[SPARK-32417] Fix flakyness of BlockManagerDecommissionIntegrationSuite #29226
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cc: @holdenk @attilapiros for review please. |
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Oh awesome, thanks for fixing this. I totally misdiagnosed this issue
(thought it was around placement of the partitions).
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Sorry it didn't work. I will make another go at it. |
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The remaining failure is in SparkR and is probably environment related. |
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| test(s"verify that an already running task which is going to cache data succeeds " + | ||
| s"on a decommissioned executor after task start") { | ||
| runDecomTest(true, false, true, 1) | ||
| } | ||
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| test(s"verify that an already running task which is going to cache data succeeds " + | ||
| s"on a decommissioned executor after iterator start") { | ||
| runDecomTest(true, false, true, 2) | ||
| } | ||
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@holdenk .. this is what I meant: I have added two tests to test the decom after "Task Start".
The first test is a negative test. It defines task start as when the driver says that the task is "launched" and as we discussed, it is expected to fail because decommissioning will be triggered before BlockManager.doPut is called and thus fail the task without starting the iterator.
The second test is what you want mentioned in your "intention": We want the decommissioning after the "iterator start" (ie user code started) to trigger a migration: The block manager will write the block anyway and it will be migrated. Do you think this second test indeed captures the intention of the test ?
The first negative test is just to demonstrate that it is a case we don't handle yet. Since we agree that it is not a case worth fixing for production, I will eventually delete it.
So now we cover the full gamut of interesting decom points in task: When it is just launched, When user code is activated, After it has ended.
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I think this isn't needed anymore. |
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Jul 28, 2020
| sc.addSparkListener(new SparkListener { | ||
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| def getCandidateExecutorToDecom: Option[String] = if (whenToDecom == TaskStarted) { | ||
| accum.value.asScala.headOption |
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I don't think this is going to work as intended, accumulators send updates back at the end of the task, unless something has changed.
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If you don't believe that is the case, can we add an assertion in here that none of the tasks have finished yet?
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I think I understand your intention with accumulators now, I don't think that's going to work. |
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An interesting failure happens when migrateDuring = true (and persist or shuffle is true): - We schedule the job with tasks on executors 0, 1, 2. - We wait 300 ms and decommission executor 0. - If the task is not yet done on executor 0, it will now fail because the block manager won't be able to save the block. This condition is easy to trigger on a loaded machine where the github checks run. - The task with retry on a different executor (1 or 2) and its shuffle blocks will land there. - No actual block migration happens here because the decommissioned executor technically failed before it could even produce a block. So this change makes two fixes to remove the above race condition. - When migrateDuring = true, wait for a task to complete and write the block, and then decommission that executor. - When migrateDuring = false, it is still possible (because of delay scheduling) for two tasks to be run on the same executor serially and one executor to go idle. In which case, we must make sure to decommission an executor that actually had a task run on it.
Now that we wait for an actual task to succeed, we don't need to wait for events prior to that: broadcast of job-info finished and task started. The waiting for the task end/success subsumes that. Simplifying the test even further.
