Running containers in Kubernetes brings its own set of complexities in terms of automation, segmentation, and efficiency. Application deployment which happens inside containers and across clouds, can multiply very easily, leaving hundreds or even thousands of entities to monitor.
Without the proper framework, It becomes very tough for enterprises to have visibility into the Kubernetes environment which will hinder them from achieving greater agility, innovation, and business growth.
To overcome this invisibility between the microservices, organizations need insights through monitoring which will not only examine the performance of containers, pods, and services but also allows monitoring teams to gauge application performance by detecting and removing bottlenecks.
Below is the list of Kubernetes monitoring best practices which will enable alerts for critical services while providing actionable insights on individual components of the cluster.
Evaluate monitoring tools frameworks
Implementing a monitoring solution to gather metrics and events from a system is always considered a Kubernetes monitoring best practice, but it is better to evaluate the strategies/framework implemented by monitoring solutions to determine which suits you the best.
Some monitoring frameworks in Kubernetes runs an agent in a sidecar which doesn’t require any change in your containerized applications. This is helpful in serverless scenarios but in the long run or when containers per node increase, this implementation can cause increased resource usage which can cause scenarios that are hard to debug.
Due to the debugging issues in the sidecar model, many monitoring tools vendors have shifted to models that have programs running as modules of the Linux kernel. This way, they can easily listen to system calls which allows them to capture all of the information necessary for troubleshooting, debugging, and analysis in a Kubernetes environment.
Implement End to End Visibility
Kubernetes monitoring tooling must be flexible enough to meet the complex demands of containers which are deployed/redeployed in different locations to roll out new features and updates.
There can be many situations where a particular pod/container only appeared for a few minutes or moved to another node in the cluster. To monitor these changes, a comprehensive monitoring solution that provides end-to-end visibility is required.
End to end communication between Kubernetes components will not only reveal where security vulnerabilities are but also provide targeted ways to improve performance by capturing all the host event activity.
Host event activity which is different from metrics and log data, is more comprehensive in information. Even a short amount of event activity can provide an abundance of data that can be later used for analysis to identify the root cause.
Monitor the Kubernetes control plane
The Kubernetes control plane which manages all of your Kubernetes cluster resources provides capabilities for pods scheduling and retrieval of secrets stored in the cluster.
Knowing the main components of the Kubernetes control plane will not only help in detecting and troubleshooting cluster latency errors but also decrease hassle to manage stakeholders involved in monitoring cluster workloads.
For example, monitoring the Kubernetes API server which is one of the major components of the control plane, provides visible communication between the cluster components, etcd which stores configuration information to be used between nodes, and cloud controller manager which runs controllers to interact with cloud providers can help alert the right teams at the right time by limiting the noise of being alerted about things that do not pertain to them.
Have granular control on system resources
It is necessary to have granular control on system resources to know what is happening across the entire host and how it is gathering information about resources like CPU, memory, network, and storage.
Granular control allows gathering of information at the kernel level about the processes/resources and how they interact with the system (file access, port management, etc.) and the metadata that describes their connections in real-world scenarios.
Many tools can report issues with the Kubernetes cluster, but the root of the issue can be better resolved by looking at the details of what the container is trying to do while it is booting up. Only by having greater visibility and granular control into the system calls users can determine the root cause and take desired measures.
Granular visibility can also be attained by implementing a performance monitoring solution that gives granular insight into the containerized applications’ performance and helps identify both bandwidth-hogging applications and container-level network errors.
Implement a SaaS-based monitoring solution
Preferring a SaaS-based monitoring solution over an on-premise solution has its own benefits. First, a SaaS-based solution can be used to scale on demand without worrying about data management at the backend. It can help achieve better cluster performance and productivity by installing the necessary agents required for monitoring compared to an on-premises solution which requires manual install and setup time.
Patches and rolling out of new features is also easy with SaaS-based monitoring solution which decreases the hassle of manually upgrading clusters while avoiding the costs for in-house hardware and software costs.
SaaS-based solutions can also implement infrastructure as a code, which is the process of managing IT infrastructure through configuration files. They connect with Kubernetes monitoring tools and watch cluster objects for defined patterns.
They can also be implemented as a controller which runs inside a Kubernetes cluster to handle updates subscription and deployment objects creation. Overall, they provide a framework that helps in the dynamic creation of alerts through states of monitoring solutions.
Choosing the right monitoring strategy is key. Traditional monitoring strategies do provide a framework but are short-lived in the long run which makes it necessary to implement best practices for ensuring high availability, reliable system performance, and easy troubleshooting.