Flask vs Semantic UI: What are the differences?
Flask: a microframework for Python based on Werkzeug, Jinja 2 and good intentions. Flask is intended for getting started very quickly and was developed with best intentions in mind; Semantic UI: A UI Component library implemented using a set of specifications designed around natural language. Semantic empowers designers and developers by creating a shared vocabulary for UI.
Flask can be classified as a tool in the "Microframeworks (Backend)" category, while Semantic UI is grouped under "Front-End Frameworks".
"Lightweight", "Python" and "Minimal" are the key factors why developers consider Flask; whereas "Easy to use and looks elegant", "Variety of components" and "Themes" are the primary reasons why Semantic UI is favored.
Flask and Semantic UI are both open source tools. Semantic UI with 45.9K GitHub stars and 4.84K forks on GitHub appears to be more popular than Flask with 45.2K GitHub stars and 12.7K GitHub forks.
According to the StackShare community, Flask has a broader approval, being mentioned in 511 company stacks & 531 developers stacks; compared to Semantic UI, which is listed in 77 company stacks and 55 developer stacks.
What is Flask?
What is Semantic UI?
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ReactQL is written in TypeScript to provide full types/Intellisense, and pick up hard-to-diagnose goofs that might later show up at runtime. React makes heavy use of Webpack 4 to handle transforming your code to an optimised client-side bundle, and in throws back just enough code needed for the initial render, while seamlessly handling
import statements asynchronously as needed, making the payload your user downloads ultimately much smaller than trying to do it by hand.
React Helmet was chosen to handle
<head> content, because it works universally, making it easy to throw back the correct
<title> and other tags on the initial render, as well as inject new tags for subsequent client-side views.
<style> tags when using #StyledComponents.
React Router handles routing, because it works both on the server and in the client. ReactQL customises it further by capturing non-200 responses on the server, redirecting or throwing back custom 404 pages as needed.
Koa is the web server that handles all incoming HTTP requests, because it's fast (TTFB < 5ms, even after fully rendering React), and its natively #async, making it easy to async/await inside routes and middleware.
Unlike our frontend, we chose Flask, a microframework, for our backend. We use it with Python 3 and Gunicorn.
One of the reasons was that I have significant experience with this framework. However, it also was a rather straightforward choice given that our backend almost only serves REST APIs, and that most of the work is talking to the database with SQLAlchemy .
We could have gone with something like Hug but it is kind of early. We might revisit that decision for new services later on.
We are in the process of building a modern content platform to deliver our content through various channels. We decided to go with Microservices architecture as we wanted scale. Microservice architecture style is an approach to developing an application as a suite of small independently deployable services built around specific business capabilities. You can gain modularity, extensive parallelism and cost-effective scaling by deploying services across many distributed servers. Microservices modularity facilitates independent updates/deployments, and helps to avoid single point of failure, which can help prevent large-scale outages. We also decided to use Event Driven Architecture pattern which is a popular distributed asynchronous architecture pattern used to produce highly scalable applications. The event-driven architecture is made up of highly decoupled, single-purpose event processing components that asynchronously receive and process events.
To build our #Backend capabilities we decided to use the following: 1. #Microservices - Java with Spring Boot , Node.js with ExpressJS and Python with Flask 2. #Eventsourcingframework - Amazon Kinesis , Amazon Kinesis Firehose , Amazon SNS , Amazon SQS, AWS Lambda 3. #Data - Amazon RDS , Amazon DynamoDB , Amazon S3 , MongoDB Atlas
To build #Webapps we decided to use Angular 2 with RxJS
#Devops - GitHub , Travis CI , Terraform , Docker , Serverless
Flask is a light, yet powerful Python web framework perfect for quickly building smaller web applications. It's a "micro-framework" that's easy to learn and simple to use, so it's perfect for those new to web development as well as those looking to rapidly develop a web application.
I use Flask for times when I need to create a REST API that interfaces with other Python code, or there is no specific reason why I'd want to use Node.JS. I prefer Flask because of its small learning curve, allowing me to get started coding as quickly as possible
This lightweight web framework enables quick REST API development while enabling easy clustering, and the usage of multiple worker processes required to scale the REST API service to meet high volume requirements.
Service to query NOAA weather forecasts data and service to build tidal current forecast maps using AWS EC2 and Geoserver
We use Semantic UI for our frotend. A heavily customised version of it, but still Semantic UI under the hood.
Used Semantic UI + Angular2 together with Spring or Node/Express for full stack web application development.
Flask drives our APIs, both the Website APIs and the majority of the REST Messaging APIs