Learn Clojure, D3 and Docker

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

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

In this course we will learn and demonstrate the fundamental concepts and APIs of D3, on our way to building several common chart types. We’ll start with the building blocks upon which all D3 visualizations are built; things like scales, interpolators, and selections. We’ll see how to load data with D3, and how to put its full name, Data Driven Documents, into action. We’ll build column and bar charts, scatter plots, and area charts. Most importantly, we’ll gain an understanding of the conventions and patterns that underlie all D3 projects and examples, enabling you to dissect, understand, and build virtually any D3 visualization on the internet.

D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG and CSS. D3’s emphasis on web standards gives you the full capabilities of modern browsers without tying yourself to a proprietary framework, combining powerful visualization components and a data-driven approach to DOM manipulation.

Build Interactive JavaScript Charts with D3 v4

This course covers D3 v3. If you'd like to learn about v4 of D3 (the latest version) [click here for the course on that](https://egghead.io/courses/build-interactive-javascript-charts-with-d3-v4).

This series will gently guide you through the first steps in getting to know D3. The curve can be steep, but with a few simple tools, you'll be charting your buns off in no time flat.

Use D3 (v3) to Build Interactive Charts with JavaScript

Clojure is a dynamic, general-purpose programming language, combining the approachability and interactive development of a scripting language with an efficient and robust infrastructure for multithreaded programming. Clojure is a compiled language, yet remains completely dynamic – every feature supported by Clojure is supported at runtime. Clojure provides easy access to the Java frameworks, with optional type hints and type inference, to ensure that calls to Java can avoid reflection.

clojure

In this lesson I show what a Clojure keyword is and why we prefer them as keys in maps; and the three ways we can look up a value in a map.

Keywords and Map Lookup in Clojure and ClojureScript

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

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

Para iniciar con Clojure es necesario realizar algunas configuraciones e instalar algunas herramientas.
Lo primero es instalar [leiningen](https://leiningen.org) y configurar tu editor de código.
En este caso configuraremos VIM. Para esto instalaremos algunos ]plugins 

* [vim-fireplace](https://github.com/tpope/vim-fireplace)
* [vim-salve](https://github.com/tpope/vim-salve)
* [coc-conjure](https://github.com/jlesquembre/coc-conjure)

Configurar ambiente de desarrollo y VIM para Clojure

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.

Access Environment Variables in a Dockerfile to Build a Front-End Container

This lesson uses some advanced concepts and some bash scripting to build a better Dockerfile. By using the concepts showed here, you will be able to build more future-proof and production-ready containers. If you prefer to focus on the content about containers, feel free to ignore this lesson and come back to it later on if needed.

Currently, we use `sed` to overwrite the hard-coded path to the API. This works well when there is a single value to replace. Now if one of your team members replaced the value of localhost:3000, your build will break.

We'll use `jq` to fix this issue. Jq is a command-line tool that makes it easy to change values in a JSON file. The base image that we used for building the application does not contain this tool, but that is not an issue. To install `jq`, you will need to download the [binary](https://github.com/stedolan/jq/releases/download/jq-${JQ_VERSION}/jq-linux64.), we'll need that to execute `jq` commands in our Dockerfile. This will allow us to update specific properties i

Modify a JSON Configuration File with jq

The containers you built in the previous lessons are running as root for a few reasons. First of all, we read and write files from the file system in order to install tools like jq or to change some configuration files. And second, we are running servers on ports that are restricted to the root user (such as port 80).

While the risks are very limited in your own environment, you will need to have security in mind when you are deploying those containers to a production server. Some hosts will prevent any container where the user is root to be running at all.

From the Dockerfile, we will specify the use that builds the container will be the `node` user that the node image gives us. To do this we will need to set permissions for the node user to add and modify specific files.

Create a Node.js Non-root Image

Now that all three tiers of your application are available in containers, you'll need to push those images to container registries. Registries are the container equivalent to repositories for your code. Registries can be public or private. 

In this lesson, we'll use Docker Hub to push our images. Docker Hub repositories allow you share container images with your team, customers, or the Docker community at large.

Docker images are pushed to Docker Hub through the `[docker push](https://docs.docker.com/engine/reference/commandline/push/)` command. A single Docker Hub repository can hold many Docker images (stored as **tags**).

Images pushed to a public registry can be downloaded by using the `docker pull` command. Once they have the image downloaded, they can use Docker run to start each container just like you did in the previous steps.

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