Tag Archives: Amazon CloudFront

Web application access control patterns using AWS services

Post Syndicated from Zili Gao original https://aws.amazon.com/blogs/architecture/web-application-access-control-patterns-using-aws-services/

The web application client-server pattern is widely adopted. The access control allows only authorized clients to access the backend server resources by authenticating the client and providing granular-level access based on who the client is.

This post focuses on three solution architecture patterns that prevent unauthorized clients from gaining access to web application backend servers. There are multiple AWS services applied in these architecture patterns that meet the requirements of different use cases.

OAuth 2.0 authentication code flow

Figure 1 demonstrates the fundamentals to all the architectural patterns discussed in this post. The blog Understanding Amazon Cognito user pool OAuth 2.0 grants describes the details of different OAuth 2.0 grants, which can vary the flow to some extent.

A typical OAuth 2.0 authentication code flow

Figure 1. A typical OAuth 2.0 authentication code flow

The architecture patterns detailed in this post use Amazon Cognito as the authorization server, and Amazon Elastic Compute Cloud instance(s) as resource server. The client can be any front-end application, such as a mobile application, that sends a request to the resource server to access the protected resources.

Pattern 1

Figure 2 is an architecture pattern that offloads the work of authenticating clients to Application Load Balancer (ALB).

Application Load Balancer integration with Amazon Cognito

Figure 2. Application Load Balancer integration with Amazon Cognito

ALB can be used to authenticate clients through the user pool of Amazon Cognito:

  1. The client sends HTTP request to ALB endpoint without authentication-session cookies.
  2. ALB redirects the request to Amazon Cognito authentication endpoint. The client is authenticated by Amazon Cognito.
  3. The client is directed back to the ALB with the authentication code.
  4. The ALB uses the authentication code to obtain the access token from the Amazon Cognito token endpoint and also uses the access token to get client’s user claims from Amazon Cognito UserInfo endpoint.
  5. The ALB prepares the authentication session cookie containing encrypted data and redirects client’s request with the session cookie. The client uses the session cookie for all further requests. The ALB validates the session cookie and decides if the request can be passed through to its targets.
  6. The validated request is forwarded to the backend instances with the ALB adding HTTP headers that contain the data from the access token and user-claims information.
  7. The backend server can use the information in the ALB added headers for granular-level permission control.

The key takeaway of this pattern is that the ALB maintains the whole authentication context by triggering client authentication with Amazon Cognito and prepares the authentication-session cookie for the client. The Amazon Cognito sign-in callback URL points to the ALB, which allows the ALB access to the authentication code.

More details about this pattern can be found in the documentation Authenticate users using an Application Load Balancer.

Pattern 2

The pattern demonstrated in Figure 3 offloads the work of authenticating clients to Amazon API Gateway.

Amazon API Gateway integration with Amazon Cognito

Figure 3. Amazon API Gateway integration with Amazon Cognito

API Gateway can support both REST and HTTP API. API Gateway has integration with Amazon Cognito, whereas it can also have control access to HTTP APIs with a JSON Web Token (JWT) authorizer, which interacts with Amazon Cognito. The ALB can be integrated with API Gateway. The client is responsible for authenticating with Amazon Cognito to obtain the access token.

  1. The client starts authentication with Amazon Cognito to obtain the access token.
  2. The client sends REST API or HTTP API request with a header that contains the access token.
  3. The API Gateway is configured to have:
    • Amazon Cognito user pool as the authorizer to validate the access token in REST API request, or
    • A JWT authorizer, which interacts with the Amazon Cognito user pool to validate the access token in HTTP API request.
  4. After the access token is validated, the REST or HTTP API request is forwarded to the ALB, and:
    • The API Gateway can route HTTP API to private ALB via a VPC endpoint.
    • If a public ALB is used, the API Gateway can route both REST API and HTTP API to the ALB.
  5. API Gateway cannot directly route REST API to a private ALB. It can route to a private Network Load Balancer (NLB) via a VPC endpoint. The private ALB can be configured as the NLB’s target.

The key takeaways of this pattern are:

  • API Gateway has built-in features to integrate Amazon Cognito user pool to authorize REST and/or HTTP API request.
  • An ALB can be configured to only accept the HTTP API requests from the VPC endpoint set by API Gateway.

Pattern 3

Amazon CloudFront is able to trigger AWS Lambda functions deployed at AWS edge locations. This pattern (Figure 4) utilizes a feature of [email protected], where it can act as an authorizer to validate the client requests that use an access token, which is usually included in HTTP Authorization header.

Using Amazon CloudFront and AWS Lambda@Edge with Amazon Cognito

Figure 4. Using Amazon CloudFront and AWS [email protected] with Amazon Cognito

The client can have an individual authentication flow with Amazon Cognito to obtain the access token before sending the HTTP request.

  1. The client starts authentication with Amazon Cognito to obtain the access token.
  2. The client sends a HTTP request with Authorization header, which contains the access token, to the CloudFront distribution URL.
  3. The CloudFront viewer request event triggers the launch of the function at [email protected]
  4. The Lambda function extracts the access token from the Authorization header, and validates the access token with Amazon Cognito. If the access token is not valid, the request is denied.
  5. If the access token is validated, the request is authorized and forwarded by CloudFront to the ALB. CloudFront is configured to add a custom header with a value that can only be shared with the ALB.
  6. The ALB sets a listener rule to check if the incoming request has the custom header with the shared value. This makes sure the internet-facing ALB only accepts requests that are forwarded by CloudFront.
  7. To enhance the security, the shared value of the custom header can be stored in AWS Secrets Manager. Secrets Manager can trigger an associated Lambda function to rotate the secret value periodically.
  8. The Lambda function also updates CloudFront for the added custom header and ALB for the shared value in the listener rule.

The key takeaways of this pattern are:

  • By default, CloudFront will remove the authorization header before forwarding the HTTP request to its origin. CloudFront needs to be configured to forward the Authorization header to the origin of the ALB. The backend server uses the access token to apply granular levels of resource access permission.
  • The use of [email protected] requires the function to sit in us-east-1 region.
  • The CloudFront-added custom header’s value is kept as a secret that can only be shared with the ALB.


The architectural patterns discussed in this post are token-based web access control methods that are fully supported by AWS services. The approach offloads the OAuth 2.0 authentication flow from the backend server to AWS services. The services managed by AWS can provide the resilience, scalability, and automated operability for applying access control to a web application.

AWS Week in Review – May 9, 2022

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/aws-week-in-review-may-9-2022/

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

Another week starts, and here’s a collection of the most significant AWS news from the previous seven days. This week is also the one-year anniversary of CloudFront Functions. It’s exciting to see what customers have built during this first year.

Last Week’s Launches
Here are some launches that caught my attention last week:

Amazon RDS supports PostgreSQL 14 with three levels of cascaded read replicas – That’s 5 replicas per instance, supporting a maximum of 155 read replicas per source instance with up to 30X more read capacity. You can now build a more robust disaster recovery architecture with the capability to create Single-AZ or Multi-AZ cascaded read replica DB instances in same or cross Region.

Amazon RDS on AWS Outposts storage auto scalingAWS Outposts extends AWS infrastructure, services, APIs, and tools to virtually any datacenter. With Amazon RDS on AWS Outposts, you can deploy managed DB instances in your on-premises environments. Now, you can turn on storage auto scaling when you create or modify DB instances by selecting a checkbox and specifying the maximum database storage size.

Amazon CodeGuru Reviewer suppression of files and folders in code reviews – With CodeGuru Reviewer, you can use automated reasoning and machine learning to detect potential code defects that are difficult to find and get suggestions for improvements. Now, you can prevent CodeGuru Reviewer from generating unwanted findings on certain files like test files, autogenerated files, or files that have not been recently updated.

Amazon EKS console now supports all standard Kubernetes resources to simplify cluster management – To make it easy to visualize and troubleshoot your applications, you can now use the console to see all standard Kubernetes API resource types (such as service resources, configuration and storage resources, authorization resources, policy resources, and more) running on your Amazon EKS cluster. More info in the blog post Introducing Kubernetes Resource View in Amazon EKS console.

AWS AppConfig feature flag Lambda Extension support for Arm/Graviton2 processors – Using AWS AppConfig, you can create feature flags or other dynamic configuration and safely deploy updates. The AWS AppConfig Lambda Extension allows you to access this feature flag and dynamic configuration data in your Lambda functions. You can now use the AWS AppConfig Lambda Extension from Lambda functions using the Arm/Graviton2 architecture.

AWS Serverless Application Model (SAM) CLI now supports enabling AWS X-Ray tracing – With the AWS SAM CLI you can initialize, build, package, test on local and cloud, and deploy serverless applications. With AWS X-Ray, you have an end-to-end view of requests as they travel through your application, making them easier to monitor and troubleshoot. Now, you can enable tracing by simply adding a flag to the sam init command.

Amazon Kinesis Video Streams image extraction – With Amazon Kinesis Video Streams you can capture, process, and store media streams. Now, you can also request images via API calls or configure automatic image generation based on metadata tags in ingested video. For example, you can use this to generate thumbnails for playback applications or to have more data for your machine learning pipelines.

AWS GameKit supports Android, iOS, and MacOS games developed with Unreal Engine – With AWS GameKit, you can build AWS-powered game features directly from the Unreal Editor with just a few clicks. Now, the AWS GameKit plugin for Unreal Engine supports building games for the Win64, MacOS, Android, and iOS platforms.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
Some other updates you might have missed:

🎂 One-year anniversary of CloudFront Functions – I can’t believe it’s been one year since we launched CloudFront Functions. Now, we have tens of thousands of developers actively using CloudFront Functions, with trillions of invocations per month. You can use CloudFront Functions for HTTP header manipulation, URL rewrites and redirects, cache key manipulations/normalization, access authorization, and more. See some examples in this repo. Let’s see what customers built with CloudFront Functions:

  • CloudFront Functions enables Formula 1 to authenticate users with more than 500K requests per second. The solution is using CloudFront Functions to evaluate if users have access to view the race livestream by validating a token in the request.
  • Cloudinary is a media management company that helps its customers deliver content such as videos and images to users worldwide. For them, [email protected] remains an excellent solution for applications that require heavy compute operations, but lightweight operations that require high scalability can now be run using CloudFront Functions. With CloudFront Functions, Cloudinary and its customers are seeing significantly increased performance. For example, one of Cloudinary’s customers began using CloudFront Functions, and in about two weeks it was seeing 20–30 percent better response times. The customer also estimates that they will see 75 percent cost savings.
  • Based in Japan, DigitalCube is a web hosting provider for WordPress websites. Previously, DigitalCube spent several hours completing each of its update deployments. Now, they can deploy updates across thousands of distributions quickly. Using CloudFront Functions, they’ve reduced update deployment times from 4 hours to 2 minutes. In addition, faster updates and less maintenance work result in better quality throughout DigitalCube’s offerings. It’s now easier for them to test on AWS because they can run tests that affect thousands of distributions without having to scale internally or introduce downtime.
  • Amazon.com is using CloudFront Functions to change the way it delivers static assets to customers globally. CloudFront Functions allows them to experiment with hyper-personalization at scale and optimal latency performance. They have been working closely with the CloudFront team during product development, and they like how it is easy to create, test, and deploy custom code and implement business logic at the edge.

AWS open-source news and updates – A newsletter curated by my colleague Ricardo to bring you the latest open-source projects, posts, events, and more. Read the latest edition here.

Reduce log-storage costs by automating retention settings in Amazon CloudWatch – By default, CloudWatch Logs stores your log data indefinitely. This blog post shows how you can reduce log-storage costs by establishing a log-retention policy and applying it across all of your log groups.

Observability for AWS App Runner VPC networking – With X-Ray support in App runner, you can quickly deploy web applications and APIs at any scale and take advantage of adding tracing without having to manage sidecars or agents. Here’s an example of how you can instrument your applications with the AWS Distro for OpenTelemetry (ADOT).

Upcoming AWS Events
It’s AWS Summits season and here are some virtual and in-person events that might be close to you:

You can now register for re:MARS to get fresh ideas on topics such as machine learning, automation, robotics, and space. The conference will be in person in Las Vegas, June 21–24.

That’s all from me for this week. Come back next Monday for another Week in Review!


Enriching Amazon Cognito features with an Amazon API Gateway proxy

Post Syndicated from Mahmoud Matouk original https://aws.amazon.com/blogs/architecture/enriching-amazon-cognito-features-with-an-amazon-api-gateway-proxy/

This post was co-written with Geoff Baskwill, member of the Architecture Enabling Team at Trend Micro. At Trend Micro, we use AWS technologies to build secure solutions to help our customers improve their security posture.

This post builds on the architecture originally published in Protect public clients for Amazon Cognito with an Amazon CloudFront proxy. Read that post to learn more about public clients and why it is helpful to implement a proxy layer.

We’ll build on the idea of passing calls to Amazon Cognito through a lightweight proxy. This pattern allows you to augment identity flows in your system with additional processing without having to change the client or the backend. For example, you can use the proxy layer to protect public clients as explained in the original post. You can also use this layer to apply additional fraud detection logic to prevent fraudulent sign up, propagate events to downstream systems for monitoring or enhanced logging, and replicate certain events to another AWS Region (for example, to build high availability and multi-Region capabilities).

The solution in the original post used Amazon CloudFront, [email protected], and AWS WAF to implement protection of public clients, and hinted that there are multiple ways to do it. In this post, we explore one of these alternatives by using Amazon API Gateway and a proxy AWS Lambda function to implement the proxy to Amazon Cognito. This alternative offers improved performance and full access to request and response elements.

Solution overview

The focus of this solution is to protect public clients of the Amazon Cognito user pool.

The workflow is shown in Figure 1 and works as follows:

  1. Configure the client application (mobile or web client) to use the API Gateway endpoint as a proxy to an Amazon Cognito regional endpoint. You also create an application client in Amazon Cognito with a secret. This means that any unauthenticated API call must have the secret hash.
  2. Use a Lambda function to add a secret hash to the relevant incoming requests before passing them on to the Amazon Cognito endpoint. This function can also be used for other purposes like logging, propagation of events, or additional validation.
  3. In the Lambda function, you must have the app client secret to be able to calculate the secret hash and add it to the request. We recommend that you keep the secret in AWS Secrets Manager and cache it for the lifetime of the function.
  4. Use AWS WAF with API Gateway to enforce rate limiting, implement allow and deny lists, and apply other rules according to your security requirements.
  5. Clients that send unauthenticated API calls to the Amazon Cognito endpoint directly are blocked and dropped because of the missing secret.

Not shown: You may want to set up a custom domain and certificate for your API Gateway endpoint.

A proxy solution to the Amazon Cognito regional endpoint

Figure 1. A proxy solution to the Amazon Cognito regional endpoint

Deployment steps

You can use the following AWS CloudFormation template to deploy this proxy pattern for your existing Amazon Cognito user pool.

Note: This template references a Lambda code package from a bucket in the us-east-1 Region. For that reason, the template can be only created in us-east-1. If you need to create the proxy solution in another Region, download the template and Lambda code package, update the template to reference another Amazon Simple Storage Service (Amazon S3) bucket that you own in the desired Region, and upload the code package to that S3 bucket. Then you can deploy your modified template in the desired Region.

launch stack

This template requires the user pool ID as input and will create several resources in your AWS account to support the following proxy pattern:

  • A new application client with a secret will be added to your Amazon Cognito user pool
  • The secret will be stored in Secrets Manager and will be read from the proxy Lambda function
  • The proxy Lambda function will be used to intercept Amazon Cognito API calls and attach client-secret to applicable requests
  • The API Gateway project provides the custom proxy endpoint that is used as the Amazon Cognito endpoint in your client applications
  • An AWS WAF WebACL provides firewall protection to the API Gateway endpoint. The WebACL includes placeholder rules for Allow and Deny lists of IPs. It also includes a rate limiting rule that will block requests from IP addresses that exceed the number of allowed requests within a five-minute period (rate limit value is provided as input to the template)
  • Several helper resources will also be created like Lambda functions, necessary AWS IAM policies, and roles to allow the solution to function properly

After you create a successful stack, you can find the endpoint URL in the outputs section of your CloudFormation stack. This is the URL we use in the next section with client applications.

Note: The template and code has been simplified for demonstration purposes. If you plan to deploy this solution in production, make sure to review these resources for compliance with your security and performance requirements. For example, you might need to enable certain logs or log encryption or use a customer managed key for encryption.

Integrating your client with proxy solution

Integrate the client application with the proxy by changing the endpoint in your client application to use the endpoint URL for the proxy API Gateway. The endpoint URL and application client ID are located in the Outputs section of the CloudFormation stack.

Next, edit your client-side code to forward calls to Amazon Cognito through the proxy endpoint and use the new application client ID. For example, if you’re using the Identity SDK, you should change this property as follows.

var poolData = {
  UserPoolId: '<USER-POOL-ID>',
  ClientId: '<APP-CLIENT-ID>',
  endpoint: 'https://<APIGATEWAY-URL>'

If you’re using AWS Amplify, change the endpoint in the aws-exports.js file by overriding the property aws_cognito_endpoint. Or, if you configure Amplify Auth in your code, you can provide the endpoint as follows.

  userPoolId: '<USER-POOL-ID>',
  userPoolWebClientId: '<APP-CLIENT-ID>',
  endpoint: 'https://<APIGATEWAY-URL>'

If you have a mobile application that uses the Amplify mobile SDK, override the endpoint in your configuration as follows (don’t include AppClientSecret parameter in your configuration).

Note that the Endpoint value contains the domain name only, not the full URL. This feature is available in the latest releases of the iOS and Android SDKs.

"CognitoUserPool": {
  "Default": {
    "AppClientId": "<APP-CLIENT-ID>",
    "Endpoint": "<APIGATEWAY-DOMAIN-NAME>",
    "PoolId": "<USER-POOL-ID>",
    "Region": "<REGION>"
WARNING: If you do an amplify push or amplify pull operation, the Amplify CLI overwrites customizations to the awsconfiguration.json and amplifyconfiguration.json files. You must manually re-apply the Endpoint customization and remove the AppClientSecret if you use the CLI to modify your cloud backend.

When to use this pattern

The same guidance for using this pattern applies as in the original post.

