All posts by Julian Wood

Building well-architected serverless applications: Implementing application workload security – part 2

2021-07-13 Julian Wood

Post Syndicated from Julian Wood original https://aws.amazon.com/blogs/compute/building-well-architected-serverless-applications-implementing-application-workload-security-part-2/

This series of blog posts uses the AWS Well-Architected Tool with the Serverless Lens to help customers build and operate applications using best practices. In each post, I address the serverless-specific questions identified by the Serverless Lens along with the recommended best practices. See the introduction post for a table of contents and explanation of the example application.

Security question SEC3: How do you implement application security in your workload?

This post continues part 1 of this security question. Previously, I cover reviewing security awareness documentation such as the Common Vulnerabilities and Exposures (CVE) database. I show how to use GitHub security features to inspect and manage code dependencies. I then show how to validate inbound events using Amazon API Gateway request validation.

Required practice: Store secrets that are used in your code securely

Store secrets such as database passwords or API keys in a secrets manager. Using a secrets manager allows for auditing access, easier rotation, and prevents exposing secrets in application source code. There are a number of AWS and third-party solutions to store and manage secrets.

AWS Partner Network (APN) member Hashicorp provides Vault to keep secrets and application data secure. Vault has a centralized workflow for tightly controlling access to secrets across applications, systems, and infrastructure. You can store secrets in Vault and access them from an AWS Lambda function to, for example, access a database. You can use the Vault Agent for AWS to authenticate with Vault, receive the database credentials, and then perform the necessary queries. You can also use the Vault AWS Lambda extension to manage the connectivity to Vault.

AWS Systems Manager Parameter Store allows you to store configuration data securely, including secrets, as parameter values.

AWS Secrets Manager enables you to replace hardcoded credentials in your code with an API call to Secrets Manager to retrieve the secret programmatically. You can protect, rotate, manage, and retrieve database credentials, API keys, and other secrets throughout their lifecycle. You can also generate secure secrets. By default, Secrets Manager does not write or cache the secret to persistent storage.

Parameter Store integrates with Secrets Manager. For more information, see “Referencing AWS Secrets Manager secrets from Parameter Store parameters.”

To show how Secrets Manager works, deploy the solution detailed in “How to securely provide database credentials to Lambda functions by using AWS Secrets Manager”.

The AWS CloudFormation stack deploys an Amazon RDS MySQL database with a randomly generated password. This is stored in Secrets Manager using a secret resource. A Lambda function behind an API Gateway endpoint returns the record count in a table from the database, using the required credentials. Lambda function environment variables store the database connection details and which secret to return for the database password. The password is not stored as an environment variable, nor in the Lambda function application code.

Lambda environment variables for Secrets Manager

The application flow is as follows:

Clients call the API Gateway endpoint
API Gateway invokes the Lambda function
The Lambda function retrieves the database secrets using the Secrets Manager API
The Lambda function connects to the RDS database using the credentials from Secrets Manager and returns the query results

View the password secret value in the Secrets Manager console, which is randomly generated as part of the stack deployment.

Example password stored in Secrets Manager

The Lambda function includes the following code to retrieve the secret from Secrets Manager. The function then uses it to connect to the database securely.

secret_name = os.environ['SECRET_NAME']
rds_host = os.environ['RDS_HOST']
name = os.environ['RDS_USERNAME']
db_name = os.environ['RDS_DB_NAME']

session = boto3.session.Session()
client = session.client(
	service_name='secretsmanager',
	region_name=region_name
)
get_secret_value_response = client.get_secret_value(
	SecretId=secret_name
)
...
secret = get_secret_value_response['SecretString']
j = json.loads(secret)
password = j['password']
...
conn = pymysql.connect(
	rds_host, user=name, passwd=password, db=db_name, connect_timeout=5)

Browsing to the endpoint URL specified in the CloudFormation output displays the number of records. This confirms that the Lambda function has successfully retrieved the secure database credentials and queried the table for the record count.

Lambda function retrieving database credentials

Audit secrets access through a secrets manager

Monitor how your secrets are used to confirm that the usage is expected, and log any changes to them. This helps to ensure that any unexpected usage or change can be investigated, and unwanted changes can be rolled back.

Hashicorp Vault uses Audit devices that keep a detailed log of all requests and responses to Vault. Audit devices can append logs to a file, write to syslog, or write to a socket.

Secrets Manager supports logging API calls with AWS CloudTrail. CloudTrail captures all API calls for Secrets Manager as events. This includes calls from the Secrets Manager console and from code calling the Secrets Manager APIs.

Viewing the CloudTrail event history shows the requests to secretsmanager.amazonaws.com. This shows the requests from the console in addition to the Lambda function.

CloudTrail showing access to Secrets Manager

Secrets Manager also works with Amazon EventBridge so you can trigger alerts when administrator-specified operations occur. You can configure EventBridge rules to alert on deleted secrets or secret rotation. You can also create an alert if anyone tries to use a secret version while it is pending deletion. This can identify and alert when there is an attempt to use an out-of-date secret.

Enforce least privilege access to secrets

Access to secrets must be tightly controlled because the secrets contain sensitive information. Create AWS Identity and Access Management (IAM) policies that enable minimal access to secrets to prevent credentials being accidentally used or compromised. Secrets that have policies that are too permissive could be misused by other environments or developers. This can lead to accidental data loss or compromised systems. For more information, see “Authentication and access control for AWS Secrets Manager”.

Rotate secrets frequently.

Rotating your workload secrets is important. This prevents misuse of your secrets since they become invalid within a configured time period.

Secrets Manager allows you to rotate secrets on a schedule or on demand. This enables you to replace long-term secrets with short-term ones, significantly reducing the risk of compromise. Secrets Manager creates a CloudFormation stack with a Lambda function to manage the rotation process for you. Secrets Manager has native integrations with Amazon RDS, Amazon Redshift, and Amazon DocumentDB. It populates the function with the Amazon Resource Name (ARN) of the secret. You specify the permissions to rotate the credentials, and how often you want to rotate the secret.

The CloudFormation stack creates a MySecretRotationSchedule resource with a MyRotationLambda function to rotate the secret every 30 days.

MySecretRotationSchedule:
    Type: AWS::SecretsManager::RotationSchedule
    DependsOn: SecretRDSInstanceAttachment
    Properties:
    SecretId: !Ref MyRDSInstanceRotationSecret
    RotationLambdaARN: !GetAtt MyRotationLambda.Arn
    RotationRules:
        AutomaticallyAfterDays: 30
MyRotationLambda:
    Type: AWS::Serverless::Function
    Properties:
    Runtime: python3.7
    Role: !GetAtt MyLambdaExecutionRole.Arn
    Handler: mysql_secret_rotation.lambda_handler
    Description: 'This is a lambda to rotate MySql user passwd'
    FunctionName: 'cfn-rotation-lambda'
    CodeUri: 's3://devsecopsblog/code.zip'      
    Environment:
        Variables:
        SECRETS_MANAGER_ENDPOINT: !Sub 'https://secretsmanager.${AWS::Region}.amazonaws.com'

View and edit the rotation settings in the Secrets Manager console.

Secrets Manager rotation settings

Manually rotate the secret by selecting Rotate secret immediately. This invokes the Lambda function, which updates the database password and updates the secret in Secrets Manager.

View the updated secret in Secrets Manager, where the password has changed.

Secrets Manager password change

Browse to the endpoint URL to confirm you can still access the database with the updated credentials.

Access endpoint with updated Secret Manager password

You can provide your own code to customize a Lambda rotation function for other databases or services. The code includes the commands required to interact with your secured service to update or add credentials.

Conclusion

Implementing application security in your workload involves reviewing and automating security practices at the application code level. By implementing code security, you can protect against emerging security threats. You can improve the security posture by checking for malicious code, including third-party dependencies.

In this post, I continue from part 1, looking at securely storing, auditing, and rotating secrets that are used in your application code.

In the next post in the series, I start to cover the reliability pillar from the Well-Architected Serverless Lens with regulating inbound request rates.

For more serverless learning resources, visit Serverless Land.

Building well-architected serverless applications: Implementing application workload security – part 1

2021-07-06 Julian Wood

Post Syndicated from Julian Wood original https://aws.amazon.com/blogs/compute/building-well-architected-serverless-applications-implementing-application-workload-security-part-1/

Security question SEC3: How do you implement application security in your workload?

Review and automate security practices at the application code level, and enforce security code review as part of development workflow. By implementing security at the application code level, you can protect against emerging security threats and reduce the attack surface from malicious code, including third-party dependencies.

Required practice: Review security awareness documents frequently

Stay up to date with both AWS and industry security best practices to understand and evolve protection of your workloads. Having a clear understanding of common threats helps you to mitigate them when developing your workloads.

The AWS Security Blog provides security-specific AWS content. The Open Web Application Security Project (OWASP) Top 10 is a guide for security practitioners to understand the most common application attacks and risks. The OWASP Top 10 Serverless Interpretation provides information specific to serverless applications.

Review and subscribe to vulnerability and security bulletins

Regularly review news feeds from multiple sources that are relevant to the technologies used in your workload. Subscribe to notification services to be informed of critical threats in near-real time.

The Common Vulnerabilities and Exposures (CVE) program identifies, defines, and catalogs publicly disclosed cybersecurity vulnerabilities. You can search the CVE list directly, for example “Python”.

CVE Python search

The US National Vulnerability Database (NVD) allows you to search by vulnerability type, severity, and impact. You can also perform advanced searching by vendor name, product name, and version numbers. GitHub also integrates with CVE, which allows for advanced searching within the CVEproject/cvelist repository.

AWS Security Bulletins are a notification system for security and privacy events related to AWS services. Subscribe to the security bulletin RSS feed to keep up to date with AWS security announcements.

The US Cybersecurity and Infrastructure Security Agency (CISA) provides alerts about current security issues, vulnerabilities, and exploits. You can receive email alerts or subscribe to the RSS feed.

AWS Partner Network (APN) member Palo Alto Networks provides the “Serverless architectures Security Top 10” list. This is a security awareness and education guide to use while designing, developing, and testing serverless applications to help minimize security risks.

Good practice: Automatically review a workload’s code dependencies/libraries

Regularly reviewing application and code dependencies is a good industry security practice. This helps detect and prevent non-certified application code, and ensure that third-party application dependencies operate as intended.

Implement security mechanisms to verify application code and dependencies before using them

Combine automated and manual security code reviews to examine application code and its dependencies to ensure they operate as intended. Automated tools can help identify overly complex application code, and common security vulnerability exposures that are already cataloged.

Manual security code reviews, in addition to automated tools, help ensure that application code works as intended. Manual reviews can include business contextual information and integrations that automated tools may not capture.

Before adding any code dependencies to your workload, take time to review and certify each dependency to ensure that you are adding secure code. Use third-party services to review your code dependencies on every commit automatically.

OWASP has a code review guide and dependency check tool that attempt to detect publicly disclosed vulnerabilities within a project’s dependencies. The tool has a command line interface, a Maven plugin, an Ant task, and a Jenkins plugin.

GitHub has a number of security features for hosted repositories to inspect and manage code dependencies.

The dependency graph allows you to explore the packages that your repository depends on. Dependabot alerts show information about dependencies that are known to contain security vulnerabilities. You can choose whether to have pull requests generated automatically to update these dependencies. Code scanning alerts automatically scan code files to detect security vulnerabilities and coding errors.

You can enable these features by navigating to the Settings tab, and selecting Security & analysis.

GitHub configure security and analysis features

Once Dependabot analyzes the repository, you can view the dependencies graph from the Insights tab. In the serverless airline example used in this series, you can view the Loyalty service package.json dependencies.

Serverless airline loyalty dependencies

Dependabot alerts for security vulnerabilities are visible in the Security tab. You can review alerts and see information about how to resolve them.

Dependabot alert

Once Dependabot alerts are enabled for a repository, you can also view the alerts when pushing code to the repository from the terminal.

Dependabot terminal alert

If you enable security updates, Dependabot can automatically create pull requests to update dependencies.

Dependabot pull requests

AWS Partner Network (APN) member Snyk has an integration with AWS Lambda to manage the security of your function code. Snyk determines what code and dependencies are currently deployed for Node.js, Ruby, and Java projects. It tests dependencies against their vulnerability database.

If you build your functions using container images, you can use Amazon Elastic Container Registry’s (ECR) image scanning feature. You can manually scan your images, or scan them on each push to your repository.

Elastic Container Registry image scanning example results

Best practice: Validate inbound events

Sanitize inbound events and validate them against a predefined schema. This helps prevent errors and increases your workload’s security posture by catching malformed events or events intentionally crafted to be malicious. The OWASP Input validation cheat sheet includes guidance for providing input validation security functionality in your applications.

Validate incoming HTTP requests against a schema

Implicitly trusting data from clients could lead to malformed data being processed. Use data type validators or web application frameworks to ensure data correctness. These should include regular expressions, value range, data structure, and data normalization.

You can configure Amazon API Gateway to perform basic validation of an API request before proceeding with the integration request to add another layer of security. This ensures that the HTTP request matches the desired format. Any HTTP request that does not pass validation is rejected, returning a 400 error response to the caller.

