Post Syndicated from Matt Cline original https://aws.amazon.com/blogs/devops/next-generation-microservices-dotnet-grpc/

Modern architectures use multiple microservices in conjunction to drive customer experiences. At re:Invent 2015, AWS senior project manager Rob Brigham described Amazon’s architecture of many single-purpose microservices – including ones that render the “Buy” button, calculate tax at checkout, and hundreds more.

Microservices commonly communicate with JSON over HTTP/1.1. These technologies are ubiquitous and human-readable, but they aren’t optimized for communication between dozens or hundreds of microservices.

Next-generation Web technologies, including gRPC and HTTP/2, significantly improve communication speed and efficiency between microservices. AWS offers the most compelling experience for builders implementing microservices. Moreover, the addition of HTTP/2 and gRPC support in Application Load Balancer (ALB) provides an end-to-end solution for next-generation microservices. ALBs can inspect and route gRPC calls, enabling features like health checks, access logs, and gRPC-specific metrics.

This post demonstrates .NET microservices communicating with gRPC via Application Load Balancers. The microservices run on AWS Graviton2 instances, utilizing a custom-built 64-bit Arm processor to deliver up to 40% better price/performance than x86.

Architecture Overview

Modern Tacos is a new restaurant offering delivery. Customers place orders via mobile app, then they receive real-time status updates as their order is prepared and delivered.

The tutorial includes two microservices: “Submit Order” and “Track Order”. The Submit Order service receives orders from the app, then it calls the Track Order service to initiate order tracking. The Track Order service provides streaming updates to the app as the order is prepared and delivered.

Each microservice is deployed in an Amazon EC2 Auto Scaling group. Each group is behind an ALB that routes gRPC traffic to instances in the group.

Comparing gRPC and JSON for microservices

Microservices typically communicate by sending JSON data over HTTP. As a text-based format, JSON is readable, flexible, and widely compatible. However, JSON also has significant weaknesses as a data interchange format. JSON’s flexibility makes enforcing a strict API specification difficult — clients can send arbitrary or invalid data, so developers must write rigorous data validation code. Additionally, performance can suffer at scale due to JSON’s relatively high bandwidth and parsing requirements. These factors also impact performance in constrained environments, such as smartphones and IoT devices. gRPC addresses all of these issues.

gRPC is an open-source framework designed to efficiently connect services. Instead of JSON, gRPC sends messages via a compact binary format called Protocol Buffers, or protobuf. Although protobuf messages are not human-readable, they utilize less network bandwidth and are faster to encode and decode. Operating at scale, these small differences multiply to a significant performance gain.

gRPC APIs define a strict contract that is automatically enforced for all messages. Based on this contract, gRPC implementations generate client and server code libraries in multiple programming languages. This allows developers to use higher-level constructs to call services, rather than programming against “raw” HTTP requests.

gRPC also benefits from being built on HTTP/2, a major revision of the HTTP protocol. In addition to the foundational performance and efficiency improvements from HTTP/2, gRPC utilizes the new protocol to support bi-directional streaming data. Implementing real-time streaming prior to gRPC typically required a completely separate protocol (such as WebSockets) that might not be supported by every client.

gRPC for .NET developers

Several recent updates have made gRPC more useful to .NET developers. .NET 5 includes significant performance improvements to gRPC, and AWS has broad support for .NET 5. In May 2021, the .NET team announced their focus on a gRPC implementation written entirely in C#, called “grpc-dotnet”, which follows C# conventions very closely.

Instead of working with JSON, dynamic objects, or strings, C# developers calling a gRPC service use a strongly-typed client, automatically generated from the protobuf specification. This obviates much of the boilerplate validation required by JSON APIs, and it enables developers to use rich data structures. Additionally, the generated code enables full IntelliSense support in Visual Studio.

