In the reconciler's filtering for tainted nodes, we use whether the
server supports disconnected clients as a gate to a bunch of our
logic, but this doesn't account for cases where the job doesn't have
`max_client_disconnect`. The only real consequence of this appears to
be that allocs on disconnected nodes are marked "complete" instead of
"lost".
* planner: expose ServerMeetsMinimumVersion via Planner interface
* filterByTainted: add flag indicating disconnect support
* allocReconciler: accept and pass disconnect support flag
* tests: update dependent tests
CSI `CreateVolume` RPC is idempotent given that the topology,
capabilities, and parameters are unchanged. CSI volumes have many
user-defined fields that are immutable once set, and many fields that
are not user-settable.
Update the `Register` RPC so that updating a volume via the API merges
onto any existing volume without touching Nomad-controlled fields,
while validating it with the same strict requirements expected for
idempotent `CreateVolume` RPCs.
Also, clarify that this state store method is used for everything, not just
for the `Register` RPC.
The allocReconciler's computeGroup function contained a significant amount of inline logic that was difficult to understand the intent of. This commit extracts inline logic into the following intention revealing subroutines. It also includes updates to the function internals also aimed at improving maintainability and renames some existing functions for the same purpose. New or renamed functions include.
Renamed functions
- handleGroupCanaries -> cancelUnneededCanaries
- handleDelayedLost -> createLostLaterEvals
- handeDelayedReschedules -> createRescheduleLaterEvals
New functions
- filterAndStopAll
- initializeDeploymentState
- requiresCanaries
- computeCanaries
- computeUnderProvisionedBy
- computeReplacements
- computeDestructiveUpdates
- computeMigrations
- createDeployment
- isDeploymentComplete
The spread iterator can panic when processing an evaluation, resulting
in an unrecoverable state in the cluster. Whenever a panicked server
restarts and quorum is restored, the next server to dequeue the
evaluation will panic.
To trigger this state:
* The job must have `max_parallel = 0` and a `canary >= 1`.
* The job must not have a `spread` block.
* The job must have a previous version.
* The previous version must have a `spread` block and at least one
failed allocation.
In this scenario, the desired changes include `(place 1+) (stop
1+), (ignore n) (canary 1)`. Before the scheduler can place the canary
allocation, it tries to find out which allocations can be
stopped. This passes back through the stack so that we can determine
previous-node penalties, etc. We call `SetJob` on the stack with the
previous version of the job, which will include assessing the `spread`
block (even though the results are unused). The task group spread info
state from that pass through the spread iterator is not reset when we
call `SetJob` again. When the new job version iterates over the
`groupPropertySets`, it will get an empty `spreadAttributeMap`,
resulting in an unexpected nil pointer dereference.
This changeset resets the spread iterator internal state when setting
the job, logging with a bypass around the bug in case we hit similar
cases, and a test that panics the scheduler without the patch.
Processing an evaluation is nearly a pure function over the state
snapshot, but we randomly shuffle the nodes. This means that
developers can't take a given state snapshot and pass an evaluation
through it and be guaranteed the same plan results.
But the evaluation ID is already random, so if we use this as the seed
for shuffling the nodes we can greatly reduce the sources of
non-determinism. Unfortunately golang map iteration uses a global
source of randomness and not a goroutine-local one, but arguably
if the scheduler behavior is impacted by this, that's a bug in the
iteration.
If processing a specific evaluation causes the scheduler (and
therefore the entire server) to panic, that evaluation will never
get a chance to be nack'd and cleared from the state store. It will
get dequeued by another scheduler, causing that server to panic, and
so forth until all servers are in a panic loop. This prevents the
operator from intervening to remove the evaluation or update the
state.
Recover the goroutine from the top-level `Process` methods for each
scheduler so that this condition can be detected without panicking the
server process. This will lead to a loop of recovering the scheduler
goroutine until the eval can be removed or nack'd, but that's much
better than taking a downtime.
* csi: resolve invalid claim states on read
It's currently possible for CSI volumes to be claimed by allocations
that no longer exist. This changeset asserts a reasonable state at
the state store level by registering these nil allocations as "past
claims" on any read. This will cause any pass through the periodic GC
or volumewatcher to trigger the unpublishing workflow for those claims.
* csi: make feasibility check errors more understandable
When the feasibility checker finds we have no free write claims, it
checks to see if any of those claims are for the job we're currently
scheduling (so that earlier versions of a job can't block claims for
new versions) and reports a conflict if the volume can't be scheduled
so that the user can fix their claims. But when the checker hits a
claim that has a GCd allocation, the state is recoverable by the
server once claim reaping completes and no user intervention is
required; the blocked eval should complete. Differentiate the
scheduler error produced by these two conditions.
