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feat: usage of predict_linear function in volume dashboard #2763

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merged 12 commits into from
Apr 25, 2024
Merged

feat: usage of predict_linear function in volume dashboard #2763

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2291dd1
feat: usage of predict_linear function in volume dashboard
Hardikl Mar 20, 2024
0e50df6
feat: handled review comments for UI
Hardikl Mar 21, 2024
ac9a9e9
Merge remote-tracking branch 'origin/main' into hl_vol_trend
Hardikl Apr 5, 2024
eea4a8b
feat: usage of predict_linear function in table in volume dashboard
Hardikl Apr 5, 2024
f21c017
feat: usage of percentage in predict_linear function
Hardikl Apr 11, 2024
a2c6dc1
Merge remote-tracking branch 'origin/main' into hl_vol_trend
Hardikl Apr 11, 2024
fe6723c
feat: usage of percentage in predict_linear function
Hardikl Apr 11, 2024
a386d8f
Merge remote-tracking branch 'origin/main' into hl_vol_trend
Hardikl Apr 16, 2024
bd70bb0
feat: handling review comments
Hardikl Apr 16, 2024
5218c59
feat: handled review comments
Hardikl Apr 17, 2024
5ef06de
Merge remote-tracking branch 'origin/main' into hl_vol_trend
Hardikl Apr 23, 2024
1e66cc7
feat: handling review comments
Hardikl Apr 23, 2024
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4 changes: 2 additions & 2 deletions cmd/tools/grafana/dashboard_test.go
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Original file line number Diff line number Diff line change
Expand Up @@ -825,11 +825,11 @@ func checkTopKRange(t *testing.T, path string, data []byte) {
expressions := make([]exprP, 0)

VisitAllPanels(data, func(_ string, key, value gjson.Result) {
doTarget("", key, value, func(path string, expr string, format string) {
doTarget("", key, value, func(path string, expr string, format string, title string) {
if format == "table" || format == "stat" {
return
}
expressions = append(expressions, newExpr(path, expr))
expressions = append(expressions, newExpr(path, expr, title))
})
})

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29 changes: 16 additions & 13 deletions cmd/tools/grafana/metrics.go
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Original file line number Diff line number Diff line change
Expand Up @@ -32,22 +32,23 @@ func doMetrics(_ *cobra.Command, _ []string) {
}

type exprP struct {
path string
expr string
vars []string
path string
expr string
vars []string
panelTitle string
}

func visitExpressionsAndQueries(path string, data []byte) {
// collect all expressions
expressions := make([]exprP, 0)
gjson.GetBytes(data, "panels").ForEach(func(key, value gjson.Result) bool {
doTarget("", key, value, func(path string, expr string, _ string) {
expressions = append(expressions, newExpr(path, expr))
doTarget("", key, value, func(path string, expr string, _ string, title string) {
expressions = append(expressions, newExpr(path, expr, title))
})
value.Get("panels").ForEach(func(key2, value2 gjson.Result) bool {
pathPrefix := fmt.Sprintf("panels[%d].", key.Int())
doTarget(pathPrefix, key2, value2, func(path string, expr string, _ string) {
expressions = append(expressions, newExpr(path, expr))
doTarget(pathPrefix, key2, value2, func(path string, expr string, _ string, title string) {
expressions = append(expressions, newExpr(path, expr, title))
})
return true
})
Expand Down Expand Up @@ -127,22 +128,24 @@ func allVariables(data []byte) map[string]variable {
return variables
}

func newExpr(path string, expr string) exprP {
func newExpr(path string, expr string, title string) exprP {
allMatches := varRe.FindAllStringSubmatch(expr, -1)
vars := make([]string, 0, len(allMatches))
for _, match := range allMatches {
vars = append(vars, match[1])
}
return exprP{
path: path,
expr: expr,
vars: vars,
path: path,
expr: expr,
vars: vars,
panelTitle: title,
}
}

func doTarget(pathPrefix string, key gjson.Result, value gjson.Result,
exprFunc func(path string, expr string, format string)) {
exprFunc func(path string, expr string, format string, title string)) {
kind := value.Get("type").String()
title := value.Get("title").String()
if kind == "row" {
return
}
Expand All @@ -151,7 +154,7 @@ func doTarget(pathPrefix string, key gjson.Result, value gjson.Result,
for i, targetN := range targetsSlice {
expr := targetN.Get("expr").String()
pathWithTarget := path + ".targets[" + strconv.Itoa(i) + "]"
exprFunc(pathWithTarget, expr, kind)
exprFunc(pathWithTarget, expr, kind, title)
}
}

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296 changes: 296 additions & 0 deletions grafana/dashboards/cmode/volume.json
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Original file line number Diff line number Diff line change
Expand Up @@ -7561,6 +7561,302 @@
],
"title": "Volume Growth Rate",
"type": "row"
},
{
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Need Datacenter column in table.
Cluster should be a column after datacenter (at start) in table.
Default sorting should be on predicted value.
Is gradient bar percentage display better than text percentage value?
Node can be removed from table.

