ClusterLoaderV2 Huge-Service Test Failure: API Responsiveness Issue

Introduction

Hey guys! We've got a situation with a failing test in our Kubernetes infrastructure. Specifically, the ClusterLoaderV2 huge-service test within the sig-scalability suite is throwing errors. This test is crucial for ensuring the scalability and performance of our clusters, so we need to get to the bottom of this ASAP. Let's dive into the details and figure out what's going on and how to fix it. This article breaks down the failure, its impact, and the steps needed to resolve it, ensuring our Kubernetes clusters remain robust and scalable.

The Problem: Failing Test Details

The test that's failing is:

• ClusterLoaderV2: huge-service: [step: 11] gathering measurements [01] - APIResponsivenessPrometheusSimple

This test is designed to measure the API responsiveness of our Kubernetes clusters under heavy load. Step 11, where the failure occurs, involves gathering measurements using Prometheus, a popular monitoring and alerting tool. The specific issue seems to be related to high latency in API requests, particularly DELETE requests for events within namespaces. Understanding this failure is crucial for maintaining the stability and performance of Kubernetes deployments, as it directly impacts the responsiveness of the API server under load.

Impact of the Failure

So, why should we care about this failing test? Well, a failing test like this can indicate several underlying problems, including:

• Performance bottlenecks: High latency suggests that our API server might be struggling to handle the load.
• Scalability issues: If the API server can't keep up, it could limit the scalability of our cluster.
• User experience degradation: Slow API responses can lead to a sluggish user experience for anyone interacting with the cluster.
• Potential for cascading failures: If the API server becomes unresponsive, it could trigger cascading failures in other parts of the system. Addressing this issue proactively is essential to prevent broader system instability and maintain a healthy Kubernetes environment.

In short, this isn't something we can just ignore. We need to fix it to ensure our clusters remain healthy and performant. This proactive approach helps in maintaining a stable and efficient Kubernetes ecosystem, which is vital for large-scale deployments.

When Did This Start?

The failures have been occurring consistently since:

• Start: 2025-10-30 07:02:18 +0000 UTC
• Latest: 2025-11-01 07:02:23 +0000 UTC

This consistent failure over several days indicates a persistent issue rather than a transient glitch. This timeline helps in narrowing down the potential causes by correlating the failures with any recent changes or deployments in the infrastructure. Identifying the onset of the issue is a critical step in the troubleshooting process, allowing for a more targeted investigation.

Which Jobs and Tests Are Affected?

Here's a breakdown of the affected jobs and tests:

• Failing Jobs:
  • master-informing
  • ec2-master-scale-performance
• Failing Tests:
  • ClusterLoaderV2: huge-service: [step: 11] gathering measurements [01] - APIResponsivenessPrometheusSimple

The master-informing job is critical as it provides insights into the master node's health and performance. The ec2-master-scale-performance job specifically tests the scalability of the master node in an AWS environment. The consistent failure in these jobs signals a potential systemic issue that needs immediate attention. Understanding which specific jobs are failing is crucial for isolating the problem and implementing targeted solutions.

Diving into the Failure Reason

The error message gives us a crucial clue:

{ "Failure" :0
[measurement call APIResponsivenessPrometheus - APIResponsivenessPrometheusSimple error: top latency metric: there should be no high-latency requests, but: [got: &{Resource:events Subresource: Verb:DELETE Scope:namespace Latency:perc50: 450ms, perc90: 57.749999999s, perc99: 1m0s Count:115 SlowCount:56}; expected perc99 <= 30s]]
:0}

This tells us that the APIResponsivenessPrometheusSimple measurement is failing because of high latency, specifically with DELETE requests for events. Let's break this down:

• Resource: events: The issue is related to Kubernetes events, which are records of actions happening in the cluster.
• Subresource: This further specifies the type of event being targeted.
• Verb: DELETE: The high latency is occurring when deleting events.
• Scope: namespace: The problem is scoped to namespace-level operations.
• Latency: perc50: 450ms, perc90: 57.749999999s, perc99: 1m0s: This is the critical part. The 99th percentile latency is a whopping 1 minute, far exceeding the expected threshold of 30 seconds.
• Count: 115 SlowCount: 56: Out of 115 requests, 56 were considered slow, indicating a significant performance issue. This high number of slow requests underscores the severity of the latency problem and the urgent need for a solution.

