Kubernetes Monitoring: Metrics, Methods, and Best Practices

What Is Kubernetes Monitoring? 

Kubernetes monitoring involves tracking the performance and resource utilization of your Kubernetes clusters. It provides insights into the health status of your clusters, including identifying overburdened nodes, detecting failed pods, and observing the overall performance of your applications.

Monitoring Kubernetes clusters involves several layers, from the infrastructure and the Kubernetes objects to the applications running within the containers. Taking a multi-layered approach helps ensure the smooth functioning of your Kubernetes environment and provides crucial data for troubleshooting and optimization efforts.

A good Kubernetes monitoring solution should provide a wide range of metrics, including CPU usage, memory consumption, network bandwidth, pod status, and more. It should also offer alerting capabilities to notify you of critical issues and potential threats to your Kubernetes environment.

This is part of a series of articles about Kubernetes architecture.

In this article:

• Why Is Kubernetes Monitoring Important?
• What Kubernetes Metrics Should You Measure?
• How to Deploy Kubernetes Monitoring 
• Kubernetes Monitoring Challenges
• Kubernetes Monitoring Best Practices

Why Is Kubernetes Monitoring Important? 

The importance of Kubernetes monitoring cannot be overstated. As your applications grow and evolve, so does your Kubernetes environment. With the increasing complexity and scale, maintaining visibility into the performance and health of your clusters becomes even more crucial.

Kubernetes monitoring provides insights into the operational status of your clusters and helps identify performance bottlenecks and resource inefficiencies. With this information, you can make informed decisions about scaling your applications, troubleshooting issues, and optimizing your resource allocation.

More importantly, Kubernetes monitoring helps ensure the availability and reliability of your applications. By detecting failures and anomalies early, you can prevent minor issues from escalating into major disruptions. This proactive approach to problem-solving can significantly improve your application’s uptime and user satisfaction.

What Kubernetes Metrics Should You Measure? 

The key to effective Kubernetes monitoring is knowing which metrics to measure. While there are hundreds of potential metrics, here are some of the most important ones.

Cluster Monitoring

Cluster monitoring involves tracking the overall performance and health of your Kubernetes cluster. Key metrics include the number of nodes, the status of the nodes, the number of running pods, and the total resource utilization of the cluster. These metrics provide a high-level view of your cluster’s health and can help identify potential issues such as overloaded nodes or insufficient resources.

Node Metrics

Node metrics provide a deeper look into the performance of individual nodes within your cluster. These metrics include the node’s CPU usage, memory consumption, disk I/O, and network bandwidth. By tracking these metrics, you can identify nodes that are under heavy load or experiencing performance issues.

Pod Monitoring

Monitoring pods involves tracking their lifecycle, resource usage, and health status. Key metrics for pod monitoring include CPU and memory usage per pod, restart count, and the status of each pod (running, waiting, or terminated). These metrics give insights into the health and performance of individual pods, enabling the identification of issues at the microservice level.

Learn more in our detailed guide to Kubernetes pod.

Deployment Metrics

Deployment metrics focus on the performance and status of your applications or services deployed within your Kubernetes cluster. Key metrics include the number of replicas, the status of the replicas, the resource utilization of the replicas, and the response time of your applications. These metrics can help you understand how your applications are performing and whether they are meeting your performance expectations.

Ingress Metrics

Ingress metrics involve monitoring the network traffic entering your Kubernetes cluster. Key metrics include the number of incoming requests, the response time, and the error rate. These metrics can help you understand the load on your cluster and can provide insights into potential network-related issues.

Persistent Storage

Persistent storage metrics involve tracking the performance and utilization of your storage resources within your Kubernetes cluster. Key metrics include the total storage capacity, the used storage, the available storage, and the I/O operations. These metrics can help you manage your storage resources effectively and can provide insights into potential storage-related issues.

How to Deploy Kubernetes Monitoring 

There are two primary ways to deploy monitoring in a Kubernetes cluster: via DaemonSets, or using third party tools.

Monitoring Using Kubernetes DaemonSets

A DaemonSet ensures that all (or some) nodes run a copy of a pod. This is particularly useful for deploying system-wide services such as log collectors or monitoring agents. You can use a DaemonSet to deploy any monitoring tool across all nodes of your cluster.

When using DaemonSets for Kubernetes monitoring, the monitoring agent runs on every node in the cluster. This means that as your cluster scales, your monitoring solution scales with it. You don’t have to worry about manually deploying monitoring agents to new nodes — Kubernetes takes care of this for you. The DaemonSet ensures that your monitoring agent is always running, even if a node goes down. This is crucial for maintaining visibility into your Kubernetes environment.

However, DaemonSets are not without their drawbacks. Because they run on every node, they can consume significant resources, especially in large clusters. It’s vital to carefully manage the resource allocation of your DaemonSet to prevent it from impacting the performance of your applications.

Kubernetes Monitoring with Third Party Tools

There’s also a large ecosystem of third-party tools available for Kubernetes monitoring. These tools often provide more advanced features and capabilities than the built-in tools. Some of the third-party tools I’ve used include Prometheus, Grafana, and Datadog.

