open-nomad/command/eval_delete.go

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// Copyright (c) HashiCorp, Inc.
// SPDX-License-Identifier: MPL-2.0
package command
import (
"errors"
"fmt"
"strings"
"github.com/hashicorp/nomad/api"
"github.com/hashicorp/nomad/api/contexts"
"github.com/posener/complete"
)
type EvalDeleteCommand struct {
Meta
filter string
yes bool
// deleteByArg is set when the command is deleting an evaluation that has
// been passed as an argument. This avoids need for confirmation.
deleteByArg bool
// numDeleted tracks the total evaluations deleted in a single run of this
// command. It provides a way to output this information to the user at the
// command completion.
numDeleted int
// client is the lazy-loaded API client and is stored here, so we don't
// need to pass it to multiple functions.
client *api.Client
}
func (e *EvalDeleteCommand) Help() string {
helpText := `
Usage: nomad eval delete [options] <evaluation>
Delete an evaluation by ID. If the evaluation ID is omitted, this command
will use the filter flag to identify and delete a set of evaluations. If ACLs
are enabled, this command requires a management ACL token.
This command should be used cautiously and only in outage situations where
there is a large backlog of evaluations not being processed. During most
normal and outage scenarios, Nomads reconciliation and state management will
handle evaluations as needed.
The eval broker is expected to be paused prior to running this command and
un-paused after. This can be done using the following two commands:
- nomad operator scheduler set-config -pause-eval-broker=true
- nomad operator scheduler set-config -pause-eval-broker=false
General Options:
` + generalOptionsUsage(usageOptsNoNamespace) + `
Eval Delete Options:
-filter
Specifies an expression used to filter evaluations by for deletion. When
using this flag, it is advisable to ensure the syntax is correct using the
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.
2022-11-14 19:08:13 +00:00
eval list command first. Note that deleting evals by filter is imprecise:
for sets of evals larger than a single raft log batch, evals can be inserted
behind the cursor and therefore be missed.
-yes
Bypass the confirmation prompt if an evaluation ID was not provided.
`
return strings.TrimSpace(helpText)
}
func (e *EvalDeleteCommand) Synopsis() string {
return "Delete evaluations by ID or using a filter"
}
func (e *EvalDeleteCommand) AutocompleteFlags() complete.Flags {
return mergeAutocompleteFlags(e.Meta.AutocompleteFlags(FlagSetClient),
complete.Flags{
"-filter": complete.PredictAnything,
"-yes": complete.PredictNothing,
})
}
func (e *EvalDeleteCommand) AutocompleteArgs() complete.Predictor {
return complete.PredictFunc(func(a complete.Args) []string {
client, err := e.Meta.Client()
if err != nil {
return nil
}
resp, _, err := client.Search().PrefixSearch(a.Last, contexts.Evals, nil)
if err != nil {
return []string{}
}
return resp.Matches[contexts.Evals]
})
}
func (e *EvalDeleteCommand) Name() string { return "eval delete" }
func (e *EvalDeleteCommand) Run(args []string) int {
flags := e.Meta.FlagSet(e.Name(), FlagSetClient)
flags.Usage = func() { e.Ui.Output(e.Help()) }
flags.StringVar(&e.filter, "filter", "", "")
flags.BoolVar(&e.yes, "yes", false, "")
if err := flags.Parse(args); err != nil {
return 1
}
args = flags.Args()
if err := e.verifyArgsAndFlags(args); err != nil {
e.Ui.Error(fmt.Sprintf("Error validating command args and flags: %v", err))
return 1
}
// Get the HTTP client and store this for use across multiple functions.
client, err := e.Meta.Client()
if err != nil {
e.Ui.Error(fmt.Sprintf("Error initializing client: %s", err))
return 1
}
e.client = client
// Ensure the eval broker is paused. This check happens multiple times on
// the leader, but this check means we can provide quick and actionable
// feedback.
schedulerConfig, _, err := e.client.Operator().SchedulerGetConfiguration(nil)
if err != nil {
e.Ui.Error(fmt.Sprintf("Error querying scheduler configuration: %s", err))
return 1
}
if !schedulerConfig.SchedulerConfig.PauseEvalBroker {
e.Ui.Error("Eval broker is not paused")
e.Ui.Output(`To delete evaluations you must first pause the eval broker by running "nomad operator scheduler set-config -pause-eval-broker=true"`)
e.Ui.Output(`After the deletion is complete, unpause the eval broker by running "nomad operator scheduler set-config -pause-eval-broker=false"`)
return 1
}
// Track the eventual exit code as there are a number of factors that
// influence this.
var exitCode int
// Call the correct function in order to handle the operator input
// correctly.
switch len(args) {
case 1:
e.deleteByArg = true
exitCode, err = e.handleEvalArgDelete(args[0])
default:
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.
