community

offs when compared with tools like rkt and podman. I think it's a mistake to standardize on Dockerfiles, but it's definitely a good idea to standardize on the CRI (which Docker now adheres to via a containerd shim by default). The best thing by far about kubernetes is the CRI[1], CNI[2] (Container Networking Interface), and CSI[3] (Container Storage Interface) standards that are coming out of it. I don't think any one realizes it yet, but they are inadvertently building secure/sandboxed computing for everyone. Containers are just sandboxed processes, and before this, most people were running basically completely unsandboxed processes (both in the cloud and on their personal computers) -- once all this stuff lands, linux is going to have some amazing features for running applications more safely -- production-grade safety for any application you run on your own machine available with standardized tooling. [0]: https://github.com/kubernetes/enhancements/blob/master/keps/... [1]: https://github.com/kubernetes/community/blob/master/contribu... [2]: https://github.com/containernetworking/cni/ [3]: https://kubernetes-csi.github.io/

uess it depends of point of view and interests.

at Google.]

se there's no performance cliff, it's just the maximum number of nodes you can run today if you want Kubernetes to meet the Kubernetes performance SLOs given the workload in the scalability tests.) [Disclaimer: I work on Kubernetes/GKE at Google.]

: * Go was chosen over C++ because, like C++, it is a systems language, but is much more accessible for building an open-source community. * Docker was (and still is) by far the most popular container runtime, and the slimmer containerd makes it even more appropriate to serve as the container runtime for a system like Kubernetes. While it's true that in Borg the container runtime and "package" (container image) management systems are separate, the tradeoffs between packaging more in the image vs. pre-installing dependencies on the host are exactly the same as with Docker images. In any event, it's very feasible to build very slim Docker images (you definitely don't need getty in your image :-). * You can read the reasons etcd was chosen in this recent comment (https://news.ycombinator.com/item?id=17476142) from a Red Hat employee who is one of the earliest contributors to Kubernetes and one of the most prolific. Regarding consensus, I didn't understand your comment; Borg uses Paxos and etcd uses Raft, but those are basically equivalent algorithms. Regarding scalability, we do continuous scalability testing as part of the Kubernetes CI pipeline, at a cluster size of 5000 nodes. If you're interested in learning more, I'd encourage you to joint the scalability SIG (https://github.com/kubernetes/community/tree/master/sig-scal...). I'm not aware that "messaging around Kubernetes has gravitated toward smaller, targeted clusters." It's true that a lot of people do use small-ish clusters, but AFAICT that's not because of scalability limitations, but rather because (1) the hosted Kubernetes offerings make it so easy to spin up clusters on demand, and (2) until recently, Kubernetes was lacking critical multi-tenancy features that would allow, say, multiple teams within a company to safely share a cluster. Regarding mixing batch and interactive/serving applications in a single cluster managed by a single control plane, this has been the intention of Kubernetes from the beginning. It's true that open-source batch systems like Hadoop and Spark have traditionally shipped with their own orchestrators/schedulers, but that's starting to change as Kubernetes becomes more popular, for example Spark now supports Kubernetes natively (https://kubernetes.io/blog/2018/03/apache-spark-23-with-nati...). In terms of features that enable batch and serving workloads to share a node and a cluster, Kubernetes has had the concept of QoS classes (https://kubernetes.io/docs/tasks/configure-pod-container/qua...) from the beginning, and as of the most recent Kubernetes release we now have priority/preemption (https://cloudplatform.googleblog.com/2018/02/get-the-most-ou...). QoS classes and priority/preemption are the two main concepts that allow batch and interactive/serving application to share nodes and clusters in Borg, and we now have them in Kubernetes. On your fifth point, I agree that this is one of the strengths of the Google production environment, but Kubernetes is limited in how prescriptive it can be in dictating how people write applications, since we want Kubernetes to work with essentially any application. This is why we have, for example, extremely flexible liveness/readiness probes in Kubernetes (https://kubernetes.io/docs/tasks/configure-pod-container/con...) rather than the expectation that every application has a built-in web server that exports a predefined /statusz endpoint. That said, we have been more prescriptive in how to build Kubernetes control plane components (for example such components generally have /healthz endpoints and export Prometheus instrumentation according to the guidelines outlined at https://github.com/kubernetes/community/blob/master/contribu...). Over time as containers and the "cloud native" architecture become more popular, I think there will be more standardization in the ways you described when people see the benefits it provides in allowing them to plug in their app immediately to standard container ecosystems. To some extent Istio (https://github.com/istio/istio) is a step in that direction, and in some sense even better because it interposes transparently rather than requiring you to build your application a particular way. For anyone interested in learning more about the evolution of cluster management systems at Google, I recommend this paper: https://ai.google/research/pubs/pub44843 While Kubernetes is definitely not the same codebase as Borg, I do think it's accurate to say that it is the descendant of Borg.

hat'll do it for you: https://github.com/kubernetes-incubator/external-dns