The Serverless Security Workshop has a module on API Gateway input validation based on the fictional Wild Rydes unicorn raid hailing service. The example shows a REST API endpoint where partner companies of Wild Rydes can submit unicorn customizations, such as branded capes, to advertise their company. The API endpoint should ensure that the request body follows specific patterns. These include checking the ImageURL is a valid URL, and the ID for Cape is a numeric value.

In API Gateway, a model defines the data structure of a payload, using the JSON schema draft 4. The model ensures that you receive the parameters in the format you expect. You can check them against regular expressions. The CustomizationPost model specifies that the ImageURL and Cape schemas should contain the following valid patterns:

    "imageUrl": {
      "type": "string",
      "title": "The Imageurl Schema",
      "pattern": "^https?:\/\/[-a-zA-Z0-9@:%_+.~#?&//=]+$"
    },
    "sock": {
      "type": "string",
      "title": " The Cape Schema ",
      "pattern": "^[0-9]*$"
    },
    …

The model is applied to the /customizations/post method as part of the Method Request. The Request Validator is set to Validate body and the CustomizationPost model is set for the Request Body.

API Gateway request validator

When testing the POST /customizations API with valid parameters using the following input:

{  
   "name":"Cherry-themed unicorn",
   "imageUrl":"https://en.wikipedia.org/wiki/Cherry#/media/File:Cherry_Stella444.jpg",
   "sock": "1",
   "horn": "2",
   "glasses": "3",
   "cape": "4"
}

The result is a valid response:

{"customUnicornId":<the-id-of-the-customization>}

Testing validation to the POST /customizations API using invalid parameters shows the input validation process.

The ImageUrl is not a valid URL:

 {  
    "name":"Cherry-themed unicorn",
    "imageUrl":"htt://en.wikipedia.org/wiki/Cherry#/media/File:Cherry_Stella444.jpg",
    "sock": "1" ,
    "horn": "2" ,
    "glasses": "3",
    "cape": "4"
 }

The Cape parameter is not a number, which shows a SQL injection attempt.

 {  
    "name":"Orange-themed unicorn",
    "imageUrl":"https://en.wikipedia.org/wiki/Orange_(fruit)#/media/File:Orange-Whole-%26-Split.jpg",
    "sock": "1",
    "horn": "2",
    "glasses": "3",
    "cape":"2); INSERT INTO Cape (NAME,PRICE) VALUES ('Bad color', 10000.00"
 }

These return a 400 Bad Request response from API Gateway before invoking the Lambda function:

{"message": "Invalid request body"}

To gain further protection, consider adding an AWS Web Application Firewall (AWS WAF) access control list to your API endpoint. The workshop includes an AWS WAF module to explore three AWS WAF rules:

Restrict the maximum size of request body
SQL injection condition as part of the request URI
Rate-based rule to prevent an overwhelming number of requests

AWS WAF ACL

AWS WAF also includes support for custom responses and request header insertion to improve the user experience and security posture of your applications.

For more API Gateway security information, see the security overview whitepaper.

Also add further input validation logic to your Lambda function code itself. For examples, see “Input Validation for Serverless”.

Conclusion

In this post, I cover reviewing security awareness documentation such as the CVE database. I show how to use GitHub security features to inspect and manage code dependencies. I then show how to validate inbound events using API Gateway request validation.

This well-architected question will be continued where I look at securely storing, auditing, and rotating secrets that are used in your application code.

For more serverless learning resources, visit Serverless Land.

Building well-architected serverless applications: Managing application security boundaries – part 2

2021-06-29 Julian Wood

Post Syndicated from Julian Wood original https://aws.amazon.com/blogs/compute/building-well-architected-serverless-applications-managing-application-security-boundaries-part-2/

This series uses the AWS Well-Architected Tool with the Serverless Lens to help customers build and operate applications using best practices. In each post, I address the nine serverless-specific questions identified by the Serverless Lens along with the recommended best practices. See the introduction post for a table of contents and explanation of the example application.

Security question SEC2: How do you manage your serverless application’s security boundaries?

This post continues part 1 of this security question. Previously, I cover how to evaluate and define resource policies, showing what policies are available for various serverless services. I show some of the features of AWS Web Application Firewall (AWS WAF) to protect APIs. Then then go through how to control network traffic at all layers. I explain how AWS Lambda functions connect to VPCs, and how to use private APIs and VPC endpoints. I walk through how to audit your traffic.

Required practice: Use temporary credentials between resources and components

Do not share credentials and permissions policies between resources to maintain a granular segregation of permissions and improve the security posture. Use temporary credentials that are frequently rotated and that have policies tailored to the access the resource needs.

Use dynamic authentication when accessing components and managed services

AWS Identity and Access Management (IAM) roles allows your applications to access AWS services securely without requiring you to manage or hardcode the security credentials. When you use a role, you don’t have to distribute long-term credentials such as a user name and password, or access keys. Instead, the role supplies temporary permissions that applications can use when they make calls to other AWS resources. When you create a Lambda function, for example, you specify an IAM role to associate with the function. The function can then use the role-supplied temporary credentials to sign API requests.

Use IAM for authorizing access to AWS managed services such as Lambda or Amazon S3. Lambda also assumes IAM roles, exposing and rotating temporary credentials to your functions. This enables your application code to access AWS services.

Use IAM to authorize access to internal or private Amazon API Gateway API consumers. See this list of AWS services that work with IAM.

Within the serverless airline example used in this series, the loyalty service uses a Lambda function to fetch loyalty points and next tier progress. AWS AppSync acts as the client using an HTTP resolver, via an API Gateway REST API /loyalty/{customerId}/get resource, to invoke the function.

To ensure only AWS AppSync is authorized to invoke the API, IAM authorization is set within the API Gateway method request.

Viewing API Gateway IAM authorization

The IAM role specifies that appsync.amazonaws.com can perform an execute-api:Invoke on the specific API Gateway resource arn:aws:execute-api:${AWS::Region}:${AWS::AccountId}:${LoyaltyApi}/*/*/*

For more information, see “Using an IAM role to grant permissions to applications”.

Use a framework such as the AWS Serverless Application Model (AWS SAM) to deploy your applications. This ensures that AWS resources are provisioned with unique per resource IAM roles. For example, AWS SAM automatically creates unique IAM roles for every Lambda function you create.

Best practice: Design smaller, single purpose functions

Creating smaller, single purpose functions enables you to keep your permissions aligned to least privileged access. This reduces the risk of compromise since the function does not require access to more than it needs.

Create single purpose functions with their own IAM role

Single purpose Lambda functions allow you to create IAM roles that are specific to your access requirements. For example, a large multipurpose function might need access to multiple AWS resources such as Amazon DynamoDB, Amazon S3, and Amazon Simple Queue Service (SQS). Single purpose functions would not need access to all of them at the same time.

With smaller, single purpose functions, it’s often easier to identify the specific resources and access requirements, and grant only those permissions. Additionally, new features are usually implemented by new functions in this architectural design. You can specifically grant permissions in new IAM roles for these functions.

Avoid sharing IAM roles with multiple cloud resources. As permissions are added to the role, these are shared across all resources using this role. For example, use one dedicated IAM role per Lambda function. This allows you to control permissions more intentionally. Even if some functions have the same policy initially, always separate the IAM roles to ensure least privilege policies.

Use least privilege access policies with your users and roles

When you create IAM policies, follow the standard security advice of granting least privilege, or granting only the permissions required to perform a task. Determine what users (and roles) must do and then craft policies that allow them to perform only those tasks.

Start with a minimum set of permissions and grant additional permissions as necessary. Doing so is more secure than starting with permissions that are too lenient and then trying to tighten them later. In the unlikely event of misused credentials, credentials will only be able to perform limited interactions.

To control access to AWS resources, AWS SAM uses the same mechanisms as AWS CloudFormation. For more information, see “Controlling access with AWS Identity and Access Management” in the AWS CloudFormation User Guide.

For a Lambda function, AWS SAM scopes the permissions of your Lambda functions to the resources that are used by your application. You add IAM policies as part of the AWS SAM template. The policies property can be the name of AWS managed policies, inline IAM policy documents, or AWS SAM policy templates.

For example, the serverless airline has a ConfirmBooking Lambda function that has UpdateItem permissions to the specific DynamoDB BookingTable resource.

Parameters:
    BookingTable:
        Type: AWS::SSM::Parameter::Value<String>
        Description: Parameter Name for Booking Table
Resources:
    ConfirmBooking:
        Type: AWS::Serverless::Function
        Properties:
            FunctionName: !Sub ServerlessAirline-ConfirmBooking-${Stage}
            Policies:
                - Version: "2012-10-17"
                  Statement:
                      Action: dynamodb:UpdateItem
                      Effect: Allow
                      Resource: !Sub "arn:${AWS::Partition}:dynamodb:${AWS::Region}:${AWS::AccountId}:table/${BookingTable}"

One of the fastest ways to scope permissions appropriately is to use AWS SAM policy templates. You can reference these templates directly in the AWS SAM template for your application, providing custom parameters as required.

The serverless patterns collection allows you to build integrations quickly using AWS SAM and AWS Cloud Development Kit (AWS CDK) templates.

The booking service uses the SNSPublishMessagePolicy. This policy gives permission to the NotifyBooking Lambda function to publish a message to an Amazon Simple Notification Service (Amazon SNS) topic.

    BookingTopic:
        Type: AWS::SNS::Topic

    NotifyBooking:
        Type: AWS::Serverless::Function
        Properties:
            Policies:
                - SNSPublishMessagePolicy:
                      TopicName: !Sub ${BookingTopic.TopicName}
        …

Auditing permissions and removing unnecessary permissions

Audit permissions regularly to help you identify unused permissions so that you can remove them. You can use last accessed information to refine your policies and allow access to only the services and actions that your entities use. Use the IAM console to view when last an IAM role was used.

IAM last used

Use IAM access advisor to review when was the last time an AWS service was used from a specific IAM user or role. You can view last accessed information for IAM on the Access Advisor tab in the IAM console. Using this information, you can remove IAM policies and access from your IAM roles.

IAM access advisor

When creating and editing policies, you can validate them using IAM Access Analyzer, which provides over 100 policy checks. It generates security warnings when a statement in your policy allows access AWS considers overly permissive. Use the security warning’s actionable recommendations to help grant least privilege. To learn more about policy checks provided by IAM Access Analyzer, see “IAM Access Analyzer policy validation”.

With AWS CloudTrail, you can use CloudTrail event history to review individual actions your IAM role has performed in the past. Using this information, you can detect which permissions were actively used, and decide to remove permissions.

AWS CloudTrail

To work out which permissions you may need, you can generate IAM policies based on access activity. You configure an IAM role with broad permissions while the application is in development. Access Analyzer reviews your CloudTrail logs. It generates a policy template that contains the permissions that the role used in your specified date range. Use the template to create a policy that grants only the permissions needed to support your specific use case. For more information, see “Generate policies based on access activity”.

IAM Access Analyzer

Conclusion

Managing your serverless application’s security boundaries ensures isolation for, within, and between components. In this post, I continue from part 1, looking at using temporary credentials between resources and components. I cover why smaller, single purpose functions are better from a security perspective, and how to audit permissions. I show how to use AWS SAM to create per-function IAM roles.

For more serverless learning resources, visit https://serverlessland.com.

Using GitHub Actions to deploy serverless applications

2021-06-24 Julian Wood

Post Syndicated from Julian Wood original https://aws.amazon.com/blogs/compute/using-github-actions-to-deploy-serverless-applications/

This post is written by Gopi Krishnamurthy, Senior Solutions Architect.

Continuous integration and continuous deployment (CI/CD) is one of the major DevOps components. This allows you to build, test, and deploy your applications rapidly and reliably, while improving quality and reducing time to market.

GitHub is an AWS Partner Network (APN) with the AWS DevOps Competency. GitHub Actions is a GitHub feature that allows you to automate tasks within your software development lifecycle. You can use GitHub Actions to run a CI/CD pipeline to build, test, and deploy software directly from GitHub.

The AWS Serverless Application Model (AWS SAM) is an open-source framework for building serverless applications. It provides shorthand syntax to express functions, APIs, databases, and event source mappings. With a few lines per resource, you can define the application you want and model it using YAML.

During deployment, AWS SAM transforms and expands the AWS SAM syntax into AWS CloudFormation syntax, enabling you to build serverless applications faster. The AWS SAM CLI allows you to build, test, and debug applications locally, defined by AWS SAM templates. You can also use the AWS SAM CLI to deploy your applications to AWS. For AWS SAM example code, see the serverless patterns collection.

In this post, you learn how to create a sample serverless application using AWS SAM. You then use GitHub Actions to build, and deploy the application in your AWS account.

New GitHub action setup-sam

A GitHub Actions runner is the application that runs a job from a GitHub Actions workflow. You can use a GitHub hosted runner, which is a virtual machine hosted by GitHub with the runner application installed. You can also host your own runners to customize the environment used to run jobs in your GitHub Actions workflows.

