Kubernetes E2e Test Failures: CSI & PersistentVolume Issues

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Kubernetes e2e Test Failures: CSI & PersistentVolume Issues

Hey guys! We've got some failing tests in our Kubernetes e2e suite, and it looks like the storage SIG needs to jump in. Let's break down what's happening so we can get this sorted out ASAP. This article dives deep into the recent failures observed in Kubernetes end-to-end (e2e) tests, specifically focusing on issues related to CSI (Container Storage Interface) volumes and PersistentVolumes. Understanding these failures is crucial for maintaining the stability and reliability of Kubernetes storage solutions.

Which Jobs Are Failing?

The primary culprit seems to be the sig-release-master-informing#gce-cos-master-serial job. This job is crucial for ensuring the stability of Kubernetes on Google Cloud Platform using Container-Optimized OS (COS) and running in a serial manner, meaning tests are executed one after another. When this job fails, it indicates a potential issue that needs immediate attention.

Which Tests Are Failing?

Digging deeper, we can see a few specific tests are consistently failing. These failures point to potential bottlenecks or bugs in our storage provisioning and management.

  • Kubernetes e2e suite.[It] [sig-storage] CSI Volumes [Driver: csi-hostpath] [Testpattern: Dynamic PV (block volmode)] pvc-deletion-performance should delete volumes at scale within performance constraints [Slow] [Serial]
  • Kubernetes e2e suite.[It] [sig-storage] CSI Volumes [Driver: csi-hostpath] [Testpattern: Dynamic PV (filesystem volmode)] volume-lifecycle-performance should provision volumes at scale within performance constraints [Slow] [Serial]
  • Kubernetes e2e suite.[It] [sig-storage] PersistentVolumes-local Stress with local volumes [Serial] should be able to process many pods and reuse local volumes

Let's break down each of these failures:

CSI Volume Deletion Performance

The pvc-deletion-performance test focuses on the speed and efficiency of deleting Persistent Volume Claims (PVCs) using the CSI hostpath driver with dynamic provisioning and block volume mode. This test is crucial because it simulates a scenario where a large number of volumes need to be deleted, and performance bottlenecks can lead to significant delays. The failure message, expected all PVCs to be in Bound state within 30m0s, suggests that the deletion process is taking longer than expected, potentially indicating issues with the CSI driver, the underlying storage system, or the Kubernetes control plane. This test is marked as [Slow] and [Serial], indicating that it's designed to identify performance bottlenecks and is executed sequentially to avoid interference from other tests.

CSI Volume Lifecycle Performance

The volume-lifecycle-performance test evaluates the provisioning speed of volumes using the CSI hostpath driver with dynamic provisioning and filesystem volume mode. Similar to the deletion test, this test aims to ensure that volumes can be provisioned at scale within acceptable timeframes. The failure message, expected all PVCs to be in Bound state within 15m0s minutes, indicates that the volume provisioning process is not meeting the performance targets. This could be due to various factors, including the CSI driver's implementation, the efficiency of the storage backend, or limitations in the Kubernetes scheduling and binding mechanisms. Understanding the root cause of this failure is essential for optimizing volume provisioning in Kubernetes environments.

PersistentVolumes-local Stress Test

The PersistentVolumes-local Stress with local volumes test assesses the ability of Kubernetes to manage local volumes under high load. This test is designed to simulate real-world scenarios where a large number of pods are created, use local volumes, and are then reused. The failure message, some pods failed to complete within 5m0s: client rate limiter Wait returned an error: context deadline exceeded, suggests that the Kubernetes API server is being overwhelmed by the number of requests, leading to rate limiting and timeouts. This could be due to excessive pod creation and deletion, inefficient volume binding, or limitations in the performance of the local storage system. Addressing this issue is critical for ensuring the scalability and stability of Kubernetes clusters that rely on local volumes.