You may prefer this solution if you are familiar with API Gateway or if you want to take advantage of the following:

  • Use CloudWatch metrics from API Gateway to monitor the behavior and health of your Amazon Cognito user pool.
  • Find and examine logs from your Lambda proxy function in the Region where you have deployed this solution.
  • Deploy your proxy function into an Amazon Virtual Private Cloud (Amazon VPC) and access sensitive data or services in the Amazon VPC or through Amazon VPC endpoints.
  • Have full access to request and response in the proxy Lambda function

Extend the proxy features

Now that you are intercepting all of the API requests to Amazon Cognito, add features to your identity layer:

  • Emit events using Amazon EventBridge when user data changes. You can do this when the proxy function receives mutating actions like UpdateUserAttribute (among others) and Amazon Cognito processes the request successfully.
  • Implement more complex rate limiting than what AWS WAF supports, like per-user rate limits regardless of where IP address requests are coming from. This can also be extended to include fraud detection, request input validation, and integration with third-party security tools.
  • Build a geo-redundant user pool that transparently mitigates regional failures by replicating mutating actions to an Amazon Cognito user pool in another Region.


This solution has the same limitations highlighted in the original post. Keep in mind that resourceful authenticated users can still make requests to the Amazon Cognito API directly using the access token they obtained from authentication. If you want to prevent this from happening, adjust the proxy to avoid returning the access token to clients or return an encrypted version of the token.


In this post, we explored an alternative solution that implements a thin proxy to Amazon Cognito endpoint. This allows you to protect your application against unwanted requests and enrich your identity flows with additional logging, event propagation, validations, and more.

Ready to get started? If you have questions about this post, start a new thread on the Amazon Cognito forum or contact AWS Support.

Deploying Sample UI Forms using React, Formik, and AWS CDK

Post Syndicated from Kevin Rivera original https://aws.amazon.com/blogs/architecture/deploying-sample-ui-forms-using-react-formik-and-aws-cdk/

Companies in many industries use UI forms to collect customer data for account registrations, online shopping, and surveys. It can be tedious to create form fields. Proper use of input validation can help users easily find and fix mistakes. Best practice is that users should not see a form filled with “this field is required” or “your email is invalid” errors until they have first attempted to complete the form.

Forms can be difficult to write, maintain, and test. They often have to be repeated in multiple areas on even the most basic interactive web application. Fortunately, third-party libraries provide front-end developers with tools to manage these complexities.

This blog post will describe an example solution for implementing simple forms for a user interface using the JavaScript libraries React and Formik. We will also use AWS resources to host the application. The blog will describe how the application is provisioned using the AWS Cloud Development Kit (CDK).

Our sample form and code

Our solution demonstrates a straightforward way for a front-end or full stack developer to rapidly create forms. We will show how a popular React form library, Formik, abstracts input field state management and reduces the amount of written code.

Our sample form will collect the user’s information (name, email, and date of birth) and store the data to a private Amazon S3 bucket for later retrieval using a presigned URL. The sample code gives developers a structure with which to build on and experiment. The code provides example integration with AWS services to host a React form application.

Figure 1 demonstrates how the user’s information flows through various AWS services and finally gets uploaded to private Amazon S3 bucket.

Figure 1. User interface communicating with API Gateway to upload a file to a S3 bucket using a presigned URL

Figure 1. User interface communicating with API Gateway to upload a file to a S3 bucket using a presigned URL

  1. Click the Upload button. The user visits the webpage, fills the form, and clicks the ‘Upload Data’ button
  2. HTTP request to Amazon API Gateway. The front end makes an HTTP request to the API Gateway
  3. Forward HTTP request. The API Gateway forwards the HTTP request to the Lambda function that generates a presigned URL for uploading data to a S3 bucket
  4. Presigned URL. The presigned URL for uploading data to a S3 bucket is returned by the Lambda function to the API Gateway as HTTP response.
  5. Forward HTTP response. The API Gateway forwards the presigned URL to the client application
  6. Upload data to Amazon S3. The client application uses the presigned URL to upload the form data to a S3 bucket

The code also demonstrates the flow of data when a download request is made by the user. The download process is shown in Figure 2.

Figure 2. User interface communicating with API Gateway to download a file from a S3 bucket using presigned URL

Figure 2. User interface communicating with API Gateway to download a file from a S3 bucket using presigned URL

  1. Click Download button. The user clicks the ‘Download Data’ button
  2. HTTP request to API Gateway. The front end makes an HTTP request to the API Gateway
  3. Forward HTTP request. The API Gateway forwards the HTTP request to the Lambda function that generates a presigned URL for downloading data from a S3 bucket
  4. Presigned URL. The presigned URL for downloading data from a S3 bucket is returned by the Lambda function to the API Gateway as HTTP response.
  5. Forward HTTP response. The API Gateway forwards the presigned URL to the client application
  6. Upload data to Amazon S3. The client application uses the presigned URL to download the form data to S3 bucket
  7. File downloads. The file downloads to user’s computer

Here are the four steps to demonstrate this solution:

  1. Provisioning the infrastructure (backend). The infrastructure will consist of:
    • An AWS Lambda function, which will generate a presigned URL when requested by the UI and respond with the URL for uploading/downloading data
    • An API Gateway, which will handle the requests and responses from UI and Lambda
    • Two separate S3 buckets, which will host the static UI forms and store the uploaded data (different buckets for each).
  2. Deploying the front end. We will use sample React/Formik code on S3.
  3. Testing. Once our code is deployed, we will test the form by uploading a file though the UI, and then retrieve that file.
  4. Clean up. Finally, we will clean up the S3 bucket.


For this walkthrough, you should have the following prerequisites:

Deploying the backend and front end

Clone code

The sample code for this application is available on GitHub. Clone the repo to follow along in a terminal.

git clone https://github.com/aws-samples/react-formik-on-aws

Install dependencies

Change the directory to the folder the clone created and install dependencies for the API.

cd formik-presigned-s3/ npm install

After installing the dependencies for the API, let’s install the dependencies in the UI.

cd ui/formik-s3-react-app
npm install


Let’s bundle our Lambda function that currently exists in the index.js file inside the resources/lambda directory. This will create our Lambda function inside a directory that our stack can read from, to create the handler.

npx esbuild resources/lambda/index.js –bundle –platform=node –target=node12 –external:aws-sdk –outfile=dist/lambda/build/index.js

Let’s go into more detail about the function of the Lambda handler. As seen in Figure 3, the handler is using three helper functions that are written in the file (isExisted, fetchUploadUrl, fetchViewUrl). It creates a presigned URL for uploads/downloads of data, confirms that the URL was created, and fetches the URL. Lines 6874 are calling the helper functions based on the API request needed.

Figure 3. Lambda’s handler function for GET request type

Figure 3. Lambda’s handler function for GET request type

Build the React app

#Make sure you are in the ui/formik-s3-react-app directory
npm run build

This command will create your index.html file and its dependencies, which will be the source of your UI site. When we deploy our stack, we will inspect the CDK code. The Lambda bundler and the React app build step work together to source the directory and create the S3 bucket that will eventually host the React application.

Note: If you are deploying AWS CDK apps into an AWS environment, you must provision these resources for a specific location and account. In this case you must run the following command:

cdk bootstrap aws://<aws_account_number>/<aws_region>

This is the error that you will see if you do not bootstrap:

This stack uses assets, so the toolkit stack must be deployed to the environment (Run "cdk bootstrap aws://aws_account_number/aws_region")

Let’s deploy!

Before we run the deploy command, let’s understand what exactly we are deploying and the advantages of the CDK.

Note: We won’t go into depth on how the AWS CDK works, but we will demonstrate implementation of the code for our infrastructure and website hosting.

Our configuration code for deploying our CDK is found in the root directory in a file called cdk.json. It’s important that we can configure certain properties. This is where we map to our bin file that creates our CDK app. As you can see in Figure 4, the app key points to bin/formic-s3.ts.

Figure 4. cdk.json file

Figure 4. Contents of cdk.json file

Now let’s look at the CDK stack code, shown in Figure 5. This can be found in the lib directory of the root file and it is called formik-s3-stack.ts.

Figure 5. CDK stack code that creates a new S3 bucket for hosting the React webpage

Figure 5. CDK stack code that creates a new S3 bucket for hosting the React webpage

This is the part of the code that creates our S3 bucket for hosting our React UI. The first few lines create the bucket name and point to the file that will be seen by the world (index.html). The deployment function has a source that will be searching for the path in your local directory where the build files were created. This will source the directory and then create it in an S3 bucket in the cloud.

Notice how our publicReadAccess is commented out. This is because it is not best practice to leave your bucket exposed publicly. For this blog, we will host this simple form site and allow public access. However, a CDN such as Amazon CloudFront should be used for distribution of traffic to keep your S3 bucket secure.

Figure 6. CDK stack code that creates a new S3 bucket

Figure 6. CDK stack code that creates a new S3 bucket for uploading and downloading data using S3 presigned URL

Figure 6 shows the second S3 bucket that will be used for our Formik data.

Figure 7. CDK code stack used to create the Lambda function

Figure 7. CDK code stack used to create the Lambda function

Figure 7 shows how to create your Lambda function, which also will be reading from the ‘bundling’ step.

Figure 8. CDK code stack used to create API Gateway

Figure 8. CDK code stack used to create API Gateway

Figure 8 shows how to create your API Gateway resources. Notice the ‘OPTIONS’ document is used here. This is because our front-end request URLs are not from the same origin as our APIs. Including the ‘OPTIONS’ document enables our browser to succeed in its preflight request and avoid any CORS issues.

Now that we understand our CDK, let’s finally DEPLOY!

npx cdk deploy

You will receive the output in the terminal that will be the storage API endpoint. You can also view this in CloudFormation under the Output tab for the stack the CDK spun up (FormikS3Stack). You should also see your S3 URL to view your React app.

What is React’s form?

Once you have your URL, you should see the form, shown in Figure 9.

Figure 9. Portal form designed using Formik in ReactJS

Figure 9. Portal form designed using Formik in ReactJS

Why is Formik so special?

Let’s preface this with how our forms had to be created using the old method, shown in Figure 10.

Figure 10. This is from https://www.bitnative.com/2020/08/19/formik-vs-plain-react-for-forms-worth-it/ showing a form without Formik

Figure 10. This is from https://www.bitnative.com/2020/08/19/formik-vs-plain-react-for-forms-worth-it/ showing a form without Formik

Figure 11 shows our code:

Figure 11. React code with the UI components

Figure 11. React code with the UI components

One of the first things you can notice when comparing both methods, is the location of your initial values. Formik handles the state of your fields. Without it, we would need to manage this with React’s state object if we were using class components, or with hooks inside functional components. With Formik, we don’t have to handle these tasks.

Another benefit of using Formik is its handling of input validation, errors, and handler functions that we can use to manage our UI (lines 7079 and 8793.) Formik reduces the need to write extra lines of code to handle validation and errors, managing states, and creating event handler logic.

Read this blog post that compares both methods of creating forms.

Making our API calls from the UI

Our Formik form is simple to implement, but one more step remains. We need to handle uploading the information, and then downloading it.

With all our resources created and our form done, we put it all together by creating our API requests, shown in Figure 12.

Figure 12. Code to upload to S3 bucket and download from S3 bucket

Figure 12. Code to upload to S3 bucket and download from S3 bucket

Due to the efficiency of AWS and Formik, we can upload and download with fewer than 50 lines of code.

Lines 1126 is where we call our API Gateway URL that our CDK created for us. With this API, when the user first clicks the upload button, the request hits the endpoint to create the presigned URL. It waits for its creation and in lines 2125 we PUT our data into our S3 bucket.

Lastly, we are able to hit that same presigned URL to download our information we uploaded into a JSON file.

Cleaning up

To avoid incurring future charges, delete the resources. Let’s run:

npx cdk destroy

You can confirm the removal by going into CloudFormation and confirming the resources were deleted.


In this blog post, we learned how we can create a simple server for our form submissions. We spun it up easily with the CDK toolkit and provisioned our resources. We hosted our UI and created a sample form using Formik, which handles state and reduces the amount of code we must write. We then hit the endpoints given to us by the deployment and tested the app by uploading and downloading our form data. Traditional form data management requires a separate function for handling data and errors in forms. This is a cleaner and more efficient way to handle form data.

For further reading:

Codacy Measures Developer Productivity using AWS Serverless

Post Syndicated from Catarina Gralha original https://aws.amazon.com/blogs/architecture/codacy-measures-developer-productivity-using-aws-serverless/

Codacy is a DevOps insights company based in Lisbon, Portugal. Since its launch in 2012, Codacy has helped software development and engineering teams reduce defects, keep technical debt in check, and ship better code, faster.

Codacy’s latest product, Pulse, is a service that helps understand and improve the performance of software engineering teams. This includes measuring metrics such as deployment frequency, lead time for changes, or mean time to recover. Codacy’s main platform is built on top of AWS products like Amazon Elastic Kubernetes Service (EKS), but they have taken Pulse one step further with AWS serverless.

In this post, we will explore the Pulse’s requirements, architecture, and the services it is built on, including AWS Lambda, Amazon API Gateway, and Amazon DynamoDB.

Pulse prototype requirements

Codacy had three clear requirements for their initial Pulse prototype.

  1. The solution must enable the development team to iterate quickly and have minimal time-to-market (TTM) to validate the idea.
  2. The solution must be easily scalable and match the demands of both startups and large enterprises alike. This was of special importance, as Codacy wanted to onboard Pulse with some of their existing customers. At the time, these customers already had massive amounts of information.
  3. The solution must be cost-effective, particularly during the early stages of the product development.

Enter AWS serverless

Codacy could have built Pulse on top of Amazon EC2 instances. However, this brings the undifferentiated heavy lifting of having to provision, secure, and maintain the instances themselves.

AWS serverless technologies are fully managed services that abstract the complexity of infrastructure maintenance away from developers and operators, so they can focus on building products.

Serverless applications also scale elastically and automatically behind the scenes, so customers don’t need to worry about capacity provisioning. Furthermore, these services are highly available by design and span multiple Availability Zones (AZs) within the Region in which they are deployed. This gives customers higher confidence that their systems will continue running even if one Availability Zone is impaired.

AWS serverless technologies are cost-effective too, as they are billed per unit of value, as opposed to billing per provisioned capacity. For example, billing is calculated by the amount of time a function takes to complete or the number of messages published to a queue, rather than how long an EC2 instance runs. Customers only pay when they are getting value out of the services, for example when serving an actual customer request.

Overview of Pulse’s solution architecture

An event is generated when a developer performs a specific action as part of their day-to-day tasks, such as committing code or merging a pull request. These events are the foundational data that Pulse uses to generate insights and are thus processed by multiple Pulse components called modules.

Let’s take a detailed look at a few of them.

Ingestion module

Figure 1. Pulse ingestion module architecture

Figure 1. Pulse ingestion module architecture

Figure 1 shows the ingestion module, which is the entry point of events into the Pulse platform and is built on AWS serverless applications as follows:

  • The ingestion API is exposed to customers using Amazon API Gateway. This defines REST, HTTP, and WebSocket APIs with sophisticated functionality such as request validation, rate limiting, and more.
  • The actual business logic of the API is implemented as AWS Lambda functions. Lambda can run custom code in a fully managed way. You only pay for the time that the function takes to run, in 1-millisecond increments. Lambda natively supports multiple languages, but customers can also bring their own runtimes or container images as needed.
  • API requests are authorized with keys, which are stored in Amazon DynamoDB, a key-value NoSQL database that delivers single-digit millisecond latency at any scale. API Gateway invokes a Lambda function that validates the key against those stored in DynamoDB (this is called a Lambda authorizer.)
  • While API Gateway provides a default domain name for each API, Codacy customizes it with Amazon Route 53, a service that registers domain names and configures DNS records. Route 53 offers a service level agreement (SLA) of 100% availability.
  • Events are stored in raw format in Pulse’s data lake, which is built on top of AWS’ object storage service, Amazon Simple Storage Service (S3). With Amazon S3, you can store massive amounts of information at low cost using simple HTTP requests. The data is highly available and durable.
  • Whenever a new event is ingested by the API, a message is published in Pulse’s message bus. (More information later in this post.)

Events module

Figure 2. Pulse events module architecture

Figure 2. Pulse events module architecture

The events module handles the aggregation and storage of events for actual consumption by customers, see Figure 2:

  • Events are consumed from the message bus and processed with a Lambda function, which stores them in Amazon Redshift.
  • Amazon Redshift is AWS’ managed data warehouse, and enables Pulse’s users to get insights and metrics by running analytical (OLAP) queries with the highest performance.
  • These metrics are exposed to customers via another API (the public API), which is also built on API Gateway.
  • The business logic for this API is implemented using Lambda functions, like the Ingestion module.

Message bus

Figure 3. Message bus architecture

Figure 3. Message bus architecture

We mentioned earlier that Pulse’s modules communicate messages with each other via the “message bus.” When something occurs at a specific component, a message (event) is published to the bus. At the same time, developers create subscriptions for each module that should receive these messages. This is known as the publisher/subscriber pattern (pub/sub for short), and is a fundamental piece of event-driven architectures.

With the message bus, you can decouple all modules from each other. In this way, a publisher does not need to worry about how many or who their subscribers are, or what to do if a new one arrives. This is all handled by the message bus.

Pulse’s message bus is built like this, shown in Figure 3:

  • Events are published via Amazon Simple Notification Service (SNS), using a construct called a topic. Topics are the basic unit of message publication and consumption. Components are subscribed to this topic, and you can filter out unwanted messages.
  • Developers configure Amazon SNS subscriptions to have the events sent to a queue, which provides a buffering layer from which workers can process messages. At the same time, queues also ensure that messages are not lost if there is an error. In Pulse’s case, these queues are implemented with Amazon Simple Queue Service (SQS).

Other modules

There are other parts of Pulse architecture that also use AWS serverless. For example, user authentication and sign-up are handled by Amazon Cognito, and Pulse’s frontend application is hosted on Amazon S3. This app is served to customers worldwide with low latency using Amazon CloudFront, a content delivery network.

