Kubernetes is an open source-system for managing containerized applications across multiple hosts. It aims to provide a “platform for automating deployment, scaling, and operations of application containers across clusters of hosts.” Originally developed by Google, it is now maintained by the Cloud Native Computing Foundation (CNCF).
After its launch as an open-source project, Kubernetes saw an overwhelming rise in popularity among developers because of its robust and flexible functioning mechanism that was a leap forward from traditional physical servers and the much more developed virtual servers.
While Kubernetes helps eliminate a lot of the manual workload associated with managing containers, it also has several complexities of its own, which only tend to increase with large-scale deployments. This necessitates the use of a Kubernetes performance monitoring tool in order to ensure that the performance and health of Kubernetes clusters are optimal at all times. Visibility down to the node, container, and application level is essential to diagnose Kubernetes performance issues and troubleshoot them faster.
Kubernetes performance monitoring with Applications Manager
Applications Manager offers end-to-end visibility into the performance of your Kubernetes environment. It auto-discovers the parts of a Kubernetes cluster, such as nodes, namespaces, deployments, replica sets, pods, and containers; maps relationships between objects; and provides detailed performance insights, all of which are essential to implementing a comprehensive Kubernetes monitoring plan.
Cluster and node statistics
To efficiently monitor Kubernetes performance and identify problems, metrics like cluster CPU usage and memory usage should be fundamentals to focus on. Nodes are either virtual servers or physical machines within clusters. These nodes contain services responsible for running pods and monitoring the CPU and memory usage details of these nodes (workers and masters), and provide insights on the overall health and availability of the entire cluster. Kubernetes cluster monitoring can help determine if you have enough nodes in your cluster, and whether the resources allocated to existing nodes are sufficient for deployed applications.
Additionally, insights into the pod usage details of your nodes can help you understand how heavy the load on a node is, and measure the overall Kubernetes node performance.
A pod is a group of containers, such as Docker, that are deployed on a host and share common resources. These pods run on nodes, and therefore contribute heavily to the memory and CPU usage of the nodes. This makes monitoring the operational status of pods an essential part of Kubernetes performance monitoring.
You need to ensure all the pods in a deployment are running, and not in a restart loop. Ensuring the availability of resources so pods don’t slip into pending state, knowing the state of containers in the pod, whether they are all running, if there have been any recent restarts, etc. are all essential factors in preventing problems with Kubernetes pod performance.
Services and deployments
Each node contains the services necessary to run pods, including the container runtime responsible for running containers in pods. These containers host applications, which makes knowing the status of services important. Tracking the number of network requests sent across containers on different nodes within a distributed service is another important aspect to consider to guarantee your deployed applications are always running optimally. Also, since the Deployment controller can change the state of pods and ReplicaSets with declarative updates, delivery of optimal Kubernetes service performance (and in turn, the seamless functioning of applications) is only possible by remaining consistently watchful of deployment availability.
Persistent storage volumes
Persistent volumes (PV) are storage instances in a cluster that point to a physical storage space. However, these PVs can only be accessed in Kubernetes clusters with the help of a persistent volume claim (PVC), which is a request to provision a PV with a specific type and configuration. These PVs can store data beyond the life cycle of a pod, and ensure the consistent state of data. This allows complex workloads to be put into containers and, by leveraging PVs, you can start using databases like MySQL, Cassandra, and even MS SQL for your applications.
Trend analysis and performance forecasting
While prioritizing critical Kubernetes performance metrics for comprehensive Kubernetes monitoring is important, analyzing attribute trends across hours, days, weeks, or any fixed duration can help you understand the load handling and efficiency of your Kubernetes clusters and organize them better. Similarly, insights into growth and utilization trends of various critical attributes such as CPU requests, CPU limits, etc. can help you plan capacity for the future.
Kubernetes offers incredible efficiency in deployment and scaling of containerized applications. However, to make the most out of Kubernetes environments, problems that can impact performance need to be identified and fixed in a timely manner. This can include issues due to architectural complexity, resource consumption, unavailability of resources, configuration errors, and more.
A Kubernetes performance monitoring tool is ideal to help spot and remediate these problems. ManageEngine Applications Manager is one such solution that can automatically discover your Kubernetes instances in production, capture detailed performance insights, and offer machine learning-powered performance forecasting for capacity planning. Besides Kubernetes performance monitoring, Applications Manager offers comprehensive monitoring for over 130 business applications across servers and infrastructure, application performance, cloud services, and end-user experience monitoring modules—all from a single console.