Kubernetes observability refers to the method by which issues may be detected and dealt with more efficiently by gaining insight into the behaviour and performance of apps running on Kubernetes as well as the underlying infrastructure and components. With its help, Kubernetes workloads may be more reliable, have less downtime and fewer outages, and run more efficiently.
This is the first in a series of essays on monitoring in Kubernetes.
Why Is Observability a Cornerstone of Kubernetes?
Kubernetes’s observability is important for several reasons, such as those listed below:
The complexity of the situation
Pods, nodes, services, and networking resources are just some of the many moving parts that make up Kubernetes. Understanding the present status of a system and pinpointing faults is greatly facilitated by a clear visualisation of its components and the relationships between them.
By collecting information from several points of view, kubernetes observability helps in ensuring the reliability of Kubernetes clusters and the applications that run on them.
Improvement in Functioning as a Whole
Monitoring and analysing performance data is useful for identifying bottlenecks in Kubernetes clusters and applications so that they may be removed.
Problem-Spotting and Solution-Building
With the information gained through increased observability, issues may be quickly diagnosed and addressed. As a result, the time spent fixing problems is reduced, and fewer users and fewer business processes are disrupted.
Processing unit (CPU), memory (memory), and storage space (storage space) utilisation monitoring may help ensure that the cluster has enough resources to operate the applications. Keeping an eye on how these resources are being used helps also guarantee that the cluster has what it needs to run the applications.
What’s the Distinction Between Monitoring and Observation Capability?
While both observability and monitoring play important roles in the Kubernetes ecosystem, they are distinct concepts.
Monitoring, in the context of computing systems, is the practise of collecting and analysing data on a system’s activity and behaviour in order to detect and diagnose potential issues. Monitoring in the context of Kubernetes is collecting information on the cluster and its parts like nodes, pods, and containers to ensure everything is working as it should.
Therefore, monitoring is a crucial aspect of Kubernetes’ observability; nevertheless, observability extends beyond monitoring to provide a more holistic view of the system. In contrast to observability’s focus on understanding and analysis, monitoring’s primary goal is issue detection.
There are a lot of variables at play.
In order to provide applications and services, a Kubernetes cluster requires communication across many different parts, including pods, nodes, services, and networking components. In the case of an issue, isolating the source of the problem to a specific part may be difficult. It might be the network, the underlying infrastructure, or the application itself if it’s causing performance issues.
Constantly shifting surroundings Due to the frequent addition, removal, and modification of cluster components, Kubernetes clusters often operate in a dynamic environment. This raises the prospect of changes to the cluster’s overall architecture and the interplay between its many parts. Due of this, it may be challenging to keep monitoring and observability tools accurately configured and up-to-date.