[ML] Fix possible race condition when closing an opening job #42506
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This change fixes a race condition that would result in an in-memory data structure becoming out-of-sync with persistent tasks in cluster state. If repeated often enough this could result in it being impossible to open any ML jobs on the affected node, as the master node would think the node had capacity to open another job but the chosen node would error during the open sequence due to its in-memory data structure being full. The race could be triggered by opening a job and then closing it a tiny fraction of a second later. It is unlikely a user of the UI could open and close the job that fast, but a script or program calling the REST API could. The nasty thing is, from the externally observable states and stats everything would appear to be fine - the fast open then close sequence would appear to leave the job in the closed state. It's only later that the leftovers in the in-memory data structure might build up and cause a problem.
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Pinging @elastic/ml-core |
droberts195
commented
May 24, 2019
| @@ -401,16 +401,12 @@ protected void doRun() { | |||
| logger.debug("Aborted opening job [{}] as it has been closed", jobId); | |||
| return; | |||
| } | |||
| if (processContext.getState() != ProcessContext.ProcessStateName.NOT_RUNNING) { | |||
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This check needs to be done under lock to prevent a close sneaking in after this check but before the open actions are performed.
| @@ -605,10 +613,10 @@ public void closeJob(JobTask jobTask, boolean restart, String reason) { | |||
| if (communicator == null) { | |||
| logger.debug("Job [{}] is being closed before its process is started", jobId); | |||
| jobTask.markAsCompleted(); | |||
| return; | |||
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Returning here without removing the entry from processByAllocation is the cause of the eventual filling up to capacity of processByAllocation. It's nasty because jobTask.markAsCompleted() causes the externally visible effects of closing the job to all look as expected. It's only the internal data structure that's left with a spurious entry.
dimitris-athanasiou
approved these changes
May 24, 2019
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Jenkins run elasticsearch-ci/1 |
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Jenkins run elasticsearch-ci/1 |
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Jenkins run elasticsearch-ci/1 |
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Jenkins run elasticsearch-ci/1 |
droberts195
added a commit
that referenced
this pull request
May 24, 2019
This change fixes a race condition that would result in an in-memory data structure becoming out-of-sync with persistent tasks in cluster state. If repeated often enough this could result in it being impossible to open any ML jobs on the affected node, as the master node would think the node had capacity to open another job but the chosen node would error during the open sequence due to its in-memory data structure being full. The race could be triggered by opening a job and then closing it a tiny fraction of a second later. It is unlikely a user of the UI could open and close the job that fast, but a script or program calling the REST API could. The nasty thing is, from the externally observable states and stats everything would appear to be fine - the fast open then close sequence would appear to leave the job in the closed state. It's only later that the leftovers in the in-memory data structure might build up and cause a problem.
droberts195
added a commit
that referenced
this pull request
May 24, 2019
This change fixes a race condition that would result in an in-memory data structure becoming out-of-sync with persistent tasks in cluster state. If repeated often enough this could result in it being impossible to open any ML jobs on the affected node, as the master node would think the node had capacity to open another job but the chosen node would error during the open sequence due to its in-memory data structure being full. The race could be triggered by opening a job and then closing it a tiny fraction of a second later. It is unlikely a user of the UI could open and close the job that fast, but a script or program calling the REST API could. The nasty thing is, from the externally observable states and stats everything would appear to be fine - the fast open then close sequence would appear to leave the job in the closed state. It's only later that the leftovers in the in-memory data structure might build up and cause a problem.
droberts195
added a commit
that referenced
this pull request
May 24, 2019
This change fixes a race condition that would result in an in-memory data structure becoming out-of-sync with persistent tasks in cluster state. If repeated often enough this could result in it being impossible to open any ML jobs on the affected node, as the master node would think the node had capacity to open another job but the chosen node would error during the open sequence due to its in-memory data structure being full. The race could be triggered by opening a job and then closing it a tiny fraction of a second later. It is unlikely a user of the UI could open and close the job that fast, but a script or program calling the REST API could. The nasty thing is, from the externally observable states and stats everything would appear to be fine - the fast open then close sequence would appear to leave the job in the closed state. It's only later that the leftovers in the in-memory data structure might build up and cause a problem.
droberts195
added a commit
that referenced
this pull request
May 24, 2019
This change fixes a race condition that would result in an in-memory data structure becoming out-of-sync with persistent tasks in cluster state. If repeated often enough this could result in it being impossible to open any ML jobs on the affected node, as the master node would think the node had capacity to open another job but the chosen node would error during the open sequence due to its in-memory data structure being full. The race could be triggered by opening a job and then closing it a tiny fraction of a second later. It is unlikely a user of the UI could open and close the job that fast, but a script or program calling the REST API could. The nasty thing is, from the externally observable states and stats everything would appear to be fine - the fast open then close sequence would appear to leave the job in the closed state. It's only later that the leftovers in the in-memory data structure might build up and cause a problem.
gurkankaymak
pushed a commit
to gurkankaymak/elasticsearch
that referenced
this pull request
May 27, 2019
…#42506) This change fixes a race condition that would result in an in-memory data structure becoming out-of-sync with persistent tasks in cluster state. If repeated often enough this could result in it being impossible to open any ML jobs on the affected node, as the master node would think the node had capacity to open another job but the chosen node would error during the open sequence due to its in-memory data structure being full. The race could be triggered by opening a job and then closing it a tiny fraction of a second later. It is unlikely a user of the UI could open and close the job that fast, but a script or program calling the REST API could. The nasty thing is, from the externally observable states and stats everything would appear to be fine - the fast open then close sequence would appear to leave the job in the closed state. It's only later that the leftovers in the in-memory data structure might build up and cause a problem.
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This change fixes a race condition that would result in an
in-memory data structure becoming out-of-sync with persistent
tasks in cluster state.
If repeated often enough this could result in it being
impossible to open any ML jobs on the affected node, as the
master node would think the node had capacity to open another
job but the chosen node would error during the open sequence
due to its in-memory data structure being full.
The race could be triggered by opening a job and then closing
it a tiny fraction of a second later. It is unlikely a user
of the UI could open and close the job that fast, but a script
or program calling the REST API could.
The nasty thing is, from the externally observable states and
stats everything would appear to be fine - the fast open then
close sequence would appear to leave the job in the closed
state. It's only later that the leftovers in the in-memory
data structure might build up and cause a problem.