Reactive Programming in Python





Python 難易度



In relation to Internet of Things, micro services, and big data, a developer is easily being expected to handle the stream of data flow. A growing fantasy of Reactive programming is being told that a paradigm can help people to face these challenges in theory and practice and to make life easier. Is it True? Or, does it SCALE?

Join the quest to to discover reactive design and data workflow implemented in Python. We’ll inspect their features and use cases of reactive programming, to name a few, Python built-in, PyFunctional, RxPy, Flexx, async and await (and asyncio), Promise, … etc., study their best practices, and discover the elegant part compared with commonly seen sequential chaining. We also want to know when it may complicate your code.

Keywords: functional, asynchronous, map, data flow, stream, react


Reactive programming is “a programming paradigm oriented around data flows and the propagation of change.” — Wikipedia “... and not just for react.js example.” -- me. To figure out the tradeoff between decisions made on using/implement of reactive pattern will be a interesting adventure, which should bring up different perspectives and design decisions on coding style and architecture.

Keith Yang

最近覺得邊騎室內腳踏車邊用電腦,離開臉書與 IG 的精神(神經?)生活很不賴。讓這隻小白鼠從大眾心理控制實驗學裡喘了一小口氣。

Recently he enjoys skateboard commute, still coffee-achemy, and indoor cycle while programming or gaming, an awesome mind vocation of leaving FB and IG.

Keith is the founder and co-organizer of, largest Python user group in Taiwan, a Lead Software Engineer at iCHEF, and was Chairperson of PyCon APAC 2015. His work mostly focuses on web/backend/cloud services since 2006, and he hands on kernel tools of virtualization on hypervisors in 2016.