aniketio-ctrl 68effaf232
chore: support for non-normalized metrics behind a feature flag (#7919)
feat(7294-services): added dot metrics boolean for services tab
2025-05-30 10:27:29 +00:00

347 lines
9.5 KiB
Go

package inframetrics
import (
"context"
"math"
"sort"
"github.com/SigNoz/signoz/pkg/query-service/app/metrics/v4/helpers"
"github.com/SigNoz/signoz/pkg/query-service/common"
"github.com/SigNoz/signoz/pkg/query-service/interfaces"
"github.com/SigNoz/signoz/pkg/query-service/model"
v3 "github.com/SigNoz/signoz/pkg/query-service/model/v3"
"github.com/SigNoz/signoz/pkg/query-service/postprocess"
"github.com/SigNoz/signoz/pkg/valuer"
"golang.org/x/exp/slices"
)
var (
metricToUseForClusters = GetDotMetrics("k8s_node_cpu_utilization")
clusterAttrsToEnrich = []string{GetDotMetrics("k8s_cluster_name")}
// TODO(srikanthccv): change this to k8s_cluster_uid after showing the missing data banner
k8sClusterUIDAttrKey = GetDotMetrics("k8s_cluster_name")
queryNamesForClusters = map[string][]string{
"cpu": {"A"},
"cpu_allocatable": {"B"},
"memory": {"C"},
"memory_allocatable": {"D"},
}
clusterQueryNames = []string{"A", "B", "C", "D"}
)
type ClustersRepo struct {
reader interfaces.Reader
querierV2 interfaces.Querier
}
func NewClustersRepo(reader interfaces.Reader, querierV2 interfaces.Querier) *ClustersRepo {
return &ClustersRepo{reader: reader, querierV2: querierV2}
}
func (n *ClustersRepo) GetClusterAttributeKeys(ctx context.Context, req v3.FilterAttributeKeyRequest) (*v3.FilterAttributeKeyResponse, error) {
req.DataSource = v3.DataSourceMetrics
req.AggregateAttribute = metricToUseForClusters
if req.Limit == 0 {
req.Limit = 50
}
attributeKeysResponse, err := n.reader.GetMetricAttributeKeys(ctx, &req)
if err != nil {
return nil, err
}
return attributeKeysResponse, nil
}
func (n *ClustersRepo) GetClusterAttributeValues(ctx context.Context, req v3.FilterAttributeValueRequest) (*v3.FilterAttributeValueResponse, error) {
req.DataSource = v3.DataSourceMetrics
req.AggregateAttribute = metricToUseForClusters
if req.Limit == 0 {
req.Limit = 50
}
attributeValuesResponse, err := n.reader.GetMetricAttributeValues(ctx, &req)
if err != nil {
return nil, err
}
return attributeValuesResponse, nil
}
func (p *ClustersRepo) getMetadataAttributes(ctx context.Context, req model.ClusterListRequest) (map[string]map[string]string, error) {
clusterAttrs := map[string]map[string]string{}
for _, key := range clusterAttrsToEnrich {
hasKey := false
for _, groupByKey := range req.GroupBy {
if groupByKey.Key == key {
hasKey = true
break
}
}
if !hasKey {
req.GroupBy = append(req.GroupBy, v3.AttributeKey{Key: key})
}
}
mq := v3.BuilderQuery{
DataSource: v3.DataSourceMetrics,
AggregateAttribute: v3.AttributeKey{
Key: metricToUseForClusters,
DataType: v3.AttributeKeyDataTypeFloat64,
},
Temporality: v3.Unspecified,
GroupBy: req.GroupBy,
}
query, err := helpers.PrepareTimeseriesFilterQuery(req.Start, req.End, &mq)
if err != nil {
return nil, err
}
query = localQueryToDistributedQuery(query)
attrsListResponse, err := p.reader.GetListResultV3(ctx, query)
if err != nil {
return nil, err
}
for _, row := range attrsListResponse {
stringData := map[string]string{}
for key, value := range row.Data {
if str, ok := value.(string); ok {
stringData[key] = str
} else if strPtr, ok := value.(*string); ok {
stringData[key] = *strPtr
}
}
clusterUID := stringData[k8sClusterUIDAttrKey]
if _, ok := clusterAttrs[clusterUID]; !ok {
clusterAttrs[clusterUID] = map[string]string{}
}
for _, key := range req.GroupBy {
clusterAttrs[clusterUID][key.Key] = stringData[key.Key]
}
}
return clusterAttrs, nil
}
func (p *ClustersRepo) getTopClusterGroups(ctx context.Context, orgID valuer.UUID, req model.ClusterListRequest, q *v3.QueryRangeParamsV3) ([]map[string]string, []map[string]string, error) {
step, timeSeriesTableName, samplesTableName := getParamsForTopClusters(req)
queryNames := queryNamesForClusters[req.OrderBy.ColumnName]
topClusterGroupsQueryRangeParams := &v3.QueryRangeParamsV3{
Start: req.Start,
End: req.End,
Step: step,
CompositeQuery: &v3.CompositeQuery{
BuilderQueries: map[string]*v3.BuilderQuery{},
QueryType: v3.QueryTypeBuilder,
PanelType: v3.PanelTypeTable,
},
}
for _, queryName := range queryNames {
query := q.CompositeQuery.BuilderQueries[queryName].Clone()
query.StepInterval = step
query.MetricTableHints = &v3.MetricTableHints{
TimeSeriesTableName: timeSeriesTableName,
SamplesTableName: samplesTableName,
}
if req.Filters != nil && len(req.Filters.Items) > 0 {
if query.Filters == nil {
query.Filters = &v3.FilterSet{Operator: "AND", Items: []v3.FilterItem{}}
}
query.Filters.Items = append(query.Filters.Items, req.Filters.Items...)