AWS has released a GitHub action called setup-sam to install AWS SAM, which is pre-installed on GitHub hosted runners. You can use this action to install a specific, or the latest AWS SAM version.

This demo uses AWS SAM to create a small serverless application using one of the built-in templates. When the code is pushed to GitHub, a GitHub Actions workflow triggers a GitHub CI/CD pipeline. This builds, and deploys your code directly from GitHub to your AWS account.

Prerequisites

A GitHub account: This post assumes you have the required permissions to configure GitHub repositories, create workflows, and configure GitHub secrets.
Create a new GitHub repository and clone it to your local environment. For this example, create a repository called github-actions-with-aws-sam.
An AWS account with permissions to create the necessary resources.
Install AWS Command Line Interface (CLI) and AWS SAM CLI locally. This is separate from using the AWS SAM CLI in a GitHub Actions runner. If you use AWS Cloud9 as your integrated development environment (IDE), AWS CLI and AWS SAM are pre-installed.
Create an Amazon S3 bucket in your AWS account to store the build package for deployment.
An AWS user with access keys, which the GitHub Actions runner uses to deploy the application. The user also write requires access to the S3 bucket.

Creating the AWS SAM application

You can create a serverless application by defining all required resources in an AWS SAM template. AWS SAM provides a number of quick-start templates to create an application.

From the CLI, open a terminal, navigate to the parent of the cloned repository directory, and enter the following:

sam init -r python3.8 -n github-actions-with-aws-sam --app-template "hello-world"

When asked to select package type (zip or image), select zip.

This creates an AWS SAM application in the root of the repository named github-actions-with-aws-sam, using the default configuration. This consists of a single AWS Lambda Python 3.8 function invoked by an Amazon API Gateway endpoint.

To see additional runtimes supported by AWS SAM and options for sam init, enter sam init -h.

Local testing

AWS SAM allows you to test your applications locally. AWS SAM provides a default event in events/event.json that includes a message body of {\"message\": \"hello world\"}.

Invoke the HelloWorldFunction Lambda function locally, passing the default event:

sam local invoke HelloWorldFunction -e events/event.json

The function response is:

{"message": "hello world"}

Test the API Gateway functionality in front of the Lambda function by first starting the API locally:

sam local start-api

AWS SAM launches a Docker container with a mock API Gateway endpoint listening on localhost:3000.
Use curl to call the hello API:

curl http://127.0.0.1:3000/hello

The API response should be:

{"message": "hello world"}

Creating the sam-pipeline.yml file

GitHub CI/CD pipelines are configured using a YAML file. This file configures what specific action triggers a workflow, such as push on main, and what workflow steps are required.

In the root of the repository containing the files generated by sam init, create the directory: .github/workflows.

Create a new file called sam-pipeline.yml under the .github/workflows directory.

sam-pipeline.yml file

Edit the sam-pipeline.yml file and add the following:

on:
  push:
    branches:
      - main
jobs:
  build-deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - uses: actions/setup-python@v2
      - uses: aws-actions/setup-sam@v1
      - uses: aws-actions/configure-aws-credentials@v1
        with:
          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
          aws-region: ##region##
      # sam build 
      - run: sam build --use-container

# Run Unit tests- Specify unit tests here 

# sam deploy
      - run: sam deploy --no-confirm-changeset --no-fail-on-empty-changeset --stack-name sam-hello-world --s3-bucket ##s3-bucket## --capabilities CAPABILITY_IAM --region ##region##

Replace ##s3-bucket## with the name of the S3 bucket previously created to store the deployment package.
Replace both ##region## with your AWS Region.

The configuration triggers the GitHub Actions CI/CD pipeline when code is pushed to the main branch. You can amend this if you are using another branch. For a full list of supported events, refer to GitHub documentation page.

You can further customize the sam build –use-container command if necessary. By default the Docker image used to create the build artifact is pulled from Amazon ECR Public. The default Python 3.8 image in this example is based on the language specified during sam init. To pull a different container image, use the --build-image option as specified in the documentation.

The AWS CLI and AWS SAM CLI are installed in the runner using the GitHub action setup-sam. To install a specific version, use the version parameter.

uses: aws-actions/setup-sam@v1
with:
  version: 1.23.0

As part of the CI/CD process, we recommend you scan your code for quality and vulnerabilities in bundled libraries. You can find these security offerings from our AWS Lambda Technology Partners.

Configuring AWS credentials in GitHub

The GitHub Actions CI/CD pipeline requires AWS credentials to access your AWS account. The credentials must include AWS Identity and Access Management (IAM) policies that provide access to Lambda, API Gateway, AWS CloudFormation, S3, and IAM resources.

These credentials are stored as GitHub secrets within your GitHub repository, under Settings > Secrets. For more information, see “GitHub Actions secrets”.

In your GitHub repository, create two secrets named AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY and enter the key values. We recommend following IAM best practices for the AWS credentials used in GitHub Actions workflows, including:

Do not store credentials in your repository code. Use GitHub Actions secrets to store credentials and redact credentials from GitHub Actions workflow logs.
Create an individual IAM user with an access key for use in GitHub Actions workflows, preferably one per repository. Do not use the AWS account root user access key.
Grant least privilege to the credentials used in GitHub Actions workflows. Grant only the permissions required to perform the actions in your GitHub Actions workflows.
Rotate the credentials used in GitHub Actions workflows regularly.
Monitor the activity of the credentials used in GitHub Actions workflows.

Deploying your application

Add all the files to your local git repository, commit the changes, and push to GitHub.

git add .
git commit -am "Add AWS SAM files"
git push

Once the files are pushed to GitHub on the main branch, this automatically triggers the GitHub Actions CI/CD pipeline as configured in the sam-pipeline.yml file.

The GitHub actions runner performs the pipeline steps specified in the file. It checks out the code from your repo, sets up Python, and configures the AWS credentials based on the GitHub secrets. The runner uses the GitHub action setup-sam to install AWS SAM CLI.

The pipeline triggers the sam build process to build the application artifacts, using the default container image for Python 3.8.

sam deploy runs to configure the resources in your AWS account using the securely stored credentials.

To view the application deployment progress, select Actions in the repository menu. Select the workflow run and select the job name build-deploy.

GitHub Actions progress

If the build fails, you can view the error message. Common errors are:

Incompatible software versions such as the Python runtime being different from the Python version on the build machine. Resolve this by installing the proper software versions.
Credentials could not be loaded. Verify that AWS credentials are stored in GitHub secrets.
Ensure that your AWS account has the necessary permissions to deploy the resources in the AWS SAM template, in addition to the S3 deployment bucket.

Testing the application

Within the workflow run, expand the Run sam deploy section.
Navigate to the AWS SAM Outputs section. The HelloWorldAPI value shows the API Gateway endpoint URL deployed in your AWS account.

AWS SAM outputs

Use curl to test the API:

curl https://<api-id>.execute-api.us-east-1.amazonaws.com/Prod/hello/

The API response should be:
{"message": "hello world"}

Cleanup

To remove the application resources, navigate to the CloudFormation console and delete the stack. Alternatively, you can use an AWS CLI command to remove the stack:

aws cloudformation delete-stack --stack-name sam-hello-world

Empty, and delete the S3 deployment bucket.

Conclusion

GitHub Actions is a GitHub feature that allows you to run a CI/CD pipeline to build, test, and deploy software directly from GitHub. AWS SAM is an open-source framework for building serverless applications.

In this post, you use GitHub Actions CI/CD pipeline functionality and AWS SAM to create, build, test, and deploy a serverless application. You use sam init to create a serverless application and tested the functionality locally. You create a sam-pipeline.yml file to define the pipeline steps for GitHub Actions.

The GitHub action setup-sam installed AWS SAM on the GitHub hosted runner. The GitHub Actions workflow uses sam build to create the application artifacts and sam deploy to deploy them to your AWS account.

For more serverless learning resources, visit https://serverlessland.com.

Building well-architected serverless applications: Managing application security boundaries – part 1

2021-06-22 Julian Wood

Post Syndicated from Julian Wood original https://aws.amazon.com/blogs/compute/building-well-architected-serverless-applications-managing-application-security-boundaries-part-1/

Security question SEC2: How do you manage your serverless application’s security boundaries?

Defining and securing your serverless application’s boundaries ensures isolation for, within, and between components.

Required practice: Evaluate and define resource policies

Resource policies are AWS Identity and Access Management (IAM) statements. They are attached to resources such as an Amazon S3 bucket, or an Amazon API Gateway REST API resource or method. The policies define what identities have fine-grained access to the resource. To see which services support resource-based policies, see “AWS Services That Work with IAM”. For more information on how resource policies and identity policies are evaluated, see “Identity-Based Policies and Resource-Based Policies”.

Understand and determine which resource policies are necessary

Resource policies can protect a component by restricting inbound access to managed services. Use resource policies to restrict access to your component based on a number of identities, such as the source IP address/range, function event source, version, alias, or queues. Resource policies are evaluated and enforced at IAM level before each AWS service applies it’s own authorization mechanisms, when available. For example, IAM resource policies for API Gateway REST APIs can deny access to an API before an AWS Lambda authorizer is called.

If you use multiple AWS accounts, you can use AWS Organizations to manage and govern individual member accounts centrally. Certain resource policies can be applied at the organizations level, providing guardrail for what actions AWS accounts within the organization root or OU can do. For more information see, “Understanding how AWS Organization Service Control Policies work”.

Review your existing policies and how they’re configured, paying close attention to how permissive individual policies are. Your resource policies should only permit necessary callers.

Implement resource policies to prevent unauthorized access

For Lambda, use resource-based policies to provide fine-grained access to what AWS IAM identities and event sources can invoke a specific version or alias of your function. Resource-based policies can also be used to control access to Lambda layers. You can combine resource policies with Lambda event sources. For example, if API Gateway invokes Lambda, you can restrict the policy to the API Gateway ID, HTTP method, and path of the request.

In the serverless airline example used in this series, the IngestLoyalty service uses a Lambda function that subscribes to an Amazon Simple Notification Service (Amazon SNS) topic. The Lambda function resource policy allows SNS to invoke the Lambda function.

Lambda resource policy document

API Gateway resource-based policies can restrict API access to specific Amazon Virtual Private Cloud (VPC), VPC endpoint, source IP address/range, AWS account, or AWS IAM users.

Amazon Simple Queue Service (SQS) resource-based policies provide fine-grained access to certain AWS services and AWS IAM identities (users, roles, accounts). Amazon SNS resource-based policies restrict authenticated and non-authenticated actions to topics.

Amazon DynamoDB resource-based policies provide fine-grained access to tables and indexes. Amazon EventBridge resource-based policies restrict AWS identities to send and receive events including to specific event buses.

For Amazon S3, use bucket policies to grant permission to your Amazon S3 resources.

The AWS re:Invent session Best practices for growing a serverless application includes further suggestions on enforcing security best practices.

Best practices for growing a serverless application

Good practice: Control network traffic at all layers

Apply controls for controlling both inbound and outbound traffic, including data loss prevention. Define requirements that help you protect your networks and protect against exfiltration.

Use networking controls to enforce access patterns

API Gateway and AWS AppSync have support for AWS Web Application Firewall (AWS WAF) which helps protect web applications and APIs from attacks. AWS WAF enables you to configure a set of rules called a web access control list (web ACL). These allow you to block, or count web requests based on customizable web security rules and conditions that you define. These can include specified IP address ranges, CIDR blocks, specific countries, or Regions. You can also block requests that contain malicious SQL code, or requests that contain malicious script. For more information, see How AWS WAF Works.

A private API endpoint is an API Gateway interface VPC endpoint that can only be accessed from your Amazon Virtual Private Cloud (Amazon VPC). This is an elastic network interface that you create in a VPC. Traffic to your private API uses secure connections and does not leave the Amazon network, it is isolated from the public internet. For more information, see “Creating a private API in Amazon API Gateway”.

To restrict access to your private API to specific VPCs and VPC endpoints, you must add conditions to your API’s resource policy. For example policies, see the documentation.

By default, Lambda runs your functions in a secure Lambda-owned VPC that is not connected to your account’s default VPC. Functions can access anything available on the public internet. This includes other AWS services, HTTPS endpoints for APIs, or services and endpoints outside AWS. The function cannot directly connect to your private resources inside of your VPC.

You can configure a Lambda function to connect to private subnets in a VPC in your account. When a Lambda function is configured to use a VPC, the Lambda function still runs inside the Lambda service VPC. The function then sends all network traffic through your VPC and abides by your VPC’s network controls. Functions deployed to virtual private networks must consider network access to restrict resource access.

AWS Lambda service VPC with VPC-to-VPT NAT to customer VPC

When you connect a function to a VPC in your account, the function cannot access the internet, unless the VPC provides access. To give your function access to the internet, route outbound traffic to a NAT gateway in a public subnet. The NAT gateway has a public IP address and can connect to the internet through the VPC’s internet gateway. For more information, see “How do I give internet access to my Lambda function in a VPC?”. Connecting a function to a public subnet doesn’t give it internet access or a public IP address.

