Golden signal update #748
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Golden signal update #748
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- rearrange tiles to match cost opt slides - added tile to show history for demand based downscaling - added tile to show history for spot usage
update image
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@yqlu can you ptal? |
yqlu
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Mar 11, 2024
dashboards/google-kubernetes-engine/gke-optimization-signals.json
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yqlu
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Mar 18, 2024
| @@ -210,7 +209,7 @@ | |||
| "plotType": "LINE", | |||
| "targetAxis": "Y1", | |||
| "timeSeriesQuery": { | |||
| "timeSeriesQueryLanguage": "{ fetch k8s_container::kubernetes.io/container/cpu/request_cores;\n fetch k8s_node::kubernetes.io/node/cpu/allocatable_cores\n} \n| ${project_id}\n| ${location}\n| ${cluster_name}\n| join\n| group_by [project_id, location, cluster_name], sum(val(0)) / sum(val(1))", | |||
| "timeSeriesQueryLanguage": "{ fetch k8s_container::kubernetes.io/container/cpu/request_cores\n| filter\n resource.namespace_name !~ '(kube|istio|gatekeeper|gke|gmp|gke-gmp)-system|gke-managed-filestorecsi|gke-mcs'\n| ${project_id}\n| ${location}\n| ${cluster_name}\n | group_by [], .aggregate()\n ;\n fetch k8s_node::kubernetes.io/node/cpu/allocatable_cores\n| ${project_id}\n| ${location}\n| ${cluster_name}\n | group_by [], .aggregate()\n} \n| join\n| value val(0) / val(1)\n| scale '%'", | |||
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If you make both the numerator and denominator | group_by [resource.cluster_name, resource.location, resource.project_id] as follows
You get the best of both worlds:
- when there are multiple clusters, you get one time series per cluster
- each time series is automatically labelled by the cluster name
- when the filters are drilled down to a single cluster, the single time series value matches the scorecard
And you should do this for Cluster Bin Packing Memory as well
| "scorecard": { | ||
| "blankView": {}, | ||
| "thresholds": [], | ||
| "timeSeriesQuery": { | ||
| "timeSeriesQueryLanguage": "fetch k8s_node\n| ${project_id}\n| ${location}\n| ${cluster_name}\n| metric 'kubernetes.io/node/cpu/allocatable_utilization'\n| group_by [],.mean\n| scale '%'", | ||
| "outputFullDuration": true, | ||
| "timeSeriesQueryLanguage": "fetch k8s_node\n| ${project_id}\n| ${location}\n| ${cluster_name}\n| metric 'kubernetes.io/node/cpu/allocatable_utilization'\n| group_by 1m,\n [value_allocatable_utilization_mean: mean(value.allocatable_utilization)]\n| every 1m\n| group_by [resource.project_id, resource.location, resource.cluster_name],\n [value_allocatable_utilization_mean_aggregate:\n aggregate(value_allocatable_utilization_mean)]\n| value\n [value_allocatable_utilization_mean_aggregate_scale:\n scale(value_allocatable_utilization_mean_aggregate, '%')]", |
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Same comment as the Demand-Based Downscaling XY chart below (the aggregate() should be a mean())
But in addition, this should be group_by [] so that we reduce it to a single line. When I test this out in a project with multiple GKE clusters I get the following error:
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