NFS is a commonly used storage option for Kubernetes, but it is not as reliable as some other options such as persistent volumes or distributed file systems. This is because NFS relies on a single server for storage, which can be a single point of failure. Additionally, NFS can experience performance degradation when handling high volumes of data or a large number of concurrent connections.

While I don't have extensive experience with it, I believe Istio and other service meshes are built for this (and more complex things). https://www.alibabacloud.com/blog/597011 Looks like it has a "global" DNS name format for inter cluster routing in addition to the local format. Services in the same Kubernetes cluster have the same DNS suffix, for example, svc.cluster.local. Kubernetes DNS provides the DNS resolution capability for these services. To deploy a similar configuration for services in the remote cluster, we name the services in the remote cluster in the .global format. The Istio installation package includes a CoreDNS server that provides the DNS resolution capability for these services. To use the DNS resolution capability, you need to direct the DNS service of Kubernetes to the CoreDNS service. Then, the CoreDNS service will act as the DNS server for the .global DNS domain.

Your ip show does mention 10.42.0.0/32, 10.42.0.200/32. No traces of 10.42.0.232, though. From LAN/outside SDN: can you connect any TCP port, or ping that address? anything that answers? I suspect nothing would. In addition to that Traefik rule, you mentioned creating a Service ... Are you sure you did it right? Look at kubectl get svc -A | grep 10.42.0.200, that would give you a working example -- something with an externalIP/externalIPs set, or some status.loadbalancer.ingress array ... configuration may vary, depending on how external IPs were implemented and kube version, .. And by the way, creating such Services: are you pointing to your application directly, or is it sent to Traefik instead? Assuming the latter: are you sure Traefik actually listens on such port. Also, while Traefik should be able to process TCP or UDP trafic, unless you can easily have several Services & backends accessible through the same proto/port combination: involving your ingress controller here might not be necessary.

Can you share output of kubectl describe pod mongodb-arbiter-0 Mainly interessting are events at the end if possible within timeframe when you start the deployment until it crashes.

The "PF" field refers to port forwarding. You may setup port forwarding, ("shift-f" in pod view). This indicator probably means you have setup some port forwarding -- in my case, it prints some weird character. Although to be honest ... I only figured it out, searching their git repository history .... Introduced here: https://github.com/derailed/k9s/commit/f1111174aa8df815e25b0a029ba1465d3a855b49#diff-5b19eaab395254884c7dff045b0e50a599c29794e80a81fe42c9667450b6e9d4

path is used to declare the service's routing address / interface address / API When using livenessProbe.httpGet as a health checker, a health query is required, i.e. it calls a uri to confirm the service health based on the returned status code The composition of this uri: scheme://podIp:port/path == http://10.244.171.183:8080/index.html

You want to use two services. One of your services should set some spec.publishNotReadyAddresses: true. That one you would use for internal communications, initializing your cluster while Pods could still be not Ready. And the other service exposing your cluster to its clients, observing the defaults readiness. Eg, for redis, I would use something like: apiVersion: v1 kind: Service metadata: name: redis-kube spec: clusterIP: None ports: - name: tcp-6379 port: 6379 protocol: TCP targetPort: 6379 - name: tcp-26379 port: 26379 protocol: TCP targetPort: 26379 publishNotReadyAddresses: true selector: name: redis-kube sessionAffinity: None type: ClusterIP Also: when trying to resolve name for the other containers in your cluster, instead of nslookup app-0, try nslookup .. to resolve app1-0, you would use nslookup app1-0.app1cluster.app or nslookup app1-0.app1cluster.app.svc.cluster.local

You can choose which CRI you would like to use directly from the join command by adding this flag: (see kubeadm join --help for details) --cri-socket string here, string will be one of the two available sockets displayed in the error: unix:///var/run/containerd/containerd.sock unix:///var/run/cri-dockerd.sock Or you can also add it to you kubeadm config as stated see doc here: https://kubernetes.io/docs/reference/config-api/kubeadm-config.v1beta3/#kubeadm-k8s-io-v1beta3-NodeRegistrationOptions