Summary and next steps

By using AWS serverless, Codacy has been able to reduce the time required to bring Pulse to market by staying focused on developing business logic, rather than managing servers. Furthermore, Codacy is confident they can handle Pulse’s growth, as this serverless architecture will scale automatically according to demand.

Creating a Multi-Region Application with AWS Services – Part 1, Compute and Security

Post Syndicated from Joe Chapman original https://aws.amazon.com/blogs/architecture/creating-a-multi-region-application-with-aws-services-part-1-compute-and-security/

Building a multi-Region application requires lots of preparation and work. Many AWS services have features to help you build and manage a multi-Region architecture, but identifying those capabilities across 200+ services can be overwhelming.

In this 3-part blog series, we’ll explore AWS services with features to assist you in building multi-Region applications. In Part 1, we’ll build a foundation with AWS security, networking, and compute services. In Part 2, we’ll add in data and replication strategies. Finally, in Part 3, we’ll look at the application and management layers.

Considerations before getting started

AWS Regions are built with multiple isolated and physically separate Availability Zones (AZs). This approach allows you to create highly available Well-Architected workloads that span AZs to achieve greater fault tolerance. There are three general reasons that you may need to expand beyond a single Region:

  • Expansion to a global audience as an application grows and its user base becomes more geographically dispersed, there can be a need to reduce latencies for different parts of the world.
  • Reducing Recovery Point Objectives (RPO) and Recovery Time Objectives (RTO) as part of disaster recovery (DR) plan.
  • Local laws and regulations may have strict data residency and privacy requirements that must be followed.

Ensuring security, identity, and compliance

Creating a security foundation starts with proper authentication, authorization, and accounting to implement the principle of least privilege. AWS Identity and Access Management (IAM) operates in a global context by default. With IAM, you specify who can access which AWS resources and under what conditions. For workloads that use directory services, the AWS Directory Service for Microsoft Active Directory Enterprise Edition can be set up to automatically replicate directory data across Regions. This allows applications to reduce lookup latencies by using the closest directory and creates durability by spanning multiple Regions.

Applications that need to securely store, rotate, and audit secrets, such as database passwords, should use AWS Secrets Manager. It encrypts secrets with AWS Key Management Service (AWS KMS) keys and can replicate secrets to secondary Regions to ensure applications are able to obtain a secret in the closest Region.

Encrypt everything all the time

AWS KMS can be used to encrypt data at rest, and is used extensively for encryption across AWS services. By default, keys are confined to a single Region. AWS KMS multi-Region keys can be created to replicate keys to a second Region, which eliminates the need to decrypt and re-encrypt data with a different key in each Region.

AWS CloudTrail logs user activity and API usage. Logs are created in each Region, but they can be centralized from multiple Regions and multiple accounts into a single Amazon Simple Storage Service (Amazon S3) bucket. As a best practice, these logs should be aggregated to an account that is only accessible to required security personnel to prevent misuse.

As your application expands to new Regions, AWS Security Hub can aggregate and link findings to a single Region to create a centralized view across accounts and Regions. These findings are continuously synced between Regions to keep you updated on global findings.

We put these features together in Figure 1.

Multi-Region security, identity, and compliance services

Figure 1. Multi-Region security, identity, and compliance services

Building a global network

For resources launched into virtual networks in different Regions, Amazon Virtual Private Cloud (Amazon VPC) allows private routing between Regions and accounts with VPC peering. These resources can communicate using private IP addresses and do not require an internet gateway, VPN, or separate network appliances. This works well for smaller networks that only require a few peering connections. However, as the number of peered connections increases, the mesh of peered connections can become difficult to manage and troubleshoot.

AWS Transit Gateway can help reduce these difficulties by creating a central transitive hub to act as a cloud router. A Transit Gateway’s routing capabilities can expand to additional Regions with Transit Gateway inter-Region peering to create a globally distributed private network.

Building a reliable, cost-effective way to route users to distributed Internet applications requires highly available and scalable Domain Name System (DNS) records. Amazon Route 53 does exactly that.

Route 53 routing policies can route traffic to a record with the lowest latency, or automatically fail over a record. If a larger failure occurs, the Route 53 Application Recovery Controller can simplify the monitoring and failover process for application failures across Regions, AZs, and on-premises.

Amazon CloudFront’s content delivery network is truly global, built across 300+ points of presence (PoP) spread throughout the world. Applications that have multiple possible origins, such as across Regions, can use CloudFront origin failover to automatically fail over the origin. CloudFront’s capabilities expand beyond serving content, with the ability to run compute at the edge. CloudFront functions make it easy to run lightweight JavaScript functions, and AWS [email protected] makes it easy to run Node.js and Python functions across these 300+ PoPs.

AWS Global Accelerator uses the AWS global network infrastructure to provide two static anycast IPs for your application. It automatically routes traffic to the closest Region deployment, and if a failure is detected it will automatically redirect traffic to a healthy endpoint within seconds.

Figure 2 brings these features together to create a global network across two Regions.

AWS VPC connectivity and content delivery

Figure 2. AWS VPC connectivity and content delivery

Building the compute layer

An Amazon Elastic Compute Cloud (Amazon EC2) instance is based on an Amazon Machine Image (AMI). An AMI specifies instance configurations such as the instance’s storage, launch permissions, and device mappings. When a new standard image needs to be created, EC2 Image Builder can be used to streamline copying AMIs to selected Regions.

Although EC2 instances and their associated Amazon Elastic Block Store (Amazon EBS) volumes live in a single AZ, Amazon Data Lifecycle Manager can automate the process of taking and copying EBS snapshots across Regions. This can enhance DR strategies by providing a relatively easy cold backup-and-restore option for EBS volumes.

As an architecture expands into multiple Regions, it can become difficult to track where instances are provisioned. Amazon EC2 Global View helps solve this by providing a centralized dashboard to see Amazon EC2 resources such as instances, VPCs, subnets, security groups, and volumes in all active Regions.

Microservice-based applications that use containers benefit from quicker start-up times. Amazon Elastic Container Registry (Amazon ECR) can help ensure this happens consistently across Regions with private image replication at the registry level. An ECR private registry can be configured for either cross-Region or cross-account replication to ensure your images are ready in secondary Regions when needed.

We bring these compute layer features together in Figure 3.

AMI and EBS snapshot copy across Regions

Figure 3. AMI and EBS snapshot copy across Regions


It’s important to create a solid foundation when architecting a multi-Region application. These foundations pave the way for you to move fast in a secure, reliable, and elastic way as you build out your application. In this post, we covered options across AWS security, networking, and compute services that have built-in functionality to take away some of the undifferentiated heavy lifting. We’ll cover data, application, and management services in future posts.

Ready to get started? We’ve chosen some AWS Solutions and AWS Blogs to help you!

Looking for more architecture content? AWS Architecture Center provides reference architecture diagrams, vetted architecture solutions, Well-Architected best practices, patterns, icons, and more!

Optimize your IoT Services for Scale with IoT Device Simulator

Post Syndicated from Ajay Swamy original https://aws.amazon.com/blogs/architecture/optimize-your-iot-services-for-scale-with-iot-device-simulator/

The IoT (Internet of Things) has accelerated digital transformation for many industries. Companies can now offer smarter home devices, remote patient monitoring, connected and autonomous vehicles, smart consumer devices, and many more products. The enormous volume of data emitted from IoT devices can be used to improve performance, efficiency, and develop new service and business models. This can help you build better relationships with your end consumers. But you’ll need an efficient and affordable way to test your IoT backend services without incurring significant capex by deploying test devices to generate this data.

IoT Device Simulator (IDS) is an AWS Solution that manufacturing companies can use to simulate data, test device integration, and improve the performance of their IoT backend services. The solution enables you to create hundreds of IoT devices with unique attributes and properties. You can simulate data without configuring and managing physical devices.

An intuitive UI to create and manage devices and simulations

IoT Device Simulator comes with an intuitive user interface that enables you to create and manage device types for data simulation. The solution also provides you with a pre-built autonomous car device type to simulate a fleet of connected vehicles. Once you create devices, you can create simulations and generate data (see Figure 1.)

Figure 1. The landing page UI enables you to create devices and simulation

Figure 1. The landing page UI enables you to create devices and simulation

Create devices and simulate data

With IDS, you can create multiple device types with varying properties and data attributes (see Figure 2.) Each device type has a topic where simulation data is sent. The supported data types are object, array, sinusoidal, location, Boolean, integer, float, and more. Refer to this full list of data types. Additionally, you can import device types via a specific JSON format or use the existing automotive demo to pre-populate connected vehicles.

Figure 2. Create multiple device types and their data attributes

Figure 2. Create multiple device types and their data attributes

Create and manage simulations

With IDS, you can create simulations with one device or multiple device types (see Figure 3.) In addition, you can specify the number of devices to simulate for each device type and how often data is generated and sent.

Figure 3. Create simulations for multiple devices

Figure 3. Create simulations for multiple devices

You can then run multiple simulations (see Figure 4) and use the data generated to test your IoT backend services and infrastructure. In addition, you have the flexibility to stop and restart the simulation as needed.

Figure 4. Run and stop multiple simulations

Figure 4. Run and stop multiple simulations

You can view the simulation in real time and observe the data messages flowing through. This way you can ensure that the simulation is working as expected (see Figure 5.) You can stop the simulation or add a new simulation to the mix at any time.

Figure 5. Observe your simulation in real time

Figure 5. Observe your simulation in real time

IoT Device Simulator architecture

Figure 6. IoT Device Simulator architecture

Figure 6. IoT Device Simulator architecture

The AWS CloudFormation template for this solution deploys the following architecture, shown in Figure 6:

  1. Amazon CloudFront serves the web interface content from an Amazon Simple Storage Service (Amazon S3) bucket.
  2. The Amazon S3 bucket hosts the web interface.
  3. Amazon Cognito user pool authenticates the API requests.
  4. An Amazon API Gateway API provides the solution’s API layer.
  5. AWS Lambda serves as the solution’s microservices and routes API requests.
  6. Amazon DynamoDB stores simulation and device type information.
  7. AWS Step Functions include an AWS Lambda simulator function to simulate devices and send messages.
  8. An Amazon S3 bucket stores pre-defined routes that are used for the automotive demo (which is a pre-built example in the solution).
  9. AWS IoT Core serves as the endpoint to which messages are sent.
  10. Amazon Location Service provides the map display showing the location of automotive devices for the automotive demo.

The IoT Device Simulator console is hosted on an Amazon S3 bucket, which is accessed via Amazon CloudFront. It uses Amazon Cognito to manage access. API calls, such as retrieving or manipulating information from the databases or running simulations, are routed through API Gateway. API Gateway calls the microservices, which will call the relevant service.

For example, when creating a new device type, the request is sent to API Gateway, which then routes the request to the microservices Lambda function. Based on the request, the microservices Lambda function recognizes that it is a request to create a device type and saves the device type to DynamoDB.

Running a simulation

When running a simulation, the microservices Lambda starts a Step Functions workflow. First, the request contains information about the simulation to be run, including the unique device type ID. Then, using the unique device type ID, Step Functions retrieves all the necessary information about each device type to run the simulation. Once all the information has been retrieved, the simulator Lambda function is run. The simulator Lambda function uses the device type information, including the message payload template. The Lambda function uses this template to build the message sent to the IoT topic specified for the device type.

When running a custom device type, the simulator generates random information based on the values provided for each attribute. For example, when the automotive simulation is run, the simulation runs a series of calculations to simulate an automobile moving along a series of pre-defined routes. Pre-defined routes are created and stored in an S3 bucket, when the solution is launched. The simulation retrieves the routes at random each time the Lambda function runs. Automotive demo simulations also show a map generated from Amazon Location Service and display the device locations as they move.

The simulator exits once the Lambda function has completed or has reached the fifteen-minute execution limit. It then passes all the necessary information back to the Step Function. Step Functions then enters a choice state and restarts the Lambda function if it has not yet surpassed the duration specified for the simulation. It then passes all the pertinent information back to the Lambda function so that it can resume where it left off. The simulator Lambda function also checks DynamoDB every thirty seconds to see if the user has manually stopped the simulation. If it has, it will end the simulation early. Once the simulation is complete, the Step Function updates the DynamoDB table.

The solution enables you to launch hundreds of devices to test backend infrastructure in an IoT workflow. The solution contains an Import/Export feature to share device types. Exporting a device type generates a JSON file that represents the device type. The JSON file can then be imported to create the same device type automatically. The solution allows the viewing of up to 100 messages while the solution is running. You can also filter the messages by topic and device and see what data each device emits.


IoT Device Simulator is designed to help customers test device integration and IoT backend services more efficiently without incurring capex for physical devices. This solution provides an intuitive web-based graphic user interface (GUI) that enables customers to create and simulate hundreds of connected devices. It is not necessary to configure and manage physical devices or develop time-consuming scripts. Although we’ve illustrated an automotive application in this post, this simulator can be used for many different industries, such as consumer electronics, healthcare equipment, utilities, manufacturing, and more.

Get started with IoT Device Simulator today.

AWS Free Tier Data Transfer Expansion – 100 GB From Regions and 1 TB From Amazon CloudFront Per Month

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-free-tier-data-transfer-expansion-100-gb-from-regions-and-1-tb-from-amazon-cloudfront-per-month/

The AWS Free Tier has been around since 2010 and allows you to use generous amounts of over 100 different AWS services. Some services offer free trials, others are free for the first 12 months after you sign up, and still others are always free, up to a per-service maximum. Our intent is to make it easy and cost-effective for you to gain experience with a wide variety of powerful services without having to pay any usage charges.

Free Tier Data Transfer Expansion
Today, as part of our long tradition of AWS price reductions, I am happy to share that we are expanding the Free Tier with additional data transfer out, as follows:

Data Transfer from AWS Regions to the Internet is now free for up to 100 GB of data per month (up from 1 GB per region). This includes Amazon EC2, Amazon S3, Elastic Load Balancing, and so forth. The expansion does not apply to the AWS GovCloud or AWS China Regions.

Data Transfer from Amazon CloudFront is now free for up to 1 TB of data per month (up from 50 GB), and is no longer limited to the first 12 months after signup. We are also raising the number of free HTTP and HTTPS requests from 2,000,000 to 10,000,000, and removing the 12 month limit on the 2,000,000 free CloudFront Function invocations per month. The expansion does not apply to data transfer from CloudFront PoPs in China.

This change is effective December 1, 2021 and takes effect with no effort on your part. As a result of this change, millions of AWS customers worldwide will no longer see a charge for these two categories of data transfer on their monthly AWS bill. Customers who go beyond one or both of these allocations will also see a reduction in their overall data transfer charges.

Your applications can run in any of 21 AWS Regions with a total of 69 Availability Zones (with more of both on the way), and can make use of the full range of CloudFront features (including SSL support and media streaming), and over 300 CloudFront PoPs, all connected across a dedicated network backbone. The network was designed with performance as a key driver, and is expanded continuously in order to meet the ever-growing needs of our customers. It is global, fully redundant, and built from parallel 100 GbE metro fibers linked via trans-oceanic cables across the Atlantic, Pacific, and Indian Oceans, as well as the Mediterranean, Red Sea, and South China Seas.


Optimizing your AWS Infrastructure for Sustainability, Part III: Networking

Post Syndicated from Katja Philipp original https://aws.amazon.com/blogs/architecture/optimizing-your-aws-infrastructure-for-sustainability-part-iii-networking/

In Part I: Compute and Part II: Storage of this series, we introduced strategies to optimize the compute and storage layer of your AWS architecture for sustainability.

This blog post focuses on the network layer of your AWS infrastructure and proposes concepts to optimize your network utilization.

Optimizing the networking layer of your AWS infrastructure

When you make your applications available to more customers, the packets that travel across the network will increase. Similarly, the larger the size of data, as well as the more distance a packet has to travel, the more resources are required to transmit it. With growing number of application users, optimizing network traffic can ensure that network resource consumption is not growing linearly.

The recommendations in the following sections will help you use your resources more efficiently for the network layer of your workload.

Reducing the network traveled per request

Reducing the data sent over the network and optimizing the path a packet takes will result in a more efficient data transfer. The following table provides metrics related to some AWS services that can help you find potential network optimization opportunities.

Service Metric/Check Source
Amazon CloudFront Cache hit rate Viewing CloudFront and [email protected] metrics
AWS Trusted Advisor check reference
Amazon Simple Storage Service (Amazon S3) Data transferred in/out of a bucket Metrics and dimensions
AWS Trusted Advisor check reference
Amazon Elastic Compute Cloud (Amazon EC2) NetworkPacketsIn/NetworkPacketsOut List the available CloudWatch metrics for your instances
AWS Trusted Advisor CloudFront Content Delivery Optimization AWS Trusted Advisor check reference

We recommend the following concepts to optimize your network utilization.

Read local, write global

The following strategies allow users to read the data from the source closest to them; thus, fewer requests travel longer distances.

  • If you are operating within a single AWS Region, you should choose a Region that is near the majority of your users. The further your users are away from the Region, the further data needs to travel through the global network.
  • If your users are spread over multiple Regions, set up multiple copies of the data to reside in each Region. Amazon Relational Database Service (Amazon RDS) and Amazon Aurora let you set up cross-Region read replicas. Amazon DynamoDB global tables allow for fast performance and alleviate network load.

Use a content delivery network

Content delivery networks (CDNs) bring your data closer to the end user. When requested, they cache static content from the original server and deliver it to the user. This shortens the distance each packet has to travel.

  • CloudFront optimizes network utilization and delivers traffic over CloudFront’s globally distributed edge network. Figure 1 shows a global user base that accesses an S3 bucket directly versus serving cached data from edge locations.
  • Trusted Advisor includes a check that recommends whether you should use a CDN for your S3 buckets. It analyzes the data transferred out of your S3 bucket and flags the buckets that could benefit from a CloudFront distribution.
Comparison of accessing an S3 bucket directly versus via a CloudFront distribution/edge locations

Figure 1. Comparison of accessing an S3 bucket directly versus via a CloudFront distribution/edge locations

Optimize CloudFront cache hit ratio

CloudFront caches different versions of an object depending upon the request headers (for example, language, date, or user-agent). You can further optimize your CDN distribution’s cache hit ratio (the number of times an object is served from the CDN versus from the origin) with a Trusted Advisor check. It automatically checks for headers that do not affect the object and then recommends a configuration to ignore those headers and not forward the request to the origin.

