the world’s largest e-commerce business for the past 20 years. DevOps Guru automatically detects operational problems, details the possible causes, and recommends remediation actions. DevOps Guru provides customers with a single console experience to search and visualize operational data by integrating data across multiple sources supporting Amazon CloudWatch, AWS Config, AWS CloudTrail, AWS CloudFormation, and AWS X-Ray and reduces the need to use multiple tools.

Getting Started with DevOps Guru
Activating DevOps Guru is as easy as accessing the AWS Management Console and clicking Enable. When enabling DevOps Guru, you can select the IAM role. You’ll then choose the AWS resources to analyze, which may include all resources in your AWS account or just specified CloudFormation StackSets. Finally, you can set an Amazon SNS topic if you want to send notifications from DevOps Guru via SNS.

DevOps Guru starts to accumulate logs and analyze your environment; it can take up to several hours. Let’s assume we have a simple serverless architecture as shown in this illustration.

When the system has an error, the operator needs to investigate if the error came from Amazon API Gateway, AWS Lambda, or AWS DynamoDB. They must then determine the root cause and how to fix the issue. With DevOps Guru, the process is now easy and simple.

When a developer accesses the management console of DevOps Guru, they will see a list of insights which is a collection of anomalies that are created during the analysis of the AWS resources configured within your application. In this case, Amazon API Gateway, AWS Lambda, and Amazon DynamoDB. Each insight contains observations, recommendations, and contextual data you can use to better understand and resolve the operational problem.

The list below shows the insight name, the status (closed or ongoing), severity, and when the insight was created. Without checking any logs, you can immediately see that in the most recent issue (line1), a problem with a Lambda function within your stack was the cause of the issue, and it was related to duration. If the issue was still occurring, the status would be listed as Ongoing. Since this issue was temporary, the status is showing Closed.


Let’s look deeper at the most recent anomaly by clicking through the first insight link. There are two tabs: Aggregated metrics and Graphed anomalies.

Aggregated metrics display metrics that are related to the insight. Operators can see which AWS CloudFormation stack created the resource that emitted the metric, the name of the resource, and its type. The red lines on a timeline indicate spans of time when a metric emitted unusual values. In this case, the operator can see the specific time of day on Nov 24 when the anomaly occurred for each metric.

Graphed anomalies display detailed graphs for each of the insight’s anomalies. Operators can investigate and look at an anomaly at the resource level and per statistic. The graphs are grouped by metric name.


By reviewing aggregated and graphed anomalies, an operator can see when the issue occurred, whether it is still ongoing, as well as the resources impacted. It appears the increased Lambda duration had a corresponding impact on API Gateway causing timeouts and resulted in 5XX errors in API Gateway.

Dev Ops Guru also provides Relevant events which are related to activities that changed your application’s configuration as illustrated below.


We can now see that a configuration change happened 2 hours before this issue occurred. If we click the point on the graph at 20:30 on 11/24, we can learn more and see the details of that change.

If you click through to the Ops event, the AWS CloudTrail logs would show that the configuration change was twofold: 1) a change in the concurrency provisioned capacity on a Lambda function and 2) the reduction in the integration timeout on an API integration latency.

recommendations to fix

The recommendations tell the operator to evaluate the provisioned concurrency for Lambda and how to troubleshoot errors in API Gateway. After further evaluation, the operator will discover this is exactly correct. The root cause is a mismatch between the Lambda provisioned concurrency setting and the API Gateway integration latency timeout. When the Lambda configuration was updated in the last deployment, it altered how this application responded to burst traffic, and it no longer fit within the API Gateway timeout window. This error is unlikely to have been found in unit testing and will occur repeatedly if the configurations are not updated.

DevOps Guru can send alerts of anomalies to operators via Amazon SNS, and it is integrated with AWS Systems Manager OpsCenter, enabling customers to receive insights directly within OpsCenter as quickly diagnose and remediate issues.

Available for Preview Today
Amazon DevOps Guru is available for preview in US East (N. Virginia), US East (Ohio), US West (Oregon), Europe (Ireland), and Asia Pacific (Tokyo). To learn more about DevOps Guru, please visit our web site and technical documentation, and get started today.

– Kame



Using NuGet with AWS CodeArtifact

Post Syndicated from John Standish original https://aws.amazon.com/blogs/devops/using-nuget-with-aws-codeartifact/

Managing NuGet packages for .NET development can be a challenge. Tasks such as initial configuration, ongoing maintenance, and scaling inefficiencies are the biggest pain points for developers and organizations. With its addition of NuGet package support, AWS CodeArtifact now provides easy-to-configure and scalable package management for .NET developers. You can use NuGet packages stored in CodeArtifact in Visual Studio, allowing you to use the tools you already know.

In this post, we show how you can provision NuGet repositories in 5 minutes. Then we demonstrate how to consume packages from your new NuGet repositories, all while using .NET native tooling.

All relevant code for this post is available in the aws-codeartifact-samples GitHub repo.


For this walkthrough, you should have the following prerequisites:

Architecture overview

Two core resource types make up CodeArtifact: domains and repositories. Domains provide an easy way manage multiple repositories within an organization. Repositories store packages and their assets. You can connect repositories to other CodeArtifact repositories, or popular public package repositories such as nuget.org, using upstream and external connections. For more information about these concepts, see AWS CodeArtifact Concepts.

The following diagram illustrates this architecture.

AWS CodeArtifact core concepts

Figure: AWS CodeArtifact core concepts

Creating CodeArtifact resources with AWS CloudFormation

The AWS CloudFormation template provided in this post provisions three CodeArtifact resources: a domain, a team repository, and a shared repository. The team repository is configured to use the shared repository as an upstream repository, and the shared repository has an external connection to nuget.org.

The following diagram illustrates this architecture.

Example AWS CodeArtifact architecture

Figure: Example AWS CodeArtifact architecture

The following CloudFormation template used in this walkthrough:

AWSTemplateFormatVersion: '2010-09-09'
Description: AWS CodeArtifact resources for dotnet

  # Create Domain
    Type: AWS::CodeArtifact::Domain
      DomainName: example-domain
        Version: 2012-10-17
          - Effect: Allow
              - !Sub arn:aws:iam::${AWS::AccountId}:root
            Resource: "*"
              - codeartifact:CreateRepository
              - codeartifact:DescribeDomain
              - codeartifact:GetAuthorizationToken
              - codeartifact:GetDomainPermissionsPolicy
              - codeartifact:ListRepositoriesInDomain

  # Create External Repository
    Type: AWS::CodeArtifact::Repository
    Condition: ProvisionNugetTeamAndUpstream
      DomainName: !GetAtt ExampleDomain.Name
      RepositoryName: my-external-repository       
        - public:nuget-org
        Version: 2012-10-17
          - Effect: Allow
              - !Sub arn:aws:iam::${AWS::AccountId}:root
            Resource: "*"
              - codeartifact:DescribePackageVersion
              - codeartifact:DescribeRepository
              - codeartifact:GetPackageVersionReadme
              - codeartifact:GetRepositoryEndpoint
              - codeartifact:ListPackageVersionAssets
              - codeartifact:ListPackageVersionDependencies
              - codeartifact:ListPackageVersions
              - codeartifact:ListPackages
              - codeartifact:PublishPackageVersion
              - codeartifact:PutPackageMetadata
              - codeartifact:ReadFromRepository

  # Create Repository
    Type: AWS::CodeArtifact::Repository
      DomainName: !GetAtt ExampleDomain.Name
      RepositoryName: my-team-repository
        - !GetAtt MyExternalRepository.Name
        Version: 2012-10-17
          - Effect: Allow
              - !Sub arn:aws:iam::${AWS::AccountId}:root
            Resource: "*"
              - codeartifact:DescribePackageVersion
              - codeartifact:DescribeRepository
              - codeartifact:GetPackageVersionReadme
              - codeartifact:GetRepositoryEndpoint
              - codeartifact:ListPackageVersionAssets
              - codeartifact:ListPackageVersionDependencies
              - codeartifact:ListPackageVersions
              - codeartifact:ListPackages
              - codeartifact:PublishPackageVersion
              - codeartifact:PutPackageMetadata
              - codeartifact:ReadFromRepository

Getting the CloudFormation template

To use the CloudFormation stack, we recommend you clone the following GitHub repo so you also have access to the example projects. See the following code:

git clone https://github.com/aws-samples/aws-codeartifact-samples.git
cd aws-codeartifact-samples/getting-started/dotnet/cloudformation/

Alternatively, you can copy the previous template into a file on your local filesystem named deploy.yml.

Provisioning the CloudFormation stack

Now that you have a local copy of the template, you need to provision the resources using a CloudFormation stack. You can deploy the stack using the AWS CLI or on the AWS CloudFormation console.

To use the AWS CLI, enter the following code:

aws cloudformation deploy \
--template-file deploy.yml \
--stack-name CodeArtifact-GettingStarted-DotNet

To use the AWS CloudFormation console, complete the following steps:

  1. On the AWS CloudFormation console, choose Create stack.
  2. Choose With new resources (standard).
  3. Select Upload a template file.
  4. Choose Choose file.
  5. Name the stack CodeArtifact-GettingStarted-DotNet.
  6. Continue to choose Next until prompted to create the stack.

Configuring your local development experience

We use the CodeArtifact credential provider to connect the Visual Studio IDE to a CodeArtifact repository. You need to download and install the AWS Toolkit for Visual Studio to configure the credential provider. The toolkit is an extension for Microsoft Visual Studio on Microsoft Windows that makes it easy to develop, debug, and deploy .NET applications to AWS. The credential provider automates fetching and refreshing the authentication token required to pull packages from CodeArtifact. For more information about the authentication process, see AWS CodeArtifact authentication and tokens.

To connect to a repository, you complete the following steps:

  1. Configure an account profile in the AWS Toolkit.
  2. Copy the source endpoint from the AWS Explorer.
  3. Set the NuGet package source as the source endpoint.
  4. Add packages for your project via your CodeArtifact repository.

Configuring an account profile in the AWS Toolkit

Before you can use the Toolkit for Visual Studio, you must provide a set of valid AWS credentials. In this step, we set up a profile that has access to interact with CodeArtifact. For instructions, see Providing AWS Credentials.

