Python is actually the first decision we made in our stack selection process. The entire dev team is familiar with the language and more importantly, it is the language of choice for most of the leading machine learning research and applications. Another thing that we considered is that using python allows us to more easily hire developers in the future. Python is generally the kind of language in which it is really easy to get something started with minimal effort, which is ideal for us given our short timeline
We are planning to choose Docker since it will allow us to build and install libraries and dependencies with ease. Its extensive use in the world will be helpful to provide us with useful discussion boards. This will be the first time any member of the dev team will be using Docker as part of their application. Given the limited readings, we have been able to do about it in the time we had, we a really excited to get to work with it. It seems to have a lot of potential that we would like to explore as a team. Another reason is that our dev team currently only has access to Windows machines and we want our application to be system agnostic. Using Docker will also help us limit the number of CI minutes our application requires.
Overview: To put it simply, we plan to use the MERN stack to build our web application. MongoDB will be used as our primary database. We will use ExpressJS alongside Node.js to set up our API endpoints. Additionally, we plan to use React to build our SPA on the client side and use Redis on the server side as our primary caching solution. Initially, while working on the project, we plan to deploy our server and client both on Heroku . However, Heroku is very limited and we will need the benefits of an Infrastructure as a Service so we will use Amazon EC2 to later deploy our final version of the application.
Serverside: nodemon will allow us to automatically restart a running instance of our node app when files changes take place. We decided to use MongoDB because it is a non relational database which uses the Document Object Model. This allows a lot of flexibility as compared to a RDMS like SQL which requires a very structural model of data that does not change too much. Another strength of MongoDB is its ease in scalability. We will use Mongoose along side MongoDB to model our application data. Additionally, we will host our MongoDB cluster remotely on MongoDB Atlas. Bcrypt will be used to encrypt user passwords that will be stored in the DB. This is to avoid the risks of storing plain text passwords. Moreover, we will use Cloudinary to store images uploaded by the user. We will also use the Twilio SendGrid API to enable automated emails sent by our application. To protect private API endpoints, we will use JSON Web Token and Passport. Also, PayPal will be used as a payment gateway to accept payments from users.
Client Side: As mentioned earlier, we will use React to build our SPA. React uses a virtual DOM which is very efficient in rendering a page. Also React will allow us to reuse components. Furthermore, it is very popular and there is a large community that uses React so it can be helpful if we run into issues. We also plan to make a cross platform mobile application later and using React will allow us to reuse a lot of our code with React Native. Redux will be used to manage state. Redux works great with React and will help us manage a global state in the app and avoid the complications of each component having its own state. Additionally, we will use Bootstrap components and custom CSS to style our app.
Other: Git will be used for version control. During the later stages of our project, we will use Google Analytics to collect useful data regarding user interactions. Moreover, Slack will be our primary communication tool. Also, we will use Visual Studio Code as our primary code editor because it is very light weight and has a wide variety of extensions that will boost productivity. Postman will be used to interact with and debug our API endpoints.
We initially though we would use Django because it seemed to have a lot of the things we needed out of the box. After a bit of research we realized that using Flask would be a better option since it is more flexible and would be lighter for our purposes. Having set up our REST api using Flask we believe that we did make the right decision. We found that the flexibility of Flask along with the many extensions available for it to be very appealing. We were able to add the functionality we needed without much difficulty thanks to the quality of the extensions and their documentation.