Learn Elasticsearch, Mongodb and Sequelize

MongoDB has a quite powerful aggregation framework that allows you to write data processing pipelines. Documents in the MongoDB collection go through a series of stages or a pipeline that transforms them into an aggregated result. In this short video series, you will learn how to write aggregations in MongoDB.

mongodb

MongoDB Aggregation Framework

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

Aggregation in MongoDB is really powerful and allows you to create solid pipelines to extract and transform data. This is an introductory video that aims to demonstrate the basics of aggregation — we will see how to create some basic pipeline to extract data using `$match` and manipulate the output using `$project` stages.

The aggregation stages/operators covered in this video are:

* [`$match`](https://docs.mongodb.com/manual/reference/operator/aggregation/match/index.html)
* [`$project`](https://docs.mongodb.com/manual/reference/operator/aggregation/project/index.html)
* [`$concat`](https://docs.mongodb.com/manual/reference/operator/aggregation/concat/)

You can see the list of all the [available operators in the MongoDB docs](https://docs.mongodb.com/manual/reference/operator/aggregation/)


Find and Manipulate Data using Aggregation in MongoDB

In our last lesson, we learnt the basics of MongoDB aggregation and saw how we can [find and manipulate data](https://egghead.io/lessons/mongodb-find-and-manipulate-data-using-aggregation-in-mongodb). In this continuation, we will see how we can sort, skip and limit records during our aggregation stages.

The aggregation stages/operators covered in this video are:

* [`$sort`](https://docs.mongodb.com/manual/reference/operator/aggregation/sort/index.html)
* [`$skip`](https://docs.mongodb.com/manual/reference/operator/aggregation/skip/index.html)
* [`$limit`](https://docs.mongodb.com/manual/reference/operator/aggregation/limit/index.html)

You can see the list of all the [available operators in the MongoDB docs.](https://docs.mongodb.com/manual/reference/operator/aggregation/)

Sort, Skip and Limit Records in MongoDB Aggregation

Mongoose provides a straight-forward, schema-based solution to model your application data. It includes built-in type casting, validation, query building, business logic hooks and more, out of the box.

mongoose

One of the great things about NoSQL databases is that you do not have to decide on your data schema while developing. As you get closer to production though, you'll often want to more clearly define the shape of the data you are working with.

Enforce Schemas in MongoDB using Mongoose Models

Unlike the relational databases, MongoDB doesn't allow you to join collections using the simple `find` queries, but you can write the join queries using the aggregation framework. In this lesson, we learn how to join two collections to get the data in the format we want.

The aggregation operators that we cover in this video are:

- [`$lookup`](https://docs.mongodb.com/manual/reference/operator/aggregation/lookup/index.html)
- [`$project`](https://docs.mongodb.com/manual/reference/operator/aggregation/project/index.html)
- [`$arrayElemAt`](https://docs.mongodb.com/manual/reference/operator/aggregation/arrayElemAt/index.html)

You can see the list of all the [available operators in the MongoDB docs.](https://docs.mongodb.com/manual/reference/operator/aggregation/)

Write Joins in MongoDB

[In our last lesson](https://egghead.io/lessons/mongodb-group-by-and-sum-in-mongodb?pl=mongodb-aggregation-framework-3e5d), we saw how we can use `$group` operator to simulate `GROUP BY` operations that you might be aware from SQL. A minor gimmick with `$group` is that it only works with the scalar field values and not arrays. There might be times when you would want to group by some array fields; in this lesson, we learn how to do that using `$unwind` operator

Here is the list of operators that we cover in this lesson:

- [$unwind](https://docs.mongodb.com/manual/reference/operator/aggregation/unwind/index.html)
- [$group](https://docs.mongodb.com/manual/reference/operator/aggregation/group/index.html)
- [$sort](https://docs.mongodb.com/manual/reference/operator/aggregation/sort/index.html)
- [$limit](https://docs.mongodb.com/manual/reference/operator/aggregation/limit/index.html)

You can see the list of all the [available operators in the MongoDB docs](https://docs.mongodb.com/manual/reference/operator/aggregation/).


Perform Group Operations on Array Fields in MongoDB

In this lesson, we learn how to group documents in a collection using `$group` operator similar to `GROUP BY` in SQL. We also look at `$sum` operator and see how we can use it to count the number of documents as well as get sum of some field in our grouped results.

Here is the list of operators that we cover in this lesson:

* [`$group`](https://docs.mongodb.com/manual/reference/operator/aggregation/group/index.html)
* [`$sum`](https://docs.mongodb.com/manual/reference/operator/aggregation/sum/index.html)
* [`$sort`](https://docs.mongodb.com/manual/reference/operator/aggregation/sort/index.html)

You can see the list of all the [available operators in the MongoDB docs](https://docs.mongodb.com/manual/reference/operator/aggregation/).


Group By and Sum in MongoDB

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.

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