Use edge-oriented services

Edge computing brings data storage and computation closer to users. By implementing this approach, you can perform data preprocessing or run machine learning algorithms on the edge.

  • Edge-oriented services applied on gateways or directly onto user devices reduce network traffic because data does not need to be sent back to the cloud server.
  • One-time, low-latency tasks are a good fit for edge use cases, like when an autonomous vehicle needs to detect objects nearby. You should generally archive data that needs to be accessed by multiple parties in the cloud, but consider factors such as device hardware and privacy regulations first.
  • CloudFront Functions can run compute on edge locations and [email protected] can generate Regional edge caches. AWS IoT Greengrass provides edge computing for Internet of Things (IoT) devices.

Reducing the size of data transmitted

Serve compressed files

In addition to caching static assets, you can further optimize network utilization by serving compressed files to your users. You can configure CloudFront to automatically compress objects, which results in faster downloads, leading to faster rendering of webpages.

Enhance Amazon EC2 network performance

Network packets consist of data that you are sending (frame) and the processing overhead information. If you use larger packets, you can pass more data in a single packet and decrease processing overhead.

Jumbo frames use the largest permissible packet that can be passed over the connection. Keep in mind that outside a single virtual private cloud (VPC), over virtual private network (VPN) or internet gateway, traffic is limited to a lower frame regardless of using jumbo frames.

Optimize APIs

If your payloads are large, consider reducing their size to reduce network traffic by compressing your messages for your REST API payloads. Use the right endpoint for your use case. Edge-optimized API endpoints are best suited for geographically distributed clients. Regional API endpoints are best suited for when you have a few clients with higher demands, because they can help reduce connection overhead. Caching your API responses will reduce network traffic and enhance responsiveness.


As your organization’s cloud adoption grows, knowing how efficient your resources are is crucial when optimizing your AWS infrastructure for environmental sustainability. Using the fewest number of resources possible and using them to their fullest will have the lowest impact on the environment.

Throughout this three-part blog post series, we introduced you to the following architectural concepts and metrics for the compute, storage, and network layers of your AWS infrastructure.

  • Reducing idle resources and maximizing utilization
  • Shaping demand to existing supply
  • Managing your data’s lifecycle
  • Using different storage tiers
  • Optimizing the path data travels through a network
  • Reducing the size of data transmitted

This is not an exhaustive list. We hope it is a starting point for you to consider the environmental impact of your resources and how you can build your AWS infrastructure to be more efficient and sustainable. Figure 2 shows an overview of how you can monitor related metrics with CloudWatch and Trusted Advisor.

Overview of services that integrate with CloudWatch and Trusted Advisor for monitoring metrics

Figure 2. Overview of services that integrate with CloudWatch and Trusted Advisor for monitoring metrics

Ready to get started? Check out the AWS Sustainability page to find out more about our commitment to sustainability. It provides information about renewable energy usage, case studies on sustainability through the cloud, and more.

Other blog posts in this series

Related information

What to Consider when Selecting a Region for your Workloads

Post Syndicated from Saud Albazei original https://aws.amazon.com/blogs/architecture/what-to-consider-when-selecting-a-region-for-your-workloads/

The AWS Cloud is an ever-growing network of Regions and points of presence (PoP), with a global network infrastructure that connects them together. With such a vast selection of Regions, costs, and services available, it can be challenging for startups to select the optimal Region for a workload. This decision must be made carefully, as it has a major impact on compliance, cost, performance, and services available for your workloads.

Evaluating Regions for deployment

There are four main factors that play into evaluating each AWS Region for a workload deployment:

  1. Compliance. If your workload contains data that is bound by local regulations, then selecting the Region that complies with the regulation overrides other evaluation factors. This applies to workloads that are bound by data residency laws where choosing an AWS Region located in that country is mandatory.
  2. Latency. A major factor to consider for user experience is latency. Reduced network latency can make substantial impact on enhancing the user experience. Choosing an AWS Region with close proximity to your user base location can achieve lower network latency. It can also increase communication quality, given that network packets have fewer exchange points to travel through.
  3. Cost. AWS services are priced differently from one Region to another. Some Regions have lower cost than others, which can result in a cost reduction for the same deployment.
  4. Services and features. Newer services and features are deployed to Regions gradually. Although all AWS Regions have the same service level agreement (SLA), some larger Regions are usually first to offer newer services, features, and software releases. Smaller Regions may not get these services or features in time for you to use them to support your workload.

Evaluating all these factors can make coming to a decision complicated. This is where your priorities as a business should influence the decision.

Assess potential Regions for the right option

Evaluate by shortlisting potential Regions.

  • Check if these Regions are compliant and have the services and features you need to run your workload using the AWS Regional Services website.
  • Check feature availability of each service and versions available, if your workload has specific requirements.
  • Calculate the cost of the workload on each Region using the AWS Pricing Calculator.
  • Test the network latency between your user base location and each AWS Region.

At this point, you should have a list of AWS Regions with varying cost and network latency that looks something Table 1:

Region Compliance Latency Cost Services / Features
Region A

15 ms $$
Region B

20 ms



Region C

80 ms $

Table 1. Region evaluation matrix

Many workloads such as high performance computing (HPC), analytics, and machine learning (ML), are not directly linked to a customer-facing application. These would not be sensitive to network latency, so you may want to select the Region with the lowest cost.

Alternatively, you may have a backend service for a game or mobile application in which network latency has a direct impact on user experience. Measure the difference in network latency between each Region, and determine if it is worth the increased cost. You can leverage the Amazon CloudFront edge network, which helps reduce latency and increases communication quality. This is because it uses a fully managed AWS network infrastructure, which connects your application to the edge location nearest to your users.

Multi-Region deployment

You can also split the workload across multiple Regions. The same workload may have some components that are sensitive to network latency and some that are not. You may determine you can benefit from both lower network latency and reduced cost at the same time. Here’s an example:

Figure 1. Multi-Region deployment optimized for feature availability

Figure 1. Multi-Region deployment optimized for feature availability

Figure 1 shows a serverless application deployed at the Bahrain Region (me-south-1) which has a close proximity to the customer base in Riyadh, Saudi Arabia. Application users enjoy a lower latency network connecting to the AWS Cloud. Analytics workloads are deployed in the Ireland Region (eu-west-1), which has a lower cost for Amazon Redshift and other features.

Note that data transfer between Regions is not free and, in this example, costs $0.115 per GB. However, even with this additional cost factored in, running the analytical workload in Ireland (eu-west-1) is still more cost-effective. You can also benefit from additional capabilities and features that may have not yet been released in the Bahrain (me-south-1) Region.

This multi-Region setup could also be beneficial for applications with a global user base. The application can be deployed in multiple secondary AWS Regions closer to the user base locations. It uses a primary AWS Region with a lower cost for consolidated services and latency-insensitive workloads.

Figure 2. Multi-Region deployment optimized for network latency

Figure 2. Multi-Region deployment optimized for network latency

Figure 2 allows for an application to span multiple Regions to serve read requests with the lowest network latency possible. Each client will be routed to the nearest AWS Region. For read requests, an Amazon Route 53 latency routing policy will be used. For write requests, an endpoint routed to the primary Region will be used. This primary endpoint can also have periodic health checks to failover to a secondary Region for disaster recovery (DR).

Other factors may also apply for certain applications such as ones that require Amazon EC2 Spot Instances. Regions differ in size, with some having three, and others up to six Availability Zones (AZ). This results in varying Spot Instance capacity available for Amazon EC2. Choosing larger Regions offers larger Spot capacity. A multi-Region deployment offers the most Spot capacity.


Selecting the optimal AWS Region is an important first step when deploying new workloads. There are many other scenarios in which splitting the workload across multiple AWS Regions can result in a better user experience and cost reduction. The four factors mentioned in this blog post can be evaluated together to find the most appropriate Region to deploy your workloads.

If the workload is bound by any regulations, shortlist the Regions that are compliant. Measure the network latency between each Region and the location of the user base. Estimate the workload cost for each Region. Check that the shortlisted Regions have the services and features your workload requires. And finally, determine if your workload can benefit from running in multiple Regions.

Dive deeper into the AWS Global Infrastructure Website for more information.

Securely Ingest Industrial Data to AWS via Machine to Cloud Solution

Post Syndicated from Ajay Swamy original https://aws.amazon.com/blogs/architecture/securely-ingest-industrial-data-to-aws-via-machine-to-cloud-solution/

As a manufacturing enterprise, maximizing your operational efficiency and optimizing output are critical factors in this competitive global market. However, many manufacturers are unable to frequently collect data, link data together, and generate insights to help them optimize performance. Furthermore, decades of competing standards for connectivity have resulted in the lack of universal protocols to connect underlying equipment and assets.

Machine to Cloud Connectivity Framework (M2C2) is an Amazon Web Services (AWS) Solution that provides the secure ingestion of equipment telemetry data to the AWS Cloud. This allows you to use AWS services to conduct analysis on your equipment data, instead of managing underlying infrastructure operations. The solution allows for robust data ingestion from industrial equipment that use OPC Data Access (OPC DA) and OPC Unified Access (OPC UA) protocols.

Secure, automated configuration and ingestion of industrial data

M2C2 allows manufacturers to ingest their shop floor data into various data destinations in AWS. These include AWS IoT SiteWise, AWS IoT Core, Amazon Kinesis Data Streams, and Amazon Simple Storage Service (S3). The solution is integrated with AWS IoT SiteWise so you can store, organize, and monitor data from your factory equipment at scale. Additionally, the solution provides customers an intuitive user interface to create, configure, monitor, and manage connections.

Automated setup and configuration

Figure 1. Automatically create and configure connections

Figure 1. Automatically create and configure connections

With M2C2, you can connect to your operational technology assets (see Figure 1). The solution automatically creates AWS IoT certificates, keys, and configuration files for AWS IoT Greengrass. This allows you to set up Greengrass to run on your industrial gateway. It also automates the deployment of any Greengrass group configuration changes required by the solution. You can define a connection with the interface, and specify attributes about equipment, tags, protocols, and read frequency for equipment data.

Figure 2. Send data to different destinations in the AWS Cloud

Figure 2. Send data to different destinations in the AWS Cloud

Once the connection details have been specified, you can send data to different destinations in AWS Cloud (see Figure 2). M2C2 provides capability to ingest data from industrial equipment using OPC-DA and OPC-UA protocols. The solution collects the data, and then publishes the data to AWS IoT SiteWise, AWS IoT Core, or Kinesis Data Streams.

Publishing data to AWS IoT SiteWise allows for end-to-end modeling and monitoring of your factory floor assets. When using the default solution configuration, publishing data to Kinesis Data Streams allows for ingesting and storing data in an Amazon S3 bucket. This gives you the capability for custom advanced analytics use cases and reporting.

You can choose to create multiple connections, and specify sites, areas, processes, and machines, by using the setup UI.

Management of connections and messages

Figure 3. Manage your connections

Figure 3. Manage your connections

M2C2 provides a straightforward connections screen (see Figure 3), where production managers can monitor and review the current state of connections. You can start and stop connections, view messages and errors, and gain connectivity across different areas of your factory floor. The Manage connections UI allows you to holistically manage data connectivity from a centralized place. You can then make changes and corrections as needed.

Architecture and workflow

Figure 4. Machine to Cloud Connectivity (M2C2) Framework architecture

Figure 4. Machine to Cloud Connectivity (M2C2) Framework architecture

The AWS CloudFormation template deploys the following infrastructure, shown in Figure 4:

  1. An Amazon CloudFront user interface that deploys into an Amazon S3 bucket configured for web hosting.
  2. An Amazon API Gateway API provides the user interface for client requests.
  3. An Amazon Cognito user pool authenticates the API requests.
  4. AWS Lambda functions power the user interface, in addition to the configuration and deployment mechanism for AWS IoT Greengrass and AWS IoT SiteWise gateway resources. Amazon DynamoDB tables store the connection metadata.
  5. An AWS IoT SiteWise gateway configuration can be used for any OPC UA data sources.
  6. An Amazon Kinesis Data Streams data stream, Amazon Kinesis Data Firehose, and Amazon S3 bucket to store telemetry data.
  7. AWS IoT Greengrass is installed and used on an on-premises industrial gateway to run protocol connector Lambda functions. These connect and read telemetry data from your OPC UA and OPC DA servers.
  8. Lambda functions are deployed onto AWS IoT Greengrass Core software on the industrial gateway. They connect to the servers and send the data to one or more configured destinations.
  9. Lambda functions that collect the telemetry data write to AWS IoT Greengrass stream manager streams. The publisher Lambda functions read from the streams.
  10. Publisher Lambda functions forward the data to the appropriate endpoint.

Data collection

The Machine to Cloud Connectivity solution uses Lambda functions running on Greengrass to connect to your on-premises OPC-DA and OPC-UA industrial devices. When you deploy a connection for an OPC-DA device, the solution configures a connection-specific OPC-DA connector Lambda. When you deploy a connection for an OPC-UA device, the solution uses the AWS IoT SiteWise Greengrass connector to collect the data.

Regardless of protocol, the solution configures a publisher Lambda function, which takes care of sending your streaming data to one or more desired destinations. Stream Manager enables the reading and writing of stream data from multiple sources and to multiple destinations within the Greengrass core. This enables each configured collector to write data to a stream. The publisher reads from that stream and sends the data to your desired AWS resource.


Machine to Cloud Connectivity (M2C2) Framework is a self-deployable solution that provides secure connectivity between your technology (OT) assets and the AWS Cloud. With M2C2, you can send data to AWS IoT Core or AWS IoT SiteWise for analytics and monitoring. You can store your data in an industrial data lake using Kinesis Data Streams and Amazon S3. Get started with Machine to Cloud Connectivity (M2C2) Framework today.

Augmenting VMware Cloud on AWS Workloads with Native AWS services

Post Syndicated from Talha Kalim original https://aws.amazon.com/blogs/architecture/augmenting-vmware-cloud-on-aws-workloads-with-native-aws-services/

VMware Cloud on AWS allows you to quickly migrate VMware workloads to a VMware-managed Software-Defined Data Center (SDDC) running in the AWS Cloud and extend your on-premises data centers without replatforming or refactoring applications.

You can use native AWS services with Virtual Machines (VMs) in the SDDC, to reduce operational overhead and lower your Total Cost of Ownership (TCO) while increasing your workload’s agility and scalability.

This post covers patterns for connectivity between native AWS services and VMware workloads. We also explore common integrations, including using AWS Cloud storage from an SDDC, securing VM workloads using AWS networking services, and using AWS databases and analytics services with workloads running in the SDDC.

Networking between SDDC and native AWS services

Establishing robust network connectivity with VMware Cloud SDDC VMs is critical to successfully integrating AWS services. This section shows you different options to connect the VMware SDDC with your native AWS account.

The simplest way to get started is to use AWS services in the connected Amazon Virtual Private Cloud (VPC) that is selected during the SDDC deployment process. Figure 1 shows this connectivity, which is automatically configured and available once the SDDC is deployed.

Figure 1. SDDC to Customer Account VPC connectivity configured at deployment

Figure 1. SDDC to Customer Account VPC connectivity configured at deployment

The SDDC Elastic Network Interface (ENI) allows you to connect to native AWS services within the connected VPC, but it doesn’t provide transitive routing beyond the connected VPC. For example, it will not connect the SDDC to other VPCs and the internet.

If you’re looking to connect to native AWS services in multiple accounts and VPCs in the same AWS Region, you have two connectivity options. These are explained in the following sections.

Attaching VPCs to VMware Transit Connect

When you need high-throughput connectivity in a multi-VPC environment, use VMware Transit Connect (VTGW), as shown in Figure 2.

Figure 2. Multi-account VPC connectivity through VMware Transit Connect VPC attachments

Figure 2. Multi-account VPC connectivity through VMware Transit Connect VPC attachments

VTGW uses a VMware-managed AWS Transit Gateway to interconnect SDDCs within an SDDC group. It also allows you to attach your VPCs in the same Region to the VTGW by providing connectivity to any SDDC within the SDDC group.

Connecting through an AWS Transit Gateway

To connect to your VPCs through an existing Transit Gateway in your account, use IPsec virtual private network (VPN) connections from the SDDC with Border Gateway Protocol (BGP)-based routing, as shown in Figure 3. Multiple IPsec tunnels to the Transit Gateway use equal-cost multi-path routing, which increases bandwidth by load-balancing traffic.

Figure 3. Multi-account VPC connectivity through an AWS Transit Gateway

Figure 3. Multi-account VPC connectivity through an AWS Transit Gateway

For scalable, high throughput connectivity to an existing Transit Gateway, connect to the SDDC via a Transit VPC that is attached to the VTGW, as shown in Figure 3. You must manually configure the routes between the VPCs and SDDC for this architecture.

In the following sections, we’ll show you how to use some of these connectivity options for common native AWS services integrations with VMware SDDC workloads.

Reducing TCO with Amazon EFS, Amazon FSx, and Amazon S3

As you are sizing your VMware Cloud on AWS SDDC, consider using AWS Cloud storage for VMs that provide files services or require object storage. Migrating these workloads to cloud storage like Amazon Simple Storage Service (Amazon S3), Amazon Elastic File System (Amazon EFS), or Amazon FSx can reduce your overall TCO through optimized SDDC sizing.

Additionally, you can reduce the undifferentiated heavy lifting involved with deploying and managing complex architectures for file services in VM disks. Figure 4 shows how these services integrate with VMs in the SDDC.

Figure 4. Connectivity examples for AWS Cloud storage services

Figure 4. Connectivity examples for AWS Cloud storage services

We recommend connecting to your S3 buckets via the VPC gateway endpoint in the connected VPC. This is a more cost-effective approach because it avoids the data processing costs associated with a VTGW and AWS PrivateLink for Amazon S3.

Similarly, the recommended approach for Amazon EFS and Amazon FSx is to deploy the services in the connected VPC for VM access through the SDDC elastic network interface. You can also connect to existing Amazon EFS and Amazon FSx file shares in other accounts and VPCs using a VTGW, but consider the data transfer costs first.