Essentially, the API server is taking too long to delete events, which is causing the test to fail. This high latency can be a symptom of various underlying issues, such as database bottlenecks, inefficient event handling, or resource contention. Pinpointing the exact cause requires a more in-depth investigation and potentially some performance profiling.

Potential Causes and Troubleshooting Steps

Okay, so we know there's high latency when deleting events. What could be causing this? Here are a few possibilities:

1. etcd Bottleneck: etcd is Kubernetes' datastore, and if it's struggling, it can cause API latency. We need to check etcd's performance metrics (CPU, memory, disk I/O) to see if it's the culprit. Analyzing etcd metrics is a critical step in diagnosing API latency issues, as etcd performance directly impacts the API server's responsiveness. Monitoring these metrics can help identify bottlenecks and inform optimization strategies.
2. High Event Volume: If there are a large number of events being generated and deleted, it could overwhelm the API server. We should investigate event generation rates and retention policies. High event volume can lead to increased load on the API server and etcd, exacerbating latency issues. Understanding event patterns and implementing effective retention policies are essential for maintaining system performance.
3. Inefficient Event Handling: There might be inefficiencies in how Kubernetes handles event deletion. We might need to look at the event controller's code and see if there are any optimizations we can make. Code-level optimizations can significantly improve event handling efficiency and reduce latency. Reviewing the event controller's logic and identifying potential bottlenecks are key steps in this process.
4. Resource Contention: The API server might be competing for resources (CPU, memory) with other components. We need to monitor resource usage across the cluster. Resource contention can lead to performance degradation and API latency issues. Monitoring resource utilization and ensuring adequate resource allocation are vital for maintaining a stable and responsive Kubernetes environment.
5. Network Issues: Although less likely, network latency between the API server and etcd could also contribute to the problem. Network latency can add overhead to API requests and exacerbate performance issues. While less common, network performance should not be overlooked as a potential factor in API latency problems.

To troubleshoot this, we need to:

• Monitor etcd: Check its CPU, memory, and disk I/O usage.
• Analyze event rates: See how many events are being created and deleted.
• Profile the API server: Identify any performance bottlenecks in the code.
• Review Kubernetes configuration: Ensure proper resource allocation and event retention policies are in place.
• Check Network Latency: Validate the network performance between the API server and etcd to rule out any connectivity issues.

By systematically investigating these areas, we can pinpoint the root cause and implement a fix. Each of these steps provides valuable insights into the system's behavior and helps in narrowing down the potential causes of the latency issue.

Relevant SIG(s)

This issue falls under the purview of:

• /sig scalability
• @kubernetes/release-team-release-signal

These SIGs (Special Interest Groups) are the experts in this area, and they'll be instrumental in helping us resolve this issue. Engaging the relevant SIGs ensures that the right expertise is applied to the problem and facilitates a collaborative approach to finding a solution. Their involvement is crucial for addressing complex issues and maintaining the health of the Kubernetes ecosystem.

Next Steps

Here's what we need to do next:

1. Gather more data: Collect detailed metrics from etcd, the API server, and other relevant components.
2. Engage the SIGs: Reach out to the scalability SIG and the release team for assistance.
3. Reproduce the issue: Try to reproduce the failure in a controlled environment.
4. Implement a fix: Once we've identified the root cause, develop and deploy a solution.

By following these steps, we can systematically address the failing test and ensure the continued scalability and performance of our Kubernetes clusters. This proactive approach is essential for maintaining a robust and reliable infrastructure.

Conclusion

The failing ClusterLoaderV2 huge-service test is a critical issue that needs our attention. The high latency in deleting events indicates a potential performance bottleneck that could impact the scalability and responsiveness of our Kubernetes clusters. By systematically troubleshooting the issue, engaging the relevant SIGs, and implementing a fix, we can ensure the continued health and performance of our infrastructure. Let's get this fixed, guys! This collaborative effort will help maintain a stable and efficient Kubernetes environment, which is vital for large-scale deployments. Ensuring the reliability of our clusters is paramount for the success of our projects and the satisfaction of our users.