Learn Elasticsearch, Rails and Svelte

Web3 is a paradigm shift in how applications are interacted with, ran, and built. 

It’s not just a Pump-and-Dump scheme on cryptocurrencies or Bored Ape NFT profile pictures you can put on Twitter.

A fundamental tenet of Web3 is bringing ownership back to the user. You no longer have to trust that your data is being properly handled on someone else’s server (although, currently Web3 ecosystem mainly depends on centralized infrastructure such as GitHub).  

This ownership is achieved through blockchain, cryptocurrencies, NFTs, and smart contracts.

A great example of this type of ownership is through how you exchange money in Web3. There is no intermediary like Stripe, or PayPal to process your transactions. You can even build your own smart contracts that receive crypto (e.g. Ethereum or ‘eth’ for short) and then process that eth how you’d like.

Building this type of smart contract is exactly what Matías is going to show you how to do right now.

Matías builds out a Web3 application that allows you to receive tips in the form of eth in a tip jar on your website. This means your application will be backed by a smart contract that’s accepts eth, stores data, and emits Solidity events.

The frontend application uses SvelteKit which you’ll see how easy it is to get up and be productive with in minutes. A huge benefit here is that the JavaScript you write is just that, JavaScript. You’ll be able to take what you learn and implement it into any framework of your choosing.

While you build this application you will learn how to:

- Write a smart contract using Solidity language
- Test the smart contract using just JavaScript through the waffle framework
- Communicate and interact with the smart contract using ethers.js in a SvelteKit application
- Deploy your smart contract to make it public and accessible to anyone

Solidity is an object-oriented, high-level language for implementing smart contracts.

solidity

Full Stack Web3 and Blockchain Development on Ethereum with SvelteKit

JavaScript community has been shaken recently by a release of Svelte 3 and for a good reason - it's a completely different approach than React or Vue. To quote the docs:

"[Svelte](https://svelte.dev/) is a radical new approach to building user interfaces. Whereas traditional frameworks like React and Vue do the bulk of their work in the browser, Svelte shifts that work into a compile step that happens when you build your app."

**The best part?** If you have 26 spare minutes you can learn the basics of Svelte 3 and start building your own apps with it!

Enjoy!



The magical disappearing UI framework

 - The web's JavaScript bloat crisis, solved. Svelte turns your templates into tiny, framework-less vanilla JavaScript.
 - Simple and familiar. Build apps out of composable, easy-to-write blocks using languages you already know.
 - Super fast, rock solid. Compile-time static analysis ensures the browser does no more work than it needs to.

svelte

Up and running with Svelte 3

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

Rails is a web-application framework that includes everything needed to create database-backed web applications according to the Model-View-Controller (MVC) pattern.

rails

Expanding on [Part 1](https://egghead.io/lessons/angularjs-rails-todo-api-part-1), this lesson will look at how to bootstrap AngularJS in a rails application as part of the asset pipeline. With Angular available, you'll see how to communicate with the Rails API using $resource.

Rails Todo API Part 2

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.

Monitor Elasticsearch cluster health and status with the _cat API

Elasticsearch has a rich set of APIs for adding data to an index but for loading massive amounts of data, you’ll find the bulk interface much more efficient and performant. In this lesson you will learn how to format your data for bulk loading, add data via the bulk endpoint with curl, and add data via the bulk endpoint using the elasticsearch npm client.

Search

Search Results