ClusterCockpit/cc-backend
1
0
Fork 0
mirror of https://github.com/ClusterCockpit/cc-backend synced 2024-12-25 21:09:05 +01:00
Code Issues Packages Releases Activity

Merge branch '263_use_median_for_statsseries' into Refactor-job-footprint

Browse Source
This commit is contained in:
Christoph Kluge 2024-07-09 09:28:21 +02:00
commit 0240997257
9 changed files with 160 additions and 30 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

7
api/schema.graphqls
View File

@ -147,9 +147,10 @@ type MetricStatistics {
}
type StatsSeries {
mean: [NullableFloat!]!
min: [NullableFloat!]!
max: [NullableFloat!]!
mean: [NullableFloat!]!
median: [NullableFloat!]!
min: [NullableFloat!]!
max: [NullableFloat!]!
}
type MetricFootprints {

72
internal/graph/generated/generated.go
View File

@ -249,9 +249,10 @@ type ComplexityRoot struct {
}
StatsSeries struct {
Max func(childComplexity int) int
Mean func(childComplexity int) int
Min func(childComplexity int) int
Max func(childComplexity int) int
Mean func(childComplexity int) int
Median func(childComplexity int) int
Min func(childComplexity int) int
}
SubCluster struct {
@ -1326,6 +1327,13 @@ func (e *executableSchema) Complexity(typeName, field string, childComplexity in
return e.complexity.StatsSeries.Mean(childComplexity), true
case "StatsSeries.median":
if e.complexity.StatsSeries.Median == nil {
break
}
return e.complexity.StatsSeries.Median(childComplexity), true
case "StatsSeries.min":
if e.complexity.StatsSeries.Min == nil {
break
@ -1860,9 +1868,10 @@ type MetricStatistics {
}
type StatsSeries {
mean: [NullableFloat!]!
min: [NullableFloat!]!
max: [NullableFloat!]!
mean: [NullableFloat!]!
median: [NullableFloat!]!
min: [NullableFloat!]!
max: [NullableFloat!]!
}
type MetricFootprints {
@ -4662,6 +4671,8 @@ func (ec *executionContext) fieldContext_JobMetric_statisticsSeries(ctx context.
switch field.Name {
case "mean":
return ec.fieldContext_StatsSeries_mean(ctx, field)
case "median":
return ec.fieldContext_StatsSeries_median(ctx, field)
case "min":
return ec.fieldContext_StatsSeries_min(ctx, field)
case "max":
@ -8649,6 +8660,50 @@ func (ec *executionContext) fieldContext_StatsSeries_mean(ctx context.Context, f
return fc, nil
}
func (ec *executionContext) _StatsSeries_median(ctx context.Context, field graphql.CollectedField, obj *schema.StatsSeries) (ret graphql.Marshaler) {
fc, err := ec.fieldContext_StatsSeries_median(ctx, field)
if err != nil {
return graphql.Null
}
ctx = graphql.WithFieldContext(ctx, fc)
defer func() {
if r := recover(); r != nil {
ec.Error(ctx, ec.Recover(ctx, r))
ret = graphql.Null
}
}()
resTmp, err := ec.ResolverMiddleware(ctx, func(rctx context.Context) (interface{}, error) {
ctx = rctx // use context from middleware stack in children
return obj.Median, nil
})
if err != nil {
ec.Error(ctx, err)
return graphql.Null
}
if resTmp == nil {
if !graphql.HasFieldError(ctx, fc) {
ec.Errorf(ctx, "must not be null")
}
return graphql.Null
}
res := resTmp.([]schema.Float)
fc.Result = res
return ec.marshalNNullableFloat2ᚕgithubᚗcomᚋClusterCockpitᚋccᚑbackendᚋpkgᚋschemaᚐFloatᚄ(ctx, field.Selections, res)
}
func (ec *executionContext) fieldContext_StatsSeries_median(ctx context.Context, field graphql.CollectedField) (fc *graphql.FieldContext, err error) {
fc = &graphql.FieldContext{
Object: "StatsSeries",
Field: field,
IsMethod: false,
IsResolver: false,
Child: func(ctx context.Context, field graphql.CollectedField) (*graphql.FieldContext, error) {
return nil, errors.New("field of type NullableFloat does not have child fields")
},
}
return fc, nil
}
func (ec *executionContext) _StatsSeries_min(ctx context.Context, field graphql.CollectedField, obj *schema.StatsSeries) (ret graphql.Marshaler) {
fc, err := ec.fieldContext_StatsSeries_min(ctx, field)
if err != nil {
@ -14431,6 +14486,11 @@ func (ec *executionContext) _StatsSeries(ctx context.Context, sel ast.SelectionS
if out.Values[i] == graphql.Null {
out.Invalids++
}
case "median":
out.Values[i] = ec._StatsSeries_median(ctx, field, obj)
if out.Values[i] == graphql.Null {
out.Invalids++
}
case "min":
out.Values[i] = ec._StatsSeries_min(ctx, field, obj)
if out.Values[i] == graphql.Null {

