## Development Environment Changes
* Added stringer to build deps
## New HTTP APIs
* Added scheduler worker config API
* Added scheduler worker info API
## New Internals
* (Scheduler)Worker API refactor—Start(), Stop(), Pause(), Resume()
* Update shutdown to use context
* Add mutex for contended server data
- `workerLock` for the `workers` slice
- `workerConfigLock` for the `Server.Config.NumSchedulers` and
`Server.Config.EnabledSchedulers` values
## Other
* Adding docs for scheduler worker api
* Add changelog message
Co-authored-by: Derek Strickland <1111455+DerekStrickland@users.noreply.github.com>
When `volumewatcher.Watcher` starts on the leader, it starts a watch
on every volume and triggers a reap of unused claims on any change to
that volume. But if a reaping is in-flight during leadership
transitions, it will fail and the event that triggered the reap will
be dropped. Perform one reap of unused claims at the start of the
watcher so that leadership transitions don't drop this event.
The task runner prestart hooks take a `joincontext` so they have the
option to exit early if either of two contexts are canceled: from
killing the task or client shutdown. Some tasks exit without being
shutdown from the server, so neither of the joined contexts ever gets
canceled and we leak the `joincontext` (48 bytes) and its internal
goroutine. This primarily impacts batch jobs and any task that fails
or completes early such as non-sidecar prestart lifecycle tasks.
Cancel the `joincontext` after the prestart call exits to fix the
leak.
The `go-getter` library was updated to 1.5.9 in #11481 to pick up a
bug fix for automatically unpacking uncompressed tar archives. But
this version had a regression in git `ref` param behavior and was
patched in 1.5.10.
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.
All breadcrumbs do not need a title property because some views
drill down by using a tab-based UI (e.g. CSI volumes and the Job Overview)
The goal is to help us identify breadcrumbs that are non-descriptive (i.e.
breadcrumbs that display as an ID).
