Learn Browserify, Elasticsearch and Jekyll

Jekyll is a blog-aware static site generator. You can write text in markdown or HTML and Jekyll uses layouts to create a static website. You can tweak how you want the site URLs to look, what data gets displayed on the site, and more.

After watching the collection you'll be able to:

* Install Jeykll and run it locally
* Create a new blog post
* Install plugins to add new features to your blog

Jekyll is a parsing engine bundled as a ruby gem used to build static websites from dynamic components such as templates, partials, liquid code, markdown, etc. Jekyll is known as "a simple, blog aware, static site generator".

jekyll

Create A Blog With Jekyll

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

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.

Bulk import data into Elasticsearch

This is more of an analysis of how the jvm heap will kill your Elasticsearch cluster than a “how-to” lesson. If you aren’t familiar with Java apps and the jvm, this 2 1/2 minutes can save you much pain, suffering, and self-loathing by showing you how Elasticsearch utilizes the jvm heap for performance and what to monitor so you know when it’s affecting you.

Understand the role of JVM heap in Elasticsearch performance

Business requirements change, new information is discovered, or usage patterns differ from the expected use. In any case, sooner or later you will find the need to reindex your data to accommodate these changes. In this lesson you will learn how to leverage the information stored in Elasticsearch and the bulk API to reindex data from one index to another.

Topics

Instructors

Content Type

Free or Pro