Diagnosing and resolving issues in Kubernetes can be quite challenging. Kubernetes, after all, is a complex system.
Resolving problems even in small K8s clusters, nodes, and containers can be tricky, and it's often hard to identify and address issues. And an issue isn't even always easy to trace – it might be a problem in a pod or pods, a single container, controller, control plane, or in multiple components.
As you might expect, the complexity grows exponentially when you are dealing with a large-scale production environment with numerous microservices involved.
These microservices are often created by different development teams. There are also instances when these microservices are collaboratively built by different teams but on a common K8s cluster.
In such scenarios, there might be confusion about who is responsible for troubleshooting what.
But don't worry – you can avoid turning Kubernetes troubleshooting into an unmanageable mess and wasting resources with the help of the following pointers.
One of the most important points in Kubernetes troubleshooting is the need for improved visibility.
Data from The State of Kubernetes Security 2020 survey show that 75 percent of Kubernetes users consider visibility as essential since it can be tedious and challenging to quickly determine and examine everything an organization has deployed over time.
Why is visibility important in K8s?
Enhanced K8s visibility really helps you gain operational and security insights. It also makes it possible to achieve the following:
- Ensuring compliance. When organizations comply with established standards or guidelines, the likelihood of getting entangled in complicated or convoluted scenarios really goes down. This also means that troubleshooting becomes much easier.
- Ascertaining proper traffic. Data traffic between services or microservices, in particular, is like a depiction of the health of a K8s system. Traffic must go where it should. Otherwise, expect serious malfunctions and critical issues.
- Knowing what is running in the K8s environment and determining if they are appropriately configured. It's excruciatingly difficult to conduct any troubleshooting if you're not familiar with your K8s environment.
- Predicting seasonal needs. With the proper knowledge and understanding of Kubernetes, you can see trends or patterns in resource usage. This helps you make projections and predictions that can also be useful in troubleshooting.
- Enabling efficient use of resources. Increased visibility allows you to determine whether or not you are using the right amount of resources relative to latency and performance history.
- Efficient troubleshooting. Having a clear grasp of everything that is going on in a Kubernetes-employing environment ultimately leads to better troubleshooting outcomes, because it's easier to find the root cause of the issues you encounter with applications and microservices.
How to improve your K8s visibility
If you want to improve your K8s visibility, you'll need to collect of two types of data: real-time and historical.
Real-time data is necessary to identify and resolve a current issue. Historial data serves as a basis in benchmarking activities against something that is considered regular or normal.
Both types of data are useful in troubleshooting and can become even more useful when they improve your Kubernetes visibility.
You can achieve increased visibility by creating a way to efficiently get and analyze real-time and historical data. You can also increase visibility by using tools that facilitate enhanced deployment and monitoring.
There are many Kubernetes tools that enable live monitoring, control, and tracing. These tools can give you robust status update pages, metrics, and OpenTracing functions that include support for observability platforms such as LightStep and Datadog.
Establish Organized and Efficient Fault Management
It is not enough to find the faults. You also need to have an organized and efficient way of managing problems. This helps prevent (or more quickly address) similar issues in the future.
As we just discussed, improved Kubernetes visibility helps you use your resources more efficiently. And you should take advantage of this efficiency advantage by coming up with an organized and efficient approach in managing your Kubernetes faults.
For starters, make it a point to master common K8s errors, like the ones described below. In many cases, the problems are just common or simple errors that many K8s devs tend to overanalyze.
Common Kubernetes Errors
CreateContainerConfigError. This fault typically comes from a missing ConfigMap or secret (K8s objects that contain sensitive data such as login credentials).
The problem can be an authentication issue in the container registry or the use of a wrong image name or tag. You can identify these issues by running the appropriate commands.
CrashLoopBackOff. This error happens when a pod could not be scheduled on a node. This might happen because you don't have enough resources on a node to run a pod or a pod that failed to mount the volumes requested.
Kubernetes Node Not Ready. This happens when a worker node is terminated or crashes, which results in the unavailability of all stateful pods residing on the shutdown node.
This usually resolves on its own after some time. If time is of the essence, though, you'll need to reschedule the stateful pods on a different running node.
For other problems, standard troubleshooting usually entails a few steps. For K8s pods troubleshooting, you'll usually need to:
- examine the output of the kubectl describe pod command,
- check for the error in the pod description,
- check for a mismatch between the API server and the local pod manifest (and the diagnosis of other pod issues through the pod logs), and
- perform Container Exec debugging, Ephemeral Debug Container debugging, and a Debug Pod command on the node.
When it comes to K8s clusters, you'll need to view the basic cluster information, retrieve the cluster logs, and implement solutions according to what problems you find.
To address an API server virtual machine crash or shutdown, for example, you'll need to restart the API server virtual machine. This solution similarly applies to control plane service crashes and Kubelet malfunctions.
You should have predefined workflows to guide your responses to various issues. Again, troubleshooting Kubernetes is not as easy as it sounds. There is nothing wrong with having cheat sheets, especially when you're dealing with uncommon issues.
Use a Kubernetes Troubleshooting Solution
For many companies or teams, using a K8s troubleshooting service can be the best way to handle Kubernetes issues.
Not every team has top-tier Kubernetes experts who can readily address problems as they emerge. And not everyone has the right tools and systematic procedures in place for dealing with container, pod, cluster, or node problems.
A troubleshooting solution can provide a unified platform for tracking K8s activities which makes it easier to trace the source of issues.
Such a system can offer an efficient way to enhance K8s visibility by providing a comprehensive timeline, for example. Or it can display all code and configuration modifications, pod logs, deployment statuses, alerts, code diffs, and other details in an organized manner.
Also, advanced Kubernetes troubleshooting services are built to serve insights on service dependencies. They make it easy to comprehend cross-service changes that are happening throughout an organization.
They can provide useful details on the chain reactions that follow after certain changes to help you identify and eventually resolve the problem.
Saying that Kubernetes troubleshooting can be simplified may seem like an oxymoron. And promising a truly simplified way to deal with K8s problems is pushing the envelope too far.
But, it is certainly not impossible to establish ways to make the troubleshooting process less complicated and tedious than it normally is.