2022-11-14 19:08:13 +00:00
exitCode, err = e.handleDeleteByFilter(e.filter)
}
// Do not exit if we got an error as it's possible this was on the
// non-first iteration, and we have therefore deleted some evals.
if err != nil {
e.Ui.Error(fmt.Sprintf("Error deleting evaluations: %s", err))
}
// Depending on whether we deleted evaluations or not, output a message so
// this is clear.
if e.numDeleted > 0 {
e.Ui.Output(fmt.Sprintf("Successfully deleted %v %s",
e.numDeleted, correctGrammar("evaluation", e.numDeleted)))
} else if err == nil {
e.Ui.Output("No evaluations were deleted")
}
return exitCode
}
// verifyArgsAndFlags ensures the passed arguments and flags are valid for what
// this command accepts and can take action on.
func (e *EvalDeleteCommand) verifyArgsAndFlags(args []string) error {
numArgs := len(args)
// The command takes either an argument or filter, but not both.
if (e.filter == "" && numArgs < 1) || (e.filter != "" && numArgs > 0) {
return errors.New("evaluation ID or filter flag required")
}
// If an argument is supplied, we only accept a single eval ID.
if numArgs > 1 {
return fmt.Errorf("expected 1 argument, got %v", numArgs)
}
return nil
}
// handleEvalArgDelete handles deletion and evaluation which was passed via
// it's ID as a command argument. This is the simplest route to take and
// doesn't require filtering or batching.
func (e *EvalDeleteCommand) handleEvalArgDelete(evalID string) (int, error) {
evalInfo, _, err := e.client.Evaluations().Info(evalID, nil)
if err != nil {
return 1, err
}
// Supplying an eval to delete by its ID will always skip verification, so
// we don't need to understand the boolean response.
code, _, err := e.batchDelete([]*api.Evaluation{evalInfo})
return code, err
}
// batchDelete is responsible for deleting the passed evaluations and asking
// any confirmation questions along the way. It will ask whether the operator
// want to list the evals before deletion, and optionally ask for confirmation
// before deleting based on input criteria.
func (e *EvalDeleteCommand) batchDelete(evals []*api.Evaluation) (int, bool, error) {
// Ask whether the operator wants to see the list of evaluations before
// moving forward with deletion. This will only happen if filters are used
// and the confirmation step is not bypassed.
if !e.yes && !e.deleteByArg {
_, listEvals := e.askQuestion(fmt.Sprintf(
"Do you want to list evals (%v) before deletion? [y/N]",
len(evals)), "")
// List the evals for deletion is the user has requested this. It can
// be useful when the list is small and targeted, but is maybe best
// avoided when deleting large quantities of evals.
if listEvals {
e.Ui.Output("")
e.Ui.Output(formatEvalList(evals, false))
e.Ui.Output("")
}
}
// Generate our list of eval IDs which is required for the API request.
ids := make([]string, len(evals))
for i, eval := range evals {
ids[i] = eval.ID
}
// If the user did not wish to bypass the confirmation step, ask this now
// and handle the response.
if !e.yes && !e.deleteByArg {
code, deleteEvals := e.askQuestion(fmt.Sprintf(
"Are you sure you want to delete %v evals? [y/N]",
len(evals)), "Cancelling eval deletion")
e.Ui.Output("")
if !deleteEvals {
return code, deleteEvals, nil
}
}
_, err := e.client.Evaluations().Delete(ids, nil)
if err != nil {
return 1, false, err
}
// Calculate how many total evaluations we have deleted, so we can output
// this at the end of the process.
curDeleted := e.numDeleted
e.numDeleted = curDeleted + len(ids)
return 0, true, nil
}
// askQuestion allows the command to ask the operator a question requiring a
// y/n response. The optional noResp is used when the operator responds no to
// a question.
func (e *EvalDeleteCommand) askQuestion(question, noResp string) (int, bool) {
answer, err := e.Ui.Ask(question)
if err != nil {
e.Ui.Error(fmt.Sprintf("Failed to parse answer: %v", err))
return 1, false
}
if answer == "" || strings.ToLower(answer)[0] == 'n' {
if noResp != "" {
e.Ui.Output(noResp)
}
return 0, false
} else if strings.ToLower(answer)[0] == 'y' && len(answer) > 1 {
e.Ui.Output("For confirmation, an exact y is required.")
return 0, false
} else if answer != "y" {
e.Ui.Output("No confirmation detected. For confirmation, an exact 'y' is required.")
return 1, false
}
return 0, true
}
func correctGrammar(word string, num int) string {
if num > 1 {
return word + "s"
}
return word
}
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.
2022-11-14 19:08:13 +00:00
func (e *EvalDeleteCommand) handleDeleteByFilter(filterExpr string) (int, error) {
// If the user did not wish to bypass the confirmation step, ask this now
// and handle the response.
if !e.yes && !e.deleteByArg {
resp, _, err := e.client.Evaluations().Count(&api.QueryOptions{
Filter: filterExpr,
})
if err != nil {
return 1, err
}
code, deleteEvals := e.askQuestion(fmt.Sprintf(
"Are you sure you want to delete %d evals? [y/N]",
resp.Count), "Cancelling eval deletion")
e.Ui.Output("")
if !deleteEvals {
return code, nil
}
}
resp, _, err := e.client.Evaluations().DeleteOpts(&api.EvalDeleteRequest{
Filter: filterExpr,
}, nil)
if err != nil {
return 1, err
}
e.numDeleted = resp.Count
return 0, nil
}