Visual Studio Toolkit for AWS Account Profile Setup

Figure: Visual Studio Toolkit for AWS Account Profile Setup

Copying the NuGet source endpoint

After you set up your profile, you can see your provisioned repositories.

  1. In the AWS Explorer pane, navigate to the repository you want to connect to.
  2. Choose your repository (right-click).
  3. Choose Copy NuGet Source Endpoint.
AWS CodeArtifact repositories shown in the AWS Explorer

Figure: AWS CodeArtifact repositories shown in the AWS Explorer


You use the source endpoint later to configure your NuGet package sources.

Setting the package source using the source endpoint

Now that you have your source endpoint, you can set up the NuGet package source.

  1. In Visual Studio, under Tools, choose Options.
  2. Choose NuGet Package Manager.
  3. Under Options, choose the + icon to add a package source.
  4. For Name , enter codeartifact.
  5. For Source, enter the source endpoint you copied from the previous step.
Configuring Nuget package sources for AWS CodeArtifact

Figure: Configuring NuGet package sources for AWS CodeArtifact


Adding packages via your CodeArtifact repository

After the package source is configured against your team repository, you can pull packages via the upstream connection to the shared repository.

  1. Choose Manage NuGet Packages for your project.
    • You can now see packages from nuget.org.
  2. Choose any package to add it to your project.
Exploring packages while connected to a AWS CodeArtifact repository

Exploring packages while connected to a AWS CodeArtifact repository

Viewing packages stored in your CodeArtifact team repository

Packages are stored in a repository you pull from, or referenced via the upstream connection. Because we’re pulling packages from nuget.org through an external connection, you can see cached copies of those packages in your repository. To view the packages, navigate to your repository on the CodeArtifact console.

Packages stored in a AWS CodeArtifact repository

Packages stored in a AWS CodeArtifact repository

Cleaning Up

When you’re finished with this walkthrough, you may want to remove any provisioned resources. To remove the resources that the CloudFormation template created, navigate to the stack on the AWS CloudFormation console and choose Delete Stack. It may take a few minutes to delete all provisioned resources.

After the resources are deleted, there are no more cleanup steps.


We have shown you how to set up CodeArtifact in minutes and easily integrate it with NuGet. You can build and push your package faster, from hours or days to minutes. You can also integrate CodeArtifact directly in your Visual Studio environment with four simple steps. With CodeArtifact repositories, you inherit the durability and security posture from the underlying storage of CodeArtifact for your packages.

As of November 2020, CodeArtifact is available in the following AWS Regions:

  • US: US East (Ohio), US East (N. Virginia), US West (Oregon)
  • AP: Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo)
  • EU: Europe (Frankfurt), Europe (Ireland), Europe (Stockholm)

For an up-to-date list of Regions where CodeArtifact is available, see AWS CodeArtifact FAQ.

About the Authors

John Standish

John Standish is a Solutions Architect at AWS and spent over 13 years as a Microsoft .Net developer. Outside of work, he enjoys playing video games, cooking, and watching hockey.

Nuatu Tseggai

Nuatu Tseggai is a Cloud Infrastructure Architect at Amazon Web Services. He enjoys working with customers to design and build event-driven distributed systems that span multiple services.

Neha Gupta

Neha Gupta is a Solutions Architect at AWS and have 16 years of experience as a Database architect/ DBA. Apart from work, she’s outdoorsy and loves to dance.

Elijah Batkoski

Elijah is a Technical Writer for Amazon Web Services. Elijah has produced technical documentation and blogs for a variety of tools and services, primarily focused around DevOps.

Automate thousands of mainframe tests on AWS with the Micro Focus Enterprise Suite

Post Syndicated from Kevin Yung original https://aws.amazon.com/blogs/devops/automate-mainframe-tests-on-aws-with-micro-focus/

Micro Focus – AWS Advanced Technology Parnter, they are a global infrastructure software company with 40 years of experience in delivering and supporting enterprise software.

We have seen mainframe customers often encounter scalability constraints, and they can’t support their development and test workforce to the scale required to support business requirements. These constraints can lead to delays, reduce product or feature releases, and make them unable to respond to market requirements. Furthermore, limits in capacity and scale often affect the quality of changes deployed, and are linked to unplanned or unexpected downtime in products or services.

The conventional approach to address these constraints is to scale up, meaning to increase MIPS/MSU capacity of the mainframe hardware available for development and testing. The cost of this approach, however, is excessively high, and to ensure time to market, you may reject this approach at the expense of quality and functionality. If you’re wrestling with these challenges, this post is written specifically for you.

To accompany this post, we developed an AWS prescriptive guidance (APG) pattern for developer instances and CI/CD pipelines: Mainframe Modernization: DevOps on AWS with Micro Focus.

Overview of solution

In the APG, we introduce DevOps automation and AWS CI/CD architecture to support mainframe application development. Our solution enables you to embrace both Test Driven Development (TDD) and Behavior Driven Development (BDD). Mainframe developers and testers can automate the tests in CI/CD pipelines so they’re repeatable and scalable. To speed up automated mainframe application tests, the solution uses team pipelines to run functional and integration tests frequently, and uses systems test pipelines to run comprehensive regression tests on demand. For more information about the pipelines, see Mainframe Modernization: DevOps on AWS with Micro Focus.

In this post, we focus on how to automate and scale mainframe application tests in AWS. We show you how to use AWS services and Micro Focus products to automate mainframe application tests with best practices. The solution can scale your mainframe application CI/CD pipeline to run thousands of tests in AWS within minutes, and you only pay a fraction of your current on-premises cost.

The following diagram illustrates the solution architecture.

Mainframe DevOps On AWS Architecture Overview, on the left is the conventional mainframe development environment, on the left is the CI/CD pipelines for mainframe tests in AWS

Figure: Mainframe DevOps On AWS Architecture Overview


Best practices

Before we get into the details of the solution, let’s recap the following mainframe application testing best practices:

  • Create a “test first” culture by writing tests for mainframe application code changes
  • Automate preparing and running tests in the CI/CD pipelines
  • Provide fast and quality feedback to project management throughout the SDLC
  • Assess and increase test coverage
  • Scale your test’s capacity and speed in line with your project schedule and requirements

Automated smoke test

In this architecture, mainframe developers can automate running functional smoke tests for new changes. This testing phase typically “smokes out” regression of core and critical business functions. You can achieve these tests using tools such as py3270 with x3270 or Robot Framework Mainframe 3270 Library.

The following code shows a feature test written in Behave and test step using py3270:

# home_loan_calculator.feature
Feature: calculate home loan monthly repayment
  the bankdemo application provides a monthly home loan repayment caculator 
  User need to input into transaction of home loan amount, interest rate and how many years of the loan maturity.
  User will be provided an output of home loan monthly repayment amount

  Scenario Outline: As a customer I want to calculate my monthly home loan repayment via a transaction
      Given home loan amount is <amount>, interest rate is <interest rate> and maturity date is <maturity date in months> months 
       When the transaction is submitted to the home loan calculator
       Then it shall show the monthly repayment of <monthly repayment>

    Examples: Homeloan
      | amount  | interest rate | maturity date in months | monthly repayment |
      | 1000000 | 3.29          | 300                     | $4894.31          |


# home_loan_calculator_steps.py
import sys, os
from py3270 import Emulator
from behave import *

@given("home loan amount is {amount}, interest rate is {rate} and maturity date is {maturity_date} months")
def step_impl(context, amount, rate, maturity_date):
    context.home_loan_amount = amount
    context.interest_rate = rate
    context.maturity_date_in_months = maturity_date

@when("the transaction is submitted to the home loan calculator")
def step_impl(context):
    # Setup connection parameters
    tn3270_host = os.getenv('TN3270_HOST')
    tn3270_port = os.getenv('TN3270_PORT')
	# Setup TN3270 connection
    em = Emulator(visible=False, timeout=120)
    em.connect(tn3270_host + ':' + tn3270_port)
	# Screen login
    em.fill_field(10, 44, 'b0001', 5)
	# Input screen fields for home loan calculator
    em.fill_field(8, 46, context.home_loan_amount, 7)
    em.fill_field(10, 46, context.interest_rate, 7)
    em.fill_field(12, 46, context.maturity_date_in_months, 7)

    # collect monthly replayment output from screen
    context.monthly_repayment = em.string_get(14, 46, 9)

@then("it shall show the monthly repayment of {amount}")
def step_impl(context, amount):
    print("expected amount is " + amount.strip() + ", and the result from screen is " + context.monthly_repayment.strip())
assert amount.strip() == context.monthly_repayment.strip()

To run this functional test in Micro Focus Enterprise Test Server (ETS), we use AWS CodeBuild.

We first need to build an Enterprise Test Server Docker image and push it to an Amazon Elastic Container Registry (Amazon ECR) registry. For instructions, see Using Enterprise Test Server with Docker.

Next, we create a CodeBuild project and uses the Enterprise Test Server Docker image in its configuration.

The following is an example AWS CloudFormation code snippet of a CodeBuild project that uses Windows Container and Enterprise Test Server:

    Type: AWS::CodeBuild::Project
      Name: !Sub '${AWS::StackName}BddTestBankDemo'
          Status: ENABLED
        Type: CODEPIPELINE
        EncryptionDisabled: true
        ComputeType: BUILD_GENERAL1_LARGE
        Image: !Sub "${EnterpriseTestServerDockerImage}:latest"
        ImagePullCredentialsType: SERVICE_ROLE
      ServiceRole: !Ref CodeBuildRole
        Type: CODEPIPELINE
        BuildSpec: bdd-test-bankdemo-buildspec.yaml

In the CodeBuild project, we need to create a buildspec to orchestrate the commands for preparing the Micro Focus Enterprise Test Server CICS environment and issue the test command. In the buildspec, we define the location for CodeBuild to look for test reports and upload them into the CodeBuild report group. The following buildspec code uses custom scripts DeployES.ps1 and StartAndWait.ps1 to start your CICS region, and runs Python Behave BDD tests:

version: 0.2
      - |
        # Run Command to start Enterprise Test Server
        CD C:\

        py -m pip install behave

        Write-Host "waiting for server to be ready ..."
        do {
          Write-Host "..."
          sleep 3  
        } until(Test-NetConnection -Port 9270 | ? { $_.TcpTestSucceeded } )

        CD C:\tests\features
        MD C:\tests\reports
        $Env:Path += ";c:\wc3270"

        $address=(Get-NetIPAddress -AddressFamily Ipv4 | where { $_.IPAddress -Match "172\.*" })
        $Env:TN3270_HOST = $address.IPAddress
        $Env:TN3270_PORT = "9270"
        behave.exe --color --junit --junit-directory C:\tests\reports
      - '**/*'
    base-directory: "C:\\tests\\reports"

In the smoke test, the team may run both unit tests and functional tests. Ideally, these tests are better to run in parallel to speed up the pipeline. In AWS CodePipeline, we can set up a stage to run multiple steps in parallel. In our example, the pipeline runs both BDD tests and Robot Framework (RPA) tests.