* csi: resolve invalid claim states on read
It's currently possible for CSI volumes to be claimed by allocations
that no longer exist. This changeset asserts a reasonable state at
the state store level by registering these nil allocations as "past
claims" on any read. This will cause any pass through the periodic GC
or volumewatcher to trigger the unpublishing workflow for those claims.
* csi: make feasibility check errors more understandable
When the feasibility checker finds we have no free write claims, it
checks to see if any of those claims are for the job we're currently
scheduling (so that earlier versions of a job can't block claims for
new versions) and reports a conflict if the volume can't be scheduled
so that the user can fix their claims. But when the checker hits a
claim that has a GCd allocation, the state is recoverable by the
server once claim reaping completes and no user intervention is
required; the blocked eval should complete. Differentiate the
scheduler error produced by these two conditions.
This PR exposes the following existing`consul-template` configuration options to Nomad jobspec authors in the `{job.group.task.template}` stanza.
- `wait`
It also exposes the following`consul-template` configuration to Nomad operators in the `{client.template}` stanza.
- `max_stale`
- `block_query_wait`
- `consul_retry`
- `vault_retry`
- `wait`
Finally, it adds the following new Nomad-specific configuration to the `{client.template}` stanza that allows Operators to set bounds on what `jobspec` authors configure.
- `wait_bounds`
Co-authored-by: Tim Gross <tgross@hashicorp.com>
Co-authored-by: Michael Schurter <mschurter@hashicorp.com>
* Fixed name of `nomad.scheduler.allocs.reschedule` metric
* Added new metrics to metrics reference documentation
* Expanded definitions of "waiting" metrics
* Changelog entry for #10236 and #10237
Adds a package `scheduler/benchmarks` with some examples of profiling
and benchmarking the scheduler, along with helpers for loading
real-world data for profiling.
This tooling comes out of work done for #11712. These test benchmarks
have not been added to CI because these particular profiles are mostly
examples and the runs will add an excessive amount of time to CI runs
for code that rarely changes in a way that has any chance of impacting
performance.
When the scheduler picks a node for each evaluation, the
`LimitIterator` provides at most 2 eligible nodes for the
`MaxScoreIterator` to choose from. This keeps scheduling fast while
producing acceptable results because the results are binpacked.
Jobs with a `spread` block (or node affinity) remove this limit in
order to produce correct spread scoring. This means that every
allocation within a job with a `spread` block is evaluated against
_all_ eligible nodes. Operators of large clusters have reported that
jobs with `spread` blocks that are eligible on a large number of nodes
can take longer than the nack timeout to evaluate (60s). Typical
evaluations are processed in milliseconds.
In practice, it's not necessary to evaluate every eligible node for
every allocation on large clusters, because the `RandomIterator` at
the base of the scheduler stack produces enough variation in each pass
that the likelihood of an uneven spread is negligible. Note that
feasibility is checked before the limit, so this only impacts the
number of _eligible_ nodes available for scoring, not the total number
of nodes.
This changeset sets the iterator limit for "large" `spread` block and
node affinity jobs to be equal to the number of desired
allocations. This brings an example problematic job evaluation down
from ~3min to ~10s. The included tests ensure that we have acceptable
spread results across a variety of large cluster topologies.
In the system scheduler, if a subset of clients are filtered by class,
we hit a code path where the `AllocMetric` has been copied, but the
`Copy` method does not instantiate the various maps. This leads to an
assignment to a nil map. This changeset ensures that the maps are
non-nil before continuing.
The `Copy` method relies on functions in the `helper` package that all
return nil slices or maps when passed zero-length inputs. This
changeset to fix the panic bug intentionally defers updating those
functions because it'll have potential impact on memory usage. See
https://github.com/hashicorp/nomad/issues/11564 for more details.
This change modifies the Nomad job register and deregister RPCs to
accept an updated option set which includes eval priority. This
param is optional and override the use of the job priority to set
the eval priority.
In order to ensure all evaluations as a result of the request use
the same eval priority, the priority is shared to the
allocReconciler and deploymentWatcher. This creates a new
distinction between eval priority and job priority.
The Nomad agent HTTP API has been modified to allow setting the
eval priority on job update and delete. To keep consistency with
the current v1 API, job update accepts this as a payload param;
job delete accepts this as a query param.
Any user supplied value is validated within the agent HTTP handler
removing the need to pass invalid requests to the server.
The register and deregister opts functions now all for setting
the eval priority on requests.
The change includes a small change to the DeregisterOpts function
which handles nil opts. This brings the function inline with the
RegisterOpts.
The system scheduler should leave allocs on draining nodes as-is, but
stop node stop allocs on nodes that are no longer part of the job
datacenters.
Previously, the scheduler did not make the distinction and left system
job allocs intact if they are already running.
I've added a failing test first, which you can see in https://app.circleci.com/jobs/github/hashicorp/nomad/179661 .
