cost-model/pkg/costmodel/costmodel.go

package costmodel

import (
	"fmt"
	"math"
	"regexp"
	"strconv"
	"strings"
	"time"

	costAnalyzerCloud "github.com/kubecost/cost-model/pkg/cloud"
	"github.com/kubecost/cost-model/pkg/clustercache"
	"github.com/kubecost/cost-model/pkg/env"
	"github.com/kubecost/cost-model/pkg/log"
	"github.com/kubecost/cost-model/pkg/prom"
	"github.com/kubecost/cost-model/pkg/thanos"
	"github.com/kubecost/cost-model/pkg/util"
	prometheusClient "github.com/prometheus/client_golang/api"
	v1 "k8s.io/api/core/v1"
	metav1 "k8s.io/apimachinery/pkg/apis/meta/v1"
	"k8s.io/apimachinery/pkg/labels"
	"k8s.io/client-go/kubernetes"
	"k8s.io/klog"

	"github.com/google/uuid"
	"golang.org/x/sync/singleflight"
)

const (
	statusAPIError = 422

	profileThreshold = 1000 * 1000 * 1000 // 1s (in ns)

	apiPrefix         = "/api/v1"
	epAlertManagers   = apiPrefix + "/alertmanagers"
	epLabelValues     = apiPrefix + "/label/:name/values"
	epSeries          = apiPrefix + "/series"
	epTargets         = apiPrefix + "/targets"
	epSnapshot        = apiPrefix + "/admin/tsdb/snapshot"
	epDeleteSeries    = apiPrefix + "/admin/tsdb/delete_series"
	epCleanTombstones = apiPrefix + "/admin/tsdb/clean_tombstones"
	epConfig          = apiPrefix + "/status/config"
	epFlags           = apiPrefix + "/status/flags"
)

// isCron matches a CronJob name and captures the non-timestamp name
var isCron = regexp.MustCompile(`^(.+)-\d{10}$`)

type CostModel struct {
	Cache        clustercache.ClusterCache
	RequestGroup *singleflight.Group
}

func NewCostModel(cache clustercache.ClusterCache) *CostModel {
	// request grouping to prevent over-requesting the same data prior to caching
	requestGroup := new(singleflight.Group)

	return &CostModel{
		Cache:        cache,
		RequestGroup: requestGroup,
	}
}

type CostData struct {
	Name            string                       `json:"name,omitempty"`
	PodName         string                       `json:"podName,omitempty"`
	NodeName        string                       `json:"nodeName,omitempty"`
	NodeData        *costAnalyzerCloud.Node      `json:"node,omitempty"`
	Namespace       string                       `json:"namespace,omitempty"`
	Deployments     []string                     `json:"deployments,omitempty"`
	Services        []string                     `json:"services,omitempty"`
	Daemonsets      []string                     `json:"daemonsets,omitempty"`
	Statefulsets    []string                     `json:"statefulsets,omitempty"`
	Jobs            []string                     `json:"jobs,omitempty"`
	RAMReq          []*util.Vector               `json:"ramreq,omitempty"`
	RAMUsed         []*util.Vector               `json:"ramused,omitempty"`
	RAMAllocation   []*util.Vector               `json:"ramallocated,omitempty"`
	CPUReq          []*util.Vector               `json:"cpureq,omitempty"`
	CPUUsed         []*util.Vector               `json:"cpuused,omitempty"`
	CPUAllocation   []*util.Vector               `json:"cpuallocated,omitempty"`
	GPUReq          []*util.Vector               `json:"gpureq,omitempty"`
	PVCData         []*PersistentVolumeClaimData `json:"pvcData,omitempty"`
	NetworkData     []*util.Vector               `json:"network,omitempty"`
	Labels          map[string]string            `json:"labels,omitempty"`
	NamespaceLabels map[string]string            `json:"namespaceLabels,omitempty"`
	ClusterID       string                       `json:"clusterId"`
}

func (cd *CostData) String() string {
	return fmt.Sprintf("\n\tName: %s; PodName: %s, NodeName: %s\n\tNamespace: %s\n\tDeployments: %s\n\tServices: %s\n\tCPU (req, used, alloc): %d, %d, %d\n\tRAM (req, used, alloc): %d, %d, %d",
		cd.Name, cd.PodName, cd.NodeName, cd.Namespace, strings.Join(cd.Deployments, ", "), strings.Join(cd.Services, ", "),
		len(cd.CPUReq), len(cd.CPUUsed), len(cd.CPUAllocation),
		len(cd.RAMReq), len(cd.RAMUsed), len(cd.RAMAllocation))
}

func (cd *CostData) GetController() (name string, kind string, hasController bool) {
	hasController = false

	if len(cd.Deployments) > 0 {
		name = cd.Deployments[0]
		kind = "deployment"
		hasController = true
	} else if len(cd.Statefulsets) > 0 {
		name = cd.Statefulsets[0]
		kind = "statefulset"
		hasController = true
	} else if len(cd.Daemonsets) > 0 {
		name = cd.Daemonsets[0]
		kind = "daemonset"
		hasController = true
	} else if len(cd.Jobs) > 0 {
		name = cd.Jobs[0]
		kind = "job"
		hasController = true

		match := isCron.FindStringSubmatch(name)
		if match != nil {
			name = match[1]
		}
	}

	return name, kind, hasController
}

const (
	queryRAMRequestsStr = `avg(
		label_replace(
			label_replace(
				avg(
					count_over_time(kube_pod_container_resource_requests_memory_bytes{container!="",container!="POD", node!=""}[%s] %s) 
					*  
					avg_over_time(kube_pod_container_resource_requests_memory_bytes{container!="",container!="POD", node!=""}[%s] %s)
				) by (namespace,container,pod,node,cluster_id) , "container_name","$1","container","(.+)"
			), "pod_name","$1","pod","(.+)"
		)
	) by (namespace,container_name,pod_name,node,cluster_id)`
	queryRAMUsageStr = `sort_desc(
		avg(
			label_replace(count_over_time(container_memory_working_set_bytes{container_name!="",container_name!="POD", instance!=""}[%s] %s), "node", "$1", "instance","(.+)") 
			* 
			label_replace(avg_over_time(container_memory_working_set_bytes{container_name!="",container_name!="POD", instance!=""}[%s] %s), "node", "$1", "instance","(.+)") 
		) by (namespace,container_name,pod_name,node,cluster_id)
	)`
	queryCPURequestsStr = `avg(
		label_replace(
			label_replace(
				avg(
					count_over_time(kube_pod_container_resource_requests_cpu_cores{container!="",container!="POD", node!=""}[%s] %s) 
					*  
					avg_over_time(kube_pod_container_resource_requests_cpu_cores{container!="",container!="POD", node!=""}[%s] %s)
				) by (namespace,container,pod,node,cluster_id) , "container_name","$1","container","(.+)"
			), "pod_name","$1","pod","(.+)"
		) 
	) by (namespace,container_name,pod_name,node,cluster_id)`
	queryCPUUsageStr = `avg(
		label_replace(
		rate( 
			container_cpu_usage_seconds_total{container_name!="",container_name!="POD",instance!=""}[%s] %s
		) , "node", "$1", "instance", "(.+)"
		)
	) by (namespace,container_name,pod_name,node,cluster_id)`
	queryGPURequestsStr = `avg(
		label_replace(
			label_replace(
				avg(
					count_over_time(kube_pod_container_resource_requests{resource="nvidia_com_gpu", container!="",container!="POD", node!=""}[%s] %s) 
					*  
					avg_over_time(kube_pod_container_resource_requests{resource="nvidia_com_gpu", container!="",container!="POD", node!=""}[%s] %s)
					* %f
				) by (namespace,container,pod,node,cluster_id) , "container_name","$1","container","(.+)"
			), "pod_name","$1","pod","(.+)"
		) 
	) by (namespace,container_name,pod_name,node,cluster_id) 
	* on (pod_name, namespace, cluster_id) group_left(container) label_replace(avg(avg_over_time(kube_pod_status_phase{phase="Running"}[%s] %s)) by (pod,namespace,cluster_id), "pod_name","$1","pod","(.+)")`
	queryPVRequestsStr = `avg(avg(kube_persistentvolumeclaim_info) by (persistentvolumeclaim, storageclass, namespace, volumename, cluster_id) 
	* 
	on (persistentvolumeclaim, namespace, cluster_id) group_right(storageclass, volumename) 
	sum(kube_persistentvolumeclaim_resource_requests_storage_bytes) by (persistentvolumeclaim, namespace, cluster_id, kubernetes_name)) by (persistentvolumeclaim, storageclass, namespace, volumename, cluster_id)`
	// queryRAMAllocationByteHours yields the total byte-hour RAM allocation over the given
	// window, aggregated by container.
	//  [line 3]     sum_over_time(each byte*min in window) / (min/hr kubecost up) = [byte*hour] by metric, adjusted for kubecost downtime
	//  [lines 2,4]  sum(") by unique container key = [byte*hour] by container
	//  [lines 1,5]  relabeling
	queryRAMAllocationByteHours = `
		label_replace(label_replace(
			sum(
				sum_over_time(container_memory_allocation_bytes{container!="",container!="POD", node!=""}[%s:1m]) / %f 
			) by (namespace,container,pod,node,cluster_id)
		, "container_name","$1","container","(.+)"), "pod_name","$1","pod","(.+)")`
	// queryCPUAllocationVCPUHours yields the total VCPU-hour CPU allocation over the given
	// window, aggregated by container.
	//  [line 3]     sum_over_time(each VCPU*mins in window) / (min/hr kubecost up) = [VCPU*hour] by metric, adjusted for kubecost downtime
	//  [lines 2,4]  sum(") by unique container key = [VCPU*hour] by container
	//  [lines 1,5]  relabeling
	queryCPUAllocationVCPUHours = `
		label_replace(label_replace(
			sum(
				sum_over_time(container_cpu_allocation{container!="",container!="POD", node!=""}[%s:1m]) / %f
			) by (namespace,container,pod,node,cluster_id)
		, "container_name","$1","container","(.+)"), "pod_name","$1","pod","(.+)")`
	// queryPVCAllocationFmt yields the total byte-hour PVC allocation over the given window.
	//  sum(all VCPU measurements within given window) = [byte*min] by metric
	//  (") / 60 = [byte*hour] by metric, assuming no missed scrapes
	//  (") * (normalization factor) = [byte*hour] by metric, normalized for missed scrapes
	//  sum(") by unique pvc = [VCPU*hour] by (cluster, namespace, pod, pv, pvc)
	// Note: normalization factor is 1.0 if no scrapes are missed and has an upper bound determined by minExpectedScrapeRate
	// so that coarse resolutions don't push normalization factors too high; e.g. 24h resolution with 1h of data would make
	// for a normalization factor of 24. With a minimumExpectedScrapeRate of 0.95, that caps the norm factor at
	queryPVCAllocationFmt = `sum(sum_over_time(pod_pvc_allocation[%s:1m])) by (cluster_id, namespace, pod, persistentvolume, persistentvolumeclaim) / 60
		* 60 / clamp_min(count_over_time(sum(pod_pvc_allocation) by (cluster_id, namespace, pod, persistentvolume, persistentvolumeclaim)[%s:1m])/%f, 60 * %f)`
	queryPVHourlyCostFmt      = `avg_over_time(pv_hourly_cost[%s])`
	queryNSLabels             = `avg_over_time(kube_namespace_labels[%s])`
	queryPodLabels            = `avg_over_time(kube_pod_labels[%s])`
	queryDeploymentLabels     = `avg_over_time(deployment_match_labels[%s])`
	queryStatefulsetLabels    = `avg_over_time(statefulSet_match_labels[%s])`
	queryPodDaemonsets        = `sum(kube_pod_owner{owner_kind="DaemonSet"}) by (namespace,pod,owner_name,cluster_id)`
	queryPodJobs              = `sum(kube_pod_owner{owner_kind="Job"}) by (namespace,pod,owner_name,cluster_id)`
	queryServiceLabels        = `avg_over_time(service_selector_labels[%s])`
	queryZoneNetworkUsage     = `sum(increase(kubecost_pod_network_egress_bytes_total{internet="false", sameZone="false", sameRegion="true"}[%s] %s)) by (namespace,pod_name,cluster_id) / 1024 / 1024 / 1024`
	queryRegionNetworkUsage   = `sum(increase(kubecost_pod_network_egress_bytes_total{internet="false", sameZone="false", sameRegion="false"}[%s] %s)) by (namespace,pod_name,cluster_id) / 1024 / 1024 / 1024`
	queryInternetNetworkUsage = `sum(increase(kubecost_pod_network_egress_bytes_total{internet="true"}[%s] %s)) by (namespace,pod_name,cluster_id) / 1024 / 1024 / 1024`
	normalizationStr          = `max(count_over_time(kube_pod_container_resource_requests_memory_bytes{}[%s] %s))`
	kubecostUpMinsPerHourStr  = `max(count_over_time(node_cpu_hourly_cost[%s:1m])) / %f`
)

