Kubernetes best practices for reliability
In Kubernetes, reliability refers to the ability of the system to consistently and predictably provide the desired functionality to its users. Specifically, reliability in Kubernetes means that the system can perform its intended tasks and services without failure or downtime, even in the face of various types of failures or disruptions.
Here are six Kubernetes best practices for reliability to help maximize workload health.
- Replicate and Distribute. Utilize Kubernetes replication controllers, deployments, or stateful sets to ensure workload replication across multiple nodes. Replicating your application instances helps in achieving high availability and fault tolerance. Distribute replicas across different availability zones or failure domains to minimize the impact of node failures.
- Detect and respond to pod problems with liveness, readiness and startup probes. Implement liveness, readiness and startup probes for your pods to ensure that Kubernetes can detect and respond to any problems with your application. Liveness probes help kubelet to determine when to restart a container, readiness probes tell the kubelet when a container is ready to accept traffic whereas the startup probe informs the kubelet when an application in the container has started.
- Scale based on resource utilization. Use Horizontal Pod Autoscaling (HPA) to automatically scale your application based on resource usage, ensuring that your application is always available to users, even during high traffic.
- Avoid downtime during updates with Rolling Updates. Use Rolling Updates to deploy new versions of your application without downtime. Rolling updates allow Kubernetes to gradually replace old pods with new ones, minimizing the impact on your application's availability.
- Persist data that is required across restarts and deployments. Use persistent storage to store data that needs to persist across pod restarts and deployments. Use a distributed storage solution such as a distributed file system or a cloud-native storage solution to ensure your application's data is always available and reliable.
- Reduce mean time to resolution (MTTR) with monitoring and debugging tools. Implement a monitoring and troubleshooting solution for your Kubernetes cluster and application. Monitor logs and metrics to detect and respond to any problems with your application or cluster. Use tools like Kubernetes Dashboard, Prometheus, and Grafana to monitor and troubleshoot your Kubernetes cluster.
Kubernetes best practices for high availability
High availability (HA) in Kubernetes refers to the ability of the system to maintain a high level of service availability and reliability, even in the face of failures or disruptions.
These eight Kubernetes high availability best practices can help organizations improve application uptime.
- Implement horizontal scaling to account for load fluctuations. Use HPA to scale down or up the replicas of a pod based on resource utilization to ensure that your application can handle varying levels of traffic and load.
- Manage application rollouts and scaling with Kubernetes deployments. Deployments provide a declarative way to manage updates and rollbacks of your application.
- Configure Kubernetes services to provide a stable IP address and DNS name for your application. A stable IP address and DNS name allow the application to be accessible by clients even if the underlying replicas are replaced or moved.
- Implement stateless architectures to allow for easy application scaling. You can scale Stateless applications by adding more replicas of the same pod without requiring any changes to the underlying infrastructure.
- Use vertical scaling to increase the resources available to a single pod. Vertical scaling works by increasing the CPU or memory resources assigned to a pod. You can use Vertical Pod Autoscaler to help you with this.
- Adopt a microservice architecture. Use microservices to break down your application into smaller, independently deployable components to scale each microservice independently based on resource requirements.
- Use resource requests and limits to ensure your application has enough resources to run smoothly. Resource requests guarantee that your application has access to the resources it needs, while resource limits prevent it from using too many resources and impacting the performance of other applications on the same node.
- Avoid resource contention with node anti-affinity to ensure pods are not scheduled on a node as other pods with similar resource requirements. Resource requests guarantee that your application has access to the resources it needs, while resource limits prevent it from using too many resources and impacting the performance of other applications on the same node.
Kubernetes best practices for networking
Kubernetes networking is the set of rules and protocols used to connect and manage the network traffic between the components of a Kubernetes cluster. Kubernetes uses a flat, virtual network that allows pods and nodes to communicate with each other regardless of their physical location. This network is created using software-defined networking (SDN) technologies, which provide a flexible, scalable, and reliable way to manage network traffic in a Kubernetes environment.
Here are ten Kubernetes networking best practices that can help administrators ensure their network services are secure and performant:
- Enable containers on different nodes to communicate using a Container Network Interface (CNI) plugin. Kubernetes supports several CNI plugins, including Calico, Flannel, and Weave Net.
- Create a dedicated network interface for your Kubernetes cluster to isolate traffic and reduce network congestion. You can achieve this using a separate network interface card (NIC) or virtual LANs (VLANs).
- Control traffic flows with network policies in your Kubernetes cluster. Network policies allow you to specify rules for inbound and outbound traffic, restricting access to specific pods or services.
- Manage traffic between services in a distributed application with a service mesh. A service mesh provides load balancing, traffic shaping, and service discovery.
- Expose services to the outside world (when needed) using ingress controllers. Ingress controllers allow you to specify routing rules and provide SSL termination for incoming traffic.
- Perform service discovery within your cluster with DNS. Kubernetes provides a built-in DNS service to resolve service names to IP addresses. However, using External DNS to manage records using Kubernetes resources in a DNS provider-agnostic way is recommended.
- Distribute traffic across multiple application instances using load balancing. Kubernetes provides built-in load balancing for services, distributing traffic across multiple service replicas.
- Distribute traffic across multiple nodes with external load balancers. External load balancers can be integrated with Kubernetes using cloud provider-specific integrations or Kubernetes Ingress.
- Prioritize traffic for critical applications with Quality of Service (QoS) classes. Kubernetes provides three QoS classes: guaranteed, burstable, and best effort.
- Test your Kubernetes cluster's network throughput to ensure it can handle the expected traffic load. Use tools like iperf to test network performance between nodes in your Kubernetes cluster.