We revisited our distributed tracing setup and incorporated Kubernetes pod metadata into it, significantly enhancing our engineers’ ability to troubleshoot problems that cut across microservices.
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September 18th, 2018 Engineering Microservices Using Kubernetes Pod Metadata to Improve Zipkin Traces By Steve Conover
August 31st, 2018 Engineering Monitoring Testing Hands-Off Deployment with Canary By Jorge Creixell and Tobias Schmidt
At SoundCloud, we follow best practices around continuous delivery, i.e. deploying small incremental changes often (many times a day). In order to improve the user experience, we’ve been exploring different ways of reducing the impact and the Mean Time to Recovery (MTTR) of faulty deployments. Enter canary releases.
August 14th, 2018 Engineering Creating Readable Spark Jobs By Luciano Molinari
July 26th, 2018 Management Engineering Getting a Team Back on Track By Arbo von Monkiewitsch
Sometimes, an important team that’s part of an otherwise healthy company culture starts tanking and the people on the team get frustrated and even quit.
In this article, I want to share what I learned when I started to manage a team — referred to as the R Team from here on out — that had huge problems when I took over as Engineering Manager, as well as explain how I got it back on track.
July 10th, 2018 Architecture Data Keeping Counts In Sync By Lorand Kasler
Track play counts are essential for providing a good creator experience on the SoundCloud platform. They not only help creators keep track of their most popular songs, but they also give creators a better understanding of their fanbase and global impact. This post is a continuation of an earlier post that discussed what we do at SoundCloud to ensure creators get their play stats (along with their other stats), both reliably and in real time.