Learn Algolia, Netlify and Twit

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

How does e-commerce work on the Jamstack? 

Jason Lengstorf and [Thor 雷神](https://twitter.com/thorwebdev) teamed up on _Learn With Jason_ to [build a serverless Stripe Checkout implementation](https://www.learnwithjason.dev/sell-products-on-the-jamstack). This collection is a cleaned up, egghead-style tutorial for selling products on a Jamstack site using Netlify Functions and Stripe Checkout.

Sell Products Using Stripe Checkout and Netlify Functions

OpenGraph images are the images you see when you paste a link into a social platform and it "unfurls" into an image, title, and description. This happens on Twitter, Discord, Slack, and many other platforms. This collection goes over everything from designing OpenGraph images in Figma, to implementing them in CodeSandbox, to returning headless browser screenshots from Netlify Functions, and finally using Cloudinary as a write-through cache. 

You will come away from this collection with the ability to ship an API that you can use on any of your sites, and also on-demand, that can generate images for not only OpenGraph unfurls, but also for Instagram, GitHub, and more. Using headless browsers with playwright to generate our images means we get full access to all the responsive power of CSS and all the logical power of JS to handle layout, importing assets like pngs, choosing different fonts, and more.

The image below is generated via the project you'll build and deploy in this course

![An example opengraph image](https://relaxed-payne-d1bfbe.netlify.com/opengraph?title=Building%20an%20OpenGraph%20image%20generation%20API%20with%20Cloudinary%20Netlify%20Functions%20and%20React&tags=figma,netlify%20functions,playwright&author=chrisbiscardi)

React is one of the web’s most popular frameworks for building JavaScript applications. 

If you know what you’re doing, React can drastically simplify how you build, use, and maintain code.

Whether you’re a React newbie or you’re ready for advanced techniques, you can level-up with egghead. Explore our in-depth courses, lessons, and community resources to build more powerful applications and crack open your career.

react

Building an OpenGraph image generation API with Cloudinary, Netlify Functions, and React

A short list of screencasts to cover essentials of [Gatsby](https://www.gatsbyjs.org/) to get started and have enough knowledge to become dangerous. 

The content comes from multiple instructors that have each covered different essentials, this cherry picks some of the most important concepts.

Videos in this collection cover these topics:
- installing plugins
- creating pages
- navigating between pages
- adding optimized images
- theming your styles
- deploying your site

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

Up and Running with Gatsby

Today’s users expect great things from their search experiences — speed, relevance, and usability, to name a few. Algolia provides developers with out-of-the-box and customizable search experiences that are a joy to build.

This introductory course on the Algolia search solution focuses on the user-facing part of search. Using the Algolia InstantSearch.js front-end library, we’ll see how to build an engaging search experience in no time. (28 minutes, to be exact.) 

**Prerequisites:** 
You don’t need previous experience with search to follow this course — we’ll use an example index with an e-commerce data set preconfigured. We will work mainly in JavaScript, so basic JS knowledge is expected.

If you want to play with your own index settings and data set, you can [create a free Algolia account here](https://www.algolia.com/users/sign_up/hacker/?utm_medium=link&utm_source=egghead&utm_campaign=education), but this is not required to follow along with this first course. 

Algolia's full suite APIs enable teams to develop unique search and discovery experiences for their customers across all platforms and devices. Type a query. Click a link. Open an app. Everything your customers do sends a signal about what they want. Algolia decodes that intent to deliver the right content - instantly.

algolia

Getting Started with Algolia InstantSearch.js

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.

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

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

This will get you started on how to setup the Javascript API client of Algolia search solution. To follow along the video having a free Algolia account already setup will make things easier (but is not required). Once the client is setup we will test it by pushing a couple of records to an index.  

Set up the Algolia JavaScript Client

We’ll load a simple JSON file containing 500 actors records from disk and push it to our Algolia index using the Javascript API client.

Push JSON Data to an Algolia Index from the JavaScript Client

We’ll learn how to push a small amount of data from a local MySQL database to an Algolia index. The first step is to connect to the DB using the `mysql` package, then select all records from the DB and finally push them to Algolia using the JS API client.

Push MySQL Data to an Algolia Index from the JS Client

Using the Lodash library, we’ll see how to split large amounts of data from a local MySQL database into smaller chunks and push them to an Algolia index.  

Push Chunks of Large MySQL Data to an Algolia Index with Lodash

The first step to implement Algolia on the front end using Instantsearch : importing and setting up the library with your credentials. This is what will allow the library to initiate the connection to Algolia servers and the first step to complete before adding some useful widgets.

Set up the Instantsearch JS Library

In this lesson we’ll build a search experience reduced to it’s 2 essential components : a search input and the displayed results. As simple as it is, to build this from scratch would require a fair amount of work. Between listening to the input, making the api call, parsing and displaying the result... To build this using Algolia’s Instantsearch library requires a few lines of code with simply two widgets: the Searchbox and the Hits widget.

Search an Algolia Index with Instantsearch JS searchbox and hits widgets

Before playing with Instantsearch widgets and build a rich search experience, the fundamental step is to understand what the library will be dealing with. In this lesson we we’ll cover the anatomy of the JSON search API response send back by Algolia for each search query and how it can be used.

Anatomy of an Algolia Search API Response

Displaying barebones Hits won’t catch your user’s attention. The UI is key to provide a good search experience and hits are the meat of this experience so they need extra attention. By using Mustache templating we’ll see how to display rich results to your users. 

Display Rich results with templates in the Hits widget for Instantsearch JS

We now have a good understanding of the API Response structure, we could go playing with instantsearch JS library right away, that said this Library is building on top of the JS API Client. To get a better grasp on how it works at a lower level, let’s play first with raw hits straight from the JS API client, we’ll take care of better UI with Instantsearch JS right after that.

Leverage the Algolia JS API Client to Display Raw Hits from the API Response

Every  good search UX make users understand instantly why a given hit is being displayed as part of the result set. One way to achieve that is to highlight the keywords from the query directly inside the Hits. This is built in the API JSON response and in this video we’ll explain how to leverage it easily.

Highlight Keywords in Query Results using the Instantsearch JS Hits Widget

Searching is obviously not limited to inputting text in a searchbox. If your dataset allows it Algolia can leverage of attributes and values. To make use of those facets on the front end, one of the most common widget that you can find in a search UI is the refinement list. With this widget, the user can filter the dataset based on facets. This widget only displays the most relevant facets for the current search context.

Filter Datasets with Facets using Instantsearch JS to add a Refinement List

The hierarchical menu widget is used to create a navigation based on a hierarchy of facet attributes. We generally implement it for categories with subcategories.

Browse results based on a hierarchy of Facets (HierarchicalMenu) in Instantsearch JS

The range input is a simple but useful widget that allows a user filter the results by selecting a numeric range with a minimum and maximum input. In an e-commerce website for instance, this widget would be useful to limit the results where the price is comprised between $X and $Y.

Search

Search Results