After a lot of playing around, I came to a working solution that I briefly mentioned in a comment in the original question. The CI is now creating a namespace on the cluster, running the dry run apply and then deleting the namespace when finished. Not sure if this is the perfect solution but it's working as I hoped. helm template . \ --values common/values-common.yaml \ --values variants/$VARIANT/values-$VARIANT.yaml \ --name-template=github-actions-test \ --set image.tag=github-actions-test \ --namespace $NAMESPACE \ --debug > dry-run.yaml kubectl create namespace $NAMESPACE --dry-run=client -o yaml | kubectl apply -f - echo "errors=$(kubectl create -f dry-run.yaml -n $NAMESPACE --dry-run=server -o yaml 2>&1 > /dev/null)" >> $GITHUB_OUTPUT kubectl delete namespace $NAMESPACE

Figured out a way, but it might not be the most elegant. First, we render the chart locally and then use tree to print out the subdirectories with their sizes in a human-readable format. helm template . --output-dir=file-size-test tree --du -h file-size-test This produces an output similar to: [340M] testing123 └── [340M] chart_name ├── [ 91K] charts │ ├── [ 58K] sub-chart │ │ └── [ 58K] templates │ │ ├── [1.3K] deployment.yaml │ │ ├── [ 411] config.yaml │ │ ├── [ 579] service.yaml │ │ └── [ 359] serviceaccount.yaml └── [340M] templates ├── [ 68M] deployment.yaml ├── [136M] config1.yaml ├── [136M] config2.yaml └── [1.3K] ingress.yaml So now I've found that the issue seems to be the massive config1.yaml and config2.yaml files. While this doesn't exactly answer the question of how to find which have been ignored, it at least points to which might not have been ignored.

The load balancer will connect to the default security group by default. This security group has a source of itself, which basically means that it blocks everything. Add the inbound rule of all traffic from "Anywhere-IPv4" to ensure that all traffic can access the load balancer.

Got it working with this helm github action https://github.com/marketplace/actions/helm-3 add secret to github as your .kube/config and this workflow step: steps: - name: 'check it out' uses: actions/checkout@v3 - name: helm-deploy uses: WyriHaximus/github-action-helm3@v2.0 with: exec: helm upgrade logstash /github/workspace/elk/logstash/ --install --wait --atomic --namespace=default --set=app.name=logstash --values=/github/workspace/elk/logstash/values.yaml kubeconfig: '${{ secrets.KUBECONFIG }}'