Prometheus is a powerful open-source monitoring system that can handle multi-dimensional data collection and querying. It’s highly flexible and customizable, making it an excellent choice for complex Kubernetes environments. Grafana is a visualization tool that can be used with Prometheus to create comprehensive dashboards.

Kubernetes Monitoring Challenges 

Here are some of the key challenges teams face when monitoring Kubernetes clusters.

Complexity

One of the biggest challenges in Kubernetes monitoring is the inherent complexity of the system. Kubernetes is a highly dynamic environment, with pods constantly being created and destroyed, services being scaled up and down, and nodes being added or removed. This dynamism makes it difficult to keep track of what’s happening in the cluster.

To deal with this complexity, it’s crucial to have a monitoring solution that can handle the dynamism of Kubernetes. This includes being able to automatically discover new nodes and services, track the state of pods and services over time, and provide real-time alerts when something goes wrong.

Scalability

Another challenge is scalability. As your Kubernetes cluster grows, so does the number of metrics you need to monitor. This can quickly become overwhelming, especially if you’re using a monitoring solution that isn’t designed to handle large-scale environments.

To overcome this challenge, you need a monitoring solution that can scale with your cluster. This includes being able to handle a high volume of metrics, provide fast query performance, and offer efficient storage and retention of metrics data.

Real-Time Monitoring

Real-time monitoring is another important challenge in Kubernetes. Given the dynamic nature of Kubernetes, it’s crucial to have real-time visibility into the state of your cluster. This allows you to quickly identify and respond to issues before they impact your applications.

However, real-time monitoring in Kubernetes is not straightforward. It requires a monitoring solution that can collect and process metrics from across the Kubernetes environment, store it in a central location, and provide real-time alerts.

Metrics Overload

Kubernetes produces a vast amount of metrics, and it can be challenging to determine which ones are important and which ones can be ignored.

To deal with this challenge, it’s important to have a clear understanding of your monitoring goals and what metrics are relevant to these goals. This includes knowing what metrics to monitor for each type of Kubernetes resource (e.g., node, pod, service) and how to interpret these metrics. It’s also helpful to use a monitoring solution that allows you to filter and aggregate metrics, so you can focus on the ones that matter most.

Kubernetes Monitoring Best Practices 

Here are some best practices that can help you overcome the challenges and effectively monitor your Kubernetes environment.

1. Choosing the Relevant Metrics

It’s important to remember that more data doesn’t necessarily mean better monitoring. The key is identifying which metrics are genuinely relevant to your system.

Choosing the right metrics requires a clear understanding of your system’s architecture and operational needs. For instance, if your primary concern is ensuring the availability of your services, you’ll likely focus on pod metrics that highlight the performance and availability of individual services. On the other hand, if you’re more concerned with overall system performance, you might find cluster and node metrics more useful.

2. Implement an Extensive Labeling Policy

Labels in Kubernetes are key-value pairs attached to objects like pods and nodes. They’re used to organize and select subsets of objects, making it easier to manage and monitor your cluster.

A well-designed labeling policy can greatly enhance your Kubernetes monitoring efforts. By assigning clear and descriptive labels to your objects, you can quickly identify and troubleshoot issues as they arise. For example, if a particular pod is underperforming, labels can help you determine if the issue is isolated to that pod, or if it’s part of a larger problem affecting all pods with the same label.

3. Preserve Historical Data

By tracking and analyzing past performance, you can identify trends and patterns that might not be apparent in real-time data. This can help you anticipate and prevent potential issues before they affect your system’s performance.

Historical data is particularly useful for capacity planning. By analyzing your system’s past resource usage, you can accurately predict future needs and make informed decisions about scaling your system. It also enables you to identify periods of peak demand, allowing you to adjust your resources accordingly to prevent slowdowns or outages.

Unfortunately, Kubernetes doesn’t inherently store historical data. This means you’ll need to use a third-party tool or service to collect and store this data.

4. Focus on the End-User Experience

While it’s essential to monitor your system’s internal metrics, it’s equally important to ensure your services are meeting your users’ expectations.

End-user experience monitoring involves tracking metrics like response times, error rates, and request rates. These metrics provide a clear picture of how your services are performing from the user’s perspective, enabling you to identify and resolve any issues that might be affecting the user experience.

One effective method for end-user experience monitoring is synthetic monitoring. This involves simulating user interactions with your services and measuring their performance. This not only provides insight into the user experience but also allows you to proactively identify and address issues before they affect your users.

Automating Kubernetes Infrastructure with Spot by NetApp

Spot Ocean from Spot by NetApp frees DevOps teams from the tedious management of their cluster’s worker nodes while helping reduce cost by up to 90%. Spot Ocean’s automated optimization delivers the following benefits:

• Container-driven autoscaling for the fastest matching of pods with appropriate nodes
• Easy management of workloads with different resource requirements in a single cluster
• Intelligent bin-packing for highly utilized nodes and greater cost-efficiency
• Cost allocation by namespaces, resources, annotation and labels
• Reliable usage of the optimal blend of spot, reserved and on-demand compute pricing models
• Automated infrastructure headroom ensuring high availability
• Right-sizing based on actual pod resource consumption  

Learn more about Spot Ocean today!