Learn Algolia, Chai and Cloudflare

Cloudflare Workers let you write JavaScript Service Workers that run directly in Cloudflare's 200+ edge locations around the world.

With a Cloudflare Worker, you can modify your site's HTTP requests and responses, make parallel requests, and even reply directly from the edge.

Cloudflare Workers are written against a similar API to the W3C Service Workers standard. However, while Service Workers run inside the end user's browser, Cloudflare Workers run on Cloudflare's servers. This means that Cloudflare Workers can implement trusted code (which the client cannot tamper with), and will work no matter what browser the user is running.

In this course, Kristian Freeman will teach you how to build and deploy a Serverless API with Cloudflare Workers. By learning how to build your own serverless JSON APIs, you can effectively manage a highly-available backend for your projects, without needing to learn DevOps or manage services.

Cloudflare secures and ensures the reliability of your external-facing resources such as websites, APIs, and applications. It protects your internal resources such as behind-the-firewall applications, teams, and devices. And it is your platform for developing globally-scalable applications.

cloudflare

Build a Serverless API with Cloudflare Workers

Cloudflare Workers is a serverless application platform that enables you to create and deploy functions to Cloudflare's Edge Network.

Unlike other serverless providers that only have regional data centers, Cloudflare's Edge Network consists of hundreds of servers all over the world.

Cloudflare Workers is a powerful platform, and this course serves as a great introduction for what it can do.

Follow along with Kristian Freeman as you build a localization engine that renders data based on the Edge location nearest to the application's user.

After this course, you'll be ready to start experimenting with your own Cloudflare Workers projects. *Need an idea?* *[Check out the suggested Portfolio Project for this course.](https://egghead.io/projects/introduction-to-cloudflare-workers)*

Introduction to Cloudflare Workers

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.

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Getting Started with Algolia InstantSearch.js

The Edge is one of the most exciting new topics in web development. At the front of the pack is Cloudflare, whose network boasts servers located in hundreds of cities all over the world instead of in regions like "East" or "West".

More servers in more locations mean a faster experience for your users.

But what about the data part of the equation?

If the application you're working on skews more toward "reading" rather than "writing" data, Cloudflare Workers KV is a perfect fit.

Once you create a KV namespace— the "KV" stands for "key-value"— Cloudflare keeps track of your application data. The most commonly accessed data will be propagated around the world to the various Edge servers, while less popular data will be stored in more central locations.

Kristian Freeman has produced courses that introduce how to build and deploy sites and JSON APIs powered by Cloudflare Workers.

Today, he will show you how Workers KV can be integrated into a Cloudflare Workers project through building a Todo App.

## The Project

![todo-app-screenshot](https://res.cloudinary.com/dg3gyk0gu/image/upload/v1639014074/course-resources/Build-data-driven-applications-on-the-edge-with-Workers-and-Workers-KV/CleanShot_2021-12-08_at_16.46.39_2x.png)

Before you groan about a bland Todo App, think about it this way: 

The Todo App is the perfect example of the CRUD operations. Cloudflare KV has its own nuances for reading and writing data through a concept called Binding that is easier to grok when you understand the larger example as a whole.

As the course progresses, Kristian shares examples of how you can store more complex key-values, along with data operations like editing and filtering.

One of the coolest features that Cloudflare KV allows for is user-specific data that is tied to the IP address the request originates from. In other words, you'll be able to share your deployed Todo app with anyone around the world, and they will only see their personal items.

## **Gain these Skills**

- Manipulating data using Cloudflare Workers KV
- Make data unique to each user without logging in
- Effectively store lightweight data in Cloudflare Workers KV
- Setting up a key-value store and connected projects
- Reading and writing to a serverless key-value store
- Storing complex values in a Cloudflare KV store

## **Prerequisites**

You'll need to have a basic understanding of what Cloudflare Workers is recommended but not necessary. This course is relatively beginner-friendly into the world of Workers KV that you can get by with no knowledge on Cloudflare Workers. 

