For the third part of this Fleet Management series, we’ll discuss what we call “fleet-wide refactoring” of code across thousands of Git repos: the tools we’ve built to enable fleet-wide refactoring and make it an everyday occurrence, how we rolled them out, and lessons we’ve learned along the way.

At Spotify, we adopted the declarative infrastructure paradigm to evolve our infrastructure platform’s configuration management and control plane approach, allowing us to manage hundreds of thousands of cloud resources across tens of thousands of different services at scale.

Instead of performing 10 major software upgrades to our infrastructure every year, what if we did 10,000 small ones? Across our entire codebase? That’s the idea behind Fleet Management: by building automation tools that can safely make changes to thousands of repos at once, we can maintain the health of our tech infrastructure continuously (instead of slowly and laboriously). More importantly, removing this low-level work from our developers’ to-do lists allows product teams to focus on solving problems way more interesting than migrating from Java 17.0.4 to 17.0.5. A healthier, more secure codebase, plus happier, more productive engineers. What’s not to like? In this first post about Fleet Management at Spotify, we describe what it means to adopt a fleet-first mindset — and the benefits we’ve seen so far.

All of us are familiar with overflow bugs. However, sometimes you write code that counts on overflow. This is a story where overflow was supposed to happen but didn’t, hence the name underflow bug.

At Spotify we take user feedback seriously. We noticed some users complaining about our shuffling algorithm playing a few songs from the same artist right after each other. The users were asking “Why isn’t your shuffling random?”. We responded “Hey! Our shuffling is random!”

So who was right? As it turns out, both we and the users were right but it’s a bit more complicated than that. It also tells a nice story about how to interpret users’ feedback.

只有当我们用正确的流失数据才有可能通过分析来正确的指导决策。本文介绍的错误都是很常见的哦！​

Daniel Ek on how he spends his time, learns and makes decisions.

技术升级或者技术迁移通常很容易烂尾，一般各大公司都有不少这样的烂尾项目。怎样解决这一问题，Spotify用自己的实践给出答案。