Kubernetes has revolutionized containerized application deployment, but effective monitoring remains a crucial challenge. Unlike traditional infrastructures, Kubernetes environments are dynamic, distributed, and short-lived, making real-time visibility essential for performance, security, and cost optimization. Without proper monitoring, teams risk application downtime, resource wastage, and security vulnerabilities.
In this blog, we explore the best practices for Kubernetes monitoring to help organizations build a robust observability strategy. By implementing these practices, you can ensure smooth operations, optimize resource utilization, and proactively detect and resolve issues before they impact users.
1. Embrace full-stack observability
Basic metrics, while providing a useful starting point, only scratch the surface of effective Kubernetes monitoring. To truly understand the complex interactions within your environment, you need observability to evaluate the performance of individual microservices, the health of underlying infrastructure components, and the intricate dependencies between them. This means integrating and analyzing data from multiple sources, namely:
- Metrics: Performance indicators like CPU usage, memory consumption, network throughput, and pod availability.
- Logs: Detailed records of application and system events, aiding in debugging and forensic analysis.
- Traces: End-to-end visibility of requests flowing through microservices to detect bottlenecks and latencies.
By employing full-stack monitoring tools like ManageEngine Applications Manager, teams gain the crucial ability to correlate data across different layers of the application stack, from the underlying infrastructure to individual microservices and database queries. This cross-layer correlation significantly accelerates the process of root cause analysis, enabling teams to quickly pinpoint the source of performance issues or errors. Improved troubleshooting capabilities, facilitated by this comprehensive visibility, minimize downtime and ensure a smoother user experience.
2. Focus on the correct Kubernetes metrics
Kubernetes provides a wealth of metrics, but collecting and analyzing them can be overwhelming and inefficient. The key to effective monitoring is to focus on the metrics that provide the most actionable insights, enabling you to quickly identify and resolve performance issues. By prioritizing key metrics, you can avoid metric overload and focus your attention on the data that truly matters. These key metrics include:
- Cluster health: Understanding the overall health of your cluster is paramount. Track node health, resource availability, and scheduler performance to ensure the stability of your Kubernetes environment.
- Pod and container performance: Monitoring the performance of individual pods and containers allows you to pinpoint resource bottlenecks and identify performance issues within specific applications.
- Application performance: Focus on application-level metrics that directly impact user experience, such as request latency, error rates, and database performance. These metrics provide the most direct insights into the quality of service your users are receiving.
Metric Category |
Specific Metric (Example) |
Importance |
|
Cluster level |
kube_node_status_condition |
Identifies unhealthy nodes impacting overall cluster stability. |
|
node_cpu_usage_seconds_total |
Tracks overall CPU resource consumption and potential bottlenecks. |
|
|
node_memory_Active_bytes |
Monitors memory pressure and potential resource shortages. |
|
|
scheduler_e2e_scheduling_duration_seconds |
Indicates scheduler performance and potential scheduling delays. |
|
|
kube_deployment_status_replicas_available |
Tracks the health and availability of deployed applications. |
|
|
Pod and container performance |
kube_pod_status_phase |
Indicates the health and status of individual pods. |
|
container_cpu_usage_seconds_total |
Identifies CPU-intensive containers and potential resource contention. |
|
|
container_memory_usage_bytes |
Monitors memory consumption by individual containers and potential memory leaks. |
|
|
container_network_transmit_bytes_total |
Tracks network bandwidth usage by individual containers. |
|
|
kube_pod_container_restarts_total |
Indicates potential instability or crashes within a pod. |
|
|
Application performance |
http_server_requests_seconds |
Measures application responsiveness and user experience. |
|
http_server_requests_total{status=”5xx”} |
Identifies application errors and potential issues. |
|
|
http_server_requests_total |
Tracks application traffic and request volume. |
|
|
database_query_time_seconds |
Identifies slow database queries impacting application performance. |
|
|
database_cache_hit_ratio |
Monitors database caching efficiency and potential performance bottlenecks. |
|
|
Custom application metrics |
Provides insights into application logic and specific functionalities. |
Pro tip: Define service-level indicators (SLIs) and service-level objectives (SLOs) to track performance and reliability effectively.
3. Implement labeling and tagging for efficient monitoring
In the dynamic and often complex world of Kubernetes, proper labeling is not just a best practice; it’s an absolute necessity for efficient operations. The ability to quickly filter, group, and troubleshoot issues depends heavily on a well-defined labeling strategy. Without proper labeling, teams can waste valuable time sifting through mountains of data, struggling to identify the root cause of problems, and ultimately impacting application performance and availability.
Why? This makes it easier to filter logs, visualize workloads, and enforce monitoring policies across clusters.
These labeling best practices help you efficiently manage your Kubernetes resources:
- Environment: Use the env label (for example, env=production, env=staging) to easily distinguish and manage workloads across different environments. This is essential for isolating production traffic from development or testing activities.
