In kubernetes we can pods on worker nodes and pods share the resources and IP address,but what if we run two diiferent pods on a same worker node does that mean that both the pods will have different IP address?
To answer the main question - yes. A node can and does run different pods. Even if you have only one Deployment you can run
kubectl describe nodes my-node
Or even
kubectl get pods --all-namespaces
To see some pods that kubernetes uses for its control plane on each node.
About the second question, it really depends on your deployment, id recommend on reading about kube proxy which is a pod running on every node! (Regarding your first question) and is in charge of the networking layer and communication within the cluster
https://kubernetes.io/docs/reference/command-line-tools-reference/kube-proxy/
The pods will have their own IP address within that node, and there are ways to directly communicate with pods
https://superuser.openstack.org/articles/review-of-pod-to-pod-communications-in-kubernetes/
https://kubernetes.io/docs/concepts/cluster-administration/networking/
Related
Cluster consists of one master and one worker node. If the master is down and worker is restarted no workloads (deployments) are started on boot. How and if it is possible to make worker resume last state without master?
Kubernetes 1.18.3
On worker node are installed: kubelet, kubectl, kubeadm
Ideally you should have more than one(typically a odd number like 3 or 5) node serving as master and accessible from worker nodes via a LoadBalancer.
The state is stored in ETCD which is accessed by worker nodes via the API Server. So without master nodes running there is no way for workers to know the desired state.
Although it's not recommended you but can use static pod as potential solution here.Static Pods are managed directly by the kubelet daemon on a specific node, without the API server observing them.Unlike Pods that are managed by the control plane (for example, a Deployment ), instead the kubelet watches each static Pod (and restarts it if it crashes).
The caveat of using static pod is since those pods are not dependent on API Server Hence static Pods cannot be managed with kubectl or other Kubernetes API clients.
I have created a K8s cluster on GCP, and I deployed an application.
Then I scaled it:
kubectl scale deployment hello-world-rest-api --replicas=3
Now when I run 'kubectl get pods', I see three pods. Their NODE value is same. I understand it means they all are deployed on same machine. But I observe that IP value for all three is different.
If NODE is same, then why is IP different?
There are several networks in a k8s cluster. The pods are on the pod network, so every pod deployed on the nodes of a k8s cluster can see each other as though they are independent nodes on a network. The pod address space is different from the node address space. So, each pod running on a node gets a unique address from the pod network, which is also different from the node network. The k8s components running on each node perform the address translation.
so i naturally run nvidia-docker and the k8s-device-plugin as a daemonset. as not all my kubernetes worker nodes have gpus, i use a nodeSelector in the daemonset to run just on nodes that i've labeled with accelerator=nvidia.
in another case, i also do the same for ingress-nginx: i label a few nodes that i want and run it as a daemonset. i then have an external (f5) load balancer that holds the VIP to the relevant DNS records for the ingress endpoints (yeah, i know there's a f5 ingress available - its on the todo list).
i've noticed that many users state that daemonsets should only be used for pods that should be running on ALL workers. is there anything inherently bad with my restriction of running daemonsets on a subset of nodes?
It's a valid use case. You can restrict the daemonset to run on the nodes that you want by using node selectors.
In Kelsey Hightower's Kubernetes Up and Running, he gives two commands :
kubectl get daemonSets --namespace=kube-system kube-proxy
and
kubectl get deployments --namespace=kube-system kube-dns
Why does one use daemonSets and the other deployments?
And what's the difference?
Kubernetes deployments manage stateless services running on your cluster (as opposed to for example StatefulSets which manage stateful services). Their purpose is to keep a set of identical pods running and upgrade them in a controlled way. For example, you define how many replicas(pods) of your app you want to run in the deployment definition and kubernetes will make that many replicas of your application spread over nodes. If you say 5 replica's over 3 nodes, then some nodes will have more than one replica of your app running.
DaemonSets manage groups of replicated Pods. However, DaemonSets attempt to adhere to a one-Pod-per-node model, either across the entire cluster or a subset of nodes. A Daemonset will not run more than one replica per node. Another advantage of using a Daemonset is that, if you add a node to the cluster, then the Daemonset will automatically spawn a pod on that node, which a deployment will not do.
DaemonSets are useful for deploying ongoing background tasks that you need to run on all or certain nodes, and which do not require user intervention. Examples of such tasks include storage daemons like ceph, log collection daemons like fluentd, and node monitoring daemons like collectd
Lets take the example you mentioned in your question: why iskube-dns a deployment andkube-proxy a daemonset?
The reason behind that is that kube-proxy is needed on every node in the cluster to run IP tables, so that every node can access every pod no matter on which node it resides. Hence, when we make kube-proxy a daemonset and another node is added to the cluster at a later time, kube-proxy is automatically spawned on that node.
Kube-dns responsibility is to discover a service IP using its name and only one replica of kube-dns is enough to resolve the service name to its IP. Hence we make kube-dns a deployment, because we don't need kube-dns on every node.
i am running ipyparallel in an kube cluster. I have several pods running on one node which is fine. But for my computation i want to help ipyparallel in loadbalancing by choosing pods evenly over all nodes.
Is there a way to get this information from inside the pods/docker?
You could use a Kubernetes Service which does round-robin loadbalancing.
If you need the IP addresses, you could do a DNS A- or SRV-Records lookup and get all IPs of all running instances: http://kubernetes.io/docs/admin/dns/