Learn Faunadb, Lodash and Serverless


Next.js is one of the most popular server-side-rendered React frameworks you can use to build production-ready serverless applications in record time. 

On the other hand, Fauna is a flexible, serverless, distributed document-relational database. Fauna has a powerful GraphQL API and custom resolver support. 

Using Next.js and Fauna, you can build robust, feature-rich, production-ready full-stack applications without the hassle of managing any servers or databases.

In this course, Shadid guides you through building your very own full-stack serverless marketplace application using Next.js, GraphQL, and Fauna. 

You will learn,

- How to work with Apollo GraphQL Client in Next.js
- How to setup Fauna GraphQL
- Data modeling in Fauna
- How to write custom Fauna GraphQL Resolvers (UDFs)
- How to authenticate with Auth0?
- Stripe payment 



GraphQL is a query language for APIs and a runtime for fulfilling those queries with your existing data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools.

graphql

Full-Stack Serverless Applications with Next.js & Fauna

Starting from a prebuilt frontend, Jason Lengstorf guides you through the creation of a "Corgi Up-boop App" that uses Netlify Functions and Hasura. You can view [the deployed final product on Netlify](https://egghead-serverless-functions.netlify.app/).

You'll set up the Netlify CLI, and configure a local development environment before writing and deploying functions with a few different use cases.

The first example function bypasses CORS restrictions to retrieve corgi data from an example third-party API.

Next, you'll configure environmental variables to securely communicate with the Unsplash API without exposing private keys to the client.

Serverless functions don't persist data by themselves, so we will use  Hasura to create a GraphQL API for tracking the number of "boops" each corgi receives.

This course is an excellent illustration of the speed of Serverless functions, and how fast you can implement functionality in your web applications.

An all-in-one workflow that combines global deployment, continuous integration, and automatic HTTPS.

netlify

Build a Corgi Up-boop Web App with Netlify Serverless Functions and Hasura

[Lodash](https://lodash.com/docs/) is a collection of utilities that have become a staple of modern web development. This collection will show you how to re-create its most common methods using simple JavaScript techniques available in any modern web browser. In addition to re-building tools like `_.merge`, `_.get` and `_.debounce` from scratch you will also learn how to replace them completely in some cases with modern syntax such as [optional chaining](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Operators/Optional_chaining), [nullish coalescing](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Operators/Nullish_coalescing_operator) and [Object.fromEntries](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Object/fromEntries).

This collection should interest the following groups:
- Those who want to better understand common JavaScript patterns
- Engineers who want to reduce their dependence on Lodash (for performance or other reasons)
- Candidates preparing for a technical job interview (where it is common to be asked how to implement some of these methods).

JavaScript® (often shortened to JS) is a lightweight, interpreted, object-oriented language with first-class functions, most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic, and supports object-oriented, imperative, and functional programming styles.

javascript

Recreating Popular JavaScript Utility Methods from Lodash

This application serves as an introduction to building products with a JAMStack and Serverless approach. We cover everything from the beginning:

* starting with the Gatsby client
* handling authentication with Netlify Identity
* Shipping a GraphQL server on Netlify Functions
* Building in access control with a serverless capable database

It uses tools that remove as many of the barriers as possible while also allowing upgrading in the future. Netlify Functions can grow into AWS Lambda, Netlify Identity can grow into Auth0 or Cognito, and FaunaDB can grow into DynamoDB.

