Volcano Metrics Not Resetting After Job Deletion

by SLV Team 49 views
Volcano Metrics Not Resetting After Job Deletion: A Deep Dive

Hey folks! 👋 I'm diving deep into a tricky issue I encountered while stress-testing Volcano. Specifically, I'm seeing that Volcano metrics related to jobs aren't properly resetting after the jobs are deleted. This is super important because accurate metrics are crucial for understanding the performance and health of the scheduler. Let's break down the problem, how to reproduce it, and what's likely going on under the hood.

The Problem: Persistent Volcano Metrics

So, here's the deal. I was running a stress test where I created thousands of nodes using Kwok and then spawned thousands of Pods. Everything seemed to be humming along, but then I hit a snag. After deleting all those Pods, I noticed that the job-related metrics in Volcano weren't resetting to zero. This is a problem because it gives a skewed view of the system's state. Imagine trying to debug a performance issue and seeing metrics that are artificially inflated because they haven't been cleaned up. Not fun, right?

As you can see from the image provided, even after deleting a massive number of pods, the unschedule_task_count metric (and likely others) remain stubbornly high. This makes it look like there are still tons of pending tasks when, in reality, they've been nuked. That's a classic example of a metric not resetting, and it can mess with monitoring, alerting, and overall system understanding.

This behavior makes it difficult to understand the true state of the system after job completion. If you are monitoring Volcano for performance issues, the metrics will provide the wrong information. So, this is a real issue. Getting these metrics to reset is critical for reliable monitoring. Without this reset, it can lead to confusion and incorrect interpretations of system behavior. This can lead to wrong assumptions about resource allocation and scheduling efficiency. It also affects the ability to make data-driven decisions regarding cluster operations.

Why This Matters

This is more than just a minor inconvenience. Accurate metrics are the lifeblood of any system that needs to be properly monitored and optimized. Here's why this metric reset issue is a big deal:

  • Misleading Performance Analysis: Inflated metrics make it look like the system is under more load than it actually is. This can lead to incorrect conclusions about bottlenecks and performance issues.
  • Inefficient Resource Allocation: If the scheduler thinks there are still pending tasks, it might make poor decisions about where to schedule new pods, potentially leading to wasted resources.
  • Difficulty in Debugging: When metrics are inaccurate, it's harder to pinpoint the root cause of problems. You end up chasing ghosts, wasting time, and increasing frustration.
  • Impact on Monitoring and Alerting: Alerts based on incorrect metrics will be triggered at the wrong times, leading to alert fatigue and making it harder to spot real problems.

In essence, these persistent metrics can erode trust in the Volcano scheduler itself, which is never a good thing. The issue affects the accuracy of performance reports and hinders the ability to make informed decisions based on the data. Let's find out how to reproduce the issue, so we can fix it.

Reproducing the Issue: Steps to Replication

Okay, let's get down to how you can recreate this issue yourself. The steps are pretty straightforward, but crucial for validating the problem and helping to fix it. Here's how to reproduce the problem:

  1. Create a Batch of Pods: Kick things off by deploying a large number of pods. This will create a bunch of jobs for the Volcano scheduler to manage.
  2. Quickly Delete the Batch: Immediately after creating the pods, delete them all. Make sure to do this quickly to simulate a scenario where jobs are terminated rapidly.
  3. Observe the Metrics: Keep a close eye on the Volcano metrics, particularly those related to job scheduling and task counts. The key here is to observe the unschedule_task_count metric, for example. The goal is to see if they reset to zero.

In my case, even though I deleted over 4,000 pending Pods, the metrics stubbornly showed over 3,000 tasks remaining. This clearly indicates a problem with the cleanup process.

Tools and Setup

To reproduce this, you'll need a Kubernetes cluster with Volcano installed. You'll also need a way to create and delete a large number of pods quickly. You can use tools like kubectl with scripting or automation, or other tools like Kwok which I used, to simulate a large number of nodes and pods efficiently. Make sure you have the necessary permissions to create and delete pods and jobs within your cluster.

Expected vs. Actual Results

The expected behavior is that the metrics related to the deleted jobs should reset to zero, or at least reflect the correct status of the system. In other words, you delete the pods, and the metrics should reflect the absence of those pods. However, the actual result I received was quite different:

  • Expected: After deleting the pods, the metrics would clear, showing that there are no pending tasks. The unschedule_task_count metric should drop down to zero or a very small number, reflecting any remaining tasks in the system.
  • Actual: Despite deleting over 4,000 pending pods, the metrics still indicated over 3,000 tasks remained. This clearly indicates that the metrics are not being properly cleaned up after job termination. The metrics showed a significant discrepancy between the actual state of the cluster and the data presented by Volcano.

