Understanding the Scope of the Problem
Restart delays in Atlantis were consuming over 50 hours of engineering time monthly, creating inefficiencies and paging on-call engineers repeatedly. These issues stemmed from prolonged restart times, which became increasingly noticeable with frequent credential rotations and project onboarding. The root cause was traced back to a silent bottleneck within Kubernetes related to persistent volume management. This bottleneck, exacerbated by millions of files accumulating in the volume, demanded immediate attention.
Atlantis operates as a Kubernetes StatefulSet and relies heavily on a PersistentVolume (PV) to store repository states. While this configuration is standard, the mounting delay of the PV during pod restarts revealed a deeper issue with how inodes were being allocated and consumed within the filesystem. Identifying the exact bottleneck required a detailed investigation into Kubernetes logs and components.
Analyzing Kubernetes Pod Initialization
Upon executing a pod restart via kubectl rollout restart statefulset, engineers observed significant delays in the pod's initialization. Despite the pod appearing immediately, its status remained stuck in the init container phase for approximately 30 minutes. Standard Kubernetes event logs offered limited insight, showing normal scheduling and image pulling operations, but failing to explain the prolonged delays.
To uncover the source of the issue, deeper logs from Kubernetes kubelet service were examined. Kubelet is responsible for mounting persistent volumes, managing secret volumes, and coordinating pod creation. Filtering these logs for entries related to Atlantis revealed a pattern: the mounting of the PV was taking an unusually long time, suggesting an underlying problem with the filesystem handling.
Identifying the Root Cause
The investigation uncovered that the persistent volume was running out of inodes, which are filesystem structures tracking file and directory metadata. This limitation was caused by the default parameters set during the creation of the filesystem. Unfortunately, the Ceph-based storage backend did not allow for custom inode allocation, leaving engineers no choice but to expand the filesystem to provide additional inodes.
Additionally, the excessive number of files and directories in the PV meant that operations like mounting and initialization became increasingly slow over time. This issue compounded as the repository state expanded, making restarts progressively more burdensome.
The One-Line Solution
After thoroughly examining both Kubernetes logs and inode management, the team implemented a straightforward yet effective fix. By optimizing the pod configuration with a single line of code, they adjusted the PV mounting process to address the inode bottleneck directly. This adjustment significantly reduced the restart time from 30 minutes to a matter of seconds, effectively eliminating the recurring delays.
This fix not only improved the operational efficiency of Atlantis but also freed up valuable engineering resources that were previously consumed by troubleshooting and managing restart issues. The solution highlighted the importance of understanding default system behaviors and preemptively addressing potential scaling concerns in Kubernetes environments.
Lessons for Future Infrastructure Management
This case underscores the need for continuous monitoring of storage utilization and system performance in Kubernetes deployments. Silent bottlenecks, like inode exhaustion, can remain hidden until they severely impact operational efficiency. Engineers must proactively assess resource constraints and understand default configurations when deploying StatefulSets reliant on persistent volumes.
Moreover, teams should incorporate logs from components like kubelet into their diagnostic workflows. These logs provide granular insights that standard Kubernetes event outputs might miss, enabling faster identification of root causes during incidents. Proactive adjustments to storage configurations can prevent costly delays and improve overall system reliability.