2018-07-17 22:25:38 +00:00
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package scheduler
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import (
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"github.com/hashicorp/nomad/nomad/structs"
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)
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2018-07-26 15:32:06 +00:00
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const (
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2018-07-30 00:49:56 +00:00
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// implicitTarget is used to represent any remaining attribute values
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// when target percentages don't add up to 100
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implicitTarget = "*"
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)
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2018-07-17 22:25:38 +00:00
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// SpreadIterator is used to spread allocations across a specified attribute
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// according to preset weights
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type SpreadIterator struct {
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ctx Context
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source RankIterator
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job *structs.Job
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tg *structs.TaskGroup
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// jobSpreads is a slice of spread stored at the job level which apply
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// to all task groups
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jobSpreads []*structs.Spread
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2018-07-26 00:28:36 +00:00
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// tgSpreadInfo is a map per task group with precomputed
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// values for desired counts and weight
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tgSpreadInfo map[string]spreadAttributeMap
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2018-07-29 00:49:09 +00:00
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2018-07-26 00:28:36 +00:00
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// sumSpreadWeights tracks the total weight across all spread
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// stanzas
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sumSpreadWeights int32
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// hasSpread is used to early return when the job/task group
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// does not have spread configured
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hasSpread bool
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2018-07-26 00:28:36 +00:00
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// groupProperySets is a memoized map from task group to property sets.
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// existing allocs are computed once, and allocs from the plan are updated
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// when Reset is called
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groupPropertySets map[string][]*propertySet
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}
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type spreadAttributeMap map[string]*spreadInfo
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type spreadInfo struct {
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weight int8
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desiredCounts map[string]float64
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}
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func NewSpreadIterator(ctx Context, source RankIterator) *SpreadIterator {
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iter := &SpreadIterator{
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ctx: ctx,
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source: source,
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groupPropertySets: make(map[string][]*propertySet),
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tgSpreadInfo: make(map[string]spreadAttributeMap),
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}
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return iter
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}
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func (iter *SpreadIterator) Reset() {
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iter.source.Reset()
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for _, sets := range iter.groupPropertySets {
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for _, ps := range sets {
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ps.PopulateProposed()
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}
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}
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}
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func (iter *SpreadIterator) SetJob(job *structs.Job) {
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iter.job = job
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if job.Spreads != nil {
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iter.jobSpreads = job.Spreads
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}
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}
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func (iter *SpreadIterator) SetTaskGroup(tg *structs.TaskGroup) {
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iter.tg = tg
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// Build the property set at the taskgroup level
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if _, ok := iter.groupPropertySets[tg.Name]; !ok {
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// First add property sets that are at the job level for this task group
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for _, spread := range iter.jobSpreads {
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pset := NewPropertySet(iter.ctx, iter.job)
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pset.SetTargetAttribute(spread.Attribute, tg.Name)
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iter.groupPropertySets[tg.Name] = append(iter.groupPropertySets[tg.Name], pset)
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}
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// Include property sets at the task group level
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for _, spread := range tg.Spreads {
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pset := NewPropertySet(iter.ctx, iter.job)
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pset.SetTargetAttribute(spread.Attribute, tg.Name)
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iter.groupPropertySets[tg.Name] = append(iter.groupPropertySets[tg.Name], pset)
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}
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}
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2018-07-26 00:28:36 +00:00
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// Check if there are any spreads configured
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iter.hasSpread = len(iter.groupPropertySets[tg.Name]) != 0
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// Build tgSpreadInfo at the task group level
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if _, ok := iter.tgSpreadInfo[tg.Name]; !ok {
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iter.computeSpreadInfo(tg)
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}
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}
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func (iter *SpreadIterator) hasSpreads() bool {
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return iter.hasSpread
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}
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func (iter *SpreadIterator) Next() *RankedNode {
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for {
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option := iter.source.Next()
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// Hot path if there is nothing to check
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if option == nil || !iter.hasSpreads() {
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return option
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}
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tgName := iter.tg.Name
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propertySets := iter.groupPropertySets[tgName]
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// Iterate over each spread attribute's property set and add a weighted score
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totalSpreadScore := 0.0
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for _, pset := range propertySets {
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nValue, errorMsg, usedCount := pset.UsedCount(option.Node, tgName)
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2018-07-31 02:59:35 +00:00
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// Add one to include placement on this node in the scoring calculation
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usedCount += 1
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// Set score to -1 if there were errors in building this attribute
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if errorMsg != "" {
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iter.ctx.Logger().Named("spread").Warn("error building spread attributes for task group", "task_group", tgName, "error", errorMsg)
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totalSpreadScore -= 1.0
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continue
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}
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spreadAttributeMap := iter.tgSpreadInfo[tgName]
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spreadDetails := spreadAttributeMap[pset.targetAttribute]
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if len(spreadDetails.desiredCounts) == 0 {
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// When desired counts map is empty the user didn't specify any targets