type PrometheusMetadata struct {
	Running            bool `json:"running"`
	KubecostDataExists bool `json:"kubecostDataExists"`
}

// ValidatePrometheus tells the model what data prometheus has on it.
func ValidatePrometheus(cli prometheusClient.Client, isThanos bool) (*PrometheusMetadata, error) {
	q := "up"
	if isThanos {
		q += thanos.QueryOffset()
	}

	ctx := prom.NewContext(cli)

	resUp, err := ctx.QuerySync(q)
	if err != nil {
		return &PrometheusMetadata{
			Running:            false,
			KubecostDataExists: false,
		}, err
	}

	if len(resUp) == 0 {
		return &PrometheusMetadata{
			Running:            false,
			KubecostDataExists: false,
		}, fmt.Errorf("no running jobs on Prometheus at %s", ctx.QueryURL().Path)
	}

	for _, result := range resUp {
		job, err := result.GetString("job")
		if err != nil {
			return &PrometheusMetadata{
				Running:            false,
				KubecostDataExists: false,
			}, fmt.Errorf("up query does not have job names")
		}

		if job == "kubecost" {
			return &PrometheusMetadata{
				Running:            true,
				KubecostDataExists: true,
			}, err
		}
	}

	return &PrometheusMetadata{
		Running:            true,
		KubecostDataExists: false,
	}, nil
}

func (cm *CostModel) ComputeCostData(cli prometheusClient.Client, clientset kubernetes.Interface, cp costAnalyzerCloud.Provider, window string, offset string, filterNamespace string) (map[string]*CostData, error) {
	queryRAMRequests := fmt.Sprintf(queryRAMRequestsStr, window, offset, window, offset)
	queryRAMUsage := fmt.Sprintf(queryRAMUsageStr, window, offset, window, offset)
	queryCPURequests := fmt.Sprintf(queryCPURequestsStr, window, offset, window, offset)
	queryCPUUsage := fmt.Sprintf(queryCPUUsageStr, window, offset)
	queryGPURequests := fmt.Sprintf(queryGPURequestsStr, window, offset, window, offset, 1.0, window, offset)
	queryPVRequests := fmt.Sprintf(queryPVRequestsStr)
	queryNetZoneRequests := fmt.Sprintf(queryZoneNetworkUsage, window, "")
	queryNetRegionRequests := fmt.Sprintf(queryRegionNetworkUsage, window, "")
	queryNetInternetRequests := fmt.Sprintf(queryInternetNetworkUsage, window, "")
	queryNormalization := fmt.Sprintf(normalizationStr, window, offset)

	// Cluster ID is specific to the source cluster
	clusterID := env.GetClusterID()

	// Submit all Prometheus queries asynchronously
	ctx := prom.NewContext(cli)
	resChRAMRequests := ctx.Query(queryRAMRequests)
	resChRAMUsage := ctx.Query(queryRAMUsage)
	resChCPURequests := ctx.Query(queryCPURequests)
	resChCPUUsage := ctx.Query(queryCPUUsage)
	resChGPURequests := ctx.Query(queryGPURequests)
	resChPVRequests := ctx.Query(queryPVRequests)
	resChNetZoneRequests := ctx.Query(queryNetZoneRequests)
	resChNetRegionRequests := ctx.Query(queryNetRegionRequests)
	resChNetInternetRequests := ctx.Query(queryNetInternetRequests)
	resChNormalization := ctx.Query(queryNormalization)

	// Pull pod information from k8s API
	podlist := cm.Cache.GetAllPods()

	podDeploymentsMapping, err := getPodDeployments(cm.Cache, podlist, clusterID)
	if err != nil {
		return nil, err
	}

	podServicesMapping, err := getPodServices(cm.Cache, podlist, clusterID)
	if err != nil {
		return nil, err
	}

	namespaceLabelsMapping, err := getNamespaceLabels(cm.Cache, clusterID)
	if err != nil {
		return nil, err
	}

	// Process Prometheus query results. Handle errors using ctx.Errors.
	resRAMRequests, _ := resChRAMRequests.Await()
	resRAMUsage, _ := resChRAMUsage.Await()
	resCPURequests, _ := resChCPURequests.Await()
	resCPUUsage, _ := resChCPUUsage.Await()
	resGPURequests, _ := resChGPURequests.Await()
	resPVRequests, _ := resChPVRequests.Await()
	resNetZoneRequests, _ := resChNetZoneRequests.Await()
	resNetRegionRequests, _ := resChNetRegionRequests.Await()
	resNetInternetRequests, _ := resChNetInternetRequests.Await()
	resNormalization, _ := resChNormalization.Await()

	if ctx.HasErrors() {
		for _, promErr := range ctx.Errors() {
			log.Errorf("ComputeCostData: Prometheus error: %s", promErr.Error())
		}

		// TODO: Categorize fatal prometheus query failures
		// return nil, fmt.Errorf("Error querying prometheus: %s", promErr.Error())
	}

	defer measureTime(time.Now(), profileThreshold, "ComputeCostData: Processing Query Data")

	normalizationValue, err := getNormalization(resNormalization)
	if err != nil {
		return nil, fmt.Errorf("Error parsing normalization values from %s: %s", queryNormalization, err.Error())
	}

	nodes, err := cm.GetNodeCost(cp)
	if err != nil {
		log.Warningf("GetNodeCost: no node cost model available: " + err.Error())
		return nil, err
	}

	// Unmounted PVs represent the PVs that are not mounted or tied to a volume on a container
	unmountedPVs := make(map[string][]*PersistentVolumeClaimData)
	pvClaimMapping, err := GetPVInfo(resPVRequests, clusterID)
	if err != nil {
		log.Warningf("GetPVInfo: unable to get PV data: %s", err.Error())
	}
	if pvClaimMapping != nil {
		err = addPVData(cm.Cache, pvClaimMapping, cp)
		if err != nil {
			return nil, err
		}
		// copy claim mappings into zombies, then remove as they're discovered
		for k, v := range pvClaimMapping {
			unmountedPVs[k] = []*PersistentVolumeClaimData{v}
		}
	}

	networkUsageMap, err := GetNetworkUsageData(resNetZoneRequests, resNetRegionRequests, resNetInternetRequests, clusterID)
	if err != nil {
		klog.V(1).Infof("[Warning] Unable to get Network Cost Data: %s", err.Error())
		networkUsageMap = make(map[string]*NetworkUsageData)
	}

	containerNameCost := make(map[string]*CostData)
	containers := make(map[string]bool)

	RAMReqMap, err := GetContainerMetricVector(resRAMRequests, true, normalizationValue, clusterID)
	if err != nil {
		return nil, err
	}
	for key := range RAMReqMap {
		containers[key] = true
	}

	RAMUsedMap, err := GetContainerMetricVector(resRAMUsage, true, normalizationValue, clusterID)
	if err != nil {
		return nil, err
	}
	for key := range RAMUsedMap {
		containers[key] = true
	}
	CPUReqMap, err := GetContainerMetricVector(resCPURequests, true, normalizationValue, clusterID)
	if err != nil {
		return nil, err
	}
	for key := range CPUReqMap {
		containers[key] = true
	}
	GPUReqMap, err := GetContainerMetricVector(resGPURequests, true, normalizationValue, clusterID)
	if err != nil {
		return nil, err
	}
	for key := range GPUReqMap {
		containers[key] = true
	}
	CPUUsedMap, err := GetContainerMetricVector(resCPUUsage, false, 0, clusterID) // No need to normalize here, as this comes from a counter
	if err != nil {
		return nil, err
	}
	for key := range CPUUsedMap {
		containers[key] = true
	}
	currentContainers := make(map[string]v1.Pod)
	for _, pod := range podlist {
		if pod.Status.Phase != v1.PodRunning {
			continue
		}
		cs, err := NewContainerMetricsFromPod(pod, clusterID)
		if err != nil {
			return nil, err
		}
		for _, c := range cs {
			containers[c.Key()] = true // captures any containers that existed for a time < a prometheus scrape interval. We currently charge 0 for this but should charge something.
			currentContainers[c.Key()] = *pod
		}
	}
	missingNodes := make(map[string]*costAnalyzerCloud.Node)
	missingContainers := make(map[string]*CostData)
	for key := range containers {
		if _, ok := containerNameCost[key]; ok {
			continue // because ordering is important for the allocation model (all PV's applied to the first), just dedupe if it's already been added.
		}
		if pod, ok := currentContainers[key]; ok {
			podName := pod.GetObjectMeta().GetName()
			ns := pod.GetObjectMeta().GetNamespace()

			nsLabels := namespaceLabelsMapping[ns+","+clusterID]
			podLabels := pod.GetObjectMeta().GetLabels()
			if podLabels == nil {
				podLabels = make(map[string]string)
			}

			for k, v := range nsLabels {
				podLabels[k] = v
			}

			nodeName := pod.Spec.NodeName
			var nodeData *costAnalyzerCloud.Node
			if _, ok := nodes[nodeName]; ok {
				nodeData = nodes[nodeName]
			}

			nsKey := ns + "," + clusterID

			var podDeployments []string
			if _, ok := podDeploymentsMapping[nsKey]; ok {
				if ds, ok := podDeploymentsMapping[nsKey][pod.GetObjectMeta().GetName()]; ok {
					podDeployments = ds
				} else {
					podDeployments = []string{}
				}
			}

			var podPVs []*PersistentVolumeClaimData
			podClaims := pod.Spec.Volumes
			for _, vol := range podClaims {
				if vol.PersistentVolumeClaim != nil {
					name := vol.PersistentVolumeClaim.ClaimName
					key := ns + "," + name + "," + clusterID
					if pvClaim, ok := pvClaimMapping[key]; ok {
						pvClaim.TimesClaimed++
						podPVs = append(podPVs, pvClaim)