For example, the “Submit Order” microservice executes this code in order to call the “Track Order” microservice:

using var channel = GrpcChannel.ForAddress("https://track-order.example.com");

var trackOrderClient = new TrackOrder.Protos.TrackOrder.TrackOrderClient(channel);

var reply = await trackOrderClient.StartTrackingOrderAsync(new TrackOrder.Protos.Order
{
    DeliverTo = "Address",
    LastUpdated = Timestamp.FromDateTime(DateTime.UtcNow),
    OrderId = order.OrderId,
    PlacedOn = order.PlacedOn,
    Status = TrackOrder.Protos.OrderStatus.Placed
});

This code calls the StartTrackingOrderAsync method on the Track Order client, which looks just like a local method call. The method intakes a data structure that supports rich data types like DateTime and enumerations, instead of the loosely-typed JSON. The methods and data structures are defined by the Track Order service’s protobuf specification, and the .NET gRPC tools automatically generate the client and data structure classes without requiring any developer effort.

Configuring ALB for gRPC

To make gRPC calls to targets behind an ALB, create a load balancer target group and select gRPC as the protocol version. You can do this through the AWS Management Console, AWS Command Line Interface (CLI), AWS CloudFormation, or AWS Cloud Development Kit (CDK).

This CDK code creates a gRPC target group:

var targetGroup = new ApplicationTargetGroup(this, "TargetGroup", new ApplicationTargetGroupProps
{
    Protocol = ApplicationProtocol.HTTPS,
    ProtocolVersion = ApplicationProtocolVersion.GRPC,
    Vpc = vpc,
    Targets = new IApplicationLoadBalancerTarget {...}
});

gRPC requests work with target groups utilizing HTTP/2, but the gRPC protocol enables additional features including health checks, request count metrics, access logs that differentiate gRPC requests, and gRPC-specific response headers. gRPC also works with native ALB features like stickiness, multiple load balancing algorithms, and TLS termination.

Deploy the Tutorial

The sample provisions AWS resources via the AWS Cloud Development Kit (CDK). The CDK code is provided in C# so that .NET developers can use a familiar language.

The solution deployment steps include:

• Configuring a domain name in Route 53.
• Deploying the microservices.
• Running the mobile app on AWS Device Farm.

The source code is available on GitHub.

Prerequisites

For this tutorial, you should have these prerequisites:

• Sign up for an AWS account.
• Complete the AWS CDK Getting Started guide.
• Install the AWS CLI and set up your AWS credentials for command-line use – or, you can use the AWS Tools for PowerShell and set up your AWS credentials for PowerShell.
• Create a public hosted zone in Amazon Route 53 for a domain name that you control. This will be the “parent” domain name for the microservices.
• Install Visual Studio 2019.
• Clone the GitHub repository to your computer.
• Open a terminal (such as Bash) or a PowerShell prompt.

Configure the environment variables needed by the CDK. In the sample commands below, replace AWS_ACCOUNT_ID with your numeric AWS account ID. Replace AWS_REGION with the name of the region where you will deploy the sample, such as us-east-1 or us-west-2.

If you’re using a *nix shell such as Bash, run these commands:

export CDK_DEFAULT_ACCOUNT=AWS_ACCOUNT_ID
export CDK_DEFAULT_REGION=AWS_REGION

If you’re using PowerShell, run these commands:

$Env:CDK_DEFAULT_ACCOUNT="AWS_ACCOUNT_ID"
$Env:CDK_DEFAULT_REGION="AWS_REGION"
Set-DefaultAWSRegion -Region AWS_REGION

Throughout this tutorial, replace RED TEXT with the appropriate value.

Save the directory path where you cloned the GitHub repository. In the sample commands below, replace EXAMPLE_DIRECTORY with this path.

In your terminal or PowerShell, run these commands:

cd EXAMPLE_DIRECTORY/src/ModernTacoShop/Common/cdk
cdk bootstrap --context domain-name=PARENT_DOMAIN_NAME
cdk deploy --context domain-name=PARENT_DOMAIN_NAME

The CDK output includes the name of the S3 bucket that will store deployment packages. Save the name of this bucket. In the sample commands below, replace SHARED_BUCKET_NAME with this name.

Deploy the Track Order microservice

Compile the Track Order microservice for the Arm microarchitecture utilized by AWS Graviton2 processors. The TrackOrder.csproj file includes a target that automatically packages the compiled microservice into a ZIP file. You will upload this ZIP file to S3 for use by CodeDeploy. Next, you will utilize the CDK to deploy the microservice’s AWS infrastructure, and then install the microservice on the EC2 instance via CodeDeploy.

