asyncio vs Flask: What are the differences?
Developers describe asyncio as "Asynchronous I/O, event loop, coroutines and tasks". This module provides infrastructure for writing single-threaded concurrent code using coroutines, multiplexing I/O access over sockets and other resources, running network clients and servers, and other related primitives. On the other hand, Flask is detailed as "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.
asyncio and Flask can be categorized as "Microframeworks (Backend)" tools.
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 asyncio is used by RolePoint, RapidSOS, and Lamoda.ru. Flask has a broader approval, being mentioned in 690 company stacks & 2934 developers stacks; compared to asyncio, which is listed in 5 company stacks and 9 developer stacks.
What is asyncio?
What is Flask?
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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
Investigating Tortoise ORM and GINO ORM...
I need to introduce some async ORM with the current stack: Tornado with asyncio loop, AIOHTTP, with PostgreSQL and MSSQL. This project revolves heavily around realtime and due to the realtime requirements, blocking during database access is not acceptable.
Considering that these ORMs are both young projects, I wondered if anybody had some experience with similar stack and these async ORMs?
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