Post Syndicated from James Beswick original https://aws.amazon.com/blogs/compute/server-side-rendering-micro-frontends-ui-composer-and-service-discovery/
This post is written by Luca Mezzalira, Principal Specialist Solutions Architect, Serverless.
The previous blog post describes the architecture for creating a server-side rendering micro-frontend in AWS. This and subsequent posts explain the different parts that compose this architecture in detail. The code for the example is available on a AWS Samples GitHub repository.
For context, this post covers the infrastructure related to the UI composer, and why you need an Amazon S3 bucket for storing static assets:
The rest of the series explores the micro-frontends composition, how to design micro-frontends using serverless services, different caching and performance optimization strategies, and the organization structure implications associated with frontend distributed systems.
A user’s request journey
The best way to navigate through this distributed system is by simulating a user request that touches all the parts implemented in the architecture.
The application example shows a product details page of a hypothetical ecommerce platform:
When a user selects an article from the catalog page, the DNS resolves the URL to an Amazon CloudFront distribution that is the reference CDN for this project.
The request is immediately fulfilled if the page is cached. Therefore, no additional logic is requested by the cloud infrastructure and the response is fast (less than the 500 ms shown in this example).
When the page is not available in the CloudFront points of presence (PoPs), the request is forwarded to the Application Load Balancer (ALB). It arrives at the AWS Fargate cluster where the UI Composer generates the page for fulfilling the request.
Using CloudFront in the architecture
CDNs are known for accelerating application delivery thanks to caching static files from nearby PoPs. CloudFront can also accelerate uncacheable content such as dynamic APIs or personalized content.
With a network of over 450 points of presence, CloudFront terminates user TCP/TLS connections within 20-30 milliseconds on average. Traffic to origin servers is carried over the AWS global network instead of the public internet. This infrastructure is a purpose-built, highly available, and low-latency private infrastructure built on a global, fully redundant, metro fiber network that is linked via terrestrial and trans-oceanic cables across the world. In addition to terminating connections close to users, CloudFront accelerates dynamic content thanks to modern internet protocols such as QUIC and TLS1.3, and persisting TCP connections to the origin servers.
CloudFront also has security benefits, offering protection in AWS against infrastructure DDoS attacks. It integrates with AWS Web Application Firewall and AWS Shield Advanced, giving you controls to block application-level DDoS attacks. CloudFront also offers native security controls such as HTTP to HTTPS redirections, CORS management, geo-blocking, tokenization, and managing security response headers.
UI Composer application logic
When the request is not fulfilled by the CloudFront cache, it is routed to the Fargate cluster. Here, multiple tasks compute and serve the page requested.
This example uses Fastify, a fast Node.js framework that is gaining popularity among the Node.js community. When the web server initializes, it loads external parameters and the template for composing a page.
const start = async () => {
try {
//load parameters
MFElist = await init();
//load catalog template
catalogTemplate = await loadFromS3(MFElist.template, MFElist.templatesBucket)
await fastify.listen({ port: PORT, host: '0.0.0.0' })
} catch (err) {
fastify.log.error(err)
process.exit(1)
}
}
To maintain team independence and avoid redeploying the UI composer for every application change, the HTML templates are loaded from an S3 bucket. All teams responsible for micro-frontends in the same page can position their micro-frontends into the right place of the HTML template and delegate the composition task to the UI composer.
In this demo, the initial parameters and the catalog template are retrieved once. However, in a real scenario, it’s more likely you retrieve the parameters at initialization and at a regular cadence. The template might be loaded at runtime for every request or have another background routine fetching the initialization parameters in a similar way.
When the request reaches the product details route, the web application logic calls a transformTemplate function. It passes the catalog template, retrieved from the S3 bucket at the server initialization. It returns a 200 response if the page is composed without any issues.
fastify.get('/productdetails', async(request, reply) => {
try{
const catalogDetailspage = await transformTemplate(catalogTemplate)
responseStream(catalogDetailspage, 200, reply)
} catch(err){
console.log(err)
throw new Error(err)
}
})
The page composition is the key responsibility of the UI composer. There are several viable approaches for composing micro-frontends in a server-side rendering system, covered in the next post.
Micro-frontends discovery
To decouple workloads for multiple teams, you must use architectural patterns that support it. In a microservices architecture, a pattern that allows independent evolution of a service without coupling the DNS or IP to any microservice is the service discovery pattern.
In this example, AWS System Managers Parameters Store acts as a services registry. Every micro-frontend available in the workload registers itself once the infrastructure is provisioned.
In this way, the UI composer can request the micro-frontend ID found inside the HTML template. It can retrieve the correct way to consume the micro-frontend API using an ARN or a remote HTTP URL, for instance.
Using ARN over HTTP requests inside the workload network can help you to reduce the latency thanks to fewer network hops. Moreover, the security is delegated to IAM policies providing a robust security implementation.
The UI composer takes care to retrieve the micro-frontends endpoints at runtime before loading them into the HTML template. This is a simpler yet powerful approach for maintaining the boundaries within your organization and allowing independent teams to evolve their architecture autonomously.
Micro-frontends discovery evolution
Using Parameter Store as a service discovery system, you can deploy a new micro-frontend by adding a new key-value into the service discovery.
A more sophisticated option could be creating a service that acts as a registry and also shapes the traffic towards different micro-frontends versions using deployment strategies like canary releases or blue/green deployments.
You can start iteratively with a simple key-value store system and evolve the architecture with a more complex approach when the workload requires, providing a robust way to roll out micro-frontends services in your system.
When this is in place, it’s likely to increase the release cadence of your micro-frontends. This is because developers often feel safer releasing in production without affecting the entire user base and they can run tests alongside real traffic.
Performance considerations
This architecture uses Fargate for composing the micro-frontends instead of Lambda functions. This allows incremental rendering offered by browsers, displaying the HTML page partially before it’s completely returned.
Consider a scenario where a micro-frontend takes longer to render due to a downstream dependency or a faulty version deployed into production. Without the streaming capability, you must wait until all the micro-frontends responses arrive, buffer them in memory, compose the page and then send the final output to the browser.
Instead, by using the streaming API offered by Node.js frameworks, you can send a partial HTML page (for example, the head tag and subsequently the rest of the page), to be rendered by a browser.
Streaming also improves server overhead, because the servers don’t have to buffer entire pages. By incrementally flushing data to browsers, servers keep memory pressure low, which lets them process more requests and save overhead costs.
However, in case your workload doesn’t require these capabilities, one or multiple Lambda functions might be suitable for your project as well, reducing the infrastructure management complexity to handle.
Conclusion
This post looks at how to use the UI Composer and micro-frontends discoverability. Once this part is developed, it won’t need to change regularly. This represents the foundation for building server-side rendering micro-frontends using HTML-over-the-wire. There might be other approaches to follow for other frameworks such as Next.js due to the architectural implementation of the framework itself.
The next post will cover how the UI composer includes micro-frontends output inside an HTML template.
For more serverless learning resources, visit Serverless Land.
Kalyan Janaki is Senior Big Data & Analytics Specialist with Amazon Web Services. He helps customers architect and build highly scalable, performant, and secure cloud-based solutions on AWS.