The following CloudFormation code snippet runs two different tests. You use the same RunOrder value to indicate the actions run in parallel.

        - Name: Tests
            - Name: RunBDDTest
                Category: Build
                Owner: AWS
                Provider: CodeBuild
                Version: 1
                ProjectName: !Ref BddTestBankDemoStage
                PrimarySource: Config
                - Name: DemoBin
                - Name: Config
              RunOrder: 1
            - Name: RunRbTest
                Category: Build
                Owner: AWS
                Provider: CodeBuild
                Version: 1
                ProjectName : !Ref RpaTestBankDemoStage
                PrimarySource: Config
                - Name: DemoBin
                - Name: Config
              RunOrder: 1  

The following screenshot shows the example actions on the CodePipeline console that use the preceding code.

Screenshot of CodePipeine parallel execution tests using a same run order value

Figure – Screenshot of CodePipeine parallel execution tests

Both DBB and RPA tests produce jUnit format reports, which CodeBuild can ingest and show on the CodeBuild console. This is a great way for project management and business users to track the quality trend of an application. The following screenshot shows the CodeBuild report generated from the BDD tests.

CodeBuild report generated from the BDD tests showing 100% pass rate

Figure – CodeBuild report generated from the BDD tests

Automated regression tests

After you test the changes in the project team pipeline, you can automatically promote them to another stream with other team members’ changes for further testing. The scope of this testing stream is significantly more comprehensive, with a greater number and wider range of tests and higher volume of test data. The changes promoted to this stream by each team member are tested in this environment at the end of each day throughout the life of the project. This provides a high-quality delivery to production, with new code and changes to existing code tested together with hundreds or thousands of tests.

In enterprise architecture, it’s commonplace to see an application client consuming web services APIs exposed from a mainframe CICS application. One approach to do regression tests for mainframe applications is to use Micro Focus Verastream Host Integrator (VHI) to record and capture 3270 data stream processing and encapsulate these 3270 data streams as business functions, which in turn are packaged as web services. When these web services are available, they can be consumed by a test automation product, which in our environment is Micro Focus UFT One. This uses the Verastream server as the orchestration engine that translates the web service requests into 3270 data streams that integrate with the mainframe CICS application. The application is deployed in Micro Focus Enterprise Test Server.

The following diagram shows the end-to-end testing components.

Regression Test the end-to-end testing components using ECS Container for Exterprise Test Server, Verastream Host Integrator and UFT One Container, all integration points are using Elastic Network Load Balancer

Figure – Regression Test Infrastructure end-to-end Setup

To ensure we have the coverage required for large mainframe applications, we sometimes need to run thousands of tests against very large production volumes of test data. We want the tests to run faster and complete as soon as possible so we reduce AWS costs—we only pay for the infrastructure when consuming resources for the life of the test environment when provisioning and running tests.

Therefore, the design of the test environment needs to scale out. The batch feature in CodeBuild allows you to run tests in batches and in parallel rather than serially. Furthermore, our solution needs to minimize interference between batches, a failure in one batch doesn’t affect another running in parallel. The following diagram depicts the high-level design, with each batch build running in its own independent infrastructure. Each infrastructure is launched as part of test preparation, and then torn down in the post-test phase.

Regression Tests in CodeBuoild Project setup to use batch mode, three batches running in independent infrastructure with containers

Figure – Regression Tests in CodeBuoild Project setup to use batch mode

Building and deploying regression test components

Following the design of the parallel regression test environment, let’s look at how we build each component and how they are deployed. The followings steps to build our regression tests use a working backward approach, starting from deployment in the Enterprise Test Server:

  1. Create a batch build in CodeBuild.
  2. Deploy to Enterprise Test Server.
  3. Deploy the VHI model.
  4. Deploy UFT One Tests.
  5. Integrate UFT One into CodeBuild and CodePipeline and test the application.

Creating a batch build in CodeBuild

We update two components to enable a batch build. First, in the CodePipeline CloudFormation resource, we set BatchEnabled to be true for the test stage. The UFT One test preparation stage uses the CloudFormation template to create the test infrastructure. The following code is an example of the AWS CloudFormation snippet with batch build enabled:

        - Name: SystemsTest
            - Name: Uft-Tests
                Category: Build
                Owner: AWS
                Provider: CodeBuild
                Version: 1
                ProjectName : !Ref UftTestBankDemoProject
                PrimarySource: Config
                BatchEnabled: true
                CombineArtifacts: true
                - Name: Config
                - Name: DemoSrc
                - Name: TestReport                
              RunOrder: 1

Second, in the buildspec configuration of the test stage, we provide a build matrix setting. We use the custom environment variable TEST_BATCH_NUMBER to indicate which set of tests runs in each batch. See the following code:

version: 0.2
  fast-fail: true
      ignore-failure: false
            - 1
            - 2
            - 3 

After setting up the batch build, CodeBuild creates multiple batches when the build starts. The following screenshot shows the batches on the CodeBuild console.

Regression tests Codebuild project ran in batch mode, three batches ran in prallel successfully

Figure – Regression tests Codebuild project ran in batch mode

Deploying to Enterprise Test Server

ETS is the transaction engine that processes all the online (and batch) requests that are initiated through external clients, such as 3270 terminals, web services, and websphere MQ. This engine provides support for various mainframe subsystems, such as CICS, IMS TM and JES, as well as code-level support for COBOL and PL/I. The following screenshot shows the Enterprise Test Server administration page.

Enterprise Server Administrator window showing configuration for CICS

Figure – Enterprise Server Administrator window

In this mainframe application testing use case, the regression tests are CICS transactions, initiated from 3270 requests (encapsulated in a web service). For more information about Enterprise Test Server, see the Enterprise Test Server and Micro Focus websites.

In the regression pipeline, after the stage of mainframe artifact compiling, we bake in the artifact into an ETS Docker container and upload the image to an Amazon ECR repository. This way, we have an immutable artifact for all the tests.

During each batch’s test preparation stage, a CloudFormation stack is deployed to create an Amazon ECS service on Windows EC2. The stack uses a Network Load Balancer as an integration point for the VHI’s integration.

The following code is an example of the CloudFormation snippet to create an Amazon ECS service using an Enterprise Test Server Docker image:

    - EtsTaskDefinition
    - EtsContainerSecurityGroup
    - EtsLoadBalancerListener
      Cluster: !Ref 'WindowsEcsClusterArn'
      DesiredCount: 1
          ContainerName: !Sub "ets-${AWS::StackName}"
          ContainerPort: 9270
          TargetGroupArn: !Ref EtsPort9270TargetGroup
      HealthCheckGracePeriodSeconds: 300          
      TaskDefinition: !Ref 'EtsTaskDefinition'
    Type: "AWS::ECS::Service"

          Image: !Sub "${AWS::AccountId}.dkr.ecr.us-east-1.amazonaws.com/systems-test/ets:latest"
            LogDriver: awslogs
              awslogs-group: !Ref 'SystemsTestLogGroup'
              awslogs-region: !Ref 'AWS::Region'
              awslogs-stream-prefix: ets
          Name: !Sub "ets-${AWS::StackName}"
          cpu: 4096
          memory: 8192
              ContainerPort: 9270
          - "powershell.exe"
          - '-F'
          - .\StartAndWait.ps1
          - 'bankdemo'
          - C:\bankdemo\
          - 'wait'
      Family: systems-test-ets
    Type: "AWS::ECS::TaskDefinition"

Deploying the VHI model

In this architecture, the VHI is a bridge between mainframe and clients.

We use the VHI designer to capture the 3270 data streams and encapsulate the relevant data streams into a business function. We can then deliver this function as a web service that can be consumed by a test management solution, such as Micro Focus UFT One.

The following screenshot shows the setup for getCheckingDetails in VHI. Along with this procedure we can also see other procedures (eg calcCostLoan) defined that get generated as a web service. The properties associated with this procedure are available on this screen to allow for the defining of the mapping of the fields between the associated 3270 screens and exposed web service.

example of VHI designer to capture the 3270 data streams and encapsulate the relevant data streams into a business function getCheckingDetails

Figure – Setup for getCheckingDetails in VHI

The following screenshot shows the editor for this procedure and is initiated by the selection of the Procedure Editor. This screen presents the 3270 screens that are involved in the business function that will be generated as a web service.

VHI designer Procedure Editor shows the procedure

Figure – VHI designer Procedure Editor shows the procedure

After you define the required functional web services in VHI designer, the resultant model is saved and deployed into a VHI Docker image. We use this image and the associated model (from VHI designer) in the pipeline outlined in this post.

For more information about VHI, see the VHI website.

The pipeline contains two steps to deploy a VHI service. First, it installs and sets up the VHI models into a VHI Docker image, and it’s pushed into Amazon ECR. Second, a CloudFormation stack is deployed to create an Amazon ECS Fargate service, which uses the latest built Docker image. In AWS CloudFormation, the VHI ECS task definition defines an environment variable for the ETS Network Load Balancer’s DNS name. Therefore, the VHI can bootstrap and point to an ETS service. In the VHI stack, it uses a Network Load Balancer as an integration point for UFT One test integration.