						// Remove entry from potential unmounted pvs
						delete(unmountedPVs, key)
					}
				}
			}

			var podNetCosts []*util.Vector
			if usage, ok := networkUsageMap[ns+","+podName+","+clusterID]; ok {
				netCosts, err := GetNetworkCost(usage, cp)
				if err != nil {
					klog.V(4).Infof("Error pulling network costs: %s", err.Error())
				} else {
					podNetCosts = netCosts
				}
			}

			var podServices []string
			if _, ok := podServicesMapping[nsKey]; ok {
				if svcs, ok := podServicesMapping[nsKey][pod.GetObjectMeta().GetName()]; ok {
					podServices = svcs
				} else {
					podServices = []string{}
				}
			}

			for i, container := range pod.Spec.Containers {
				containerName := container.Name

				// recreate the key and look up data for this container
				newKey := NewContainerMetricFromValues(ns, podName, containerName, pod.Spec.NodeName, clusterID).Key()

				RAMReqV, ok := RAMReqMap[newKey]
				if !ok {
					klog.V(4).Info("no RAM requests for " + newKey)
					RAMReqV = []*util.Vector{&util.Vector{}}
				}
				RAMUsedV, ok := RAMUsedMap[newKey]
				if !ok {
					klog.V(4).Info("no RAM usage for " + newKey)
					RAMUsedV = []*util.Vector{&util.Vector{}}
				}
				CPUReqV, ok := CPUReqMap[newKey]
				if !ok {
					klog.V(4).Info("no CPU requests for " + newKey)
					CPUReqV = []*util.Vector{&util.Vector{}}
				}
				GPUReqV, ok := GPUReqMap[newKey]
				if !ok {
					klog.V(4).Info("no GPU requests for " + newKey)
					GPUReqV = []*util.Vector{&util.Vector{}}
				}
				CPUUsedV, ok := CPUUsedMap[newKey]
				if !ok {
					klog.V(4).Info("no CPU usage for " + newKey)
					CPUUsedV = []*util.Vector{&util.Vector{}}
				}

				var pvReq []*PersistentVolumeClaimData
				var netReq []*util.Vector
				if i == 0 { // avoid duplicating by just assigning all claims to the first container.
					pvReq = podPVs
					netReq = podNetCosts
				}

				costs := &CostData{
					Name:            containerName,
					PodName:         podName,
					NodeName:        nodeName,
					Namespace:       ns,
					Deployments:     podDeployments,
					Services:        podServices,
					Daemonsets:      getDaemonsetsOfPod(pod),
					Jobs:            getJobsOfPod(pod),
					Statefulsets:    getStatefulSetsOfPod(pod),
					NodeData:        nodeData,
					RAMReq:          RAMReqV,
					RAMUsed:         RAMUsedV,
					CPUReq:          CPUReqV,
					CPUUsed:         CPUUsedV,
					GPUReq:          GPUReqV,
					PVCData:         pvReq,
					NetworkData:     netReq,
					Labels:          podLabels,
					NamespaceLabels: nsLabels,
					ClusterID:       clusterID,
				}
				costs.CPUAllocation = getContainerAllocation(costs.CPUReq, costs.CPUUsed, "CPU")
				costs.RAMAllocation = getContainerAllocation(costs.RAMReq, costs.RAMUsed, "RAM")
				if filterNamespace == "" {
					containerNameCost[newKey] = costs
				} else if costs.Namespace == filterNamespace {
					containerNameCost[newKey] = costs
				}
			}
		} else {
			// The container has been deleted. Not all information is sent to prometheus via ksm, so fill out what we can without k8s api
			klog.V(4).Info("The container " + key + " has been deleted. Calculating allocation but resulting object will be missing data.")
			c, err := NewContainerMetricFromKey(key)
			if err != nil {
				return nil, err
			}
			RAMReqV, ok := RAMReqMap[key]
			if !ok {
				klog.V(4).Info("no RAM requests for " + key)
				RAMReqV = []*util.Vector{&util.Vector{}}
			}
			RAMUsedV, ok := RAMUsedMap[key]
			if !ok {
				klog.V(4).Info("no RAM usage for " + key)
				RAMUsedV = []*util.Vector{&util.Vector{}}
			}
			CPUReqV, ok := CPUReqMap[key]
			if !ok {
				klog.V(4).Info("no CPU requests for " + key)
				CPUReqV = []*util.Vector{&util.Vector{}}
			}
			GPUReqV, ok := GPUReqMap[key]
			if !ok {
				klog.V(4).Info("no GPU requests for " + key)
				GPUReqV = []*util.Vector{&util.Vector{}}
			}
			CPUUsedV, ok := CPUUsedMap[key]
			if !ok {
				klog.V(4).Info("no CPU usage for " + key)
				CPUUsedV = []*util.Vector{&util.Vector{}}
			}

			node, ok := nodes[c.NodeName]
			if !ok {
				klog.V(4).Infof("Node \"%s\" has been deleted from Kubernetes. Query historical data to get it.", c.NodeName)
				if n, ok := missingNodes[c.NodeName]; ok {
					node = n
				} else {
					node = &costAnalyzerCloud.Node{}
					missingNodes[c.NodeName] = node
				}
			}
			namespacelabels, ok := namespaceLabelsMapping[c.Namespace+","+c.ClusterID]
			if !ok {
				klog.V(3).Infof("Missing data for namespace %s", c.Namespace)
			}

			costs := &CostData{
				Name:            c.ContainerName,
				PodName:         c.PodName,
				NodeName:        c.NodeName,
				NodeData:        node,
				Namespace:       c.Namespace,
				RAMReq:          RAMReqV,
				RAMUsed:         RAMUsedV,
				CPUReq:          CPUReqV,
				CPUUsed:         CPUUsedV,
				GPUReq:          GPUReqV,
				NamespaceLabels: namespacelabels,
				ClusterID:       c.ClusterID,
			}
			costs.CPUAllocation = getContainerAllocation(costs.CPUReq, costs.CPUUsed, "CPU")
			costs.RAMAllocation = getContainerAllocation(costs.RAMReq, costs.RAMUsed, "RAM")
			if filterNamespace == "" {
				containerNameCost[key] = costs
				missingContainers[key] = costs
			} else if costs.Namespace == filterNamespace {
				containerNameCost[key] = costs
				missingContainers[key] = costs
			}
		}
	}
	// Use unmounted pvs to create a mapping of "Unmounted-<Namespace>" containers
	// to pass along the cost data
	unmounted := findUnmountedPVCostData(unmountedPVs, namespaceLabelsMapping)
	for k, costs := range unmounted {
		klog.V(4).Infof("Unmounted PVs in Namespace/ClusterID: %s/%s", costs.Namespace, costs.ClusterID)

		if filterNamespace == "" {
			containerNameCost[k] = costs
		} else if costs.Namespace == filterNamespace {
			containerNameCost[k] = costs
		}
	}

	err = findDeletedNodeInfo(cli, missingNodes, window)
	if err != nil {
		klog.V(1).Infof("Error fetching historical node data: %s", err.Error())
	}

	err = findDeletedPodInfo(cli, missingContainers, window)
	if err != nil {
		klog.V(1).Infof("Error fetching historical pod data: %s", err.Error())
	}
	return containerNameCost, err
}

func findUnmountedPVCostData(unmountedPVs map[string][]*PersistentVolumeClaimData, namespaceLabelsMapping map[string]map[string]string) map[string]*CostData {
	costs := make(map[string]*CostData)
	if len(unmountedPVs) == 0 {
		return costs
	}

	for k, pv := range unmountedPVs {
		keyParts := strings.Split(k, ",")
		if len(keyParts) != 3 {
			klog.V(1).Infof("Unmounted PV used key with incorrect parts: %s", k)
			continue
		}

		ns, _, clusterID := keyParts[0], keyParts[1], keyParts[2]

		namespacelabels, ok := namespaceLabelsMapping[ns+","+clusterID]
		if !ok {
			klog.V(3).Infof("Missing data for namespace %s", ns)
		}

		// Should be a unique "Unmounted" cost data type
		name := "unmounted-pvs"

		metric := NewContainerMetricFromValues(ns, name, name, "", clusterID)
		key := metric.Key()

		if costData, ok := costs[key]; !ok {
			costs[key] = &CostData{
				Name:            name,
				PodName:         name,
				NodeName:        "",
				Namespace:       ns,
				NamespaceLabels: namespacelabels,
				Labels:          namespacelabels,
				ClusterID:       clusterID,
				PVCData:         pv,
			}
		} else {
			costData.PVCData = append(costData.PVCData, pv...)
		}
	}

	return costs
}

func findDeletedPodInfo(cli prometheusClient.Client, missingContainers map[string]*CostData, window string) error {
	if len(missingContainers) > 0 {
		queryHistoricalPodLabels := fmt.Sprintf(`kube_pod_labels{}[%s]`, window)

		podLabelsResult, err := prom.NewContext(cli).QuerySync(queryHistoricalPodLabels)
		if err != nil {
			log.Errorf("failed to parse historical pod labels: %s", err.Error())
		}
		podLabels := make(map[string]map[string]string)
		if podLabelsResult != nil {
			podLabels, err = parsePodLabels(podLabelsResult)
			if err != nil {
				log.Errorf("failed to parse historical pod labels: %s", err.Error())
			}
		}
		for key, costData := range missingContainers {
			cm, _ := NewContainerMetricFromKey(key)
			labels, ok := podLabels[cm.PodName]
			if !ok {
				log.Errorf("unable to find historical data for pod '%s'", cm.PodName)
				labels = make(map[string]string)
			}
			for k, v := range costData.NamespaceLabels {
				labels[k] = v
			}
			costData.Labels = labels
		}
	}

	return nil
}

func findDeletedNodeInfo(cli prometheusClient.Client, missingNodes map[string]*costAnalyzerCloud.Node, window string) error {
	if len(missingNodes) > 0 {
		defer measureTime(time.Now(), profileThreshold, "Finding Deleted Node Info")

		queryHistoricalCPUCost := fmt.Sprintf(`avg_over_time(node_cpu_hourly_cost[%s])`, window)
		queryHistoricalRAMCost := fmt.Sprintf(`avg_over_time(node_ram_hourly_cost[%s])`, window)
		queryHistoricalGPUCost := fmt.Sprintf(`avg_over_time(node_gpu_hourly_cost[%s])`, window)

		ctx := prom.NewContext(cli)
		cpuCostResCh := ctx.Query(queryHistoricalCPUCost)
		ramCostResCh := ctx.Query(queryHistoricalRAMCost)
		gpuCostResCh := ctx.Query(queryHistoricalGPUCost)

		cpuCostRes, _ := cpuCostResCh.Await()
		ramCostRes, _ := ramCostResCh.Await()
		gpuCostRes, _ := gpuCostResCh.Await()
		if ctx.HasErrors() {
			return ctx.Errors()[0]
		}

		cpuCosts, err := getCost(cpuCostRes)
		if err != nil {
			return err
		}
		ramCosts, err := getCost(ramCostRes)
		if err != nil {
			return err
		}
		gpuCosts, err := getCost(gpuCostRes)
		if err != nil {
			return err
		}

		if len(cpuCosts) == 0 {
			klog.V(1).Infof("Kubecost prometheus metrics not currently available. Ingest this server's /metrics endpoint to get that data.")
		}

		for node, costv := range cpuCosts {
			if _, ok := missingNodes[node]; ok {
				missingNodes[node].VCPUCost = fmt.Sprintf("%f", costv[0].Value)
			}
		}
		for node, costv := range ramCosts {
			if _, ok := missingNodes[node]; ok {
				missingNodes[node].RAMCost = fmt.Sprintf("%f", costv[0].Value)
			}
		}
		for node, costv := range gpuCosts {
			if _, ok := missingNodes[node]; ok {
				missingNodes[node].GPUCost = fmt.Sprintf("%f", costv[0].Value)
			}
		}

