from Docker Compose on a single machine, to Heroku and similar systems, to something like Snakemake for computational pipelines.

if what you care about is downtime, your first thought shouldn’t be “how do I reduce deployment downtime from 1 second to 1ms”, it should be “how can I ensure database schema changes don’t prevent rollback if I screw something up.”

The features Kubernetes provides for reliability (health checks, rolling deploys), can be implemented much more simply, or already built-in in many cases. For example, nginx can do health checks on worker processes, and you can use docker-autoheal or something similar to automatically restart those processes.

Scaling for many web applications is typically bottlenecked by the database, not the web workers.

Kubernetes might be useful if you need to scale a lot. But let’s consider some alternatives

Distributed applications are really hard to write correctly. Really. The more moving parts, the more these problems come in to play. Distributed applications are hard to debug. You need whole new categories of instrumentation and logging to getting understanding that isn’t quite as good as what you’d get from the logs of a monolithic application.

you need to spin up a complete K8s system just to test anything, via a VM or nested Docker containers.

“Kubernetes is a large system with significant operational complexity. The assessment team found configuration and deployment of Kubernetes to be non-trivial, with certain components having confusing default settings, missing operational controls, and implicitly defined security controls.”

Before you can run a single application, you need the following highly-simplified architecture

the Kubernetes codebase has significant room for improvement. The codebase is large and complex, with large sections of code containing minimal documentation and numerous dependencies, including systems external to Kubernetes.

Kubernetes has plenty of moving parts—concepts, subsystems, processes, machines, code—and that means plenty of problems.

Knative

Kubernetes

Installing runtime

apt-get update && apt-get install -y apt-transport-https curl curl -s https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add - cat <<EOF >/etc/apt/sources.list.d/kubernetes.list deb https://apt.kubernetes.io/ kubernetes-xenial main EOF apt-get update apt-get install -y kubelet kubeadm kubectl apt-mark hold kubelet kubeadm kubectl

Joining your nodes

sudo -i
apt-get update && apt-get upgrade -y
apt-get install -y docker.io

apt-get update && apt-get install -y apt-transport-https curl
curl -s https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add -
cat <<EOF >/etc/apt/sources.list.d/kubernetes.list
deb https://apt.kubernetes.io/ kubernetes-xenial main
EOF
apt-get update
apt-get install -y kubelet kubeadm kubectl
apt-mark hold kubelet kubeadm kubectl

OpenShift vs Kubernetes

Pipeline de CI/CD no Kubernetes usando Jenkins e Spinnaker

这张图给出了谷歌在2015年提出的Inception-v3模型。这个模型在ImageNet数据集上可以达到95%的正确率。然而，这个模型中有2500万个参数，分类一张图片需要50亿次加法或者乘法运算。