Kubernetes Observability: Pillars, Tools & Best Practices

Kubernetes observability is the ability to monitor, measure, and understand the internal state and behavior of Kubernetes clusters and workloads. It involves collecting metrics, logs, and traces across pods, nodes, and services to detect issues, optimize performance, and ensure system reliability.

The observability concept is an evolution of traditional monitoring strategies. Simple monitoring workflows typically revolve around individual metrics that lack broader context, whereas observability aims to offer a holistic view of all the components in a system. It also explains how the system’s current state arose, not just what it is.

These characteristics are crucial to Kubernetes operations. Kubernetes clusters are usually large-scale environments involving many different components. Operators need clear insights into what’s happening and why, from control plane services to compute Nodes, storage integrations, and running workloads. Observable clusters support informed decision-making when investigating performance problems, capacity issues, and unexpected Pod terminations.

Kubernetes observability pillars

Observability has three main pillars: logs, metrics, and traces.

• Metrics are quantitative time-series data describing specific aspects of system performance at a moment in time, such as CPU utilization or the count of failed Pods.
• Logs are timestamped messages generated by system components describing errors, warnings, and current events.
• Traces are detailed records of the end-to-end path between microservices taken by individual requests. Trace data allows you to see how requests move through your cluster, including the time taken at each step.

Kubernetes clusters should include tools that collect data for all three pillars. Exposing metrics, logs, and traces lets operators deeply interrogate a cluster’s state, enabling more effective Kubernetes debugging and optimization.

Observability data collected from Kubernetes clusters is useful in many different scenarios. Here are some of the key ways in which Kubernetes observability benefits DevOps teams:

1. Gain visibility into cluster activity: Observability tools let you analyze what’s happening in your cluster using metrics, logs, and traces. Without them, you have limited insights because Kubectl only provides simple lists of resources.
2. Monitor Node resource consumption: Monitoring Node resource utilization metrics like CPU and memory consumption lets you identify cluster scaling issues to improve your workload distribution.
3. Understand service utilization stats: Cluster operators need visibility into the usage of different services to make informed scaling decisions. Monitoring load changes also helps pinpoint performance problems caused by new changes.
4. Inspect application logs: Developers use app logs to record important events. Scraping the log data from Kubernetes Pods makes this data available to analyze when debugging errors.
5. Efficiently respond to incidents and failures: Easy access to metrics, logs, and traces lets operators respond more effectively to Kubernetes incidents. The data explains which components have failed and why, helping reduce incident resolution times.
6. Stay informed of cluster costs: Managing costs is one of the biggest challenges for Kubernetes operators, as it’s not always clear what each resource contributes to the bill. Cost monitoring solutions provide real-time insights into actual cluster spending.
7. Detect security issues: Observability suites can work alongside security tools to highlight potential misconfigurations in your cluster, such as the presence of Pods without an appropriate security context configured.
8. Analyze network traffic patterns: Traces allow you to see how data moves through your Kubernetes cluster to reach different Pods and Services. Analyzing network usage can help you optimize your microservices architecture.
9. Effectively solve Kubernetes problems: Detailed observability data gives you the answers you need to start making targeted improvements in performance, errors, scaling, or costs.

Overall, observability data is useful whenever you need to know what your cluster is doing or why. A comprehensive observability system gives you the answers you need when investigating cluster issues or deciding on changes. But, although these benefits mean observability should be a must-have for cluster operators, it presents several practical challenges.

Implementing an effective Kubernetes observability strategy can be surprisingly tricky. 

Because Kubernetes doesn’t come with built-in observability features, you must install external tools to capture your metrics, logs, and traces. These solutions must be correctly configured and integrated to get the most value from your data.

Common Kubernetes observability problems include:

• Multiple data types to manage: Metrics, logs, and traces all contribute to Kubernetes observability, but each data type is typically handled by a different tool.
• Multiple different services and resources to monitor: Kubernetes clusters have large monitoring surfaces, including the control plane, Nodes, and deployed applications. Multiple monitoring agents are required to fully capture a cluster’s state.
• Cluster resources are dynamic and short-lived: Resources within Kubernetes clusters change frequently, such as when Pods are replaced, Deployments are scaled, or Jobs are created. When tools aren’t configured correctly, it can be harder to spot patterns in observability data.
• Large data volumes can be costly to retain: Large-scale Kubernetes clusters with many resources can quickly produce very high volumes of observability data. Too much noise makes analysis difficult and leads to excess storage costs.
• Risk of data becoming siloed in different tools and services: Observability works best when it’s holistic and integrated, but the multi-faceted nature of Kubernetes clusters, involving metrics, logs, and traces for a variety of cluster components, can increase the risk that data will become stuck in individual tools. This prevents holistic analysis.

You can solve Kubernetes observability challenges by choosing observability solutions for Kubernetes clusters. Your implementation will be more likely to succeed if you use official installation methods, tune data retention settings, and look for tools designed to work together. 

In the following sections, we’ll look at some top choices and best practices.

