Kubernetes Observability Best Practices

Kubernetes Observability Best Practices

The digital transformation is taking place at an accelerated pace. To keep up with it, companies across all sectors and industries have to master and ace innovation. Nothing serves this effort better than ditching the outdated and traditional on-premises data centers and switching to multi-cloud environments. However, organizations can benefit from unprecedented innovation by using Kubernetes as their primary application and platform. Development teams can fuel and boost the speeds of their digital capabilities and services by adopting cloud-native technologies, such as containers and microservices. These resources allow them to drive customer success and adapt to their business needs that are rapidly evolving.

Managing and automating containerized applications and workloads becomes easy with Kubernetes, but these cloud-native environments pose a real challenge for maintaining visibility into them. Kubernetes is undoubtedly portable and flexible due to its dynamic abstraction layer. However, the layer also often gives birth to some unique and complex errors that are difficult to prevent, troubleshoot, and find. What is even more difficult, though, is to connect the business outcomes with the complex web of data that monitoring tools generate.

The following Kubernetes observability best practices, however, allow and ensure that the organizations are able to overcome these challenges and deal with them easily and swiftly.

Automation and AIOps are Essential

Distributed platforms like Kubernetes are highly complex as well as dynamic. They enable better scalability and much faster innovation. Their pods, nodes, and clusters tend to continuously change, so it is practically very difficult to manually instrument and configure the monitoring capabilities. The time that IT teams should be spending in developing and launching new services for driving business success is spent wasted on scrambling to keep up with the rate of change of their Kubernetes environments and trying to gain insight into their application’s health.

The solution to this problem is the automatic discovery of services as the existing ones scale, and new ones come online and instrument them in real-time. The teams can become able to do that with the help of AIOps that provides them with continuous automation. The application and platform teams can constantly monitor performance anomalies and system degradation. This makes the operation of large-scale environments simple.

What is more, is that the teams are allowed to determine which technical changes benefit the business the most, and they can prioritize tasks to ensure great impacts. The teams can not only prevent problems before they occur and negatively impact the user experience but refocus on delivering the best outcomes for the customers as well as the business by optimizing services. In short, AIOps helps organizations maintain continuous observability by allowing them a complete view of their Kubernetes environments.

A Full-Stack Approach is Key

Workloads and microservices constantly change, which makes it difficult to keep track of and maintain observability. Additionally, Kubernetes is usually deployed across multiple environments by organizations which make the endeavor of observability even more complicated. Whether it is the on-premises servers or through managed services, such as GKE, AKS, and EKS, the IT teams deploy microservices across many platforms. They do that as Kubernetes allows them this flexibility by being able to run on any cloud. The different organizations use different cloud platform metrics and monitoring tools to manage their Kubernetes environments.

The manual correlation and collection of the observability data for a full context and a bigger picture from so many sources is a highly time-wasting task. If the cross-team collaboration is not on point and the teams are siloed with point monitoring solutions, the process becomes even slower and ineffective.

Having a common data model in a single platform to unify all Kubernetes traces, logs, and metrics is an effective approach to observability. It helps break down silos between teams and fosters and aids collaboration across the organization. Furthermore, the application and platform teams need to have a unified and common accessible view across the entire environment. For this purpose, technology and traditional stacks and services that an organization is running alongside its Kubernetes deployments should also be included in that data model. This way, the teams can optimize Kubernetes applications and workloads more successfully by utilizing a greater context that this end-to-end proposition to observability offers.

View Data in Context of the User

Kubernetes observability best practices not just aim at providing organizations with access to more and more data but also at allowing them to identify the improvement needs within their technology stack. By getting just a backend perspective, application owners and developers often get limited as traces, logs, and metrics do not tell the whole story. The user experience on the front end of the application and the underlying cloud platform on the backend needs to be connected and compared with the code the organizations push into production to understand the effects the business outcomes receive from Kubernetes performance. The effectiveness can easily be determined by combining the real-time business metrics, like conversion rates and insights of user experience, with Kubernetes monitoring data.

The application topology mapping capabilities allow the teams to easily gain visibility to these insights by automatic visualization of all dependencies and relationships across the wider cloud technology stack and within a Kubernetes environment, including real-time user experience data. IT teams can identify and ascertain the overall impacts on the business of different issues by vertical mapping of dependencies between workloads, pods, hosts, and clusters, as well as the horizontal mapping between services, applications, and data centers.

This correlation of backend performance and real-time user experience enables digital teams and business leaders to deliver better user experiences and improve their services. They can make better decisions about their further investment plans pertaining to their digital infrastructure and optimization of their systems.

Conclusion for Best Practices with Kubernetes Observability

The Kubernetes Observability best practices are essential for all organizations that use Kubernetes. Through embracing the benefits of AI and automation, observability challenges of the Kubernetes architecture can be swiftly tackled. These best practices ensure organizations are able to continuously monitor data and utilize it to better their services and make their user experience more efficient.