Integrating AWS networking and content delivery services

Using various AWS networking and content delivery services with VMware Cloud on AWS workloads will provide robust traffic management, security, and fast content delivery. Figure 5 shows how AWS networking and content delivery services integrate with workloads running on VMs.

Figure 5. Connectivity examples for AWS networking and content delivery services

Figure 5. Connectivity examples for AWS networking and content delivery services

Deploy Elastic Load Balancing (ELB) services in a VPC subnet that has network reachability to the SDDC VMs. This includes the connected VPC over the SDDC elastic network interface, a VPC attached via VTGW, and VPCs attached to a Transit Gateway connected to the SDDC.

VTGW connectivity should be used when the design requires using existing networking services in other VPCs. For example, if you have a dedicated internet ingress/egress VPC. An internal ELB can also be used for load-balancing traffic between services running in SDDC VMs and services running within AWS VPCs.

Use Amazon CloudFront, a global content delivery service, to integrate with load balancers, S3 buckets for static content, or directly with publicly accessible SDDC VMs. Additionally, use Amazon Route 53 to provide public and private DNS services for VMware Cloud on AWS. Deploy services such as AWS WAF and AWS Shield to provide comprehensive network security for VMware workloads in the SDDC.

Integrating with AWS database and analytics services

Data is one the most valuable assets in an organization, and databases are often the most demanding and critical workloads running in on-premises VMware environments.

A common customer pattern to reduce TCO for storage-heavy or memory-intensive databases is to use purpose-built Databases on AWS like Amazon Relational Database Service (RDS). Amazon RDS lets you migrate on-premises relational databases to the cloud and integrate it with SDDC VMs. Using AWS databases also reduces operational overhead you may incur with tasks associated with managing availability, scalability, and disaster recovery (DR).

With AWS Analytics services integrations, you can take advantage of the close proximity of data within VMware Cloud on AWS data stores to gain meaningful insights from your business data. For example, you can use Amazon Redshift to create a data warehouse to run analytics at scale on relational data from transactional systems, operational databases, and line-of-business applications running within the SDDC.

Figure 6 shows integration options for AWS databases and analytics services with VMware Cloud on AWS VMs.

Figure 6. Connectivity examples for AWS Database and Analytics services

Figure 6. Connectivity examples for AWS Database and Analytics services

We recommend deploying and consuming database services in the connected VPC. If you have existing databases in other accounts or VPCs that require integration with VMware VMs, connect them using the VTGW.

Analytics services can involve ingesting large amounts of data from various sources, including from VMs within the SDDC, creating a significant amount of data traffic. In such scenarios, we recommend using the SDDC connected VPC to deploy any required interface endpoints for analytics services to achieve a cost-effective architecture.


VMware Cloud on AWS is one of the fastest ways to migrate on-premises VMware workloads to the cloud. In this blog post, we provided different architecture options for connecting the SDDC to native AWS services. This lets you evaluate your requirements to select the most cost-effective option for your workload.

The example integrations covered in this post are common AWS service integrations, including storage, network, and databases. They are a great starting point, but the possibilities are endless. Integrating services like Amazon Machine Learning (Amazon ML), and Serverless on AWS allows you to deliver innovative services to your users, often without having to re-factor existing application backends running on VMware Cloud on AWS.

Additional Resources

If you need to integrate VMware Cloud on AWS with an AWS service, explore the following resources and reach out to us at AWS.

Building well-architected serverless applications: Optimizing application performance – part 2

Post Syndicated from Julian Wood original https://aws.amazon.com/blogs/compute/building-well-architected-serverless-applications-optimizing-application-performance-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.

PERF 1. Optimizing your serverless application’s performance

This post continues part 1 of this security question. Previously, I cover measuring and optimizing function startup time. I explain cold and warm starts and how to reuse the Lambda execution environment to improve performance. I show a number of ways to analyze and optimize the initialization startup time. I explain how only importing necessary libraries and dependencies increases application performance.

Good practice: Design your function to take advantage of concurrency via asynchronous and stream-based invocations

AWS Lambda functions can be invoked synchronously and asynchronously.

Favor asynchronous over synchronous request-response processing.

Consider using asynchronous event processing rather than synchronous request-response processing. You can use asynchronous processing to aggregate queues, streams, or events for more efficient processing time per invocation. This reduces wait times and latency from requesting apps and functions.

When you invoke a Lambda function with a synchronous invocation, you wait for the function to process the event and return a response.

Synchronous invocation

Synchronous invocation

As synchronous processing involves a request-response pattern, the client caller also needs to wait for a response from a downstream service. If the downstream service then needs to call another service, you end up chaining calls that can impact service reliability, in addition to response times. For example, this POST /order request must wait for the response to the POST /invoice request before responding to the client caller.

Example synchronous processing

Example synchronous processing

The more services you integrate, the longer the response time, and you can no longer sustain complex workflows using synchronous transactions.

Asynchronous processing allows you to decouple the request-response using events without waiting for a response from the function code. This allows you to perform background processing without requiring the client to wait for a response, improving client performance. You pass the event to an internal Lambda queue for processing and Lambda handles the rest. An external process, separate from the function, manages polling and retries. Using this asynchronous approach can also make it easier to handle unpredictable traffic with significant volumes.

Asynchronous invocation

Asynchronous invocation

For example, the client makes a POST /order request to the order service. The order service accepts the request and returns that it has been received, without waiting for the invoice service. The order service then makes an asynchronous POST /invoice request to the invoice service, which can then process independently of the order service. If the client must receive data from the invoice service, it can handle this separately via a GET /invoice request.

Example asynchronous processing

Example asynchronous processing

You can configure Lambda to send records of asynchronous invocations to another destination service. This helps you to troubleshoot your invocations. You can also send messages or events that can’t be processed correctly into a dedicated Amazon Simple Queue Service (SQS) dead-letter queue for investigation.

You can add triggers to a function to process data automatically. For more information on which processing model Lambda uses for triggers, see “Using AWS Lambda with other services”.

Asynchronous workflows handle a variety of use cases including data Ingestion, ETL operations, and order/request fulfillment. In these use-cases, data is processed as it arrives and is retrieved as it changes. For example asynchronous patterns, see “Serverless Data Processing” and “Serverless Event Submission with Status Updates”.

For more information on Lambda synchronous and asynchronous invocations, see the AWS re:Invent presentation “Optimizing your serverless applications”.

Tune batch size, batch window, and compress payloads for high throughput

When using Lambda to process records using Amazon Kinesis Data Streams or SQS, there are a number of tuning parameters to consider for performance.

You can configure a batch window to buffer messages or records for up to 5 minutes. You can set a limit of the maximum number of records Lambda can process by setting a batch size. Your Lambda function is invoked whichever comes first.

For high volume SQS standard queue throughput, Lambda can process up to 1000 concurrent batches of records per second. For more information, see “Using AWS Lambda with Amazon SQS”.

For high volume Kinesis Data Streams throughput, there are a number of options. Configure the ParallelizationFactor setting to process one shard of a Kinesis Data Stream with more than one Lambda invocation simultaneously. Lambda can process up to 10 batches in each shard. For more information, see “New AWS Lambda scaling controls for Kinesis and DynamoDB event sources.” You can also add more shards to your data stream to increase the speed at which your function can process records. This increases the function concurrency at the expense of ordering per shard. For more details on using Kinesis and Lambda, see “Monitoring and troubleshooting serverless data analytics applications”.

Kinesis enhanced fan-out can maximize throughput by dedicating a 2 MB/second input/output channel per second per consumer instead of 2 MB per shard. For more information, see “Increasing stream processing performance with Enhanced Fan-Out and Lambda”.

Kinesis stream producers can also compress records. This is at the expense of additional CPU cycles for decompressing the records in your Lambda function code.

Required practice: Measure, evaluate, and select optimal capacity units

Capacity units are a unit of consumption for a service. They can include function memory size, number of stream shards, number of database reads/writes, request units, or type of API endpoint. Measure, evaluate and select capacity units to enable optimal configuration of performance, throughput, and cost.

Identify and implement optimal capacity units.

For Lambda functions, memory is the capacity unit for controlling the performance of a function. You can configure the amount of memory allocated to a Lambda function, between 128 MB and 10,240 MB. The amount of memory also determines the amount of virtual CPU available to a function. Adding more memory proportionally increases the amount of CPU, increasing the overall computational power available. If a function is CPU-, network- or memory-bound, then changing the memory setting can dramatically improve its performance.

Choosing the memory allocated to Lambda functions is an optimization process that balances performance (duration) and cost. You can manually run tests on functions by selecting different memory allocations and measuring the time taken to complete. Alternatively, use the AWS Lambda Power Tuning tool to automate the process.

The tool allows you to systematically test different memory size configurations and depending on your performance strategy – cost, performance, balanced – it identifies what is the most optimum memory size to use. For more information, see “Operating Lambda: Performance optimization – Part 2”.

AWS Lambda Power Tuning report

AWS Lambda Power Tuning report

Amazon DynamoDB manages table processing throughput using read and write capacity units. There are two different capacity modes, on-demand and provisioned.

On-demand capacity mode supports up to 40K read/write request units per second. This is recommended for unpredictable application traffic and new tables with unknown workloads. For higher and predictable throughputs, provisioned capacity mode along with DynamoDB auto scaling is recommended. For more information, see “Read/Write Capacity Mode”.

For high throughput Amazon Kinesis Data Streams with multiple consumers, consider using enhanced fan-out for dedicated 2 MB/second throughput per consumer. When possible, use Kinesis Producer Library and Kinesis Client Library for effective record aggregation and de-aggregation.

Amazon API Gateway supports multiple endpoint types. Edge-optimized APIs provide a fully managed Amazon CloudFront distribution. These are better for geographically distributed clients. API requests are routed to the nearest CloudFront Point of Presence (POP), which typically improves connection time.

Edge-optimized API Gateway deployment

Edge-optimized API Gateway deployment

Regional API endpoints are intended when clients are in the same Region. This helps you to reduce request latency and allows you to add your own content delivery network if necessary.

Regional endpoint API Gateway deployment

Regional endpoint API Gateway deployment

Private API endpoints are API endpoints that can only be accessed from your Amazon Virtual Private Cloud (VPC) using an interface VPC endpoint. For more information, see “Creating a private API in Amazon API Gateway”.

For more information on endpoint types, see “Choose an endpoint type to set up for an API Gateway API”. For more general information on API Gateway, see the AWS re:Invent presentation “I didn’t know Amazon API Gateway could do that”.

AWS Step Functions has two workflow types, standard and express. Standard Workflows have exactly once workflow execution and can run for up to one year. Express Workflows have at-least-once workflow execution and can run for up to five minutes. Consider the per-second rates you require for both execution start rate and the state transition rate. For more information, see “Standard vs. Express Workflows”.

Performance load testing is recommended at both sustained and burst rates to evaluate the effect of tuning capacity units. Use Amazon CloudWatch service dashboards to analyze key performance metrics including load testing results. I cover performance testing in more detail in “Regulating inbound request rates – part 1”.

For general serverless optimization information, see the AWS re:Invent presentation “Serverless at scale: Design patterns and optimizations”.


Evaluate and optimize your serverless application’s performance based on access patterns, scaling mechanisms, and native integrations. You can improve your overall experience and make more efficient use of the platform in terms of both value and resources.

This post continues from part 1 and looks at designing your function to take advantage of concurrency via asynchronous and stream-based invocations. I cover measuring, evaluating, and selecting optimal capacity units.

This well-architected question will continue in part 3 where I look at integrating with managed services directly over functions when possible. I cover optimizing access patterns and applying caching where applicable.

For more serverless learning resources, visit Serverless Land.

Architecting a Highly Available Serverless, Microservices-Based Ecommerce Site

Post Syndicated from Senthil Kumar original https://aws.amazon.com/blogs/architecture/architecting-a-highly-available-serverless-microservices-based-ecommerce-site/

The number of ecommerce vendors is growing globally—they often handle large traffic at different times of the day and different days of the year. This, in addition to building, managing, and maintaining IT infrastructure on-premises data centers can present challenges to ecommerce businesses’ scalability and growth.

This blog provides you a Serverless on AWS solution that offloads the undifferentiated heavy lifting of managing resources and ensures your businesses’ architecture can handle peak traffic.

Common architecture set up versus serverless solution

The following sections describe a common monolithic architecture and our suggested alternative approach: setting up microservices-based order submission and product search modules. These modules are independently deployable and scalable.

Typical monolithic architecture

Figure 1 shows how a typical on-premises ecommerce infrastructure with different tiers is set up:

  • Web servers serve static assets and proxy requests to application servers
  • Application servers process ecommerce business logic and authentication logic
  • Databases store user and other dynamic data
  • Firewall and load balancers provide network components for load balancing and network security
Monolithic on-premises ecommerce infrastructure with different tiers

Figure 1. Monolithic on-premises ecommerce infrastructure with different tiers

Monolithic architecture tightly couples different layers of the application. This prevents them from being independently deployed and scaled.

Microservices-based modules

Order submission workflow module

This three-layer architecture can be set up in the AWS Cloud using serverless components:

  • Static content layer (Amazon CloudFront and Amazon Simple Storage Service (Amazon S3)). This layer stores static assets on Amazon S3. By using CloudFront in front of the S3 storage cache, you can deliver assets to customers globally with low latency and high transfer speeds.
  • Authentication layer (Amazon Cognito or customer proprietary layer). Ecommerce sites deliver authenticated and unauthenticated content to the user. With Amazon Cognito, you can manage users’ sign-up, sign-in, and access controls, so this authentication layer ensures that only authenticated users have access to secure data.
  • Dynamic content layer (AWS Lambda and Amazon DynamoDB). All business logic required for the ecommerce site is handled by the dynamic content layer. Using Lambda and DynamoDB ensures that these components are scalable and can handle peak traffic.

As shown in Figure 2, the order submission workflow is split into two sections: synchronous and asynchronous.

By splitting the order submission workflow, you allow users to submit their order details and get an orderId. This makes sure that they don’t have to wait for backend processing to complete. This helps unburden your architecture during peak shopping periods when the backend process can get busy.

Microservices-based order submission workflow

Figure 2. Microservices-based order submission workflow

The details of the order, such as credit card information in encrypted form, shipping information, etc., are stored in DynamoDB. This action invokes an asynchronous workflow managed by AWS Step Functions.

Figure 3 shows sample step functions from the asynchronous process. In this scenario, you are using external payment processing and shipping systems. When both systems get busy, step functions can manage long-running transactions and also the required retry logic. It uses a decision-based business workflow, so if a payment transaction fails, the order can be canceled. Or, once payment is successful, the order can proceed.

Amazon Simple Notification Service (Amazon SNS) notifies users whenever their order status changes. You can even extend Step Functions to have it react based on status of shipping.

Sample AWS Step Functions asynchronous workflow that uses external payment processing service and shipping system

Figure 3. Sample AWS Step Functions asynchronous workflow that uses external payment processing service and shipping system

Product search module

Our product search module is set up using the following serverless components:

  • Amazon Elasticsearch Service (Amazon ES) stores product data, which is updated whenever product-related data changes.
  • Lambda formats the data.
  • Amazon API Gateway allows users to search without authentication. As shown in Figure 4, searching for products on the ecommerce portal does not require users to log in. All traffic via API Gateway is unauthenticated.
Microservices-based product search workflow module with dynamic traffic through API Gateway

Figure 4. Microservices-based product search workflow module with dynamic traffic through API Gateway

Replicating data across Regions

If your ecommerce application runs on multiple Regions, it may require the content and data to be replicated. This allows the application to handle local traffic from that Region and also act as a failover option if the application fails in another Region. The content and data are replicated using the multi-Region replication features of Amazon S3 and DynamoDB global tables.

Figure 5 shows a multi-Region ecommerce site built on AWS with serverless services. It uses the following features to make sure that data between all Regions are in sync for data/assets that do not need data residency compliance:

  • Amazon S3 multi-Region replication keeps static assets in sync for assets.
  • DynamoDB global tables keeps dynamic data in sync across Regions.

Assets that are specific to their Region are stored in Regional specific buckets.

Data replication for a multi-Region ecommerce website built using serverless components

Figure 5. Data replication for a multi-Region ecommerce website built using serverless components

Amazon Route 53 DNS web service manages traffic failover from one Region to another. Route 53 provides different routing policies, and depending on your business requirement, you can choose the failover routing policy.

Best practices

Now that we’ve shown you how to build these applications, make sure you follow these best practices to effectively build, deploy, and monitor the solution stack:

  • Infrastructure as Code (IaC). A well-defined, repeatable infrastructure is important for managing any solution stack. AWS CloudFormation allows you to treat your infrastructure as code and provides a relatively easy way to model a collection of related AWS and third-party resources.
  • AWS Serverless Application Model (AWS SAM). An open-source framework. Use it to build serverless applications on AWS.
  • Deployment automation. AWS CodePipeline is a fully managed continuous delivery service that automates your release pipelines for fast and reliable application and infrastructure updates.
  • AWS CodeStar. Allows you to quickly develop, build, and deploy applications on AWS. It provides a unified user interface, enabling you to manage all of your software development activities in one place.
  • AWS Well-Architected Framework. Provides a mechanism for regularly evaluating your workloads, identifying high risk issues, and recording your improvements.
  • Serverless Applications Lens. Documents how to design, deploy, and architect serverless application workloads.
  • Monitoring. AWS provides many services that help you monitor and understand your applications, including Amazon CloudWatch, AWS CloudTrail, and AWS X-Ray.


In this blog post, we showed you how to architect a highly available, serverless, and microservices-based ecommerce website that operates in multiple Regions.

We also showed you how to replicate data between different Regions for scaling and if your workload fails. These serverless services reduce the burden of building and managing physical IT infrastructure to help you focus more on building solutions.

Related information

Protect public clients for Amazon Cognito by using an Amazon CloudFront proxy

Post Syndicated from Mahmoud Matouk original https://aws.amazon.com/blogs/security/protect-public-clients-for-amazon-cognito-by-using-an-amazon-cloudfront-proxy/

In Amazon Cognito user pools, an app client is an entity that has permission to call unauthenticated API operations (that is, operations that don’t have an authenticated user), such as operations to sign up, sign in, and handle forgotten passwords. In this post, I show you a solution designed to protect these API operations from unwanted bots and distributed denial of service (DDoS) attacks.

