What is Flask?
Who uses Flask?
Here are some stack decisions, common use cases and reviews by companies and developers who chose Flask in their tech stack.
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
One of our top priorities at Pinterest is fostering a safe and trustworthy experience for all Pinners. As Pinterest’s user base and ads business grow, the review volume has been increasing exponentially, and more content types require moderation support. To solve greater engineering and operational challenges at scale, we needed a highly-reliable and performant system to detect, report, evaluate, and act on abusive content and users and so we created Pinqueue.
Pinqueue-3.0 serves as a generic platform for content moderation and human labeling. Under the hood, Pinqueue3.0 is a Flask + React app powered by Pinterest’s very own Gestalt UI framework. On the backend, Pinqueue3.0 heavily relies on PinLater, a Pinterest-built reliable asynchronous job execution system, to handle the requests for enqueueing and action-taking. Using PinLater has significantly strengthened Pinqueue3.0’s overall infra with its capability of processing a massive load of events with configurable retry policies.
Hundreds of millions of people around the world use Pinterest to discover and do what they love, and our job is to protect them from abusive and harmful content. We’re committed to providing an inspirational yet safe experience to all Pinners. Solving trust & safety problems is a joint effort requiring expertise across multiple domains. Pinqueue3.0 not only plays a critical role in responsively taking down unsafe content, it also has become an enabler for future ML/automation initiatives by providing high-quality human labels. Going forward, we will continue to improve the review experience, measure review quality and collaborate with our machine learning teams to solve content moderation beyond manual reviews at an even larger scale.
We initially though we would use Django because it seemed to have a lot of the things we needed out of the box. After a bit of research we realized that using Flask would be a better option since it is more flexible and would be lighter for our purposes. Having set up our REST api using Flask we believe that we did make the right decision. We found that the flexibility of Flask along with the many extensions available for it to be very appealing. We were able to add the functionality we needed without much difficulty thanks to the quality of the extensions and their documentation.
I need your help in choosing either Spring Boot or ASP.NET Core. Both frameworks seem to have mature ecosystems. I would like to hear your thoughts on the following points:
- Difficulty level of both frameworks
- Level of community support
- Career prospects i.e do Spring based jobs pay more or vice versa
- which one will be helpful if I decide to transition towards a more specialized field like data engineering.
I am asking this because it is something that I am also exploring in parallel. I know that Python and #SQL play a huge role in big data.
I'm planning to build a cloud-based project for satellite image browsing, analysis, and downloading. Satellite image includes Sentinel-2, Landsat 8, MODIS, and other more open-source Earth observation datasets, assuming that time span is limited to the recent few years given a relatively limited storage space (i.e., TB-level). Previously, I chose Python due to its advantage of downloading and preprocessing satellite images, and consequently chose Flask as a microservice for scientific calculation. There was a separate service for tile map service to publish tms with stored satellite images using
geotrellis. All I want to accomplish is similar to LandViewer. I want to know if it is applicable and scalable to build a fullstack framework using Django? Plus, is it only necessary to store downloaded original satellite image in advance? Any advice on schema design or tech stacks sharing would be appreciated.