Implementing a Kubernetes observability strategy is typically a multi-step process. You need to define your observability objectives, choose automated tools to fulfill them, and install and configure your solutions in your cluster. 

Once your tools are up and running, you can use them to analyze collected data and drive incremental cluster improvements.

Try using the following high-level process to get started with your Kubernetes observability plan:

1. Set observability aims: Define your expected observability outcomes, such as resource usage, shorter incident response times, or reduced operating costs.
2. Choose tools: Evaluate, install, and configure observability tools (such as those in the list below) to continually scrape, organize, and store your cluster’s metrics, logs, and traces.
3. Configure alerts: Use alerting solutions to inform team members when important events occur in real-time.
4. Monitor collected data: Use query and visualization tools to inspect your collected observability data. The data should allow you to investigate specific problems efficiently and track general trends over time.
5. Iterate on your implementation: Regularly review your observability strategy to identify improvement opportunities. For instance, you may find that you need extra tools and alerts or that you’re collecting too many unnecessary metrics.

You should also consider who will have access to your observability data. It’s good practice to open access to as many stakeholders as possible. This lets developers, operators, and product managers engage with available logs and metrics throughout the DevOps lifecycle. Making observability a collaborative process can help you analyze complex events, such as understanding why more Pod failures occurred after deploying a new feature.

Let’s look at some of the most popular Kubernetes observability tools. These solutions let you collect and analyze metrics, logs, traces, and other types of observability data. 

Each tool generally focuses on one use case, so you should combine several options to build a complete cluster observability implementation.

1. Metrics-Server

Metrics-Server is a simple Kubernetes monitoring tool maintained as part of the Kubernetes project. It collects Pod and Node-level resource consumption metrics such as CPU and memory utilization. The server scrapes metrics every 15 seconds.

Metrics-Server is primarily designed as a backend tool for Kubernetes autoscaling. It provides the data that Kubernetes needs to make autoscaling decisions. 

However, you can also directly access Metrics-Server data using the kubectl top command. This lets you conveniently monitor key real-time Pod and Node stats in your terminal, but the data isn’t comprehensive enough to be used as a full observability solution.

2. Kube-State-Metrics

Kube-State-Metrics is another Kubernetes add-on project. It provides a service that exposes metrics about the objects within your Kubernetes cluster. The data is generated by listening to Kubernetes API server events.

Kube-State-Metrics data is one of the main ways to understand what’s happening in a Kubernetes cluster at the workload level. Hundreds of different metrics are supported, such as the number of Pods in different states, Deployment rollout progress, and CronJob schedule times.

Kube-State-Metrics exposes its metrics in Prometheus format. You can scrape and query them with any Prometheus instance, or use Grafana to visualize your metrics on a dashboard.

3. Kube-Prometheus-Stack (Prometheus and Grafana)

On that note, Prometheus and Grafana are staples of the Kubernetes observability ecosystem. Prometheus is a time-series database that’s ideal for storing and querying metrics data, whereas Grafana is a popular dashboard-based visualization tool that’s easy to use with Prometheus.

Kube-Prometheus-Stack is an active community project that brings Kubernetes, Prometheus, Grafana, and Kube-State-Metrics together. It’s a single Helm chart that installs and configures a complete observability stack in your cluster. 

The chart includes a Prometheus instance that automatically scrapes Kubernetes control plane, Node, and Kube-State-Metrics data. Prometheus is accompanied by a built-in set of Grafana dashboards that provide useful visualizations with zero configuration.

You can learn more about getting started with Kube-Prometheus-Stack.

4. Elastic Stack (ELK)

The Elastic Stack (ELK) is the combination of Elasticsearch, Kibana, and Logstash. Together, these components implement a full log storage and indexing system.

Using ELK with Kubernetes lets you scrape the logs from your Pods and cluster control plane components. You can then search, filter, and transform logs to find the information you’re looking for. ELK also ensures you can retain logs after a Pod terminates or is destroyed, unlike when manually retrieving logs via kubectl logs.

It’s easy to deploy ECK in Kubernetes using the stack’s dedicated Kubernetes operator.

5. Fluentd

Fluentd is a popular unified data collector for logging systems. It’s a CNCF project that pairs well with Kubernetes.

Installing Fluentd in your cluster lets you collect logs and send them to external destinations such as an Elasticsearch instance. A wide variety of filtering, parsing, and transformation plugins allow you to process your logs before they’re stored.

6. Alertmanager

Alertmanager is a highly popular alerting system. It’s part of the Prometheus project, but runs as a separate component. However, Alertmanager is also deployed automatically when you use the Kube-Prometheus-Stack Helm chart discussed above.

You can configure Alertmanager with precise rules for when different alerts should be triggered. Alertmanager consumes your Prometheus metrics data to determine when an alert is firing, then sends a notification to one or more receivers. 

Many different receiver options are supported, including email, PagerDuty, chat platforms, and custom integrations. This lets you stay informed of important events in your cluster without needing to check your other observability tools manually.

7. OpenTelemetry

OpenTelemetry is a telemetry solution designed to generate and collect system traces. Kubernetes system components support the OpenTelemetry format, allowing you to trace activity throughout your cluster.