	}
	return nil
}

func getContainerAllocation(req []*util.Vector, used []*util.Vector, allocationType string) []*util.Vector {
	// The result of the normalize operation will be a new []*util.Vector to replace the requests
	allocationOp := func(r *util.Vector, x *float64, y *float64) bool {
		if x != nil && y != nil {
			x1 := *x
			if math.IsNaN(x1) {
				klog.V(1).Infof("[Warning] NaN value found during %s allocation calculation for requests.", allocationType)
				x1 = 0.0
			}
			y1 := *y
			if math.IsNaN(y1) {
				klog.V(1).Infof("[Warning] NaN value found during %s allocation calculation for used.", allocationType)
				y1 = 0.0
			}

			r.Value = math.Max(x1, y1)
		} else if x != nil {
			r.Value = *x
		} else if y != nil {
			r.Value = *y
		}

		return true
	}

	return util.ApplyVectorOp(req, used, allocationOp)
}

func addPVData(cache clustercache.ClusterCache, pvClaimMapping map[string]*PersistentVolumeClaimData, cloud costAnalyzerCloud.Provider) error {
	cfg, err := cloud.GetConfig()
	if err != nil {
		return err
	}
	// Pull a region from the first node
	var defaultRegion string
	nodeList := cache.GetAllNodes()
	if len(nodeList) > 0 {
		defaultRegion = nodeList[0].Labels[v1.LabelZoneRegion]
	}

	storageClasses := cache.GetAllStorageClasses()
	storageClassMap := make(map[string]map[string]string)
	for _, storageClass := range storageClasses {
		params := storageClass.Parameters
		storageClassMap[storageClass.ObjectMeta.Name] = params
		if storageClass.GetAnnotations()["storageclass.kubernetes.io/is-default-class"] == "true" || storageClass.GetAnnotations()["storageclass.beta.kubernetes.io/is-default-class"] == "true" {
			storageClassMap["default"] = params
			storageClassMap[""] = params
		}
	}

	pvs := cache.GetAllPersistentVolumes()
	pvMap := make(map[string]*costAnalyzerCloud.PV)
	for _, pv := range pvs {
		parameters, ok := storageClassMap[pv.Spec.StorageClassName]
		if !ok {
			klog.V(4).Infof("Unable to find parameters for storage class \"%s\". Does pv \"%s\" have a storageClassName?", pv.Spec.StorageClassName, pv.Name)
		}
		var region string
		if r, ok := pv.Labels[v1.LabelZoneRegion]; ok {
			region = r
		} else {
			region = defaultRegion
		}

		cacPv := &costAnalyzerCloud.PV{
			Class:      pv.Spec.StorageClassName,
			Region:     region,
			Parameters: parameters,
		}
		err := GetPVCost(cacPv, pv, cloud, region)
		if err != nil {
			return err
		}
		pvMap[pv.Name] = cacPv
	}

	for _, pvc := range pvClaimMapping {
		if vol, ok := pvMap[pvc.VolumeName]; ok {
			pvc.Volume = vol
		} else {
			klog.V(4).Infof("PV not found, using default")
			pvc.Volume = &costAnalyzerCloud.PV{
				Cost: cfg.Storage,
			}
		}
	}

	return nil
}

func GetPVCost(pv *costAnalyzerCloud.PV, kpv *v1.PersistentVolume, cp costAnalyzerCloud.Provider, defaultRegion string) error {
	cfg, err := cp.GetConfig()
	if err != nil {
		return err
	}
	key := cp.GetPVKey(kpv, pv.Parameters, defaultRegion)
	pvWithCost, err := cp.PVPricing(key)
	if err != nil {
		pv.Cost = cfg.Storage
		return err
	}
	if pvWithCost == nil || pvWithCost.Cost == "" {
		pv.Cost = cfg.Storage
		return nil // set default cost
	}
	pv.Cost = pvWithCost.Cost
	return nil
}

func (cm *CostModel) GetNodeCost(cp costAnalyzerCloud.Provider) (map[string]*costAnalyzerCloud.Node, error) {
	cfg, err := cp.GetConfig()
	if err != nil {
		return nil, err
	}

	nodeList := cm.Cache.GetAllNodes()
	nodes := make(map[string]*costAnalyzerCloud.Node)

	for _, n := range nodeList {
		name := n.GetObjectMeta().GetName()
		nodeLabels := n.GetObjectMeta().GetLabels()
		nodeLabels["providerID"] = n.Spec.ProviderID

		cnode, err := cp.NodePricing(cp.GetKey(nodeLabels, n))
		if err != nil {
			klog.Infof("Error getting node pricing. Error: %s", err.Error())
			if cnode != nil {
				nodes[name] = cnode
				continue
			} else {
				cnode = &costAnalyzerCloud.Node{
					VCPUCost: cfg.CPU,
					RAMCost:  cfg.RAM,
				}
			}
		}
		newCnode := *cnode
		if newCnode.InstanceType == "" {
			newCnode.InstanceType = n.Labels[v1.LabelInstanceType]
		}
		if newCnode.Region == "" {
			newCnode.Region = n.Labels[v1.LabelZoneRegion]
		}
		newCnode.ProviderID = n.Spec.ProviderID

		var cpu float64
		if newCnode.VCPU == "" {
			cpu = float64(n.Status.Capacity.Cpu().Value())
			newCnode.VCPU = n.Status.Capacity.Cpu().String()
		} else {
			cpu, err = strconv.ParseFloat(newCnode.VCPU, 64)
			if err != nil {
				klog.V(1).Infof("[Warning] parsing VCPU value: \"%s\" as float64", newCnode.VCPU)
			}
		}
		if math.IsNaN(cpu) {
			klog.V(1).Infof("[Warning] cpu parsed as NaN. Setting to 0.")
			cpu = 0
		}

		var ram float64
		if newCnode.RAM == "" {
			newCnode.RAM = n.Status.Capacity.Memory().String()
		}
		ram = float64(n.Status.Capacity.Memory().Value())
		if math.IsNaN(ram) {
			klog.V(1).Infof("[Warning] ram parsed as NaN. Setting to 0.")
			ram = 0
		}

		newCnode.RAMBytes = fmt.Sprintf("%f", ram)

		// Azure does not seem to provide a GPU count in its pricing API. GKE supports attaching multiple GPUs
		// So the k8s api will often report more accurate results for GPU count under status > capacity > nvidia.com/gpu than the cloud providers billing data
		// not all providers are guaranteed to use this, so don't overwrite a Provider assignment if we can't find something under that capacity exists
		gpuc := 0.0
		q, ok := n.Status.Capacity["nvidia.com/gpu"]
		if ok {
			gpuCount := q.Value()
			if gpuCount != 0 {
				newCnode.GPU = fmt.Sprintf("%d", q.Value())
				gpuc = float64(gpuCount)
			}
		} else {
			gpuc, err = strconv.ParseFloat(newCnode.GPU, 64)
			if err != nil {
				gpuc = 0.0
			}
		}
		if math.IsNaN(gpuc) {
			klog.V(1).Infof("[Warning] gpu count parsed as NaN. Setting to 0.")
			gpuc = 0.0
		}

		if newCnode.GPU != "" && newCnode.GPUCost == "" {
			// We couldn't find a gpu cost, so fix cpu and ram, then accordingly
			klog.V(4).Infof("GPU without cost found for %s, calculating...", cp.GetKey(nodeLabels, n).Features())

			defaultCPU, err := strconv.ParseFloat(cfg.CPU, 64)
			if err != nil {
				klog.V(3).Infof("Could not parse default cpu price")
				return nil, err
			}
			if math.IsNaN(defaultCPU) {
				klog.V(1).Infof("[Warning] defaultCPU parsed as NaN. Setting to 0.")
				defaultCPU = 0
			}

			defaultRAM, err := strconv.ParseFloat(cfg.RAM, 64)
			if err != nil {
				klog.V(3).Infof("Could not parse default ram price")
				return nil, err
			}
			if math.IsNaN(defaultRAM) {
				klog.V(1).Infof("[Warning] defaultRAM parsed as NaN. Setting to 0.")
				defaultRAM = 0
			}

			defaultGPU, err := strconv.ParseFloat(cfg.GPU, 64)
			if err != nil {
				klog.V(3).Infof("Could not parse default gpu price")
				return nil, err
			}
			if math.IsNaN(defaultGPU) {
				klog.V(1).Infof("[Warning] defaultGPU parsed as NaN. Setting to 0.")
				defaultGPU = 0
			}

			cpuToRAMRatio := defaultCPU / defaultRAM
			if math.IsNaN(cpuToRAMRatio) {
				klog.V(1).Infof("[Warning] cpuToRAMRatio[defaultCPU: %f / defaultRam: %f] is NaN. Setting to 0.", defaultCPU, defaultRAM)
				cpuToRAMRatio = 0
			}

			gpuToRAMRatio := defaultGPU / defaultRAM
			if math.IsNaN(gpuToRAMRatio) {
				klog.V(1).Infof("[Warning] gpuToRAMRatio is NaN. Setting to 0.")
				gpuToRAMRatio = 0
			}

			ramGB := ram / 1024 / 1024 / 1024
			if math.IsNaN(ramGB) {
				klog.V(1).Infof("[Warning] ramGB is NaN. Setting to 0.")
				ramGB = 0
			}

			ramMultiple := gpuc*gpuToRAMRatio + cpu*cpuToRAMRatio + ramGB
			if math.IsNaN(ramMultiple) {
				klog.V(1).Infof("[Warning] ramMultiple is NaN. Setting to 0.")
				ramMultiple = 0
			}

			var nodePrice float64
			if newCnode.Cost != "" {
				nodePrice, err = strconv.ParseFloat(newCnode.Cost, 64)
				if err != nil {
					klog.V(3).Infof("Could not parse total node price")
					return nil, err
				}
			} else {
				nodePrice, err = strconv.ParseFloat(newCnode.VCPUCost, 64) // all the price was allocated the the CPU
				if err != nil {
					klog.V(3).Infof("Could not parse node vcpu price")
					return nil, err
				}
			}
			if math.IsNaN(nodePrice) {
				klog.V(1).Infof("[Warning] nodePrice parsed as NaN. Setting to 0.")
				nodePrice = 0
			}

			ramPrice := (nodePrice / ramMultiple)
			if math.IsNaN(ramPrice) {
				klog.V(1).Infof("[Warning] ramPrice[nodePrice: %f / ramMultiple: %f] parsed as NaN. Setting to 0.", nodePrice, ramMultiple)
				ramPrice = 0
			}

