How to observe Kubernetes metrics and deployments: A comprehensive guide

Kubernetes is a highly dynamic container orchestration system, and monitoring it effectively is essential to maintaining performance, reliability, and security. Observability in Kubernetes ensures high availability and optimal system performance. Since Kubernetes environments are dynamic and distributed, monitoring deployments, pods, and system metrics effectively requires a comprehensive observability approach. It involves tracking real-time metrics, logs, and traces to understand how applications and infrastructure components behave. Learn more about what is Kubernetes monitoring here.

This guide covers how to perform Kubernetes monitoring for both metrics and deployments in depth, using both native tools (kubectl, Metrics Server) and third-party solutions (Prometheus, Grafana, ManageEngine Applications Manager).

The imperative of Kubernetes observability: Why it matters more than ever

Kubernetes environments are inherently ephemeral and complex. Workloads are constantly being created, scaled, and destroyed. This dynamism, coupled with the distributed nature of microservices architectures, necessitates a robust observability strategy. Without it, you risk:

• Unpredictable performance: Ephemeral workloads and auto-scaling can lead to unexpected resource fluctuations, impacting application performance.
• Troubleshooting nightmares: Distributed microservices make it challenging to pinpoint the root cause of issues without end-to-end request tracking.
• Security vulnerabilities: Lack of visibility into application behavior can leave your systems vulnerable to security threats.
• Resource wastage: Inefficient resource allocation can lead to unnecessary costs.

To mitigate these risks and ensure the smooth operation of your Kubernetes deployments, you must embrace the three pillars of observability:

• Metrics: Real-time numerical data that provides a quantitative view of system and application performance.
• Logs: Text-based records of events and errors, offering contextual insights into application behavior.
• Traces: End-to-end tracking of requests as they propagate through microservices, revealing dependencies and latency.

By combining these elements, DevOps teams can detect anomalies, troubleshoot issues, and optimize performance.

Observing Kubernetes metrics: The pulse of your cluster

Metrics provide a real-time, quantitative view of your Kubernetes environment. Kubernetes provides multiple built-in and external tools to capture metrics. Here are a couple of ways to fetch the Kubernetes metrics:

1. Leveraging the Kubernetes Metrics Server for basic resource insights:

The Metrics Server is a lightweight, in-cluster aggregator of resource usage data. It's ideal for:

• Horizontal Pod Autoscaling (HPA): Enabling dynamic scaling based on resource utilization.
• Quick resource checks: Providing a snapshot of CPU and memory usage across nodes and pods.
• Troubleshooting sudden spikes: Identifying immediate resource bottlenecks.

Installation:

Use the following bash script:

Verification:

Common commands:

Kubernetes exposes key resource utilization data through the Metrics Server. Given below are some of the commands to fetch the resource utilization from the Metrics Server:

• kubectl top nodes: Node CPU/memory usage.
• kubectl top pods --all-namespaces: Pod CPU/memory usage.
• kubectl describe pod <pod-name>: Detailed pod info (resource requests/limits).
• kubectl get pods --all-namespaces: Pod status list.

Limitations:

The Metrics Server does not store historical data, limiting its use for long-term trend analysis.

2. Prometheus for Kubernetes metrics

Prometheus is the de facto standard for Kubernetes metrics monitoring, since it offers:

• Long-term storage: This enables historical data analysis and trend identification.
• PromQL: A powerful query language for extracting granular insights from metrics.
• Grafana integration: Providing rich visualizations and dashboards.

Steps to set up Prometheus for Kubernetes monitoring:

Deploy Prometheus using Helm:

Verify installation:

kubectl get pods -n monitoring

Access Prometheus dashboard:

kubectl port-forward svc/prometheus-server 9090:80 -n monitoring

Key metrics and PromQL queries:

Queries	Description
up{job="kubernetes-nodes"}	Checks the availability of nodes.
sum(rate(node_cpu_seconds_total{mode="user"}[5m])) by (node)	Calculates CPU usage per node.
sum(kube_pod_container_status_restarts_total) by (pod, namespace)	Tracks pod restart counts.
histogram_quantile(0.95, rate(http_server_requests_seconds[5m]))	Measures 95th percentile request latency.

Alternatively,

For a streamlined Kubernetes monitoring experience, ManageEngine Applications Manager offers preconfigured dashboards and AI-driven anomaly detection, bypassing the complexity of manual Prometheus setups.

Observing Kubernetes deployments

There are two methods by which you can observe Kubernetes deployments. They are:

1. Using Kubectl to monitor deployments

You can observe real-time Kubernetes deployments using:

Command	Description
kubectl get deployments --all-namespaces	Lists all deployments.
kubectl describe deployment <deployment-name>	Describes a specific deployment.
kubectl rollout status deployment <deployment-name>	Monitors rolling updates.
kubectl logs -f <pod-name>	To view detailed pod logs.

2. Visualizing deployments with Grafana

By combining Grafana's visualization capabilities with Prometheus's metrics collection, you gain powerful insights into Kubernetes deployments. Here are the steps to get visual reports of the Kubernetes deployments in Grafana:

• Install Grafana via Helm:

  helm install grafana grafana/grafana

• Access the dashboard:

  kubectl port-forward svc/grafana 3000:80

• Import pre-built Kubernetes monitoring dashboards from Grafana Labs.

