Grafana Cloud

Grafana Kubernetes Monitoring

Grafana Kubernetes Monitoring provides a simplified approach that enables you to monitor your Kubernetes fleet. You can use its rich interface to drill into your data, as well as take advantage of preconfigured dashboards, recording rules, and alert rules. With Kubernetes Monitoring, you can collect and store telemetry data about your infrastructure in Grafana Cloud, including:

  • Metrics
  • Pod logs
  • Cluster events
  • Traces
  • Cost metrics

Kubernetes Monitoring home page

Benefits of Kubernetes Monitoring

All companies that use Kubernetes, regardless of size, should monitor their available resource utilization. If a fleet is underprovisioned, the performance and availability of applications and services are at serious risk. Conversely, fleets with overprovisioned resources run the risk of wasting money and resources that could be better applied elsewhere. With Kubernetes Monitoring, you can achieve optimal resource utilization that results in a reliable, performant, and economical infrastructure and software.

Kubernetes Monitoring provides crucial information about your infrastructure with:

  • Comprehensive monitoring and visibility
  • Advanced warning of usage spikes and increasing error rates
  • Insight into usage to optimize resources
  • Easier discovery of issues and troubleshooting
  • Real-time and predicted costs
  • Management and refinement of stored metrics

Access data in one platform

You can access your data in one platform to explore your infrastructure and analyze the health of your Clusters, Pods, and containers. As you drill down to examine and troubleshoot, you remain within the same Kubernetes Monitoring GUI.

Obtain and monitor logs

Like metrics, Kubernetes does not provide a native storage solution for logs. While monitoring Kubernetes Clusters with kube-state-metrics is highly recommended, without a logging solution, troubleshooting workflows can be incomplete. With logs, you can identify the root cause of an issue more quickly.

Kubernetes Monitoring uses Grafana Loki as its log aggregator, which was built to be compatible with Prometheus. The two technologies share labels, which means you can correlate your Kubernetes telemetry. Correlated Kubernetes metrics and logs mean you can:

  • Identify root causes faster.
  • Remove the burden of setting up and configuring multiple technologies.

Monitor and manage resource efficiency and use

Kubernetes resources that are not optimized can significantly impact both budget and performance. An underprovisioned Kubernetes infrastructure leads to applications that lag, underperform, are unstable, or do not function. An overprovisioned infrastructure becomes costly.

To manage CPU, RAM, and storage, and mitigate the threat of an unstable infrastructure, you must monitor your resource usage to:

  • Ensure that there are enough allocated resources. This decreases the risk of Pod or container eviction as well as undesired performance of your microservices and applications.
  • Eliminate unused or stranded resources.

Solve Node health and resource usage issues

Kubernetes Nodes are the machines in a Cluster that run your applications and store your data. You can run Kubernetes on a number of platforms, including public clouds, private clouds, bare metal servers, and even your laptop. However, unhealthy Nodes can cause exponential errors, unhealthy Deployments, or other events that may be frequent or infrequent.

There are two types of Nodes in a Kubernetes Cluster:

  • Worker Nodes: To host your application containers, grouped as Pods
  • Control plane Nodes: To run the services that are required to control the Kubernetes Cluster

While Clusters act as the spine of your Kubernetes architecture, Nodes form the vertebrae. A healthy backbone of efficient Nodes is required for your Clusters to stay up and your applications to run fast. You could use an expensive autoscaler that buys increasingly more cloud resources and spans more Nodes. While you have seemingly endless resources, it’s still difficult to pinpoint where the actual issues are.

Instead, you can take a data-driven approach for better capacity utilization, resource management, Pod placement, and issue resolution through Kubernetes Monitoring.

Plan, budget, and estimate cost from resource usage forecasts

By predicting and forecasting resource utilization efficiency, you can determine how much of a particular resource will be required for a given project or activity. This insight allows for better planning, budgeting, and cost estimations. More information means teams make better choices regarding resource allocation and infrastructure set up. Ultimately, the power to forecast resource utilization allows organizations to make adjustments proactively to improve efficiency and reduce waste.

Get started

Get started easily by using a simple configuration process with Grafana Kubernetes Monitoring Helm chart to set up Kubernetes Monitoring.

Other configuration methods

There are many available methods you can use to configure Kubernetes Monitoring for your infrastructure data. Refer to Configure manually for infrastructure.

To configure data about an application running in Kubernetes, refer to Configure manually for applications.

What is out of the box

These features are included with Kubernetes Monitoring:

  • Drilling into your data using a single GUI
  • Kubernetes home “crow’s nest” view, showing a snapshot of Cluster, Node, Pod, and container counts, as well as any issues that need attention
  • Efficiency view dedicated to exploring and examining resource usage
  • Cost view for you to analyze and manage your infrastructure costs and potential savings
  • Preconfigured dashboards for analyzing resource usage and cluster operations, from the multi-cluster level down to individual containers and pods
  • Recording rules that increase the speed of dashboard queries and the evaluation of alerting rules
  • Alerting rules for alerting on conditions that you want to be informed about
  • An event handler to watch for Kubernetes events in your clusters