			cpuPrice := ramPrice * cpuToRAMRatio
			gpuPrice := ramPrice * gpuToRAMRatio

			newCnode.VCPUCost = fmt.Sprintf("%f", cpuPrice)
			newCnode.RAMCost = fmt.Sprintf("%f", ramPrice)
			newCnode.RAMBytes = fmt.Sprintf("%f", ram)
			newCnode.GPUCost = fmt.Sprintf("%f", gpuPrice)
		} else if newCnode.RAMCost == "" {
			// We couldn't find a ramcost, so fix cpu and allocate ram accordingly
			klog.V(4).Infof("No RAM cost found for %s, calculating...", cp.GetKey(nodeLabels, n).Features())

			defaultCPU, err := strconv.ParseFloat(cfg.CPU, 64)
			if err != nil {
				klog.V(3).Infof("Could not parse default cpu price")
				return nil, err
			}
			if math.IsNaN(defaultCPU) {
				klog.V(1).Infof("[Warning] defaultCPU parsed as NaN. Setting to 0.")
				defaultCPU = 0
			}

			defaultRAM, err := strconv.ParseFloat(cfg.RAM, 64)
			if err != nil {
				klog.V(3).Infof("Could not parse default ram price")
				return nil, err
			}
			if math.IsNaN(defaultRAM) {
				klog.V(1).Infof("[Warning] defaultRAM parsed as NaN. Setting to 0.")
				defaultRAM = 0
			}

			cpuToRAMRatio := defaultCPU / defaultRAM
			if math.IsNaN(cpuToRAMRatio) {
				klog.V(1).Infof("[Warning] cpuToRAMRatio[defaultCPU: %f / defaultRam: %f] is NaN. Setting to 0.", defaultCPU, defaultRAM)
				cpuToRAMRatio = 0
			}

			ramGB := ram / 1024 / 1024 / 1024
			if math.IsNaN(ramGB) {
				klog.V(1).Infof("[Warning] ramGB is NaN. Setting to 0.")
				ramGB = 0
			}

			ramMultiple := cpu*cpuToRAMRatio + ramGB
			if math.IsNaN(ramMultiple) {
				klog.V(1).Infof("[Warning] ramMultiple is NaN. Setting to 0.")
				ramMultiple = 0
			}

			var nodePrice float64
			if newCnode.Cost != "" {
				nodePrice, err = strconv.ParseFloat(newCnode.Cost, 64)
				if err != nil {
					klog.V(3).Infof("Could not parse total node price")
					return nil, err
				}
			} else {
				nodePrice, err = strconv.ParseFloat(newCnode.VCPUCost, 64) // all the price was allocated the the CPU
				if err != nil {
					klog.V(3).Infof("Could not parse node vcpu price")
					return nil, err
				}
			}
			if math.IsNaN(nodePrice) {
				klog.V(1).Infof("[Warning] nodePrice parsed as NaN. Setting to 0.")
				nodePrice = 0
			}

			ramPrice := (nodePrice / ramMultiple)
			if math.IsNaN(ramPrice) {
				klog.V(1).Infof("[Warning] ramPrice[nodePrice: %f / ramMultiple: %f] parsed as NaN. Setting to 0.", nodePrice, ramMultiple)
				ramPrice = 0
			}

			cpuPrice := ramPrice * cpuToRAMRatio

			if defaultRAM != 0 {
				newCnode.VCPUCost = fmt.Sprintf("%f", cpuPrice)
				newCnode.RAMCost = fmt.Sprintf("%f", ramPrice)
			} else { // just assign the full price to CPU
				if cpu != 0 {
					newCnode.VCPUCost = fmt.Sprintf("%f", nodePrice/cpu)
				} else {
					newCnode.VCPUCost = fmt.Sprintf("%f", nodePrice)
				}
			}
			newCnode.RAMBytes = fmt.Sprintf("%f", ram)

			klog.V(4).Infof("Computed \"%s\" RAM Cost := %v", name, newCnode.RAMCost)
		}

		nodes[name] = &newCnode
	}

	cp.ApplyReservedInstancePricing(nodes)

	return nodes, nil
}

func getPodServices(cache clustercache.ClusterCache, podList []*v1.Pod, clusterID string) (map[string]map[string][]string, error) {
	servicesList := cache.GetAllServices()
	podServicesMapping := make(map[string]map[string][]string)
	for _, service := range servicesList {
		namespace := service.GetObjectMeta().GetNamespace()
		name := service.GetObjectMeta().GetName()
		key := namespace + "," + clusterID
		if _, ok := podServicesMapping[key]; !ok {
			podServicesMapping[key] = make(map[string][]string)
		}
		s := labels.Nothing()
		if service.Spec.Selector != nil && len(service.Spec.Selector) > 0 {
			s = labels.Set(service.Spec.Selector).AsSelectorPreValidated()
		}
		for _, pod := range podList {
			labelSet := labels.Set(pod.GetObjectMeta().GetLabels())
			if s.Matches(labelSet) && pod.GetObjectMeta().GetNamespace() == namespace {
				services, ok := podServicesMapping[key][pod.GetObjectMeta().GetName()]
				if ok {
					podServicesMapping[key][pod.GetObjectMeta().GetName()] = append(services, name)
				} else {
					podServicesMapping[key][pod.GetObjectMeta().GetName()] = []string{name}
				}
			}
		}
	}
	return podServicesMapping, nil
}

func getPodStatefulsets(cache clustercache.ClusterCache, podList []*v1.Pod, clusterID string) (map[string]map[string][]string, error) {
	ssList := cache.GetAllStatefulSets()
	podSSMapping := make(map[string]map[string][]string) // namespace: podName: [deploymentNames]
	for _, ss := range ssList {
		namespace := ss.GetObjectMeta().GetNamespace()
		name := ss.GetObjectMeta().GetName()

		key := namespace + "," + clusterID
		if _, ok := podSSMapping[key]; !ok {
			podSSMapping[key] = make(map[string][]string)
		}
		s, err := metav1.LabelSelectorAsSelector(ss.Spec.Selector)
		if err != nil {
			klog.V(2).Infof("Error doing deployment label conversion: " + err.Error())
		}
		for _, pod := range podList {
			labelSet := labels.Set(pod.GetObjectMeta().GetLabels())
			if s.Matches(labelSet) && pod.GetObjectMeta().GetNamespace() == namespace {
				sss, ok := podSSMapping[key][pod.GetObjectMeta().GetName()]
				if ok {
					podSSMapping[key][pod.GetObjectMeta().GetName()] = append(sss, name)
				} else {
					podSSMapping[key][pod.GetObjectMeta().GetName()] = []string{name}
				}
			}
		}
	}
	return podSSMapping, nil

}

func getPodDeployments(cache clustercache.ClusterCache, podList []*v1.Pod, clusterID string) (map[string]map[string][]string, error) {
	deploymentsList := cache.GetAllDeployments()
	podDeploymentsMapping := make(map[string]map[string][]string) // namespace: podName: [deploymentNames]
	for _, deployment := range deploymentsList {
		namespace := deployment.GetObjectMeta().GetNamespace()
		name := deployment.GetObjectMeta().GetName()

		key := namespace + "," + clusterID
		if _, ok := podDeploymentsMapping[key]; !ok {
			podDeploymentsMapping[key] = make(map[string][]string)
		}
		s, err := metav1.LabelSelectorAsSelector(deployment.Spec.Selector)
		if err != nil {
			klog.V(2).Infof("Error doing deployment label conversion: " + err.Error())
		}
		for _, pod := range podList {
			labelSet := labels.Set(pod.GetObjectMeta().GetLabels())
			if s.Matches(labelSet) && pod.GetObjectMeta().GetNamespace() == namespace {
				deployments, ok := podDeploymentsMapping[key][pod.GetObjectMeta().GetName()]
				if ok {
					podDeploymentsMapping[key][pod.GetObjectMeta().GetName()] = append(deployments, name)
				} else {
					podDeploymentsMapping[key][pod.GetObjectMeta().GetName()] = []string{name}
				}
			}
		}
	}
	return podDeploymentsMapping, nil
}

func getPodDeploymentsWithMetrics(deploymentLabels map[string]map[string]string, podLabels map[string]map[string]string) (map[string]map[string][]string, error) {
	podDeploymentsMapping := make(map[string]map[string][]string)

	for depKey, depLabels := range deploymentLabels {
		kt, err := NewKeyTuple(depKey)
		if err != nil {
			continue
		}

		namespace := kt.Namespace()
		name := kt.Key()
		clusterID := kt.ClusterID()

		key := namespace + "," + clusterID
		if _, ok := podDeploymentsMapping[key]; !ok {
			podDeploymentsMapping[key] = make(map[string][]string)
		}
		s := labels.Set(depLabels).AsSelectorPreValidated()
		for podKey, pLabels := range podLabels {
			pkey, err := NewKeyTuple(podKey)
			if err != nil {
				continue
			}
			podNamespace := pkey.Namespace()
			podName := pkey.Key()
			podClusterID := pkey.ClusterID()

			labelSet := labels.Set(pLabels)
			if s.Matches(labelSet) && podNamespace == namespace && podClusterID == clusterID {
				deployments, ok := podDeploymentsMapping[key][podName]
				if ok {
					podDeploymentsMapping[key][podName] = append(deployments, name)
				} else {
					podDeploymentsMapping[key][podName] = []string{name}
				}
			}
		}
	}

	// Remove any duplicate data created by metric names
	pruneDuplicateData(podDeploymentsMapping)

	return podDeploymentsMapping, nil
}

func getPodServicesWithMetrics(serviceLabels map[string]map[string]string, podLabels map[string]map[string]string) (map[string]map[string][]string, error) {
	podServicesMapping := make(map[string]map[string][]string)

	for servKey, servLabels := range serviceLabels {
		kt, err := NewKeyTuple(servKey)
		if err != nil {
			continue
		}

		namespace := kt.Namespace()
		name := kt.Key()
		clusterID := kt.ClusterID()

		key := namespace + "," + clusterID
		if _, ok := podServicesMapping[key]; !ok {
			podServicesMapping[key] = make(map[string][]string)
		}
		s := labels.Nothing()
		if servLabels != nil && len(servLabels) > 0 {
			s = labels.Set(servLabels).AsSelectorPreValidated()
		}

		for podKey, pLabels := range podLabels {
			pkey, err := NewKeyTuple(podKey)
			if err != nil {
				continue
			}
			podNamespace := pkey.Namespace()
			podName := pkey.Key()
			podClusterID := pkey.ClusterID()

			labelSet := labels.Set(pLabels)
			if s.Matches(labelSet) && podNamespace == namespace && podClusterID == clusterID {
				services, ok := podServicesMapping[key][podName]
				if ok {
					podServicesMapping[key][podName] = append(services, name)
				} else {
					podServicesMapping[key][podName] = []string{name}
				}
			}
		}
	}

	// Remove any duplicate data created by metric names
	pruneDuplicateData(podServicesMapping)

	return podServicesMapping, nil
}

// This method alleviates an issue with metrics that used a '_' to replace '-' in deployment
// and service names. To avoid counting these as multiple deployments/services, we'll remove
// the '_' version. Not optimal, but takes care of the issue
func pruneDuplicateData(data map[string]map[string][]string) {
	for _, podMap := range data {
		for podName, values := range podMap {
			podMap[podName] = pruneDuplicates(values)
		}
	}
}