You can control the VPC settings for your Lambda functions using AWS IAM condition keys. For example, you can require that all functions in your organization are connected to a VPC. You can also specify the subnets and security groups that the function’s users can and can’t use.

Unsolicited inbound traffic to a Lambda function isn’t permitted by default. There is no direct network access to the execution environment where your functions run. When connected to a VPC, function outbound traffic comes from your own network address space.

You can use security groups, which act as a virtual firewall to control outbound traffic for functions connected to a VPC. Use security groups to permit your Lambda function to communicate with other AWS resources. For example, a security group can allow the function to connect to an Amazon ElastiCache cluster.

To filter or block access to certain locations, use VPC routing tables to configure routing to different networking appliances. Use network ACLs to block access to CIDR IP ranges or ports, if necessary. For more information about the differences between security groups and network ACLs, see “Compare security groups and network ACLs.”

In addition to API Gateway private endpoints, several AWS services offer VPC endpoints, including Lambda. You can use VPC endpoints to connect to AWS services from within a VPC without an internet gateway, NAT device, VPN connection, or AWS Direct Connect connection.

Using tools to audit your traffic

When you configure a Lambda function to use a VPC, or use private API endpoints, you can use VPC Flow Logs to audit your traffic. VPC Flow Logs allow you to capture information about the IP traffic going to and from network interfaces in your VPC. Flow log data can be published to Amazon CloudWatch Logs or S3 to see where traffic is being sent to at a granular level. Here are some flow log record examples. For more information, see “Learn from your VPC Flow Logs”.

Block network access when required

In addition to security groups and network ACLs, third-party tools allow you to disable outgoing VPC internet traffic. These can also be configured to allow traffic to AWS services or allow-listed services.

Conclusion

Managing your serverless application’s security boundaries ensures isolation for, within, and between components. In this post, I cover how to evaluate and define resource policies, showing what policies are available for various serverless services. I show some of the features of AWS WAF to protect APIs. Then I review how to control network traffic at all layers. I explain how Lambda functions connect to VPCs, and how to use private APIs and VPC endpoints. I walk through how to audit your traffic.

This well-architected question will be continued where I look at using temporary credentials between resources and components. I cover why smaller, single purpose functions are better from a security perspective, and how to audit permissions. I show how to use AWS Serverless Application Model (AWS SAM) to create per-function IAM roles.

For more serverless learning resources, visit https://serverlessland.com.

Caching data and configuration settings with AWS Lambda extensions

2021-03-02 Julian Wood

Post Syndicated from Julian Wood original https://aws.amazon.com/blogs/compute/caching-data-and-configuration-settings-with-aws-lambda-extensions/

This post is written by Hari Ohm Prasath Rajagopal, Senior Modernization Architect and Vamsi Vikash Ankam, Technical Account Manager

In this post, I show how to build a flexible in-memory AWS Lambda caching layer using Lambda extensions. Lambda functions use REST API calls to access the data and configuration from the cache. This can reduce latency and cost when consuming data from AWS services such as Amazon DynamoDB, AWS Systems Manager Parameter Store, and AWS Secrets Manager.

Applications making frequent API calls to retrieve static data can benefit from a caching layer. This can reduce the function’s latency, particularly for synchronous requests, as the data is retrieved from the cache instead of an external service. The cache can also reduce costs by reducing the number of calls to downstream services.

There are two types of cache to consider in this situation:

Data cache for caching data from databases such as Amazon Relational Database Service (Amazon RDS), or DynamoDB, etc.
Configuration cache for caching data from a configuration management system such as Parameter Store, AWS AppConfig, or AWS Secrets Manager.

Lambda extensions are a new way for tools to integrate more easily into the Lambda execution environment and control and participate in Lambda’s lifecycle. They use the Extensions API, a new HTTP interface, to register for lifecycle events during function initialization, invocation, and shutdown.

They can also use environment variables to add options and tools to the runtime, or use wrapper scripts to customize the runtime startup behavior. The Lambda cache uses Lambda extensions to run as a separate process.

To learn more about how to use extensions with your functions, read “Introducing AWS Lambda extensions”.

Creating a cache using Lambda extensions

To set up the example, visit the GitHub repo, and follow the instructions in the README.md file.

The demo uses AWS Serverless Application Model (AWS SAM) to deploy the infrastructure. The walkthrough requires AWS SAM CLI (minimum version 0.48) and an AWS account.

To install the example:

Create an AWS account if you do not already have one and login.
Clone the repo to your local development machine:

git clone https://github.com/aws-samples/aws-lambda-extensions
cd aws-lambda-extensions/cache-extension-demo/

If you are not running in a Linux environment, ensure that your build architecture matches the Lambda execution environment by compiling with GOOS=linux and GOARCH=amd64.

GOOS=linux GOARCH=amd64

Build the Go binary extension with the following command:

go build -o bin/extensions/cache-extension-demo main.go

Ensure that the extensions files are executable:

chmod +x bin/extensions/cache-extension-demo

Update the parameters region value in ../example-function/config.yaml with the Region where you are deploying the function.

parameters:
  - region: us-west-2

Build the function dependencies.

cd SAM
sam build

AWS SAM build

Deploy the AWS resources specified in the template.yml file:

sam deploy --guided

During the prompts:
Enter a stack name cache-extension-demo.
Enter the same AWS Region specified previously.
Accept the default DatabaseName. You can specify a custom database name, and also update the ../example-function/config.yaml and index.js files with the new database name.
Enter MySecret as the Secrets Manager secret.
Accept the defaults for the remaining questions.

AWS SAM Deploy

AWS SAM deploys:

A DynamoDB table.
The Lambda function ExtensionsCache-DatabaseEntry, which puts a sample item into the DynamoDB table.
An AWS Systems Manager Parameter Store parameter called CacheExtensions_Parameter1 with a value of MyParameter.
An AWS Secrets Manager secret called secret_info with a value of MySecret.
A Lambda layer called Cache_Extension_Layer.
A Lambda function using Nodejs.12 called ExtensionsCache-SampleFunction. This reads the cached values via the extension from either the DynamoDB table, Parameter Store, or Secrets Manager.
IAM permissions

The cache extension is delivered as a Lambda layer and added to ExtensionsCache-SampleFunction.

It is written as a self-contained binary in Golang, which makes the extension compatible with all of the supported runtimes. The extension caches the data from DynamoDB, Parameter Store, and Secrets Manager, and then runs a local HTTP endpoint to service the data. The Lambda function retrieves the configuration data from the cache using a local HTTP REST API call.

Here is the architecture diagram.

Extensions cache architecture diagram

Once deployed, the extension performs the following steps:

On start-up, the extension reads the config.yaml file, which determines which resources to cache. The file is deployed as part of the Lambda function.
The boolean CACHE_EXTENSION_INIT_STARTUP Lambda environment variable specifies whether to load into cache the items specified in config.yaml. If false, the extension initializes an empty map with the names.
The extension retrieves the required data based on the resources in the config.yaml file. This includes the data from DynamoDB, the configuration from Parameter Store, and the secret from Secrets Manager. The data is stored in memory.
The extension starts a local HTTP server using TCP port 4000, which serves the cache items to the function. The Lambda function accesses the local in-memory cache by invoking the following endpoint: http://localhost:4000/<cachetype>?name=<name>.
If the data is not available in the cache, or has expired, the extension accesses the corresponding AWS service to retrieve the data. It is cached first and then returned to the Lambda function. The CACHE_EXTENSION_TTL Lambda environment variable defines the refresh interval (defined based on Go time format, for example: 30s, 3m, etc.)

This sequence diagram explains the data flow:

Extensions cache sequence diagram

Testing the example application

Once the AWS SAM template is deployed, navigate to the AWS Lambda console.

Select the function starting with the name ExtensionsCache-SampleFunction. Within the function code, the options array specifies which data to return from the cache. This is initially set to path: '/dynamodb?name=DynamoDbTable-pKey1-sKey1'
Choose Configure test events to configure a test event.
Enter a name for the Event name, accept the default payload, and select Create.
Select Test to invoke the function. This returns the cached data from DynamoDB and logs the output.

Successfully retrieve DynamoDB data from cache

In the index.js file, amend the path statement to retrieve the Parameter Store configuration:

const options = {
  "hostname": "localhost",
  "port": 4000,
  "path": "/parameters?name=CacheExtensions_Parameter1",
  "method": "GET"
}

Select Deploy to save the function configuration and select Test. The function returns the Parameter Store configuration item:

Successfully retrieve Parameter Store data from cache

In the function code, amend the path statement to retrieve the Secrets Manager secret:

const options = {
  "hostname": "localhost",
  "port": 4000,
  "path": "/parameters?name=/aws/reference/secretsmanager/secret_info",
  "method": "GET"
}

Select Deploy to save the function configuration and select Test. The function returns the secret:

Successfully retrieve Secrets Manager data from cache

The benefits of using Lambda extensions

There are a number of benefits to using a Lambda extension for this solution:

Improved Lambda function performance as data is cached in memory by the extension during initialization.
Fewer AWS API calls to external services, this can reduce costs and helps avoid throttling limits if services are accessed frequently.
Cache data is stored in memory and not in a file within the Lambda execution environment. This means that no additional process is required to manage the lifecycle of the file. In-memory is also more secure, as data is not persisted to disk for subsequent function invocations.
The function requires less code, as it only needs to communicate with the extension via HTTP to retrieve the data. The function does not have to have additional libraries installed to communicate with DynamoDB, Parameter Store, Secrets Manager, or the local file system.
The cache extension is a Golang compiled binary and the executable can be shared with functions running other runtimes like Node.js, Python, Java, etc.
Using a YAML template to store the details of what to cache makes it easier to configure and add additional services.

Comparing the performance benefit

To test the performance of the cache extension, I compare two tests:

A Golang Lambda function that accesses a secret from AWS Secrets Manager for every invocation.
The ExtensionsCache-SampleFunction, previously deployed using AWS SAM. This uses the cache extension to access the secrets from Secrets Manager, the function reads the value from the cache.

Both functions are configured with 512 MB of memory and the function timeout is set to 30 seconds.

I use Artillery to load test both Lambda functions. The load runs for 100 invocations over 2 minutes. I use Amazon CloudWatch metrics to view the function average durations.

Test 1 shows a duration of 43 ms for the first invocation as a cold start. Subsequent invocations average 22 ms.

Test 1 performance results

Test 2 shows a duration of 16 ms for the first invocation as a cold start. Subsequent invocations average 3 ms.

Test 2 performance results

Using the Lambda extensions caching layer shows a significant performance improvement. Cold start invocation duration is reduced by 62% and subsequent invocations by 80%.

In this example, the CACHE_EXTENSION_INIT_STARTUP environment variable flag is not configured. With the flag enabled for the extension, data is pre-fetched during extension initialization and the cold start time is further reduced.

Conclusion

Using Lambda extensions is an effective way to cache static data from external services in Lambda functions. This reduces function latency and costs. This post shows how to build both a data and configuration cache using DynamoDB, Parameter Store, and Secrets Manager.

To set up the walkthrough demo in this post, visit the GitHub repo, and follow the instructions in the README.md file.

The extension uses a local configuration file to determine which values to cache, and retrieves the items from the external services. A Lambda function retrieves the values from the local cache using an HTTP request, without having to communicate with the external services directly. In this example, this results in an 80% reduction in function invocation time.

For more serverless learning resources, visit https://serverlessland.com.

Working with Lambda layers and extensions in container images

2020-12-03 Julian Wood

Post Syndicated from Julian Wood original https://aws.amazon.com/blogs/compute/working-with-lambda-layers-and-extensions-in-container-images/

In this post, I explain how to use AWS Lambda layers and extensions with Lambda functions packaged and deployed as container images.

Previously, Lambda functions were packaged only as .zip archives. This includes functions created in the AWS Management Console. You can now also package and deploy Lambda functions as container images.

You can use familiar container tooling such as the Docker CLI with a Dockerfile to build, test, and tag images locally. Lambda functions built using container images can be up to 10 GB in size. You push images to an Amazon Elastic Container Registry (ECR) repository, a managed AWS container image registry service. You create your Lambda function, specifying the source code as the ECR image URL from the registry.

Lambda container image support

Lambda functions packaged as container images do not support adding Lambda layers to the function configuration. However, there are a number of solutions to use the functionality of Lambda layers with container images. You take on the responsible for packaging your preferred runtimes and dependencies as a part of the container image during the build process.

Understanding how Lambda layers and extensions work as .zip archives

If you deploy function code using a .zip archive, you can use Lambda layers as a distribution mechanism for libraries, custom runtimes, and other function dependencies.

When you include one or more layers in a function, during initialization, the contents of each layer are extracted in order to the /opt directory in the function execution environment. Each runtime then looks for libraries in a different location under /opt, depending on the language. You can include up to five layers per function, which count towards the unzipped deployment package size limit of 250 MB. Layers are automatically set as private, but they can be shared with other AWS accounts, or shared publicly.