But if you want to get the most out of this course, we would recommend taking [Introduction to Cloudflare Workers](https://egghead.io/courses/introduction-to-cloudflare-workers-5aa3) to get a better understanding of how Workers KV comes into play. 

## **What's Next? (You might also like these resources:)**

Follow along with Kristian Freeman as you build a localization engine that renders data based on the Edge location nearest to the application's user. → [Introduction to Cloudflare Workers](https://egghead.io/courses/introduction-to-cloudflare-workers-5aa3) 

Learn how to build your own serverless APIs so you can manage a highly available backend for your projects. No Learning DevOps or managing services necessary. →  [Build a Serverless API with Cloudflare Workers](https://egghead.io/courses/build-a-serverless-api-with-cloudflare-workers-d67ca551)

Using Cloudflare Pages and Cloudflare Workers, it's easy to create dynamic front ends that you can deploy in just seconds from GitHub. → [Deploy Dynamic Frontend Applications with Cloudflare Pages](https://egghead.io/courses/deploy-dynamic-frontend-applications-with-cloudflare-pages-6aaa00a1)

Build Data-Driven Applications on the Edge with Workers and Workers KV

Learn how to create dynamic front ends using both Cloudflare Pages and Cloudflare Workers. 

With Cloudflare Pages, you can deploy static sites in seconds from Github. In this course, you will learn how to get started by going through the setup process and integrating Contentful and Airtable. 

And we will the frontend dynamic by writing a serverless function with Cloudflare Workers. We set that up by using the Cloudflare Workers CLI, Wrangler, which makes it easy to connect to your Cloudflare account and create serverless functions. 

And then we connect them with our example project, where we need to send submitted form data from our front end over to Airtable. 

So check out this course and learn to use the Jamstack with Cloudflare today!

Deploy Dynamic Frontend Applications with Cloudflare Pages

Chai is a BDD / TDD assertion library for node and the browser that can be delightfully paired with any javascript testing framework.

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[Chai](https://npmjs.com/package/chai) assertions work great with the [Mocha](https://npmjs.com/package/mocha) testing framework. In this lesson we'll walk through out to install Chai and [karma-chai](https://npmjs.com/package/karma-chai) so you can use the `expect` assertions in your tests.

Use Chai assertions for tests in a Karma project

Every dependable library needs to have tests to ensure that you don't push accidental bugs. In this lesson, see how you can set up mocha and chai to start testing your library.

Setting up Unit Testing with Mocha and Chai

Now that you've got everything set up with mocha and chai, it's time to actually write tests. In this lesson you'll learn how to use `describe` and `it` to outline your tests and make sure you don't break your library in the future.

Unit Testing with Mocha and Chai

In this lesson, we will use Chai's request method to test our Node application's API responses.
By the end of this lesson, you will know how to:
- install the prerequisites to use mocha and chai in your application
- test for HTTP status response codes
- test for a string of text on a page
- test for a json response and validate the properties of the object
- write tests that not only verify the response of your application, but the behavior as well


Test Node RESTful API with Mocha and Chai

Mocha is a feature-rich JavaScript test framework running on Node.js and in the browser, making asynchronous testing simple and fun. Mocha tests run serially, allowing for flexible and accurate reporting, while mapping uncaught exceptions to the correct test cases.

mocha

Writing great ES6 style Promises for Node.js is only half the battle. Your great modules must include tests as well to ensure future iterations don't break them. In this lesson, I show you a simple ES6 Promise in Node.js, then walk you through creating tests in Mocha using chai and chai-as-promised to test both resolve and reject methods.

Testing ES6 Promises in Node.js using Mocha and Chai

In this lesson, you will learn how to verify your API server is returning HTTP 400 responses when clients submit incorrect data. Returning HTTP 400 ensures that your clients are notified of the incorrect usage. Testing for them ensures your API returns errors instead of incorrect responses when supplied with incorrect data.

Test for HTTP 400 responses with Mocha and Chai

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

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

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

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

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

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.