Fixes https://github.com/hashicorp/nomad/issues/11373
Fix a bug where the scheduler may panic when preemption is enabled. The conditions are a bit complicated:
A job with higher priority that schedule multiple allocations that preempt other multiple allocations on the same node, due to port/network/device assignments.
The cause of the bug is incidental mutation of internal cached data. `RankedNode` computes and cache proposed allocations in https://github.com/hashicorp/nomad/blob/v1.1.6/scheduler/rank.go#L42-L53 . But scheduler then mutates the list to remove pre-emptable allocs in https://github.com/hashicorp/nomad/blob/v1.1.6/scheduler/rank.go#L293-L294, and `RemoveAllocs` mutates and sets the tail of cached slice with `nil`s triggering a nil-pointer derefencing case.
I fixed the issue by avoiding the mutation in `RemoveAllocs` - the micro-optimization there doesn't seem necessary.
Fixes https://github.com/hashicorp/nomad/issues/11342
Add a new hostname string parameter to the network block which
allows operators to specify the hostname of the network namespace.
Changing this causes a destructive update to the allocation and it
is omitted if empty from API responses. This parameter also supports
interpolation.
In order to have a hostname passed as a configuration param when
creating an allocation network, the CreateNetwork func of the
DriverNetworkManager interface needs to be updated. In order to
minimize the disruption of future changes, rather than add another
string func arg, the function now accepts a request struct along with
the allocID param. The struct has the hostname as a field.
The in-tree implementations of DriverNetworkManager.CreateNetwork
have been modified to account for the function signature change.
In updating for the change, the enhancement of adding hostnames to
network namespaces has also been added to the Docker driver, whilst
the default Linux manager does not current implement it.
When a system or sysbatch job specify constraints that none of the
current nodes meet, report a warning to the user.
Also, for sysbatch job, mark the job as dead as a result.
A sample run would look like:
```
$ nomad job run ./example.nomad
==> 2021-08-31T16:57:35-04:00: Monitoring evaluation "b48e8882"
2021-08-31T16:57:35-04:00: Evaluation triggered by job "example"
==> 2021-08-31T16:57:36-04:00: Monitoring evaluation "b48e8882"
2021-08-31T16:57:36-04:00: Evaluation status changed: "pending" -> "complete"
==> 2021-08-31T16:57:36-04:00: Evaluation "b48e8882" finished with status "complete" but failed to place all allocations:
2021-08-31T16:57:36-04:00: Task Group "cache" (failed to place 1 allocation):
* Constraint "${meta.tag} = bar": 2 nodes excluded by filter
* Constraint "${attr.kernel.name} = linux": 1 nodes excluded by filter
$ nomad job status example
ID = example
Name = example
Submit Date = 2021-08-31T16:57:35-04:00
Type = sysbatch
Priority = 50
Datacenters = dc1
Namespace = default
Status = dead
Periodic = false
Parameterized = false
Summary
Task Group Queued Starting Running Failed Complete Lost
cache 0 0 0 0 0 0
Allocations
No allocations placed
```
When a node becomes ready, create an eval for all system jobs across
namespaces.
The previous code uses `job.ID` to deduplicate evals, but that ignores
the job namespace. Thus if there are multiple jobs in different
namespaces sharing the same ID/Name, only one will be considered for
running in the new node. Thus, Nomad may skip running some system jobs
in that node.
This PR implements a new "System Batch" scheduler type. Jobs can
make use of this new scheduler by setting their type to 'sysbatch'.
Like the name implies, sysbatch can be thought of as a hybrid between
system and batch jobs - it is for running short lived jobs intended to
run on every compatible node in the cluster.
As with batch jobs, sysbatch jobs can also be periodic and/or parameterized
dispatch jobs. A sysbatch job is considered complete when it has been run
on all compatible nodes until reaching a terminal state (success or failed
on retries).
Feasibility and preemption are governed the same as with system jobs. In
this PR, the update stanza is not yet supported. The update stanza is sill
limited in functionality for the underlying system scheduler, and is
not useful yet for sysbatch jobs. Further work in #4740 will improve
support for the update stanza and deployments.
Closes#2527
Updates to the datacenter field should be destructive for any allocation that
is on a node no longer in the list of datacenters, but inplace for any
allocation on a node that is still in the list. Add a check for this change to
the system and generic schedulers after we've checked the task definition for
updates and obtained the node for each current allocation.
The `QuotaIterator` is used as the source of nodes passed into feasibility
checking for constraints. Every node that passes the quota check counts the
allocation resources agains the quota, and as a result we count nodes which
will be later filtered out by constraints. Therefore for jobs with
constraints, nodes that are feasibility checked but fail have been counted
against quotas. This failure mode is order dependent; if all the unfiltered
nodes happen to be quota checked first, everything works as expected.
This changeset moves the `QuotaIterator` to happen last among all feasibility
checkers (but before ranking). The `QuotaIterator` will never receive filtered
nodes so it will calculate quotas correctly.