Lambda Extensions are a way to augment your Lambda functions and are deployed as Lambda layers. You can use Lambda Extensions to integrate functions with your preferred monitoring, observability, security, and governance tools. You can choose from a broad set of tools provided by AWS, AWS Lambda Ready Partners, and AWS Partners, or create your own Lambda Extensions. For more information, see “Introducing AWS Lambda Extensions – In preview.”

Extensions can run in either of two modes, internal and external. An external extension runs as an independent process in the execution environment. They can start before the runtime process, and can continue after the function invocation is fully processed. Internal extensions run as part of the runtime process, in-process with your code.

Lambda searches the /opt/extensions directory and starts initializing any extensions found. Extensions must be executable as binaries or scripts. As the function code directory is read-only, extensions cannot modify function code.

It helps to understand that Lambda layers and extensions are just files copied into specific file paths in the execution environment during the function initialization. The files are read-only in the execution environment.

Understanding container images with Lambda

A container image is a packaged template built from a Dockerfile. The image is assembled or built from commands in the Dockerfile, starting from a parent or base image, or from scratch. Each command then creates a new layer in the image, which is stacked in order on top of the previous layer. Once built from the packaged template, a container image is immutable and read-only.

For Lambda, a container image includes the base operating system, the runtime, any Lambda extensions, your application code, and its dependencies. Lambda provides a set of open-source base images that you can use to build your container image. Lambda uses the image to construct the execution environment during function initialization. You can use the AWS Serverless Application Model (AWS SAM) CLI or native container tools such as the Docker CLI to build and test container images locally.

Using Lambda layers in container images

Container layers are added to a container image, similar to how Lambda layers are added to a .zip archive function.

There are a number of ways to use container image layering to add the functionality of Lambda layers to your Lambda function container images.

Use a container image version of a Lambda layer

A Lambda layer publisher may have a container image format equivalent of a Lambda layer. To maintain the same file path as Lambda layers, the published container images must have the equivalent files located in the /opt directory. An image containing an extension must include the files in the /opt/extensions directory.

An example Lambda function, packaged as a .zip archive, is created with two layers. One layer contains shared libraries, and the other layer is a Lambda extension from an AWS Partner.

aws lambda create-function –region us-east-1 –function-name my-function \

aws lambda create-function --region us-east-1 --function-name my-function \  
    --role arn:aws:iam::123456789012:role/lambda-role \
    --layers \
        "arn:aws:lambda:us-east-1:123456789012:layer:shared-lib-layer:1" \
        "arn:aws:lambda:us-east-1:987654321987:extensions-layer:1" \
    …

The corresponding Dockerfile syntax for a function packaged as a container image includes the following lines. These pull the container image versions of the Lambda layers and copy them into the function image. The shared library image is pulled from ECR and the extension image is pulled from Docker Hub.

FROM public.ecr.aws/myrepo/shared-lib-layer:1 AS shared-lib-layer
# Layer code
WORKDIR /opt
COPY --from=shared-lib-layer /opt/ .

FROM aws-partner/extensions-layer:1 as extensions-layer
# Extension  code
WORKDIR /opt/extensions
COPY --from=extensions-layer /opt/extensions/ .

Copy the contents of a Lambda layer into a container image

You can use existing Lambda layers, and copy the contents of the layers into the function container image /opt directory during docker build.

You need to build a Dockerfile that includes the AWS Command Line Interface to copy the layer files from Amazon S3.

The Dockerfile to add two layers into a single image includes the following lines to copy the Lambda layer contents.

FROM alpine:latest

ARG AWS_DEFAULT_REGION=${AWS_DEFAULT_REGION:-"us-east-1"}
ARG AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID:-""}
ARG AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY:-""}
ENV AWS_DEFAULT_REGION=${AWS_DEFAULT_REGION}
ENV AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID}
ENV AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY}

RUN apk add aws-cli curl unzip

RUN mkdir -p /opt

RUN curl $(aws lambda get-layer-version-by-arn --arn arn:aws:lambda:us-east-1:1234567890123:layer:shared-lib-layer:1 --query 'Content.Location' --output text) --output layer.zip
RUN unzip layer.zip -d /opt
RUN rm layer.zip

RUN curl $(aws lambda get-layer-version-by-arn --arn arn:aws:lambda:us-east-1:987654321987:extensions-layer:1 --query 'Content.Location' --output text) --output layer.zip
RUN unzip layer.zip -d /opt
RUN rm layer.zip

To run the AWS CLI, specify your AWS_ACCESS_KEY, and AWS_SECRET_ACCESS_KEY, and include the required AWS_DEFAULT_REGION as command-line arguments.

docker build . -t layer-image1:latest \
--build-arg AWS_DEFAULT_REGION=us-east-1 \
--build-arg AWS_ACCESS_KEY_ID=AKIAIOSFODNN7EXAMPLE \
--build-arg AWS_SECRET_ACCESS_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY

This creates a container image containing the existing Lambda layer and extension files. This can be pushed to ECR and used in a function.

Build a container image from a Lambda layer

You can repackage and publish Lambda layer file content as container images. Creating separate container images for different layers allows you to add them to multiple functions, and share them in a similar way as Lambda layers.

You can create a separate container image containing the files from a single layer, or combine the files from multiple layers into a single image. If you create separate container images for layer files, you then add these images into your function image.

There are two ways to manage language code dependencies. You can pre-build the dependencies and copy the files into the container image, or build the dependencies during docker build.

In this example, I migrate an existing Python application. This comprises a Lambda function and extension, from a .zip archive to separate function and extension container images. The extension writes logs to S3.

You can choose how to store images in repositories. You can either push both images to the same ECR repository with different image tags, or push to different repositories. In this example, I use separate ECR repositories.

To set up the example, visit the GitHub repo and follow the instructions in the README.md file.

The existing example extension uses a makefile to install boto3 using pip install with a requirements.txt file. This is migrated to the docker build process. I must add a Python runtime to be able to run pip install as part of the build process. I use python:3.8-alpine as a minimal base image.

I create separate Dockerfiles for the function and extension. The extension Dockerfile contains the following lines.

FROM python:3.8-alpine AS installer
#Layer Code
COPY extensionssrc /opt/
COPY extensionssrc/requirements.txt /opt/
RUN pip install -r /opt/requirements.txt -t /opt/extensions/lib

FROM scratch AS base
WORKDIR /opt/extensions
COPY --from=installer /opt/extensions .

I build, tag, login, and push the extension container image to an existing ECR repository.

docker build -t log-extension-image:latest  .
docker tag log-extension-image:latest 123456789012.dkr.ecr.us-east-1.amazonaws.com/log-extension-image:latest
aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 123456789012.dkr.ecr.us-east-1.amazonaws.com
docker push 123456789012.dkr.ecr.us-east-1.amazonaws.com/log-extension-image:latest

The function Dockerfile contains the following lines, which add the files from the previously created extension image to the function image. There is no need to run pip install for the function as it does not require any additional dependencies.

FROM 123456789012.dkr.ecr.us-east-1.amazonaws.com/log-extension-image:latest AS layer
FROM public.ecr.aws/lambda/python:3.8
# Layer code
WORKDIR /opt
COPY --from=layer /opt/ .
# Function code
WORKDIR /var/task
COPY app.py .
CMD ["app.lambda_handler"]

I build, tag, and push the function container image to a separate existing ECR repository. This creates an immutable image of the Lambda function.

docker build -t log-extension-function:latest  .
docker tag log-extension-function:latest 123456789012.dkr.ecr.us-east-1.amazonaws.com/log-extension-function:latest
docker push 123456789012.dkr.ecr.us-east-1.amazonaws.com/log-extension-function:latest

The function requires a unique S3 bucket to store the logs files, which I create in the S3 console. I create a Lambda function from the ECR repository image, and specify the bucket name as a Lambda environment variable.

aws lambda create-function --region us-east-1  --function-name log-extension-function \
--package-type Image --code ImageUri=123456789012.dkr.ecr.us-east-1.amazonaws.com/log-extension-function:latest \
--role "arn:aws:iam:: 123456789012:role/lambda-role" \
--environment  "Variables": {"S3_BUCKET_NAME": "s3-logs-extension-demo-logextensionsbucket-us-east-1"}

For subsequent extension code changes, I need to update both the extension and function images. If only the function code changes, I need to update the function image. I push the function image as the :latest image to ECR. I then update the function code deployment to use the updated :latest ECR image.

aws lambda update-function-code --function-name log-extension-function --image-uri 123456789012.dkr.ecr.us-east-1.amazonaws.com/log-extension-function:latest

Using custom runtimes with container images

With .zip archive functions, custom runtimes are added using Lambda layers. With container images, you no longer need to copy in Lambda layer code for custom runtimes.

You can build your own custom runtime images starting with AWS provided base images for custom runtimes. You can add your preferred runtime, dependencies, and code to these images. To communicate with Lambda, the image must implement the Lambda Runtime API. We provide Lambda runtime interface clients for all supported runtimes, or you can implement your own for additional runtimes.

Running extensions in container images

A Lambda extension running in a function packaged as a container image works in the same way as a .zip archive function. You build a function container image including the extension files, or adding an extension image layer. Lambda looks for any external extensions in the /opt/extensions directory and starts initializing them. Extensions must be executable as binaries or scripts.

Internal extensions modify the Lambda runtime startup behavior using language-specific environment variables, or wrapper scripts. For language-specific environment variables, you can set the following environment variables in your function configuration to augment the runtime command line.

JAVA_TOOL_OPTIONS (Java Corretto 8 and 11)
NODE_OPTIONS (Node.js 10 and 12)
DOTNET_STARTUP_HOOKS (.NET Core 3.1)

An example Lambda environment variable for JAVA_TOOL_OPTIONS:

-javaagent:"/opt/ExampleAgent-0.0.jar"

Wrapper scripts delegate the runtime start-up to a script. The script can inject and alter arguments, set environment variables, or capture metrics, errors, and other diagnostic information. The following runtimes support wrapper scripts: Node.js 10 and 12, Python 3.8, Ruby 2.7, Java 8 and 11, and .NET Core 3.1

You specify the script by setting the value of the AWS_LAMBDA_EXEC_WRAPPER environment variable as the file system path of an executable binary or script, for example:

/opt/wrapper_script

Conclusion

You can now package and deploy Lambda functions as container images in addition to .zip archives. Lambda functions packaged as container images do not directly support adding Lambda layers to the function configuration as .zip archives do.

In this post, I show a number of solutions to use the functionality of Lambda layers and extensions with container images, including example Dockerfiles.

I show how to migrate an existing Lambda function and extension from a .zip archive to separate function and extension container images. Follow the instructions in the README.md file in the GitHub repository.

For more serverless learning resources, visit https://serverlessland.com.

Using AWS Lambda extensions to send logs to custom destinations

2020-11-12 Julian Wood

Post Syndicated from Julian Wood original https://aws.amazon.com/blogs/compute/using-aws-lambda-extensions-to-send-logs-to-custom-destinations/

You can now send logs from AWS Lambda functions directly to a destination of your choice using AWS Lambda Extensions. Lambda Extensions are a new way for monitoring, observability, security, and governance tools to easily integrate with AWS Lambda. For more information, see “Introducing AWS Lambda Extensions – In preview”.

To help you troubleshoot failures in Lambda functions, AWS Lambda automatically captures and streams logs to Amazon CloudWatch Logs. This stream contains the logs that your function code and extensions generate, in addition to logs the Lambda service generates as part of the function invocation.

Previously, to send logs to a custom destination, you typically configure and operate a CloudWatch Log Group subscription. A different Lambda function forwards logs to the destination of your choice.

Logging tools, running as Lambda extensions, can now receive log streams directly from within the Lambda execution environment, and send them to any destination. This makes it even easier for you to use your preferred extensions for diagnostics.

Today, you can use extensions to send logs to Coralogix, Datadog, Honeycomb, Lumigo, New Relic, and Sumo Logic.

Overview

To receive logs, extensions subscribe using the new Lambda Logs API.

Lambda Logs API

The Lambda service then streams the logs directly to the extension. The extension can then process, filter, and route them to any preferred destination. Lambda still sends the logs to CloudWatch Logs.

You deploy extensions, including ones that use the Logs API, as Lambda layers, with the AWS Management Console and AWS Command Line Interface (AWS CLI). You can also use infrastructure as code tools such as AWS CloudFormation, the AWS Serverless Application Model (AWS SAM), Serverless Framework, and Terraform.

