stale-issue-message: "This issue has been automatically marked as stale because it has been open 360 days with no activity. Remove stale label or comment or this will be closed in 180 days"
        days-before-stale: 360
        days-before-close: 180
        stale-issue-label: "status:stale"
        exempt-issue-labels: 'type:feature,type:with reproduction steps,type:has pull request'
        stale-pr-label: 'status:stale'

          exempt-pr-labels: "override-stale"
          #Limit the No. of API calls in one run default value is 30.
          operations-per-run: 1000
          days-before-issue-stale: 180
          days-before-issue-close: 365
          stale-issue-label: "stale"
          # reason for closed the issue default value is not_planned
          close-issue-reason: completed

 */

pipeline {
  agent { node { label 'ubuntu' } }
  options {
    durabilityHint('PERFORMANCE_OPTIMIZED')
    buildDiscarder(logRotator(numToKeepStr: '20', artifactNumToKeepStr: '5'))
    timeout(time: 180, unit: 'MINUTES')
  }
  parameters {
    string( defaultValue: 'master', description: 'Core Its branch (default master)',
            name: 'ITS_BRANCH' )
  }
  stages {
    stage("Build Maven Core") {
      steps {

                display = ParameterDisplay.PROMPT,
                allowEmpty = true
            )
        }

        cleanup {
            history(days = 180)
        }

        this.init()
    }
}

class PublishFinalRelease(branch: VersionedSettingsBranch) : PublishRelease(
    promotedBranch = branch.branchName,
    prepTask = "prepFinalRelease",

    name = "Flaky Test Quarantine - ${os.asName()} ${arch.asName()}"
    description = "Run all flaky tests skipped multiple times"

    applyDefaultSettings(os = os, arch = arch, buildJvm = BuildToolBuildJvm, timeout = 180)

    val testsWithOs = model.stages.filter {
        it.stageName in listOf(
            StageName.QUICK_FEEDBACK_LINUX_ONLY,
            StageName.QUICK_FEEDBACK,
            StageName.PULL_REQUEST_FEEDBACK,

 */
public class SetContainsBenchmark {
  // Start at 4.88 then multiply by 2*2^phi <evil cackle> - The goal is be uniform
  // yet visit a variety of "values-relative-to-the-next-power-of-2"
  @Param({"5", "30", "180", "1100", "6900", "43000", "260000"}) // "1600000", "9800000"
  private int size;

  // TODO(kevinb): look at exact (==) hits vs. equals() hits?
  @Param({"0.2", "0.8"})
  private double hitRate;

  @Param("true")

 */
public class SetContainsBenchmark {
  // Start at 4.88 then multiply by 2*2^phi <evil cackle> - The goal is be uniform
  // yet visit a variety of "values-relative-to-the-next-power-of-2"
  @Param({"5", "30", "180", "1100", "6900", "43000", "260000"}) // "1600000", "9800000"
  private int size;

  // TODO(kevinb): look at exact (==) hits vs. equals() hits?
  @Param({"0.2", "0.8"})
  private double hitRate;

  @Param("true")

			return
		}

		if db.Statement.Schema != nil {
			for _, c := range db.Statement.Schema.UpdateClauses {
				db.Statement.AddClause(c)
			}
		}

		if db.Statement.SQL.Len() == 0 {
			db.Statement.SQL.Grow(180)
			db.Statement.AddClauseIfNotExists(clause.Update{})
			if _, ok := db.Statement.Clauses["SET"]; !ok {
				if set := ConvertToAssignments(db.Statement); len(set) != 0 {
					defer delete(db.Statement.Clauses, "SET")

ah","vermont","virginia","washington","west virginia","wisconsin","wyoming"],errorMessage:"",errorMessageKey:"badCustomVal"}),a.formUtils.addValidator({name:"longlat",validatorFunction:function(a){var b=/^[-+]?([1-8]?\d(\.\d+)?|90(\.0+)?),\s*[-+]?(180(\.0+)?|((1[0-7]\d)|([1-9]?\d))(\.\d+)?)$/;return b.test(a)},errorMessage:"",errorMessageKey:"badCustomVal"});var b=function(b){var c=[];return a.each(b,function(a,b){c.push(b.substr(0,1).toUpperCase()+b.substr(1,b.length))}),c.sort(),c};a.fn.sugges...

number then GCD algorithm provides affinity towards odd number erasure sets to provide for uniform distribution across nodes*. This is to ensure that same number of drives are pariticipating in any erasure set. For example if you have 2 nodes with 180 drives then GCD is 15 but this would lead to uneven distribution, one of the nodes would participate more drives. To avoid this the affinity is given towards nodes which leads to next best GCD factor of 12 which provides uniform distribution.

-...