Kubernetes Monitoring: A Practical Guide

What Is Kubernetes Monitoring? 

Kubernetes monitoring is the process of monitoring the health and performance of a Kubernetes cluster and the applications running on it. This includes collecting metrics and logs, detecting and alerting on issues, and visualizing the state of the cluster and applications. 

Kubernetes monitoring tools typically use various data sources, such as Kubernetes APIs, application logs, and infrastructure metrics, to provide insights into the health and performance of a cluster and its components. Effective monitoring is critical for ensuring the reliability and availability of Kubernetes-based applications.

This is part of an extensive series of guides about cloud security.

The Benefits of Kubernetes Monitoring 

A Kubernetes monitoring solution provides several benefits, including:

• Detecting and alerting on issues: Kubernetes monitoring tools provide real-time insights into the health and performance of the cluster and applications running on it. This enables teams to detect and resolve issues before they impact end-users.
• Tracking issues in a distributed environment: Kubernetes allows organizations to deploy and manage microservices-based applications. This can make it difficult to track issues, but monitoring tools can help by providing insights into the behavior of each service and their interactions.
• Providing insights into health and performance: Kubernetes monitoring tools provide insights into the state of the cluster, as well as the health and performance of individual components. This information can be used to optimize resource allocation and improve application performance.
• Understanding resource utilization: Kubernetes monitoring tools track resource utilization, such as CPU and memory usage, across the cluster and individual applications. This helps teams identify potential bottlenecks and optimize resource allocation.

What Kubernetes Metrics Should You Measure?

Kubernetes Control Plane Metrics

Kubernetes control plane metrics provide insights into the state and performance of core components that manage the cluster. These include the API server, etcd, controller manager, and scheduler. Monitoring control plane metrics is essential for ensuring the health and availability of the entire cluster.

1. API Server Metrics: The API server handles communication between users, controllers, and the Kubernetes cluster. Key metrics include request rates, error rates (e.g., 4xx or 5xx), and latency, which help identify API server overload or performance issues.
2. etcd Metrics: etcd serves as the cluster’s distributed key-value store. Critical metrics to monitor include db size, disk write latency, leader elections, and snapshot durations. Slow etcd performance can lead to cluster instability.
3. Controller Manager Metrics: This component ensures the desired state of the cluster is maintained. Important metrics include controller reconcile rates, failure counts, and queue lengths to detect delays in applying changes.
4. Scheduler Metrics: The scheduler assigns workloads to nodes. Metrics like scheduling latency, scheduling attempts, and pending pods help ensure workloads are being scheduled efficiently.

Kubernetes Node Metrics

Node metrics provide visibility into the health and resource utilization of the physical or virtual machines that form the Kubernetes cluster. Monitoring node metrics ensures clusters operate reliably and resources are used effectively.

5. CPU Usage: Monitoring CPU utilization helps identify nodes under heavy load or potential resource exhaustion. High CPU usage over time may indicate an overloaded node.
6. Memory Usage: Track memory usage to prevent out-of-memory (OOM) errors, which can cause workloads to crash. Metrics include memory consumption per node and memory pressure.
7. Disk I/O and Usage: Monitor disk read/write operations and disk space utilization to detect storage bottlenecks or insufficient capacity.
8. Network Metrics: Measure network throughput, packet loss, and latency. These metrics can help identify connectivity issues between nodes or external services.

Kubernetes Container Metrics

Container metrics provide insights into resource usage and behavior for individual containers, which are the smallest deployable units in Kubernetes. Monitoring containers helps ensure application performance and resource efficiency.

9. CPU and Memory Usage: Track the CPU and memory consumption of each container to detect resource-hungry containers or potential bottlenecks.
10. Container Restarts: Frequent restarts indicate instability, such as crashes caused by insufficient resources or application errors.
11. Disk and Network Usage: Monitor the read/write operations and network throughput for containers. Sudden spikes or declines can indicate issues like data bottlenecks or communication failures.
12. Container Status: Keep track of container states (running, terminated, or waiting) to detect failures and ensure workloads are healthy.

Kubernetes Pod Metrics

Pod metrics provide insights into the health, performance, and resource usage of pods, which run one or more containers. Monitoring pod-level metrics ensures workloads function reliably within the cluster.

13. Pod CPU and Memory Usage: Monitor the aggregated CPU and memory usage of all containers within a pod. This helps identify pods consuming excessive resources.
14. Pod Restarts: High restart counts indicate pod instability, often caused by resource issues or application failures.
15. Pod Status: Track pod states (pending, running, succeeded, or failed) to detect scheduling issues, crashes, or unscheduled pods due to resource constraints.
16. Pod Latency: Measure how long it takes for pods to start and become ready. High pod startup latency can impact application availability and performance.

Kubernetes Monitoring Methods

Kubernetes monitoring can be achieved through various methods, each offering unique benefits and covering different layers of the Kubernetes ecosystem. Choosing the right method or a combination of methods depends on the monitoring needs, cluster size, and performance requirements.