This test aims to verify that local volumes can be effectively managed and reused across multiple pods. The error message, "some pods failed to complete within 5m0s: client rate limiter Wait returned an error: context deadline exceeded", points to issues with the Kubernetes API server being overwhelmed, possibly due to excessive pod creation and deletion or inefficient volume management.

We can check out the latest prow log for more details. Prow logs are invaluable for debugging these kinds of issues.

Since When Has It Been Failing?

These tests have been failing since 2025-11-06 14:56:01 +0000 UTC. This gives us a timeframe to investigate and correlate with any recent changes or deployments.

Reason for Failure (if possible)

Let's dive into the error messages for each test to understand the potential causes. We'll use code blocks to highlight the key error messages.

pvc-deletion-performance Failure

{ failed [FAILED] expected all PVCs to be in Bound state within 30m0s
In [It] at: k8s.io/kubernetes/test/e2e/storage/testsuites/pvcdeletionperf.go:244 @ 11/09/25 19:19:34.587
}

This error indicates that the PVCs are not being deleted within the expected timeframe (30 minutes). This could be due to issues with the CSI driver, slow storage backend operations, or problems within the Kubernetes control plane.

volume-lifecycle-performance Failure

{ failed [FAILED] expected all PVCs to be in Bound state within 15m0s minutes
In [It] at: k8s.io/kubernetes/test/e2e/storage/testsuites/volumeperf.go:209 @ 11/09/25 20:11:53.551
}

Similar to the deletion test, this failure suggests that PVCs are not being bound (provisioned) within the expected 15-minute window. This could stem from similar root causes as the deletion failure, such as CSI driver issues or slow storage provisioning.

PersistentVolumes-local Stress Test Failure

{ failed [FAILED] some pods failed to complete within 5m0s: client rate limiter Wait returned an error: context deadline exceeded
In [It] at: k8s.io/kubernetes/test/e2e/storage/persistent_volumes-local.go:639 @ 11/10/25 00:34:15.155
}

This error points to a client-side rate-limiting issue. The Kubernetes API server is likely being overwhelmed with requests, causing the client to exceed its rate limit and experience timeouts. This often happens when there are a large number of concurrent operations, such as pod creation and deletion, especially when dealing with local volumes.

Digging Deeper into the Failures

To get a clearer picture, let's analyze each failure in more detail. We need to consider potential causes and how to address them.

CSI Volume Performance Issues

The CSI (Container Storage Interface) is a critical component in Kubernetes for enabling communication between the Kubernetes control plane and storage providers. When CSI volumes exhibit performance issues, it can lead to significant delays in provisioning, deletion, and overall application performance. Several factors can contribute to these issues:

  • CSI Driver Implementation: The efficiency of the CSI driver plays a crucial role in volume performance. A poorly implemented driver can introduce bottlenecks, leading to slower operations. It's essential to ensure that the CSI driver is well-optimized and follows best practices for handling storage requests.
  • Storage Backend Performance: The underlying storage system's performance is another critical factor. If the storage backend is slow or experiencing issues, it can directly impact the performance of CSI volume operations. Monitoring the storage backend's health and performance metrics is crucial for identifying potential bottlenecks.
  • Kubernetes Control Plane Overhead: The Kubernetes control plane's overhead can also contribute to performance issues. Excessive API calls, inefficient scheduling, or resource contention within the control plane can lead to delays in volume operations. Optimizing the control plane's configuration and resource allocation can help mitigate these issues.
  • Network Latency: Network latency between the Kubernetes nodes and the storage backend can also impact CSI volume performance. High latency can increase the time it takes to provision and delete volumes, leading to performance degradation. Ensuring a low-latency network connection is essential for optimal CSI volume performance.

To address CSI volume performance issues, it's essential to investigate each of these potential causes. Analyzing logs, monitoring performance metrics, and conducting thorough testing can help identify the root cause and implement appropriate solutions. Some common solutions include optimizing the CSI driver implementation, improving the storage backend's performance, tuning the Kubernetes control plane, and ensuring a low-latency network connection.

