Book Image

MEAN Cookbook

By : Nicholas McClay
Book Image

MEAN Cookbook

By: Nicholas McClay

Overview of this book

The MEAN Stack is a framework for web application development using JavaScript-based technologies; MongoDB, Express, Angular, and Node.js. If you want to expand your understanding of using JavaScript to produce a fully functional standalone web application, including the web server, user interface, and database, then this book can help guide you through that transition. This book begins by configuring the frontend of the MEAN stack web application using the Angular JavaScript framework. We then implement common user interface enhancements before moving on to configuring the server layer of our MEAN stack web application using Express for our backend APIs. You will learn to configure the database layer of your MEAN stack web application using MongoDB and the Mongoose framework, including modeling relationships between documents. You will explore advanced topics such as optimizing your web application using WebPack as well as the use of automated testing with the Mocha and Chai frameworks. By the end of the book, you should have acquired a level of proficiency that allows you to confidently build a full production-ready and scalable MEAN stack application.
Table of Contents (13 chapters)

Creating sub-documents in Mongoose models

Sub-documents are a common way to compose a child model into a parent model's schema. Consider, for example, the creation of an order record in an e-commerce system. This record would contain references to a user model, a list of product models, and a payment and shipping model. In an SQL database, each of these entities would have a referenced ID within their own normalized table, using several JOIN operations to merge the necessary parts together into this new order model construct. By default, MongoDB approaches this problem radically differently, with the entirety of the schema simply embedding the sub-document references directly into its own order schema definition.

This approach is a trade-off. It gives you expressive, flexible JSON model representations that remain performant while still allowing rich querying and searching...