To protect Amazon Cognito services and customers, Amazon Cognito applies request rate quotas on all API categories, and throttles rapid calls that exceed the assigned quota. For that reason, you must ensure your applications control who can call unauthenticated API operations and at what rate, so that user calls aren’t throttled because of unwanted or misconfigured clients that call these API operations at high rates.

App clients fall into one of two categories: public clients (used from web or mobile applications) and private or confidential clients (used from a secured backend). Public clients shouldn’t have secrets, because it isn’t possible to protect secrets in these types of clients. Confidential clients, on the other hand, use a secret to authorize calls to unauthenticated operations. In these clients, the secret can be protected in the backend.

The benefit of using a confidential app client with a secret in Amazon Cognito is that unauthenticated API operations will accept only the calls that include the secret hash for this client, and will drop calls with an invalid or missing secret. In this way, you control who calls these API operations. Public applications can use a confidential app client by implementing a lightweight proxy layer in front of the Amazon Cognito endpoint, and then using this proxy to add a secret hash in relevant requests before passing the requests to Amazon Cognito.

There are multiple options that you can use to implement this proxy. One option is to use Amazon CloudFront and [email protected] to add the secret hash to the incoming requests. When you use a CloudFront proxy, you can also use AWS WAF, which gives you tools to detect and block unwanted clients. From [email protected], you can also integrate with other services (like Amazon Fraud Detector or third-party bot detection services) to help you detect possible fraudulent requests and block them. The CloudFront proxy, with the right set of security tools, helps protect your Amazon Cognito user pool from unwanted clients.

Solution overview

To implement this lightweight proxy pattern, you need to create an application client with a secret. Unauthenticated API calls to this client must include the secret hash which is added to the request from the proxy layer. Client applications use an SDK like AWS Amplify, the Amazon Cognito Identity SDK, or a mobile SDK to communicate with Amazon Cognito. By default, the SDK sends requests to the Regional Amazon Cognito endpoint. Your application must override the default endpoint by manually adding an “Endpoint” property in the app configuration. See the Integrate the client application with the proxy section later in this post for more details.

Figure 1 shows how this works, step by step.

Figure 1: A proxy solution to the Amazon Cognito Regional endpoint

Figure 1: A proxy solution to the Amazon Cognito Regional endpoint

The workflow is as follows:

  1. You configure the client application (mobile or web client) to use a CloudFront endpoint as a proxy to an Amazon Cognito Regional endpoint. You also create an application client in Amazon Cognito with a secret. This means that any unauthenticated API call must have the secret hash.
  2. Clients that send unauthenticated API calls to the Amazon Cognito endpoint directly are blocked and dropped because of the missing secret.
  3. You use [email protected] to add a secret hash to the relevant incoming requests before passing them on to the Amazon Cognito endpoint.
  4. From [email protected], you must have the app client secret to be able to calculate the secret hash and add it to the request. It’s recommended that you keep the secret in AWS Secrets Manager and cache it for the lifetime of the function.
  5. You use AWS WAF with CloudFront distribution to enforce rate limiting, allow and deny lists, and other rule groups according to your security requirements.

When to use this pattern

It’s a best practice to use this proxy pattern with clients that use SDKs to integrate with Amazon Cognito user pools. Examples include mobile applications that use the iOS or Android SDK, or web applications that use client-side libraries like Amplify or the Amazon Cognito Identity SDK to integrate with Amazon Cognito.

You don’t need to use a proxy pattern with server-side applications that use an AWS SDK to integrate with Amazon Cognito user pools from a protected backend, because server-side applications can natively use confidential clients and protect the secret in the backend.

You can’t use this solution with applications that use Hosted UI and OAuth 2.0 endpoints to integrate with Amazon Cognito user pools. This includes federation scenarios where users sign in with an external identity provider (IdP).

Implementation and deployment details

Before you deploy this solution, you need a user pool and an application client that has the client secret. When you have these in place, choose the following Launch Stack button to launch a CloudFormation stack in your account and deploy the proxy solution.

Select the Launch Stack button to launch the template

Note: The CloudFormation stack must be created in the us-east-1 AWS Region, but the user pool itself can exist in any supported Region.

The template takes the parameters shown in Figure 2 below.

Figure 2: CloudFormation stack creation with initial parameters

Figure 2: CloudFormation stack creation with initial parameters

The parameters in Figure 2 include:

  • AdvancedSecurityEnabled is a flag that indicates whether advanced security is enabled in the user pool or not. This flag determines which version of the Lambda function is deployed. Notice that if you change this flag as part of a stack update, it overrides the function code, so if you have any manual changes, make sure to back up your changes.
  • AppClientSecret is the secret for your application client. This secret is stored in Secrets Manager and accessed from [email protected] as needed.
  • LambdaS3BucketName is the bucket that hosts the Lambda code package. You don’t need to change this parameter unless you have a requirement to modify or extend the solution with your own Lambda function.
  • RateLimit is the maximum number of calls from a single IP address that are allowed within a 5 minute period. Values between 100 requests and 20 million requests are valid for RateLimit.
  • Important: provide a value suitable for your application and security requirements.

  • UserPoolId is the ID of your user pool. This value is used by [email protected] when needed (for example, to call admin APIs, which require the user pool ID).
  • UserPoolRegion is the AWS Region where you created your user pool. This value is used to determine which Amazon Cognito Regional endpoint to proxy the calls to.

This template creates several resources in your AWS account, as follows:

  1. A CloudFront distribution that serves as a proxy to an Amazon Cognito Regional endpoint.
  2. An AWS WAF web access control list (ACL) with rules for the allow list, deny list, and rate limit.
  3. A Lambda function to be deployed at the edge and assigned to the origin request event.
  4. A secret in Secrets Manager, to hold the values of the application client secret and user pool ID.

After you create the stack, the CloudFront distribution domain name is available on the Outputs tab in the CloudFront console, as shown in Figure 3. This is the value that’s used as the Endpoint property in your client-side application. You can optionally add an alternative domain name to the CloudFront distribution if you prefer to use your own custom domain.

Figure 3: The output of the CloudFormation stack creation, displaying the CloudFront domain name

Figure 3: The output of the CloudFormation stack creation, displaying the CloudFront domain name

Use [email protected] to add a secret hash to the request

As explained earlier, the purpose of having this proxy is to be able to inject the secret hash in unauthenticated API calls before passing them to the Amazon Cognito endpoint. This injection is achieved by a Lambda function that intercepts incoming requests at the edge (the CloudFront distribution) before passing them to the origin (the Amazon Cognito Regional endpoint).

The Lambda function that is deployed to the edge has two versions. One is a simple pass-through proxy that only adds the secret hash, and this version is used if Amazon Cognito advanced security isn’t enabled. The other version is a proxy that uses the AdminInitiateAuth and AdminRespondToAuthChallenge API operations instead of unauthenticated API operations for the user authentication and challenge response. This allows the proxy layer to propagate the client IP address to the Amazon Cognito endpoint, which guides the adaptive authentication features of advanced security. The version that is deployed by the stack is determined by the AdvancedSecurityEnabled flag when you create or update the CloudFormation stack.

You can extend this solution by manually modifying the Lambda function with your own processing logic. For example, you can integrate with fraud detection or bot detection services to evaluate the request and decide to proceed or reject the call. Note that after making any change to the Lambda function code, you must deploy a new version to the edge location. To do that from the Lambda console, navigate to Actions, choose Deploy to [email protected], and then choose Use existing CloudFront trigger on this function.

Important: If you update the stack from CloudFormation and change the value of the AdvancedSecurityEnabled flag, the new value overrides the Lambda code with the default version for the choice. In that case, all manual changes are lost.

Allow or block requests

The template that is provided in this blog post creates a web ACL with three rules: AllowList, DenyList, and RateLimit. These rules are evaluated in order and determine which requests are allowed or blocked. The template also creates four IP sets, as shown in Figure 4, to hold the values of allowed or blocked IPs for both IPv4 and IPv6 address types.

Figure 4: The CloudFormation template creates IP sets in the AWS WAF console for allow and deny lists

Figure 4: The CloudFormation template creates IP sets in the AWS WAF console for allow and deny lists

If you want to always allow requests from certain clients, for example, trusted enterprise clients or server-side clients in cases where a large volume of requests is coming from the same IP address like a VPN gateway, add these IP addresses to the corresponding AllowList IP set. Similarly, if you want to always block traffic from certain IPs, add those IPs to the corresponding DenyList IP set.

Requests from sources that aren’t on the allow list or deny list are evaluated based on the volume of calls within 5 minutes, and sources that exceed the defined rate limit within 5 minutes are automatically blocked. If you want to change the defined rate limit, you can do so by updating the CloudFormation stack and providing a different value for the RateLimit parameter. Or you can modify this value directly in the AWS WAF console by editing the RateLimit rule.

Note: You can also use AWS Managed Rules for AWS WAF to add additional protection according to your security needs.

Integrate the client application with the proxy

You can integrate the client application with the proxy by changing the Endpoint in your client application to use the CloudFront distribution domain name. The domain name is located in the Outputs section of the CloudFormation stack.

You then need to edit your client-side code to forward calls to Amazon Cognito through the proxy endpoint. For example, if you’re using the Identity SDK, you should change this property as follows.

var poolData = {
  UserPoolId: '<USER-POOL-ID>',
  ClientId: '<APP-CLIENT-ID>',
  endpoint: 'https://<CF-DISTRIBUTION-DOMAIN>'

If you’re using AWS Amplify, you can change the endpoint in the aws-exports.js file by overriding the property aws_cognito_endpoint. Or, if you configure Amplify Auth in your code, you can provide the endpoint as follows.

  userPoolId: '<USER-POOL-ID>',
  userPoolWebClientId: '<APP-CLIENT-ID>',
  endpoint: 'https://<CF-DISTRIBUTION-DOMAIN>'

If you have a mobile application that uses the Amplify mobile SDK, you can override the endpoint in your configuration as follows (don’t include AppClientSecret parameter in your configuration). Note that the Endpoint value contains the domain name only, not the full URL. This feature is available in the latest releases of the iOS and Android SDKs.

"CognitoUserPool": {
  "Default": {
    "AppClientId": "<APP-CLIENT-ID>",
    "Endpoint": "<CF-DISTRIBUTION-DOMAIN>",
    "PoolId": "<USER-POOL-ID>",
    "Region": "<REGION>"

Warning: The Amplify CLI overwrites customizations to the awsconfiguration.json and amplifyconfiguration.json files if you do an amplify push or amplify pull operation. You must manually re-apply the Endpoint customization and remove the AppClientSecret if you use the CLI to modify your cloud backend.

Solution limitations

This solution has these limitations:

  • If advanced security features are enabled for the user pool, Amazon Cognito calculates risk for user events. If you use this proxy pattern, the IP address that is propagated in user events is the proxy IP address, which causes risk calculation for SignUp, ForgotPassword, and ResendCode events to be inaccurate. On the other hand, Sign-In events still have the client IP address propagated correctly, and risk calculation and adaptive authentication for Sign-In events aren’t affected by the use of this proxy.
  • This solution is not applicable to Hosted UI, OAuth 2.0 endpoints, and federation flows.
  • Authenticated and admin API operations (which require developer credentials or an access token) aren’t covered in this solution. These API operations don’t require a secret hash, and they use other authentication mechanisms.
  • Using this proxy solution with mobile apps requires an update to the application. The update might take time to be available in the relevant app store, and you must depend on end users to update their app. Plan ahead of time to use the solution with mobile apps.

How to detect unusual behavior

In this section, I share with you the steps to detect, quickly analyze and respond to unwanted clients. It’s a best practice to configure monitoring and alarms that help you to detect unexpected spikes in activity. Additionally, I show you how to be ready to quickly identify clients that are calling your resources at a higher-than-usual rate.

Monitor utilization compared to quotas

Amazon Cognito integrates with Service Quotas, which monitor service utilization compared to quotas. These metrics help you detect unexpected spikes and be alerted if you’re approaching your quota for a certain API category. Approaching your quota indicates that there is a risk that calls from legitimate users will be throttled.

To view utilization versus quota metrics

  1. In the Service Quotas console, choose Service Quotas, choose AWS Services, and then choose Amazon Cognito User Pools.
  2. Under Service quotas, enter the search term rate of. This shows you the list of API categories and the assigned quotas for each category.
    Figure 5: The Service Quotas console showing Amazon Cognito API category rate quotas

    Figure 5: The Service Quotas console showing Amazon Cognito API category rate quotas

  3. Choose any of the API categories to see utilization versus quota metrics.
    Figure 6: The Service Quotas console showing utilization vs quota metrics for Amazon Cognito UserCreation APIs

    Figure 6: The Service Quotas console showing utilization vs quota metrics for Amazon Cognito UserCreation APIs

  4. You can also create alarms from this page to alert you if utilization is above a pre-defined threshold. You can create alarms starting at 50 percent utilization. It’s recommended that you create multiple alarms, for example at the 50 percent, 70 percent, and 90 percent thresholds, and configure CloudWatch alarms as appropriate.
    Figure 7: Creating an alarm for the utilization of the UserCreation API category

    Figure 7: Creating an alarm for the utilization of the UserCreation API category

Analyze CloudTrail logs with Athena

If you detect an unexpected spike in traffic to a certain API category, the next step is to identify the sources of this spike. You can do that by using CloudTrail logs or, after you deploy and use this proxy solution, CloudFront logs as sources of information. You can then analyze these logs by using Amazon Athena queries.

The first step is to create Athena tables from CloudTrail and CloudFront logs. You can do that by following these steps for CloudTrail and similar steps for CloudFront. After you have these tables created, you can create a set of queries that help you identify unwanted clients. Here are a couple of examples:

  • Use the following query to identify clients with the highest call rate to the InitiateAuth API operation within the timeframe you noticed the spike (change the eventtime value to reflect the attack window).
    SELECT sourceipaddress, count(*)
    FROM "default"."cloudtrail_logs"
    WHERE eventname='InitiateAuth'
    AND eventtime >= '2021-03-01T00:00:00Z'and eventtime < '2021-03-31T00:00:00Z'
    GROUP BY sourceipaddress
    LIMIT 10

  • Use the following query to identify clients that come through CloudFront with the highest error rate.
    SELECT count(*) as count, request_ip
    FROM "default"."cloudfront_logs"
    WHERE status>500
    GROUP BY request_ip

After you identify sources that are calling your service with a higher-than-usual rate, you can block these clients by adding them to the DenyList IP set that was created in AWS WAF.

Analyze CloudTrail events with CloudWatch Logs Insights

It’s a best practice to configure your trail to send events to CloudWatch Logs. After you do this, you can interactively search and analyze your Amazon Cognito CloudTrail events with CloudWatch Logs Insights to identify errors, unusual activity, or unusual user behavior in your account.


In this post, I showed you how to implement a lightweight proxy to an Amazon Cognito endpoint, which can be used with an application client secret to control access to unauthenticated API operations. This approach, together with security tools such as AWS WAF, helps provide protection for these API operations from unwanted clients. I also showed you strategies to help detect an ongoing attack and quickly analyze, identify, and block unwanted clients.

For more strategies for DDoS mitigation, see the AWS Best Practices for DDoS Resiliency.

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, start a new thread on the Amazon Cognito forum or contact AWS Support.

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


Mahmoud Matouk

Mahmoud is a Senior Solutions Architect with the Amazon Cognito team. He helps AWS customers build secure and innovative solutions for various identity and access management scenarios.

Should I Run my Containers on AWS Fargate, AWS Lambda, or Both?

Post Syndicated from Rob Solomon original https://aws.amazon.com/blogs/architecture/should-i-run-my-containers-on-aws-fargate-aws-lambda-or-both/

Containers have transformed how companies build and operate software. Bundling both application code and dependencies into a single container image improves agility and reduces deployment failures. But what compute platform should you choose to be most efficient, and what factors should you consider in this decision?

With the release of container image support for AWS Lambda functions (December 2020), customers now have an additional option for building serverless applications using their existing container-oriented tooling and DevOps best practices. In addition, a single container image can be configured to run on both of these compute platforms: AWS Lambda (using serverless functions) or AWS Fargate (using containers).

Three key factors can influence the decision of what platform you use to deploy your container: startup time, task runtime, and cost. That decision may vary each time a task is initiated, as shown in the three scenarios following.

Design considerations for deploying a container

Total task duration consists of startup time and runtime. The startup time of a containerized task is the time required to provision the container compute resource and deploy the container. Task runtime is the time it takes for the application code to complete.

Startup time: Some tasks must complete quickly. For example, when a user waits for a web response, or when a series of tasks is completed in sequential order. In those situations, the total duration time must be minimal. While the application code may be optimized to run faster, startup time depends on the chosen compute platform as well. AWS Fargate container startup time typically takes from 60 to 90 seconds. AWS Lambda initial cold start can take up to 5 seconds. Following that first startup, the same containerized function has negligible startup time.

Task runtime: The amount of time it takes for a task to complete is influenced by the compute resources allocated (vCPU and memory) and application code. AWS Fargate lets you select vCPU and memory size. With AWS Lambda, you define the amount of allocated memory. Lambda then provisions a proportional quantity of vCPU. In both AWS Fargate and AWS Lambda uses, increasing the amount of compute resources may result in faster completion time. However, this will depend on the application. While the additional compute resources incur greater cost, the total duration may be shorter, so the overall cost may also be lower.

AWS Lambda has a maximum limit of 15 minutes of runtime. Lambda shouldn’t be used for these tasks to avoid the likelihood of timeout errors.

Figure 1 illustrates the proportion of startup time to total duration. The initial steepness of each line shows a rapid decrease in startup overhead. This is followed by a flattening out, showing a diminishing rate of efficiency. Startup time delay becomes less impactful as the total job duration increases. Other factors (such as cost) become more significant.

Figure 1. Ratio of startup time as a function to overall job duration for each service

Figure 1. Ratio of startup time as a function to overall job duration for each service

Cost: When making the choice between Fargate and Lambda, it is important to understand the different pricing models. This way, you can make the appropriate selection for your needs.

