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Little Silk Road

Abstract:

This project aims to create service oriented architecture for an application.

The backend exposes data through a set of API endpoints that communicate with a separate front end application. The API handles CRUD operations and handles one-to-many relationships with SQL and ActiveRecord queries.

The frontend consumes data from the Rails API. Additionally, it enhances user experience with a new interface to sort through sellers and their goods and services.

Installation Instructions:

There are two halves to this application, a server and an interface.

To install and run the server:

  1. Navigate to an empty directory
  2. Fork and clone this repository
  3. cd into cloned repo
  4. In a new terminal run rails server
  • Ensure the server is connected to port 3000

To install this application to work with the server:

  1. Navigate to another empty directory
  2. Fork and clone this repository
  3. cd into cloned repo
  4. Run npm install
  5. Run npm run dev to start developing.
  6. Navigate to http://localhost:5173/ in browser.

Preview of App:

(Provide ONE gif or screenshot of your application - choose the "coolest" piece of functionality to show off. gifs preferred!)


Context:

This project was completed over the course of an 8 day sprint by a team of Turing students.

Learning Goals:

  • Use ActiveRecord and SQL to write queries that deal with one-to-many database relationships
  • Expose API endpoints to CRUD database resources
  • Validate models and handle sad paths for invalid data input
  • Test both happy and sad path functionality based on JSON contracts
  • Use MVC to organize code effectively, lmiting data logic in controllers and serializers
  • Track user stories with GitHub Projects
  • Improve an existing FE application by:
    • Styling the user interface
    • Refactoring JavaScript code
    • Adding an additional FE feature
    • Practice individual research (articles, videos, mentors)

Contributors:

Bloom, Stefan

Chirchirillo, Joe

Fallenius, Karl

Messersmith, Renee

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  • JavaScript 67.4%
  • CSS 18.1%
  • HTML 14.5%