The following code is an example of a ECS Task Definition CloudFormation snippet that creates a VHI service in Amazon ECS Fargate and integrates it with an ETS server:

    - EtsService
    Type: AWS::ECS::TaskDefinition
      Family: systems-test-vhi
      NetworkMode: awsvpc
        - FARGATE
      ExecutionRoleArn: !Ref FargateEcsTaskExecutionRoleArn
      Cpu: 2048
      Memory: 4096
        - Cpu: 2048
          Name: !Sub "vhi-${AWS::StackName}"
          Memory: 4096
            - Name: esHostName 
              Value: !GetAtt EtsInternalLoadBalancer.DNSName
            - Name: esPort
              Value: 9270
          Image: !Ref "${AWS::AccountId}.dkr.ecr.us-east-1.amazonaws.com/systems-test/vhi:latest"
            - ContainerPort: 9680
            LogDriver: awslogs
              awslogs-group: !Ref 'SystemsTestLogGroup'
              awslogs-region: !Ref 'AWS::Region'
              awslogs-stream-prefix: vhi


Deploying UFT One Tests

UFT One is a test client that uses each of the web services created by the VHI designer to orchestrate running each of the associated business functions. Parameter data is supplied to each function, and validations are configured against the data returned. Multiple test suites are configured with different business functions with the associated data.

The following screenshot shows the test suite API_Bankdemo3, which is used in this regression test process.

the screenshot shows the test suite API_Bankdemo3 in UFT One test setup console, the API setup for getCheckingDetails

Figure – API_Bankdemo3 in UFT One Test Editor Console

For more information, see the UFT One website.

Integrating UFT One and testing the application

The last step is to integrate UFT One into CodeBuild and CodePipeline to test our mainframe application. First, we set up CodeBuild to use a UFT One container. The Docker image is available in Docker Hub. Then we author our buildspec. The buildspec has the following three phrases:

  • Setting up a UFT One license and deploying the test infrastructure
  • Starting the UFT One test suite to run regression tests
  • Tearing down the test infrastructure after tests are complete

The following code is an example of a buildspec snippet in the pre_build stage. The snippet shows the command to activate the UFT One license:

version: 0.2
# . . .
      - |
        # Activate License
        $process = Start-Process -NoNewWindow -RedirectStandardOutput LicenseInstall.log -Wait -File 'C:\Program Files (x86)\Micro Focus\Unified Functional Testing\bin\HP.UFT.LicenseInstall.exe' -ArgumentList @('concurrent', 10600, 1, ${env:AUTOPASS_LICENSE_SERVER})        
        Get-Content -Path LicenseInstall.log
        if (Select-String -Path LicenseInstall.log -Pattern 'The installation was successful.' -Quiet) {
          Write-Host 'Licensed Successfully'
        } else {
          Write-Host 'License Failed'
          exit 1

The following command in the buildspec deploys the test infrastructure using the AWS Command Line Interface (AWS CLI)

aws cloudformation deploy --stack-name $stack_name `
--template-file cicd-pipeline/systems-test-pipeline/systems-test-service.yaml `
--parameter-overrides EcsCluster=$cluster_arn `
--capabilities CAPABILITY_IAM

Because ETS and VHI are both deployed with a load balancer, the build detects when the load balancers become healthy before starting the tests. The following AWS CLI commands detect the load balancer’s target group health:

$vhi_health_state = (aws elbv2 describe-target-health --target-group-arn $vhi_target_group_arn --query 'TargetHealthDescriptions[0].TargetHealth.State' --output text)
$ets_health_state = (aws elbv2 describe-target-health --target-group-arn $ets_target_group_arn --query 'TargetHealthDescriptions[0].TargetHealth.State' --output text)          

When the targets are healthy, the build moves into the build stage, and it uses the UFT One command line to start the tests. See the following code:

$process = Start-Process -Wait  -NoNewWindow -RedirectStandardOutput UFTBatchRunnerCMD.log `
-FilePath "C:\Program Files (x86)\Micro Focus\Unified Functional Testing\bin\UFTBatchRunnerCMD.exe" `
-ArgumentList @("-source", "${env:CODEBUILD_SRC_DIR_DemoSrc}\bankdemo\tests\API_Bankdemo\API_Bankdemo${env:TEST_BATCH_NUMBER}")

The next release of Micro Focus UFT One (November or December 2020) will provide an exit status to indicate a test’s success or failure.

When the tests are complete, the post_build stage tears down the test infrastructure. The following AWS CLI command tears down the CloudFormation stack:

	  	- |
		  Write-Host "Clean up ETS, VHI Stack"
		  aws cloudformation delete-stack --stack-name $stack_name
          aws cloudformation wait stack-delete-complete --stack-name $stack_name

At the end of the build, the buildspec is set up to upload UFT One test reports as an artifact into Amazon Simple Storage Service (Amazon S3). The following screenshot is the example of a test report in HTML format generated by UFT One in CodeBuild and CodePipeline.

UFT One HTML report shows regression testresult and test detals

Figure – UFT One HTML report

A new release of Micro Focus UFT One will provide test report formats supported by CodeBuild test report groups.


In this post, we introduced the solution to use Micro Focus Enterprise Suite, Micro Focus UFT One, Micro Focus VHI, AWS developer tools, and Amazon ECS containers to automate provisioning and running mainframe application tests in AWS at scale.

The on-demand model allows you to create the same test capacity infrastructure in minutes at a fraction of your current on-premises mainframe cost. It also significantly increases your testing and delivery capacity to increase quality and reduce production downtime.

A demo of the solution is available in AWS Partner Micro Focus website AWS Mainframe CI/CD Enterprise Solution. If you’re interested in modernizing your mainframe applications, please visit Micro Focus and contact AWS mainframe business development at [email protected].


Micro Focus


Peter Woods

Peter Woods

Peter has been with Micro Focus for almost 30 years, in a variety of roles and geographies including Technical Support, Channel Sales, Product Management, Strategic Alliances Management and Pre-Sales, primarily based in Europe but for the last four years in Australia and New Zealand. In his current role as Pre-Sales Manager, Peter is charged with driving and supporting sales activity within the Application Modernization and Connectivity team, based in Melbourne.

Leo Ervin

Leo Ervin

Leo Ervin is a Senior Solutions Architect working with Micro Focus Enterprise Solutions working with the ANZ team. After completing a Mathematics degree Leo started as a PL/1 programming with a local insurance company. The next step in Leo’s career involved consulting work in PL/1 and COBOL before he joined a start-up company as a technical director and partner. This company became the first distributor of Micro Focus software in the ANZ region in 1986. Leo’s involvement with Micro Focus technology has continued from this distributorship through to today with his current focus on cloud strategies for both DevOps and re-platform implementations.

Kevin Yung

Kevin Yung

Kevin is a Senior Modernization Architect in AWS Professional Services Global Mainframe and Midrange Modernization (GM3) team. Kevin currently is focusing on leading and delivering mainframe and midrange applications modernization for large enterprise customers.

Fine-tuning blue/green deployments on application load balancer

Post Syndicated from Raghavarao Sodabathina original https://aws.amazon.com/blogs/devops/blue-green-deployments-with-application-load-balancer/

In a traditional approach to application deployment, you typically fix a failed deployment by redeploying an older, stable version of the application. Redeployment in traditional data centers is typically done on the same set of resources due to the cost and effort of provisioning additional resources. Applying the principles of agility, scalability, and automation capabilities of AWS can shift the paradigm of application deployment. This enables a better deployment technique called blue/green deployment.

Blue/green deployments provide near-zero downtime release and rollback capabilities. The fundamental idea behind blue/green deployment is to shift traffic between two identical environments that are running different versions of your application. The blue environment represents the current application version serving production traffic. In parallel, the green environment is staged running a newer version of your application. After the green environment is ready and tested, production traffic is redirected from blue to green. If any problems are identified, you can roll back by reverting traffic to the blue environment.

Canary deployments are a pattern for the slow rollout of new version of an existing application. The canary deployments incrementally deploy the new version, making it visible to new users in a slow fashion. As you gain confidence in the deployment, you can deploy it to replace the current version in its entirety.

AWS provides several options to help you automate and streamline your blue/green deployments and canary deployments, one such approach is using Application Load Balancer weighted target group feature. In this post, we  will cover the concepts of  target group stickiness, load balancer stickiness,  connection draining and  how they influence traffic shifting for  canary  and blue/green deployments when using the Application Load Balancer weighted target group feature .

Application Load Balancer weighted target groups

A target group is used to route requests to one or more registered targets like Amazon Elastic Compute Cloud (Amazon EC2) instances, fixed IP addresses, or AWS Lambda functions, among others. When creating a load balancer, you create one or more listeners and configure listener rules to direct the traffic to a target group.

Application Load Balancers now support weighted target groups routing. With this feature, you can add more than one target group to the forward action of a listener rule, and specify a weight for each group. For example, when you define a rule as having two target groups with weights of 9 and 1, the load balancer routes 90% of the traffic to the first target group and 10% to the other target group. You can create and configure your weighted target groups by using AWS Console , AWS CLI or AWS SDK.

For more information, see How do I set up weighted target groups for my Application Load Balancer?

Target group level stickiness

You can set target group stickiness to make sure clients get served from a specific target group for a configurable duration of time to ensure consistent experience. Target group stickiness is different from the already existing load balancer stickiness (also known as sticky sessions). Sticky sessions make sure that the requests from a client are always sticking to a particular target within a target group. Target group stickiness only ensures the requests are sent to a particular target group.

You can enable target group level stickiness using the AWS Command Line Interface (AWS CLI) with the TargetGroupStickinessConfig parameter, as shown in the following CLI command:

aws elbv2 modify-listener \
    --listener-arn " < LISTENER ARN > " \
    --default-actions \
       "Type": "forward",
       "Order": 1,
       "ForwardConfig": {
          "TargetGroups": [
             {"TargetGroupArn": "<Blue Target Group ARN>", "Weight": 90}, \
             {"TargetGroupArn": "<Green Target Group ARN>", "Weight": 10}, \
          "TargetGroupStickinessConfig": {
             "Enabled": true,
             "DurationSeconds": 120

In the next sections, we will see how to fine-tune weighted target group  configuration to achieve effective canary deployments and blue/green deployments.