// Determine if there is an underscore in the value of a slice. If so, replace _ with -, and then
// check to see if the result exists in the slice. If both are true, then we DO NOT include that
// original value in the new slice.
func pruneDuplicates(s []string) []string {
	m := sliceToSet(s)

	for _, v := range s {
		if strings.Contains(v, "_") {
			name := strings.Replace(v, "_", "-", -1)
			if !m[name] {
				m[name] = true
			}
			delete(m, v)
		}
	}

	return setToSlice(m)
}

// Creates a map[string]bool containing the slice values as keys
func sliceToSet(s []string) map[string]bool {
	m := make(map[string]bool)
	for _, v := range s {
		m[v] = true
	}
	return m
}

func setToSlice(m map[string]bool) []string {
	var result []string
	for k, _ := range m {
		result = append(result, k)
	}
	return result
}

func costDataPassesFilters(costs *CostData, namespace string, cluster string) bool {
	passesNamespace := namespace == "" || costs.Namespace == namespace
	passesCluster := cluster == "" || costs.ClusterID == cluster

	return passesNamespace && passesCluster
}

// Finds the a closest multiple less than value
func floorMultiple(value int64, multiple int64) int64 {
	return (value / multiple) * multiple
}

// Attempt to create a key for the request. Reduce the times to minutes in order to more easily group requests based on
// real time ranges. If for any reason, the key generation fails, return a uuid to ensure uniqueness.
func requestKeyFor(startString string, endString string, windowString string, filterNamespace string, filterCluster string, remoteEnabled bool) string {
	fullLayout := "2006-01-02T15:04:05.000Z"
	keyLayout := "2006-01-02T15:04Z"

	sTime, err := time.Parse(fullLayout, startString)
	if err != nil {
		klog.V(1).Infof("[Warning] Start=%s failed to parse when generating request key: %s", startString, err.Error())
		return uuid.New().String()
	}
	eTime, err := time.Parse(fullLayout, endString)
	if err != nil {
		klog.V(1).Infof("[Warning] End=%s failed to parse when generating request key: %s", endString, err.Error())
		return uuid.New().String()
	}

	// We "snap" start time and duration to their closest 5 min multiple less than itself, by
	// applying a snapped duration to a snapped start time.
	durMins := int64(eTime.Sub(sTime).Minutes())
	durMins = floorMultiple(durMins, 5)

	sMins := int64(sTime.Minute())
	sOffset := sMins - floorMultiple(sMins, 5)

	sTime = sTime.Add(-time.Duration(sOffset) * time.Minute)
	eTime = sTime.Add(time.Duration(durMins) * time.Minute)

	startKey := sTime.Format(keyLayout)
	endKey := eTime.Format(keyLayout)

	return fmt.Sprintf("%s,%s,%s,%s,%s,%t", startKey, endKey, windowString, filterNamespace, filterCluster, remoteEnabled)
}

// Executes a range query for cost data
func (cm *CostModel) ComputeCostDataRange(cli prometheusClient.Client, clientset kubernetes.Interface, cp costAnalyzerCloud.Provider,
	startString, endString, windowString string, resolutionHours float64, filterNamespace string, filterCluster string, remoteEnabled bool) (map[string]*CostData, error) {
	// Create a request key for request grouping. This key will be used to represent the cost-model result
	// for the specific inputs to prevent multiple queries for identical data.
	key := requestKeyFor(startString, endString, windowString, filterNamespace, filterCluster, remoteEnabled)

	klog.V(4).Infof("ComputeCostDataRange with Key: %s", key)

	// If there is already a request out that uses the same data, wait for it to return to share the results.
	// Otherwise, start executing.
	result, err, _ := cm.RequestGroup.Do(key, func() (interface{}, error) {
		return cm.costDataRange(cli, clientset, cp, startString, endString, windowString, resolutionHours, filterNamespace, filterCluster, remoteEnabled)
	})

	data, ok := result.(map[string]*CostData)
	if !ok {
		return nil, fmt.Errorf("Failed to cast result as map[string]*CostData")
	}

	return data, err
}

func (cm *CostModel) costDataRange(cli prometheusClient.Client, clientset kubernetes.Interface, cp costAnalyzerCloud.Provider, startString, endString, windowString string, resolutionHours float64, filterNamespace string, filterCluster string, remoteEnabled bool) (map[string]*CostData, error) {
	layout := "2006-01-02T15:04:05.000Z"

	start, err := time.Parse(layout, startString)
	if err != nil {
		klog.V(1).Infof("Error parsing time " + startString + ". Error: " + err.Error())
		return nil, err
	}

	end, err := time.Parse(layout, endString)
	if err != nil {
		klog.V(1).Infof("Error parsing time " + endString + ". Error: " + err.Error())
		return nil, err
	}

	window, err := time.ParseDuration(windowString)
	if err != nil {
		klog.V(1).Infof("Error parsing time " + windowString + ". Error: " + err.Error())
		return nil, err
	}

	clusterID := env.GetClusterID()

	durHrs := end.Sub(start).Hours() + 1

	if remoteEnabled == true {
		remoteLayout := "2006-01-02T15:04:05Z"
		remoteStartStr := start.Format(remoteLayout)
		remoteEndStr := end.Format(remoteLayout)
		klog.V(1).Infof("Using remote database for query from %s to %s with window %s", startString, endString, windowString)
		return CostDataRangeFromSQL("", "", windowString, remoteStartStr, remoteEndStr)
	}

	ctx := prom.NewContext(cli)

	// Query for the average number of minutes per hour that Kubecost was up
	// in the given range by averaging the number of up minutes-per-hour for
	// each window in the range. Use that number in the RAM and CPU allocation
	// queries as the adjutsment factor, scaling only if Kubecost was down
	// for fewer than 3 minutes (as a heuristic for a reasonable amount of
	// time to interpolate). Otherwise, use 60 minutes per hour and assume
	// that this period of time is during Kubecost start-up or a long-term
	// downtime for which we don't want to interpolate.
	queryKubecostUpMinsPerHour := fmt.Sprintf(kubecostUpMinsPerHourStr, windowString, window.Hours())
	resKubecostUp, err := ctx.QueryRangeSync(queryKubecostUpMinsPerHour, start, end, window)
	if err != nil {
		log.Errorf("costDataRange: error querying Kubecost up: %s", err)
		return nil, err
	}

	kubecostMinsPerHour := 0.0
	num := 0
	if len(resKubecostUp) > 0 {
		for _, val := range resKubecostUp[0].Values {
			kubecostMinsPerHour += val.Value
			num++
		}
		kubecostMinsPerHour /= float64(num)
	}
	if kubecostMinsPerHour <= 57.0 {
		kubecostMinsPerHour = 60.0
	}

	// TODO niko/queryfix rewrite PVCAllocation query too, and remove this
	// Use a heuristic to tell the difference between missed scrapes and an incomplete window
	// of data due to fresh install, etc.
	minimumExpectedScrapeRate := 0.95

	queryRAMAlloc := fmt.Sprintf(queryRAMAllocationByteHours, windowString, kubecostMinsPerHour)
	queryCPUAlloc := fmt.Sprintf(queryCPUAllocationVCPUHours, windowString, kubecostMinsPerHour)
	queryRAMRequests := fmt.Sprintf(queryRAMRequestsStr, windowString, "", windowString, "")
	queryRAMUsage := fmt.Sprintf(queryRAMUsageStr, windowString, "", windowString, "")
	queryCPURequests := fmt.Sprintf(queryCPURequestsStr, windowString, "", windowString, "")
	queryCPUUsage := fmt.Sprintf(queryCPUUsageStr, windowString, "")
	queryGPURequests := fmt.Sprintf(queryGPURequestsStr, windowString, "", windowString, "", resolutionHours, windowString, "")
	queryPVRequests := fmt.Sprintf(queryPVRequestsStr)
	queryPVCAllocation := fmt.Sprintf(queryPVCAllocationFmt, windowString, windowString, resolutionHours, minimumExpectedScrapeRate)
	queryPVHourlyCost := fmt.Sprintf(queryPVHourlyCostFmt, windowString)
	queryNetZoneRequests := fmt.Sprintf(queryZoneNetworkUsage, windowString, "")
	queryNetRegionRequests := fmt.Sprintf(queryRegionNetworkUsage, windowString, "")
	queryNetInternetRequests := fmt.Sprintf(queryInternetNetworkUsage, windowString, "")
	queryNormalization := fmt.Sprintf(normalizationStr, windowString, "")

	queryProfileStart := time.Now()

	// Submit all queries for concurrent evaluation
	resChRAMRequests := ctx.QueryRange(queryRAMRequests, start, end, window)
	resChRAMUsage := ctx.QueryRange(queryRAMUsage, start, end, window)
	resChRAMAlloc := ctx.QueryRange(queryRAMAlloc, start, end, window)
	resChCPURequests := ctx.QueryRange(queryCPURequests, start, end, window)
	resChCPUUsage := ctx.QueryRange(queryCPUUsage, start, end, window)
	resChCPUAlloc := ctx.QueryRange(queryCPUAlloc, start, end, window)
	resChGPURequests := ctx.QueryRange(queryGPURequests, start, end, window)
	resChPVRequests := ctx.QueryRange(queryPVRequests, start, end, window)
	resChPVCAlloc := ctx.QueryRange(queryPVCAllocation, start, end, window)
	resChPVHourlyCost := ctx.QueryRange(queryPVHourlyCost, start, end, window)
	resChNetZoneRequests := ctx.QueryRange(queryNetZoneRequests, start, end, window)
	resChNetRegionRequests := ctx.QueryRange(queryNetRegionRequests, start, end, window)
	resChNetInternetRequests := ctx.QueryRange(queryNetInternetRequests, start, end, window)
	resChNSLabels := ctx.QueryRange(fmt.Sprintf(queryNSLabels, windowString), start, end, window)
	resChPodLabels := ctx.QueryRange(fmt.Sprintf(queryPodLabels, windowString), start, end, window)
	resChServiceLabels := ctx.QueryRange(fmt.Sprintf(queryServiceLabels, windowString), start, end, window)
	resChDeploymentLabels := ctx.QueryRange(fmt.Sprintf(queryDeploymentLabels, windowString), start, end, window)
	resChStatefulsetLabels := ctx.QueryRange(fmt.Sprintf(queryStatefulsetLabels, windowString), start, end, window)
	resChJobs := ctx.QueryRange(queryPodJobs, start, end, window)
	resChDaemonsets := ctx.QueryRange(queryPodDaemonsets, start, end, window)
	resChNormalization := ctx.QueryRange(queryNormalization, start, end, window)

	// Pull k8s pod, controller, service, and namespace details
	podlist := cm.Cache.GetAllPods()

	podDeploymentsMapping, err := getPodDeployments(cm.Cache, podlist, clusterID)
	if err != nil {
		return nil, fmt.Errorf("error querying the kubernetes API: %s", err)
	}

	podStatefulsetsMapping, err := getPodStatefulsets(cm.Cache, podlist, clusterID)
	if err != nil {
		return nil, fmt.Errorf("error querying the kubernetes API: %s", err)
	}

	podServicesMapping, err := getPodServices(cm.Cache, podlist, clusterID)
	if err != nil {
		return nil, fmt.Errorf("error querying the kubernetes API: %s", err)
	}

	namespaceLabelsMapping, err := getNamespaceLabels(cm.Cache, clusterID)
	if err != nil {
		return nil, fmt.Errorf("error querying the kubernetes API: %s", err)
	}