The CDK stack deploys these resources:

• An Amazon EC2 Auto Scaling group.
• An Application Load Balancer (ALB) using gRPC, targeting the Auto Scaling group and configured with microservice health checks.
• A subdomain for the microservice, targeting the ALB.
• A DynamoDB table used by the microservice.
• CodeDeploy infrastructure to deploy the microservice to the Auto Scaling group.

If you’re using the AWS CLI, run these commands:

cd EXAMPLE_DIRECTORY/src/ModernTacoShop/TrackOrder/src/
dotnet publish --runtime linux-arm64 --self-contained
aws s3 cp ./bin/TrackOrder.zip s3://SHARED_BUCKET_NAME
etag=$(aws s3api head-object --bucket SHARED_BUCKET_NAME \
    --key TrackOrder.zip --query ETag --output text)
cd ../cdk
cdk deploy

The CDK output includes the name of the CodeDeploy deployment group. Use this name to run the next command:

aws deploy create-deployment --application-name ModernTacoShop-TrackOrder \
    --deployment-group-name TRACK_ORDER_DEPLOYMENT_GROUP_NAME \
    --s3-location bucket=SHARED_BUCKET_NAME,bundleType=zip,key=TrackOrder.zip,etag=$etag \
    --file-exists-behavior OVERWRITE

If you’re using PowerShell, run these commands:

cd EXAMPLE_DIRECTORY/src/ModernTacoShop/TrackOrder/src/
dotnet publish --runtime linux-arm64 --self-contained
Write-S3Object -BucketName SHARED_BUCKET_NAME `
    -Key TrackOrder.zip `
    -File ./bin/TrackOrder.zip
Get-S3ObjectMetadata -BucketName SHARED_BUCKET_NAME `
    -Key TrackOrder.zip `
    -Select ETag `
    -OutVariable etag
cd ../cdk
cdk deploy

The CDK output includes the name of the CodeDeploy deployment group. Use this name to run the next command:

New-CDDeployment -ApplicationName ModernTacoShop-TrackOrder `
    -DeploymentGroupName TRACK_ORDER_DEPLOYMENT_GROUP_NAME `
    -S3Location_Bucket SHARED_BUCKET_NAME `
    -S3Location_BundleType zip `
    -S3Location_Key TrackOrder.zip `
    -S3Location_ETag $etag[0] `
    -RevisionType S3 `
    -FileExistsBehavior OVERWRITE

Deploy the Submit Order microservice

The steps to deploy the Submit Order microservice are identical to the Track Order microservice. See that section for details.

If you’re using the AWS CLI, run these commands:

cd EXAMPLE_DIRECTORY/src/ModernTacoShop/SubmitOrder/src/
dotnet publish --runtime linux-arm64 --self-contained
aws s3 cp ./bin/SubmitOrder.zip s3://SHARED_BUCKET_NAME
etag=$(aws s3api head-object --bucket SHARED_BUCKET_NAME \
    --key SubmitOrder.zip --query ETag --output text)
cd ../cdk
cdk deploy

The CDK output includes the name of the CodeDeploy deployment group. Use this name to run the next command:

aws deploy create-deployment --application-name ModernTacoShop-SubmitOrder \
    --deployment-group-name SUBMIT_ORDER_DEPLOYMENT_GROUP_NAME \
    --s3-location bucket=SHARED_BUCKET_NAME,bundleType=zip,key=SubmitOrder.zip,etag=$etag \
    --file-exists-behavior OVERWRITE

If you’re using PowerShell, run these commands:

cd EXAMPLE_DIRECTORY/src/ModernTacoShop/SubmitOrder/src/
dotnet publish --runtime linux-arm64 --self-contained
Write-S3Object -BucketName SHARED_BUCKET_NAME `
    -Key SubmitOrder.zip `
    -File ./bin/SubmitOrder.zip
Get-S3ObjectMetadata -BucketName SHARED_BUCKET_NAME `
    -Key SubmitOrder.zip `
    -Select ETag `
    -OutVariable etag
cd ../cdk
cdk deploy