Canary deployments with Application Load Balancer weighted target group

The canary deployment pattern allows you to roll out a new version of your application to a subset of users before making it widely available. This can be helpful in validating the stability of a new version of the application or performing A/B testing.

For this use case, you want to perform canary deployment for your application and test it by driving only 10% of the incoming traffic to your new version for 12 hours. You need to create two weighted target groups for your Application Load Balancer and use target group stickiness set to a duration of 12 hours. When target group stickiness is enabled, the requests from a client are sent to the same target group for the specified time duration.

Blue and green target groups with weights 90 and 10 for canary deployment

Figure 1: Blue and green target groups with weights 90 and 10 for canary deployment

We can define a rule as having two target groups, blue and green, with weights of 90 and 10, respectively, and enable target group level stickiness with a duration of 12 hours (43,200 seconds). The following table summarizes this configuration. See the following CLI command:

aws elbv2 modify-listener \
    --listener-arn " < LISTENER ARN > " \
    --default-actions \
       "Type": "forward",
       "Order": 1,
       "ForwardConfig": {
          "TargetGroups": [
             {"TargetGroupArn": "<Blue Target Group ARN>", "Weight": 90}, \
             {"TargetGroupArn": "<Green Target Group ARN>", "Weight": 10}, \
          "TargetGroupStickinessConfig": {
             "Enabled": true,
             "DurationSeconds": 43200

At this point, the users with existing sessions continue to be sent to the blue target group running version 1, and 10% of the new users without a session are sent to the green target group up to 12 hours running version 2, as illustrated in the following diagram.

Blue-green deployment architecture with 90% blue traffic and 10% green traffic.

Figure 2: Blue-green deployment architecture with 90% blue traffic and 10% green traffic.

When you’re confident that the new version is performing well and stable, you can update the target group weights for your blue and green target groups to be 0% and 100%, respectively, to ensure that all the traffic is shifted to your green target group. You may still see some traffic flowing into the blue target group for existing users with active session whose target group stickiness duration (in this case target group stickiness duration is 12 hours) has not expired.

Recommendation:  As illustrated above, target group stickiness duration still influences the traffic shift between blue and green targets. So we recommend you to reduce the target group stickiness duration from 12 hours to 5 minutes or less depending upon your use case to ensure that the existing users going to the blue target group also fully transition to the green target group at the earliest. Some of our customers are using target group stickiness duration as 5 minutes to shift their traffic to green target group  after successful canary testing.

The recommended value of stickiness may vary across application types. For example, for a typical 3-tier front-end deployment, lower  target group stickiness value is desirable. However, for middle tier deployment,  the target group stickiness duration value may need to be higher to account for longer transactions.

Blue/green deployments with Application Load Balancer weighted target group

For this use case, you want you perform blue/green deployment for your application to provide near-zero downtime release and rollback capabilities. You can create two weighted target groups called blue and green with the following weights applied as an initial configuration.

Blue/green deployment configuration with blue target group 100% and green target group 0%

Figure 3: Blue/green deployment configuration with blue target group 100% and green target group 0%

When you’re ready to perform the deployment, you can change the weights for blue and green targets groups to be 0% and 100%, respectively, to shift the traffic completely to your newer version of the application.

Blue/green deployment configuration with blue target group 0% and green target group 100%

Figure 4: Blue/green deployment configuration with blue target group 0% and green target group 100%

When you’re performing blue/green deployment using weighted target groups, the recommendation is to not enable target group level stickiness so that traffic shifts immediately from the blue target group to the green target group. See the following CLI command:

aws elbv2 modify-listener \
    --listener-arn "<LISTENER ARN>" \
    --default-actions \
       "Type": "forward",
       "Order": 1,
       "ForwardConfig": {
          "TargetGroups": [
             {"TargetGroupArn": "<Blue Target Group>", "Weight": 0}, \
             {"TargetGroupArn": "<Green Target Group>", "Weight": 100}, \

The following diagram shows the updated architecture.

Blue-green deployment architecture with 0% blue traffic and 100% green traffic

Figure 5: Blue-green deployment architecture with 0% blue traffic and 100% green traffic

If you need to enable target group level stickiness, you can ensure that all traffic transitions from the blue target group to the green target group by keeping the target group level stickiness duration as low as possible (5 minutes or less).

In the following code, the target group level stickiness is enabled for a duration of 5 minutes and traffic is completely shifted from the blue target group to the green target group:

aws elbv2 modify-listener \
    --listener-arn "<LISTENER ARN> " \
    --default-actions \
       "Type": "forward",
       "Order": 1,
       "ForwardConfig": {
          "TargetGroups": [
             {"TargetGroupArn": "<Blue Target Group>", "Weight": 0}, \
             {"TargetGroupArn": "<Green Target Group>", "Weight": 100}, \
          "TargetGroupStickinessConfig": {
             "Enabled": true,
             "DurationSeconds": 300

The existing users with connection stickiness to the blue target group continue to the blue target group until the 5-minute duration elapses from the last request time.

Recommendation:  As illustrated above, target group stickiness duration still influences the traffic shift between blue and green targets. So we recommend you to reduce the target group stickiness duration from  5 minutes to 1 minute or less depending upon your use case to ensure that all users transition into the green target group at the earliest.

As recommended above, the recommended value of stickiness may vary across application types.

Connection draining

To provide near-zero downtime release with blue/green deployment, you want to avoid breaking open network connections while taking an instance out of service, updating its software, or replacing it with a fresh instance that contains updated software.  In the above use cases, you can ensure graceful transition between  blue and green target groups by enabling the connection draining feature for your Elastic Load Balancers. You can do this from the AWS Management Console, the AWS CLI, or by calling the ModifyLoadBalancerAttributes function in the Elastic Load Balancing API. You can enable the feature and enter a timeout between 1 second and 1 hour. The connection time out duration depends upon your application profile. If  your application is stateless like your customers are using your website, connection time out duration of lowest value is preferable. Applications that are  transactions heavy  and connection oriented sessions like web sockets, we recommend you to choose relatively high connection draining duration as it will impact the customer experience adversely.

Load balancer stickiness

In addition to the target group level stickiness, Application Load Balancer also supports load balancer level stickiness. When a load balancer first receives a request from a client, it routes the request to a target, generates a cookie named AWSALB that encodes information about the selected target, encrypts the cookie, and includes the cookie in the response to the client. The client should include the cookie that it receives in subsequent requests to the load balancer. When the load balancer receives a request from a client that contains the cookie, if sticky sessions are enabled for the target group and the request goes to the same target, the load balancer detects the cookie and routes the request to the same target. If the cookie is present but can’t be decoded, or if it refers to a target that was deregistered or is unhealthy, the load balancer selects a new target and updates the cookie with information about the new target.

You can enable Application Load Balancer stickiness using the AWS CLI or the console. You can specify a value between 1 second–7 days.

In the context of blue/green and canary deployments, the load balancer stickiness has no influence on the traffic shifting behavior using the weighted target groups because target group stickiness takes precedence over load balancer stickiness.


In this post, we showed how to perform canary and blue/green deployments with Application Load Balancer’s weighted target group feature and how target group level stickiness impacts your canary and blue/green deployments. We also demonstrated how quickly you can enable ELB connection draining to provide near-zero downtime release with blue/green deployment. We hope that you find these recommendations helpful when you build a blue/green deployment with Application Load Balancer. You can reach out to AWS Solutions Architects and AWS Support teams for further assistance.


Raghavarao Sodabathina is an Enterprise Solutions Architect at AWS, focusing on Data Analytics, AI/ML, and Serverless Platform. He engages with customers to create innovative solutions that address customer business problems and accelerate the adoption of AWS services. In his spare time, Raghavarao enjoys spending time with his family, reading books, and watching movies.



Siva Rajamani is a Boston-based Enterprise Solutions Architect for AWS. He enjoys working closely with customers, supporting their digital transformation and AWS adoption journey. His core areas of focus are Serverless, Application Integration, and Security. Outside of work, he enjoys outdoor activities and watching documentaries.




TAGS: blue-green deployments

Automating deployments to Raspberry Pi devices using AWS CodePipeline

Post Syndicated from Ahmed ElHaw original https://aws.amazon.com/blogs/devops/automating-deployments-to-raspberry-pi-devices-using-aws-codepipeline/

Managing applications deployments on Raspberry Pi can be cumbersome, especially in headless mode and at scale when placing the devices outdoors and out of reach such as in home automation projects, in the yard (for motion detection) or on the roof (as a humidity and temperature sensor). In these use cases, you have to remotely connect via secure shell to administer the device.

It can be complicated to keep physically connecting when you need a monitor, keyboard, and mouse. Alternatively, you can connect via SSH in your home local network, provided your client workstation is also on the same private network.

In this post, we discuss using Raspberry Pi as a headless server with minimal-to-zero direct interaction by using AWS CodePipeline. We examine two use cases:

  • Managing and automating operational tasks of the Raspberry Pi, running Raspbian OS or any other Linux distribution. For more information about this configuration, see Manage Raspberry Pi devices using AWS Systems Manager.
  • Automating deployments to one or more Raspberry Pi device in headless mode (in which you don’t use a monitor or keyboard to run your device). If you use headless mode but still need to do some wireless setup, you can enable wireless networking and SSH when creating an image.

Solution overview

Our solution uses the following services:

We use CodePipeline to manage continuous integration and deployment to Raspberry Pi running Ubuntu Server 18 for ARM. As of this writing, CodeDeploy agents are supported on Windows OS, Red Hat, and Ubuntu.

For this use case, we use the image ubuntu-18.04.4-preinstalled-server-arm64+raspi3.img.

To close the loop, you edit your code or commit new revisions from your PC or Amazon Elastic Compute Cloud (Amazon EC2) to trigger the pipeline to deploy to Pi. The following diagram illustrates the architecture of our automated pipeline.


Solution Overview architectural diagram

Setting up a Raspberry Pi device

To set up a CodeDeploy agent on a Raspberry Pi device, the device should be running an Ubuntu Server 18 for ARM, which is supported by the Raspberry Pi processor architecture and the CodeDeploy agent, and it should be connected to the internet. You will need a keyboard and a monitor for the initial setup.