… captures @holdenk's intent
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Thanks for pointing this out @holdenk. As a spark n00b I have certainly learnt quite a bit by fixing this test. I tried another approach and also added the asserts you asked for. I learnt that while spark does not update the accumulator until the task completes, it does graciously trigger a listener event when the accumulators are received from an executor via the heartbeat mechanism. I am using that to know for sure that the task has started for "real". Please take a look. |
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Yeah if you wait long enough for a heartbeat and check the event that should do the trick, but to avoid a flaky test your going to need a good length of time. I'll take another look tomorrow afternoon (have meetings all morning). |
holdenk
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Jul 29, 2020
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For some reasons, the test seems still flaky:
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…onIntegrationSuite to reduce flakyness ### What changes were proposed in this pull request? As reported by HyukjinKwon, BlockManagerDecommissionIntegrationSuite test is apparently still flaky (even after #29226): #29226 (comment). The new flakyness is because the executors are not launching in the 6 seconds time out I had given them when run under github checks. Bumped up the timeouts. ### Why are the changes needed? To make this test not flaky so that it can give us high signal if decommissioning regresses. ### Does this PR introduce _any_ user-facing change? No, unit test only check. ### How was this patch tested? No new tests. Just github and jenkins. Closes #29388 from agrawaldevesh/more_bm_harden. Authored-by: Devesh Agrawal <[email protected]> Signed-off-by: Dongjoon Hyun <[email protected]>
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[SPARK-21040][CORE] Speculate tasks which are running on decommission executors This PR adds functionality to consider the running tasks on decommission executors based on some config. In spark-on-cloud , we sometimes already know that an executor won't be alive for more than fix amount of time. Ex- In AWS Spot nodes, once we get the notification, we know that a node will be gone in 120 seconds. So if the running tasks on the decommissioning executors may run beyond currentTime+120 seconds, then they are candidate for speculation. Currently when an executor is decommission, we stop scheduling new tasks on those executors but the already running tasks keeps on running on them. Based on the cloud, we might know beforehand that an executor won't be alive for more than a preconfigured time. Different cloud providers gives different timeouts before they take away the nodes. For Ex- In case of AWS spot nodes, an executor won't be alive for more than 120 seconds. We can utilize this information in cloud environments and take better decisions about speculating the already running tasks on decommission executors. Yes. This PR adds a new config "spark.executor.decommission.killInterval" which they can explicitly set based on the cloud environment where they are running. Added UT. Closes apache#28619 from prakharjain09/SPARK-21040-speculate-decommission-exec-tasks. Authored-by: Prakhar Jain <[email protected]> Signed-off-by: Holden Karau <[email protected]> [SPARK-20629][CORE][K8S] Copy shuffle data when nodes are being shutdown This pull request adds the ability to migrate shuffle files during Spark's decommissioning. The design document associated with this change is at https://docs.google.com/document/d/1xVO1b6KAwdUhjEJBolVPl9C6sLj7oOveErwDSYdT-pE . To allow this change the `MapOutputTracker` has been extended to allow the location of shuffle files to be updated with `updateMapOutput`. When a shuffle block is put, a block update message will be sent which triggers the `updateMapOutput`. Instead of rejecting remote puts of shuffle blocks `BlockManager` delegates the storage of shuffle blocks to it's shufflemanager's resolver (if supported). A new, experimental, trait is added for shuffle resolvers to indicate they handle remote putting of blocks. The existing block migration code is moved out into a separate file, and a producer/consumer model is introduced for migrating shuffle files from the host as quickly as possible while not overwhelming other executors. Recomputting shuffle blocks can be expensive, we should take advantage of our decommissioning time to migrate these blocks. This PR introduces two new configs parameters, `spark.storage.decommission.shuffleBlocks.enabled` & `spark.storage.decommission.rddBlocks.enabled` that control which blocks should be migrated during storage decommissioning. New unit test & expansion of the Spark on K8s decom test to assert that decommisioning with shuffle block migration means that the results are not recomputed even when the original executor is terminated. This PR is a cleaned-up version of the previous WIP PR I made apache#28331 (thanks to attilapiros for his very helpful reviewing on it :)). Closes apache#28708 from holdenk/SPARK-20629-copy-shuffle-data-when-nodes-are-being-shutdown-cleaned-up. Lead-authored-by: Holden Karau <[email protected]> Co-authored-by: Holden Karau <[email protected]> Co-authored-by: “attilapiros” <[email protected]> Co-authored-by: Attila Zsolt Piros <[email