OpenTelemetry provides a Helm chart that installs the collector in your cluster. A dedicated operator also simplifies the process of configuring telemetry targets, such as Kubernetes control plane services or your own apps. 

Collected data can be streamed to several different backends, including Prometheus, Jaeger, and Elasticsearch.

8. Kubecost

Kubecost is a dedicated Kubernetes cost monitoring solution. It interfaces with your cloud provider’s pricing tables to provide transparent real-time insights into cluster costs. The tool also suggests savings opportunities, such as right-sizing Nodes to reduce resource wastage.

Kubecost was originally an open-source project, but it’s now a commercial tool owned by IBM. However, you can choose OpenCost as a fully open-source alternative.

Maintained by the CNCF, OpenCost is the core cost allocation engine originally built by Kubecost. Kubecost then layers additional proprietary features on top of OpenCost.

You should now have a deeper understanding of what Kubernetes observability means and how it’s implemented. Let’s wrap up with a summary of five key best practices for observability success at scale.

1. Enable alerts and notifications for key events – Using tools like Alertmanager to get notified when events occur lets you respond to cluster incidents proactively, rather than reactively. Operators will know immediately if Pods fail to schedule or a Node goes offline, for instance. This helps shorten incident durations.
2. Consistently label Kubernetes resources to track them in your data – Labeling your Kubernetes resources using a consistent structure makes them easier to identify in monitoring data.
   
   Kubernetes-aware monitoring tools may natively support the Kubernetes recommended labels to let you easily filter data to specific deployments, workloads, and components, for instance. You can also use custom labels to help you drill down to just data about the resource usage you’re interested in.
3. Instrument your applications to enable metrics and log scraping – Making the apps within your cluster observable enables more precise activity analysis.
   
   For instance, you can use Prometheus client libraries to expose key metrics from your applications, such as the number of orders being placed or the number of unique users logging in. Analyzing this data alongside cluster-level stats gives you the most complete picture of what’s happening in your environment.
4. Only collect data you actually need – A busy Kubernetes cluster can quickly produce vast volumes of observability data. It may seem tempting to keep all the default metrics provided by tools like Kube-Prometheus-Stack and Kube-State-Metrics enabled, but if you’re not actively monitoring them, they waste storage capacity and may increase performance overheads.
   
   It’s more efficient to selectively enable metrics that support your defined Kubernetes observability aims. Regularly tune data retention periods to avoid excess storage use.
5. Keep monitoring strategies aligned around compliance requirements – Logs and traces may contain sensitive data sent in requests or stored by your application.
   
   You should audit your observability strategy to ensure that the data collected and your use of it are compatible with your organization’s compliance and privacy requirements. Restricting access to observability data can hinder collaboration, but it may be necessary for certain metrics and log sources.

Spacelift allows you to connect to and orchestrate all of your infrastructure tooling, including infrastructure as code, version control systems, observability tools, control and governance solutions, and cloud providers. 

It enables powerful CI/CD workflows for OpenTofu, Terraform, Pulumi, Kubernetes, and more. It also supports observability integrations with Prometheus and Datadog, letting you monitor the activity in your Spacelift stacks precisely.

With Spacelift, you get:

• Multi-IaC workflows
• Stack dependencies: You can create dependencies between stacks and pass outputs from one to another to build an environment promotion pipeline more easily.
• Unlimited policies and integrations: Spacelift allows you to implement any type of guardrails and integrate with any tool you want. You can control how many approvals you need for a run, which resources can be created, which parameters those resources can have, what happens when a pull request is open, and where to send your notifications data.
• High flexibility: You can customize what happens before and after runner phases, bring your own image, and even modify the default workflow commands.
• Self-service infrastructure via Blueprints: You can define infrastructure templates that are easily deployed. These templates can have policies/integrations/contexts/drift detection embedded inside them for reliable deployment.
• Drift detection and remediation: Ensure the reliability of your infrastructure by detecting and remediating drift.

If you want to learn more about Spacelift, create a free account or book a demo with one of our engineers.

Implementing a Kubernetes observability strategy enables you to understand what’s happening in your clusters. Metrics, logs, and traces let you efficiently pinpoint the causes of problems for easier debugging. The data tells you not just what’s currently happened, but also how your cluster’s state has changed over time.

Because Kubernetes isn’t preconfigured for observability, you need to use tools like Metrics Server, Kube-Prometheus-Stack, and ELK to monitor your cluster’s components. These tools continually scrape your cluster’s data, ready for you to analyze. 

Other solutions provide vital insights for specific operational tasks, such as Kubecost for real-time cost monitoring. You should also configure alerts to inform you about key events in your Kubernetes environments as they happen.

Finally, remember that good Kubernetes observability depends on your cluster’s infrastructure being observable too. It’s easy to lose track of compute nodes, networking components, and cloud accounts at scale, especially if you’re using multiple cluster providers. Manage your clusters using an IaC platform like Spacelift to gain clear visibility into your infrastructure resources.