2
internal/metricdata/metricdata.go
View File

@ -263,7 +263,7 @@ func cacheKey(
// For /monitoring/job/<job> and some other places, flops_any and mem_bw need
// to be available at the scope 'node'. If a job has a lot of nodes,
// statisticsSeries should be available so that a min/mean/max Graph can be
// statisticsSeries should be available so that a min/median/max Graph can be
// used instead of a lot of single lines.
func prepareJobData(
job *schema.Job,

37
internal/util/statistics.go
View File

@ -7,6 +7,10 @@ package util
import (
"github.com/ClusterCockpit/cc-backend/pkg/schema"
"golang.org/x/exp/constraints"
"fmt"
"math"
"sort"
)
func Min[T constraints.Ordered](a, b T) T {
@ -34,3 +38,36 @@ func LoadJobStat(job *schema.JobMeta, metric string) float64 {
return 0.0
}
func sortedCopy(input []float64) []float64 {
sorted := make([]float64, len(input))
copy(sorted, input)
sort.Float64s(sorted)
return sorted
}
func Mean(input []float64) (float64, error) {
if len(input) == 0 {
return math.NaN(), fmt.Errorf("input array is empty: %#v", input)
}
sum := 0.0
for _, n := range input {
sum += n
}
return sum / float64(len(input)), nil
}
func Median(input []float64) (median float64, err error) {
c := sortedCopy(input)
// Even numbers: add the two middle numbers, divide by two (use mean function)
// Odd numbers: Use the middle number
l := len(c)
if l == 0 {
return math.NaN(), fmt.Errorf("input array is empty: %#v", input)
} else if l%2 == 0 {
median, _ = Mean(c[l/2-1 : l/2+1])
} else {
median = c[l/2]
}
return median, nil
}

46
pkg/schema/metrics.go
View File

@ -10,6 +10,8 @@ import (
"math"
"sort"
"unsafe"
"github.com/ClusterCockpit/cc-backend/internal/util"
)
type JobData map[string]map[MetricScope]*JobMetric
@ -36,6 +38,7 @@ type MetricStatistics struct {
type StatsSeries struct {
Mean []Float `json:"mean"`
Median []Float `json:"median"`
Min []Float `json:"min"`
Max []Float `json:"max"`
Percentiles map[int][]Float `json:"percentiles,omitempty"`
@ -120,7 +123,7 @@ func (jd *JobData) Size() int {
for _, metric := range scopes {
if metric.StatisticsSeries != nil {
n += len(metric.StatisticsSeries.Max)
n += len(metric.StatisticsSeries.Mean)
n += len(metric.StatisticsSeries.Median)
n += len(metric.StatisticsSeries.Min)
}
@ -149,53 +152,74 @@ func (jm *JobMetric) AddStatisticsSeries() {
}
}
min, mean, max := make([]Float, n), make([]Float, n), make([]Float, n)
// mean := make([]Float, n)
min, median, max := make([]Float, n), make([]Float, n), make([]Float, n)
i := 0
for ; i < m; i++ {
smin, ssum, smax := math.MaxFloat32, 0.0, -math.MaxFloat32
seriesCount := len(jm.Series)
// ssum := 0.0
smin, smed, smax := math.MaxFloat32, make([]float64, seriesCount), -math.MaxFloat32
notnan := 0
for j := 0; j < len(jm.Series); j++ {
for j := 0; j < seriesCount; j++ {
x := float64(jm.Series[j].Data[i])
if math.IsNaN(x) {
continue
}
notnan += 1
ssum += x
// ssum += x
smed[j] = x
smin = math.Min(smin, x)
smax = math.Max(smax, x)
}
if notnan < 3 {
min[i] = NaN
mean[i] = NaN
// mean[i] = NaN
median[i] = NaN
max[i] = NaN
} else {
min[i] = Float(smin)
mean[i] = Float(ssum / float64(notnan))
// mean[i] = Float(ssum / float64(notnan))
max[i] = Float(smax)
medianRaw, err := util.Median(smed)
if err != nil {
median[i] = NaN
} else {
median[i] = Float(medianRaw)
}
}
}
for ; i < n; i++ {
min[i] = NaN
mean[i] = NaN
// mean[i] = NaN
median[i] = NaN
max[i] = NaN
}
if smooth {
for i := 2; i < len(mean)-2; i++ {
for i := 2; i < len(median)-2; i++ {
if min[i].IsNaN() {
continue
}
min[i] = (min[i-2] + min[i-1] + min[i] + min[i+1] + min[i+2]) / 5
max[i] = (max[i-2] + max[i-1] + max[i] + max[i+1] + max[i+2]) / 5
mean[i] = (mean[i-2] + mean[i-1] + mean[i] + mean[i+1] + mean[i+2]) / 5
// mean[i] = (mean[i-2] + mean[i-1] + mean[i] + mean[i+1] + mean[i+2]) / 5
// Reduce Median further
smoothRaw := []float64{float64(median[i-2]), float64(median[i-1]), float64(median[i]), float64(median[i+1]), float64(median[i+2])}
smoothMedian, err := util.Median(smoothRaw)
if err != nil {
median[i] = NaN
} else {
median[i] = Float(smoothMedian)
}
}
}
jm.StatisticsSeries = &StatsSeries{Mean: mean, Min: min, Max: max}
jm.StatisticsSeries = &StatsSeries{Median: median, Min: min, Max: max} // Mean: mean
}
func (jd *JobData) AddNodeScope(metric string) bool {