Follow these instructions for your initial setup:

  1. Download the Ubuntu image.

Pick the image based on your Raspberry Pi model. For this use case, we use Raspberry Pi 4 with Ubuntu 18.04.4 LTS.

  1. Burn the Ubuntu image to your microSD using a disk imager software (or other reliable tool). For instructions, see Create an Ubuntu Image for a Raspberry Pi on Windows.
  2. Configure WiFi on the Ubuntu server.

After booting from the newly flashed microSD, you can configure the OS.

  1. To enable DHCP, enter the following YAML (or create the yaml file if it doesn’t exist) to /etc/netplan/wireless.yaml:
  version: 2
      dhcp4: yes
      dhcp6: no
        "<your network ESSID>":
          password: "<your wifi password>"

Replace the variables <your network ESSID> and <your wifi password> with your wireless network SSID and password, respectively.

  1. Run the netplan by entering the following command:
[email protected]:~$ sudo netplan try

Installing CodeDeploy and registering Raspberry Pi as an on-premises instance

When the Raspberry Pi is connected to the internet, you’re ready to install the AWS Command Line Interface (AWS CLI) and the CodeDeploy agent to manage automated deployments through CodeDeploy.

To register an on-premises instance, you must use an AWS Identity and Access Management (IAM) identity to authenticate your requests. You can choose from the following options for the IAM identity and registration method you use:

  • An IAM user ARN. This is best for registering a single on-premises instance.
  • An IAM role to authenticate requests with periodically refreshed temporary credentials generated with the AWS Security Token Service (AWS STS). This is best for registering a large number of on-premises instances.

For this post, we use the first option and create an IAM user and register a single Raspberry Pi. You can use this procedure for a handful of devices. Make sure you limit the privileges of the IAM user to what you need to achieve; a scoped-down IAM policy is given in the documentation instructions. For more information, see Use the register command (IAM user ARN) to register an on-premises instance.

  1. Install the AWS CLI on Raspberry Pi with the following code:
[email protected]:~$ sudo apt install awscli
  1. Configure the AWS CLI and enter your newly created IAM access key, secret access key, and Region (for example, eu-west-1):
[email protected]:~$ sudo aws configure
AWS Access Key ID [None]: <IAM Access Key>
AWS Secret Access Key [None]: <Secret Access Key>
Default region name [None]: <AWS Region>
Default output format [None]: Leave default, press Enter.
  1. Now that the AWS CLI running on the Raspberry Pi has access to CodeDeploy API operations, you can register the device as an on-premises instance:
[email protected]:~$ sudo aws deploy register --instance-name rpi4UbuntuServer --iam-user-arn arn:aws:iam::<AWS_ACCOUNT_ID>:user/Rpi --tags Key=Name,Value=Rpi4 --region eu-west-1
Registering the on-premises instance... DONE
Adding tags to the on-premises instance... DONE

Tags allow you to assign metadata to your AWS resources. Each tag is a simple label consisting of a customer-defined key and an optional value that can make it easier to manage, search for, and filter resources by purpose, owner, environment, or other criteria.

When working with on-premises instances with CodeDeploy, tags are mandatory to select the instances for deployment. For this post, we tag the first device with Key=Name,Value=Rpi4. Generally speaking, it’s good practice to use tags on all applicable resources.

You should see something like the following screenshot on the CodeDeploy console.

CodeDeploy console

Or from the CLI, you should see the following output:

[email protected]:~$ sudo aws deploy list-on-premises-instances
    "instanceNames": [
  1. Install the CodeDeploy agent:
[email protected]:~$ sudo aws deploy install --override-config --config-file /etc/codedeploy-agent/conf/codedeploy.onpremises.yml --region eu-west-1

If the preceding command fails due to dependencies, you can get the CodeDeploy package and install it manually:

[email protected]:~$ sudo apt-get install ruby
[email protected]:~$ sudo wget https://aws-codedeploy-us-west-2.s3.amazonaws.com/latest/install
--2020-03-28 18:58:15--  https://aws-codedeploy-us-west-2.s3.amazonaws.com/latest/install
Resolving aws-codedeploy-us-west-2.s3.amazonaws.com (aws-codedeploy-us-west-2.s3.amazonaws.com)...
Connecting to aws-codedeploy-us-west-2.s3.amazonaws.com (aws-codedeploy-us-west-2.s3.amazonaws.com)||:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 13819 (13K) []
Saving to: ‘install’
install 100%[====================================================================>]  13.50K  --.-KB/s    in 0.003s 
2020-03-28 18:58:16 (3.81 MB/s) - ‘install’ saved [13819/13819]
[email protected]:~$ sudo chmod +x ./install
[email protected]:~$ sudo ./install auto

 Check the service status with the following code:

[email protected]:~$ sudo service codedeploy-agent status
codedeploy-agent.service - LSB: AWS CodeDeploy Host Agent
   Loaded: loaded (/etc/init.d/codedeploy-agent; generated)
   Active: active (running) since Sat 2020-08-15 14:18:22 +03; 17s ago
     Docs: man:systemd-sysv-generator(8)
    Tasks: 3 (limit: 4441)
   CGroup: /system.slice/codedeploy-agent.service
           └─4243 codedeploy-agent: master 4243

Start the service (if not started automatically):

[email protected]:~$ sudo service codedeploy-agent start

Congratulations! Now that the CodeDeploy agent is installed and the Raspberry Pi is registered as an on-premises instance, CodeDeploy can deploy your application build to the device.

Creating your source stage

You’re now ready to create your source stage.

  1. On the CodeCommit console, under Source, choose Repositories.
  2. Choose Create repository.

For instructions on connecting your repository from your local workstation, see Setup for HTTPS users using Git credentials.

CodeCommit repo

  1. In the root directory of the repository, you should include an AppSpec file for an EC2/On-Premises deployment, where the filename must be yml for a YAML-based file. The file name is case-sensitive.

AppSpec file

The following example code is from the appspec.yml file:

version: 0.0
os: linux
  - source: /
    destination: /home/ubuntu/AQI/
    - location: scripts/testGPIO.sh
      timeout: 60
      runas: root
    - location: scripts/testSensors.sh
      timeout: 300
      runas: root
    - location: startpublishdht11toshadow.sh
    - location: startpublishnovatoshadow.sh
      timeout: 300
      runas: root

The files section defines the files to copy from the repository to the destination path on the Raspberry Pi.

The hooks section runs one time per deployment to an instance. If an event hook isn’t present, no operation runs for that event. This section is required only if you’re running scripts as part of the deployment. It’s useful to implement some basic testing before and after installation of your application revisions. For more information about hooks, see AppSpec ‘hooks’ section for an EC2/On-Premises deployment.

Creating your deploy stage

To create your deploy stage, complete the following steps:

  1. On the CodeDeploy console, choose Applications.
  2. Create your application and deployment group.
    1. For Deployment type, select In-place.

Deployment group

  1. For Environment configuration, select On-premises instances.
  2. Add the tags you registered the instance with in the previous step (for this post, we add the key-value pair Name=RPI4.

on-premises tags

Creating your pipeline

You’re now ready to create your pipeline.

  1. On the CodePipeline console, choose Pipelines.
  2. Choose Create pipeline.
  3. For Pipeline name, enter a descriptive name.
  4. For Service role¸ select New service role.
  5. For Role name, enter your service role name.
  6. Leave the advanced settings at their default.
  7. Choose Next.


  Pipeline settings

  1. For Source provider, choose AWS CodeCommit
  2. For Repository name, choose the repository you created earlier.
  3. For Branch name, enter your repository branch name.
  4. For Change detection options, select Amazon CloudWatch Events.
  5. Choose Next.

Source stage


As an optional step, you can add a build stage, depending on whether your application is built with an interpreted language like Python or a compiled one like .NET C#. CodeBuild creates a fully managed build server on your behalf that runs the build commands using the buildspec.yml in the source code root directory.


  1. For Deploy provider, choose AWS CodeDeploy.
  2. For Region, choose your Region.
  3. For Application name, choose your application.
  4. For Deployment group, choose your deployment group.
  5. Choose Next.

Deploy stage

  1. Review your settings and create your pipeline.

Cleaning up

If you no longer plan to deploy to your Raspberry PI and want remove the CodeDeploy agent from your device, you can clean up with the following steps.

Uninstalling the agent

Automatically uninstall the CodeDeploy agent and remove the configuration file from an on-premises instance with the following code:

[email protected]:~$ sudo aws deploy uninstall
(Reading database ... 238749 files and directories currently installed.)
Removing codedeploy-agent (1.0-1.1597) ...
Processing triggers for systemd (237-3ubuntu10.39) ...
Processing triggers for ureadahead (0.100.0-21) ...
Uninstalling the AWS CodeDeploy Agent... DONE
Deleting the on-premises instance configuration... DONE

The uninstall command does the following:

  1. Stops the running CodeDeploy agent on the on-premises instance.
  2. Uninstalls the CodeDeploy agent from the on-premises instance.
  3. Removes the configuration file from the on-premises instance. (For Ubuntu Server and RHEL, this is /etc/codedeploy-agent/conf/codedeploy.onpremises.yml. For Windows Server, this is C:\ProgramData\Amazon\CodeDeploy\conf.onpremises.yml.)

De-registering the on-premises instance

This step is only supported using the AWS CLI. To de-register your instance, enter the following code:

[email protected]:~$ sudo aws deploy deregister --instance-name rpi4UbuntuServer --region eu-west-1
Retrieving on-premises instance information... DONE
IamUserArn: arn:aws:iam::XXXXXXXXXXXX:user/Rpi
Tags: Key=Name,Value=Rpi4
Removing tags from the on-premises instance... DONE
Deregistering the on-premises instance... DONE
Deleting the IAM user policies... DONE
Deleting the IAM user access keys... DONE
Deleting the IAM user (Rpi)... DONE

Optionally, delete your application from CodeDeploy, and your repository from CodeCommit and CodePipeline from the respective service consoles.


You’re now ready to automate your deployments to your Raspberry Pi or any on-premises supported operating system. Automated deployments and source code version control frees up more time in developing your applications. Continuous deployment helps with the automation and version tracking of your scripts and applications deployed on the device.

For more information about IoT projects created using a Raspberry Pi, see my Air Pollution demo and Kid Monitor demo.