Figure 2 shows a cost analysis of Lambda vs Fargate. This is for the entire range of configurations for a runtime task. For most of the range of configurable memory, AWS Lambda is more expensive per second than even the most expensive configuration of Fargate.

Figure 2. Total cost for both AWS Lambda and AWS Fargate based on task duration

Figure 2. Total cost for both AWS Lambda and AWS Fargate based on task duration

From a cost perspective, AWS Fargate is more cost-effective for tasks running for several seconds or longer. If cost is the only factor at play, then Fargate would be the better choice. But the savings gained by using Fargate may be offset by the business value gained from the shorter Lambda function startup time.

Dynamically choose your compute platform

In the following scenarios, we show how a single container image can serve multiple use cases. The decision to run a given containerized application on either AWS Lambda or AWS Fargate can be determined at runtime. This decision depends on whether cost, speed, or duration are the priority.

In Figure 3, an image-processing AWS Batch job runs on a nightly schedule, processing tens of thousands of images to extract location information. When run as a batch job, image processing may take 1–2 hours. The job pulls images stored in Amazon Simple Storage Service (S3) and writes the location metadata to Amazon DynamoDB. In this case, AWS Fargate provides a good combination of compute and cost efficiency. An added benefit is that it also supports tasks that exceed 15 minutes. If a single image is submitted for real-time processing, response time is critical. In that case, the same image-processing code can be run on AWS Lambda, using the same container image. Rather than waiting for the next batch process to run, the image is processed immediately.

Figure 3. One-off invocation of a typically long-running batch job

Figure 3. One-off invocation of a typically long-running batch job

In Figure 4, a SaaS application uses an AWS Lambda function to allow customers to submit complex text search queries for files stored in an Amazon Elastic File System (EFS) volume. The task should return results quickly, which is an ideal condition for AWS Lambda. However, a small percentage of jobs run much longer than the average, exceeding the maximum duration of 15 minutes.

A straightforward approach to avoid job failure is to initiate an Amazon CloudWatch alarm when the Lambda function times out. CloudWatch alarms can automatically retry the job using Fargate. An alternate approach is to capture historical data and use it to create a machine learning model in Amazon SageMaker. When a new job is initiated, the SageMaker model can predict the time it will take the job to complete. Lambda can use that prediction to route the job to either AWS Lambda or AWS Fargate.

Figure 4. Short duration tasks with occasional outliers running longer than 15 minutes

Figure 4. Short duration tasks with occasional outliers running longer than 15 minutes

In Figure 5, a customer runs a containerized legacy application that encompasses many different kinds of functions, all related to a recurring data processing workflow. Each function performs a task of varying complexity and duration. These can range from processing data files, updating a database, or submitting machine learning jobs.

Using a container image, one code base can be configured to contain all of the individual functions. Longer running functions, such as data preparation and big data analytics, are routed to Fargate. Shorter duration functions like simple queries can be configured to run using the container image in AWS Lambda. By using AWS Step Functions as an orchestrator, the process can be automated. In this way, a monolithic application can be broken up into a set of “Units of Work” that operate independently.

Figure 5. Heterogeneous function orchestration

Figure 5. Heterogeneous function orchestration


If your job lasts milliseconds and requires a fast response to provide a good customer experience, use AWS Lambda. If your function is not time-sensitive and runs on the scale of minutes, use AWS Fargate. For tasks that have a total duration of under 15 minutes, customers must decide based on impacts to both business and cost. Select the service that is the most effective serverless compute environment to meet your requirements. The choice can be made manually when a job is scheduled or by using retry logic to switch to the other compute platform if the first option fails. The decision can also be based on a machine learning model trained on historical data.

Micro-frontend Architectures on AWS

Post Syndicated from Bryant Bost original https://aws.amazon.com/blogs/architecture/micro-frontend-architectures-on-aws/

A microservice architecture is characterized by independent services that are focused on a specific business function and maintained by small, self-contained teams. Microservice architectures are used frequently for web applications developed on AWS, and for good reason. They offer many well-known benefits such as development agility, technological freedom, targeted deployments, and more. Despite the popularity of microservices, many frontend applications are still built in a monolithic style. For example, they have one large code base that interacts with all backend microservices, and is maintained by a large team of developers.

Monolith Frontend

Figure 1. Microservice backend with monolith frontend

What is a Micro-frontend?

The micro-frontend architecture introduces microservice development principles to frontend applications. In a micro-frontend architecture, development teams independently build and deploy “child” frontend applications. These applications are combined by a “parent” frontend application that acts as a container to retrieve, display, and integrate the various child applications. In this parent/child model, the user interacts with what appears to be a single application. In reality, they are interacting with several independent applications, published by different teams.

Micro Frontend

Figure 2. Microservice backend with micro-frontend

Micro-frontend Benefits

Compared to a monolith frontend, a micro-frontend offers the following benefits:

  • Independent artifacts: A core tenet of microservice development is that artifacts can be deployed independently, and this remains true for micro-frontends. In a micro-frontend architecture, teams should be able to independently deploy their frontend applications with minimal impact to other services. Those changes will be reflected by the parent application.
  • Autonomous teams: Each team is the expert in its own domain. For example, the billing service team members have specialized knowledge. This includes the data models, the business requirements, the API calls, and user interactions associated with the billing service. This knowledge allows the team to develop the billing frontend faster than a larger, less specialized team.
  • Flexible technology choices: Autonomy allows each team to make technology choices that are independent from other teams. For instance, the billing service team could develop their micro-frontend using Vue.js and the profile service team could develop their frontend using Angular.
  • Scalable development: Micro-frontend development teams are smaller and are able to operate without disrupting other teams. This allows us to quickly scale development by spinning up new teams to deliver additional frontend functionality via child applications.
  • Easier maintenance: Keeping frontend repositories small and specialized allows them to be more easily understood, and this simplifies long-term maintenance and testing. For instance, if you want to change an interaction on a monolith frontend, you must isolate the location and dependencies of the feature within the context of a large codebase. This type of operation is greatly simplified when dealing with the smaller codebases associated with micro-frontends.

Micro-frontend Challenges

Conversely, a micro-frontend presents the following challenges:

  • Parent/child integration: A micro-frontend introduces the task of ensuring the parent application displays the child application with the same consistency and performance expected from a monolith application. This point is discussed further in the next section.
  • Operational overhead: Instead of managing a single frontend application, a micro-frontend application involves creating and managing separate infrastructure for all teams.
  • Consistent user experience: In order to maintain a consistent user experience, the child applications must use the same UI components, CSS libraries, interactions, error handling, and more. Maintaining consistency in the user experience can be difficult for child applications that are at different stages in the development lifecycle.

Building Micro-frontends

The most difficult challenge with the micro-frontend architecture pattern is integrating child applications with the parent application. Prioritizing the user experience is critical for any frontend application. In the context of micro-frontends, this means ensuring a user can seamlessly navigate from one child application to another inside the parent application. We want to avoid disruptive behavior such as page refreshes or multiple logins. At its most basic definition, parent/child integration involves the parent application dynamically retrieving and rendering child applications when the parent app is loaded. Rendering the child application depends on how the child application was built, and this can be done in a number of ways. Two of the most popular methods of parent/child integration are:

  1. Building each child application as a web component.
  2. Importing each child application as an independent module. These modules either declares a function to render itself or is dynamically imported by the parent application (such as with module federation).

Registering child apps as web components:

        <script src="https://shipping.example.com/shipping-service.js"></script>
        <script src="https://profile.example.com/profile-service.js"></script>
        <script src="https://billing.example.com/billing-service.js"></script>
        <title>Parent Application</title>
        <shipping-service />
        <profile-service />
        <billing-service />

Registering child apps as modules:

        <script src="https://shipping.example.com/shipping-service.js"></script>
        <script src="https://profile.example.com/profile-service.js"></script>
        <script src="https://billing.example.com/billing-service.js"></script>
     <title>Parent Application</title>
        // Load and render the child applications form their JS bundles.

The following diagram shows an example micro-frontend architecture built on AWS.

AWS Micro Frontend

Figure 3. Micro-frontend architecture on AWS

In this example, each service team is running a separate, identical stack to build their application. They use the AWS Developer Tools and deploy the application to Amazon Simple Storage Service (S3) with Amazon CloudFront. The CI/CD pipelines use shared components such as CSS libraries, API wrappers, or custom modules stored in AWS CodeArtifact. This helps drive consistency across parent and child applications.

When you retrieve the parent application, it should prompt you to log in to an identity provider and retrieve JWTs. In this example, the identity provider is an Amazon Cognito User Pool. After a successful login, the parent application retrieves the child applications from CloudFront and renders them inside the parent application. Alternatively, the parent application can elect to render the child applications on demand, when you navigate to a specific route. The child applications should not require you to log in again to the Amazon Cognito user pool. They should be configured to use the JWT obtained by the parent app or silently retrieve a new JWT from Amazon Cognito.


Micro-frontend architectures introduce many of the familiar benefits of microservice development to frontend applications. A micro-frontend architecture also simplifies the process of building complex frontend applications by allowing you to manage small, independent components.

How to protect sensitive data for its entire lifecycle in AWS

Post Syndicated from Raj Jain original https://aws.amazon.com/blogs/security/how-to-protect-sensitive-data-for-its-entire-lifecycle-in-aws/

Many Amazon Web Services (AWS) customer workflows require ingesting sensitive and regulated data such as Payments Card Industry (PCI) data, personally identifiable information (PII), and protected health information (PHI). In this post, I’ll show you a method designed to protect sensitive data for its entire lifecycle in AWS. This method can help enhance your data security posture and be useful for fulfilling the data privacy regulatory requirements applicable to your organization for data protection at-rest, in-transit, and in-use.

An existing method for sensitive data protection in AWS is to use the field-level encryption feature offered by Amazon CloudFront. This CloudFront feature protects sensitive data fields in requests at the AWS network edge. The chosen fields are protected upon ingestion and remain protected throughout the entire application stack. The notion of protecting sensitive data early in its lifecycle in AWS is a highly desirable security architecture. However, CloudFront can protect a maximum of 10 fields and only within HTTP(S) POST requests that carry HTML form encoded payloads.

If your requirements exceed CloudFront’s native field-level encryption feature, such as a need to handle diverse application payload formats, different HTTP methods, and more than 10 sensitive fields, you can implement field-level encryption yourself using the [email protected] feature in CloudFront. In terms of choosing an appropriate encryption scheme, this problem calls for an asymmetric cryptographic system that will allow public keys to be openly distributed to the CloudFront network edges while keeping the corresponding private keys stored securely within the network core. One such popular asymmetric cryptographic system is RSA. Accordingly, we’ll implement a [email protected] function that uses asymmetric encryption using the RSA cryptosystem to protect an arbitrary number of fields in any HTTP(S) request. We will discuss the solution using an example JSON payload, although this approach can be applied to any payload format.

A complex part of any encryption solution is key management. To address that, I use AWS Key Management Service (AWS KMS). AWS KMS simplifies the solution and offers improved security posture and operational benefits, detailed later.

Solution overview

You can protect data in-transit over individual communications channels using transport layer security (TLS), and at-rest in individual storage silos using volume encryption, object encryption or database table encryption. However, if you have sensitive workloads, you might need additional protection that can follow the data as it moves through the application stack. Fine-grained data protection techniques such as field-level encryption allow for the protection of sensitive data fields in larger application payloads while leaving non-sensitive fields in plaintext. This approach lets an application perform business functions on non-sensitive fields without the overhead of encryption, and allows fine-grained control over what fields can be accessed by what parts of the application.

A best practice for protecting sensitive data is to reduce its exposure in the clear throughout its lifecycle. This means protecting data as early as possible on ingestion and ensuring that only authorized users and applications can access the data only when and as needed. CloudFront, when combined with the flexibility provided by [email protected], provides an appropriate environment at the edge of the AWS network to protect sensitive data upon ingestion in AWS.

Since the downstream systems don’t have access to sensitive data, data exposure is reduced, which helps to minimize your compliance footprint for auditing purposes.

The number of sensitive data elements that may need field-level encryption depends on your requirements. For example:

  • For healthcare applications, HIPAA regulates 18 personal data elements.
  • In California, the California Consumer Privacy Act (CCPA) regulates at least 11 categories of personal information—each with its own set of data elements.

The idea behind field-level encryption is to protect sensitive data fields individually, while retaining the structure of the application payload. The alternative is full payload encryption, where the entire application payload is encrypted as a binary blob, which makes it unusable until the entirety of it is decrypted. With field-level encryption, the non-sensitive data left in plaintext remains usable for ordinary business functions. When retrofitting data protection in existing applications, this approach can reduce the risk of application malfunction since the data format is maintained.

The following figure shows how PII data fields in a JSON construction that are deemed sensitive by an application can be transformed from plaintext to ciphertext with a field-level encryption mechanism.

Figure 1: Example of field-level encryption

Figure 1: Example of field-level encryption

You can change plaintext to ciphertext as depicted in Figure 1 by using a [email protected] function to perform field-level encryption. I discuss the encryption and decryption processes separately in the following sections.

Field-level encryption process

Let’s discuss the individual steps involved in the encryption process as shown in Figure 2.

Figure 2: Field-level encryption process

Figure 2: Field-level encryption process

Figure 2 shows CloudFront invoking a [email protected] function while processing a client request. CloudFront offers multiple integration points for invoking L[email protected] functions. Since you are processing a client request and your encryption behavior is related to requests being forwarded to an origin server, you want your function to run upon the origin request event in CloudFront. The origin request event represents an internal state transition in CloudFront that happens immediately before CloudFront forwards a request to the downstream origin server.

You can associate your [email protected] with CloudFront as described in Adding Triggers by Using the CloudFront Console. A screenshot of the CloudFront console is shown in Figure 3. The selected event type is Origin Request and the Include Body check box is selected so that the request body is conveyed to [email protected]

Figure 3: Configuration of Lambda@Edge in CloudFront

Figure 3: Configuration of [email protected] in CloudFront

The [email protected] function acts as a programmable hook in the CloudFront request processing flow. You can use the function to replace the incoming request body with a request body with the sensitive data fields encrypted.

The process includes the following steps:

Step 1 – RSA key generation and inclusion in [email protected]

You can generate an RSA customer managed key (CMK) in AWS KMS as described in Creating asymmetric CMKs. This is done at system configuration time.

Note: You can use your existing RSA key pairs or generate new ones externally by using OpenSSL commands, especially if you need to perform RSA decryption and key management independently of AWS KMS. Your choice won’t affect the fundamental encryption design pattern presented here.

The RSA key creation in AWS KMS requires two inputs: key length and type of usage. In this example, I created a 2048-bit key and assigned its use for encryption and decryption. The cryptographic configuration of an RSA CMK created in AWS KMS is shown in Figure 4.

Figure 4: Cryptographic properties of an RSA key managed by AWS KMS

Figure 4: Cryptographic properties of an RSA key managed by AWS KMS

Of the two encryption algorithms shown in Figure 4— RSAES_OAEP_SHA_256 and RSAES_OAEP_SHA_1, this example uses RSAES_OAEP_SHA_256. The combination of a 2048-bit key and the RSAES_OAEP_SHA_256 algorithm lets you encrypt a maximum of 190 bytes of data, which is enough for most PII fields. You can choose a different key length and encryption algorithm depending on your security and performance requirements. How to choose your CMK configuration includes information about RSA key specs for encryption and decryption.

Using AWS KMS for RSA key management versus managing the keys yourself eliminates that complexity and can help you:

  • Enforce IAM and key policies that describe administrative and usage permissions for keys.
  • Manage cross-account access for keys.
  • Monitor and alarm on key operations through Amazon CloudWatch.
  • Audit AWS KMS API invocations through AWS CloudTrail.
  • Record configuration changes to keys and enforce key specification compliance through AWS Config.
  • Generate high-entropy keys in an AWS KMS hardware security module (HSM) as required by NIST.
  • Store RSA private keys securely, without the ability to export.
  • Perform RSA decryption within AWS KMS without exposing private keys to application code.
  • Categorize and report on keys with key tags for cost allocation.
  • Disable keys and schedule their deletion.

You need to extract the RSA public key from AWS KMS so you can include it in the AWS Lambda deployment package. You can do this from the AWS Management Console, through the AWS KMS SDK, or by using the get-public-key command in the AWS Command Line Interface (AWS CLI). Figure 5 shows Copy and Download options for a public key in the Public key tab of the AWS KMS console.

Figure 5: RSA public key available for copy or download in the console

Figure 5: RSA public key available for copy or download in the console

Note: As we will see in the sample code in step 3, we embed the public key in the [email protected] deployment package. This is a permissible practice because public keys in asymmetric cryptography systems aren’t a secret and can be freely distributed to entities that need to perform encryption. Alternatively, you can use [email protected] to query AWS KMS for the public key at runtime. However, this introduces latency, increases the load against your KMS account quota, and increases your AWS costs. General patterns for using external data in [email protected] are described in Leveraging external data in [email protected].

Step 2 – HTTP API request handling by CloudFront

CloudFront receives an HTTP(S) request from a client. CloudFront then invokes [email protected] during origin-request processing and includes the HTTP request body in the invocation.

Step 3 – [email protected] processing

The [email protected] function processes the HTTP request body. The function extracts sensitive data fields and performs RSA encryption over their values.

