During unusual outage recovery scenarios on large clusters, a backlog of
millions of evaluations can appear. In these cases, the `eval delete` command can
put excessive load on the cluster by listing large sets of evals to extract the
IDs and then sending larges batches of IDs. Although the command's batch size
was carefully tuned, we still need to be JSON deserialize, re-serialize to
MessagePack, send the log entries through raft, and get the FSM applied.
To improve performance of this recovery case, move the batching process into the
RPC handler and the state store. The design here is a little weird, so let's
look a the failed options first:
* A naive solution here would be to just send the filter as the raft request and
let the FSM apply delete the whole set in a single operation. Benchmarking with
1M evals on a 3 node cluster demonstrated this can block the FSM apply for
several minutes, which puts the cluster at risk if there's a leadership
failover (the barrier write can't be made while this apply is in-flight).
* A less naive but still bad solution would be to have the RPC handler filter
and paginate, and then hand a list of IDs to the existing raft log
entry. Benchmarks showed this blocked the FSM apply for 20-30s at a time and
took roughly an hour to complete.
Instead, we're filtering and paginating in the RPC handler to find a page token,
and then passing both the filter and page token in the raft log. The FSM apply
recreates the paginator using the filter and page token to get roughly the same
page of evaluations, which it then deletes. The pagination process is fairly
cheap (only abut 5% of the total FSM apply time), so counter-intuitively this
rework ends up being much faster. A benchmark of 1M evaluations showed this
blocked the FSM apply for 20-30ms at a time (typical for normal operations) and
completes in less than 4 minutes.
Note that, as with the existing design, this delete is not consistent: a new
evaluation inserted "behind" the cursor of the pagination will fail to be
deleted.
Add a new `Eval.Count` RPC and associated HTTP API endpoints. This API is
designed to support interactive use in the `nomad eval delete` command to get a
count of evals expected to be deleted before doing so.
The state store operations to do this sort of thing are somewhat expensive, but
it's cheaper than serializing a big list of evals to JSON. Note that although it
seems like this could be done as an extra parameter and response field on
`Eval.List`, having it as its own endpoint avoids having to change the response
body shape and lets us avoid handling the legacy filter params supported by
`Eval.List`.
The `related` query param is used to indicate that the request should
return a list of related (next, previous, and blocked) evaluations.
Co-authored-by: Jasmine Dahilig <jasmine@hashicorp.com>
These API endpoints now return results in chronological order. They
can return results in reverse chronological order by setting the
query parameter ascending=true.
- Eval.List
- Deployment.List
API queries can request pagination using the `NextToken` and `PerPage`
fields of `QueryOptions`, when supported by the underlying API.
Add a `NextToken` field to the `structs.QueryMeta` so that we have a
common field across RPCs to tell the caller where to resume paging
from on their next API call. Include this field on the `api.QueryMeta`
as well so that it's available for future versions of List HTTP APIs
that wrap the response with `QueryMeta` rather than returning a simple
list of structs. In the meantime callers can get the `X-Nomad-NextToken`.
Add pagination to the `Eval.List` RPC by checking for pagination token
and page size in `QueryOptions`. This will allow resuming from the
last ID seen so long as the query parameters and the state store
itself are unchanged between requests.
Add filtering by job ID or evaluation status over the results we get
out of the state store.
Parse the query parameters of the `Eval.List` API into the arguments
expected for filtering in the RPC call.
