open-nomad/nomad/structs/eval.go

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// Copyright (c) HashiCorp, Inc.
// SPDX-License-Identifier: MPL-2.0
package structs
const (
// EvalDeleteRPCMethod is the RPC method for batch deleting evaluations
// using their IDs.
//
// Args: EvalDeleteRequest
// Reply: EvalDeleteResponse
EvalDeleteRPCMethod = "Eval.Delete"
)
// EvalDeleteRequest is the request object used when operators are manually
// deleting evaluations. The number of evaluation IDs within the request must
// not be greater than MaxEvalIDsPerDeleteRequest.
type EvalDeleteRequest struct {
EvalIDs []string
eval delete: move batching of deletes into RPC handler and state (#15117) 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.
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// Filter specifies the go-bexpr filter expression to be used for deleting a
// set of evaluations that matches the filter
Filter string
WriteRequest
}
// EvalDeleteResponse is the response object when one or more evaluation are
// deleted manually by an operator.
type EvalDeleteResponse struct {
eval delete: move batching of deletes into RPC handler and state (#15117) 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.
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Count int // how many Evaluations were safe to delete and/or matched the filter
WriteMeta
}