Many moons ago, I was working at the New York Times and created a library called Store, which was “a Java library for effortless, reactive data loading.” We built Store using RxJava and patterns adopted from Guava’s Cache implementation. Today’s app users expect data updates to flow in and out of the UI without having to do things like pulling to refresh or navigating back and forth between screens. Reactive front ends led me to think of how we can have declarative data stores with simple APIs that abstract complex features like multi-request throttling and disk caching that are needed in modern mobile applications.
Dropbox is a big user of Python. It’s our most widely used language both for backend services and the desktop client app (we are also heavy users of Go, TypeScript, and Rust). At our scale—millions of lines of Python—the dynamic typing in Python made code needlessly hard to understand and started to seriously impact productivity. To mitigate this, we have been gradually migrating our code to static type checking using mypy, likely the most popular standalone type checker for Python. (Mypy is an open source project, and the core team is employed by Dropbox.)
Dropbox has been one of the first companies to adopt Python static type checking at this scale.
Open source is not just for software. The same benefits of rapid innovation and community validation apply to hardware specifications as well. That’s why I’m happy to write that the v1.0 of the RunBMC hardware spec has been contributed to Open Compute Project (OCP). Before I get into what BMCs (baseboard management controllers) are and why modern data centers are dependent on them, let’s zoom out to what companies operating at cloud scale have learned.
Cloud software companies like Dropbox have millions, and in some cases, billions of users. When these cloud companies started building out their own data centers,
Until very recently, Dropbox had a technical strategy on mobile of sharing code between iOS and Android via C++. The idea behind this strategy was simple—write the code once in C++ instead of twice in Java and Objective C. We adopted this C++ strategy back in 2013, when our mobile engineering team was relatively small and needed to support a fast growing mobile roadmap. We needed to find a way to leverage this small team to quickly ship lots of code on both Android and iOS.
We have now completely backed off from this strategy in favor of using each platforms’ native languages (primarily Swift and Kotlin,
Before We Get Started
This article assumes a working knowledge of Redux, React, React-Redux, TypeScript, and uses a little bit of Lodash for convenience. If you’re not familiar with those subjects, you might need to do some Googling. You can find the final version of all the code here.
Redux has become the go-to state management system for React applications. While plenty of material exists about Redux best practices in Single Page Applications (SPAs), there isn’t a lot of material on putting together a store for a large, monolithic application.
The Dropbox desktop client is relied on by millions of users across the world to save their most important files and keep them in sync across their devices. Weighing in at over 1 million lines of Python logic, we had a massive surface area for potential issues in our migration from Python 2 to Python 3. In this process, we knew that we had to be worthy of the trust that users place in Dropbox and keep their information safe.
Over the last few months, we’ve explored why and how we rolled out our Python 3 migration,