Logging extensions from AWS Lambda Ready Partners and AWS Partners available at launch

Today, you can use logging extensions with the following tools:

The Datadog extension now makes it easier than ever to collect your serverless application logs for visualization, analysis, and archival. Paired with Datadog’s AWS integration, end-to-end distributed tracing, and real-time enhanced AWS Lambda metrics, you can proactively detect and resolve serverless issues at any scale.
Lumigo provides monitoring and debugging for modern cloud applications. With the open source extension from Lumigo, you can send Lambda function logs directly to an S3 bucket, unlocking new post processing use cases.
New Relic enables you to efficiently monitor, troubleshoot, and optimize your Lambda functions. New Relic’s extension allows you send your Lambda service platform logs directly to New Relic’s unified observability platform, allowing you to quickly visualize data with minimal latency and cost.
Coralogix is a log analytics and cloud security platform that empowers thousands of companies to improve security and accelerate software delivery, allowing you to get deep insights without paying for the noise. Coralogix can now read Lambda function logs and metrics directly, without using Cloudwatch or S3, reducing the latency, and cost of observability.
Honeycomb is a powerful observability tool that helps you debug your entire production app stack. Honeycomb’s extension decreases the overhead, latency, and cost of sending events to the Honeycomb service, while increasing reliability.
The Sumo Logic extension enables you to get instant visibility into the health and performance of your mission-critical applications using AWS Lambda. With this extension and Sumo Logic’s continuous intelligence platform, you can now ensure that all your Lambda functions are running as expected, by analyzing function, platform, and extension logs to quickly identify and remediate errors and exceptions.

You can also build and use your own logging extensions to integrate your organization’s tooling.

Showing a logging extension to send logs directly to S3

This demo shows an example of using a simple logging extension to send logs to Amazon Simple Storage Service (S3).

To set up the example, visit the GitHub repo and follow the instructions in the README.md file.

The example extension runs a local HTTP endpoint listening for HTTP POST events. Lambda delivers log batches to this endpoint. The example creates an S3 bucket to store the logs. A Lambda function is configured with an environment variable to specify the S3 bucket name. Lambda streams the logs to the extension. The extension copies the logs to the S3 bucket.

Lambda environment variable specifying S3 bucket

The extension uses the Extensions API to register for INVOKE and SHUTDOWN events. The extension, using the Logs API, then subscribes to receive platform and function logs, but not extension logs.

As the example is an asynchronous system, logs for one invoke may be processed during the next invocation. Logs for the last invoke may be processed during the SHUTDOWN event.

Testing the function from the Lambda console, Lambda sends logs to CloudWatch Logs. The logs stream shows logs from the platform, function, and extension.

Lambda logs visible in CloudWatch Logs

The logging extension also receives the log stream directly from Lambda, and copies the logs to S3.

Browsing to the S3 bucket, the log files are available.

S3 bucket containing copied logs.

Downloading the file shows the log lines. The log contains the same platform and function logs, but not the extension logs, as specified during the subscription.

[{'time': '2020-11-12T14:55:06.560Z', 'type': 'platform.start', 'record': {'requestId': '49e64413-fd42-47ef-b130-6fd16f30148d', 'version': '$LATEST'}},
{'time': '2020-11-12T14:55:06.774Z', 'type': 'platform.logsSubscription', 'record': {'name': 'logs_api_http_extension.py', 'state': 'Subscribed', 'types': ['platform', 'function']}},
{'time': '2020-11-12T14:55:06.774Z', 'type': 'platform.extension', 'record': {'name': 'logs_api_http_extension.py', 'state': 'Ready', 'events': ['INVOKE', 'SHUTDOWN']}},
{'time': '2020-11-12T14:55:06.776Z', 'type': 'function', 'record': 'Function: Logging something which logging extension will send to S3\n'}, {'time': '2020-11-12T14:55:06.780Z', 'type': 'platform.end', 'record': {'requestId': '49e64413-fd42-47ef-b130-6fd16f30148d'}}, {'time': '2020-11-12T14:55:06.780Z', 'type': 'platform.report', 'record': {'requestId': '49e64413-fd42-47ef-b130-6fd16f30148d', 'metrics': {'durationMs': 4.96, 'billedDurationMs': 100, 'memorySizeMB': 128, 'maxMemoryUsedMB': 87, 'initDurationMs': 792.41}, 'tracing': {'type': 'X-Amzn-Trace-Id', 'value': 'Root=1-5fad4cc9-70259536495de84a2a6282cd;Parent=67286c49275ac0ad;Sampled=1'}}}]

Lambda has sent specific logs directly to the subscribed extension. The extension has then copied them directly to S3.

For more example log extensions, see the Github repository.

How do extensions receive logs?

Extensions start a local listener endpoint to receive the logs using one of the following protocols:

TCP – Logs are delivered to a TCP port in Newline delimited JSON format (NDJSON).
HTTP – Logs are delivered to a local HTTP endpoint through PUT or POST, as an array of records in JSON format. http://sandbox:${PORT}/${PATH}. The $PATH parameter is optional.

AWS recommends using an HTTP endpoint over TCP because HTTP tracks successful delivery of the log messages to the local endpoint that the extension sets up.

Once the endpoint is running, extensions use the Logs API to subscribe to any of three different logs streams:

Function logs that are generated by the Lambda function.
Lambda service platform logs (such as the START, END, and REPORT logs in CloudWatch Logs).
Extension logs that are generated by extension code.

The Lambda service then sends logs to endpoint subscribers inside of the execution environment only.

Even if an extension subscribes to one or more log streams, Lambda continues to send all logs to CloudWatch.

Performance considerations

Extensions share resources with the function, such as CPU, memory, disk storage, and environment variables. They also share permissions, using the same AWS Identity and Access Management (IAM) role as the function.

Log subscriptions consume memory resources as each subscription opens a new memory buffer to store the logs. This memory usage counts towards memory consumed within the Lambda execution environment.

For more information on resources, security and performance with extensions, see “Introducing AWS Lambda Extensions – In preview”.

What happens if Lambda cannot deliver logs to an extension?

The Lambda service stores logs before sending to CloudWatch Logs and any subscribed extensions. If Lambda cannot deliver logs to the extension, it automatically retries with backoff. If the log subscriber crashes, Lambda restarts the execution environment. The logs extension re-subscribes, and continues to receive logs.

When using an HTTP endpoint, Lambda continues to deliver logs from the last acknowledged delivery. With TCP, the extension may lose logs if an extension or the execution environment fails.

The Lambda service buffers logs in memory before delivery. The buffer size is proportional to the buffering configuration used in the subscription request. If an extension cannot process the incoming logs quickly enough, the buffer fills up. To reduce the likelihood of an out of memory event due to a slow extension, the Lambda service drops records and adds a platform.logsDropped log record to the affected extension to indicate the number of dropped records.

Disabling logging to CloudWatch Logs

Lambda continues to send logs to CloudWatch Logs even if extensions subscribe to the logs stream.

To disable logging to CloudWatch Logs for a particular function, you can amend the Lambda execution role to remove access to CloudWatch Logs.

{
"Version": "2012-10-17",
"Statement": [
    {
        "Effect": "Deny",
        "Action": [
            "logs:CreateLogGroup",
            "logs:CreateLogStream",
            "logs:PutLogEvents"
        ],
        "Resource": [
            "arn:aws:logs:*:*:*"
        ]
    }
  ]
}

Logs are no longer delivered to CloudWatch Logs for functions using this role, but are still streamed to subscribed extensions. You are no longer billed for CloudWatch logging for these functions.

Pricing

Logging extensions, like other extensions, share the same billing model as Lambda functions. When using Lambda functions with extensions, you pay for requests served and the combined compute time used to run your code and all extensions, in 100 ms increments. To learn more about the billing for extensions, visit the Lambda FAQs page.

Conclusion

Lambda extensions enable you to extend the Lambda service to more easily integrate with your favorite tools for monitoring, observability, security, and governance.

Extensions can now subscribe to receive log streams directly from the Lambda service, in addition to CloudWatch Logs. Today, you can install a number of available logging extensions from AWS Lambda Ready Partners and AWS Partners. Extensions make it easier to use your existing tools with your serverless applications.

To try the S3 demo logging extension, follow the instructions in the README.md file in the GitHub repository.

Extensions are now available in preview in all commercial regions other than the China regions.

For more serverless learning resources, visit https://serverlessland.com.

Building Extensions for AWS Lambda – In preview

2020-10-08 Julian Wood

Post Syndicated from Julian Wood original https://aws.amazon.com/blogs/compute/building-extensions-for-aws-lambda-in-preview/

AWS Lambda is announcing a preview of Lambda Extensions, a new way to easily integrate Lambda with your favorite monitoring, observability, security, and governance tools. Extensions enable tools to integrate deeply into the Lambda execution environment to control and participate in Lambda’s lifecycle. This simplified experience makes it easier for you to use your preferred tools across your application portfolio today.

In this post I explain how Lambda extensions work, the changes to the Lambda lifecycle, and how to build an extension. To learn how to use extensions with your functions, see the companion blog post “Introducing AWS Lambda extensions”.

Extensions are built using the new Lambda Extensions API, which provides a way for tools to get greater control during function initialization, invocation, and shut down. This API builds on the existing Lambda Runtime API, which enables you to bring custom runtimes to Lambda.

You can use extensions from AWS, AWS Lambda Ready Partners, and open source projects for use-cases such as application performance monitoring, secrets management, configuration management, and vulnerability detection. You can also build your own extensions to integrate your own tooling using the Extensions API.

There are extensions available today for AppDynamics, Check Point, Datadog, Dynatrace, Epsagon, HashiCorp, Lumigo, New Relic, Thundra, Splunk, AWS AppConfig, and Amazon CloudWatch Lambda Insights. For more details on these, see “Introducing AWS Lambda extensions”.

The Lambda execution environment

Lambda functions run in a sandboxed environment called an execution environment. This isolates them from other functions and provides the resources, such as memory, specified in the function configuration.

Lambda automatically manages the lifecycle of compute resources so that you pay for value. Between function invocations, the Lambda service freezes the execution environment. It is thawed if the Lambda service needs the execution environment for subsequent invocations.

Previously, only the runtime process could influence the lifecycle of the execution environment. It would communicate with the Runtime API, which provides an HTTP API endpoint within the execution environment to communicate with the Lambda service.

Lambda and Runtime API

The runtime uses the API to request invocation events from Lambda and deliver them to the function code. It then informs the Lambda service when it has completed processing an event. The Lambda service then freezes the execution environment.

The runtime process previously exposed two distinct phases in the lifecycle of the Lambda execution environment: Init and Invoke.

1. Init: During the Init phase, the Lambda service initializes the runtime, and then runs the function initialization code (the code outside the main handler). The Init phase happens either during the first invocation, or in advance if Provisioned Concurrency is enabled.

2. Invoke: During the invoke phase, the runtime requests an invocation event from the Lambda service via the Runtime API, and invokes the function handler. It then returns the function response to the Runtime API.

After the function runs, the Lambda service freezes the execution environment and maintains it for some time in anticipation of another function invocation.

If the Lambda function does not receive any invokes for a period of time, the Lambda service shuts down and removes the environment.

Previous Lambda lifecycle

With the addition of the Extensions API, extensions can now influence, control, and participate in the lifecycle of the execution environment. They can use the Extensions API to influence when the Lambda service freezes the execution environment.

AWS Lambda execution environment with the Extensions API

Extensions are initialized before the runtime and the function. They then continue to run in parallel with the function, get greater control during function invocation, and can run logic during shut down.

Extensions allow integrations with the Lambda service by introducing the following changes to the Lambda lifecycle:

An updated Init phase. There are now three discrete Init tasks: extensions Init, runtime Init, and function Init. This creates an order where extensions and the runtime can perform setup tasks before the function code runs.
Greater control during invocation. During the invoke phase, as before, the runtime requests the invocation event and invokes the function handler. In addition, extensions can now request lifecycle events from the Lambda service. They can run logic in response to these lifecycle events, and respond to the Lambda service when they are done. The Lambda service freezes the execution environment when it hears back from the runtime and all extensions. In this way, extensions can influence the freeze/thaw behavior.
Shutdown phase: we are now exposing the shutdown phase to let extensions stop cleanly when the execution environment shuts down. The Lambda service sends a shut down event, which tells the runtime and extensions that the environment is about to be shut down.

New Lambda lifecycle with extensions

Each Lambda lifecycle phase starts with an event from the Lambda service to the runtime and all registered extensions. The runtime and extensions signal that they have completed by requesting the Next invocation event from the Runtime and Extensions APIs. Lambda freezes the execution environment and all extensions when there are no pending events.

Lambda lifecycle for execution environment, runtime, extensions, and function.png

For more information on the lifecycle phases and the Extensions API, see the documentation.

How are extensions delivered and run?

You deploy extensions as Lambda layers, which are ZIP archives containing shared libraries or other dependencies.

To add a layer, use the AWS Management Console, AWS Command Line Interface (AWS CLI), or infrastructure as code tools such as AWS CloudFormation, the AWS Serverless Application Model (AWS SAM), and Terraform.

When the Lambda service starts the function execution environment, it extracts the extension files from the Lambda layer into the /opt directory. Lambda then looks for any extensions in the /opt/extensions directory and starts initializing them. Extensions need to be executable as binaries or scripts. As the function code directory is read-only, extensions cannot modify function code.

Extensions can run in either of two modes, internal and external.

