Troubleshooting Kubernetes in the real world means dealing with highly complex distributed systems.
The three pillars — Understanding, Managing, and Preventing — are essential in this process.
Here's how these three pillars can be applied for effective troubleshooting of any Kubernetes challenge.
Complex systems lead to complex problems. Oftentimes, when faced with an overwhelming Kubernetes issue, developers struggle to figure out where to begin looking for the underlying cause.
From there, they also have to understand what triggered the problem, how to fix the immediate issue, and how to prevent the problem from happening again.
The three pillars help bring order to this process, and it all begins with understanding the cause.
Unsurprisingly, simply understanding the issue and its cause is where the average team invests 80 percent of their resources.
Just understanding what went wrong, why it went wrong, and where you should go next is often the most daunting part of troubleshooting in Kubernetes.
Typically, the process starts with an analysis of what changes have occurred in the system that could have led to the problem.
On paper, analyzing changes sounds simple, but in a complex distributed system — especially one based in Kubernetes — it means looking at deployment logs, traces, metrics, pod health, resource caps, connections, and countless config files, along with any third-party integrations.
If there was ever a need for a modern example of finding a needle in a haystack, troubleshooting in Kubernetes would be the perfect showcase.
After analyzing changes that could have caused the problem, teams then move on to checking on events to review what's happening within the system.
This could be overloaded systems, data loss, service breakdown, and so much more.
Teams then review the data and metrics they have compiled for moments just like these, attempting to extract some understanding about what's behaving differently.
Fortunately, many tools exist to assist teams with troubleshooting in Kubernetes, but it's important to know when to use which ones.
In the understanding phase, the best ones to pull out of your hat would be monitoring, observability, live debugging, and logging tools.
Oftentimes, inter-dependent microservices are managed by different teams.
That in itself is often practical and unproblematic so long as proper communication between these teams is maintained at all times.
Without an open line of communication between teams, incidents can take longer to resolve, especially with the chance for the simplest of changes to cause problems.
The actions a team takes in response to an issue could be as quick as restarting a system or as serious as reverting to an older version — it all comes down to the cause of the problem.
Once you understand the underlying issue, teams should have runbooks (how-to guide) that help them manage any incident, providing them with tasks and actions to help them respond to any kind of alert.
Too often, teams end up turning to a seasoned engineer who is able to fix an issue based on unwritten tradition, which can be helpful at the moment, but detrimental in the long run.
Creating reliable runbooks will allow any engineer on staff to resolve problems, regardless of experience, helping facilitate real-time troubleshooting and improving microservices resiliency.
The most important pillar of all, prevention ensures that teams take proactive measures and plan accordingly so that incidents don't occur again in the future.
Aside from writing up definitive policies and rules following the incident, teams need to sit down and discuss every phase, from recognition to remediation.
For example, everyone should get together to review the actions they took to understand the problem and make note of how long they took to identify the issue.
It's also useful to record how long it took before the issue was escalated to the "management" phase and handed off to relevant teams to be resolved.
Make note of how the responsibilities were delegated and how teams can ensure better communication and collaboration in the future.
Keywords like "transparency" should certainly come up, and real-time progress updates are also essential in Kubernetes troubleshooting.
Ultimately, you should come up with a canonical order of tasks that need to be completed in response to each alert and incident your teams may run into again.
From there, you can begin to consider automation and orchestration to better respond to and prevent future incidents.
This allows you to get closer to the "self-healing" systems that tech experts dream about.
Tools that help make your systems more adaptive and resilient, like Gremlin, ChaosIQ, and Shoreline, can be very useful here.
When dealing with extremely complex distributed systems like Kubernetes often supports, tracking down the simplest of mistakes can create major challenges.
By following these three pillars, you'll be able to prepare your teams for the worst, and help them spend less time on trial-and-error and more time on preserving and optimizing system performance.
Of course, no team can create runbooks and ensure resiliency overnight.
Adservio can help your business troubleshoot and prevent problems in Kubernetes. For learning more about how we can help you feel free to contact us!