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Visual Studio Code worked really well for us as well, it worked well with all our polyglot services and the .Net core integration had great cross-platform developer experience (to be fair, F# was a bit trickier) - actually, each of our team members used a different OS (Ubuntu, macos, windows). Our production deployment ran for a time on Docker Swarm until we've decided to adopt Kubernetes with almost seamless migration process.
After our positive experience of running .Net core workloads in containers and developing Tweek's .Net services on non-windows machines, C# had gained back some of its popularity (originally lost to Node.js), and other teams have been using it for developing microservices, k8s sidecars (like https://github.com/Soluto/airbag), cli tools, serverless functions and other projects...
I've used .NET for many years, but only in recent years, after Microsoft introduced .NET Core, I've found a new love and excitement for the technology again. The main driver for us using .NET Core is not that it is cross platform compatible, open source or blazingly fast (which it is!), but the fact that we can use (what we consider) the best programming languages (mainly F# and C#) to carry out our jobs without sacrificing the other benefits.
Today we run most of our web infrastructure on .NET Core in Docker containers, deployed into a Kubernetes cluster which spans across multiple time zones in the Google Cloud and we couldn't be happier. Due to the portability of the .NET Core platform we are even able to develop many new services as serverless functions with F# which has become an absolute game changer.
How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:
Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.
Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:
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