Internal extensions run as part of the runtime process, in-process with your code. They are not separate processes. Internal extensions allow you to modify the startup of the runtime process using language-specific environment variables and wrapper scripts. You can use language-specific environment variables to add options and tools to the runtime for Java Correto 8 and 11, Node.js 10 and 12, and .NET Core 3.1. Wrapper scripts allow you to delegate the runtime startup to your script to customize the runtime startup behavior. You can use wrapper scripts with Node.js 10 and 12, Python 3.8, Ruby 2.7, Java 8 and 11, and .NET Core 3.1. For more information, see “Modifying-the-runtime-environment”.
External extensions allow you to run separate processes from the runtime but still within the same execution environment as the Lambda function. External extensions can start before the runtime process, and can continue after the runtime shuts down. External extensions work with Node.js 10 and 12, Python 3.7 and 3.8, Ruby 2.5 and 2.7, Java Corretto 8 and 11, .NET Core 3.1, and custom runtimes.

External extensions can be written in a different language to the function. We recommend implementing external extensions using a compiled language as a self-contained binary. This makes the extension compatible with all of the supported runtimes. If you use a non-compiled language, ensure that you include a compatible runtime in the extension.

Extensions run in the same execution environment as the function, so share resources such as CPU, memory, and disk storage with the function. They also share environment variables, in addition to permissions, using the same AWS Identity and Access Management (IAM) role as the function.

For more details on resources, security, and performance with extensions, see the companion blog post “Introducing AWS Lambda extensions”.

For example extensions and wrapper scripts to help you build your own extensions, see the GitHub repository.

Showing extensions in action

The demo shows how external extensions integrate deeply with functions and the Lambda runtime. The demo creates an example Lambda function with a single extension using either the AWS CLI, or AWS SAM.

The example shows how an external extension can start before the runtime, run during the Lambda function invocation, and shut down after the runtime shuts down.

To set up the example, visit the GitHub repo, and follow the instructions in the README.md file.

The example Lambda function uses the custom provided.al2 runtime based on Amazon Linux 2. Using the custom runtime helps illustrate in more detail how the Lambda service, Runtime API, and the function communicate. The extension is delivered using a Lambda layer.

The runtime, function, and extension, log their status events to Amazon CloudWatch Logs. The extension initializes as a separate process and waits to receive the function invocation event from the Extensions API. It then sleeps for 5 seconds before calling the API again to register to receive the next event. The extension sleep simulates the processing of a parallel process. This could, for example, collect telemetry data to send to an external observability service.

When the Lambda function is invoked, the extension, runtime and function perform the following steps. I walk through the steps using the log output.

1. The Lambda service adds the configured extension Lambda layer. It then searches the /opt/extensions folder, and finds an extension called extension1.sh. The extension executable launches before the runtime initializes. It registers with the Extensions API to receive INVOKE and SHUTDOWN events using the following API call.

curl -sS -LD "$HEADERS" -XPOST "http://${AWS_LAMBDA_RUNTIME_API}/2020-01-01/extension/register" --header "Lambda-Extension-Name: ${LAMBDA_EXTENSION_NAME}" -d "{ \"events\": [\"INVOKE\", \"SHUTDOWN\"]}" > $TMPFILE

Extension discovery, registration, and start

2. The Lambda custom provided.al2 runtime initializes from the bootstrap file.

Runtime initialization

3. The runtime calls the Runtime API to get the next event using the following API call. The HTTP request is blocked until the event is received.

curl -sS -LD "$HEADERS" -X GET "http://${AWS_LAMBDA_RUNTIME_API}/2018-06-01/runtime/invocation/next" > $TMPFILE &

The extension calls the Extensions API and waits for the next event. The HTTP request is again blocked until one is received.

curl -sS -L -XGET "http://${AWS_LAMBDA_RUNTIME_API}/2020-01-01/extension/event/next" --header "Lambda-Extension-Identifier: ${EXTENSION_ID}" > $TMPFILE &

Runtime and extension call APIs to get the next event

4. The Lambda service receives an invocation event. It sends the event payload to the runtime using the Runtime API. It sends an event to the extension informing it about the invocation, using the Extensions API.

Runtime and extension receive event

5. The runtime invokes the function handler. The function receives the event payload.

Runtime invokes handler

6. The function runs the handler code. The Lambda runtime receives back the function response and sends it back to the Runtime API with the following API call.

curl -sS -X POST "http://${AWS_LAMBDA_RUNTIME_API}/2018-06-01/runtime/invocation/$REQUEST_ID/response" -d "$RESPONSE" > $TMPFILE

Runtime receives function response and sends to Runtime API

7. The Lambda runtime then waits for the next invocation event (warm start).

Runtime waits for next event

8. The extension continues processing for 5 seconds, simulating the processing of a companion process. The extension finishes, and uses the Extensions API to register again to wait for the next event.

Extension processing

9. The function invocation report is logged.

Function invocation report

10. When Lambda is about to shut down the execution environment, it sends the Runtime API a shut down event.

Lambda runtime shut down event

11. Lambda then sends a shut down event to the extensions. The extension finishes processing and then shuts down after the runtime.

Lambda extension shut down event

The demo shows the steps the runtime, function, and extensions take during the Lambda lifecycle.

An external extension registers and starts before the runtime. When Lambda receives an invocation event, it sends it to the runtime. It then sends an event to the extension informing it about the invocation. The runtime invokes the function handler, and the extension does its own processing of the event. The extension continues processing after the function invocation completes. When Lambda is about to shut down the execution environment, it sends a shut down event to the runtime. It then sends one to the extension, so it can finish processing.

To see a sequence diagram of this flow, see the Extensions API documentation.

Pricing

Extensions share the same billing model as Lambda functions. When using Lambda functions with extensions, you pay for requests served and the combined compute time used to run your code and all extensions, in 100 ms increments. To learn more about the billing for extensions, visit the Lambda FAQs page.

Conclusion

Lambda extensions enable you to extend Lambda’s execution environment to more easily integrate with your favorite tools for monitoring, observability, security, and governance.

Extensions can run additional code; before, during, and after a function invocation. There are extensions available today from AWS Lambda Ready Partners. These cover use-cases such as application performance monitoring, secrets management, configuration management, and vulnerability detection. Extensions make it easier to use your existing tools with your serverless applications. For more information on the available extensions, see the companion post “Introducing Lambda Extensions – In preview“.

You can also build your own extensions to integrate your own tooling using the new Extensions API. For example extensions and wrapper scripts, see the GitHub repository.

Extensions are now available in preview in the following Regions: us-east-1, us-east-2, us-west-1, us-west-2, ca-central-1, eu-west-1, eu-west-2, eu-west-3, eu-central-1, eu-north-1, eu-south-1, sa-east-1, me-south-1, ap-northeast-1, ap-northeast-2, ap-northeast-3, ap-southeast-1, ap-southeast-2, ap-south-1, and ap-east-1.

For more serverless learning resources, visit https://serverlessland.com.

Introducing AWS Lambda Extensions – In preview

2020-10-08 Julian Wood

Post Syndicated from Julian Wood original https://aws.amazon.com/blogs/compute/introducing-aws-lambda-extensions-in-preview/

AWS Lambda is announcing a preview of Lambda Extensions, a new way to easily integrate Lambda with your favorite monitoring, observability, security, and governance tools. In this post I explain how Lambda extensions work, how you can begin using them, and the extensions from AWS Lambda Ready Partners that are available today.

Extensions help solve a common request from customers to make it easier to integrate their existing tools with Lambda. Previously, customers told us that integrating Lambda with their preferred tools required additional operational and configuration tasks. In addition, tools such as log agents, which are long-running processes, could not easily run on Lambda.

Extensions are a new way for tools to integrate deeply into the Lambda environment. There is no complex installation or configuration, and this simplified experience makes it easier for you to use your preferred tools across your application portfolio today. You can use extensions for use-cases such as:

capturing diagnostic information before, during, and after function invocation
automatically instrumenting your code without needing code changes
fetching configuration settings or secrets before the function invocation
detecting and alerting on function activity through hardened security agents, which can run as separate processes from the function

You can use extensions from AWS, AWS Lambda Ready Partners, and open source projects. There are extensions available today for AppDynamics, Check Point, Datadog, Dynatrace, Epsagon, HashiCorp, Lumigo, New Relic, Thundra, Splunk SignalFX, AWS AppConfig, and Amazon CloudWatch Lambda Insights.

You can learn how to build your own extensions, in the companion post “Building Extensions for AWS Lambda – In preview“.

Overview

Lambda Extensions is designed to be the easiest way to plug in the tools you use today without complex installation or configuration management. You deploy extensions as Lambda layers, with the AWS Management Console and AWS Command Line Interface (AWS CLI). You can also use infrastructure as code tools such as AWS CloudFormation, the AWS Serverless Application Model (AWS SAM), Serverless Framework, and Terraform. You can use Stackery to automate the integration of extensions from Epsagon, New Relic, Lumigo, and Thundra.

There are two components to the Lambda Extensions capability: the Extensions API and extensions themselves. Extensions are built using the new Lambda Extensions API which provides a way for tools to get greater control during function initialization, invocation, and shut down. This API builds on the existing Lambda Runtime API, which enables you to bring custom runtimes to Lambda.

AWS Lambda execution environment with the Extensions API

Most customers will use extensions without needing to know about the capabilities of the Extensions API that enables them. You can just consume capabilities of an extension by configuring the options in your Lambda functions. Developers who build extensions use the Extensions API to register for function and execution environment lifecycle events.

Extensions can run in either of two modes – internal and external.

Internal extensions run as part of the runtime process, in-process with your code. They allow you to modify the startup of the runtime process using language-specific environment variables and wrapper scripts. Internal extensions enable use cases such as automatically instrumenting code.
External extensions allow you to run separate processes from the runtime but still within the same execution environment as the Lambda function. External extensions can start before the runtime process, and can continue after the runtime shuts down. External extensions enable use cases such as fetching secrets before the invocation, or sending telemetry to a custom destination outside of the function invocation. These extensions run as companion processes to Lambda functions.

For more information on the Extensions API and the changes to the Lambda lifecycle, see “Building Extensions for AWS Lambda – In preview”

AWS Lambda Ready Partners extensions available at launch

Today, you can use extensions with the following AWS and AWS Lambda Ready Partner’s tools, and there are more to come:

AppDynamics provides end-to-end transaction tracing for AWS Lambda. With the AppDynamics extension, it is no longer mandatory for developers to include the AppDynamics tracer as a dependency in their function code, making tracing transactions across hybrid architectures even simpler.
The Datadog extension brings comprehensive, real-time visibility to your serverless applications. Combined with Datadog’s existing AWS integration, you get metrics, traces, and logs to help you monitor, detect, and resolve issues at any scale. The Datadog extension makes it easier than ever to get telemetry from your serverless workloads.
The Dynatrace extension makes it even easier to bring AWS Lambda metrics and traces into the Dynatrace platform for intelligent observability and automatic root cause detection. Get comprehensive, end-to-end observability with the flip of a switch and no code changes.
Epsagon helps you monitor, troubleshoot, and lower the cost for your Lambda functions. Epsagon’s extension reduces the overhead of sending traces to the Epsagon service, with minimal performance impact to your function.
HashiCorp Vault allows you to secure, store, and tightly control access to your application’s secrets and sensitive data. With the Vault extension, you can now authenticate and securely retrieve dynamic secrets before your Lambda function invokes.
Lumigo provides a monitoring and observability platform for serverless and microservices applications. The Lumigo extension enables the new Lumigo Lambda Profiler to see a breakdown of function resources, including CPU, memory, and network metrics. Receive actionable insights to reduce Lambda runtime duration and cost, fix bottlenecks, and increase efficiency.
Check Point CloudGuard provides full lifecycle security for serverless applications. The CloudGuard extension enables Function Self Protection data aggregation as an out-of-process extension, providing detection and alerting on application layer attacks.
New Relic provides a unified observability experience for your entire software stack. The New Relic extension uses a simpler companion process to report function telemetry data. This also requires fewer AWS permissions to add New Relic to your application.
Thundra provides an application debugging, observability and security platform for serverless, container and virtual machine (VM) workloads. The Thundra extension adds asynchronous telemetry reporting functionality to the Thundra agents, getting rid of network latency.
Splunk offers an enterprise-grade cloud monitoring solution for real-time full-stack visibility at scale. The Splunk extension provides a simplified runtime-independent interface to collect high-resolution observability data with minimal overhead. Monitor, manage, and optimize the performance and cost of your serverless applications with Splunk Observability solutions.
AWS AppConfig helps you manage, store, and safely deploy application configurations to your hosts at runtime. The AWS AppConfig extension integrates Lambda and AWS AppConfig seamlessly. Lambda functions have simple access to external configuration settings quickly and easily. Developers can now dynamically change their Lambda function’s configuration safely using robust validation features.
Amazon CloudWatch Lambda Insights enables you to efficiently monitor, troubleshoot, and optimize Lambda functions. The Lambda Insights extension simplifies the collection, visualization, and investigation of detailed compute performance metrics, errors, and logs. You can more easily isolate and correlate performance problems to optimize your Lambda environments.

You can also build and use your own extensions to integrate your organization’s tooling. For instance, the Cloud Foundations team at Square has built their own extension. They say:

The Cloud Foundations team at Square works to make the cloud accessible and secure. We partnered with the Security Infrastructure team, who builds infrastructure to secure Square’s sensitive data, to enable serverless applications at Square, and provide mTLS identities to Lambda.