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// Use even spreading scoring algorithm for this scenario
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scoreBoost := evenSpreadScoreBoost(pset, option.Node)
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totalSpreadScore += scoreBoost
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} else {
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// Get the desired count
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desiredCount, ok := spreadDetails.desiredCounts[nValue]
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if !ok {
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// See if there is an implicit target
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desiredCount, ok = spreadDetails.desiredCounts[implicitTarget]
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if !ok {
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// The desired count for this attribute is zero if it gets here
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// so use the maximum possible penalty for this node
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totalSpreadScore -= 1.0
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continue
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}
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}
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// Calculate the relative weight of this specific spread attribute
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spreadWeight := float64(spreadDetails.weight) / float64(iter.sumSpreadWeights)
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// Score Boost is proportional the difference between current and desired count
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// It is negative when the used count is greater than the desired count
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// It is multiplied with the spread weight to account for cases where the job has
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// more than one spread attribute
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scoreBoost := ((desiredCount - float64(usedCount)) / desiredCount) * spreadWeight
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totalSpreadScore += scoreBoost
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}
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}
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if totalSpreadScore != 0.0 {
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option.Scores = append(option.Scores, totalSpreadScore)
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iter.ctx.Metrics().ScoreNode(option.Node, "allocation-spread", totalSpreadScore)
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}
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return option
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}
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}
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2018-07-26 22:14:27 +00:00
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// evenSpreadScoreBoost is a scoring helper that calculates the score
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// for the option when even spread is desired (all attribute values get equal preference)
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func evenSpreadScoreBoost(pset *propertySet, option *structs.Node) float64 {
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combinedUseMap := pset.GetCombinedUseMap()
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if len(combinedUseMap) == 0 {
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// Nothing placed yet, so return 0 as the score
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return 0.0
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}
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// Get the nodes property value
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nValue, ok := getProperty(option, pset.targetAttribute)
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// Maximum possible penalty when the attribute isn't set on the node
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if !ok {
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return -1.0
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}
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currentAttributeCount := combinedUseMap[nValue]
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minCount := uint64(0)
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maxCount := uint64(0)
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for _, value := range combinedUseMap {
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if minCount == 0 || value < minCount {
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minCount = value
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2018-07-26 22:14:27 +00:00
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if maxCount == 0 || value > maxCount {
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maxCount = value
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}
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}
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2018-07-27 21:15:32 +00:00
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// calculate boost based on delta between the current and the minimum
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var deltaBoost float64
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if minCount == 0 {
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deltaBoost = -1.0
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} else {
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delta := int(minCount - currentAttributeCount)
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deltaBoost = float64(delta) / float64(minCount)
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}
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if currentAttributeCount != minCount {
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// Boost based on delta between current and min
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return deltaBoost
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} else if minCount == maxCount {
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// Maximum possible penalty when the distribution is even
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return -1.0
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} else if minCount == 0 {
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// Maximum possible boost
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return 1.0
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}
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2019-05-15 17:00:24 +00:00
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2018-07-31 02:59:35 +00:00
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// Penalty based on delta from max value
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delta := int(maxCount - minCount)
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deltaBoost = float64(delta) / float64(minCount)
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return deltaBoost
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2018-07-26 22:14:27 +00:00
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}
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2018-07-17 22:25:38 +00:00
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// computeSpreadInfo computes and stores percentages and total values
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// from all spreads that apply to a specific task group
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func (iter *SpreadIterator) computeSpreadInfo(tg *structs.TaskGroup) {
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spreadInfos := make(spreadAttributeMap, len(tg.Spreads))
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totalCount := tg.Count
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2018-07-18 15:53:37 +00:00
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// Always combine any spread stanzas defined at the job level here
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combinedSpreads := make([]*structs.Spread, 0, len(tg.Spreads)+len(iter.jobSpreads))
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combinedSpreads = append(combinedSpreads, tg.Spreads...)
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combinedSpreads = append(combinedSpreads, iter.jobSpreads...)
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for _, spread := range combinedSpreads {
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2018-07-26 15:32:06 +00:00
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si := &spreadInfo{weight: spread.Weight, desiredCounts: make(map[string]float64)}
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sumDesiredCounts := 0.0
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for _, st := range spread.SpreadTarget {
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desiredCount := (float64(st.Percent) / float64(100)) * float64(totalCount)
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si.desiredCounts[st.Value] = desiredCount
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sumDesiredCounts += desiredCount
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}
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// Account for remaining count only if there is any spread targets
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if sumDesiredCounts > 0 && sumDesiredCounts < float64(totalCount) {
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2018-07-26 15:32:06 +00:00
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remainingCount := float64(totalCount) - sumDesiredCounts
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si.desiredCounts[implicitTarget] = remainingCount
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}
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spreadInfos[spread.Attribute] = si
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iter.sumSpreadWeights += int32(spread.Weight)
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}
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iter.tgSpreadInfo[tg.Name] = spreadInfos
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}
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