Learn Cloudflare, Elasticsearch and Mocha

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)*

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

Introduction to Cloudflare Workers

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

Using NPM, you can install Cloudflare Workers' CLI (command-line interface), Wrangler, by typing `npm install -g @cloudflare/wrangler` into your command line. Wrangler allows you to generate and publish Cloudflare Workers projects. We'll also use the `wrangler login` command which will give you a link to authorize Wrangler. This will set up an API token specifically for your Wrangler installation that will allow you to publish and read all the Workers and Workers KV information that you need.

Install and Configure the Cloudflare Workers CLI Wrangler

To deploy a Cloudflare Workers serverless function, you'll need to sign up for a free Cloudflare Workers account at [workers.cloudflare.com](http://workers.cloudflare.com/), and select your unique workers.dev subdomain to deploy your functions to.

Create a Cloudflare Workers Account

Wranglers generate command, `wrangler generate`, allows you to create new Cloudflare Workers projects. Using the URL parameter, you can also generate new projects based on Cloudflare's starter templates, or open-source projects created by other developers, but for now, you will just use the default. To do this you'll only need to give it a name, for example, `wrangler generate my-worker`,  this will create a new project called my-worker. 

Generate New Cloudflare Workers Projects with Wrangler's generate Command

We'll take a look at the default code in `index.js` in a Workers project. We'll take a second to understand how it works before building the rest of our project. 

First will see what our Worker looks like in an actual browser by running `wrangler preview`. In a terminal, and we'll check out how Serverless functions in Cloudflare Workers listen for HTTPS requests via the "fetch" event and respond to them by creating and returning a new Response.

Write Your First Cloudflare Workers Serverless Function

Using `wrangler whoami` in a terminal we'll grab our account id so we can paste our id into the double quotes of `account_id = ""`  inside of `wrangler.toml` file. Using the `wrangler dev` command we can look at our Workers serverless function and test it before deploying it. Once we're ready to deploy, we can use the `wrangler publish` command to deploy it to our unique workers.dev subdomain.

Preview and Publish Your Cloudflare Workers Project

By updating the response body and setting a corresponding Content-Type header, we can render HTML pages from directly inside of our Cloudflare Workers function.

We will update our Cloudflare Worker to return a call to a template function that we define. That template function returns a string of HTML that we will render in the browser.

Render HTML Pages with Cloudflare Workers

Up until now, we've been rendering plain responses, not based on any information coming in with a request. The request.cf object sent with every incoming HTTP request to a Workers function has a ton of interesting information, including the regionality of the request, and which Cloudflare data center is responding to the request. Using this information, we'll render a dynamic HTML page based on where the user visiting our site is in the world.

Render Cloudflare Region Data for a Request Using request.cf

Deploying to your unique workers.dev subdomain is a great way to test your serverless functions before they're ready to go to production. Once you are ready to deploy to production, you can use Wrangler's environment feature to deploy to your zone, or Cloudflare-managed domain, by providing the "-e" (environment) flag when publishing.

You can check this worker out [here](https://hello-world-worker.signalnerve.com/).

Deploy to a Custom Domain with Cloudflare Wrangler Environments

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)

Topics

Instructors

Content Type