Kubernetes monitoring represents a critical system of reporting designed to help DevOps and IT teams identify issues and proactively manage complex Kubernetes clusters. Effective Kubernetes monitoring allows for real-time management of the entire containerized infrastructure. It tracks uptime and cluster resource utilizations like memory, CPU, and storage. It also tracks cluster component interactions. Kubernetes monitoring allows cluster operators to monitor functionality. It reports when the number of pods required isn’t running, when resource utilization approaches critical limits, and when a pod or node cannot join a cluster due to a failure or configuration error. Kubernetes monitoring gives you insight into the cluster’s internal health, resource counts, and performance metrics. It can also enable you to quickly discover and resolve issues through proactive alerts and machine learning-based anomaly detection. The right Kubernetes monitoring tool helps you understand the status and health of Kubernetes clusters and the applications running on them by viewing logs, metrics, and traces they generate through a unified lens. Kubernetes (K8s) is an open-source container orchestration system for automating software deployment, scaling, and managing containerized applications. Originally developed by Google in 2014, the project is now maintained by the Cloud Native Computing Foundation (CNCF).
The Multilayered Challenge of Containerized Observability
Applications running in a containerized environment like Kubernetes pose a unique monitoring challenge: how to diagnose and resolve issues with hundreds of microservices on thousands, or millions, of containers, running in ephemeral and disposable pods. A successful Kubernetes monitoring solution has specific requirements. It must monitor all layers of the technology stack. This includes the host systems where Kubernetes is running. It includes Kubernetes core components, nodes, pods, and containers running within the cluster. It includes all of the applications and services running in Kubernetes containers. The solution must automatically detect and monitor services as they appear dynamically. It must provide a way to correlate related data so that you can group and explore related metrics, logs, and other observability data. This guide describes how to use Elastic Observability to observe all layers of the application, including the orchestration software itself. This involves collecting logs and metrics from Kubernetes and the applications. It involves collecting trace data from applications deployed with Kubernetes. It involves centralizing the data in the Elastic Stack. It involves exploring the data in real-time using tailored dashboards and Observability UIs. This guide describes how to deploy Elastic monitoring agents as DaemonSets using the Elastic Agent manifest files.
Monitoring Infrastructure Layers
To achieve comprehensive visibility, you need to monitor the health and performance of several distinct layers. You must monitor the hosts where Kubernetes components are running. Each host produces metrics like CPU, memory, disk utilization, and disk and network I/O. You must monitor Kubernetes containers, which produce their own set of metrics. You must monitor the applications running as Kubernetes pods, such as application servers and databases, each producing its own set of metrics. Additional Kubernetes resources like services, deployments, and cronjobs are valuable assets of the whole infrastructure and produce their own set of metrics that need monitoring. The Elastic Agent along with the Kubernetes integration provides a unified solution to monitor all layers of the Kubernetes technology stack, so you don’t need multiple technologies to collect metrics. There are several options for collecting metrics about Kubernetes clusters and the workloads running on top of them. These include collecting Kubernetes metrics from the kubelet API. It includes collecting Kubernetes metrics from kube-state-metrics. It includes collecting Kubernetes metrics from the Kubernetes API server. It includes collecting Kubernetes metrics from the Kubernetes proxy. It includes collecting Kubernetes metrics from the Kubernetes scheduler. It includes collecting Kubernetes metrics from the Kubernetes controller-manager. It includes collecting Kubernetes events from the Kubernetes API Server. Collecting and analyzing logs of both Kubernetes core components and various applications running on top of Kubernetes is a powerful tool for Kubernetes observability.
Log Analysis and Application Performance Monitoring
From the centralized data store, you can quickly search and filter your log data. You can get information about the structure of log fields. You can display your findings in a visualization. Then, you can filter your log data and dive deeper into individual logs to find and troubleshoot issues. Relevant reference materials include exploring logs in Discover for an overview of viewing logs in Discover. It includes filtering logs in Discover for more on filtering logs in Discover. You can quickly triage and troubleshoot application performance problems with the help of Elastic application performance monitoring (APM). Think of a latency spike. APM can help you narrow the scope of your investigation to a single service. Because you’ve also ingested and correlated logs and metrics, you can then link the problem to CPU and memory utilization or error log entries of a particular Kubernetes pod. Application monitoring data is streamed from your applications running in Kubernetes to APM, where it is validated, processed, and transformed into Elasticsearch documents. There are many ways to deploy APM when working with Kubernetes, but this guide assumes that you’re using an Elastic Cloud Hosted deployment. If you haven’t done so already, enable APM in the Elastic Cloud Console. If you want to manage APM yourself, there are a few alternative options. One option is Elastic Cloud on Kubernetes (ECK), which is the Elastic recommended approach for managing APM Server deployed with Kubernetes.
