Flask vs Spark Framework: 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; Spark Framework: A micro framework for creating web applications in Kotlin and Java 8 with minimal effort. It is a simple and expressive Java/Kotlin web framework DSL built for rapid development. Its intention is to provide an alternative for Kotlin/Java developers that want to develop their web applications as expressive as possible and with minimal boilerplate.
Flask and Spark Framework belong to "Microframeworks (Backend)" category of the tech stack.
Flask is an open source tool with 45.7K GitHub stars and 12.8K GitHub forks. Here's a link to Flask's open source repository on GitHub.
Netflix, reddit, and Lyft are some of the popular companies that use Flask, whereas Spark Framework is used by Kasa Smart, AfricanStockPhoto, and Khartec ltd. Flask has a broader approval, being mentioned in 690 company stacks & 2934 developers stacks; compared to Spark Framework, which is listed in 5 company stacks and 4 developer stacks.
What is Flask?
What is Spark Framework?
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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
Flask drives our APIs, both the Website APIs and the majority of the REST Messaging APIs