Learn Cloudflare, Elasticsearch and Mocha

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

The “Edge” is where you write and store your code where your data is being produced. This allows for a quick and efficient data transfer, eliminating or severely reducing lag. 

Supabase is a suite of open-source tools wrapping a PostgreSQL database. It provides the building blocks of an app - database hosting, auth, file storage, real-time, and edge functions - so we can focus on what makes our app unique.

Cloudflare Workers are serverless functions that run at the Edge! In this course, we will use the Wrangler CLI tool for interfacing with your Cloudflare account and creating or publishing your Workers.

KV Storage is a cache that Cloudflare makes available to our Workers. It replicates across multiple CDN nodes, making it super performant to cache data.

Combined, these three things give you the benefits of that reduced lag in a smooth and streamlined process. 

Throughout this course, Jon will teach you how to create a small blog that hits the key points of how to:

- Query Supabase from Cloudflare Workers
- Read and write KV Storage cache from Cloudflare Worker
- Cache Supabase response at the Edge with KV storage

And more! 

Cache Supabase data at the Edge with Cloudflare Workers and KV Storage

In this course, you will learn the basics of using Elasticsearch within your application. You will learn how to store and retrieve data in Elasticsearch as well as how to leverage its powerful search features.
 
You will be able to design indices, types and maps for your application to leverage the most from Elasticsearch and you will learn how to monitor the health and performance of your Elasticsearch servers to identify and resolve capacity issues before they impact your application.

Throughout this course, we will be using the [Simpsons dataset](https://www.kaggle.com/wcukierski/the-simpsons-by-the-data) containing all of the Simpsons episodes, characters, and lines. It will be the foundation for learning to retrieve data from Elasticsearch, perform complex searches, and understand and implement best practices for storing data in Elasticsearch. 

elasticsearch

Get Started with Elasticsearch

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

Closing and opening indices on Elasticsearch allow you to free up resources in your cluster when they aren’t needed, preventing you from scaling or growing your cluster to support unused indices. In this lesson, you will see how easy it is to do both and learn to do so on your own indices.

Close and open an Elasticsearch index

In this lesson, you will learn how to add new documents to the Elasticsearch data store using HTTP methods and the Elasticsearch API. You will also learn how Elasticsearch uniquely identifies each document and what happens when you attempt to create a document that already exists.

Add data to Elasticsearch

Elasticsearch provides several methods for using its powerful search features. In this lesson, you will be introduced to the `_search` endpoint, the datapoints returned in results, how to set a query timeout (and what a timeout *doesn’t* do), and how to search specific indices and types.

Search for data in Elasticsearch using the _search endpoint

Elasticsearch provides a powerful API over http for accessing its features. In this lesson, you will be introduced to the API and learn how to use it to get data from Elasticsearch using the browser and the popular command curl.
We will see how adding  query stream parameters can exclude or include the properties that we desire from our data.

Setup is specified in the `README.md` of the repository linked below.

Get data from Elasticsearch by id using http

By default, search results are limited to the top 10 results. In this lesson, you will learn how to change the pagination size, paginate through results, and you will learn about the performance implications of pagination on Elasticsearch.

Paginate through search results in Elasticsearch

Elasticsearch offers two API endpoints for updating documents. In this lesson you will learn how to use those endpoints to replace entire documents in the store and update partial information within a document.

Update data in Elasticsearch

Elasticsearch provides a full featured client for nodejs available via npm. In this lesson, you will learn to how install the client and use it to retrieve data from your Elasticsearch server.
We will walk through retrieving data through callbacks as well as using promises.

Get data from Elasticsearch by id using the Elasticsearch npm client

Simple searches can be done using query parameters, also known as “Search Lite” by Elasticsearch. You will learn how to perform query parameter searches in this lesson and also discover the upper limits of its usability in this lesson.

Search Elasticsearch using Query Parameters

Query DSL is based on JSON and allows for Leaf and Compound query clauses.  The Query DSL is what you will want to use to write your production queries.  It makes your queries more flexible, precise, easier to read and easier to debug.

Search for data in Elasticsearch using queryDSL language

Using Full Text Search is where the search capabilities of Elasticsearch start to really set it apart from databases. In this lesson, you will learn how to perform full text searches against your data, interpret the results, and understand how the relevance score is impacts your search results. If you are following the examples in this course, be sure to import the Simpsons episode scripts for this lesson by running `node utils/episode_scripts.js` from the git repo directory.

Search for data in Elasticsearch using full text search

Aggregation queries can be thought of as similar to GROUP BY in SQL or the Aggregation Framework in MongoDB, but much more powerful. In this lesson you will learn how to create aggregation queries to group documents and perform rollups and calculations based on the results. Aggregation documentation can be found [here](https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations.html)

Perform analytics using Elasticsearch aggregation queries

Creating an alias can make searching easier by providing a friendlier name for the index. It can also be useful to create a filtered subset of the data, providing a better search experience for the client. You will learn how to do both in this lesson.

Create an alias for an Elasticsearch index

The index is the top-most schema level for your data. You will learn how to create an index in this lesson. You will be introduced to shards and replicas and how decisions about them will impact future performance, scalability and resiliency. You also learn what the elasticsearch cluster health status “green”, “yellow”, and “red” mean, not only in terms of cluster health but how it impacts your query search results.

Create an index using the Elasticsearch API

In this lesson, you will learn how to delete a single index, multiple indices, or all indices on your Elasticsearch cluster. We’ll see how a simple `DELETE`  request can delete any index you like.

Delete an index using the Elasticsearch API

Types can probably be best thought of as a Class for your index and a map is the definition for that type. In this lesson, you will learn how to create a mapping type for an index based on sample data, verify it, then store data in the index using that type. You will also learn how Elasticsearch can automatically create them for you (known as Dynamic Mapping), and you’ll learn what mapping explosion is and how to avoid it.

Create a type and map in an index

Elasticsearch has an in-depth set of APIs for accessing the health and performance of the cluster. In this lesson, you will learn how to access them using the _cat API endpoint, designed for console use. You will also learn some of the key metrics to monitor to identify issues and performance problems with your Elasticsearch cluster before it impacts your application and clients, including how to tell if your Elasticsearch cluster isn’t returning results based on all of your data.

Search

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