Learn Docker, Mongodb and Sequelize

While "It works on my computer" might give you an excuse to close a bug ticket in the backlog, it doesn't really work when you're working on a team.

You need to ensure that your application can run regardless of what hardware your teammates are running or libraries they have installed.

Docker solves this problem by collecting everything your application needs to run into a container.

Containers aren't just for source code. They can hold configuration, scripts, and even their own filesystem. It's similar to a Virtual Machine, without having to bring a full operating system along for the ride.

Because containers run in isolation, there is some configuration involved to allow for files to be saved and containers to communicate with one another.

One of the most useful ways to get your head around Docker is to see it in action.

Follow along with Joel Lord as he works through the process of preparing an application to run in separate containers for the frontend and backend of an application that searches for gifs and re-encodes them with a caption supplied by the user.

You'll see how to configure the separate containers to communicate with one another, work with environment variables, and persist data to your local machine. Along the way, you'll pick up some tips on useful commands and bash scripting. Finally, you'll learn how to use Docker Compose to make it easy to run multiple containers simultaneously.


Docker containers wrap a piece of software in a complete filesystem that contains everything needed to run: code, runtime, system tools, system libraries – anything that can be installed on a server. This guarantees that the software will always run the same, regardless of its environment.

docker

Containerize Full-Stack JavaScript Applications with Docker

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 we will take a simple Node.js app that connects to a MongoDB database and uses an Express web server, and learn how to setup a full software development deployment process as well as how to properly “Dockerize” the app. 

We’ll step through setting up proper version control techniques, managing configuring within the environment, setting up proper build/deploy/run processes, and all with abiding by Docker best practices. We’ll also make sure our deployment is setup for scale, and have consistent development practices in place for reliable app deployments.

Build a Twelve-Factor Node.js App with Docker

Zeit’s mission statement is to ‘Make Cloud computing as easy and accessible as Mobile computing.’ To that end, they’ve released some pretty amazing and powerful tools. In this course we’re going to take a look at `now`, their deployment tool, to see why it’s hands-down the simplest way to launch node applications, docker containers and even static files to the cloud.


We’ll also dig into some of now’s more advanced capabilities, including host aliasing, environment variables and integration with hosted databases.

Deploy Web Apps with Zeit Now

Kubernetes is an API to a computer. Multiple computers actually. Here we'll talk about what K8s is, how you can choose between the plethora of K8s related hosting options, and what the process looks like for getting some containers running.

WTF is Kubernetes (K8s)

Use [Hasura](https://hasura.io/), an open source GraphQL engine to setup a backend.

You need to have Docker installed. You can [install](https://docs.docker.com/install/) from the Docker website.

Set up a GraphQL Backend with Hasura and Docker

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

PostgreSQL is a powerful, open source object-relational database system.

postgres

YugabyteDB is an open source, high-performance distributed SQL database for powering global, internet-scale applications.YugabyteDB is also a cloud-native database, so it can be deployed across both public and private clouds, including Kubernetes environments. In regards to serving as a backend for microservices, YugabyteDB brings together three must-haves: a PostgreSQL-compatible SQL API, low-latency read performance, and globally distributed write scalability. YugabyteDB with its global data distribution brings data close to users for multi-region and multi-cloud deployments.

In this lesson we will see how you can run YugabyteDB Distributed SQL cluster using docker-compose.

Running YugabyteDB Cluster Locally using docker-compose

In this lesson we will see how you can run Hasura GraphQL engine on top of YugabyteDB Distributed SQL

YugabyteDB is an open source, high-performance distributed SQL database for powering global, internet-scale applications.YugabyteDB is also a cloud-native database, so it can be deployed across both public and private clouds, including Kubernetes environments. In regards to serving as a backend for microservices, YugabyteDB brings together three must-haves: a PostgreSQL-compatible SQL API, low-latency read performance, and globally distributed write scalability. YugabyteDB with its global data distribution brings data close to users for multi-region and multi-cloud deployments.

Set up Hasura GraphQL engine with YugabyteDB distributed SQL

When we need to spin up a database instance for our new project, installing the database management system directly on our local machine is almost always a bad idea. Luckily, Docker is here to help us practically reduce the complexity of dealing with "missing dependencies" and weird error messages down to zero.

