Roachtest Restore Failure: TPCE 400GB On GCE - Troubleshooting

by Henrik Larsen 63 views

Hey guys,

We've got a bit of a situation here – a roachtest restore/tpce/400GB/gce/nodes=4/cpus=8/lowmem has failed on release-25.2, specifically at commit ecd970dd0e4ca527d9ceaca368f8806a2471bc3c. This failure is a bit concerning, so let's dive into the details and figure out what's going on.

The Breakdown

The test timed out after an hour (1h0m0s), which is a pretty long time. This suggests that something might be hanging or running much slower than expected. The artifacts and logs are available in /artifacts/restore/tpce/400GB/gce/nodes=4/cpus=8/lowmem/run_1, so we'll definitely want to take a close look at those.

Key Parameters

Here are the parameters that were used for this test run:

  • arch=amd64
  • cloud=gce
  • coverageBuild=false
  • cpu=8
  • encrypted=false
  • fs=ext4
  • localSSD=false
  • runtimeAssertionsBuild=false
  • ssd=0

These parameters give us a good idea of the environment in which the test was running. The fact that it's on GCE, with 8 CPUs and lowmem specified, is particularly relevant. It suggests we might be running into resource constraints or specific issues related to the Google Cloud environment.

Helpful Resources

There are a few resources we can use to investigate this further:

Digging Deeper into the Roachtest Failure

Let's break down this roachtest failure a bit more so we can understand the potential causes and how to tackle them. We're dealing with a restore/tpce/400GB test, which means we're trying to restore a 400GB TPCE dataset. This is a significant amount of data, and the fact that it's running on GCE with nodes=4, cpus=8, and lowmem tells us that we're likely pushing the limits of the system. When analyzing any test failure, especially one involving large datasets and resource constraints, it's crucial to focus on a few key areas. These include resource utilization, network performance, disk I/O, and the specifics of the restore process itself.

Resource Utilization

First, we need to check if the nodes are running out of memory or CPU. The lowmem parameter suggests that memory could be a bottleneck. We should examine the Grafana dashboards linked above to see how memory, CPU, and disk I/O were behaving during the test. Look for spikes in CPU usage, memory swapping, or disk I/O that could indicate a resource bottleneck. If memory is the issue, we might need to consider increasing the memory allocation for the nodes or optimizing the restore process to reduce memory consumption. CPU spikes might indicate inefficient queries or processes during the restore. High disk I/O could mean the disks are struggling to keep up with the data being restored. It's also worth checking if there are any other processes running on the nodes that might be consuming resources and interfering with the test. This could include monitoring agents, system processes, or even other tests running concurrently. Use tools like top, htop, and iostat on the nodes themselves to get a real-time view of resource utilization if the Grafana dashboards don't provide enough detail.

Network Performance

Second, we need to investigate network performance. Restoring 400GB of data involves a lot of data transfer, so network latency or bandwidth limitations could be a factor. We should check the network metrics in Grafana to see if there are any signs of network congestion or packet loss. If the network is the bottleneck, we might need to consider optimizing the network configuration or provisioning more bandwidth. Look for metrics like network throughput, latency, and packet loss. High latency or packet loss can significantly slow down the restore process. It's also worth checking the network configuration to ensure that the nodes are properly connected and that there are no firewall rules or other network policies that might be interfering with the data transfer. Tools like ping, traceroute, and iperf can be used to diagnose network issues.

Disk I/O

Third, disk I/O is a critical factor. The speed at which data can be written to disk will directly impact the restore time. We should check the disk I/O metrics in Grafana to see if the disks are saturated. If the disks are the bottleneck, we might need to consider using faster disks or optimizing the disk configuration. Look for metrics like disk read/write latency, disk queue length, and disk utilization. High disk latency or queue length can indicate that the disks are struggling to keep up with the write load. If local SSDs are not being used (localSSD=false), the performance will be significantly lower compared to using SSDs. Consider using SSDs if performance is critical. Also, ensure that the file system (fs=ext4) is properly configured and optimized for the workload. Different file systems have different performance characteristics, and ext4 might not be the optimal choice for all scenarios.

Restore Process Specifics

Fourth, let's focus on the restore process itself. Are there any specific errors or warnings in the logs that might indicate a problem? Is the restore process correctly configured? Are there any known issues with the restore process for large TPCE datasets? The logs in /artifacts/restore/tpce/400GB/gce/nodes=4/cpus=8/lowmem/run_1 are crucial here. Look for any error messages, warnings, or stack traces that might indicate a problem. Pay close attention to the timestamps to correlate the errors with the resource utilization metrics. The restore process might involve multiple phases, such as reading data from the backup, writing data to disk, and rebuilding indexes. Each phase can have its own performance bottlenecks. It's also important to consider the configuration of the restore process. Are there any settings that can be tuned to improve performance, such as the number of concurrent workers or the buffer size? Check the documentation for the CockroachDB restore command for details on available options.

Previous Failures and Potential Solutions

It's worth noting that there's a mention of a similar failure on other branches (#151981). This suggests that the issue might not be specific to the current commit or branch and could be a recurring problem. The previous failure is tagged with A-disaster-recovery, C-test-failure, O-roachtest, O-robot, T-disaster-recovery, branch-release-24.3.19-rc, and release-blocker, which indicates that it's considered a serious issue that could block releases. This makes it even more important to get to the bottom of this failure. Looking at the previous issue might give us some clues as to the root cause and potential solutions.

Potential Solutions and Workarounds

Based on the information we have so far, here are some potential solutions and workarounds we can consider:

  1. Increase Resources: If memory or CPU is the bottleneck, try increasing the resources allocated to the nodes. This might involve increasing the instance size on GCE or using a different machine type with more memory and CPU cores.
  2. Optimize the Restore Process: Look for ways to optimize the restore process, such as tuning the configuration parameters or using a more efficient restore strategy. This might involve experimenting with different settings for the CockroachDB restore command.
  3. Investigate Network Issues: If network performance is the bottleneck, investigate potential network issues and try to resolve them. This might involve checking the network configuration, optimizing network settings, or provisioning more bandwidth.
  4. Use Faster Disks: If disk I/O is the bottleneck, consider using faster disks, such as SSDs. This can significantly improve the restore performance.
  5. Break Down the Restore: If the 400GB restore is too large, consider breaking it down into smaller chunks. This might involve restoring subsets of the data in parallel or using a different restore strategy.
  6. Investigate Code Changes: Look at the code changes between the last successful test run and the current failure to see if there are any changes that might have introduced the issue. This might involve using git bisect to narrow down the problematic commit.

Next Steps

Okay, so what are the next steps? First, we need to thoroughly analyze the logs and Grafana dashboards. This will give us a better understanding of what's happening during the test and where the bottlenecks are. Next, we should try to reproduce the failure locally. This will make it easier to debug and experiment with different solutions. Once we have a better understanding of the root cause, we can start implementing and testing potential solutions. Finally, we need to ensure that the fix is robust and doesn't introduce any new issues.

Engage the Team

This issue is tagged with @cockroachdb/disaster-recovery, so the relevant team is already notified. It's important to keep the team updated on our progress and collaborate on finding a solution. We should also use the Jira issue (CRDB-53568) to track the progress and document our findings.

Monitoring and Prevention

Finally, once we've resolved this issue, we should think about how we can prevent similar issues from occurring in the future. This might involve adding more monitoring, improving our testing procedures, or implementing better resource management.

Conclusion

This roachtest failure is definitely a serious issue, but by working together and systematically investigating the problem, we can find a solution and ensure that CockroachDB remains reliable and performant. Let's get those logs analyzed and figure this out!