Top 10 Critical Kubernetes Reliability Risks

In Kubernetes environments, reliability risks represent potential points of failure that can lead to outages if not addressed proactively. By understanding and remedying these risks, you can improve the stability and resilience of your Kubernetes clusters.

PerfectScale’s newest Industry Report, State of Kubernetes Efficiency explores the significant findings from our comprehensive data analysis, diving into the  resilience challenges faced by Kubernetes environments today. 

Working closely with large enterprise customers, PerfectScale found critical reliability risks in nearly every organization’s production environments. 

Let’s take a look at the top ten critical Kubernetes reliability risks—and what you can do to find and mitigate them before they cause an outage:

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Top Ten Critical Kubernetes Reliability Risks

According to the PerfectScale industry report, several key errors in Kubernetes deployments have been identified. Here’s a breakdown of the most prevalent issues, their observed frequency, and their impact on Kubernetes clusters.

1. MemLimitNotSet (26%)

Without setting memory limits, pods can consume an uncontrolled amount of RAM. This can lead to memory exhaustion on the host node, potentially triggering the Out of Memory (OOM) killer and causing system instability. Setting memory limits helps prevent these issues by capping the RAM usage of pods.

2. CpuRequestNotSet (16%)

Deploying pods without CPU requests can lead to inadequate resource allocation. This may result in pods being scheduled on nodes without sufficient CPU capacity, causing performance degradation and possible CPU exhaustion. Setting CPU requests ensures that pods are allocated the necessary CPU resources.

3. MemRequestNotSet (15%)

Without memory requests, Kubernetes cannot guarantee the allocation of adequate RAM for pods, leading to potential OOM events and the CrashLoopBackOff state. Defining memory requests ensures that each pod has the minimum required RAM, preventing unexpected terminations.

4. CpuThrottling (15%)

CPU throttling occurs when pods exceed their allocated CPU limits, leading to reduced performance and longer response times. This can severely impact application performance, especially under high load conditions. Monitoring and adjusting CPU limits can help mitigate this issue.

5. UnderProvisionedCpuRequest (13%)

When CPU requests are set too low, pods may not receive sufficient CPU resources, leading to performance bottlenecks. This under-provisioning can cause slowdowns and increased latency in applications. It is crucial to accurately estimate and set CPU requests based on application needs.

6. UnderProvisionedMemRequest (6%)

Similar to CPU requests, under-provisioned memory requests can lead to insufficient RAM allocation for pods, causing performance issues and potential OOM events. Properly setting memory requests based on application requirements is essential for stable operation.

7. RestartObserved (6%)

Frequent pod restarts can indicate underlying issues such as application errors, resource constraints, or failed liveness probes. These restarts can lead to service disruptions and increased overhead. Identifying and resolving the root causes of restarts is crucial for maintaining application stability.

8. UnderprovisionedMemLimit (2%)

Setting memory limits too low can cause pods to hit their memory cap prematurely, leading to application crashes and OOM events. Ensuring that memory limits are adequately provisioned based on the application's memory usage patterns helps prevent these issues.

9. OOM (1%)

Out of Memory (OOM) events occur when a pod exceeds the available RAM on the host node. This results in the OOM killer terminating the pod to free up memory, which can disrupt services and cause data loss. Setting appropriate memory requests and limits helps prevent OOM events.

10. UnderprovisionedCPULimit (0%)

No observed instances of under-provisioned CPU limits. Properly set CPU limits ensure that pods do not exceed their allocated CPU resources, maintaining system stability and performance.

Kubernetes is complex, but understanding the health of your clusters is simple

Addressing these top Kubernetes reliability risks requires proactive monitoring, meticulous configuration management, and rapid incident response capabilities. By adopting best practices for resource allocation, deployment strategies, and fault tolerance mechanisms, organizations can mitigate risks effectively and maintain robust Kubernetes deployments.

Once you’ve learned these basics, you’ll be much more confident in understanding the state of your own Kubernetes clusters.

PerfectScale is the industry's only Kubernetes cost optimization platform designed to improve cost efficiency, application stability, and resilience.  PerfectScale will provide you with all Kubernetes resources and their health displayed on out-of-the-box dashboards. PerfectScale K8s optimization platform is free to start. Give it a try to see if it’s the right solution for you!