Gatsby is a blazing fast static site generator built with React. Gatsby gives you all of the modern JavaScript tools you want; set up and ready to go out of the box.

gatsby

Building a Serverless JAMStack Todo app with Netlify, Gatsby, GraphQL, and FaunaDB

This collection is a sequel to the [Building a Serverless JAMStack Todo app with Netlify, Gatsby, GraphQL, and FaunaDB](https://egghead.io/playlists/building-a-serverless-jamstack-todo-app-with-netlify-gatsby-graphql-and-faunadb-53bb) collection. In this collection we take the TODO application we built and convert it to run using Netlify Identity, AWS Lambda (using the serverless framework) and DynamoDB. We cover

* Fauna vs DynamoDB and when to use each
* Setting up AWS accounts
* Creating DynamoDB tables and data modeling differences between Fauna and Dynamo
* Converting our Netlify Functions deployment to a Serverless Framework deployment
* Implementing Custom authorizer functions on AWS

It uses tools that remove as many of the barriers as possible. Netlify Functions grows into Serverless Framework adn AWS Lambda, Netlify Identity is kept around, and FaunaDB can grows into DynamoDB.



FaunaDB is a global serverless database that gives you ubiquitous, low latency access to app data, without sacrificing data correctness. It eliminates layers of app code for manually handling data anomalies, security, and scale, creating a friendlier dev experience for you and better app experience for your users.

faunadb

Converting a Serverless App to run on AWS Lambda and DynamoDB with Serverless Framework

FaunaDB is a global serverless database that gives you ubiquitous, low latency access to app data, without sacrificing data correctness. It eliminates layers of app code for manually handling data anomalies, security, and scale, creating a friendlier dev experience for you and better app experience for your users.

Fauna is inspired by [Calvin](https://blog.acolyer.org/2019/03/29/calvin-fast-distributed-transactions-for-partitioned-database-systems/), has [built-in GraphQL integration](https://docs.fauna.com/fauna/current/api/graphql/), and a query language based on lambda calculus. Be sure to check out the [Jepsen analysis](https://jepsen.io/analyses/faunadb-2.5.4) and the associated [chart of consistency models](https://jepsen.io/consistency)

The Complete Guide to FaunaDB

Create a serverless application and deploy it in under thirty minutes!

We'll be using Netlify, Vite, TypeScript, and React in our project. This stack synergizes extremely well for rapidly developing professional-quality applications. 

If you haven't heard of Vite, it's the French word for quick! It's also a build tool that consists of a dev-server and an extremely fast bundler. It makes for a rapid development loop.

As for the rest. Netlify makes hosting fast and easy with serverless functions and continuous deployment. React enables you to quickly create reusable components. And TypeScript helps you write cleaner code that is easier to refactor. 

So get started and learn how to use this stack in your own applications!

Deploy a Serverless API React Application with TypeScript

A modern JavaScript utility library delivering modularity, performance & extras.

lodash

This lesson shows how to refactor your old loops into using a simpler and more powerful lodash-style. We will start by looking at how many people traditionally write JavaScript `for` loops and then talk about the alternate style and benefits that Lodash offers.

Lodash: Refactoring Simple For Loops

AngularJS is an open-source JavaScript framework, maintained by Google, that assists with running single-page applications. Its goal is to augment web-based applications with model–view–controller (MVC) capability, in an effort to make both development and testing easier.

angularjs

Lodash has an enormous amount of utility functions for dealing with collections and objects in javascript. You can use it in your AngularJS applications to "debounce" user input, or add delay between user actions. This is a common technique for text input to hold firing service calls until the user stops typing for a fraction of a second.

Debounce (delay) User Input in AngularJS with Lodash

Imagine if looking at your business logic read as simple as a story from a children's book. That's exactly what we'll cover by learning how to compose small pieces of business logic and also set them up for further composition in the future.

We're showing a list of fictional tweets in a trivial React component created with create-react-app, however the example is not React specific and can be applied to any framework/environment.

Our example will refactor out business logic from a non-composable function to small composable pieces. To accomplish this we'll be using flow, filter and map from lodash/fp, which is included in the regular lodash npm package.

This approach works well in any environment where you're not relying on object references to your original data, as our functional approach creates new collection objects instead of mutating them.

Guide to lodash/fp: https://github.com/lodash/lodash/wiki/FP-Guide


Refactor Business Logic to Composable Functions with Lodash

Lodash's "sortBy" method helps you sort data in your collections and "sortedIndex" helps you find where to place new data. In this video, John walks you through how to use each of these methods and how they can work together.

