What Pros Come With Using A Kubernetes Registry
Kubernetes is an orchestration tool which means that if you have a complex application made up of multiple containers. Kubernetes tools will help you achieve high availability, scalability, and disaster recovery of your application setup. But what do all these things mean, and how does Kubernetes Registry help you achieve all these things. Read on to find out more about the pros of using Kubernetes.
Advantages of Kubernetes Registry
The Kubernetes registry is an image pull secret that your deployment uses to authenticate with a Docker registry. Following are some of the pros that come with using a Kubernetes registry:
High Availability and Scalability
Consider you have two worker nodes of the Kubernetes cluster. Each server holds a replica of the application and a database application; an ingress component also handles every incoming request to your application. For example, if someone accesses your app website on a browser, the request will come into effect. An ingress component will be their parts on each server, replicated.
Now when a user visits your app website on browser, a request is made and handled by ingress first, and as ingress is load-balanced, there are replicas of ingress on multiple servers. Ingress will then forward that request to the service for application, which is a load balancer. That will then direct that request to the respective replicas of the pod. Considering that request database access was necessary, the app will then make another request to database service, which is also loaded balanced and pass a request to one of the replicas.
Of course, this is a simplistic view; you can have ten servers and ten replicas of your database ingress applications. However, this setup demonstrates that from the entry point of the request into the cluster till the last endpoint, which is a database, every component gets replicated and load-balanced. This means that in this whole setup, there is no bottleneck where a request handling, for example, could stop the entire application and make the responses slower for a user.
It must be noted that your application or the way your application is designed should also support this replication and request handling because these are just tools that Kubernetes offers you to make your properly designed application highly available and highly scalable.
With this setup, if a server completely crashes and all the parts running on it die, you would still have replicas of your application running, so there will be no downtime. In the meantime, a Kubernetes master process called controller manager would schedule new replicas of the dead pods on another worker node and recover the previous load-balanced and replicated application state.
While the node servers run the applications, the master processes on the master nodes monitor the cluster state and ensure that if a pod dies, it automatically gets restarted. Likewise, if something crashes in the cluster, it automatically gets recovered.
An important master component used to manage the cluster mechanism to run correctly is the etcd store, which stores the cluster state like the resources available on the nodes and the pod state. So at any given time, for example, if a pod dies and etcd store gets updated about it, and that is how the controller management would know that it should intervene and make sure new pod gets restarted so when that happens again, etcd store gets updated.
As etcd always has the current state of the cluster, it is also a crucial component in disaster recovery of Kubernetes clustered applications. The way disaster recovery mechanism can be implemented, how Kubernetes is to create etcd backups and store them into remote storage and these backups are in the form of etcd snapshots. Now Kubernetes does not manage or take care of backing up the etcd snapshots on remote storage; this is the responsibility of the Kubernetes cluster administrator. So this storage could be entirely outside the cluster on a different server or even cloud storage. A JFrog Kubernetes registry, for example, enables you to leverage Multi-Site DevOps with Federated Repositories anywhere around the globe.
An important note here is that etcd does not store database or application data. That data usually gets stored on remote storage, where the application pods have reference to the warehouse to read and write the data from. Like the etcd snapshots backup location, this remote storage is not managed by Kubernetes and must be reliably backed up and stored outside of the cluster. This is usually how the production cluster is set up.
Now considering a reliable backup, replication of etcd snapshot, and application data are in place. If the whole cluster were to crash, including the worker nodes and the master nodes themselves, it would be possible to recover the cluster state on completely new machines with the new master node and new worker nodes using the etcd snapshot and the application data. Of course, you can even avoid any downtime between the cluster crash and a new cluster creation by keeping a backup cluster so that it can immediately take over when the active cluster or the current cluster crashes or dies.
You can achieve this setup with load balancers and replicas without Kubernetes, for example, on AWS instances using the AWS load balancer, etc. However, you have a couple of advantages with Kubernetes that you do not have with other tools or create this setup yourself.
One is that replication becomes much easier using Kubernetes. You only have to declare how many replicas of a particular application, be it your application or a database application, you need. Then, the Kubernetes component takes care of actually replicating it.
The second one is the Kubernetes self-healing feature so what it means is that if a pod dies, there should be a process that monitors the state that detects that a replica failed and automatically restarts a new one. So again, you have this feature out of the box from Kubernetes.
The third one is the smart scheduling feature of Kubernetes which means that, for example, if you have 50 worker servers, your application containers will run on. With Kubernetes, you do not have to decide where to run your container. Instead, you say you need a new pod replica, and Kubernetes smart scheduler goes and finds the best-fitting work node among those 50 worker nodes to schedule your container by comparing how many resources a worker node has available or free.
Overall, many features you could also do elsewhere on other platforms like AWS are more straightforward. It is easier to create and configure in Kubernetes, like serving as a load balancer.