When the client-side actions of a CSI client RPC succeed but we get
disconnected during the RPC or we fail to checkpoint the claim state, we want
to be able to retry the client RPC without getting blocked by the client-side
state (ex. mount points) already having been cleaned up in previous calls.
Using the count of node claims from earlier in the `CSIVolume.Unpublish RPC
doesn't correctly account for cases where the RPC was interrupted but
checkpointed. Instead, we'll check the current allocation count and status to
determine whether we need to send a controller unpublish.
Upgrade our consul/api import to the equivelent of consul@v1.8.1 which includes
a bug fix necessary for #6913. If consul would publish a proper api/ submodule tag
we could reference that.
Add a Postrun hook to send the `CSIVolume.Unpublish` RPC to the server. This
may forward client RPCs to the node plugins or to the controller plugins,
depending on whether other allocations on this node have claims on this
volume.
By making clients responsible for running the `CSIVolume.Unpublish` RPC (and
making the RPC available to a `nomad volume detach` command), the
volumewatcher becomes only used by the core GC job and we no longer need
async volume GC from job deregister and node update.
This changeset updates `nomad/volumewatcher` to take advantage of the
`CSIVolume.Unpublish` RPC. This lets us eliminate a bunch of code and
associated tests. The raft batching code can be safely dropped, as the
characteristic times of the CSI RPCs are on the order of seconds or even
minutes, so batching up raft RPCs added complexity without any real world
performance wins.
Includes refactor w/ test cleanup and dead code elimination in volumewatcher
The documentation encourages operators to run multiple controller plugin
instances for HA, but the client RPCs don't take advantage of this by retrying
when the RPC fails in cases when the plugin is unavailable (because the node
has drained or the alloc has failed but we haven't received an updated
fingerprint yet).
This changeset tries all known controllers on ready nodes before giving up,
and adds tests that exercise the client RPC routing and retries.
This log line should be rare since:
1. Most tokens should be logged synchronously, not via this async
batched method. Async revocation only takes place when Vault
connectivity is lost and after leader election so no revocations are
missed.
2. There should rarely be >1 batch (1,000) tokens to revoke since the
above conditions should be brief and infrequent.
3. Interval is 5 minutes, so this log line will be emitted at *most*
once every 5 minutes.
What makes this log line rare is also what makes it interesting: due to
a bug prior to Nomad 0.11.2 some tokens may never get revoked. Therefore
Nomad tries to re-revoke them on every leader election. This caused a
massive buildup of old tokens that would never be properly revoked and
purged. Nomad 0.11.3 mostly fixed this but still had a bug in purging
revoked tokens via Raft (fixed in #8553).
The nomad.vault.distributed_tokens_revoked metric is only ticked upon
successful revocation and purging, making any bugs or slowness in the
process difficult to detect.
Logging before a potentially slow revocation+purge operation is
performed will give users much better indications of what activity is
going on should the process fail to make it to the metric.
This makes use of the PR I recently had merged to eslint-plugin-ember-a11y-testing
to add linting that ensures an accessibility audit is called at least once per acceptance
test file. When I have added linting for component tests, it can apply there too.
I added exclusions for the filesystem browser tests, which are covered by behaviors/fs
and for the search test which will involve significant overrides to Ember Power Select
default templates.
Fixes https://github.com/hashicorp/nomad/issues/8544
This PR fixes a bug where using `nomad job plan ...` always report no change if the submitted job contain scaling.
The issue has three contributing factors:
1. The plan endpoint doesn't populate the required scaling policy ID; unlike the job register endpoint
2. The plan endpoint suppresses errors on job insertion - the job insertion fails here, because the scaling policy is missing the required ID
3. The scheduler reports no update necessary when the relevant job isn't in store (because the insertion failed)
This PR fixes the first two factors. Changing the scheduler to be more strict might make sense, but may violate some idempotency invariant or make the scheduler more brittle.
