Forever 42 App is a full-stack e-commerce web application that creates a storefront (and backend) for customers to shop for fictitious fashion items. You can
search for books, look at
manage your cart,
view your orders, and more.
|email@example.com||Macrometa Forever 42|
The goal of this Demo is to provide a fully-functional web application that utilizes multi-model Macrometa GDN. Increasingly, modern web apps are built using a multitude of different data models. Developers break their large applications into individual components and select the best data model for each job.
Let's consider this
Forever 42 Demo App as an example. The app contains multiple experiences such a
recommendations, and a
top sellers list. For each of these use cases, the app makes use of a purpose-built data model so the developer never has to compromise on functionality, performance, or scale.
This demo includes the following components:
- Product catalog/shopping cart - Macrometa GDN Docs offers fast, predictable performance for the key-value lookups needed in the product catalog, as well as the shopping cart and order history.
- Search - Macrometa GDN Search service enables full-text search for our storefront, enabling users to find products based on a variety of terms including product name, and category.
- Recommendations - Macrometa GDN Graphs provides social recommendations based on what user's friends have purchased, scaling as the storefront grows with more products, and users.
- Top sellers list - Macrometa GDN Stream Apps reads order information from GDN Docs Streams, creating a leaderboard of the “Top 20” purchased or fashion products.
- Serverless service backend – Cloudflare Workers and Macrometa GDN C8QL powers the interface layer between the frontend and backend, and invokes serverless compute with low latency in region closest to the user.
- Web application blueprint – We include a React web application pre-integrated out-of-the-box with tools such as React Bootstrap, Redux, React Router, internationalization, and more.
Credits: The inspiration for this demo is the AWS demo available at https://github.com/aws-samples/aws-bookstore-demo-app