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@@ -187,8 +187,8 @@ const (
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queryRAMAllocationByteHours = `
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label_replace(label_replace(
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sum(
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- sum_over_time(container_memory_allocation_bytes{container!="",container!="POD", node!=""}[%s:1m]) / %f
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- ) by (namespace,container,pod,node,cluster_id)
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+ sum_over_time(container_memory_allocation_bytes{container!="",container!="POD", node!=""}[%s])
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+ ) by (namespace,container,pod,node,cluster_id) * (scalar(avg(prometheus_target_interval_length_seconds)) / 60 / 60)
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, "container_name","$1","container","(.+)"), "pod_name","$1","pod","(.+)")`
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// queryCPUAllocationVCPUHours yields the total VCPU-hour CPU allocation over the given
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// window, aggregated by container.
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@@ -198,8 +198,8 @@ const (
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queryCPUAllocationVCPUHours = `
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label_replace(label_replace(
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sum(
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- sum_over_time(container_cpu_allocation{container!="",container!="POD", node!=""}[%s:1m]) / %f
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- ) by (namespace,container,pod,node,cluster_id)
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+ sum_over_time(container_cpu_allocation{container!="",container!="POD", node!=""}[%s])
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+ ) by (namespace,container,pod,node,cluster_id) * (scalar(avg(prometheus_target_interval_length_seconds)) / 60 / 60)
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, "container_name","$1","container","(.+)"), "pod_name","$1","pod","(.+)")`
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// queryPVCAllocationFmt yields the total byte-hour PVC allocation over the given window.
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// sum(all VCPU measurements within given window) = [byte*min] by metric
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@@ -209,8 +209,7 @@ const (
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// Note: normalization factor is 1.0 if no scrapes are missed and has an upper bound determined by minExpectedScrapeRate
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// so that coarse resolutions don't push normalization factors too high; e.g. 24h resolution with 1h of data would make
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// for a normalization factor of 24. With a minimumExpectedScrapeRate of 0.95, that caps the norm factor at
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- queryPVCAllocationFmt = `sum(sum_over_time(pod_pvc_allocation[%s:1m])) by (cluster_id, namespace, pod, persistentvolume, persistentvolumeclaim) / 60
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- * 60 / clamp_min(count_over_time(sum(pod_pvc_allocation) by (cluster_id, namespace, pod, persistentvolume, persistentvolumeclaim)[%s:1m])/%f, 60 * %f)`
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+ queryPVCAllocationFmt = `sum(sum_over_time(pod_pvc_allocation[%s])) by (cluster_id, namespace, pod, persistentvolume, persistentvolumeclaim) * scalar(avg(prometheus_target_interval_length_seconds)/60/60)`
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queryPVHourlyCostFmt = `avg_over_time(pv_hourly_cost[%s])`
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queryNSLabels = `avg_over_time(kube_namespace_labels[%s])`
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queryPodLabels = `avg_over_time(kube_pod_labels[%s])`
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@@ -1531,20 +1530,15 @@ func (cm *CostModel) costDataRange(cli prometheusClient.Client, clientset kubern
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kubecostMinsPerHour = 60.0
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}
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- // TODO niko/queryfix rewrite PVCAllocation query too, and remove this
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- // Use a heuristic to tell the difference between missed scrapes and an incomplete window
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- // of data due to fresh install, etc.
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- minimumExpectedScrapeRate := 0.95
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-
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- queryRAMAlloc := fmt.Sprintf(queryRAMAllocationByteHours, windowString, kubecostMinsPerHour)
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- queryCPUAlloc := fmt.Sprintf(queryCPUAllocationVCPUHours, windowString, kubecostMinsPerHour)
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+ queryRAMAlloc := fmt.Sprintf(queryRAMAllocationByteHours, windowString)
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+ queryCPUAlloc := fmt.Sprintf(queryCPUAllocationVCPUHours, windowString)
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queryRAMRequests := fmt.Sprintf(queryRAMRequestsStr, windowString, "", windowString, "")
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queryRAMUsage := fmt.Sprintf(queryRAMUsageStr, windowString, "", windowString, "")
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queryCPURequests := fmt.Sprintf(queryCPURequestsStr, windowString, "", windowString, "")
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queryCPUUsage := fmt.Sprintf(queryCPUUsageStr, windowString, "")
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queryGPURequests := fmt.Sprintf(queryGPURequestsStr, windowString, "", windowString, "", resolutionHours, windowString, "")
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queryPVRequests := fmt.Sprintf(queryPVRequestsStr)
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- queryPVCAllocation := fmt.Sprintf(queryPVCAllocationFmt, windowString, windowString, resolutionHours, minimumExpectedScrapeRate)
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+ queryPVCAllocation := fmt.Sprintf(queryPVCAllocationFmt, windowString)
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queryPVHourlyCost := fmt.Sprintf(queryPVHourlyCostFmt, windowString)
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queryNetZoneRequests := fmt.Sprintf(queryZoneNetworkUsage, windowString, "")
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queryNetRegionRequests := fmt.Sprintf(queryRegionNetworkUsage, windowString, "")
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