Since beginning work on Lambda, we have focused on creating a streamlined developer experience. Teams adopting Lambda need to learn a lot about AWS, and we see extensions as a way to abstract away common use cases. For our initial exploration, we wanted to make accessing secrets easy, as with our current tools each Lambda function usually pulls 3-5 secrets.

The extension we built and open source fetches secrets on cold starts, before the Lambda function is invoked. Each function includes a configuration file that specifies which secrets to pull. We knew this configuration was key, as Lambda functions should only be doing work they need to do. The secrets are cached in the local /tmp directory, which the function reads when it needs the secret data. This makes Lambda functions not only faster, but reduces the amount of code for accessing secrets.

Showing extensions in action with AWS AppConfig

This demo shows an example of using the AWS AppConfig with a Lambda function. AWS AppConfig is a capability of AWS Systems Manager to create, manage, and quickly deploy application configurations. It lets you dynamically deploy external configuration without having to redeploy your applications. As AWS AppConfig has robust validation features, all configuration changes can be tested safely before rolling out to your applications.

AWS AppConfig has an available extension which gives Lambda functions access to external configuration settings quickly and easily. The extension runs a separate local process to retrieve and cache configuration data from the AWS AppConfig service. The function code can then fetch configuration data faster using a local call rather than over the network.

To set up the example, visit the GitHub repo and follow the instructions in the README.md file.

The example creates an AWS AppConfig application, environment, and configuration profile. It stores a loglevel value, initially set to normal.

AWS AppConfig application, environment, and configuration profile

An AWS AppConfig deployment runs to roll out the initial configuration.

AWS AppConfig deployment

The example contains two Lambda functions that include the AWS AppConfig extension. For a list of the layers that have the AppConfig extension, see the blog post “AWS AppConfig Lambda Extension”.

As extensions share the same permissions as Lambda functions, the functions have execution roles that allow access to retrieve the AWS AppConfig configuration.

Lambda function add layer

The functions use the extension to retrieve the loglevel value from AWS AppConfig, returning the value as a response. In a production application, this value could be used within function code to determine what level of information to send to CloudWatch Logs. For example, to troubleshoot an application issue, you can change the loglevel value centrally. Subsequent function invocations for both functions use the updated value.

Both Lambda functions are configured with an environment variable that specifies which AWS AppConfig configuration profile and value to use.

Lambda environment variable specifying AWS AppConfig profile

The functions also return whether the invocation is a cold start.

Running the functions with a test payload returns the loglevel value normal. The first invocation is a cold start.

{
  "event": {
    "hello": "world"
  },
  "ColdStart": true,
  "LogLevel": "normal"
}

Subsequent invocations return the same value with ColdStart set to false.

{
  "event": {
    "hello": "world"
  },
  "ColdStart": false,
  "LogLevel": "normal"
}

Create a new AWS Config hosted configuration profile version setting the loglevel value to verbose. Run a new AWS AppConfig deployment to update the value. The extension for both functions retrieves the new value. The function configuration itself is not changed.

Running another test invocation for both functions returns the updated value still without a cold start.

{
  "event": {
    "hello": "world"
  },
  "ColdStart": false,
  "LogLevel": "verbose"
}

AWS AppConfig has worked seamlessly with Lambda to update a dynamic external configuration setting for multiple Lambda functions without having to redeploy the function configuration.

The only function configuration required is to add the layer which contains the AWS AppConfig extension.

Pricing

Resources, security, and performance with extensions

Extensions run in the same execution environment as the function code. Therefore, they share resources with the function, such as CPU, memory, disk storage, and environment variables. They also share permissions, using the same AWS Identity and Access Management (IAM) role as the function.

You can configure up to 10 extensions per function, using up to five layers at a time. Multiple extensions can be included in a single layer.

The size of the extensions counts towards the deployment package limit. This cannot exceed the unzipped deployment package size limit of 250 MB.

External extensions are initialized before the runtime is started so can increase the delay before the function is invoked. Today, the function invocation response is returned after all extensions have completed. An extension that takes time to complete can increase the delay before the function response is returned. If an extension performs compute-intensive operations, function execution duration may increase. To measure the additional time the extension runs after the function invocation, use the new PostRuntimeExtensionsDuration CloudWatch metric to measure the extra time the extension takes after the function execution. To understand the impact of a specific extension, you can use the Duration and MaxMemoryUsed CloudWatch metrics, and run different versions of your function with and without the extension. Adding more memory to a function also proportionally increases CPU and network throughput.

The function and all extensions must complete within the function’s configured timeout setting which applies to the entire invoke phase.

Conclusion

Lambda extensions enable you to extend the Lambda service to more easily integrate with your favorite tools for monitoring, observability, security, and governance.

Today, you can install a number of available extensions from AWS Lambda Ready Partners. These cover use-cases such as application performance monitoring, secrets management, configuration management, and vulnerability detection. Extensions make it easier to use your existing tools with your serverless applications.

You can also build extensions to integrate your own tooling using the new Extensions API. For more information, see the companion post “Building Extensions for AWS Lambda – In preview“.

For more serverless learning resources, visit https://serverlessland.com.

Introducing IAM and Lambda authorizers for Amazon API Gateway HTTP APIs

2020-09-25 Julian Wood

Post Syndicated from Julian Wood original https://aws.amazon.com/blogs/compute/introducing-iam-and-lambda-authorizers-for-amazon-api-gateway-http-apis/

Amazon API Gateway HTTP APIs enable you to create RESTful APIs with lower latency and lower cost than API Gateway REST APIs.

The API Gateway team is continuing work to improve and migrate popular REST API features to HTTP APIs. We are adding two of the most requested features, AWS Identity and Access Management (IAM) authorizers and AWS Lambda authorizers.

HTTP APIs already support JWT authorizers as a part of OpenID Connect (OIDC) and OAuth 2.0 frameworks. For more information, see “Simple HTTP API with JWT Authorizer.”

IAM authorization

AWS IAM roles and policies offer flexible, robust, and fully managed access controls, without writing any code. You can use IAM roles and policies to control who can create and manage your APIs, in addition to who can invoke them. IAM authorization for HTTP API routes is the best choice for internal or private APIs called by other AWS services like AWS Lambda.

IAM authorization for HTTP API APIs is similar to that for REST APIs. IAM access is determined by identity policies, which are attached to IAM users, groups, or roles. These policies define what identity can access which HTTP APIs routes. See “AWS Services That Work with IAM.”

Lambda authorization

A Lambda authorizer is a Lambda function which API Gateway calls for an authorization check when a client makes a request to an HTTP API route. You can use Lambda authorizers to implement custom authorization schemes to comply with your security requirements.

New authorizer features

HTTP API Lambda authorizers have some new features compared to REST APIs. There is a new payload and response format, including a simple Boolean authorization option.

New payload versions and response format

Lambda authorizers for HTTP APIs introduce a new payload format, version 2.0. If you need compatibility to use the same Lambda authorizers for both REST and HTTP APIs, you can continue to use version 1.0.

The payload format version also determines the request format and response structure that you must send to and return from your Lambda authorizer function. The version 2.0 payload context now allows non-string values. With version 1.0, your Lambda authorizer must return an IAM policy that allows or denies access to your API route. This is the same existing functionality as REST APIs. You can use standard IAM policy syntax in the policy. For examples of IAM policies, see “Control access for invoking an API.”

If you choose the new 2.0 format version when configuring the authorizer, you can now return either a Boolean value, or an IAM policy. The Boolean value enables simple responses from the authorizer without having to construct an IAM policy, and is in the format:

{
  "isAuthorized": true/false,
  "context": {
    "exampleKey": "exampleValue"
  }
}

The context object is optional. You can pass context properties on to Lambda integrations or access logs by using $context.authorizer.property. To learn more, see “Customizing HTTP API access logs.”

Caching authorizer responses

You can enable caching for a Lambda authorizer for up to one hour. To enable caching, your authorizer must have at least one identity source. API Gateway calls the Lambda authorizer function only when all of the specified identity sources are present. API Gateway uses the identity sources as the cache key. If a client specifies the same identity source parameters within the cache TTL, API Gateway uses the cached authorizer result. The Lambda authorizer function is not invoked.

Caching is enabled at the API Gateway level per authorizer. It is important to understand the effect of caching, particularly with simple responses and multiple routes. When using a simple response, the authorizer fully allows or denies all API requests that match the cached identity source values.

For example, you have two different routes using the same Lambda authorizer with a simple response. Both routes have different access requirements. The first route allows access to GET /list-users with an Authorization header with the value SecretTokenUsers. The second route denies access using the same header to GET /list-admins.

The Lambda authorizer has a single identity source, $request.header.Authorization, with the following code:

$request.header.Authorization.
exports.handler = async(event, context) => {
    let response = {
        "isAuthorized": false,
        "context": {
            "AuthInfo": "defaultdeny"
        }
    };
    if ((event.routeKey === "GET /list-users") && (event.headers.Authorization === "SecretTokenUsers")) {
        response = {
            "isAuthorized": true,
            "context": {
                "AuthInfo": "true-users"
            }
        };
    }
    if ((event.routeKey === "GET /list-admins") && (event.headers.authorization === "SecretTokenUsers")) {
        response = {
            "isAuthorized": false,
            "context": {
                "AuthInfo": "false-admins",
            }
        };
    }
    return response;
};

As both routes share the same identity source parameter, a cache result from successfully accessing /list-users with the Authorization header could allow access to /list-admins which is not intended. To cache responses differently per route, add $context.routeKey as an additional identity source. This creates a cache key that is unique for each route.

If more granular permissions are required, disable simple responses and return an IAM policy instead.

Testing Lambda authorizers

You have an existing Lambda function behind an HTTP API and want to add a Lambda authorizer using the new Boolean simple response. Create a new Lambda authorizer function with the following code.

exports.handler = async(event, context) => {
    let response = {
        "isAuthorized": false,
        "context": {
            "AuthInfo": "defaultdeny"
        }
    };
    if (event.headers.Authorization === "secretToken") {
        response = {
            "isAuthorized": true,
            "context": {
                "AuthInfo": "Customer1"
            }
        };
    }
    return response;
};

The authorizer returns true if a header called Authorization has the value secretToken.

To create an authorizer, browse to the API Gateway console. Navigate to your HTTP API, choose Authorization under Develop, select the Attach authorizers to routes tab, and choose Create and attach an authorizer.

Create and attach HTTP API authorizer

Create the Lambda authorizer, pointing to your Lambda authorizer function. Select Payload format version 2.0 with a Simple response.

Create Lambda simple authorizer settings

Enable caching and add two identity sources, $request.header.Authorization and $context.routeKey, to ensure that your cache key is unique when adding multiple routes.

Add caching and identity sources to Lambda authorizer

Choose Create and attach. The route is now using a Lambda authorizer.

HTTP API route includes Lambda authorizer

The following examples to test the API authentication use Postman but you can use any HTTP client.

Send a GET request to the HTTP APIs URL without specifying any authorization header.

Postman unauthorized GET request

API Gateway returns a 401 Unauthorized response, as expected. The required $request.header.Authorization identity source is not provided, so the Lambda authorizer is not called.

Enter a valid Authorization header key, but an invalid value.

Postman Forbidden GET request

API Gateway returns a 403 Forbidden response as the request is now passed to the Lambda authorizer, which has evaluated the value, and returned "isAuthorized": false.

Supply a valid Authorization header key and value.

Postman successful authorized GET request

API Gateway authorizes the request using the Lambda authorizer and sends the request to the Lambda function integration which returns a successful 200 response.

For more Lambda authorizer code examples see “Custom Authorizer Blueprints for AWS Lambda.”

AWS CloudFormation support

Lambda authorizers for HTTP APIs are configured as AWS::ApiGatewayV2::Authorizer CloudFormation resources. Today, they are imported into AWS Serverless Application Model (AWS SAM) applications as native CloudFormation resources.

LambdaAuthorizer:
    Type: 'AWS::ApiGatewayV2::Authorizer'
    Properties:
    Name: LambdaAuthorizer
    ApiId: !Ref HttpApi
    AuthorizerType: REQUEST
    AuthorizerUri: arn:aws:apigateway:{region}:lambda:path/2015-03-31/functions/arn:aws:lambda: {region}:{account id}:function:{Function name}/invocations
    IdentitySource:
        - $request.header.Authorization
    AuthorizerPayloadFormatVersion: 2.0

Conclusion

IAM and Lambda authorizers are two of the most requested features for Amazon API Gateway HTTP APIs. You can now use IAM authorization in a similar way to API Gateway REST APIs. Lambda authorizers for HTTP APIs offer the option of a simpler Boolean response with the new version 2.0 payload and response format. You configure identity sources to specify the location of data that’s required to authorize a request, which are also used as the cache key.

These authorizers are generally available in all AWS Regions where API Gateway is available. To learn more about options for protecting your APIs, you can read the documentation. For more information about Amazon API Gateway, visit the product page.

For the latest blogs, videos, and training for AWS Serverless, see https://serverlessland.com/.