Kubernetes will be using the same server or we can use multiple servers with k8s. if yes then how it will be work ?
In case of one instance full then would it create a new instance to route everything to the new server?
If anyone can show a real example of K8s then it would be great!
For this I can suggest Kubernetes docs to start reading from but briefly,
Kubernetes deals with resources or networking in the Master nodes (Control Plane).
Worker nodes simply have the kube-proxy and basic control mechanisms coming from kubelet service. You still can not control your cluster from worker nodes.
And yes K8s can use multiple servers for LoadBalancing. This is a Possibility.
When it comes to K8s you do not have to work in a single zone so therefore you do not have to have all the pods in the same server.
So, in a single zone if you have one master and multiple worker nodes you will be using Master's scheduler and LoadBalancer to manage the resources or the traffic if necessary. If you have multiple Master nodes, then you will be using Masters' schedulers and etc.
For a real example of K8s just search for Highly-Available Kubernetes Clusters and switch to Images section. You can have a visualized opinion about them that way.
I hope I was a little bit of help. But the docs could be more helpful I suppose.
Related
I know many services provided a managed control plane. GKE, Digital Ocean. I want a Kubernetes cluster using those service as they provide reliablity. But I want to expand my kubernetes cluster using a number of physical machines, old machines, raspberry pis etc that I have in my local office.
Is it possible? If so, how can I do that?
I found two questions in stackoverflow related to this, but both go with unsatisfactory answers
Add external node to GCP Kubernetes Cluster
Here the answer seems to be using kubeadm init - which really is not about adding a worker node, but doing making the control plane HA.
GKE with Aws worker nodes
Says it is possible, but does not mention how to add external worker nodes.
I am new to the Kubernetes, and I'm trying to understand that how can I apply it for my use-case scenario.
I managed to install a 3-node cluster on VMs within the same network. Searching about K8S's concepts and reading related articles, still I couldn't find answer for my below question. Please let me know if you have knowledge on this:
I've noticed that internal DNS service of K8S applies on the pods and this way services can find each other with hostnames instead of IPs.
Is this applicable for communication between pods of different nodes or this is only within the services inside a single node? (In other words, do we have a dns service on the node level in the K8S, or its only about pods?)
The reason for this question is the scenario that I have in mind:
I need to deploy a micro-service application (written in Java) with K8S. I made docker images from each service in my application and its working locally. Currently, these services are connected via pre-defined IP addresses.
Is there a way to run each of these services within a separate K8S node and use from its DNS service to connect the nodes without pre-defining IPs?
A service serves as an internal endpoint and (depending on the configuration) load balancer to one or several pods behind it. All communication typically is done between services, not between pods. Pods run on nodes, services don't really run anything, they are just routing traffic to the appropriate pods.
A service is a cluster-wide configuration that does not depend on a node, thus you can use a service name in the whole cluster, completely independent from where a pod is located.
So yes, your use case of running pods on different nodes and communicate between service names is a typical setup.
I try to deploy a set of k8s on the cloud, there are two options:the masters are in trust to the cloud provider or maintained by myself.
so i wonder about that if the masters in trust will leak the data on workers?
Shortly, will the master know the data on workers/nodes?
The abstractions in Kubernetes are very well defined with clear boundaries. You have to understand the concept of Volumes first. As defined here,
A Kubernetes volume is essentially a directory accessible to all
containers running in a pod. In contrast to the container-local
filesystem, the data in volumes is preserved across container
restarts.
Volumes are attached to the containers in a pod and There are several types of volumes
You can see the layers of abstraction source
Master to Cluster communication
There are two primary communication paths from the master (apiserver) to the cluster. The first is from the apiserver to the kubelet process which runs on each node in the cluster. The second is from the apiserver to any node, pod, or service through the apiserver’s proxy functionality.
Also, you should check the CCM - The cloud controller manager (CCM) concept (not to be confused with the binary) was originally created to allow cloud specific vendor code and the Kubernetes core to evolve independent of one another. The cloud controller manager runs alongside other master components such as the Kubernetes controller manager, the API server, and scheduler. It can also be started as a Kubernetes addon, in which case it runs on top of Kubernetes.
Hope this answers all your questions related to Master accessing the data on Workers.
If you are still looking for more secure ways, check 11 Ways (Not) to Get Hacked
Short answer: yes the control plane can access all of your data.
Longer and more realistic answer: probably don't worry about it. It is far more likely that any successful attack against the control plane would be just as successful as if you were running it yourself. The exact internal details of GKE/AKS/EKS are a bit fuzzy, but all three providers have a lot of experience running multi-tenant systems and it wouldn't be negligent to trust that they have enough protections in place against lateral escalations between tenants on the control plane.
The Kubernetes HA documentation shows that you can ensure availability in the case of the failure of an apiserver by having multiple instances behind a load balancer.
However, it doesn't cover what happens if the Kubernetes is deployed across multiple availability zones. There is some documentation here but it doesn't really go into failure scenarios.
What is best practice here? Should you pin the api-servers to instances inside each AZ? What happens in the event of a split brain? If I have a pod running in one AZ and it becomes unavailable to the rest of the world, what happens to it?
I specifically want to know about a custom on-premise installation, not AWS or GCE.
I'm currently learning about Kubernetes and still trying to figure it out. I get the general use of it but I think that there still plenty of things I'm missing, here's one of them. If I want to run Kubernetes on my public cloud, like GCE or AWS, will Kubernetes spin up new VMs by itself in order to make more compute for new pods that might be needed? Or will it only use a certain amount of VMs that were pre-configured as the compute pool. I heard Brendan say, in his talk in CoreOS fest, that Kubernetes sees the VMs as a "sea of compute" and the user doesn't have to worry about which VM is running which pod - I'm interested to know where that pool of compute comes from, is it configured when setting up Kubernetes? Or will it scale by itself and create new machines as needed?
I hope I managed to be coherent.
Thanks!
Kubernetes supports scaling, but not auto-scaling. The addition and removal of new pods (VMs) in a Kubernetes cluster is performed by replication controllers. The size of a replication controller can be changed by updating the replicas field. This can be performed in a couple ways:
Using kubectl, you can use the scale command.
Using the Kubernetes API, you can update your config with a new value in the replicas field.
Kubernetes has been designed for auto-scaling to be handled by an external auto-scaler. This is discussed in responsibilities of the replication controller in the Kubernetes docs.