The following code is sample source code for the [email protected] function implemented in Python 3.7:

import Crypto
import base64
import json
from Crypto.Cipher import PKCS1_OAEP
from Crypto.PublicKey import RSA

# PEM-formatted RSA public key copied over from AWS KMS or your own public key.
RSA_PUBLIC_KEY = "-----BEGIN PUBLIC KEY-----<your key>-----END PUBLIC KEY-----"

# Example sensitive data field names in a JSON object. 
PII_SENSITIVE_FIELD_NAMES = ["fname", "lname", "email", "ssn", "dob", "phone"]


def lambda_handler(event, context):
    # Extract HTTP request and its body as per documentation:
    # https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/lambda-event-structure.html
    http_request = event['Records'][0]['cf']['request']
    body = http_request['body']
    org_body = base64.b64decode(body['data'])
    mod_body = protect_sensitive_fields_json(org_body)
    body['action'] = 'replace'
    body['encoding'] = 'text'
    body['data'] = mod_body
    return http_request

def protect_sensitive_fields_json(body):
    # Encrypts sensitive fields in sample JSON payload shown earlier in this post.
    # [{"fname": "Alejandro", "lname": "Rosalez", … }]
    person_list = json.loads(body.decode("utf-8"))
    for person_data in person_list:
        for field_name in PII_SENSITIVE_FIELD_NAMES:
            if field_name not in person_data:
            plaintext = person_data[field_name]
            ciphertext = RSA_CIPHER_OBJ.encrypt(bytes(plaintext, 'utf-8'))
            ciphertext_b64 = base64.b64encode(ciphertext).decode()
            # Optionally, add unique prefix/suffix patterns to ciphertext
            person_data[field_name] = CIPHERTEXT_PREFIX + ciphertext_b64 + CIPHERTEXT_SUFFIX 
    return json.dumps(person_list)

The event structure passed into the [email protected] function is described in [email protected] Event Structure. Following the event structure, you can extract the HTTP request body. In this example, the assumption is that the HTTP payload carries a JSON document based on a particular schema defined as part of the API contract. The input JSON document is parsed by the function, converting it into a Python dictionary. The Python native dictionary operators are then used to extract the sensitive field values.

Note: If you don’t know your API payload structure ahead of time or you’re dealing with unstructured payloads, you can use techniques such as regular expression pattern searches and checksums to look for patterns of sensitive data and target them accordingly. For example, credit card primary account numbers include a Luhn checksum that can be programmatically detected. Additionally, services such as Amazon Comprehend and Amazon Macie can be leveraged for detecting sensitive data such as PII in application payloads.

While iterating over the sensitive fields, individual field values are encrypted using the standard RSA encryption implementation available in the Python Cryptography Toolkit (PyCrypto). The PyCrypto module is included within the [email protected] zip archive as described in [email protected] deployment package.

The example uses the standard optimal asymmetric encryption padding (OAEP) and SHA-256 encryption algorithm properties. These properties are supported by AWS KMS and will allow RSA ciphertext produced here to be decrypted by AWS KMS later.

Note: You may have noticed in the code above that we’re bracketing the ciphertexts with predefined prefix and suffix strings:

person_data[field_name] = CIPHERTEXT_PREFIX + ciphertext_b64 + CIPHERTEXT_SUFFIX

This is an optional measure and is being implemented to simplify the decryption process.

The prefix and suffix strings help demarcate ciphertext embedded in unstructured data in downstream processing and also act as embedded metadata. Unique prefix and suffix strings allow you to extract ciphertext through string or regular expression (regex) searches during the decryption process without having to know the data body format or schema, or the field names that were encrypted.

Distinct strings can also serve as indirect identifiers of RSA key pair identifiers. This can enable key rotation and allow separate keys to be used for separate fields depending on the data security requirements for individual fields.

You can ensure that the prefix and suffix strings can’t collide with the ciphertext by bracketing them with characters that don’t appear in cyphertext. For example, a hash (#) character cannot be part of a base64 encoded ciphertext string.

Deploying a Lambda function as a [email protected] function requires specific IAM permissions and an IAM execution role. Follow the [email protected] deployment instructions in Setting IAM Permissions and Roles for [email protected].

Step 4 – [email protected] response

The [email protected] function returns the modified HTTP body back to CloudFront and instructs it to replace the original HTTP body with the modified one by setting the following flag:

http_request['body']['action'] = 'replace'

Step 5 – Forward the request to the origin server

CloudFront forwards the modified request body provided by [email protected] to the origin server. In this example, the origin server writes the data body to persistent storage for later processing.

Field-level decryption process

An application that’s authorized to access sensitive data for a business function can decrypt that data. An example decryption process is shown in Figure 6. The figure shows a Lambda function as an example compute environment for invoking AWS KMS for decryption. This functionality isn’t dependent on Lambda and can be performed in any compute environment that has access to AWS KMS.

Figure 6: Field-level decryption process

Figure 6: Field-level decryption process

The steps of the process shown in Figure 6 are described below.

Step 1 – Application retrieves the field-level encrypted data

The example application retrieves the field-level encrypted data from persistent storage that had been previously written during the data ingestion process.

Step 2 – Application invokes the decryption Lambda function

The application invokes a Lambda function responsible for performing field-level decryption, sending the retrieved data to Lambda.

Step 3 – Lambda calls the AWS KMS decryption API

The Lambda function uses AWS KMS for RSA decryption. The example calls the KMS decryption API that inputs ciphertext and returns plaintext. The actual decryption happens in KMS; the RSA private key is never exposed to the application, which is a highly desirable characteristic for building secure applications.

Note: If you choose to use an external key pair, then you can securely store the RSA private key in AWS services like AWS Systems Manager Parameter Store or AWS Secrets Manager and control access to the key through IAM and resource policies. You can fetch the key from relevant vault using the vault’s API, then decrypt using the standard RSA implementation available in your programming language. For example, the cryptography toolkit in Python or javax.crypto in Java.

The Lambda function Python code for decryption is shown below.

import base64
import boto3
import re

kms_client = boto3.client('kms')

# This lambda function extracts event body, searches for and decrypts ciphertext 
# fields surrounded by provided prefix and suffix strings in arbitrary text bodies 
# and substitutes plaintext fields in-place.  
def lambda_handler(event, context):    
    org_data = event["body"]
    mod_data = unprotect_fields(org_data, CIPHERTEXT_PREFIX, CIPHERTEXT_SUFFIX)
    return mod_data

# Helper function that performs non-greedy regex search for ciphertext strings on
# input data and performs RSA decryption of them using AWS KMS 
def unprotect_fields(org_data, prefix, suffix):
    regex_pattern = prefix + "(.*?)" + suffix
    mod_data_parts = []
    cursor = 0

    # Search ciphertexts iteratively using python regular expression module
    for match in re.finditer(regex_pattern, org_data):
        mod_data_parts.append(org_data[cursor: match.start()])
            # Ciphertext was stored as Base64 encoded in our example. Decode it.
            ciphertext = base64.b64decode(match.group(1))

            # Decrypt ciphertext using AWS KMS  
            decrypt_rsp = kms_client.decrypt(
            decrypted_val = decrypt_rsp["Plaintext"].decode("utf-8")
        except Exception as e:
            print ("Exception: " + str(e))
            return None
        cursor = match.end()

    return "".join(mod_data_parts)

The function performs a regular expression search in the input data body looking for ciphertext strings bracketed in predefined prefix and suffix strings that were added during encryption.

While iterating over ciphertext strings one-by-one, the function calls the AWS KMS decrypt() API. The example function uses the same RSA encryption algorithm properties—OAEP and SHA-256—and the Key ID of the public key that was used during encryption in [email protected]

Note that the Key ID itself is not a secret. Any application can be configured with it, but that doesn’t mean any application will be able to perform decryption. The security control here is that the AWS KMS key policy must allow the caller to use the Key ID to perform the decryption. An additional security control is provided by Lambda execution role that should allow calling the KMS decrypt() API.

Step 4 – AWS KMS decrypts ciphertext and returns plaintext

To ensure that only authorized users can perform decrypt operation, the KMS is configured as described in Using key policies in AWS KMS. In addition, the Lambda IAM execution role is configured as described in AWS Lambda execution role to allow it to access KMS. If both the key policy and IAM policy conditions are met, KMS returns the decrypted plaintext. Lambda substitutes the plaintext in place of ciphertext in the encapsulating data body.

Steps three and four are repeated for each ciphertext string.

Step 5 – Lambda returns decrypted data body

Once all the ciphertext has been converted to plaintext and substituted in the larger data body, the Lambda function returns the modified data body to the client application.


In this post, I demonstrated how you can implement field-level encryption integrated with AWS KMS to help protect sensitive data workloads for their entire lifecycle in AWS. Since your [email protected] is designed to protect data at the network edge, data remains protected throughout the application execution stack. In addition to improving your data security posture, this protection can help you comply with data privacy regulations applicable to your organization.

Since you author your own [email protected] function to perform standard RSA encryption, you have flexibility in terms of payload formats and the number of fields that you consider to be sensitive. The integration with AWS KMS for RSA key management and decryption provides significant simplicity, higher key security, and rich integration with other AWS security services enabling an overall strong security solution.

By using encrypted fields with identifiers as described in this post, you can create fine-grained controls for data accessibility to meet the security principle of least privilege. Instead of granting either complete access or no access to data fields, you can ensure least privileges where a given part of an application can only access the fields that it needs, when it needs to, all the way down to controlling access field by field. Field by field access can be enabled by using different keys for different fields and controlling their respective policies.

In addition to protecting sensitive data workloads to meet regulatory and security best practices, this solution can be used to build de-identified data lakes in AWS. Sensitive data fields remain protected throughout their lifecycle, while non-sensitive data fields remain in the clear. This approach can allow analytics or other business functions to operate on data without exposing sensitive data.

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

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Raj Jain

Raj is a Senior Cloud Architect at AWS. He is passionate about helping customers build well-architected applications in AWS. Raj is a published author in Bell Labs Technical Journal, has authored 3 IETF standards, and holds 12 patents in internet telephony and applied cryptography. In his spare time, Raj enjoys outdoors, cooking, reading, and travel.

Serving Content Using a Fully Managed Reverse Proxy Architecture in AWS

Post Syndicated from Leonardo Machado original https://aws.amazon.com/blogs/architecture/serving-content-using-fully-managed-reverse-proxy-architecture/

With the trends to autonomous teams and microservice style architectures, web frontend tiers are challenged to become more flexible and integrate different components with independent architectures and technology stacks. Two scenarios are prominent:

  • Micro-Frontends, where there is a single page application and components within this page are owned by different teams
  • Web portals, where there is a landing page and subsections of the presence are owned by different teams. In the following we will refer to these as components as well.

What these scenarios have in common is that they consist of loosely coupled components that are seamlessly hidden to the end user behind a common interface. Often, a reverse proxy serves content from one single entry domain but retrieves the content from different origins. In the example in Figure 1 (below) we want to address one specific domain name, and depending on the path prefix, we retrieve the content from an on-premises webserver, from a webserver running on Amazon Elastic Cloud Compute (EC2), or from Amazon S3 Static Hosting, in the figure represented by the prefixes /hotels, /pets, and /cars, respectively. If we forward the path to the webserver without the path prefix, the component would not know what prefix it is run under and the prefix could be changed any time without impacting the component, thus making the component context-unaware.

Figure 1 - Architecture, AWS Amplify Console

Figure 1: Architecture, AWS Amplify Console

Some common requirements to these approaches are:

  • Components should be technology-agnostic, each component should be able to choose the technology stack independently.
  • Each component can be maintained by a dedicated autonomous team without depending on other teams.
  • All components are served from the same domain name. For example, this could have implications on search engine optimization.
  • Components should be unaware of the context where it is used.

The traditional approach would be to run a reverse proxy tier with rewrite rules to different origins. In this post we look into managed alternatives in AWS that take away the heavy lifting of running and scaling the proxy infrastructure.

Note: AWS Application Load Balancer can be used as a reverse proxy, but it only supports static targets (fixed IP address), no dynamic targets (domain name). Thus, we do not consider it here.

AWS Amplify Console

The AWS Amplify Console provides a Git-based workflow for hosting fullstack serverless web apps with continuous deployment. Amplify Console also offers a rewrites and redirects feature, which can be used for forwarding incoming requests with different path patterns to different origins (see Figure 2).

Figure 2 - Dashboard, AWS Amplify Console (rewrites and redirects feature)

Figure 2: Dashboard, AWS Amplify Console (rewrites and redirects feature)

Note: In Figure 2, <*> stands for a wildcard that matches any pattern. Target addresses must be HTTPS (no HTTP allowed).

This architectural option is the simplest to setup and manage and is the best approach for teams looking for the least management effort. AWS Amplify Console offers a simple interface for easily mapping incoming patterns to target addresses. It also makes it easy to serve additional static content if needed. Configuration options are limited and more complex scenarios cannot be implemented.

If you want to rewrite paths to remove the path prefix, you can accomplish this by using the wildcard pattern. The source address would contain the path prefix, but the target address would omit the prefix as seen in Figure 2.

When looking at pricing compared to the other approaches it is important to look at the outgoing traffic. With higher volumes, this can get expensive.

Amazon API Gateway

Amazon API Gateway is a fully managed service that makes it easy for developers to create, publish, maintain, monitor, and secure APIs at any scale. API Gateway’s REST API type allows users to setup HTTP proxy integrations, which can be used for forwarding incoming requests with different path patterns to different origin servers according to the API specifications (Figure 3).

Figure 3 - Dashboard, Amazon API Gateway (HTTP proxy integration)

Figure 3: Dashboard, Amazon API Gateway (HTTP proxy integration)

Note: In Figure 3, {proxy+} and {proxy} stand for the same wildcard pattern.

API Gateway, in comparison to Amplify Console, is better suited when looking for a higher customization degree. API Gateway offers multiple customization and monitoring features, such as custom gateway responses and dashboard monitoring.

Similar to Amplify Console, API Gateway provides a feature to rewrite paths and thus remove context from the path using the {proxy} wildcard.

API Gateway REST API pricing is based on the number of API calls as well as any external data transfers. External data transfers are charged at the EC2 data transfer rate.

Note: The HTTP integration type in API Gateway REST APIs does not support forwarding trailing slashes. If this is needed for your application, consider other integration types such as AWS Lambda integration or AWS service integration.

Amazon CloudFront and AWS [email protected]

Amazon CloudFront is a fast content delivery network (CDN) service that securely delivers data, videos, applications, and APIs to customers globally with low latency and high transfer speeds. CloudFront is able to route incoming requests with different path patterns to different origins or origin groups by configuring its cache behavior rules (Figure 4).

Figure 4 - Dashboard, CloudFront (Cache Behavior)

Figure 4: Dashboard, CloudFront (Cache Behavior)

Additionally, Amazon CloudFront allows for integration with AWS [email protected] functions. [email protected] runs your code in response to events generated by CloudFront. In this scenario we can use [email protected] to change the path pattern before forwarding a request to the origin and thus removing the context. For details on see this detailed re:Invent session.

This approach offers most control over caching behavior and customization. Being able to add your own custom code through a custom Lambda function adds an entire new range of possibilities when processing your request. This enables you to do everything from simple HTTP request and response processing at the edge to more advanced functionality, such as website security, real-time image transformation, intelligent bot mitigation, and search engine optimization.

Amazon CloudFront is charged by request and by [email protected] invocation. The data traffic out is charged with the CloudFront regional data transfer out pricing.


With AWS Amplify Console, Amazon API Gateway, and Amazon CloudFront, we have seen three approaches to implement a reverse proxy pattern using managed services from AWS. The easiest approach to start with is AWS Amplify Console. If you run into more complex scenarios consider API Gateway. For most flexibility and when data traffic cost becomes a factor look into Amazon CloudFront with [email protected]

Fast and Cost-Effective Image Manipulation with Serverless Image Handler

Post Syndicated from Ajay Swamy original https://aws.amazon.com/blogs/architecture/fast-and-cost-effective-image-manipulation-with-serverless-image-handler/

As a modern company, you most likely have both a web-based and mobile app platform to provide content to customers who view it on a range of devices. This means you need to store multiple versions of images, depending on the device. The resulting image management can be a headache as it can be expensive and cumbersome to manage.

Serverless Image Handler (SIH) is an AWS Solution Implementation you use to store a single version of every image featured in your content, while dynamically delivering different versions at runtime based on your end user’s device. The solution simplifies code, saves on storage costs, and is ideal for use with web applications and mobile apps. SIH features include the ability to resize images, change background colors, apply formatting, and add watermarks.

Architecture overview

The SIH solution utilizes an AWS CloudFormation template to deploy the solution within minutes, and it’s for those of you who have multiple image assets needing an option to dynamically change or manipulate customer-facing images. SIH deploys best-in-class AWS services such as Amazon CloudFront, Amazon API Gateway, and AWS Lambda functions, and it connects to your Amazon Simple Storage Service (Amazon S3) bucket for storage.

Deploying this solution with the default parameters builds the following environment in AWS Cloud:

SIH: Emvironment in AWS Cloud-2

SIH uses the following AWS services:

  • Amazon CloudFront to quickly and securely  deliver images to your end users at scale
  • AWS Lambda to run code for image manipulation without the need for provisioning or managing servers (thereby reducing costs and overhead)
  • Your Amazon S3 bucket for storage of your image assets
  • AWS Secrets Manager to support the signing of image URLs so that image access is protected

How does Serverless Image Handler work?

When an HTTP request is received from a customer device, it is passed from CloudFront to API Gateway, and then forwarded to the Lambda function for processing. If the image is cached by CloudFront because of an earlier request, CloudFront will return the cached image instead of forwarding the request to the API Gateway. This reduces latency and eliminates the cost of reprocessing the image.

Requests that are not cached are passed to the API Gateway, and the entire request is forwarded to the Lambda function. The Lambda function retrieves the original image from your Amazon S3 bucket and uses Sharp (the open source image processing software) to return a modified version of the image to the API Gateway. SIH also utilizes Thumbor to apply dynamic filters on the fly. Additionally, the solution generates a CloudFront domain name that supports caching in CloudFront. The newly manipulated image is now cached at CloudFront for easy access and retrieval. The end-to-end request and response can be secured by using the solution’s signed URL feature via AWS Secrets Manager, which allows you to prevent unauthorized use of your proprietary images.

Lastly, SIH uses Amazon Rekognition for face detection in images submitted for smart cropping, allowing for easy cropping for specific content and image needs.

Code example of image manipulation

Please refer to the SIH implementation guide to quickly set up and use SIH. Using Node.js, you can create an image request as illustrated below. The code block specifies the image location as myImageBucket and specifies edits of grayscale :true to change the image to grayscale.

const imageRequest = JSON.stringify({
    bucket: “myImageBucket”,
    key: “myImage.jpg”,
    edits: {
        grayscale: true

const url = `${CloudFrontUrl}/${Buffer.from(imageRequest).toString(‘base64’)}`;

With the generated URL, SIH can serve the grayscale image.


If you’re looking for a fast and cost-effective solution for image management, Serverless Image Handler provides a great way to manipulate and serve images on the fly with speed and security. Learn more about SIH and watch the accompanying Solving with AWS Solutions video below.