Alternatively,

ManageEngine Applications Manager integrates directly with Kubernetes to visualize deployments and monitor anomalies in real time. It enhances Kubernetes monitoring by seamlessly integrating with Prometheus, allowing it to ingest existing Prometheus metrics and centralize them alongside other IT infrastructure data. This provides pre-built dashboards, AI-driven anomaly detection for reduced alert fatigue, and correlation capabilities for faster troubleshooting, while also offering auto-discovery of new Kubernetes resources and maintaining vendor neutrality, simplifying Kubernetes observability without requiring complex, standalone Prometheus setups.

Observing Kubernetes logs for deeper insights

Logs are indispensable for debugging, troubleshooting, and gaining a deeper understanding of your application's inner workings. Effective log observability is crucial for identifying deployment failures, pinpointing performance bottlenecks, and maintaining the overall health of your Kubernetes environment. Here are 2 methods for observing logs:

1. Native Kubernetes logging

The Kubernetes command-line tool, kubectl, provides direct access to pod logs, offering a quick and efficient way to inspect application behavior in real-time.

Command	Purpose	Use Case
kubectl logs <pod-name>	Retrieves the logs from the primary container within the specified pod. This command is ideal for quickly inspecting the output of a single application process.	To rapidly diagnose issues within a single-container pod, check application initialization messages, or monitor the output of a specific process.
kubectl logs <pod-name> --all-containers	Aggregates and displays the logs from all containers within the specified pod. This is particularly useful for debugging complex pods with multiple sidecar containers or microservices that interact closely.	To troubleshoot interactions between multiple containers, examine the logs of sidecar containers (e.g., logging agents, service meshes), or analyze the behavior of multi-component applications.

2. Centralized log aggregation with Fluentd & Elasticsearch

For large-scale Kubernetes deployments, a centralized log aggregation solution is essential. The EFK stack (Fluentd, Elasticsearch, Kibana) provides a robust and scalable platform for log management and analysis.

Observing Kubernetes traces for Microservices monitoring

In microservice architectures, where requests traverse multiple services, distributed tracing is indispensable for understanding request flow and pinpointing performance bottlenecks.

Jaeger for Kubernetes tracing

Jaeger is a leading open-source tracing solution, that allows you to visualize request paths and identify latency issues across your microservices.

Steps to implement:

• Install Jaeger via Helm:

  helm install jaeger jaegertracing/jaeger

• Instrument applications with OpenTelemetry SDKs.

• Access the Jaeger UI:

  kubectl port-forward svc/jaeger-query 16686:80

Alternatively,

ManageEngine Applications Manager streamlines tracing. Avoid the overhead of installing and configuring Jaeger, and the complexity of SDK instrumentation. Applications Manager offers integrated distributed tracing, simplifying microservices observability.

In conclusion, effective Kubernetes observability is not a one-time setup; it's an ongoing process of monitoring, analyzing, and optimizing. By embracing the three pillars of observability and leveraging the right tools, you can ensure the health, performance, and security of your Kubernetes deployments.

Automate Kubernetes observability with ManageEngine Applications Manager

Let's face it, manually configuring observability by implementing the aforementioned guidelines is cumbersome. For enterprises seeking a streamlined, hassle-free Kubernetes monitor solution that empowers them with proactive insights and operational efficiency, ManageEngine Applications Manager emerges as a powerful and intuitive platform. It's designed to alleviate the burden of complex Kubernetes monitoring setups, offering a robust suite of features that deliver immediate value and long-term benefits. Its core capabilities include:

• Pre-configured Kubernetes monitoring dashboards: Eliminate the time-consuming process of building custom dashboards from scratch. Applications Manager provides ready-to-use dashboards that offer instant visibility into the health and performance of your Kubernetes clusters, nodes, pods, and containers. These dashboards are meticulously crafted to display critical metrics and logs, enabling teams to quickly identify trends, anomalies, and potential issues.
• Real-time alerts for cluster, node, and pod health: Stay ahead of potential problems with real-time alerts that notify you of critical events and performance deviations. Applications Manager's intelligent alerting system monitors the health of your Kubernetes infrastructure, providing immediate notifications for node failures, pod crashes, resource exhaustion, and other critical conditions. This ensures that teams can react swiftly to incidents and minimize downtime.
• AI-driven anomaly detection: Go beyond traditional threshold-based alerting with AI-driven anomaly detection. Applications Manager's machine learning algorithms analyze historical data and identify unusual patterns in your Kubernetes metrics. This enables you to proactively detect subtle performance degradations and potential issues that might otherwise go unnoticed. It also reduces alert fatigue by reducing false positives.
• Multi-cluster visibility: Gain a unified view of your Kubernetes deployments across multiple clusters and cloud providers. ManageEngine Applications Manager provides centralized visibility, enabling you to monitor and manage your entire Kubernetes ecosystem from a single pane of glass. This simplifies multi-cluster management and ensures consistent monitoring practices across all environments.
• Easy integration with Prometheus: Seamlessly integrate with your existing observability tools and workflows. ManageEngine Applications Manager offers out-of-the-box integration with popular tools like Prometheus to leverage your existing investments and create a cohesive observability ecosystem. This vendor neutral approach is very important.

With the right observability strategy in place, organizations can proactively detect issues, optimize resource utilization, and maintain the resilience of their Kubernetes infrastructure.

Why choose Applications Manager?

With its intuitive interface, robust alerting capabilities, and flexible deployment options, Applications Manager empowers organizations to reduce downtime, enhance operational efficiency, and deliver superior user experiences. Whether you’re managing on-premise, cloud, or hybrid environments, Applications Manager simplifies the complexity of IT monitoring.

Elevate your Kuberneteds monitoring game with Applications Manager. Download now and experience the difference, or schedule a personalized demo for a guided tour.