Flask vs Jinja2: What are the differences?
What is 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.
What is Jinja2? Full featured template engine for Python. Jinja2 is a full featured template engine for Python. It has full unicode support, an optional integrated sandboxed execution environment, widely used and BSD licensed.
Flask can be classified as a tool in the "Microframeworks (Backend)" category, while Jinja2 is grouped under "Templating Languages & Extensions".
"Lightweight" is the top reason why over 261 developers like Flask, while over 4 developers mention "It is simple to use" as the leading cause for choosing Jinja2.
Flask and Jinja2 are both open source tools. It seems that Flask with 44.8K GitHub stars and 12.6K forks on GitHub has more adoption than Jinja2 with 6.25K GitHub stars and 1.21K GitHub forks.
According to the StackShare community, Flask has a broader approval, being mentioned in 502 company stacks & 509 developers stacks; compared to Jinja2, which is listed in 20 company stacks and 23 developer stacks.
What is Flask?
What is Jinja2?
Need advice about which tool to choose?Ask the StackShare community!
Sign up to add, upvote and see more prosMake informed product decisions
What are the cons of using Jinja2?
Sign up to get full access to all the companiesMake informed product decisions
Sign up to get full access to all the tool integrationsMake informed product decisions
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.
django와 flask에서 html을 다룰때 jinja를 통해 다룹니다. 이것으로 템플릿을 나누어 header, footer를 별도로 관리하며 | 를 사용해 함수를 만들어 데이터를 수정하기도 합니다.
특히 summernote를 이용과 해쉬태그 만들기를 하면서 jinja에 대한 이해가 높아졌습니다.
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