protected]> Signed-off-by: Holden Karau <[email protected]> [SPARK-24266][K8S] Restart the watcher when we receive a version changed from k8s Restart the watcher when it failed with a HTTP_GONE code from the kubernetes api. Which means a resource version has changed. For more relevant information see here: fabric8io/kubernetes-client#1075 No Running spark-submit to a k8s cluster. Not sure how to make an automated test for this. If someone can help me out that would be great. Closes apache#28423 from stijndehaes/bugfix/k8s-submit-resource-version-change. Authored-by: Stijn De Haes <[email protected]> Signed-off-by: Holden Karau <[email protected]> [SPARK-32217] Plumb whether a worker would also be decommissioned along with executor This PR is a giant plumbing PR that plumbs an `ExecutorDecommissionInfo` along with the DecommissionExecutor message. The primary motivation is to know whether a decommissioned executor would also be loosing shuffle files -- and thus it is important to know whether the host would also be decommissioned. In the absence of this PR, the existing code assumes that decommissioning an executor does not loose the whole host with it, and thus does not clear the shuffle state if external shuffle service is enabled. While this may hold in some cases (like K8s decommissioning an executor pod, or YARN container preemption), it does not hold in others like when the cluster is managed by a Standalone Scheduler (Master). This is similar to the existing `workerLost` field in the `ExecutorProcessLost` message. In the future, this `ExecutorDecommissionInfo` can be embellished for knowing how long the executor has to live for scenarios like Cloud spot kills (or Yarn preemption) and the like. No Tweaked an existing unit test in `AppClientSuite` Closes apache#29032 from agrawaldevesh/plumb_decom_info. Authored-by: Devesh Agrawal <[email protected]> Signed-off-by: Holden Karau <[email protected]> [SPARK-32199][SPARK-32198] Reduce job failures during decommissioning This PR reduces the prospect of a job loss during decommissioning. It fixes two holes in the current decommissioning framework: - (a) Loss of decommissioned executors is not treated as a job failure: We know that the decommissioned executor would be dying soon, so its death is clearly not caused by the application. - (b) Shuffle files on the decommissioned host are cleared when the first fetch failure is detected from a decommissioned host: This is a bit tricky in terms of when to clear the shuffle state ? Ideally you want to clear it the millisecond before the shuffle service on the node dies (or the executor dies when there is no external shuffle service) -- too soon and it could lead to some wastage and too late would lead to fetch failures. The approach here is to do this clearing when the very first fetch failure is observed on the decommissioned block manager, without waiting for other blocks to also signal a failure. Without them decommissioning a lot of executors at a time leads to job failures. The task scheduler tracks the executors that were decommissioned along with their `ExecutorDecommissionInfo`. This information is used by: (a) For handling a `ExecutorProcessLost` error, or (b) by the `DAGScheduler` when handling a fetch failure. No Added a new unit test `DecommissionWorkerSuite` to test the new behavior by exercising the Master-Worker decommissioning. I chose to add a new test since the setup logic was quite different from the existing `WorkerDecommissionSuite`. I am open to changing the name of the newly added test suite :-) - Should I add a feature flag to guard these two behaviors ? They seem safe to me that they should only get triggered by decommissioning, but you never know :-). Closes apache#29014 from agrawaldevesh/decom_harden. Authored-by: Devesh Agrawal <[email protected]> Signed-off-by: Holden Karau <[email protected]> [SPARK-32417] Fix flakyness of BlockManagerDecommissionIntegrationSuite This test tries to fix the flakyness of BlockManagerDecommissionIntegrationSuite. Make the block manager decommissioning test be less flaky An interesting failure happens when migrateDuring = true (and persist or shuffle is true): - We schedule the job with tasks on executors 0, 1, 2. - We wait 300 ms and decommission executor 0. - If the task is not yet done on executor 0, it will now fail because the block manager won't be able to save the block. This condition is easy to trigger on a loaded machine where the github checks run. - The task with retry on a different executor (1 or 2) and its shuffle blocks will land there. - No actual block migration happens here because the decommissioned executor technically failed before it could even produce a block. To remove the above race, this change replaces the fixed wait for 300 ms to wait for an actual task to succeed. When a task has succeeded, we know its blocks would have been written for sure and thus its executor would certainly be forced to migrate those blocks when it is decommissioned. The change always decommissions an executor on which a real task finished successfully instead of picking the first executor. Because the system may choose to schedule nothing on the first executor and instead run the two tasks on one executor. I have had bad luck with BlockManagerDecommissionIntegrationSuite and it has failed several times on my PRs. So fixing it. No, unit test only change. Github checks. Ran this test 