Given how kubernetes is intended for high availability online services, I'm surprised the topic isn't more clearly consolidated in the kubernetes documentation (or more prominent in both the CKA and CKS curriculum). I did find docs from https://docs.aws.amazon.com/eks/latest/userguide/update-workers.html and https://cloud.google.com/kubernetes-engine/docs/concepts/node-pool-upgrade-strategies , covering different strategies (and technicalities) for node pool upgrades. There are also a number of related ongoing kubernetes feature https://github.com/kubernetes/kubernetes/issues/4301 , to more intelligently move pods. OS patches do not strictly necessitate a new AMI (machine image), but kernel and glibc patches do require a restart (disrupting all pods anyway) and a fresh AMI ensures consistency between nodes. To drain a node has the effect of cordoning it off (that is, tainting it with an annotation to prevent scheduling of new pods) and (gracefully) evicting its current pods. Note, it does not affect static/mirror pods (which are outside of API control) and daemonset pods (since their controller ignores the taint). The kubectl drain command blocks until complete (requiring parallel invocations to drain multiple nodes), and requires a --force argument if the node hosts any bare pods (pods with no controller to replace them). Pod disruption budgets are objects created to specify how many of an application's pods should be kept available during voluntary disruptions. For a long-running stateful application, a restrictive PDB can be used to prohibit certain pods from being evicted (which prevents drain from succeeding). The cluster autoscaler deployment is responsible for instructing ASGs (AWS EC2 autoscaling groups) to launch additional (suitable) instances if there exist (pending) pods with no place (node) to be scheduled to. It also drains and terminates instances, but only after several conditions are all satisfied for an extended period (10min): the node has low utilisation (less than half its capacity requested by its hosted pods); there is suitable space available on other nodes (accounting for affinities, etc); the autoscaler would dare to evict the pods (no inhibiting disruption budgets or annotations, no kube-system pods other than static or daemonset or with explicit disruption budgets, no bare pods, no local storage); and the cluster did not need more nodes. (Note cluster autoscaling is intentionally not concerned with actual CPU load. Also differs from default ASG behaviour by not preferentially terminating the instances with oldest launch templates.) You could manually remove an instance with the command aws autoscaling terminate-instance-in-auto-scaling-group --instance-id --should-decrement-desired-capacity. (Note, may have to use --no-should-.. if the ASG is already at its MinSize.) The kubelet should attempt (using systemd) to detect whenever an instance is shutting down, and to delay it by a grace period so that remaining pods have an opportunity to shut down cleanly. The node controller is responsible for creating node objects which represent the currently available instances, and deleting those nodes when the corresponding instance is terminated. Note that deletions in kubernetes generally cascade. There may be cases where node deletion could discard data that a pod had claimed from a dynamically provisioned "persistent" volume. Obviously any data stored locally on the node will be jettisoned. When a set of replica pods is scaled down (such as for deployments using horizontal pod autoscalers) the replicaset controller preferentially deletes a pod that has been ready for a shorter time (or none), as well as trying to leave the pods more evenly spread between nodes. This means that load fluctuations tend to only relinquish the youngest nodes, and will not organically migrate pods from cordoned older nodes to newer nodes unless the older pods were manually annotated with negative deletion costs. During a rollout restart, the deployment controller maintains pod availability within a tolerance (by default ±25%, rounded upward). Thus, for deployments with less than 4 replicas, a rollout restart will ready a healthy replacement pod before it deletes each original pod. This surge mechanism avoids disruption to the application (whereas draining relies on rectification by other controllers after a disruption). Rollout is an alternative approach that could be used to relocate pods from cordoned nodes onto other (new) nodes.

Kubelet would usually bind to whichever interface holds your default gateway. I suspect your SDN pods are failing to start up due to this. At that stage, I would check for netstat, ip routes and iptables rules. You may want to remove the internal network thing. Deploying Kubernetes, it is recommended for your nodes to run in a LAN, using loadbalancers to expose your ingresses. Otherwise, you need your default gateway to point to your internal network. And then, you'll need some kind of router, NAT-ing traffic while sending it back to your bridged network.

Running means that container(s) in your Pod is/are not crashed. Entrypoint started, and command is still running. Creating that yaml (assuming we fixed case / caps characters aren't at the right place, ...). Pod will be Running. Why: because process has no reason to crash. Because if I inspect that image it uses, entrypoint/cmd is just some /bin/sh -c /bin/sh. Shell will start and keep running.

I've figured it out Lables in K8S are for filtering, and not sorting. I've just added a version field outside labels and it worked.

On inspection $ kubectl describe pod hello-minikube $ kubectl get nodes I found that I was running x86 container on ARM Macbook M1. Installed on x86 Linux and everything works as expected https://github.com/kubernetes/minikube/issues/13129

I had the same issue when trying Microsofts tutorial. Adding host under the rules section worked for me. Example: spec: ingressClassName: nginx rules: - host: {your_app}.northeurope.cloudapp.azure.com http: paths: - path: /test(/|$)(.*) pathType: Prefix backend: service: name: mockserver-service port: number: 1234

Helm upgrade is making revisions to the deployment which means, you can roll back to the last deployment. regarding the restart of current pods, it will happen based on your definition of Deployment.spec.strategy.rollingUpdate will recreate the pods!

You have commented out the count check of the aws_eks_cluster.eks data source that would make the data source conditional, depending on the k8s_cluster_type setting. Uncommenting the count argument should make it skip the data source and not complain about it not being found. data "aws_eks_cluster" "eks" { count = var.k8s_cluster_type == "oke" ? 0 : 1 name = var.eks_cluster_name }