The CDK output includes the name of the CodeDeploy deployment group. Use this name to run the next command:

New-CDDeployment -ApplicationName ModernTacoShop-SubmitOrder `
    -DeploymentGroupName SUBMIT_ORDER_DEPLOYMENT_GROUP_NAME `
    -S3Location_Bucket SHARED_BUCKET_NAME `
    -S3Location_BundleType zip `
    -S3Location_Key SubmitOrder.zip `
    -S3Location_ETag $etag[0] `
    -RevisionType S3 `
    -FileExistsBehavior OVERWRITE

Data flow diagram

Test the microservices

Once the CodeDeploy deployments have completed, test both microservices.

First, check the load balancers’ status. Go to Target Groups in the AWS Management Console, which will list one target group for each microservice. Click each target group, then click “Targets” in the lower details pane. Every EC2 instance in the target group should have a “healthy” status.

Next, verify each microservice via gRPCurl. This tool lets you invoke gRPC services from the command line. Install gRPCurl using the instructions, and then test each microservice:

grpcurl submit-order.PARENT_DOMAIN_NAME:443 modern_taco_shop.SubmitOrder/HealthCheck
grpcurl track-order.PARENT_DOMAIN_NAME:443 modern_taco_shop.TrackOrder/HealthCheck

If a service is healthy, it will return an empty JSON object.

Run the mobile app

You will run a pre-compiled version of the app on AWS Device Farm, which lets you test on a real device without managing any infrastructure. Alternatively, compile your own version via the AndroidApp.FrontEnd project within the solution located at EXAMPLE_DIRECTORY/src/ModernTacoShop/AndroidApp/AndroidApp.sln.

Go to Device Farm in the AWS Management Console. Under “Mobile device testing projects”, click “Create a new project”. Enter “ModernTacoShop” as the project name, and click “Create Project”. In the ModernTacoShop project, click the “Remote access” tab, then click “Start a new session”. Under “Choose a device”, select the Google Pixel 3a running OS version 10, and click “Confirm and start session”.

Once the session begins, click “Upload” in the “Install applications” section. Unzip and upload the APK file located at EXAMPLE_DIRECTORY/src/ModernTacoShop/AndroidApp/com.example.modern_tacos.grpc_tacos.apk.zip, or upload an APK that you created.

Once the app has uploaded, drag up from the bottom of the device screen in order to reach the “All apps” screen. Click the ModernTacos app to launch it.

Once the app launches, enter the parent domain name in the “Domain Name” field. Click the “+” and “-“ buttons next to each type of taco in order to create your order, then click “Submit Order”. The order status will initially display as “Preparing”, and will switch to “InTransit” after about 30 seconds. The Track Order service will stream a random route to the app, updating with new position data every 5 seconds. After approximately 2 minutes, the order status will change to “Delivered” and the streaming updates will stop.

Once you’ve run a successful test, click “Stop session” in the console.

Cleaning up

To avoid incurring charges, use the cdk destroy command to delete the stacks in the reverse order that you deployed them.

You can also delete the resources via CloudFormation in the AWS Management Console.

In addition to deleting the stacks, you must delete the Route 53 hosted zone and the Device Farm project.

Conclusion

This post demonstrated multiple next-generation technologies for microservices, including end-to-end HTTP/2 and gRPC communication over Application Load Balancer, AWS Graviton2 processors, and .NET 5. These technologies enable builders to create microservices applications with new levels of performance and efficiency.

Matt Cline

Matt Cline is a Solutions Architect at Amazon Web Services, supporting customers in his home city of Pittsburgh PA. With a background as a full-stack developer and architect, Matt is passionate about helping customers deliver top-quality applications on AWS. Outside of work, Matt builds (and occasionally finishes) scale models and enjoys running a tabletop role-playing game for his friends.

Ulili Nhaga

Ulili Nhaga is a Cloud Application Architect at Amazon Web Services in San Diego, California. He helps customers modernize, architect, and build highly scalable cloud-native applications on AWS. Outside of work, Ulili loves playing soccer, cycling, Brazilian BBQ, and enjoying time on the beach.