100 times, 10 at a time in parallel in a script. Closes apache#29226 from agrawaldevesh/block-manager-decom-flaky. Authored-by: Devesh Agrawal <[email protected]> Signed-off-by: Holden Karau <[email protected]> [SPARK-31197][CORE] Shutdown executor once we are done decommissioning Exit the executor when it has been asked to decommission and there is nothing left for it to do. This is a rebase of apache#28817 If we want to use decommissioning in Spark's own scale down we should terminate the executor once finished. Furthermore, in graceful shutdown it makes sense to release resources we no longer need if we've been asked to shutdown by the cluster manager instead of always holding the resources as long as possible. The decommissioned executors will exit and the end of decommissioning. This is sort of a user facing change, however decommissioning hasn't been in any releases yet. I changed the unit test to not send the executor exit message and still wait on the executor exited message. Closes apache#29211 from holdenk/SPARK-31197-exit-execs-redone. Authored-by: Holden Karau <[email protected]> Signed-off-by: Holden Karau <[email protected]> Connect decommissioning to dynamic scaling Because the mock always says there is an RDD we may replicate more than once, and now that there are independent threads Make Spark's dynamic allocation use decommissioning Track the decommissioning executors in the core dynamic scheduler so we don't scale down too low, update the streaming ExecutorAllocationManager to also delegate to decommission Fix up executor add for resource profile Fix our exiting and cleanup thread for better debugging next time. Cleanup the locks we use in decommissioning and clarify some more bits. Verify executors decommissioned, then killed by external external cluster manager are re-launched Verify some additional calls are not occuring in the executor allocation manager suite. Dont' close the watcher until the end of the test Use decommissionExecutors and set adjustTargetNumExecutors to false so that we can match the pattern for killExecutor/killExecutors bump numparts up to 6 Revert "bump numparts up to 6" This reverts commit daf96dd. Small coment & visibility cleanup CR feedback/cleanup Cleanup the merge [SPARK-21040][CORE] Speculate tasks which are running on decommission executors This PR adds functionality to consider the running tasks on decommission executors based on some config. In spark-on-cloud , we sometimes already know that an executor won't be alive for more than fix amount of time. Ex- In AWS Spot nodes, once we get the notification, we know that a node will be gone in 120 seconds. So if the running tasks on the decommissioning executors may run beyond currentTime+120 seconds, then they are candidate for speculation. Currently when an executor is decommission, we stop scheduling new tasks on those executors but the already running tasks keeps on running on them. Based on the cloud, we might know beforehand that an executor won't be alive for more than a preconfigured time. Different cloud providers gives different timeouts before they take away the nodes. For Ex- In case of AWS spot nodes, an executor won't be alive for more than 120 seconds. We can utilize this information in cloud environments and take better decisions about speculating the already running tasks on decommission executors. Yes. This PR adds a new config "spark.executor.decommission.killInterval" which they can explicitly set based on the cloud environment where they are running. Added UT. Closes apache#28619 from prakharjain09/SPARK-21040-speculate-decommission-exec-tasks. Authored-by: Prakhar Jain <[email protected]> Signed-off-by: Holden Karau <[email protected]> [SPARK-20629][CORE][K8S] Copy shuffle data when nodes are being shutdown This pull request adds the ability to migrate shuffle files during Spark's decommissioning. The design document associated with this change is at https://docs.google.com/document/d/1xVO1b6KAwdUhjEJBolVPl9C6sLj7oOveErwDSYdT-pE . To allow this change the `MapOutputTracker` has been extended to allow the location of shuffle files to be updated with `updateMapOutput`. When a shuffle block is put, a block update message will be sent which triggers the `updateMapOutput`. Instead of rejecting remote puts of shuffle blocks `BlockManager` delegates the storage of shuffle blocks to it's shufflemanager's resolver (if supported). A new, experimental, trait is added for shuffle resolvers to indicate they handle remote putting of blocks. The existing block migration code is moved out into a separate file, and a producer/consumer model is introduced for migrating shuffle files from the host as quickly as possible while not overwhelming other executors. Recomputting shuffle blocks can be expensive, we should take advantage of our decommissioning time to migrate these blocks. This PR introduces two new configs parameters, `spark.storage.decommission.shuffleBlocks.enabled` & `spark.storage.decommission.rddBlocks.enabled` that control which blocks should be migrated during storage decommissioning. New unit test & expansion of the Spark on K8s decom test to assert that decommisioning with shuffle block migration means that the results are not recomputed even when the original executor is terminated. This PR is a cleaned-up version of the previous WIP PR I made apache#28331 (thanks to attilapiros for his very helpful reviewing