1. Metrics-Based Monitoring

Metrics-based monitoring involves collecting, analyzing, and visualizing numeric data points over time. These metrics can provide insights into resource utilization, performance, and health across Kubernetes components.

• Tools: Prometheus is a popular choice for metrics collection and alerting in Kubernetes. It gathers metrics from the Kubernetes API server, nodes, and container runtimes using pull-based scraping.
• Use Cases: Track resource utilization (CPU, memory, disk), monitor control plane performance, and set up alerts for threshold breaches.

2. Log-Based Monitoring

Logs provide detailed, event-driven records of activities within Kubernetes clusters, including application-level events and system events. Analyzing logs helps detect issues, debug failures, and trace problems in distributed environments.

• Tools: The ELK Stack (Elasticsearch, Logstash, and Kibana) and Fluentd are commonly used for log collection, aggregation, and visualization.
• Use Cases: Troubleshoot application errors, detect failures in the control plane, and analyze cluster behavior over time.

3. Tracing-Based Monitoring

Distributed tracing tracks requests as they traverse through multiple microservices within a Kubernetes cluster. It helps identify latency issues, bottlenecks, and service dependencies.

• Tools: OpenTelemetry and Jaeger are widely used for distributed tracing in Kubernetes environments.
• Use Cases: Pinpoint latency between services, optimize request flows, and troubleshoot performance bottlenecks in microservices-based applications.

4. Event Monitoring

Event monitoring focuses on tracking Kubernetes events such as pod creations, deletions, failures, and scaling activities. These events provide insight into cluster operations and issues.

• Tools: Kubernetes kubectl get events command or tools like Prometheus Alertmanager can capture events for analysis and alerting.
• Use Cases: Detect pod failures, monitor scaling activities, and ensure workloads are scheduled and running as expected.

5. End-to-End Monitoring

End-to-end monitoring integrates metrics, logs, and tracing to provide a comprehensive view of the Kubernetes ecosystem. This method combines infrastructure-level insights with application-level performance data.

• Tools: Full-stack management and observability platforms like Komodor provide unified dashboards for monitoring clusters, applications, and user experiences.
• Use Cases: Correlate infrastructure issues with application behavior, optimize resource usage, and monitor SLAs or SLOs.

Tips from the expert

Itiel Shwartz

Co-Founder & CTO

Itiel is the CTO and co-founder of Komodor. He’s a big believer in dev empowerment and moving fast, has worked at eBay, Forter and Rookout (as the founding engineer). Itiel is a backend and infra developer turned “DevOps”, an avid public speaker that loves talking about things such as cloud infrastructure, Kubernetes, Python, observability, and R&D culture.

In my experience, here are tips that can help you optimize Kubernetes monitoring:

Use Service Mesh for Enhanced Observability

Implement a service mesh like Istio or Linkerd to enhance observability in your Kubernetes cluster. Service meshes provide built-in monitoring, tracing, and logging capabilities for microservices.

Adopt Prometheus and Grafana

Use Prometheus for collecting and storing metrics, and Grafana for visualizing them. These open-source tools are widely adopted in the Kubernetes ecosystem and provide powerful monitoring and alerting capabilities.

Configure Detailed Alerts

Set up detailed alerts to notify your team about critical issues. Use alerting tools like Alertmanager to define alerting rules based on the metrics collected by Prometheus.

Monitor Kubernetes Control Plane

Keep an eye on the health and performance of the Kubernetes control plane components (API server, etcd, scheduler, controller manager). Issues with these components can affect the entire cluster.

Use Node Exporter

Deploy Node Exporter on all your nodes to collect hardware and OS-level metrics. This helps in monitoring the physical and virtual machines that run your Kubernetes workloads.

Kubernetes Monitoring Challenges 

Here are some of the main challenges involved in monitoring Kubernetes.

Ephemeral Components

Kubernetes is designed to support a highly dynamic and ephemeral environment. Pods and containers are created and destroyed frequently, making it difficult to monitor them. To address this challenge, Kubernetes monitoring tools must be able to track and monitor the entire lifecycle of a pod or container, from creation to termination.

Limited  Observability

Monitoring in Kubernetes is often limited by the observability of the system. It can be difficult to gain visibility into the inner workings of a pod or container. This is because Kubernetes is an orchestration platform that manages the deployment and scaling of containers. It is not a monitoring platform, so it does not provide granular visibility into the behavior of containers. 

Learn more in our detailed guide to Kubernetes observability

Complexity of Metrics

Kubernetes is a complex system that generates a large number of metrics. Control plane metrics, such as the API server and the kubelet, are important for understanding the state of the cluster, but they are not sufficient for monitoring application performance. There are also pod churn metrics, which reflect the rate of creation and termination of pods in the cluster. It can be challenging to manage and analyze multiple metrics to gain meaningful insights into the cluster. 