About the author

Ahmed ElHaw is a Sr. Solutions Architect at Amazon Web Services (AWS) with background in telecom, web development and design, and is passionate about spatial computing and AWS serverless technologies. He enjoys providing technical guidance to customers, helping them architect and build solutions that make the best use of AWS. Outside of work he enjoys spending time with his kids and playing video games.

Rapid and flexible Infrastructure as Code using the AWS CDK with AWS Solutions Constructs

Post Syndicated from Biff Gaut original https://aws.amazon.com/blogs/devops/rapid-flexible-infrastructure-with-solutions-constructs-cdk/


As workloads move to the cloud and all infrastructure becomes virtual, infrastructure as code (IaC) becomes essential to leverage the agility of this new world. JSON and YAML are the powerful, declarative modeling languages of AWS CloudFormation, allowing you to define complex architectures using IaC. Just as higher level languages like BASIC and C abstracted away the details of assembly language and made developers more productive, the AWS Cloud Development Kit (AWS CDK) provides a programming model above the native template languages, a model that makes developers more productive when creating IaC. When you instantiate CDK objects in your Typescript (or Python, Java, etc.) application, those objects “compile” into a YAML template that the CDK deploys as an AWS CloudFormation stack.

AWS Solutions Constructs take this simplification a step further by providing a library of common service patterns built on top of the CDK. These multi-service patterns allow you to deploy multiple resources with a single object, resources that follow best practices by default – both independently and throughout their interaction.

Comparison of an Application stack with Assembly Language, 4th generation language and Object libraries such as Hibernate with an IaC stack of CloudFormation, AWS CDK and AWS Solutions Constructs

Application Development Stack vs. IaC Development Stack

Solution overview

To demonstrate how using Solutions Constructs can accelerate the development of IaC, in this post you will create an architecture that ingests and stores sensor readings using Amazon Kinesis Data Streams, AWS Lambda, and Amazon DynamoDB.

An architecture diagram showing sensor readings being sent to a Kinesis data stream. A Lambda function will receive the Kinesis records and store them in a DynamoDB table.

Prerequisite – Setting up the CDK environment

Tip – If you want to try this example but are concerned about the impact of changing the tools or versions on your workstation, try running it on AWS Cloud9. An AWS Cloud9 environment is launched with an AWS Identity and Access Management (AWS IAM) role and doesn’t require configuring with an access key. It uses the current region as the default for all CDK infrastructure.

To prepare your workstation for CDK development, confirm the following:

  • Node.js 10.3.0 or later is installed on your workstation (regardless of the language used to write CDK apps).
  • You have configured credentials for your environment. If you’re running locally you can do this by configuring the AWS Command Line Interface (AWS CLI).
  • TypeScript 2.7 or later is installed globally (npm -g install typescript)

Before creating your CDK project, install the CDK toolkit using the following command:

npm install -g aws-cdk

Create the CDK project

  1. First create a project folder called stream-ingestion with these two commands:

mkdir stream-ingestion
cd stream-ingestion

  1. Now create your CDK application using this command:

npx [email protected] init app --language=typescript

Tip – This example will be written in TypeScript – you can also specify other languages for your projects.

At this time, you must use the same version of the CDK and Solutions Constructs. We’re using version 1.68.0 of both based upon what’s available at publication time, but you can update this with a later version for your projects in the future.

Let’s explore the files in the application this command created:

  • bin/stream-ingestion.ts – This is the module that launches the application. The key line of code is:

new StreamIngestionStack(app, 'StreamIngestionStack');

This creates the actual stack, and it’s in StreamIngestionStack that you will write the CDK code that defines the resources in your architecture.

  • lib/stream-ingestion-stack.ts – This is the important class. In the constructor of StreamIngestionStack you will add the constructs that will create your architecture.

During the deployment process, the CDK uploads your Lambda function to an Amazon S3 bucket so it can be incorporated into your stack.

  1. To create that S3 bucket and any other infrastructure the CDK requires, run this command:

cdk bootstrap

The CDK uses the same supporting infrastructure for all projects within a region, so you only need to run the bootstrap command once in any region in which you create CDK stacks.

  1. To install the required Solutions Constructs packages for our architecture, run the these two commands from the command line:

npm install @aws-solutions-constructs/[email protected]
npm install @aws-solutions-constructs/[email protected]

Write the code

First you will write the Lambda function that processes the Kinesis data stream messages.

  1. Create a folder named lambda under stream-ingestion
  2. Within the lambda folder save a file called lambdaFunction.js with the following contents:
var AWS = require("aws-sdk");

// Create the DynamoDB service object
var ddb = new AWS.DynamoDB({ apiVersion: "2012-08-10" });

AWS.config.update({ region: process.env.AWS_REGION });

// We will configure our construct to 
// look for the .handler function
exports.handler = async function (event) {
  try {
    // Kinesis will deliver records 
    // in batches, so we need to iterate through
    // each record in the batch
    for (let record of event.Records) {
      const reading = parsePayload(record.kinesis.data);
      await writeRecord(record.kinesis.partitionKey, reading);
  } catch (err) {
    console.log(`Write failed, err:\n${JSON.stringify(err, null, 2)}`);
    throw err;

// Write the provided sensor reading data to the DynamoDB table
async function writeRecord(partitionKey, reading) {

  var params = {
    // Notice that Constructs automatically sets up 
    // an environment variable with the table name.
    TableName: process.env.DDB_TABLE_NAME,
    Item: {
      partitionKey: { S: partitionKey },  // sensor Id
      timestamp: { S: reading.timestamp },
      value: { N: reading.value}

  // Call DynamoDB to add the item to the table
  await ddb.putItem(params).promise();

// Decode the payload and extract the sensor data from it
function parsePayload(payload) {

  const decodedPayload = Buffer.from(payload, "base64").toString(

  // Our CLI command will send the records to Kinesis
  // with the values delimited by '|'
  const payloadValues = decodedPayload.split("|", 2)
  return {
    value: payloadValues[0],
    timestamp: payloadValues[1]

We won’t spend a lot of time explaining this function – it’s pretty straightforward and heavily commented. It receives an event with one or more sensor readings, and for each reading it extracts the pertinent data and saves it to the DynamoDB table.

You will use two Solutions Constructs to create your infrastructure:

The aws-kinesisstreams-lambda construct deploys an Amazon Kinesis data stream and a Lambda function.

  • aws-kinesisstreams-lambda creates the Kinesis data stream and Lambda function that subscribes to that stream. To support this, it also creates other resources, such as IAM roles and encryption keys.

The aws-lambda-dynamodb construct deploys a Lambda function and a DynamoDB table.

  • aws-lambda-dynamodb creates an Amazon DynamoDB table and a Lambda function with permission to access the table.
  1. To deploy the first of these two constructs, replace the code in lib/stream-ingestion-stack.ts with the following code:
import * as cdk from "@aws-cdk/core";
import * as lambda from "@aws-cdk/aws-lambda";
import { KinesisStreamsToLambda } from "@aws-solutions-constructs/aws-kinesisstreams-lambda";

import * as ddb from "@aws-cdk/aws-dynamodb";
import { LambdaToDynamoDB } from "@aws-solutions-constructs/aws-lambda-dynamodb";

export class StreamIngestionStack extends cdk.Stack {
  constructor(scope: cdk.Construct, id: string, props?: cdk.StackProps) {
    super(scope, id, props);

    const kinesisLambda = new KinesisStreamsToLambda(
        lambdaFunctionProps: {
          // Where the CDK can find the lambda function code
          runtime: lambda.Runtime.NODEJS_10_X,
          handler: "lambdaFunction.handler",
          code: lambda.Code.fromAsset("lambda"),

    // Next Solutions Construct goes here

Let’s explore this code:

  • It instantiates a new KinesisStreamsToLambda object. This Solutions Construct will launch a new Kinesis data stream and a new Lambda function, setting up the Lambda function to receive all the messages in the Kinesis data stream. It will also deploy all the additional resources and policies required for the architecture to follow best practices.
  • The third argument to the constructor is the properties object, where you specify overrides of default values or any other information the construct needs. In this case you provide properties for the encapsulated Lambda function that informs the CDK where to find the code for the Lambda function that you stored as lambda/lambdaFunction.js earlier.
  1. Now you’ll add the second construct that connects the Lambda function to a new DynamoDB table. In the same lib/stream-ingestion-stack.ts file, replace the line // Next Solutions Construct goes here with the following code:
    // Define the primary key for the new DynamoDB table
    const primaryKeyAttribute: ddb.Attribute = {
      name: "partitionKey",
      type: ddb.AttributeType.STRING,

    // Define the sort key for the new DynamoDB table
    const sortKeyAttribute: ddb.Attribute = {
      name: "timestamp",
      type: ddb.AttributeType.STRING,

    const lambdaDynamoDB = new LambdaToDynamoDB(
        // Tell construct to use the Lambda function in
        // the first construct rather than deploy a new one
        existingLambdaObj: kinesisLambda.lambdaFunction,
        tablePermissions: "Write",
        dynamoTableProps: {
          partitionKey: primaryKeyAttribute,
          sortKey: sortKeyAttribute,
          billingMode: ddb.BillingMode.PROVISIONED,
          removalPolicy: cdk.RemovalPolicy.DESTROY

    // Add autoscaling
    const readScaling = lambdaDynamoDB.dynamoTable.autoScaleReadCapacity({
      minCapacity: 1,
      maxCapacity: 50,

      targetUtilizationPercent: 50,

Let’s explore this code:

  • The first two const objects define the names and types for the partition key and sort key of the DynamoDB table.
  • The LambdaToDynamoDB construct instantiated creates a new DynamoDB table and grants access to your Lambda function. The key to this call is the properties object you pass in the third argument.
    • The first property sent to LambdaToDynamoDB is existingLambdaObj – by setting this value to the Lambda function created by KinesisStreamsToLambda, you’re telling the construct to not create a new Lambda function, but to grant the Lambda function in the other Solutions Construct access to the DynamoDB table. This illustrates how you can chain many Solutions Constructs together to create complex architectures.
    • The second property sent to LambdaToDynamoDB tells the construct to limit the Lambda function’s access to the table to write only.
    • The third property sent to LambdaToDynamoDB is actually a full properties object defining the DynamoDB table. It provides the two attribute definitions you created earlier as well as the billing mode. It also sets the RemovalPolicy to DESTROY. This policy setting ensures that the table is deleted when you delete this stack – in most cases you should accept the default setting to protect your data.
  • The last two lines of code show how you can use statements to modify a construct outside the constructor. In this case we set up auto scaling on the new DynamoDB table, which we can access with the dynamoTable property on the construct we just instantiated.

