Why Kubernetes Troubleshooting May Not Be As Straightforward And Simple As Adopters Would Like It To Be

2021-10-29 by Clayton Richard

Kubernetes troubleshooting is a crucial facet of K8s adoption. It should be taken seriously as an essential part of operating a containerized system. As DevOps enthusiast Pavan Belagatti of DZone characterizes it, Kubernetes is “something everybody wants but few people truly understand.” The troubleshooting part is even more complicated.


Running containers is not going to be a walk in the park even for experienced IT teams. It entails computing mastery and the solving of different kinds of problems including in the areas of service discovery, flexible scaling, error tolerance, and rolling deployment. Having Kubernetes as an orchestration tool has its advantages, but it also presents new challenges.


Discussed below are some of the most important details K8s adopters should consider especially as they deal with the troubleshooting requirements and the issues that may be encountered when it comes to Kubernetes security.

Troubleshooting vs. debugging

Is troubleshooting the same as debugging? Not exactly. Troubleshooting is mainly about finding the root cause of the problems or issues encountered in a system at the macro or general level. Debugging, on the other hand, is a more focused type of troubleshooting as it specifically addresses problems in the code of a software or system. As the term suggests, it targets the fixing of bugs, errors, or anomalies in a code.


Kubernetes debugging can be considered as a more tactical solution in solving issues or exceptions in a system. It is a form of troubleshooting that concentrates on finding and fixing the erroneous or unintended behavior of a program. This could be a logic problem, a syntax issue, or a typo in the code that causes unexpected behavior and unwanted outcomes. Because of the narrower scope of debugging, it may be quicker to accomplish although not necessarily easier.


So what is the point in mentioning the difference between Kubernetes troubleshooting and debugging? The idea here is that some teams may find it easy to troubleshoot certain problems, especially the common ones, but have a hard time debugging because they do not have a full grasp of how Kubernetes really works. This usually happens when teams rely on troubleshooting tips and advice they find online and successfully solve problems without actually having a thorough understanding of K8s.


Common Kubernetes problems like overhead-limiting CPU resources, for instance, maybe immediately addressed by switching the server default governor mode from Powersave to Performance. If the problem persists, the CPU limit may then be removed. For problems that require debugging, more technical know-how is required. Those who are new to deploying Kubernetes may find this quite challenging.

System complexity

There is no doubt that Kubernetes is a complex system. Drew Bradstock, Product Lead for the Google Kubernetes Engine (GKE), admits it saying that "despite 6 years of progress, Kubernetes is still incredibly complex." It’s not surprising that troubleshooting is equally or even more complex than learning how to use it. Diagnosing problems even in just a small K8s cluster can be quite difficult.


Enterprises that deploy Kubernetes in a large-scale production environment can expect bigger complexities and more complicated potential problems. The bigger the environment is, the more difficult it would be to have visibility with multitudes of moving parts involved. This scenario results in low visibility, which significantly affects management and operational efficiency.


To cope with the complexity of K8s deployment, it would be necessary to employ several tools for the gathering of crucial information useful in troubleshooting. There are also tools necessary in identifying and diagnosing issues to ensure that troubleshooting efforts are directed to where the core problems lie. It would be very difficult to manually go over nodes, pods, containers, and other components to trace problems and implement the appropriate solutions.


Another challenge in Kubernetes troubleshooting is in dealing with the microservices created by different teams. It is not uncommon to have multiple microservices comprising different applications or systems. This results in the possible lack of clarity as far as the division of (troubleshooting) responsibility is concerned. For example, if a pod turns out to be problematic, an organization may not have clear guidance as to who should be responsible for the correction. Should it be DevOps or specific application development teams?


Enterprises cannot just have a team appointed to handle K8s troubleshooting requirements. They should also have established protocols and clear responsibility assignments and awareness. If troubleshooting is undertaken, it also helps that teams are aware that something is already being done about an identified issue. Otherwise, different teams might be working on an issue at the same time with many having a hard time figuring out what to do because they are trying to address something that may not be part of their accountability and proficiency.


Kubernetes troubleshooting can easily become chaotic without a broad understanding of K8s deployment and management. It can lead to serious inefficiency that means a waste of time and resources, which can also have an adverse effect on users and the functionality of the applications being run.

From bottom-up to horizontal

One of the best practices when it comes to Kubernetes troubleshooting is the bottom-up approach. This is done by listing all pods in the cluster where issues have been identified. This is recommended to thoroughly examine the problem and determine the status of an issue, whether it has already been reported as an error, crashing, or not ready. The list helps provide a clear starting point for the troubleshooting process, making it easier to drill down into the problem. It serves as a guide on what to find using Kubectl, for instance, to get more information.


If the fault or issue is still not yet clearly identified or diagnosed, it helps to do a horizontal examination. This means going over the configuration maps, nodes, volumes, ingresses, secrets. It is important to check them to see to it that they are aligned with what is required by the containerized app being operated. Sometimes the secrets used may not be the right ones for a specific environment.


If the problem is not with Kubernetes, it could be something in the application itself, so it is important to check the app logs. Doing this, however, requires the troubleshooter to be highly familiar with the app being scrutinized. Otherwise, it would be difficult to determine if something is wrong or unusual with the operation of the app.


No pain, no gain

Again, Kubernetes is a complex system. Enterprises that decide to adopt it must be prepared to expend the time and effort necessary to master it and become adequately proficient in troubleshooting problems. It would be difficult to succeed with Kubernetes deployment and troubleshooting without understanding K8s well enough and by only relying on the tips, guides, and insights shared by other adopters or the K8s user community.


Mastering Kubernetes troubleshooting can be a self-taught journey, but this may not be a suitable approach for enterprises. Organizations with serious businesses to run need reliable troubleshooting and support, and cannot afford to wait for a long time before their teams reach the right level of expertise. The good thing is that there are third-party providers of Kubernetes troubleshooting and debugging, so companies can focus on their more important tasks and relegate the troubleshooting work to experienced experts. These troubleshooting services cost some amount, but they may be more convenient and advantageous to some.

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