PersistentVolumes-local Stress Test Failure Analysis

The PersistentVolumes-local stress test failure is particularly concerning because it indicates a potential scalability issue within the Kubernetes cluster. The error message suggests that the API server is being overwhelmed, leading to rate limiting and timeouts. This can occur when a large number of pods are created or deleted in a short period, especially when using local volumes. Local volumes are directly attached to the nodes, and managing them can be resource-intensive for the API server.

Several factors can contribute to this failure:

  • High Pod Density: If the cluster is running a high density of pods, the API server may struggle to handle the increased load. Reducing the number of pods per node or optimizing pod scheduling can help alleviate this issue.
  • Inefficient Volume Management: Inefficient volume management practices can also contribute to API server overload. For example, creating and deleting volumes frequently can generate a large number of API calls, overwhelming the server. Implementing volume caching or optimizing volume lifecycle management can help reduce the load on the API server.
  • API Server Resource Constraints: The API server's resource constraints can also limit its ability to handle requests. Increasing the API server's CPU, memory, or other resources can improve its performance and prevent rate limiting.
  • Network Congestion: Network congestion can also contribute to API server overload. If the network is congested, API requests may take longer to process, leading to timeouts and rate limiting. Optimizing network configuration and ensuring sufficient bandwidth can help mitigate this issue.

To address the PersistentVolumes-local stress test failure, it's essential to investigate each of these potential causes. Monitoring API server performance metrics, analyzing network traffic, and reviewing volume management practices can help identify the root cause and implement appropriate solutions. Some common solutions include reducing pod density, optimizing volume management, increasing API server resources, and mitigating network congestion.

Testgrid Link

You can find more details on Testgrid: https://testgrid.k8s.io/sig-release-master-informing#gce-cos-master-serial

Testgrid is our central dashboard for tracking test results. It provides a historical view of test runs and makes it easy to spot patterns and regressions. Use this link to see the trends for this specific job.

Anything Else We Need to Know?

No response provided in the initial report.

It would be helpful to gather more information, such as:

  • Were there any recent changes to the CSI driver or storage backend?
  • Have there been any changes to the Kubernetes cluster configuration?
  • Are there any resource constraints on the nodes or the control plane?

Relevant SIG(s)

This issue falls under the purview of /sig storage. The Storage Special Interest Group is responsible for the design, development, and maintenance of Kubernetes storage features. They're the right folks to get involved in fixing this.

Next Steps

So, what should we do now? Here’s a plan of action:

  1. Engage SIG Storage: Ping the sig-storage channel in the Kubernetes Slack workspace and bring this issue to their attention. The more eyes on this, the better.
  2. Gather More Data: We need to dig deeper. Let's check the logs from the failing tests, the CSI driver, and the storage backend. Look for error messages, warnings, or any anomalies that could provide clues.
  3. Reproduce the Issue: If possible, try to reproduce the failure in a staging environment. This will allow us to experiment with different solutions without impacting production.
  4. Isolate the Cause: Once we have enough data, we can start isolating the root cause. Is it a bug in the CSI driver? A performance bottleneck in the storage backend? A configuration issue in Kubernetes?
  5. Implement a Fix: Once the cause is identified, we can implement a fix. This might involve patching the CSI driver, tuning the storage backend, or adjusting Kubernetes configurations.
  6. Verify the Fix: After implementing the fix, we need to verify that it resolves the issue. Run the failing tests again and monitor the system for any regressions.

By following these steps, we can effectively troubleshoot and resolve these Kubernetes e2e test failures. Let's work together to ensure the stability and reliability of our storage solutions!

In conclusion, addressing these Kubernetes e2e test failures requires a systematic approach. By understanding the specific issues, gathering relevant data, and engaging the appropriate SIG, we can effectively troubleshoot and resolve these problems, ensuring the stability and reliability of our Kubernetes storage solutions. Remember, collaboration and thorough investigation are key to maintaining a healthy Kubernetes environment.