Observe & Troubleshoot Your Kubernetes Environments with Dynamic Service Graph

Kubernetes workloads are highly dynamic, ephemeral, and are deployed on a distributed and agile infrastructure. Application developers, DevOps teams, and site reliability engineers (SREs) often require better visibility of their different microservices, what their dependencies are, how they are interconnected, and which other clients and applications access them. This makes Kubernetes observability challenges unique. While Kubernetes helps to meet the needs of deploying and managing distributed applications, its observability challenges require a Kubernetes-native approach.

Traditional monitoring and observability solutions create data silos by collecting data at different levels (e.g. infrastructure, cluster, and application levels), or from a large number of ephemeral objects that generate data across a distributed environment. Traditional monitoring and observability solutions then stitch this data together to provide a near real-time snapshot view. This approach is not scalable given the high volume of granular data generated at each level, as well as Kubernetes’ distributed nature. It also starts to become expensive and budget unfriendly to run traditional monitoring solutions, as they require higher resource consumption (high-performance memory, more compute, and higher bandwidth).

In contrast, a Kubernetes-native observability solution can visualize all information with all relationship context intact and provide a high-fidelity view of the environment. This Continue reading

Top Three Takeaways from DockerCon 2017

Open source collaboration, persistent storage, and security were some themes to keep in mind.