Lodash - sortBy and sortedIndex

Property access in Javascript can be problematic - especially when dealing with nested Objects and Arrays. Doing it manually and in a safe manner requires tons of boilerplate inside conditionals and results in a defensive style of coding. In this lesson we look at why this is a problem & how to overcome it using the `get` method from the popular utility library `Lodash`

Safer property access with Lodash's 'get' method

Logic often gets added to our loops. We need to iterate over a collection, and get the sum of values, for instance. This approach can lead to complicated methods, that are hard to test. Using Lodash (Underscore), we are going to extract logic in a large loop body into well named methods. The result will be cleaner and easier to understand.

Refactoring: Replace Loop with Collection Closure using Lodash

Learn how to use Object.assign to combine multiple objects together. This pattern is helpful when writing libraries that have a set of default options where you want to allow a user to override specific options. You'll also learn how to use a third party library to recursively merge nested objects together.

Combine Objects with Object.assign and Lodash merge

Lodash is a toolkit of Javascript functions that provides clean, performant methods for manipulating objects and collections. It is a "fork" of the Underscore library and provides additional functionality as well as some serious performance improvements. If you aren't using Lodash, you should be.

Introduction to Lodash

We walk through creating a new Database in the FaunaDB console complete with some demo data. We do some exploration of the data, introducing concepts like Collections and Indexes, before showing an example FQL query that runs against the database. This sets us up to talk about FQL in future videos

Creating your first FaunaDB database

FaunaDB is a Calvin inspired hosted database marketed towards JAMStack and serverless users. It is geo-replicated across multiple providers (AWS, GCP, and soon Azure), uses a lambda calculus based query language (FQL) or the built-in GraphQL support, and is quick to get started with. Don't worry about the vocabulary here (Consistency, lambda calculus, etc), FaunaDB can be quickly put to work without reading any papers and without knowing what lambda calculus is.

* [Calvin Paper](http://cs.yale.edu/homes/thomson/publications/calvin-sigmod12.pdf) (warning: pdf link)
  - [Morning Paper Overview of Calvin Paper](https://blog.acolyer.org/2019/03/29/calvin-fast-distributed-transactions-for-partitioned-database-systems/) if you don't want to read the whole paper
* [jepsen report](https://jepsen.io/analyses/faunadb-2.5.4)

An Introduction to FaunaDB

FQL is the main query language for interacting with FaunaDB. It supports all JSON types, plus some additional types like Ref, Date, and Set. FQL is based in expressions and as such everything returns a value. This makes it easy to compose logic into larger queries.

Code shown in the shell:

```
Match(Index("all_customers"))
```

Overview of the FaunaDB Query Language

When working with FQL it's often the case that you'll encounter `Ref`s. `Ref`s don't have any fields on them though! So how do we access the fields? `Get`.

```
Collections()
```

```
Collection("customers")
```

```
Ref(Collection("customers"), "244075679454331401")
```

```
Get(Ref(Collection("customers"), "244075679454331401"))
```

```
Get(Collection("customers"))
```



Refs and Get in FaunaDB

FQL Page objects have additional fields like `before`, and `data`, that may make it seem like you need to `Select` to get at the underlying array of results. However, FQL supports passing Page objects directly to functions that operate on arrays and the function will transparently operate on the contained `data` field.

```
Paginate(Match(Index("all_customers")))
```

```
Drop(1, Paginate(Match(Index("all_customers"))))
```

```
Take(1, Paginate(Match(Index("all_customers"))))
```

FQL Page objects can be arguments to array functions

`Ref`s are opaque. To access the fields on a document associated with a Ref, we first need to run `Get` on our results and then we can access fields using `Select`

```
Map(
  Paginate(Match(Index("all_customers"))),
  Lambda("X", Get(Var("X")))
)
```

```
Map(
  Paginate(Match(Index("all_customers"))),
  Lambda("X", Select(["data", "firstName"], Get(Var("X"))))
)
```

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