Key Reasons for Implementing Kubernetes Observability

Companies depend on Kubernetes monitoring tools to analyze and understand the performance of their clusters. In this article, Yoni Farin, co-founder and CTO of Coralogix, discusses what to know about Kubernetes observability and how to implement it using better full-stack observability tools.

As companies expand and scale their production software offerings, container orchestration platforms are required to manage, deploy, and maintain availability for software.

Kubernetes is a popular open-source container orchestration platform that can run locally on a private data center or in the cloud. Major cloud providers, such as AWS, Azure, and GCP, all offer managed Kubernetes services.Â

More specifically, Kubernetes observability refers to monitoring, understanding, and efficiently troubleshooting Kubernetes clusters and applications running on them. While Kubernetes can keep container workloads running, the platform also requires many moving parts, which can cause significant issues like outages and cost spikes. Thus, the complexity of managing distributed systems requires a high level of observability to keep these issues in check.Â

What Are the Pillars of Kubernetes Observability?

Your software should be integrated to use the pillars of observabilityOpens a new window , which underscore a single premise: â€œObservability enables you to understand what is happening in your software by looking at externally available information.â€ Let’s dive into the three pillars of observability.Â

Data sets allow DevOps teams to monitor Kubernetes clusters and the software running on them. Once monitoring is achieved, observability can follow.Â

Three data types make up the pillars of observability. Each pillar stores a different representation of the state of a Kubernetes cluster. Used separately, DevOps teams can monitor a Kubernetes environment but used together, DevOps teams can observe the environment.Â

Logs: Logs are records generated by containerized applications running on Kubernetes clusters. These files contain valuable information to troubleshoot issues within the application. Logs contain contextual information, including which node and pod ran the application, event time, and the user or endpoint connected. Kubernetes also generates logs giving contextual information about the Kubernetes environment itself. These logs help ensure the health and efficiency of the entire environment and any custom settings.
Metrics: Metrics are time-bound numerical representations of event counts. This data helps give insight into when events occur more or less often than expected. Metrics are easily queried, making them ideal for alerting support teams when an issue occurs.Â
Traces: Traces are additional data added to logs that link events occurring in different applications. These give causative context to logs that ease troubleshooting, helping support teams and tools understand the entire data flow through distributed systems. Traces are also used to identify bottlenecks in the system, showing where data flows slowly and where the Kubernetes configuration may need improvements.

Monitoring vs. Observability

Monitoring and observability primarily use the same data but provide different information to support teams. While monitoring a Kubernetes environment, you can verify that clusters, nodes, containers, and pods are functioning as expected based on your configuration.

Kubernetes observability also provides a comprehensive picture of the environment’s performance, giving insights that enable teams to improve performance, stabilize the infrastructure, and reduce downtime by predicting problems.Â Â

See More: How to Choose the Right Delivery Model for Kubernetes

Why Kubernetes Observability Is Good for Business

Kubernetes observability allows companies to monitor and analyze the performance and cost of running containerized applications. Specific benefits include:

Increased Efficiency: Organizations can identify and optimize resource utilization and reduce wasted resources when they have a complete picture of the Kubernetes environment. This can lead to better user experience and lower costs.
Faster Troubleshooting: DevOps teams gain visibility into the underlying infrastructure and behavior of software, enabling them to troubleshoot errors as they arise. This reduces downtime and improves application reliability.
Improved Security: Companies can monitor and analyze security-related events such as unauthorized access attempts or data breaches. Identifying and addressing these issues quickly improves the company’s overall security posture.

How To Implement Kubernetes Observability

Implementing Kubernetes observability can be complex. Here are some guidelines to ensure you get the most out of your observability setup.Â

Breakdown team silos

It’s common in distributed software to have multiple teams working on different parts of a company’s software offering. They might use Kubernetes but could also use different cloud providers or data centers to run the software. For full-stack observability, teams should come together on how observability will be implemented across the software. Teams should develop a common logging model and agree on what tools will be used to collect observability data.

Correlate centralized data

Once you’ve scaled your software, observability data should be collected in a common location. Data should be analyzed together to get a complete picture of software health and stability. If you keep observability data separated, tools won’t be able to add contextual analysis since they do not have a complete picture of the entire software offering.Â

Leverage Machine Learning and AI

Kubernetes observability requires a platform that handles the dynamic nature of a Kubernetes environment. The observability platform must adapt to changes quickly, which may be difficult for DevOps teams to manage manually and can divert attention away from building new software features.Â

Instead of building observability platforms manually, leverage existing software that utilizes Machine Learning and AI to detect issues specific to your software. Some DevOps are using ML algorithms to help teams figure out the success of certain projects and the ones that don’t measure up. Essentially, full-stack observability platforms can keep up with the dynamic environment of ML and AI, as well as inform DevOps teams when abnormal events are occurring.

Building Efficiency with Kubernetes Observability

Container orchestration tools like Kubernetes are necessary when operating distributed containers at scale. Setting up proper Kubernetes observability is critical to ensuring your software’s continued health and stability.Â

Kubernetes observability should utilize logs, metrics, and traces to ensure your containers are running properly. Your full-stack observability tool should use centralized data stores to analyze and detect issues, ideally using machine learning to gain real-time insights. Utilizing such a tool can improve security, user experience, and resolution time for errors.Â

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