Build a SRE Observability Stack for Kubernetes with Rootly

Learn to build a robust SRE observability stack for Kubernetes using metrics, logs, and traces. See how Rootly unifies SRE tools for incident tracking.

An observability stack provides deep insight into your systems' health. For a complex, dynamic environment like Kubernetes, it isn't just helpful—it's essential. The ephemeral nature of pods, constant churn from deployments, and complex service-to-service communication create blind spots that traditional monitoring can't penetrate. To truly understand what’s happening inside your clusters, you need to move beyond knowing what is broken to understanding why.

This requires building a stack founded on the three pillars of observability: metrics, logs, and traces. These elements work together to provide a complete picture of system behavior. This article offers a practical guide for building a cohesive SRE observability stack for Kubernetes and shows how Rootly integrates to streamline incident management from detection to resolution.

The Three Pillars of Observability Explained

A solid observability strategy is built on three distinct but complementary types of telemetry data. Understanding each one is the first step toward gaining full visibility into your Kubernetes clusters [2].

Metrics

Metrics are numerical, time-series data representing a system's performance and health. They are lightweight and ideal for tracking trends, building dashboards, and alerting on known failure conditions. In Kubernetes, key metrics include:

Infrastructure Metrics: Container-level resource usage like CPU, memory, and network I/O.
Cluster State Metrics: Data on the state of Kubernetes objects, like the number of running pods in a deployment.
Application Metrics: Custom data exposed by your applications, often in a Prometheus-compatible format.

A key challenge with metrics is cardinality. Using labels with many unique values, like a request_id, can cause storage requirements to swell, leading to high costs and slow query performance.

Logs

Logs are timestamped records of discrete events. While metrics tell you that CPU usage is high, logs can help you understand why by showing specific error messages or application behavior. Using structured logs in a format like JSON is far more powerful than plain text, allowing aggregation tools like Loki or Elasticsearch to parse, index, and query the data efficiently [7].

The biggest risk with logging is volume. Unfiltered, unstructured logs are not only expensive to store but also slow to search. Without a strategy for structured logging and appropriate filtering, logs can become a costly data swamp that provides little value.

Traces

Traces represent the end-to-end journey of a single request as it travels through a distributed system. In a microservices architecture, one user action might trigger calls across dozens of services. Traces connect these individual operations into a complete view of the request, making them essential for identifying performance bottlenecks and debugging latency issues across services [4].

The main tradeoff with tracing is performance overhead. Instrumenting code to generate traces adds a small amount of latency. Capturing every single trace can be resource-intensive, so most systems rely on sampling. However, sampling means you might miss the specific errored request you need to analyze.

Assembling Your Core Observability Stack

Building a modern observability stack involves choosing tools for data collection, storage, visualization, and alerting. A popular and effective stack for Kubernetes relies on open-source standards to provide flexibility and avoid vendor lock-in [6].

Data Collection and Processing: OpenTelemetry

OpenTelemetry has emerged as the cloud-native standard for instrumenting applications to generate and export telemetry data in a vendor-neutral format [3]. Its collector can be deployed as a DaemonSet to gather data from every node or as a Deployment for cluster-wide aggregation. By using OpenTelemetry, you decouple instrumentation from your backend, giving you the freedom to switch observability vendors without rewriting application code.

Storage, Visualization, and Alerting: The "PLG" Stack

A common open-source combination for the backend is Prometheus, Loki, and Grafana.

Prometheus uses a pull-based model to scrape and store metrics from configured endpoints.
Loki is a log aggregation system designed to be cost-effective and easy to operate.
Grafana is the industry standard for visualization, allowing SREs to build dashboards that query Prometheus, Loki, and tracing backends like Jaeger or Tempo [8].
Alertmanager integrates with Prometheus to handle alerting, deduplicating, and routing notifications to the right teams.

While this open-source stack offers immense power, its primary cost is operational overhead. Your team is responsible for deploying, scaling, and maintaining each component. Setting up high availability and long-term storage for Prometheus, for example, is a non-trivial engineering task [5].

Centralizing Incident Management with Rootly

Your observability stack is excellent at identifying what is wrong, but an alert is just the beginning of an incident. The critical next step is orchestrating the response. By connecting your monitoring tools to an incident management platform, you can build a powerful SRE observability stack for Kubernetes with Rootly that handles the full incident lifecycle.

From Alert to Action

Instead of manually reacting when an alert fires, you can configure your alerting pipeline to automatically trigger a Rootly workflow. An alert from Alertmanager or an escalation from PagerDuty can instantly declare an incident in Rootly. This integration bridges the gap between detection and response by kicking off automated workflows that reduce manual toil and Mean Time to Resolution (MTTR).

Streamline Your Response in One Place

Rootly acts as the command center for incident response, making it one of the most effective SRE tools for incident tracking and resolution. From a single platform, your team can:

Automatically create a dedicated Slack channel, page the on-call engineer, and start a video conference.
Execute predefined playbooks to run diagnostic commands and attach the output directly to the incident timeline.
Keep stakeholders informed with integrated status pages.
Maintain a real-time incident timeline where all actions, commands, and decisions are automatically logged.

This automation ensures a consistent, auditable response process and provides the rapid insight needed to manage incidents effectively.

Closing the Loop: Retrospectives and Learning

An incident isn't truly over until you've learned from it. Rootly uses the structured data captured in the incident timeline—including key metrics charts from Grafana—to automatically generate a retrospective. The timeline, key events, and involved personnel are pre-populated, facilitating a blameless post-mortem focused on identifying contributing factors and creating actionable follow-ups. This process turns every incident into a data-driven learning opportunity and helps you build a winning SRE observability stack for Kubernetes that fosters continuous improvement.

Conclusion

A robust SRE observability stack for Kubernetes is founded on the three pillars—metrics, logs, and traces—and powered by open-source tools like OpenTelemetry, Prometheus, and Grafana [1]. These tools provide the deep visibility required to understand complex, cloud-native systems.

However, visibility alone isn't enough. The stack is incomplete without a dedicated incident management platform to turn data into action. Rootly unifies your stack by connecting alerts to a structured, efficient, and automated response process. By orchestrating everything from initial detection to post-incident learning, Rootly helps your team resolve incidents faster and build more resilient systems.

Ready to see how Rootly can integrate with your existing tools? Book a demo or start your free trial today.