In this lesson, we will learn how to get a PostgreSQL instance running locally with Docker Compose, so we can work on the database integration of our project and test things out with ease. To follow this lesson, you will need to have Docker pre-installed.

Set up and run a PostgreSQL instance locally with Docker Compose

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

JavaScript® (often shortened to JS) is a lightweight, interpreted, object-oriented language with first-class functions, most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic, and supports object-oriented, imperative, and functional programming styles.

javascript

First, we'll get a tour of the application and what it does then we will start configuring a fresh project. 

The application UI is simple. It asks for a keyword and will search and display a random GIF from Giphy. Once you are satisfied with the image, you will be able to add a caption to it. The server will deconstruct the GIF frames, add the caption and re-create the GIF for you. Everything is then saved to a database so that all the users can benefit from your creations.

The tech involved to do this is a Vue frontend, Node.js backend, and MySQL database. I'll walk you through cloning, installing dependencies, configuring the giphy API key that we use, and starting the application. You'll notice at the end we'll need to configure the database. We'll get an error but this is expected!

Configure and Run a Node.js API Powered by a Vue Application

In this lesson, you will learn how to start basic containers and how to use them in your day to day developer life. You will also learn how to start a database container that can be used with the demo application for this course.

Running Your First Container

You will learn how to create your own images that will include all the source code of your application, along with a specific version of Node.js and all the dependencies that are needed. Once you are done, you will be able to share this image with other people and this will ensure that everyone is running the same environment. It will event let people that don’t have Node.js run this server. This will also have the added benefit of cleaning up everything once you close the application.

You'll notice that we will run into the same error stating we can't connect to the database. This is expected and we'll fix this in [Allow Container to Communicate through Docker Networks](https://egghead.io/lessons/docker-allow-container-to-communicate-through-docker-networks)

Create a Custom Image with a Dockerfile running Node.js

In the last lesson [Create a Custom Image with a Dockerfile running Node.js](https://egghead.io/lessons/docker-create-a-custom-image-with-a-dockerfile-running-node-js?pl=introduction-to-docker-afaf), our backend stopped working again because it couldn’t find the database.

One of the advantages of running containers is that they run in isolation. As far as the container is concerned, nothing exists outside of it. It behaves as if it was its own operating system. This is why the backend can’t find the database on "localhost". The two containers are running on the same host but as far as the backend is concerned, there is no database. The same goes for the database. It can’t see anything outside of what is running in its container therefore it can’t see the backend application running.

When you need a way for multiple containers to talk to each other, Docker has a tool called networks to help you. We'll use a network to allow our backend to successfully connect to the database.

Allow Containers to Communicate through Docker Networks

For your front end, a VueJs application has been created from the CLI. VueJS, just like React and Angular applications have a development mode as well as a production mode.

Once you are ready to deploy this application, you won’t want to use the same code as you do when you develop. In fact, you will need to run another script to build a production application. This script will take all of your JavaScript files and merge everything into a single minified JavaScript file. It will do the same for your CSS. By doing so, you will end up with a minimal set of files that can be served over any static file webserver. In this case, you will be using an Nginx server to serve those files. Nginx is an open-source, high-performance HTTP server that will be able to handle many requests to your front end.

We'll do this with a multi-step build in a Dockerfile that will discard the first container that we use to build the source code and run it in a container that's built on top of the nginx image.

Containerize the Front End of an Application with a Multi-Step Docker Build

In the previous lesson, [Containerize the Front End of an Application with a Multi-Step Docker Build](https://egghead.io/lessons/egghead-containerize-the-front-end-with-a-multi-step-build?pl=introduction-to-docker-afaf), we managed to put all of our front-end in a container and we can now distribute this container to the rest of our team. This will work well with stand-alone websites but as soon as you need to connect to an API, this could become an issue. 

You will need to specify the base URL of your server and this would need to be done as an environment variable. The problem here is that since our application lives in a browser, it can’t read the environment variables from the server.

In the last lesson, you’ve seen that all requests were made to `http://localhost:3000`. This means that this value was hard-coded somewhere in the front end source code. The first thing you will want to do is to find out where this was coded and make sure that it is only defined in one place.

The fix here will be to create a bash script that updates the hardcoded `localhost:3000` URL to a BASE_URL environment variable and update the instances that are used within the frontend app to your environment variable. This script will be called in the projects Dockerfile and override the original entry point.

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