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a microframework for Python based on Werkzeug, Jinja 2 and good intentions.
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What is Flask?

Flask is intended for getting started very quickly and was developed with best intentions in mind.
Flask is a tool in the Microframeworks (Backend) category of a tech stack.
Flask is an open source tool with 45.4K GitHub stars and 12.7K GitHub forks. Here’s a link to Flask's open source repository on GitHub

Who uses Flask?

Companies
662 companies reportedly use Flask in their tech stacks, including Netflix, reddit, and Lyft.

Developers
2819 developers on StackShare have stated that they use Flask.

Flask Integrations

Bugsnag, Sentry, Airbrake, Stormpath, and KeyCDN are some of the popular tools that integrate with Flask. Here's a list of all 9 tools that integrate with Flask.

Why developers like Flask?

Here’s a list of reasons why companies and developers use Flask
Flask Reviews

Here are some stack decisions, common use cases and reviews by companies and developers who chose Flask in their tech stack.

Jeyabalaji Subramanian
Jeyabalaji Subramanian
CTO at FundsCorner · | 12 upvotes · 72.8K views
atFundsCornerFundsCorner
Amazon SQS
Sentry
GitLab CI
Slack
Google Compute Engine
Netlify
AWS Lambda
Zappa
vuex
Vuetify
Vue.js
Swagger UI
MongoDB
Flask
Python

At FundsCorner, we are on a mission to enable fast accessible credit to India’s Kirana Stores. We are an early stage startup with an ultra small Engineering team. All the tech decisions we have made until now are based on our core philosophy: "Build usable products fast".

Based on the above fundamentals, we chose Python as our base language for all our APIs and micro-services. It is ultra easy to start with, yet provides great libraries even for the most complex of use cases. Our entire backend stack runs on Python and we cannot be more happy with it! If you are looking to deploy your API as server-less, Python provides one of the least cold start times.

We build our APIs with Flask. For backend database, our natural choice was MongoDB. It frees up our time from complex database specifications - we instead use our time in doing sensible data modelling & once we finalize the data model, we integrate it into Flask using Swagger UI. Mongo supports complex queries to cull out difficult data through aggregation framework & we have even built an internal framework called "Poetry", for aggregation queries.

Our web apps are built on Vue.js , Vuetify and vuex. Initially we debated a lot around choosing Vue.js or React , but finally settled with Vue.js, mainly because of the ease of use, fast development cycles & awesome set of libraries and utilities backing Vue.

You simply cannot go wrong with Vue.js . Great documentation, the library is ultra compact & is blazing fast. Choosing Vue.js was one of the critical decisions made, which enabled us to launch our web app in under a month (which otherwise would have taken 3 months easily). For those folks who are looking for big names, Adobe, and Alibaba and Gitlab are using Vue.

By choosing Vuetify, we saved thousands of person hours in designing the CSS files. Vuetify contains all key material components for designing a smooth User experience & it just works! It's an awesome framework. All of us at FundsCorner are now lifelong fanboys of Vue.js and Vuetify.

On the infrastructure side, all our API services and backend services are deployed as server less micro-services through Zappa. Zappa makes your life super easy by packaging everything that is required to deploy your code as AWS Lambda. We are now addicted to the single - click deploys / updates through Zappa. Try it out & you will convert!

Also, if you are using Zappa, you can greatly simplify your CI / CD pipelines. Do try it! It's just awesome! and... you will be astonished by the savings you have made on AWS bills at end of the month.

Our CI / CD pipelines are built using GitLab CI. The documentation is very good & it enables you to go from from concept to production in minimal time frame.

We use Sentry for all crash reporting and resolution. Pro tip, they do have handlers for AWS Lambda , which made our integration super easy.

All our micro-services including APIs are event-driven. Our background micro-services are message oriented & we use Amazon SQS as our message pipe. We have our own in-house workflow manager to orchestrate across micro - services.

