Learn Elasticsearch, Immutable and Nightmare

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

im·mu·ta·ble i(m)ˈmyo͞odəb(ə)l/ adjective - **unchanging over time or unable to be changed**.

Mutating data is the source of never ending headaches in software systems, and JavaScript code is no exception. Immutable data structures, pure functions, uni-directional data flow... you've probably been hearing these terms.

Immutable.js provides us the data structures and utilities that we need to start introducing this powerful concept of immutability into our applications.

Immutable.js provides many Persistent Immutable data structures including: List, Stack, Map, OrderedMap, Set, OrderedSet and Record.

immutable

Learn how to use Immutable.js

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.

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Sometimes our update logic is asynchronous. In this lesson we will investigate two ways in which we can write asynchronous update logic. 

…But, we will also discuss why asynchronous producers should be avoided, and how that can be done by better separating our logic. 

This lesson also demonstrates the “createDraft” and “finishDraft” APIs

Write Asynchronous Producers in Immer and Why You Shouldn’t

In this lesson we will send patches over a WebSocket to our server, and distribute them again from there.

Create a Simple WebSocket Server to Distribute Patches from Immer

Immutable.js offers methods to break  immutable structures into subsets much like Array--for instance it has the all powerful slice()--and unlike Array it offers functional methods like take() and skip(). You get the best of both the imperative and functional worlds.

Working with Subsets of an Immutable.js Map()

Equality checking deeply nested objects is painful. Fortunately Immutable.js makes this task easy with its .is() and .isSubset() methods. Let's see how we can take two different Immutable Maps() and check for equality.

Equality Checking with .is() and More

Immutable.js offers the fromJS() method to build immutable structures from objects and array. Objects are converted into maps. Arrays are converted into lists. The fromJS() method can also take a reviver function for custom conversions.

Using fromJS() to Convert Plain JavaScript Objects into Immutable Data

Understanding Immutable.js's Map() and List() structures will likely take you as far as you want to go with immutable programming. They have only small semantic differences between each other and the remaining structures in the Immutable.js family. Sequence, however, has one major difference: it's lazy--which opens a new realm of functional possibilities. Let's write a simple sequence to start.

Exploring Sequences and Range() in Immutable.js

Immutable.js iterables offer the reduce() method, a powerful and often misunderstood functional operator on which map(), filter(), groupBy(), etc. are built. The concept is simple: reduce transforms your iterable into something else, that's all. The name is misleading as you may or may not actually "reduce" anything. Let's replicate the groupBy() and filter() methods with reduce to illustrate how it works.

Transforming Immutable Data with Reduce

While Immutable.js offers .is() to confirm value equality between iterables it comes at the cost of referencing each key and value in both objects. For lightning fast equality checks, Immutable.js can produce a hash code based on an iterable's content. If two iterables have the same content, their hash codes will be the same. It's worth noting that this technique is unsuitable for mission critical application development since there is a chance, however slight, that checksums like these might collide. This is outlined here: https://en.wikipedia.org/wiki/Collision_(computer_science)

Lightning Fast Immutable.js Equality Checks with Hash Codes

The Immutable.js Record() allows you to model your immutable data much like you would model data with native Javascript classes or objects. It differs from native classes because it cannot be mutated after it's creation and it *always* has a default value. It's an excellent construct in which to piece together your stores, be them Flux or some other storage implementation. Let's quickly create an Immutable Record().

Immutable.Record() as data models

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.

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