You will probably find yourself getting data from third parties while operating on a cluster of microservices. For instance, web services that exist outside of your cluster. A significant increase in network traffic can cause some of your microservices to suffer, and this struggle may spread to all of your services.
Now that you know these technical problems, it is time to consider fault tolerance mechanics and how to effectively leverage them in particular use cases.
But
what exactly is fault tolerance?
Fault tolerance in a Kubernetes cluster refers to using specific deployment and coding standards that enable the Kubernetes cluster to bounce from mistakes smoothly and without harming the user experience.
Creating Microservices That Are Fault Tolerant
Due to high resource use, while awaiting unsuccessful or delayed answers, the cluster can collapse. Microservices accumulate I/O threads while they await the External Provider’s response. Let’s try utilizing Quarkus to fix this.
In this case, we must inform the microservice to take two actions:
- Add a timeout to an HTTP request to release the resources used while you are awaiting a response.
- Add a circuit breaker, which means temporarily halting all HTTP requests to the external service provider after a predetermined number of failures.
Fault tolerance or High-availability(HA) architecture
By enabling the scheduling of containers across numerous nodes and several availability zones (AZs) in the cloud, Kubernetes aids in enhancing dependability. By using anti-affinity, you may limit which nodes in the pod can schedule based on labels on running pods on the node rather than on labels on nodes. For the pod to be qualified to execute on a node using node selection, the node must contain each of the above key-value pairs as labels. Use anti-affinity or node selection when creating a Kubernetes deployment to help distribute your apps throughout the Kubernetes cluster for high availability.
Kubernetes HA means there is no single source of failure in a Kubernetes component. Examples of components of the state that Kubernetes stores can be a Kubernetes API server or the etcd database. How do you verify that these parts are HA?
Suppose you are running Kubernetes locally, and there are three master servers and a single computer serving as a load balancer. Even though you have numerous masters, the Kubernetes API has a single point of failure since you only have one load balancer. You must avoid this.
Because K8S recommends deploying several extra instances depending on each redundant component (for example, API server 2+, etcd odd number 3+, and kube-scheduler 2+), even if one of the redundant components in your Kubernetes cluster goes down, the cluster continues to function. What happens if you lose an additional component? If there are three masters and one of them dies, the two surviving masters overwork and degrade or perhaps lose another master.
Autoscaling and resource limitations
The Kubernetes scheduler works well because of resource requirements and restrictions for CPU and memory. Other pods may go without resources if the entire node’s CPU and memory permit to use of a single pod. By preventing pods from using up all of a node’s resources, setting restrictions on what they may use improves dependability and solves the “noisy neighboring problem.”
In turn, autoscaling can improve cluster dependability by enabling the cluster to adapt to variations in load. By scaling your application pods and cluster nodes, Horizontal Pod Autoscaler (HPA) and cluster autoscaling jointly deliver a stable cluster.
Good resource requests and limitations are essential for reliability, and the Cluster Autoscaler will struggle to function well if your resource demands do not configure appropriately. The scheduler informs the Cluster Autoscaler when a pod won’t fit on the available nodes, and the resource request informs it whether adding a new node would enable the pod to execute.
Probes for liveliness and readiness
The idea of “self-healing” is a crucial aspect of cluster stability. The aim behind this is to identify cluster problems and remedy them automatically. The liveness & readiness probes in Kubernetes are an implementation of this idea.
A liveness probe, which determines if a container is operating or alive, is essential to a Kubernetes cluster’s efficient operation. Kubernetes will immediately send a signal to destroy the pod to which the container goes if this probe gets pushed into a failing condition. A defective or non-functioning pod will also run endlessly, wasting resources and perhaps resulting in application failures if each container in the pod lacks a liveness probe.
On the contrary side, a signal uses a readiness probe when a container is prepared to service traffic. If the pod is a part of a Kubernetes service, Kubernetes does not add it to the service’s list of accessible endpoints until all of its containers have been given the “ready” status. Using this technique, you may block sick pods from handling traffic or processing requests, which stops your application’s faults from being exposed.
Both probes run periodic tests to see how well your containers are performing on the Kubernetes cluster. Each probe includes two states: pass & fail, as well as a threshold for the number of times it must be in either condition before it changes. These two probe kinds allow the cluster to “self-heal” when they are properly set up on all of your containers. Pods will be immediately destroyed or put out of service if problems in containers are automatically recognized.
In a Kubernetes system, reliability equates to stability, simplified operations and development, and improved user experience. With the appropriate setup, dependability is considerably simpler to accomplish in a Kubernetes system. Or, to put it another way, mistakes in the configuration are common. When creating a stable and dependable Kubernetes cluster, several aspects must be taken into account, including the potential need for application updates and modifications to the cluster’s configuration. Setting resource requests and restrictions, leveraging liveness and readiness probes, autoscaling pods based on a measure that depicts application demand, and other steps are included.
Learn Kubernetes online and enhance your career
Get certified in Kubernetes and improve your future career prospects better.
Enroll in Cognixia’s Docker and Kubernetes certification course, upskill yourself and make your way toward success & a better future. Get the best online learning experience with hands-on, live, interactive, instructor-led online sessions with our Kubernetes online training. In this highly competitive world, Cognixia is here to provide you with an immersible learning experience and help you enhance your skillset as well as knowledge with engaging online training that will enable you to add immense value to your organization.
Our Kubernetes online training will cover the basic-to-advanced level concepts of Docker and Kubernetes. This Kubernetes certification course offers you an opportunity to connect with the industry’s expert trainers, develop your competencies to meet industry & organizational standards, and learn about real-world best practices.
This Docker and Kubernetes Certification course will cover the following –
- Essentials of Docker
- Overview of Kubernetes
- Minikube
- Kubernetes Cluster
- Overview Kubernetes Pod
- Kubernetes Client
- Creating and modifying ConfigMaps and Secrets
- Replication Controller and Replica Set
- Deployment
- DaemonSet
- Jobs
- NameSpaces
- Dashboard
- Services
- Exploring the Kubernetes API and Key Metadata
- Managing Specialized Workloads
- Volumes and configuration Data
- Scaling
- RBAC
- Monitoring and logging
- Maintenance and troubleshooting
- The ecosystem
Prerequisites for Docker & Kubernetes Certification
- Basic command knowledge of Linux
- Basic understanding of DevOps
- Basic knowledge of YAML programming language (beneficial, not mandatory)
