What is Flask?
Who uses Flask?
Why developers like Flask?
Here are some stack decisions, common use cases and reviews by companies and developers who chose Flask in their tech stack.
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.
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.
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.
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.
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