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Around the time of their Series A, Pinterest’s stack included Python and Django, with Tornado and Node.js as web servers. Memcached / Membase and Redis handled caching, with RabbitMQ handling queueing. Nginx, HAproxy and Varnish managed static-delivery and load-balancing, with persistent data storage handled by MySQL.
Some may wonder why did we choose Grails ? Really good question :) We spent quite some time to evaluate what framework to go with and the battle was between Play Scala and Grails ( Groovy ). We have enough experience with both and, to be honest, I absolutely in love with Scala; however, the tipping point for us was the potential speed of development. Grails allows much faster development pace than Play , and as of right now this is the most important parameter. We might convert later though. Also, worth mentioning, by default Grails comes with Gradle as a build tool, so why change?
Play is a central framework/component/library (not sure what to call things these days) in Scala. We <3 Scala, and therefore we <3 Play.
Play is on of several frameworks we are prototyping and vetting for various public-facing websites, and may ultimately be the framework behind the main datapile.io website.
SpreadServe's RealTimeWebServer is built in Tornado. Spreadsheets loaded into SpreadServeEngine instances are projected into browsers using Tornado. Server side recalcs are pushed to the browser using web sockets.
setup an api for a client with tornado backend. incredibly fast and lightweight. unfortunately breaks down when using third party libraries which block internally.
I used Play to build a configuration UI for the service, which let you create and manage the menus (a hierarchical tree of options and actions).
Tornado with Async/Await coroutines provided in Python 3.5 make up for an excellent stack for a micro-service.