Configuration and Deployment Methods
To monitor Kubernetes, you need to have a metrics server running in the cluster. You need kube-state-metrics turned on. You need a collection mechanism deployed. You need a Kubernetes monitoring tool that can handle Kubernetes metrics and logs. You need an agent deployed to collect metrics and logs. To get full visibility into the entire environment, a comprehensive observability tool can monitor Kubernetes data as well as application traces, metrics, and logs. Many Kubernetes monitoring solutions use a DaemonSet approach because they’re relatively easy to provision. A DaemonSet is a specialized pod that ensures that a copy of its workload runs on all nodes within the cluster. You can also configure alerts to ensure your teams are able to respond quickly to any security or performance events. The data gained allows you to optimize the health, performance, and security configurations of your clusters. This leads to resource utilization and reduced costs. Kubernetes monitoring allows you to ensure resources are consumed optimally by teams or applications. It allows you to automatically utilize new resources when a new node joins a cluster. It allows you to redeploy workloads to available nodes when hosts go down. It allows you to provision updates and rollbacks more efficiently.
Integration Standards and Operational Efficiency
By unifying your logs, metrics, and APM traces at scale in a single view, you can effectively govern the complexity of highly distributed cloud-native applications. You also get actionable observability for your cloud-native tech stack and cloud monitoring. This enables you to proactively detect and resolve issues in sprawling hybrid and multi-cloud ecosystems. All the host systems, including Kubernetes core components, nodes, pods, and containers within the cluster, and all of the applications and services should be tracked. The system must automatically detect and monitor services as they appear dynamically. It must provide a way to collect and correlate data so that you can group and explore related metrics, logs, traces, and other observability data. It must integrate with open standards, like Prometheus and OpenTelemetry, to gather additional metrics. There are best practices for observing and securing application and service workflows on Kubernetes using Elasticsearch and OpenTelemetry. Kubernetes is different from Docker because it operates containerized applications at scale. Docker is a set of software development tools that allow you to build, share, and run individual containers. Docker employs a client-server architecture with simple commands and automation through a single API. It provides an easy way to package and distribute containerized applications. Container images built with Docker can run on a platform that supports containers, like Kubernetes or Docker Swarm. Kubernetes is better than Docker for running, managing, scheduling, and orchestrating vast volumes of containers across multiple servers and clusters. Kubernetes is favored by most larger businesses to monitor their health and efficiently balance loads. Crucially, Kubernetes comes with an API and command line tool that allows you to automate operations.
Core Kubernetes Terminology
Understanding the following terms is essential for effective monitoring. Clusters are a set of worker machines, called nodes, that run containerized applications. Every cluster has at least one worker node. A node is a worker machine in Kubernetes. The term Master (node) is a legacy term, used as a synonym for nodes hosting the control plane. Worker (node) refers to worker nodes that host the Pods that are the components of the application workload. A Pod is the smallest and simplest Kubernetes object. A Pod represents a set of running containers on your cluster. A Container is a lightweight and portable executable image that contains software and all of its dependencies. A Controller, in Kubernetes, is a control loop that watches the state of the cluster, then makes or requests changes where needed. Each controller tries to move the current cluster state closer to the desired state. Kubelet is an agent that runs on each node in the cluster.
Conclusion
Kubernetes monitoring transcends simple resource tracking; it serves as the nervous system for modern, ephemeral infrastructure. By unifying logs, metrics, and traces from every layer—host, container, and application—into a single observability platform like the Elastic Stack, organizations can transform complex diagnostic challenges into streamlined, data-driven resolutions. The ability to automatically detect dynamic services, correlate latency spikes with specific node resource exhaustion, and leverage standard integrations like OpenTelemetry ensures that DevOps teams maintain proactive control over sprawling, multi-cloud environments. As container orchestration continues to scale in complexity, the shift from isolated monitoring tools to unified, agent-based observability architectures becomes not just a best practice, but a fundamental requirement for operational resilience and cost efficiency.