That’s all it takes to create the all resources to deploy your architecture.

  1. Save all the files, then compile the Typescript into a CDK program using this command:

npm run build

  1. Finally, launch the stack using this command:

cdk deploy

(Enter “y” in response to Do you wish to deploy all these changes (y/n)?)

You will see some warnings where you override CDK default values. Because you are doing this intentionally you may disregard these, but it’s always a good idea to review these warnings when they occur.

Tip – Many mysterious CDK project errors stem from mismatched versions. If you get stuck on an inexplicable error, check package.json and confirm that all CDK and Solutions Constructs libraries have the same version number (with no leading caret ^). If necessary, correct the version numbers, delete the package-lock.json file and node_modules tree and run npm install. Think of this as the “turn it off and on again” first response to CDK errors.

You have now deployed the entire architecture for the demo – open the CloudFormation stack in the AWS Management Console and take a few minutes to explore all 12 resources that the program deployed (and the 380 line template generated to created them).

Feed the Stream

Now use the CLI to send some data through the stack.

Go to the Kinesis Data Streams console and copy the name of the data stream. Replace the stream name in the following command and run it from the command line.

aws kinesis put-records \
--stream-name StreamIngestionStack-KinesisLambdaConstructKinesisStreamXXXXXXXX-XXXXXXXXXXXX \
--records \
PartitionKey=1301,'Data=15.4|2020-08-22T01:16:36+00:00' \

Tip – If you are using the AWS CLI v2, the previous command will result in an “Invalid base64…” error because v2 expects the inputs to be Base64 encoded by default. Adding the argument --cli-binary-format raw-in-base64-out will fix the issue.

To confirm that the messages made it through the service, open the DynamoDB console – you should see the two records in the table.

Now that you’ve got it working, pause to think about what you just did. You deployed a system that can ingest and store sensor readings and scale to handle heavy loads. You did that by instantiating two objects – well under 60 lines of code. Experiment with changing some property values and deploying the changes by running npm run build and cdk deploy again.


To clean up the resources in the stack, run this command:

cdk destroy


Just as languages like BASIC and C allowed developers to write programs at a higher level of abstraction than assembly language, the AWS CDK and AWS Solutions Constructs allow us to create CloudFormation stacks in Typescript, Java, or Python instead JSON or YAML. Just as there will always be a place for assembly language, there will always be situations where we want to write CloudFormation templates manually – but for most situations, we can now use the AWS CDK and AWS Solutions Constructs to create complex and complete architectures in a fraction of the time with very little code.

AWS Solutions Constructs can currently be used in CDK applications written in Typescript, Javascript, Java and Python and will be available in C# applications soon.

About the Author

Biff Gaut has been shipping software since 1983, from small startups to large IT shops. Along the way he has contributed to 2 books, spoken at several conferences and written many blog posts. He is now a Principal Solutions Architect at AWS working on the AWS Solutions Constructs team, helping customers deploy better architectures more quickly.

Getting started with DevOps automation

Post Syndicated from Jared Murrell original https://github.blog/2020-10-29-getting-started-with-devops-automation/

This is the second post in our series on DevOps fundamentals. For a guide to what DevOps is and answers to common DevOps myths check out part one.

What role does automation play in DevOps?

First things first—automation is one of the key principles for accelerating with DevOps. As noted in my last blog post, it enables consistency, reliability, and efficiency within the organization, making it easier for teams to discover and troubleshoot problems. 

However, as we’ve worked with organizations, we’ve found not everyone knows where to get started, or which processes can and should be automated. In this post, we’ll discuss a few best practices and insights to get teams moving in the right direction.

A few helpful guidelines

The path to DevOps automation is continually evolving. Before we dive into best practices, there are a few common guidelines to keep in mind as you’re deciding what and how you automate. 

  • Choose open standards. Your contributors and team may change, but that doesn’t mean your tooling has to. By maintaining tooling that follows common, open standards, you can simplify onboarding and save time on specialized training. Community-driven standards for packaging, runtime, configuration, and even networking and storage—like those found in Kubernetes—also become even more important as DevOps and deployments move toward the cloud.
  • Use dynamic variables. Prioritizing reusable code will reduce the amount of rework and duplication you have, both now and in the future. Whether in scripts or specialized tools, securely using externally-defined variables is an easy way to apply your automation to different environments without needing to change the code itself.
  • Use flexible tooling you can take with you. It’s not always possible to find a tool that fits every situation, but using a DevOps tool that allows you to change technologies also helps reduce rework when companies change direction. By choosing a solution with a wide ecosystem of partner integrations that works with any cloud, you’ll be able to  define your unique set of best practices and reach your goals—without being restricted by your toolchain.

DevOps automation best practices

Now that our guidelines are in place, we can evaluate which sets of processes we need to automate. We’ve broken some best practices for DevOps automation into four categories to help you get started. 

1. Continuous integration, continuous delivery, and continuous deployment

We often think of the term “DevOps” as being synonymous with “CI/CD”. At GitHub we recognize that DevOps includes so much more, from enabling contributors to build and run code (or deploy configurations) to improving developer productivity. In turn, this shortens the time it takes to build and deliver applications, helping teams add value and learn faster. While CI/CD and DevOps aren’t precisely the same, CI/CD is still a core component of DevOps automation.

  • Continuous integration (CI) is a process that implements testing on every change, enabling users to see if their changes break anything in the environment. 
  • Continuous delivery (CD) is the practice of building software in a way that allows you to deploy any successful release candidate to production at any time.
  • Continuous deployment (CD) takes continuous delivery a step further. With continuous deployment, every successful change is automatically deployed to production. Since some industries and technologies can’t immediately release new changes to customers (think hardware and manufacturing), adopting continuous deployment depends on your organization and product.

Together, continuous integration and continuous delivery (commonly referred to as CI/CD) create a collaborative process for people to work on projects through shared ownership. At the same time, teams can maintain quality control through automation and bring new features to users with continuous deployment. 

2. Change management

Change management is often a critical part of business processes. Like the automation guidelines, there are some common principles and tooling that development and operations teams can use to create consistency.  

  • Version control: The practice of using version control has a long history rooted in helping people revert changes and learn from past decisions. From RCS to SVN, CVS to Perforce, ClearCase to Git, version control is a staple for enabling teams to collaborate by providing a common workflow and code base for individuals to work with. 
  • Change control: Along with maintaining your code’s version history, having a system in place to coordinate and facilitate changes helps to maintain product direction, reduces the probability of harmful changes to your code, and encourages a collaborative process.
  • Configuration management: Configuration management makes it easier for everyone to manage complex deployments through templates and manage changes at scale with proper controls and approvals.

3. ‘X’ as code

By now, you also may have heard of “infrastructure as code,” “configuration as code,” “policy as code,” or some of the other “as code” models. These models provide a declarative framework for managing different aspects of your operating environments through high level abstractions. Stated another way, you provide variables to a tool and the output is consistently the same, allowing you to recreate your resources consistently. DevOps implements the “as code” principle with several goals, including: an auditable change trail for compliance, collaborative change process via version control, a consistent, testable and reliable way of deploying resources, and as a way to lower the learning curve for new team members. 

  • Infrastructure as code (IaC) provides a declarative model for creating immutable infrastructure using the same versioning and workflow that developers use for source code. As changes are introduced to your infrastructure requirements, new infrastructure is defined, tested, and deployed with new configurations through automated declarative pipelines.
  • Platform as code (PaC) provides a declarative model for services similar to how infrastructure as code provides a framework for recreating the same infrastructure—allowing you to rapidly deploy services to existing infrastructure with high-level abstractions.
  • Configuration as code (CaC) brings the next level of declarative pipelining by defining the configuration of your applications as versioned resources.
  • Policy as code brings versioning and the DevOps workflow to security and policy management. 

4. Continuous monitoring

Operational insights are an invaluable component of any production environment. In order to understand the behaviors of your software in production, you need to have information about how it operates. Continuous monitoring—the processes and technology that monitor performance and stability of applications and infrastructure throughout the software lifecycle—provides operations teams with data to help troubleshoot, and development teams the information needed to debug and patch. This also leads into an important aspect of security, where DevSecOps takes on these principles with a security focus. Choosing the right monitoring tools can be the difference between a slight service interruption and a major outage. When it comes to gaining operational insights, there are some important considerations: 

  • Logging gives you a continuous stream of data about your business’ critical components. Application logs, infrastructure logs, and audit logs all provide important data that helps teams learn and improve products.
  • Monitoring provides a level of intelligence and interpretation to the raw data provided in logs and metrics. With advanced tooling, monitoring can provide teams with correlated insights beyond what the raw data provides.
  • Alerting provides proactive notifications to respective teams to help them stay ahead of major issues. When effectively implemented, these alerts not only let you know when something has gone wrong, but can also provide teams with critical debugging information to help solve the problem quickly.
  • Tracing takes logging a step further, providing a deeper level of application performance and behavioral insights that can greatly impact the stability and scalability of applications in production environments.

Putting DevOps automation into action

At this point, we’ve talked much about automation in the DevOps space, so is DevOps all about automation? Put simply, no. Automation is an important means to accomplishing this work efficiently between teams. Whether you’re new to DevOps or migrating from another set of automation solutions, testing new tooling with a small project or process is a great place to start. It will lay the foundation for scaling and standardizing automation across your entire organization, including how to measure effectiveness and progression toward your goals. 

Regardless of which toolset you choose to automate your DevOps workflow, evaluating your teams’ current workflows and the information you need to do your work will help guide you to your tool and platform selection, and set the stage for success. Here are a few more resources to help you along the way:

Want to see what DevOps automation looks like in practice? See how engineers at Wiley build faster and more securely with GitHub Actions.