Learn Elasticsearch, Grep 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

CMD+F in your text editor can only get you so far! In this course, we will learn to use grep and find, two powerful command-line utilities, to search through and explore our codebase. Learn to search specific files and folders, using regular expressions to look for -- or ignore! -- particular phrases. We'll cover how to search recursively, get more context on your results, and some simple tricks to make your searches faster and more targeted.


Once you're familiar with grep and find, you'll be able to quickly answer questions such as: Do I use this function anywhere other than my tests? Where does this file get imported? Whether you're working on a new feature, reading a codebase for the first time, or refactoring existing code, grep and find can help you work faster and more effectively.

A Unix command used to search files for the occurrence of a string of characters that matches a specified pattern.

grep

Use Grep for Fast Search from the Command Line

Sometimes you'll be looking for a string, but won't know which file it's in; or you'll want to find all usages of it within a directory. Learn how to use grep -r to recursively search all the files in a directory. 

Search directory contents recursively using grep

Learn how to use find to identify filenames matching specified patterns. We'll use find to identify all .jsx files in a directory, or all test files in a directory.

Use find to search for filename patterns

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

Learn to use git grep to only search through the tracked files in a git repo. This is especially useful when you want to exclude build artifacts or locally installed dependencies, such as webpack bundles or the node_modules directory. You'll also note that git grep is automatically colorized - we'll see how to get the same coloring effect with grep --color.

Search the contents of a git repository with git grep

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

By default, grep displays the file path and the line where a match is found. Learn to use the flags -A, -B, -C, and -n to get more context, and view line numbers as well as the lines surrounding a pattern match.


Show context around matches using grep

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

By using wildcards and glob patterns with the special characters . and *, you can create regular expressions that describe what kind of text you're looking for, instead of just plaintext strings. 

Search for basic patterns using grep

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

Learn to use grep's extended regular expressions to describe more complex patterns. The `?` and `+` special characters describe optional patterns. The `?` character matches zero or one instance of the preceding term, and the `+` character matches one or more instances of the preceding term. To use these characters, you'll need to either escape them with backslashes or turn on extended regular expressions with the `-E` flag.

Use grep's extended regular expressions to find complex patterns

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.

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