What is Kubernetes monitoring?
Kubernetes monitoring refers to the process of observing and analyzing the health, performance, and security of the various components of a Kubernetes cluster. Key components that need to be monitored include clusters, pods, nodes and control plane components such as the API server, etcd, controller manager, and scheduler.
A key aspect of Kubernetes monitoring is Kubernetes logging. Logging in Kubernetes differs significantly from traditional virtual machines or physical servers because in those environments, logs are retained on the machine even if the application crashes. In Kubernetes, however, logs are tied to the life cycle of ephemeral pods and containers. When a pod is deleted or a container crashes, its logs can be lost unless they are aggregated and stored externally.
To address this, Kubernetes integrates with centralized logging solutions that collect, store, and analyze logs across the cluster, enabling developers and operations teams to maintain visibility into application behavior and troubleshoot issues, even after pods are terminated.
Which components should you monitor in Kubernetes?
The major components to monitor in Kubernetes are:
Clusters: The cluster contains the core components of Kubernetes, that is, nodes and pods. Cluster monitoring involves evaluating the overall health of the cluster, optimizing resource utilization—including memory, CPU, and disk—and monitoring key metrics such as the number of active pods per node, API server latency, and the health of both nodes and pods.
Nodes: Monitoring the node provides a detailed analysis of individual node performance. This includes monitoring disk I/O latency, disk space usage, inbound and outbound network traffic, and node availability for scheduling. Critical CPU and memory usage metrics should be tracked to prevent resource exhaustion, and ensure nodes maintain optimal performance.
Pods: Monitoring the pods helps track state changes to identify potential scheduling issues. Pod monitoring involves monitoring restart counts, as a higher restart rate might indicate issues with the underlying infrastructure. This also helps analyze resource consumption and efficiency.
Ingress controller: Monitoring the ingress controllers helps measure critical parameters related to incoming traffic, such as request rates, HTTP response codes, error rates, and ingress latency. This monitoring ensures that the traffic entering the Kubernetes environment is secure and resilient against potential failures.
Control plane: Monitoring the control plane offers insights into the Kubernetes control plane’s performance with metrics such as API server request latency, scheduler efficiency, etcd health, and controller manager efficiency. These metrics are vital for maintaining the overall stability and performance of the Kubernetes cluster.
Kubernetes logs: Your key security indicators
Kubernetes logs provide critical insights into the health and security of your cluster. By collecting and analyzing Kubernetes logs, you can identify and troubleshoot issues at various levels, from application errors within pods to node-level problems to control plane failures. API logs help track API calls and access attempts, making them useful for audits. Event logs capture all significant events within the cluster, such as pod evictions, failures, and restarts, and provide a comprehensive view of cluster activity. This visibility enhances both performance and security in your Kubernetes environment. Additionally, Kubernetes logs play a key role in evaluating the success of application deployments and ensuring continuous improvement.
Monitoring and logging best practices
By adhering to best practices for logging and monitoring, businesses might maximize resource usage, improve user experience, and decrease downtime. Organizations will be able to maintain high availability and increase operational efficiency by doing this. Here are some best practices for monitoring and logging Kubernetes:
- Constantly monitor the control plane: Ensure continuous monitoring of key components such as the API server, etcd, controller manager, and scheduler. Control plane failures can lead to significant downtime or disruptions, so early detection is crucial to mitigate potential issues.
- Retain historical data: Utilize tools for archiving logs and retain historical data. Accessing previous logs helps in troubleshooting, pattern analysis, and forecasting future performance trends.
- Real-time alerts: Set up alerts for critical metrics like resource utilization and downtime. Instant notifications allow for quick resolution of issues before they worsen.
- Define resource limits: Set appropriate resource limits for your pods to ensure efficiency and to prevent any single pod from monopolizing resources. Regularly review and adjust these limits based on actual usage patterns.
- Centralize logging: Use a centralized logging solution to streamline the collection and analysis of logs across your cluster. Tools like ManageEngine Log360 enhance log filtering, and anomaly detection. These logs can also be correlated to identify threats quickly and resolve them.
- Set log retention policies: Define clear policies for log retention, archival, and rotation. This helps manage storage efficiently and prevents excessive disk usage.
Monitoring and logging challenges
Challenges with respect to Kubernetes monitoring and logging include:
- The sheer volume of metrics generated in a Kubernetes environment can be overwhelming, making it difficult to identify which metrics are most relevant for performance and reliability.
- Finding monitoring and logging tools that can scale seamlessly with a growing Kubernetes platform is a significant hurdle. Even if we manage to identify and collect data, the platform should be able to correlate and detect risks.
- Monitoring distributed components (microservices architecture) can be challenging because problems in one part of the application may impact other parts, making it difficult to pinpoint the root cause of issues.
Kubernetes application monitoring vs. Kubernetes monitoring
Kubernetes monitoring is different from Kubernetes application monitoring.
Kubernetes monitoring provides provides crucial insights into the overall health and functioning of the cluster by monitoring the cluster's core components—such as the control plane, etcd, nodes, pods, CPU usage on nodes, pod configurations, and kubelets.
While Kubernetes monitoring focuses on the performance of the cluster and its components, application performance monitoring focuses on the performance of the applications running on these Kubernetes clusters.
Kubernetes applications monitoring occurs when your Kubernetes cluster runs multiple applications. It’s important for monitoring each application’s CPU usage, memory, response times, error rates, latency, and traffic to identify areas for optimization.
