MacOS-M1-Stable Jobs Queued: Investigation Needed

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Hey folks, it looks like we've got a situation on our hands! The macos-m1-stable jobs are currently experiencing some serious queueing issues, and we need to get to the bottom of it ASAP. This is a P2 priority alert, meaning it's pretty important, and we need to jump on this to avoid any major disruptions. Let's break down what's happening, why it matters, and how we can tackle it.

Understanding the Alert and Its Implications

First things first, let's get familiar with the alert details. It's a pretty comprehensive snapshot of what's going down, so we can use it as our roadmap for this investigation. The alert popped up on October 10th at 2:36 pm PDT and is currently in the FIRING state, which means things are still not ideal.

The alert is triggered by the pytorch-dev-infra team. The core of the issue revolves around the macos-m1-stable runner type, which is facing some significant delays. The description mentions that the jobs are queuing for a long time. The alert's Reason section is where we get some specific numbers, and that's the really crucial stuff. It highlights that for the macos-m1-stable runner, the max_queue_size has reached 64, and the max_queue_time_mins has hit 51 minutes. This is far from ideal, and these long wait times can stall the build process and cause some serious headaches for developers who are waiting for their code to get tested. The threshold breach indicator confirms that things have crossed their limits and our investigation is very necessary.

The Impact of Job Queuing

Why is this a big deal? Well, when jobs get stuck in a queue, it has several negative effects:

  • Reduced Developer Productivity: Developers spend more time waiting for their builds and tests to complete. This causes frustration and reduces overall productivity. No one likes to stare at a loading screen for extended periods!
  • Slowed Release Cycles: Longer build times mean it takes longer to get new features and bug fixes out the door. This can delay the releases and impact how quickly users can get new features.
  • Resource Waste: While jobs are waiting, resources such as the servers are essentially sitting idle. This is not the most efficient use of our infrastructure.
  • Increased Risk of Errors: The longer a job is delayed, the greater the chance that it will be affected by changes in the environment, leading to unexpected errors.

So, to sum it up, these queueing issues have the potential to cause a lot of problems.

Diving Deeper: Analyzing the Queueing Metrics

To fully understand what's going on with these macOS-M1-stable jobs, let's take a closer look at the metrics. The alert directs us to http://hud.pytorch.org/metrics, which is a great starting point, as well as https://pytorchci.grafana.net/alerting/grafana/aez5q4um9pd6of/view?orgId=1, where more details can be extracted.

When looking at the HUD metrics, we'll want to focus on specific areas:

  • Runner Utilization: How busy are the macos-m1-stable runners? Are they constantly at full capacity? If runners are always busy, it suggests that we don't have enough resources. This is like trying to squeeze everyone into a too-small room; eventually, someone's going to be waiting outside.
  • Job Duration: How long do the jobs typically take to complete? If jobs are taking a long time, it will obviously increase the queueing time. Investigate whether the time taken for the jobs has increased recently.
  • Job Frequency: How many jobs are being submitted to these runners? A sudden influx of jobs can quickly overwhelm the available resources.
  • Resource Consumption: Look at metrics like CPU usage, memory usage, and disk I/O. A resource bottleneck on the runners might be slowing things down. A runner that is maxed out is like a traffic jam, with everything moving slowly.

Key Metrics to Monitor

Here are some key metrics to keep an eye on:

  • Queue Size: This is the number of jobs waiting to run. The alert tells us this is 64 which is a very high number.
  • Queue Time: The amount of time jobs are spending in the queue. The alert tells us this is around 51 minutes.
  • Runner Availability: Number of macos-m1-stable runners available and their state (idle, busy, etc.).
  • Job Completion Rate: The rate at which jobs are finishing.

By analyzing these metrics, we should gain insights into the root cause of the queueing issue. Is it a lack of resources? Are jobs taking too long? Are too many jobs being submitted simultaneously? These are the key questions we need to answer.

Troubleshooting Steps and Potential Solutions

Now, let's put on our detective hats and brainstorm some potential solutions. Based on the alert, here are some things we can look into:

  • Check the Runners' Health: Ensure the runners themselves are healthy. Check for any errors in the runner logs that might be causing delays. Check for any crashes or system errors that might be leading to downtime.
  • Increase Runner Capacity: If the runners are consistently at full capacity, we might need to add more macos-m1-stable runners. This is a very practical solution if we find that there are more jobs in queue than there is capacity.
  • Optimize Job Configuration: Review the job configuration to identify any areas where jobs can be optimized. Perhaps the job setup can be made more efficient. Look for any jobs that can be parallelized to take advantage of multiple cores.
  • Review Resource Allocation: Make sure that the runners have sufficient resources (CPU, memory, disk space). A resource shortage can significantly slow down job execution.
  • Analyze Job Dependencies: Check the jobs to ensure they are not waiting on some other job to complete. Sometimes, the order of the jobs can affect the queueing time.
  • Investigate Network Issues: Slow network speeds between the runners and the build system can also cause delays. Test and verify network connectivity.
  • Prioritize Critical Jobs: Consider implementing a system where critical jobs are prioritized to ensure they are not stuck in the queue for extended periods.

Actionable Steps to Take

Here’s a more structured approach:

  1. Gather Data: Collect detailed metrics data (queue size, queue time, runner utilization, etc.).
  2. Review Runner Logs: Inspect the logs for any errors or unusual behavior.
  3. Analyze Job Performance: Analyze job execution times to identify performance bottlenecks.
  4. Test & Validate Solutions: Implement any proposed solutions in a test environment.
  5. Monitor & Iterate: Continuously monitor the system to ensure everything runs smoothly and to identify any emerging issues.

Conclusion and Next Steps

So, we have identified a problem, analyzed the impact, and put together some key investigation steps. We now need to focus our efforts on the macos-m1-stable runner queueing issues. By diving deep into the metrics, examining the logs, and implementing the troubleshooting steps, we will be able to get to the root of the issue and restore order. Remember, communication is key! We must keep each other informed of our findings, solutions, and progress. This is a team effort, and together, we will get it done.

Let's get to work, guys! We need to get those jobs running smoothly again! Also, the Runbook and View Alert links provided in the alert details provide direct access to the information and monitoring tools we need.

Stay tuned for updates as we work to resolve this issue.