2
web/frontend/src/JobFootprint.svelte
View File

@ -101,7 +101,7 @@
// Calculate Avg from jobMetrics
const jm = jobMetrics.find((jm) => jm.name === fm && jm.scope === "node");
if (jm?.metric?.statisticsSeries) {
const noNan = jm.metric.statisticsSeries.mean.filter(function (val) {
const noNan = jm.metric.statisticsSeries.median.filter(function (val) {
return val != null;
});
mv = round(mean(noNan), 2);

4
web/frontend/src/Metric.svelte
View File

@ -33,7 +33,7 @@
error = null;
let selectedScope = minScope(scopes);
let statsPattern = /(.*)-stats$/
let statsPattern = /(.*)-stat$/
let statsSeries = rawData.map((data) => data?.statisticsSeries ? data.statisticsSeries : null)
let selectedScopeIndex
@ -92,7 +92,7 @@
{#each availableScopes as scope, index}
<option value={scope}>{scope}</option>
{#if statsSeries[index]}
<option value={scope + '-stats'}>stats series ({scope})</option>
<option value={scope + '-stat'}>stats series ({scope})</option>
{/if}
{/each}
{#if availableScopes.length == 1 && metricConfig?.scope != "node"}

2
web/frontend/src/joblist/Row.svelte
View File

@ -50,7 +50,7 @@
timestep
statisticsSeries {
min
mean
median
max
}
series {

18
web/frontend/src/plots/MetricPlot.svelte
View File

@ -216,7 +216,7 @@
// conditional hide series color markers:
if (
useStatsSeries === true || // Min/Max/Avg Self-Explanatory
useStatsSeries === true || // Min/Max/Median Self-Explanatory
dataSize === 1 || // Only one Y-Dataseries
dataSize > 6
) {
@ -296,7 +296,7 @@
}
const longestSeries = useStatsSeries
? statisticsSeries.mean.length
? statisticsSeries.median.length
: series.reduce((n, series) => Math.max(n, series.data.length), 0);
const maxX = longestSeries * timestep;
let maxY = null;
@ -346,13 +346,15 @@
if (useStatsSeries) {
plotData.push(statisticsSeries.min);
plotData.push(statisticsSeries.max);
plotData.push(statisticsSeries.mean);
plotData.push(statisticsSeries.median);
// plotData.push(statisticsSeries.mean);
if (forNode === true) {
// timestamp 0 with null value for reversed time axis
if (plotData[1].length != 0) plotData[1].push(null);
if (plotData[2].length != 0) plotData[2].push(null);
if (plotData[3].length != 0) plotData[3].push(null);
// if (plotData[4].length != 0) plotData[4].push(null);
}
plotSeries.push({
@ -368,11 +370,17 @@
stroke: "green",
});
plotSeries.push({
label: "mean",
label: "median",
scale: "y",
width: lineWidth,
stroke: "black",
});
// plotSeries.push({
// label: "mean",
// scale: "y",
// width: lineWidth,
// stroke: "blue",
// });
plotBands = [
{ series: [2, 3], fill: "rgba(0,255,0,0.1)" },
@ -422,7 +430,7 @@
// Draw plot type label:
let textl = `${scope}${plotSeries.length > 2 ? "s" : ""}${
useStatsSeries
? ": min/avg/max"
? ": min/median/max"
: metricConfig != null && scope != metricConfig.scope
? ` (${metricConfig.aggregation})`
: ""