Learn more in our detailed guide to Kubernetes metrics.

What Are Kubernetes Monitoring Tools? 

Kubernetes monitoring tools are software programs that help monitor the health and performance of Kubernetes clusters, including the nodes, pods, and containers running within them. These tools provide visibility into key metrics such as CPU and memory usage, network activity, and application performance, and can help identify issues and troubleshoot problems in real-time.

Kubernetes monitoring tools are essential for maintaining the health and performance of modern cloud-native applications, and can help DevOps teams identify issues and optimize performance in real-time.

Kubernetes Monitoring Best Practices 

Monitor the End-User Experience 

Monitoring the end-user experience is important when running Kubernetes workloads because it allows organizations to ensure that their applications are performing as expected for their users. End-user monitoring helps to identify issues that impact the user experience, such as slow page load times, error messages, and unresponsive pages.

By monitoring the end-user experience, organizations can quickly identify and resolve issues that affect their users, improving their satisfaction and overall experience with the application. This can be done using tools that track metrics such as response times, page load times, and error rates. These tools can be integrated with Kubernetes monitoring tools to provide a comprehensive view of the application’s performance and its impact on end-users.

Monitor the Cloud Environment

Monitoring Kubernetes in the cloud involves monitoring both the Kubernetes cluster and the cloud infrastructure that it runs on. This includes monitoring IAM events to ensure that only authorized users and applications are accessing the cluster. Cloud APIs should also be monitored to detect any unauthorized access attempts or unusual activity. Monitoring cloud costs is important to ensure that the cluster is optimized for cost efficiency. Network performance should be monitored to identify any issues that may be impacting application performance. 

Organizations can use a combination of cloud-specific monitoring tools and Kubernetes monitoring tools. Cloud-specific tools, such as cloud security and cost management tools, can be used to track IAM events, cloud APIs, and cloud costs. Kubernetes monitoring tools can be used to monitor the performance of the cluster and the applications running on it, as well as network performance. 

Use Labeling and Annotation

Using extensive labels and tags in Kubernetes is important for organizing, identifying, and managing resources within a Kubernetes cluster. Labels are key/value pairs that are assigned to Kubernetes resources, such as pods and services. Tags, on the other hand, are metadata that can be assigned to resources for the purpose of classification and identification.

Labels and tags enable Kubernetes administrators and developers to group, filter, and search resources based on specific criteria. This is especially important in large and complex environments where it can be difficult to manage and track resources. For example, labels and tags can be used to group resources based on their function, environment, version, and other attributes. This can simplify deployment, scaling, and management of resources within a Kubernetes cluster.

Organizations should define a consistent labeling and tagging strategy and apply it consistently across their Kubernetes resources. Kubernetes tools, such as kubectl and Kubernetes dashboards, can be used to manage and filter resources based on their labels and tags.

Leverage Historical Data for Future Planning

Capturing historical data is important for predicting future performance in a Kubernetes cluster. Historical data can be used to identify trends and patterns that can help predict future resource utilization and performance. By analyzing historical data, organizations can identify resource-intensive workloads, peak usage periods, and other factors that impact the performance of the cluster.

Kubernetes monitoring tools can be used to collect and store data about the cluster’s performance over time. This data can include metrics such as CPU usage, memory usage, and network traffic. Once this data is captured, it can be used to build models that can predict future performance based on past behavior. These models can be used to identify potential performance issues and plan for future capacity needs.

Learn more in our detailed guide to Kubernetes monitoring best practices (coming soon)

Kubernetes Monitoring with Komodor

Komodor is the Continuous Kubernetes Reliability Platform, designed to democratize K8s expertise across the organization and enable engineering teams to leverage its full value.

Komodor’s platform empowers developers to confidently monitor and troubleshoot their workloads while allowing cluster operators to enforce standardization and optimize performance. 

By leveraging Komodor, companies of all sizes significantly improve reliability, productivity, and velocity. Or, to put it simply – Komodor helps you spend less time and resources on managing Kubernetes, and more time on innovating at scale.

If you are interested in checking out Komodor, use this link to sign up for a Free Trial.

See Additional Guides on Key Cloud Security Topics

Together with our content partners, we have authored in-depth guides on several other topics that can also be useful as you explore the world of cloud security.

SSPM

Authored by Cynet

• What Is SSPM? Overview, Features and Best Practices
• SaaS Security: The Challenge and 7 Critical Best Practices
• 7 SaaS Security Best Practices You Must Know

Cloud Containers

Authored by Atlantic

• Cloud Containers 
• Containers Cloud Computing: Containers vs. Virtual Machines 
• Container Hosting: What Is Container Hosting?

Secret Management

Authored by Configu

• Secret Management: Why, How, and 5 Critical Best Practices
• Kubernetes Secrets: A Practical Guide
• GitHub Secrets: The Basics and 4 Critical Best Practices