The discrepancy between what was expected and what actually happened highlights a potential bug in the job cleanup process. The fact that the metrics fail to reflect the actual state of the system is the core of the issue. The inability to reset metrics leads to inaccurate performance reports. It also affects the ability to make informed decisions based on the data.

Volcano Version and Relevant Information

It's important to provide details about the environment to allow others to reproduce the same issue. The version of Volcano I'm using is v1.13.0. This is an important piece of information, as the issue may be specific to a particular version or range of versions.

Here's some additional information that might be relevant:

  • Cluster Configuration: The underlying Kubernetes cluster configuration, including the number of nodes, resource limits, and any custom configurations that might influence the scheduler's behavior.
  • Workload Details: The type of workloads that were deployed as part of the stress test. The specifics of the pod configurations, resource requests, and any dependencies are included.
  • Resource Usage: Monitoring of resource usage during the test can help identify bottlenecks. CPU usage, memory consumption, and network I/O are included.

Deep Dive into the Code: The Cleanup Logic

I dug into the Volcano code to try and understand the logic behind the metrics cleanup. Here's a snippet of the relevant code: This is where the magic (or the potential problem) happens. I took a look at the code for cleaning up the metrics. Here's the relevant section from the SchedulerCache:

func (sc *SchedulerCache) processCleanupJob() {
    job, shutdown := sc.DeletedJobs.Get()
    if shutdown {
        return
    }

    defer sc.DeletedJobs.Done(job)

    sc.Mutex.Lock()
    defer sc.Mutex.Unlock()

    if schedulingapi.JobTerminated(job) {
        oldJob, found := sc.Jobs[job.UID]
        if !found {
            klog.V(3).Infof("Failed to find Job <%v:%v/%v>, ignore it", job.UID, job.Namespace, job.Name)
            sc.DeletedJobs.Forget(job)
            return
        }
        newPgVersion := oldJob.PgUID
        oldPgVersion := job.PgUID
        klog.V(5).Infof("Just add pguid:%v, try to delete pguid:%v", newPgVersion, oldPgVersion)
        if oldPgVersion == newPgVersion {
            delete(sc.Jobs, job.UID)
            metrics.DeleteJobMetrics(job.Name, string(job.Queue), job.Namespace)
            klog.V(3).Infof("Job <%v:%v/%v> was deleted.", job.UID, job.Namespace, job.Name)
        }
        sc.DeletedJobs.Forget(job)
    } else {
        // Retry
        sc.retryDeleteJob(job)
    }
}

Let's break down what's happening in this code:

  1. Job Retrieval: The processCleanupJob function retrieves a job from the DeletedJobs queue.
  2. Mutex Protection: It then acquires a mutex to protect access to shared data structures.
  3. Job Termination Check: It checks if the job has been terminated using schedulingapi.JobTerminated(job).
  4. Job Deletion and Metric Reset: If the job is terminated, it attempts to delete the job from the sc.Jobs map and calls metrics.DeleteJobMetrics to remove the metrics.
  5. Failure Handling: If the job is not found or other errors occur, it logs an informational message.
  6. Retry Mechanism: If the job is not yet terminated, it retries the deletion later.

Potential Areas for Investigation

Based on this code, here are a few areas that could be contributing to the issue:

  • Timing Issues: There could be a timing issue where the job is deleted before the metrics are properly updated. This can result in metrics not being reset properly.
  • Race Conditions: Race conditions might occur if multiple processes are trying to clean up metrics simultaneously.
  • JobTerminated Function: The schedulingapi.JobTerminated(job) function might not accurately reflect the true state of the job, which can cause the job deletion process to be skipped.
  • DeleteJobMetrics Function: The metrics.DeleteJobMetrics function is responsible for removing the metrics associated with the job. Issues with this function will lead to the metrics not resetting.
  • Concurrency Issues: Concurrency issues could prevent the metrics from being deleted properly. The metrics might not be consistent across the cluster.

I examined the code and the cleanup logic, and I believe the potential solutions might include improving how jobs are identified as terminated or ensuring the delete metrics function is correctly deleting the metrics.

Conclusion and Next Steps

So, to wrap things up, I've run into an issue where Volcano metrics aren't resetting after job deletion. This leads to inaccurate performance data and potentially poor resource allocation decisions. I've outlined the steps to reproduce the problem, provided the relevant code snippet, and pointed out some potential areas where the issue might be lurking. Now, the next steps are:

  • Debugging: I'll be diving deeper into the code to understand exactly why these metrics aren't being reset.
  • Testing: Writing some tests to reproduce the issue and verify potential fixes.
  • Pull Request: If I find a solution, I'll submit a pull request to fix the problem.

I hope this helps you guys if you encounter the same problem! Let me know if you have any ideas, suggestions, or if you've seen this issue before. Together, we can make Volcano even better. Happy scheduling!