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  • Datacenter column added
  • Cluster column moved to 2nd
  • Node column removed
  • % changed to gradient
  • Default sort desc for predicted value.
image

"collapsed": true,
"datasource": "${DS_PROMETHEUS}",
"gridPos": {
"h": 1,
"w": 24,
"x": 0,
"y": 25
},
"id": 136,
"panels": [
{
"datasource": "${DS_PROMETHEUS}",
"description": "This table would show 15 day predicted value of size used percentage for top volumes.",
"fieldConfig": {
"defaults": {
"custom": {
"align": "left",
"cellOptions": {
"type": "auto"
},
"filterable": true,
"inspect": false
},
"decimals": 2,
"mappings": [
{
"options": {
"from": 100,
"result": {
"index": 0,
"text": "100%"
},
"to": 999999
},
"type": "range"
},
{
"options": {
"from": -9999999,
"result": {
"index": 1,
"text": "0%"
},
"to": 0
},
"type": "range"
}
],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "rgb(31, 176, 196)",
"value": null
}
]
},
"unit": "percent",
"unitScale": true
},
"overrides": [
{
"matcher": {
"id": "byName",
"options": "Current Value"
},
"properties": [
{
"id": "custom.width",
"value": 170
},
{
"id": "custom.displayMode",
"value": "gradient-gauge"
}
]
},
{
"matcher": {
"id": "byName",
"options": "SVM"
},
"properties": [
{
"id": "custom.width",
"value": 260
}
]
},
{
"matcher": {
"id": "byName",
"options": "Volume"
},
"properties": [
{
"id": "custom.width",
"value": 330
}
]
},
{
"matcher": {
"id": "byName",
"options": "Cluster"
},
"properties": [
{
"id": "custom.width",
"value": 280
}
]
},
{
"matcher": {
"id": "byName",
"options": "Node"
},
"properties": [
{
"id": "custom.width",
"value": 260
}
]
},
{
"matcher": {
"id": "byName",
"options": "Aggr"
},
"properties": [
{
"id": "custom.width",
"value": 260
}
]
},
{
"matcher": {
"id": "byName",
"options": "Predicted 15th Day Value"
},
"properties": [
{
"id": "custom.width",
"value": 220
},
{
"id": "custom.displayMode",
"value": "gradient-gauge"
}
]
},
{
"matcher": {
"id": "byName",
"options": "Datacenter"
},
"properties": [
{
"id": "custom.width",
"value": 190
}
]
}
]
},
"gridPos": {
"h": 7,
"w": 24,
"x": 0,
"y": 12
},
"id": 149,
"interval": "1m",
"maxDataPoints": 2,
"options": {
"cellHeight": "sm",
"footer": {
"countRows": false,
"fields": "",
"reducer": [
"sum"
],
"show": false
},
"showHeader": true,
"sortBy": [
{
"desc": true,
"displayName": "Predicted 15th Day Value"
}
]
},
"pluginVersion": "10.3.1",
"targets": [
{
"datasource": "${DS_PROMETHEUS}",
"editorMode": "code",
"expr": "predict_linear(volume_size_used_percent{datacenter=~\"$Datacenter\",cluster=~\"$Cluster\",svm=~\"$SVM\",volume=~\"$Volume\"}[3d], 15*24*3600) and topk($TopResources, avg_over_time(volume_size_used_percent{datacenter=~\"$Datacenter\",cluster=~\"$Cluster\",svm=~\"$SVM\",volume=~\"$Volume\"}[3h] @ end()))",
"format": "table",
"hide": false,
"instant": true,
"interval": "",
"intervalFactor": 1,
"legendFormat": "",
"refId": "A"
},
{
"datasource": "${DS_PROMETHEUS}",
"editorMode": "code",
"exemplar": false,
"expr": "volume_size_used_percent{datacenter=~\"$Datacenter\",cluster=~\"$Cluster\",svm=~\"$SVM\",volume=~\"$Volume\"} and topk($TopResources, avg_over_time(volume_size_used_percent{datacenter=~\"$Datacenter\",cluster=~\"$Cluster\",svm=~\"$SVM\",volume=~\"$Volume\"}[3h] @ end()))",
"format": "table",
"hide": false,
"instant": true,
"legendFormat": "",
"range": false,
"refId": "B"
}
],
"title": "Top $TopResources Volumes Per Size Used Percentage Trend",
"transformations": [
{
"id": "merge",
"options": {}
},
{
"id": "organize",
"options": {
"excludeByName": {
"Time": true,
"Value": false,
"Value #C": false,
"__name__": true,
"datacenter": false,
"instance": true,
"job": true,
"node": true,
"state": true,
"style": true
},
"includeByName": {},
"indexByName": {
"Time": 5,
"Value #A": 10,
"Value #B": 9,
"__name__": 11,
"aggr": 4,
"cluster": 1,
"datacenter": 0,
"instance": 6,
"job": 7,
"style": 8,
"svm": 3,
"volume": 2
},
"renameByName": {
"Value": "Predicted Value ",
"Value #A": "Predicted 15th Day Value",
"Value #B": "Current Value",
"Value #C": "Size",
"Value #D": "",
"Value #E": "Dedupe Space Saved",
"Value #F": "Compression Space Saved",
"Value #G": "Total Space Saved",
"Value #H": "Logical Space Used",
"Value #I": "Physical Space Used",
"Value #J": "Clone Split Estimate",
"__name__": "",
"aggr": "Aggr",
"clone_parent_snapshot": "Clone Parent Snapshot",
"clone_parent_svm": "Clone Parent SVM",
"clone_parent_volume": "Clone Parent Volume",
"cluster": "Cluster",
"datacenter": "Datacenter",
"junction_path": "Junction Path",
"svm": "SVM",
"volume": "Volume"
}
}
}
],
"type": "table"
}
],
"targets": [
{
"datasource": "${DS_PROMETHEUS}",
"refId": "A"
}
],
"title": "Forecast Volume Capacity",
"type": "row"
}
],
"refresh": "",
Expand Down
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