	// Process query results. Handle errors afterwards using ctx.Errors.
	resRAMRequests, _ := resChRAMRequests.Await()
	resRAMUsage, _ := resChRAMUsage.Await()
	resRAMAlloc, _ := resChRAMAlloc.Await()
	resCPURequests, _ := resChCPURequests.Await()
	resCPUUsage, _ := resChCPUUsage.Await()
	resCPUAlloc, _ := resChCPUAlloc.Await()
	resGPURequests, _ := resChGPURequests.Await()
	resPVRequests, _ := resChPVRequests.Await()
	resPVCAlloc, _ := resChPVCAlloc.Await()
	resPVHourlyCost, _ := resChPVHourlyCost.Await()
	resNetZoneRequests, _ := resChNetZoneRequests.Await()
	resNetRegionRequests, _ := resChNetRegionRequests.Await()
	resNetInternetRequests, _ := resChNetInternetRequests.Await()
	resNSLabels, _ := resChNSLabels.Await()
	resPodLabels, _ := resChPodLabels.Await()
	resServiceLabels, _ := resChServiceLabels.Await()
	resDeploymentLabels, _ := resChDeploymentLabels.Await()
	resStatefulsetLabels, _ := resChStatefulsetLabels.Await()
	resDaemonsets, _ := resChDaemonsets.Await()
	resJobs, _ := resChJobs.Await()
	resNormalization, _ := resChNormalization.Await()

	measureTime(queryProfileStart, profileThreshold, fmt.Sprintf("costDataRange(%fh): Prom/k8s Queries", durHrs))
	defer measureTime(time.Now(), profileThreshold, fmt.Sprintf("costDataRange(%fh): Processing Query Data", durHrs))

	if ctx.HasErrors() {
		for _, promErr := range ctx.Errors() {
			log.Errorf("CostDataRange: Prometheus error: %s", promErr.Error())
		}

		// TODO: Categorize fatal prometheus query failures
		// return nil, fmt.Errorf("Error querying prometheus: %s", promErr.Error())
	}

	profileStart := time.Now()

	normalizationValue, err := getNormalizations(resNormalization)
	if err != nil {
		msg := fmt.Sprintf("error computing normalization for start=%s, end=%s, window=%s, res=%f", start, end, window, resolutionHours*60*60)
		if pce, ok := err.(prom.CommError); ok {
			return nil, pce.Wrap(msg)
		}
		return nil, fmt.Errorf("%s: %s", msg, err)
	}

	measureTime(profileStart, profileThreshold, fmt.Sprintf("costDataRange(%fh): compute normalizations", durHrs))

	profileStart = time.Now()

	pvClaimMapping, err := GetPVInfo(resPVRequests, clusterID)
	if err != nil {
		// Just log for compatibility with KSM less than 1.6
		klog.Infof("Unable to get PV Data: %s", err.Error())
	}
	if pvClaimMapping != nil {
		err = addPVData(cm.Cache, pvClaimMapping, cp)
		if err != nil {
			return nil, fmt.Errorf("pvClaimMapping: %s", err)
		}
	}

	pvCostMapping, err := GetPVCostMetrics(resPVHourlyCost, clusterID)
	if err != nil {
		klog.V(1).Infof("Unable to get PV Hourly Cost Data: %s", err.Error())
	}

	unmountedPVs := make(map[string][]*PersistentVolumeClaimData)
	pvAllocationMapping, err := GetPVAllocationMetrics(resPVCAlloc, clusterID)
	if err != nil {
		klog.V(1).Infof("Unable to get PV Allocation Cost Data: %s", err.Error())
	}
	if pvAllocationMapping != nil {
		addMetricPVData(pvAllocationMapping, pvCostMapping, cp)
		for k, v := range pvAllocationMapping {
			unmountedPVs[k] = v
		}
	}

	measureTime(profileStart, profileThreshold, fmt.Sprintf("costDataRange(%fh): process PV data", durHrs))

	profileStart = time.Now()

	nsLabels, err := GetNamespaceLabelsMetrics(resNSLabels, clusterID)
	if err != nil {
		klog.V(1).Infof("Unable to get Namespace Labels for Metrics: %s", err.Error())
	}
	if nsLabels != nil {
		appendNamespaceLabels(namespaceLabelsMapping, nsLabels)
	}

	podLabels, err := GetPodLabelsMetrics(resPodLabels, clusterID)
	if err != nil {
		klog.V(1).Infof("Unable to get Pod Labels for Metrics: %s", err.Error())
	}

	serviceLabels, err := GetServiceSelectorLabelsMetrics(resServiceLabels, clusterID)
	if err != nil {
		klog.V(1).Infof("Unable to get Service Selector Labels for Metrics: %s", err.Error())
	}

	deploymentLabels, err := GetDeploymentMatchLabelsMetrics(resDeploymentLabels, clusterID)
	if err != nil {
		klog.V(1).Infof("Unable to get Deployment Match Labels for Metrics: %s", err.Error())
	}

	statefulsetLabels, err := GetStatefulsetMatchLabelsMetrics(resStatefulsetLabels, clusterID)
	if err != nil {
		klog.V(1).Infof("Unable to get Deployment Match Labels for Metrics: %s", err.Error())
	}

	measureTime(profileStart, profileThreshold, fmt.Sprintf("costDataRange(%fh): process labels", durHrs))

	profileStart = time.Now()

	podStatefulsetMetricsMapping, err := getPodDeploymentsWithMetrics(statefulsetLabels, podLabels)
	if err != nil {
		klog.V(1).Infof("Unable to get match Statefulset Labels Metrics to Pods: %s", err.Error())
	}
	appendLabelsList(podStatefulsetsMapping, podStatefulsetMetricsMapping)

	podDeploymentsMetricsMapping, err := getPodDeploymentsWithMetrics(deploymentLabels, podLabels)
	if err != nil {
		klog.V(1).Infof("Unable to get match Deployment Labels Metrics to Pods: %s", err.Error())
	}
	appendLabelsList(podDeploymentsMapping, podDeploymentsMetricsMapping)

	podDaemonsets, err := GetPodDaemonsetsWithMetrics(resDaemonsets, clusterID)
	if err != nil {
		klog.V(1).Infof("Unable to get Pod Daemonsets for Metrics: %s", err.Error())
	}

	podJobs, err := GetPodJobsWithMetrics(resJobs, clusterID)
	if err != nil {
		klog.V(1).Infof("Unable to get Pod Jobs for Metrics: %s", err.Error())
	}

	podServicesMetricsMapping, err := getPodServicesWithMetrics(serviceLabels, podLabels)
	if err != nil {
		klog.V(1).Infof("Unable to get match Service Labels Metrics to Pods: %s", err.Error())
	}
	appendLabelsList(podServicesMapping, podServicesMetricsMapping)

	networkUsageMap, err := GetNetworkUsageData(resNetZoneRequests, resNetRegionRequests, resNetInternetRequests, clusterID)
	if err != nil {
		klog.V(1).Infof("Unable to get Network Cost Data: %s", err.Error())
		networkUsageMap = make(map[string]*NetworkUsageData)
	}

	measureTime(profileStart, profileThreshold, fmt.Sprintf("costDataRange(%fh): process deployments, services, and network usage", durHrs))

	profileStart = time.Now()

	containerNameCost := make(map[string]*CostData)
	containers := make(map[string]bool)
	otherClusterPVRecorded := make(map[string]bool)

	RAMReqMap, err := GetNormalizedContainerMetricVectors(resRAMRequests, normalizationValue, clusterID)
	if err != nil {
		if pce, ok := err.(prom.CommError); ok {
			return nil, pce.Wrap("GetNormalizedContainerMetricVectors(RAMRequests)")
		}
		return nil, fmt.Errorf("GetNormalizedContainerMetricVectors(RAMRequests): %s", err)
	}
	for key := range RAMReqMap {
		containers[key] = true
	}

	RAMUsedMap, err := GetNormalizedContainerMetricVectors(resRAMUsage, normalizationValue, clusterID)
	if err != nil {
		if pce, ok := err.(prom.CommError); ok {
			return nil, pce.Wrap("GetNormalizedContainerMetricVectors(RAMUsage)")
		}
		return nil, fmt.Errorf("GetNormalizedContainerMetricVectors(RAMUsage): %s", err)
	}
	for key := range RAMUsedMap {
		containers[key] = true
	}

	CPUReqMap, err := GetNormalizedContainerMetricVectors(resCPURequests, normalizationValue, clusterID)
	if err != nil {
		if pce, ok := err.(prom.CommError); ok {
			return nil, pce.Wrap("GetNormalizedContainerMetricVectors(CPURequests)")
		}
		return nil, fmt.Errorf("GetNormalizedContainerMetricVectors(CPURequests): %s", err)
	}
	for key := range CPUReqMap {
		containers[key] = true
	}

	// No need to normalize here, as this comes from a counter, namely:
	// rate(container_cpu_usage_seconds_total) which properly accounts for normalized rates
	CPUUsedMap, err := GetContainerMetricVectors(resCPUUsage, clusterID)
	if err != nil {
		if pce, ok := err.(prom.CommError); ok {
			return nil, pce.Wrap("GetContainerMetricVectors(CPUUsage)")
		}
		return nil, fmt.Errorf("GetContainerMetricVectors(CPUUsage): %s", err)
	}
	for key := range CPUUsedMap {
		containers[key] = true
	}

	RAMAllocMap, err := GetContainerMetricVectors(resRAMAlloc, clusterID)
	if err != nil {
		if pce, ok := err.(prom.CommError); ok {
			return nil, pce.Wrap("GetContainerMetricVectors(RAMAllocations)")
		}
		return nil, fmt.Errorf("GetContainerMetricVectors(RAMAllocations): %s", err)
	}
	for key := range RAMAllocMap {
		containers[key] = true
	}

	CPUAllocMap, err := GetContainerMetricVectors(resCPUAlloc, clusterID)
	if err != nil {
		if pce, ok := err.(prom.CommError); ok {
			return nil, pce.Wrap("GetContainerMetricVectors(CPUAllocations)")
		}
		return nil, fmt.Errorf("GetContainerMetricVectors(CPUAllocations): %s", err)
	}
	for key := range CPUAllocMap {
		containers[key] = true
	}

	GPUReqMap, err := GetNormalizedContainerMetricVectors(resGPURequests, normalizationValue, clusterID)
	if err != nil {
		if pce, ok := err.(prom.CommError); ok {
			return nil, pce.Wrap("GetContainerMetricVectors(GPURequests)")
		}
		return nil, fmt.Errorf("GetContainerMetricVectors(GPURequests): %s", err)
	}
	for key := range GPUReqMap {
		containers[key] = true
	}

	measureTime(profileStart, profileThreshold, fmt.Sprintf("costDataRange(%fh): GetContainerMetricVectors", durHrs))

	profileStart = time.Now()

