Skip to main content

Set up a web server for learning HTTP headers


Motivation


We all follow the client-server model using the HTTP protocol for most of our web apps today. In development, we simply may have a backend API server and a frontend (web pages or mobile apps) only. However, it seemed that a proxy server is always required for production. In fact, most of the hardest issues in production come from integration. The requests and responses might be modified by the proxy server. Therefore, the understanding of HTTP protocol is one of the key skills to resolve those issues.

I wanted to dive deep into HTTP with some core concepts such as caching, cookies, and CORS. I didn't intend to go quickly rather than moved slowly to have a well understanding of what I do.

Prepare a server

The easiest way is to use my laptop as a server then I can just use "localhost". I can also use ngrok to make my web server online. Finally, I use an online tool such as RedBot to check the HTTP headers.

To make it more excited though, I deployed the app on AWS EC2. It was quite easy to launch an EC2 instance by using the wizard setup of AWS. Just simply use default settings for most of the cases. I was just curious about how to creating a new VPC (Virtual Private Cloud). To understand what exactly the IPv4 address and CIDR are, I first started to grab some knowledge about those topics by watching the following videos on YouTube


Next, I tried to create and do configure a virtual private network using Cisco Packet Tracer. I needed to configure IP address for involved devices: routers, servers, and laptop. Also, I did configure the routing tables for each router so that I could send packets among devices.

Coding

I used Express for my web server simply serving static files. I configured the HTTP header easily through passing the options of "express.static" middleware.

Deployment

Docker is my favourite tool for deployment. I created an image for my container running on my EC2 instance which I have already installed Docker. I also applied some best practices of Node.js and Docker. I built and pushed my Docker image into Docker Hub so that I could pull the image within the EC2 instance.

Happy coding!

Comments

Popular posts from this blog

Avoiding Time-Wasting Pitfalls in Agile Estimation

If you do Scrum at work, you might be very familiar to the estimation in Planning 1 . My PO has once complained to my team that why it took too long for estimating just a story. Wasting time results in the planning timebox is violated. I give you some advice from my experience: Estimation is estimation, not measure. When you read some requirements, you see some risks but you actually don't know how complicated it will be.  Don't try to influence the others by explaining how to do it in too detail. Just keep in mind that you know the business domain pertaining to customer needs and estimate how much effort you will spend for it. The effort should be compared to your baseline one that you use for a simple requirement. The bottom line is we do "relative estimation", not absolute estimation. For example, you are asked to estimate the height of a building. Basically, you just need to answer "how many times higher is the build than your height"; you do...

Math fundamentals and Katex

It was really tough for me to understand many articles about data science due to the requirements of understanding mathematics (especially linear algebra). I’ve started to gain some basic knowledges about Math by reading a book first. The great tool Typora and stackedit with supporting Katex syntax simply helps me to display Math-related symbols. Let’s start! The fundamental ideas of mathematics: “doing math” with numbers and functions. Linear algebra: “doing math” with vectors and linear transformations. 1. Solving equations Solving equations means finding the value of the unknown in the equation. To find the solution, we must break the problem down into simpler steps. E.g: x 2 − 4 = 4 5 x 2 − 4 + 4 = 4 5 + 4 x 2 = 4 9 x = 4 9 ∣ x ∣ = 7 x = 7  or  x = − 7 \begin{aligned} x^2 - 4 &= 45\\ x^2 - 4 + 4 &= 45 + 4\\ x^2 &= 49\\ \sqrt{x}&=\sqrt{49}\\ |x| &= 7\\ x=7 &\text{ or } x=-7 \end{aligned} x 2 − 4 x 2 − 4 + 4 x 2 x ​ ∣ x ∣ x = 7 ​ = 4 5 = 4 ...

How I did customize "rasa-nlu-trainer" as my own tool

Check out my implementation here Background I wanted to have a tool for human beings to classify intents and extract entities of texts which were obtained from a raw dataset such as Rocket.chat's conversation, Maluuba Frames or  here . Then, the output (labeled texts) could be consumed by an NLU tool such as Rasa NLU. rasa-nlu-trainer was a potential one which I didn't need to build an app from scratch. However, I needed to add more of my own features to fulfill my needs. They were: 1. Loading/displaying raw texts stored by a database such as MongoDB 2. Manually labeling intents and entities for the loaded texts 3. Persisting labeled texts into the database I firstly did look up what rasa-nlu-trainer 's technologies were used in order to see how to implement my mentioned features. At first glance rasa-nlu-trainer was bootstrapped with Create React App. Create React App is a tool to create a React app with no build configuration, as it said. This too...

My must-have apps for daily work

There is no doubt that cool apps can help us be more productive and enjoyable at work. For the time being, I really love the following apps which are used by me almost every day. 1. A personal Kanban In fact, a personal kanban is the most useful app for me. Why does it matter? It is not just a to-do list, but it keeps me motivated every day because it helps me be able to know what my "big picture" is. I usually set up my plans together with a path to reach them.  KanbanFlow  is my preferred tool. KanbanFlow 2. A terminal Needless to say, a terminal is a must-have app for every developer, especially the ones use macOS/Linux. Due to its importance, I love to decorate and enhance it to be super exciting with various tools such as  iTerm ,  oh-my- zsh , and  thefuck . ;) iTerm + oh-my-zsh 3. A documentation "ecosystem" As a developer, I can not remember all things that I have experimented a day. Moreover, a document is really useful for sharing an...

Sharing a virtualenv across several Python projects using Pipenv

There is a standard library for all projects in Python. However, several projects don’t always have the same dependencies all the time. That is where virtual environments come to play. You can follow this official document to use two separated tools  virtualenv and pip to  fulfill that need. My preferred alternative is to use pipenv . Pipenv is easy to use and convenient. The following are my steps to make a shared virtualenv for my all projects which requires the same dependencies. Step 1. Create an isolated virtualenv. python -m venv my-shared-env Step 2. Create a symbolic link to the created virtualenv. cd project_1 ln -s ~/.local/share/virtualenvs/my-shared-env .venv I have encountered the following issue at step 1. FileNotFoundError: [Errno 2] No such file or directory: '{my_project_path}/.venv/bin/pip': '{my_project_path}/.venv/bin/pip' The root cause was I tried to create virtualenv by running pipenv install and renaming the generated virtualenv to ...