Kubernetes Observability: Lessons Learned From Running Kubernetes in Production

Observability has become a cornerstone of modern DevOps and software engineering, especially as applications grow in complexity and scale. With the shift toward microservices architecture and multi-cloud or hybrid-cloud environments, traditional monitoring methods often fall short. This has led to the adoption of logs, metrics, and traces as the three pillars of observability, providing a holistic view of application performance.

However, achieving effective observability in Kubernetes remains a challenge. The inherent complexity of Kubernetes, coupled with the vast ecosystem of observability tools, often results in a steep learning curve and high costs. In this blog, we’ll explore key considerations for Kubernetes observability, implementation challenges, potential solutions, and the often-overlooked importance of the developer experience.

Considerations for Kubernetes Observability

Before diving into tool selection, it’s crucial to define the scope of what needs to be observed in your Kubernetes environment. This includes:

• Cluster Components: API server, etcd, controller manager, scheduler

• Node Components: kubelet, kube-proxy, container runtime

• Other Resources: CoreDNS, storage plugins, Ingress controllers

• Network: CNI, service mesh

• Security and Access: Audit logs, security policies

• Applications: Both internal and third-party applications

Additionally, components outside Kubernetes, such as databases, serverless functions, and external data lakes, must also be considered. Understanding the users and consumers of these observability tools is equally important. For instance, internal clusters may have different requirements compared to multi-tenant SaaS clusters, and the expertise levels of developers versus DevOps/SRE teams will influence tool selection.

Challenges and Recommendations for Observability Implementation

Once the scope and audience are defined, the next step is choosing the right tools. Broadly, there are two options: open-source and commercial/SaaS solutions.

Open-Source Observability Stack

Open-source tools like Prometheus, Loki, Tempo, and Grafana offer a robust ecosystem for observability. However, they often operate as individual microservices, requiring integration and standardization. OpenTelemetry has emerged as a framework to unify metrics, logs, and traces, making it easier to manage telemetry data across tools.

Challenges:

• No single tool covers all aspects of observability.

• Storage scalability issues with tools like Prometheus.

• Requires monitoring the health of the observability stack itself.

Recommendations:

• Standardize on OpenTelemetry for consistent data collection.

• Optimize storage and telemetry data volume to avoid unnecessary costs.

Commercial Observability Stack

Commercial tools offer a seamless experience by integrating logs, metrics, and traces into a single interface. However, cost control is a significant challenge, especially with high cardinality from Kubernetes metadata.

Challenges:

• High costs due to indexing and storage of metadata.

• Performance issues from excessive tags and dimensions.

Recommendations:

• Use filters and pipeline logic to index only essential data.

• Downsample repetitive data points to reduce costs.

Remembering the Developer Experience

A common pitfall in observability is over-optimizing for ops teams while neglecting developers. To ensure observability is useful for everyone, consider the following:

• Access: Ensure logs, dashboards, and alerts are easily accessible. Integrate tools with IDEs or Slack for quick access.

• Onboarding: Provide adequate training for developers to use observability tools effectively.

• Standardization vs. Flexibility: Balance standardized formats like JSON with human-readable presentations for better usability.

Aligning the goals of developers and ops teams is crucial. Tools should be easy to integrate, produce intuitive dashboards, and provide actionable insights without overwhelming users with noise.

Final Thoughts

Observability in Kubernetes is complex but essential for managing modern, cloud-native applications. Whether using open-source or commercial tools, the key is to balance functionality, cost, and usability. As the industry evolves, advancements in AI and generative technologies promise to enhance observability tooling and user experience.

At ZippyOPS, we specialize in providing consulting, implementation, and management services for DevOps, DevSecOps, DataOps, Cloud, Automated Ops, AI Ops, ML Ops, Microservices, Infrastructure, and Security. Explore our services, products, and solutions to optimize your observability strategy.

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By focusing on the right tools, cost optimization, and developer experience, you can build a robust Kubernetes observability strategy that drives performance and reliability. Let ZippyOPS guide you through this journey with our expertise and tailored solutions.