- Microservice: Use the service label (for example, service=payment, service=auth) to identify and group related microservices. This simplifies monitoring and troubleshooting, enabling you to focus on specific components of your application.
- Version: Use the version label (for example, version=v1.2.3, version=v1.2.4) to track deployments and facilitate rollbacks. This is critical for managing application updates and ensuring the ability to revert to previous versions if necessary.
4. Configure smart alerting to prevent fatigue
Too many alerts lead to noise; too few lead to blind spots. Meaningful, actionable alerts are essential for effective Kubernetes monitoring, but they must be combined with smart alerting techniques to avoid both alert fatigue and missed issues.
Alert types
- Critical: Alert on events requiring immediate action (for example, service downtime, high pod eviction rate, node failures).
- Warning: Alert on potential issues that may require attention (for example, increased latency, CPU/memory nearing limits).
- Informational: Track important events for informational purposes (for example, successful deployments, auto-scaling events).
Advanced techniques
- Thresholds: Set up thresholds for all the aforementioned priorities.
- Anomaly detection: Leverage AI-driven platforms like Moogsoft to detect unusual behavior.
- Alert deduplication and correlation: Reduce false positives by deduplicating and correlating alerts.
5. Monitor multi-cluster and hybrid cloud deployments
The distributed nature of Kubernetes deployments across multiple clusters and cloud providers makes centralized visibility paramount for preventing operational blind spots. Unified monitoring prevents fragmentation and enhances reliability across different infrastructures.
Effectively monitoring hybrid Kubernetes deployments, which often span on-premises data centers and multiple cloud providers, requires a specialized approach. Leveraging cloud monitoring tools like ManageEngine Applications Manager is crucial for achieving the unified visibility needed to prevent operational blind spots and ensure consistent performance across all environments. These hybrid cloud monitoring tools are designed to integrate seamlessly with Kubernetes, providing deep insights into application performance, resource utilization, and the health of the underlying infrastructure, regardless of where it resides.
6. Optimize monitoring for high-cardinality data
Kubernetes generates vast amounts of high-cardinality data, which can overwhelm monitoring systems. Optimize data collection to avoid performance issues:
- Reduce unnecessary metric collection by filtering high-cardinality labels.
- Use downsampling and retention policies in Prometheus to optimize storage.
- Apply adaptive sampling in distributed tracing to capture only essential data.
Here’s an example: Instead of logging every request, log only slow responses or high-latency database queries.
7. Strengthen security in Kubernetes monitoring
Securing Kubernetes monitoring requires a multi-layered approach that addresses various potential vulnerabilities. Just as you wouldn’t rely on a single lock to secure your home, you shouldn’t rely on a single security measure to protect your monitoring systems. A comprehensive security strategy should include:
- Role-based access control (RBAC): Implement RBAC to control access to monitoring dashboards and ensure that only authorized users can view and modify monitoring configurations.
- Data protection (Encryption): Encrypt logs and metrics both in transit and at rest to protect sensitive operational data from unauthorized access.
- Activity monitoring (Auditing): Regularly audit API requests and cluster events to detect suspicious activity and identify potential security breaches. These audit logs can also be invaluable for forensic analysis in the event of an incident.
8. Automate and scale your monitoring setup
Kubernetes monitoring must scale with workloads. Automation ensures consistency and efficiency. Best practices include:
- Employing GitOps for configuration management.
- Scripting for log rotation, metric collection, and alert tuning.
- Scaling monitoring components (for example, Prometheus, Grafana) based on demand (for example, using HPA).
- Leveraging ManageEngine Applications Manager’s auto-discovery to dynamically track new Kubernetes resources.
A use case scenario is configuring Horizontal Pod Autoscaler (HPA) to scale monitoring services based on the ingestion rate.
9. Conduct continuous testing and optimization
Investing in continuous improvement of your Kubernetes monitoring strategy yields significant long-term benefits. By regularly testing, refining, and optimizing your approach, you can ensure that your monitoring system remains a valuable asset for years to come. This proactive approach helps you:
- Proactively identify and address performance issues: Regular load testing and chaos engineering help you uncover potential problems before they impact users.
- Reduce downtime and improve application reliability: By identifying blind spots in your monitoring coverage, you can improve your incident response processes and minimize downtime.
- Optimize resource utilization: By gaining deeper insights into application behavior, you can optimize resource allocation and reduce costs.
- Maintain compliance: Regularly reviewing and updating your monitoring configurations helps you ensure that your monitoring system continues to meet regulatory requirements.
About ManageEngine Applications Manager
Monitoring Kubernetes can be complex, but it doesn’t have to be. ManageEngine Applications Manager simplifies the process with a comprehensive suite of features designed specifically for Kubernetes environments. Gain real-time visibility into your clusters, nodes, pods, and containers, and leverage AI-driven anomaly detection to proactively identify and address potential issues with our Kubernetes monitor. Automate key tasks and optimize resource utilization for maximum efficiency. Try it today to see how easy Kubernetes monitoring can be.