on it :)). Closes apache#28708 from holdenk/SPARK-20629-copy-shuffle-data-when-nodes-are-being-shutdown-cleaned-up. Lead-authored-by: Holden Karau <[email protected]> Co-authored-by: Holden Karau <[email protected]> Co-authored-by: “attilapiros” <[email protected]> Co-authored-by: Attila Zsolt Piros <[email protected]> Signed-off-by: Holden Karau <[email protected]> [SPARK-24266][K8S] Restart the watcher when we receive a version changed from k8s Restart the watcher when it failed with a HTTP_GONE code from the kubernetes api. Which means a resource version has changed. For more relevant information see here: fabric8io/kubernetes-client#1075 No Running spark-submit to a k8s cluster. Not sure how to make an automated test for this. If someone can help me out that would be great. Closes apache#28423 from stijndehaes/bugfix/k8s-submit-resource-version-change. Authored-by: Stijn De Haes <[email protected]> Signed-off-by: Holden Karau <[email protected]> [SPARK-32217] Plumb whether a worker would also be decommissioned along with executor This PR is a giant plumbing PR that plumbs an `ExecutorDecommissionInfo` along with the DecommissionExecutor message. The primary motivation is to know whether a decommissioned executor would also be loosing shuffle files -- and thus it is important to know whether the host would also be decommissioned. In the absence of this PR, the existing code assumes that decommissioning an executor does not loose the whole host with it, and thus does not clear the shuffle state if external shuffle service is enabled. While this may hold in some cases (like K8s decommissioning an executor pod, or YARN container preemption), it does not hold in others like when the cluster is managed by a Standalone Scheduler (Master). This is similar to the existing `workerLost` field in the `ExecutorProcessLost` message. In the future, this `ExecutorDecommissionInfo` can be embellished for knowing how long the executor has to live for scenarios like Cloud spot kills (or Yarn preemption) and the like. No Tweaked an existing unit test in `AppClientSuite` Closes apache#29032 from agrawaldevesh/plumb_decom_info. Authored-by: Devesh Agrawal <[email protected]> Signed-off-by: Holden Karau <[email protected]> [SPARK-32199][SPARK-32198] Reduce job failures during decommissioning This PR reduces the prospect of a job loss during decommissioning. It fixes two holes in the current decommissioning framework: - (a) Loss of decommissioned executors is not treated as a job failure: We know that the decommissioned executor would be dying soon, so its death is clearly not caused by the application. - (b) Shuffle files on the decommissioned host are cleared when the first fetch failure is detected from a decommissioned host: This is a bit tricky in terms of when to clear the shuffle state ? Ideally you want to clear it the millisecond before the shuffle service on the node dies (or the executor dies when there is no external shuffle service) -- too soon and it could lead to some wastage and too late would lead to fetch failures. The approach here is to do this clearing when the very first fetch failure is observed on the decommissioned block manager, without waiting for other blocks to also signal a failure. Without them decommissioning a lot of executors at a time leads to job failures. The task scheduler tracks the executors that were decommissioned along with their `ExecutorDecommissionInfo`. This information is used by: (a) For handling a `ExecutorProcessLost` error, or (b) by the `DAGScheduler` when handling a fetch failure. No Added a new unit test `DecommissionWorkerSuite` to test the new behavior by exercising the Master-Worker decommissioning. I chose to add a new test since the setup logic was quite different from the existing `WorkerDecommissionSuite`. I am open to changing the name of the newly added test suite :-) - Should I add a feature flag to guard these two behaviors ? They seem safe to me that they should only get triggered by decommissioning, but you never know :-). Closes apache#29014 from agrawaldevesh/decom_harden. Authored-by: Devesh Agrawal <[email protected]> Signed-off-by: Holden Karau <[email protected]> [SPARK-32417] Fix flakyness of BlockManagerDecommissionIntegrationSuite This test tries to fix the flakyness of BlockManagerDecommissionIntegrationSuite. Make the block manager decommissioning test be less flaky An interesting failure happens when migrateDuring = true (and persist or shuffle is true): - We schedule the job with tasks on executors 0, 1, 2. - We wait 300 ms and decommission executor 0. - If the task is not yet done on executor 0, it will now fail because the block manager won't be able to save the block. This condition is easy to trigger on a loaded machine where the github checks run. - The task with retry on a different executor (1 or 2) and its shuffle blocks will land there. - No actual block migration happens here because the decommissioned executor technically failed before it could even produce a block. To remove the above race, this change replaces the fixed wait for 300 ms to wait for an actual task to succeed. When a task has succeeded, we know its blocks would have been written for sure and thus its executor would certainly be forced to migrate those blocks when it is decommissioned. The change always decommissions an executor on which a real task finished successfully instead of picking