}
topClusterGroupsQueryRangeParams.CompositeQuery.BuilderQueries[queryName] = query
}
queryResponse, _, err := p.querierV2.QueryRange(ctx, orgID, topClusterGroupsQueryRangeParams)
if err != nil {
return nil, nil, err
}
formattedResponse, err := postprocess.PostProcessResult(queryResponse, topClusterGroupsQueryRangeParams)
if err != nil {
return nil, nil, err
}
if len(formattedResponse) == 0 || len(formattedResponse[0].Series) == 0 {
return nil, nil, nil
}
if req.OrderBy.Order == v3.DirectionDesc {
sort.Slice(formattedResponse[0].Series, func(i, j int) bool {
return formattedResponse[0].Series[i].Points[0].Value > formattedResponse[0].Series[j].Points[0].Value
})
} else {
sort.Slice(formattedResponse[0].Series, func(i, j int) bool {
return formattedResponse[0].Series[i].Points[0].Value < formattedResponse[0].Series[j].Points[0].Value
})
}
max := math.Min(float64(req.Offset+req.Limit), float64(len(formattedResponse[0].Series)))
paginatedTopClusterGroupsSeries := formattedResponse[0].Series[req.Offset:int(max)]
topClusterGroups := []map[string]string{}
for _, series := range paginatedTopClusterGroupsSeries {
topClusterGroups = append(topClusterGroups, series.Labels)
}
allClusterGroups := []map[string]string{}
for _, series := range formattedResponse[0].Series {
allClusterGroups = append(allClusterGroups, series.Labels)
}
return topClusterGroups, allClusterGroups, nil
}
func (p *ClustersRepo) GetClusterList(ctx context.Context, orgID valuer.UUID, req model.ClusterListRequest) (model.ClusterListResponse, error) {
resp := model.ClusterListResponse{}
if req.Limit == 0 {
req.Limit = 10
}
if req.OrderBy == nil {
req.OrderBy = &v3.OrderBy{ColumnName: "cpu", Order: v3.DirectionDesc}
}
if req.GroupBy == nil {
req.GroupBy = []v3.AttributeKey{{Key: k8sClusterUIDAttrKey}}
resp.Type = model.ResponseTypeList
} else {
resp.Type = model.ResponseTypeGroupedList
}
step := int64(math.Max(float64(common.MinAllowedStepInterval(req.Start, req.End)), 60))
query := NodesTableListQuery.Clone()
query.Start = req.Start
query.End = req.End
query.Step = step
for _, query := range query.CompositeQuery.BuilderQueries {
query.StepInterval = step
if req.Filters != nil && len(req.Filters.Items) > 0 {
if query.Filters == nil {
query.Filters = &v3.FilterSet{Operator: "AND", Items: []v3.FilterItem{}}
}
query.Filters.Items = append(query.Filters.Items, req.Filters.Items...)
}
query.GroupBy = req.GroupBy
}
clusterAttrs, err := p.getMetadataAttributes(ctx, req)
if err != nil {
return resp, err
}
topClusterGroups, allClusterGroups, err := p.getTopClusterGroups(ctx, orgID, req, query)
if err != nil {
return resp, err
}
groupFilters := map[string][]string{}
for _, topClusterGroup := range topClusterGroups {
for k, v := range topClusterGroup {
groupFilters[k] = append(groupFilters[k], v)
}
}
for groupKey, groupValues := range groupFilters {
hasGroupFilter := false
if req.Filters != nil && len(req.Filters.Items) > 0 {
for _, filter := range req.Filters.Items {
if filter.Key.Key == groupKey {
hasGroupFilter = true
break
}
}
}
if !hasGroupFilter {
for _, query := range query.CompositeQuery.BuilderQueries {
query.Filters.Items = append(query.Filters.Items, v3.FilterItem{
Key: v3.AttributeKey{Key: groupKey},
Value: groupValues,
Operator: v3.FilterOperatorIn,
})
}
}
}
queryResponse, _, err := p.querierV2.QueryRange(ctx, orgID, query)
if err != nil {
return resp, err
}
formattedResponse, err := postprocess.PostProcessResult(queryResponse, query)
if err != nil {
return resp, err
}
records := []model.ClusterListRecord{}
for _, result := range formattedResponse {
for _, row := range result.Table.Rows {
record := model.ClusterListRecord{
CPUUsage: -1,
CPUAllocatable: -1,
MemoryUsage: -1,
MemoryAllocatable: -1,
}
if clusterUID, ok := row.Data[k8sClusterUIDAttrKey].(string); ok {
record.ClusterUID = clusterUID
}
if cpu, ok := row.Data["A"].(float64); ok {
record.CPUUsage = cpu
}
if cpuAllocatable, ok := row.Data["B"].(float64); ok {
record.CPUAllocatable = cpuAllocatable
}
if mem, ok := row.Data["C"].(float64); ok {
record.MemoryUsage = mem
}
if memoryAllocatable, ok := row.Data["D"].(float64); ok {
record.MemoryAllocatable = memoryAllocatable
}
record.Meta = map[string]string{}
if _, ok := clusterAttrs[record.ClusterUID]; ok && record.ClusterUID != "" {
record.Meta = clusterAttrs[record.ClusterUID]
}
for k, v := range row.Data {
if slices.Contains(clusterQueryNames, k) {
continue
}
if labelValue, ok := v.(string); ok {
record.Meta[k] = labelValue
}
}
records = append(records, record)
}
}
resp.Total = len(allClusterGroups)
resp.Records = records
resp.SortBy(req.OrderBy)
return resp, nil
}