	// Request metrics can show up after pod eviction and completion.
	// This method synchronizes requests to allocations such that when
	// allocation is 0, so are requests
	applyAllocationToRequests(RAMAllocMap, RAMReqMap)
	applyAllocationToRequests(CPUAllocMap, CPUReqMap)

	measureTime(profileStart, profileThreshold, fmt.Sprintf("costDataRange(%fh): applyAllocationToRequests", durHrs))

	profileStart = time.Now()

	missingNodes := make(map[string]*costAnalyzerCloud.Node)
	missingContainers := make(map[string]*CostData)
	for key := range containers {
		if _, ok := containerNameCost[key]; ok {
			continue // because ordering is important for the allocation model (all PV's applied to the first), just dedupe if it's already been added.
		}
		c, _ := NewContainerMetricFromKey(key)
		RAMReqV, ok := RAMReqMap[key]
		if !ok {
			klog.V(4).Info("no RAM requests for " + key)
			RAMReqV = []*util.Vector{}
		}
		RAMUsedV, ok := RAMUsedMap[key]
		if !ok {
			klog.V(4).Info("no RAM usage for " + key)
			RAMUsedV = []*util.Vector{}
		}
		CPUReqV, ok := CPUReqMap[key]
		if !ok {
			klog.V(4).Info("no CPU requests for " + key)
			CPUReqV = []*util.Vector{}
		}
		CPUUsedV, ok := CPUUsedMap[key]
		if !ok {
			klog.V(4).Info("no CPU usage for " + key)
			CPUUsedV = []*util.Vector{}
		}
		RAMAllocsV, ok := RAMAllocMap[key]
		if !ok {
			klog.V(4).Info("no RAM allocation for " + key)
			RAMAllocsV = []*util.Vector{}
		}
		CPUAllocsV, ok := CPUAllocMap[key]
		if !ok {
			klog.V(4).Info("no CPU allocation for " + key)
			CPUAllocsV = []*util.Vector{}
		}
		GPUReqV, ok := GPUReqMap[key]
		if !ok {
			klog.V(4).Info("no GPU requests for " + key)
			GPUReqV = []*util.Vector{}
		}

		var node *costAnalyzerCloud.Node
		if n, ok := missingNodes[c.NodeName]; ok {
			node = n
		} else {
			node = &costAnalyzerCloud.Node{}
			missingNodes[c.NodeName] = node
		}

		nsKey := c.Namespace + "," + c.ClusterID
		podKey := c.Namespace + "," + c.PodName + "," + c.ClusterID

		namespaceLabels, ok := namespaceLabelsMapping[nsKey]
		if !ok {
			klog.V(3).Infof("Missing data for namespace %s", c.Namespace)
		}

		pLabels := podLabels[podKey]
		if pLabels == nil {
			pLabels = make(map[string]string)
		}

		for k, v := range namespaceLabels {
			pLabels[k] = v
		}

		var podDeployments []string
		if _, ok := podDeploymentsMapping[nsKey]; ok {
			if ds, ok := podDeploymentsMapping[nsKey][c.PodName]; ok {
				podDeployments = ds
			} else {
				podDeployments = []string{}
			}
		}

		var podStatefulSets []string
		if _, ok := podStatefulsetsMapping[nsKey]; ok {
			if ss, ok := podStatefulsetsMapping[nsKey][c.PodName]; ok {
				podStatefulSets = ss
			} else {
				podStatefulSets = []string{}
			}

		}

		var podServices []string
		if _, ok := podServicesMapping[nsKey]; ok {
			if svcs, ok := podServicesMapping[nsKey][c.PodName]; ok {
				podServices = svcs
			} else {
				podServices = []string{}
			}
		}

		var podPVs []*PersistentVolumeClaimData
		var podNetCosts []*util.Vector

		// For PVC data, we'll need to find the claim mapping and cost data. Will need to append
		// cost data since that was populated by cluster data previously. We do this with
		// the pod_pvc_allocation metric
		podPVData, ok := pvAllocationMapping[podKey]
		if !ok {
			klog.V(4).Infof("Failed to locate pv allocation mapping for missing pod.")
		}

		// Delete the current pod key from potentially unmounted pvs
		delete(unmountedPVs, podKey)

		// For network costs, we'll use existing map since it should still contain the
		// correct data.
		var podNetworkCosts []*util.Vector
		if usage, ok := networkUsageMap[podKey]; ok {
			netCosts, err := GetNetworkCost(usage, cp)
			if err != nil {
				klog.V(3).Infof("Error pulling network costs: %s", err.Error())
			} else {
				podNetworkCosts = netCosts
			}
		}

		// Check to see if any other data has been recorded for this namespace, pod, clusterId
		// Follow the pattern of only allowing claims data per pod
		if !otherClusterPVRecorded[podKey] {
			otherClusterPVRecorded[podKey] = true

			podPVs = podPVData
			podNetCosts = podNetworkCosts
		}

		pds := []string{}
		if ds, ok := podDaemonsets[podKey]; ok {
			pds = []string{ds}
		}

		jobs := []string{}
		if job, ok := podJobs[podKey]; ok {
			jobs = []string{job}
		}

		costs := &CostData{
			Name:            c.ContainerName,
			PodName:         c.PodName,
			NodeName:        c.NodeName,
			NodeData:        node,
			Namespace:       c.Namespace,
			Services:        podServices,
			Deployments:     podDeployments,
			Daemonsets:      pds,
			Statefulsets:    podStatefulSets,
			Jobs:            jobs,
			RAMReq:          RAMReqV,
			RAMUsed:         RAMUsedV,
			CPUReq:          CPUReqV,
			CPUUsed:         CPUUsedV,
			RAMAllocation:   RAMAllocsV,
			CPUAllocation:   CPUAllocsV,
			GPUReq:          GPUReqV,
			Labels:          pLabels,
			NamespaceLabels: namespaceLabels,
			PVCData:         podPVs,
			NetworkData:     podNetCosts,
			ClusterID:       c.ClusterID,
		}

		if costDataPassesFilters(costs, filterNamespace, filterCluster) {
			containerNameCost[key] = costs
			missingContainers[key] = costs
		}
	}

	measureTime(profileStart, profileThreshold, fmt.Sprintf("costDataRange(%fh): build CostData map", durHrs))

	unmounted := findUnmountedPVCostData(unmountedPVs, namespaceLabelsMapping)
	for k, costs := range unmounted {
		klog.V(4).Infof("Unmounted PVs in Namespace/ClusterID: %s/%s", costs.Namespace, costs.ClusterID)

		if costDataPassesFilters(costs, filterNamespace, filterCluster) {
			containerNameCost[k] = costs
		}
	}

	w := end.Sub(start)
	w += window
	if w.Minutes() > 0 {
		wStr := fmt.Sprintf("%dm", int(w.Minutes()))
		err = findDeletedNodeInfo(cli, missingNodes, wStr)
		if err != nil {
			klog.V(1).Infof("Error fetching historical node data: %s", err.Error())
		}
	}

	return containerNameCost, nil
}

func applyAllocationToRequests(allocationMap map[string][]*util.Vector, requestMap map[string][]*util.Vector) {
	// The result of the normalize operation will be a new []*util.Vector to replace the requests
	normalizeOp := func(r *util.Vector, x *float64, y *float64) bool {
		// Omit data (return false) if both x and y inputs don't exist
		if x == nil || y == nil {
			return false
		}

		// If the allocation value is 0, 0 out request value
		if *x == 0 {
			r.Value = 0
		} else {
			r.Value = *y
		}

		return true
	}

	// Run normalization on all request vectors in the mapping
	for k, requests := range requestMap {

		// Only run normalization where there are valid allocations
		allocations, ok := allocationMap[k]
		if !ok {
			delete(requestMap, k)
			continue
		}

		// Replace request map with normalized
		requestMap[k] = util.ApplyVectorOp(allocations, requests, normalizeOp)
	}
}

func addMetricPVData(pvAllocationMap map[string][]*PersistentVolumeClaimData, pvCostMap map[string]*costAnalyzerCloud.PV, cp costAnalyzerCloud.Provider) {
	cfg, err := cp.GetConfig()
	if err != nil {
		klog.V(1).Infof("Failed to get provider config while adding pv metrics data.")
		return
	}

	for _, pvcDataArray := range pvAllocationMap {
		for _, pvcData := range pvcDataArray {
			costKey := fmt.Sprintf("%s,%s", pvcData.VolumeName, pvcData.ClusterID)

			pvCost, ok := pvCostMap[costKey]
			if !ok {
				pvcData.Volume = &costAnalyzerCloud.PV{
					Cost: cfg.Storage,
				}
				continue
			}

			pvcData.Volume = pvCost
		}
	}
}

// Append labels into nsLabels iff the ns key doesn't already exist
func appendNamespaceLabels(nsLabels map[string]map[string]string, labels map[string]map[string]string) {
	for k, v := range labels {
		if _, ok := nsLabels[k]; !ok {
			nsLabels[k] = v
		}
	}
}

func appendLabelsList(mainLabels map[string]map[string][]string, labels map[string]map[string][]string) {
	for k, v := range labels {
		mainLabels[k] = v
	}
}

func getNamespaceLabels(cache clustercache.ClusterCache, clusterID string) (map[string]map[string]string, error) {
	nsToLabels := make(map[string]map[string]string)
	nss := cache.GetAllNamespaces()
	for _, ns := range nss {
		labels := make(map[string]string)
		for k, v := range ns.Labels {
			labels[SanitizeLabelName(k)] = v
		}
		nsToLabels[ns.Name+","+clusterID] = labels
	}
	return nsToLabels, nil
}

func getDaemonsetsOfPod(pod v1.Pod) []string {
	for _, ownerReference := range pod.ObjectMeta.OwnerReferences {
		if ownerReference.Kind == "DaemonSet" {
			return []string{ownerReference.Name}
		}
	}
	return []string{}
}

func getJobsOfPod(pod v1.Pod) []string {
	for _, ownerReference := range pod.ObjectMeta.OwnerReferences {
		if ownerReference.Kind == "Job" {
			return []string{ownerReference.Name}
		}
	}
	return []string{}
}

func getStatefulSetsOfPod(pod v1.Pod) []string {
	for _, ownerReference := range pod.ObjectMeta.OwnerReferences {
		if ownerReference.Kind == "StatefulSet" {
			return []string{ownerReference.Name}
		}
	}
	return []string{}
}

type PersistentVolumeClaimData struct {
	Class        string                `json:"class"`
	Claim        string                `json:"claim"`
	Namespace    string                `json:"namespace"`
	ClusterID    string                `json:"clusterId"`
	TimesClaimed int                   `json:"timesClaimed"`
	VolumeName   string                `json:"volumeName"`
	Volume       *costAnalyzerCloud.PV `json:"persistentVolume"`
	Values       []*util.Vector        `json:"values"`
}

func measureTime(start time.Time, threshold time.Duration, name string) {
	elapsed := time.Since(start)
	if elapsed > threshold {
		klog.V(3).Infof("[Profiler] %s: %s", elapsed, name)
	}
}

func measureTimeAsync(start time.Time, threshold time.Duration, name string, ch chan string) {
	elapsed := time.Since(start)
	if elapsed > threshold {
		ch <- fmt.Sprintf("%s took %s", name, time.Since(start))
	}
}