We host our static websites on Netlify. One of the cool things about Netlify is the automated CI / CD on git push. You just do a git push to deploy! Again, it is super simple to use and it just works. We were dogmatic about going server less even on static web sites & you can go server less on Netlify in a few minutes. It's just a few clicks away.

We use Google Compute Engine, especially Google Vision for our AI experiments.

For Ops automation, we use Slack. Slack provides a super-rich API (through Slack App) through which you can weave magical automation on boring ops tasks.

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David Sugar
David Sugar
Chief at Cherokees Of Idaho · | 7 upvotes · 55.3K views
ExpressJS
Flask
Sinatra
Node.js
PHP
Python
Perl
Ruby
Java
C++
#Piwitch
#SipWitchQt
#Bayonne

My view of the enterprise software stack I think is different than most. I find that I use C++ and #Qt in many of the roles most used Java and typically in #SipWitchQt and #Bayonne. I also have come to adopt Ruby in those other places where I had used Perl, Python , and PHP in the past, and certainly in preference to Node.js. In particular I am starting to really like Ruby and Sinatra over Python and Flask or Node.js with ExpressJS for writing quick web api and microservices, hence why I am using Sinatra in #PiWitch going forward. I do not pick a language because of popularity, but rather based on whether I can be effective in it for the problem I am trying solve.

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Pierre Chapuis
Pierre Chapuis
at Pierre Chapuis · | 5 upvotes · 16K views
atChilliChilli
Gunicorn
Python
SQLAlchemy
Hug
Flask

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.

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StackShare Editors
StackShare Editors
Flask
AWS EC2
Celery
Datadog
PagerDuty
Airflow
StatsD
Grafana

Data science and engineering teams at Lyft maintain several big data pipelines that serve as the foundation for various types of analysis throughout the business.

Apache Airflow sits at the center of this big data infrastructure, allowing users to “programmatically author, schedule, and monitor data pipelines.” Airflow is an open source tool, and “Lyft is the very first Airflow adopter in production since the project was open sourced around three years ago.”

There are several key components of the architecture. A web UI allows users to view the status of their queries, along with an audit trail of any modifications the query. A metadata database stores things like job status and task instance status. A multi-process scheduler handles job requests, and triggers the executor to execute those tasks.

Airflow supports several executors, though Lyft uses CeleryExecutor to scale task execution in production. Airflow is deployed to three Amazon Auto Scaling Groups, with each associated with a celery queue.

Audit logs supplied to the web UI are powered by the existing Airflow audit logs as well as Flask signal.

Datadog, Statsd, Grafana, and PagerDuty are all used to monitor the Airflow system.

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MoonSoo Kim
MoonSoo Kim
Chief Technology Officer at N R I S E - INTERACTIVE / FACTORY · | 1 upvotes · 10.6K views
atnrisenrise
Flask

어플리케이션 서버는 flask 로 작성되었습니다. 미니멀하며, 어플리케이션 서버를 구축하는데 필요한 것들은 대부분 준비되어 있습니다. Flask

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Sharone Zitzman
Sharone Zitzman
Head of Developer Relations at Appsflyer · | 1 upvotes · 10K views
atCloudifyCloudify
Flask

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. Flask

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Flask Alternatives & Comparisons

What are some alternatives to Flask?
Django
Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design.
Tornado
By using non-blocking network I/O, Tornado can scale to tens of thousands of open connections, making it ideal for long polling, WebSockets, and other applications that require a long-lived connection to each user.
ExpressJS
Express is a minimal and flexible node.js web application framework, providing a robust set of features for building single and multi-page, and hybrid web applications.
Node.js
Node.js uses an event-driven, non-blocking I/O model that makes it lightweight and efficient, perfect for data-intensive real-time applications that run across distributed devices.
React
Lots of people use React as the V in MVC. Since React makes no assumptions about the rest of your technology stack, it's easy to try it out on a small feature in an existing project.
See all alternatives

Flask's Stats

Flask's Followers
2755 developers follow Flask to keep up with related blogs and decisions.
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