the first executor. Because the system may choose to schedule nothing on the first executor and instead run the two tasks on one executor. I have had bad luck with BlockManagerDecommissionIntegrationSuite and it has failed several times on my PRs. So fixing it. No, unit test only change. Github checks. Ran this test 100 times, 10 at a time in parallel in a script. Closes apache#29226 from agrawaldevesh/block-manager-decom-flaky. Authored-by: Devesh Agrawal <[email protected]> Signed-off-by: Holden Karau <[email protected]> [SPARK-31197][CORE] Shutdown executor once we are done decommissioning Exit the executor when it has been asked to decommission and there is nothing left for it to do. This is a rebase of apache#28817 If we want to use decommissioning in Spark's own scale down we should terminate the executor once finished. Furthermore, in graceful shutdown it makes sense to release resources we no longer need if we've been asked to shutdown by the cluster manager instead of always holding the resources as long as possible. The decommissioned executors will exit and the end of decommissioning. This is sort of a user facing change, however decommissioning hasn't been in any releases yet. I changed the unit test to not send the executor exit message and still wait on the executor exited message. Closes apache#29211 from holdenk/SPARK-31197-exit-execs-redone. Authored-by: Holden Karau <[email protected]> Signed-off-by: Holden Karau <[email protected]> Connect decommissioning to dynamic scaling Because the mock always says there is an RDD we may replicate more than once, and now that there are independent threads Make Spark's dynamic allocation use decommissioning Track the decommissioning executors in the core dynamic scheduler so we don't scale down too low, update the streaming ExecutorAllocationManager to also delegate to decommission Fix up executor add for resource profile Fix our exiting and cleanup thread for better debugging next time. Cleanup the locks we use in decommissioning and clarify some more bits. Verify executors decommissioned, then killed by external external cluster manager are re-launched Verify some additional calls are not occuring in the executor allocation manager suite. Dont' close the watcher until the end of the test Use decommissionExecutors and set adjustTargetNumExecutors to false so that we can match the pattern for killExecutor/killExecutors bump numparts up to 6 Revert "bump numparts up to 6" This reverts commit daf96dd. Small coment & visibility cleanup CR feedback/cleanup Fix up the merge CR feedback, move adjustExecutors to a common utility function Exclude some non-public APIs Remove junk More CR feedback Fix adjustExecutors backport This test fails for me locally and from what I recall it's because we use a different method of resolving the bind address than upstream so disabling the test This test fails for me locally and from what I recall it's because we use a different method of resolving the bind address than upstream so disabling the test Cleanup and drop watcher changes from the backport
holdenk
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…onIntegrationSuite to reduce flakyness ### What changes were proposed in this pull request? As reported by HyukjinKwon, BlockManagerDecommissionIntegrationSuite test is apparently still flaky (even after apache#29226): apache#29226 (comment). The new flakyness is because the executors are not launching in the 6 seconds time out I had given them when run under github checks. Bumped up the timeouts. ### Why are the changes needed? To make this test not flaky so that it can give us high signal if decommissioning regresses. ### Does this PR introduce _any_ user-facing change? No, unit test only check. ### How was this patch tested? No new tests. Just github and jenkins. Closes apache#29388 from agrawaldevesh/more_bm_harden. Authored-by: Devesh Agrawal <[email protected]> Signed-off-by: Dongjoon Hyun <[email protected]>
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What changes were proposed in this pull request?
This test tries to fix the flakyness of BlockManagerDecommissionIntegrationSuite.
Description of the problem
Make the block manager decommissioning test be less flaky
An interesting failure happens when migrateDuring = true (and persist or shuffle is true):
the block manager won't be able to save the block. This condition is
easy to trigger on a loaded machine where the github checks run.
blocks will land there.
executor technically failed before it could even produce a block.
To remove the above race, this change replaces the fixed wait for 300 ms to wait for an actual task to succeed. When a task has succeeded, we know its blocks would have been written for sure and thus its executor would certainly be forced to migrate those blocks when it is decommissioned.
The change always decommissions an executor on which a real task finished successfully instead of picking the first executor. Because the system may choose to schedule nothing on the first executor and instead run the two tasks on one executor.
Why are the changes needed?
I have had bad luck with BlockManagerDecommissionIntegrationSuite and it has failed several times on my PRs. So fixing it.
Does this PR introduce any user-facing change?
No, unit test only change.
How was this patch tested?
Github checks. Ran this test 100 times, 10 at a time in parallel in a script.