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time_series_query.go
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time_series_query.go
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package buffered
import (
"context"
"encoding/json"
"fmt"
"math"
"net/http"
"regexp"
"sort"
"strconv"
"strings"
"time"
"github.com/grafana/grafana-plugin-sdk-go/backend"
sdkHTTPClient "github.com/grafana/grafana-plugin-sdk-go/backend/httpclient"
"github.com/grafana/grafana-plugin-sdk-go/data"
"github.com/grafana/grafana/pkg/infra/log"
"github.com/grafana/grafana/pkg/infra/tracing"
"github.com/grafana/grafana/pkg/tsdb/intervalv2"
"github.com/grafana/grafana/pkg/tsdb/prometheus/middleware"
"github.com/grafana/grafana/pkg/tsdb/prometheus/utils"
"github.com/grafana/grafana/pkg/util/maputil"
apiv1 "github.com/prometheus/client_golang/api/prometheus/v1"
"github.com/prometheus/common/model"
"go.opentelemetry.io/otel/attribute"
)
// Internal interval and range variables
const (
varInterval = "$__interval"
varIntervalMs = "$__interval_ms"
varRange = "$__range"
varRangeS = "$__range_s"
varRangeMs = "$__range_ms"
varRateInterval = "$__rate_interval"
)
// Internal interval and range variables with {} syntax
// Repetitive code, we should have functionality to unify these
const (
varIntervalAlt = "${__interval}"
varIntervalMsAlt = "${__interval_ms}"
varRangeAlt = "${__range}"
varRangeSAlt = "${__range_s}"
varRangeMsAlt = "${__range_ms}"
varRateIntervalAlt = "${__rate_interval}"
)
const legendFormatAuto = "__auto"
var (
legendFormat = regexp.MustCompile(`\{\{\s*(.+?)\s*\}\}`)
safeRes = 11000
)
type Buffered struct {
intervalCalculator intervalv2.Calculator
tracer tracing.Tracer
client apiv1.API
log log.Logger
ID int64
URL string
TimeInterval string
}
// New creates and object capable of executing and parsing a Prometheus queries. It's "buffered" because there is
// another implementation capable of streaming parse the response.
func New(roundTripper http.RoundTripper, tracer tracing.Tracer, settings backend.DataSourceInstanceSettings, plog log.Logger) (*Buffered, error) {
promClient, err := CreateClient(roundTripper, settings.URL)
if err != nil {
return nil, fmt.Errorf("error creating prom client: %v", err)
}
jsonData, err := utils.GetJsonData(settings)
if err != nil {
return nil, fmt.Errorf("error getting jsonData: %w", err)
}
timeInterval, err := maputil.GetStringOptional(jsonData, "timeInterval")
if err != nil {
return nil, err
}
return &Buffered{
intervalCalculator: intervalv2.NewCalculator(),
tracer: tracer,
log: plog,
client: promClient,
TimeInterval: timeInterval,
ID: settings.ID,
URL: settings.URL,
}, nil
}
func (b *Buffered) ExecuteTimeSeriesQuery(ctx context.Context, req *backend.QueryDataRequest) (*backend.QueryDataResponse, error) {
// Add headers from the request to context so they are added later on by a context middleware. This is because
// prom client does not allow us to do this directly.
addHeaders := make(map[string]string)
if req.Headers["FromAlert"] == "true" {
addHeaders["FromAlert"] = "true"
}
ctxWithHeaders := sdkHTTPClient.WithContextualMiddleware(ctx, middleware.ReqHeadersMiddleware(addHeaders))
queries, err := b.parseTimeSeriesQuery(req)
if err != nil {
result := backend.QueryDataResponse{
Responses: backend.Responses{},
}
return &result, fmt.Errorf("error parsing time series query: %v", err)
}
return b.runQueries(ctxWithHeaders, queries)
}
func (b *Buffered) runQueries(ctx context.Context, queries []*PrometheusQuery) (*backend.QueryDataResponse, error) {
result := backend.QueryDataResponse{
Responses: backend.Responses{},
}
for _, query := range queries {
b.log.Debug("Sending query", "start", query.Start, "end", query.End, "step", query.Step, "query", query.Expr)
ctx, endSpan := utils.StartTrace(ctx, b.tracer, "datasource.prometheus", []utils.Attribute{
{Key: "expr", Value: query.Expr, Kv: attribute.Key("expr").String(query.Expr)},
{Key: "start_unixnano", Value: query.Start, Kv: attribute.Key("start_unixnano").Int64(query.Start.UnixNano())},
{Key: "stop_unixnano", Value: query.End, Kv: attribute.Key("stop_unixnano").Int64(query.End.UnixNano())},
})
defer endSpan()
response := make(map[TimeSeriesQueryType]interface{})
timeRange := apiv1.Range{
Step: query.Step,
// Align query range to step. It rounds start and end down to a multiple of step.
Start: alignTimeRange(query.Start, query.Step, query.UtcOffsetSec),
End: alignTimeRange(query.End, query.Step, query.UtcOffsetSec),
}
if query.RangeQuery {
rangeResponse, _, err := b.client.QueryRange(ctx, query.Expr, timeRange)
if err != nil {
b.log.Error("Range query failed", "query", query.Expr, "err", err)
result.Responses[query.RefId] = backend.DataResponse{Error: err}
continue
}
response[RangeQueryType] = rangeResponse
}
if query.InstantQuery {
instantResponse, _, err := b.client.Query(ctx, query.Expr, query.End)
if err != nil {
b.log.Error("Instant query failed", "query", query.Expr, "err", err)
result.Responses[query.RefId] = backend.DataResponse{Error: err}
continue
}
response[InstantQueryType] = instantResponse
}
// This is a special case
// If exemplar query returns error, we want to only log it and continue with other results processing
if query.ExemplarQuery {
exemplarResponse, err := b.client.QueryExemplars(ctx, query.Expr, timeRange.Start, timeRange.End)
if err != nil {
b.log.Error("Exemplar query failed", "query", query.Expr, "err", err)
} else {
response[ExemplarQueryType] = exemplarResponse
}
}
frames, err := parseTimeSeriesResponse(response, query)
if err != nil {
return &result, err
}
// The ExecutedQueryString can be viewed in QueryInspector in UI
for _, frame := range frames {
frame.Meta.ExecutedQueryString = "Expr: " + query.Expr + "\n" + "Step: " + query.Step.String()
}
result.Responses[query.RefId] = backend.DataResponse{
Frames: frames,
}
}
return &result, nil
}
func formatLegend(metric model.Metric, query *PrometheusQuery) string {
var legend = metric.String()
if query.LegendFormat == legendFormatAuto {
// If we have labels set legend to empty string to utilize the auto naming system
if len(metric) > 0 {
legend = ""
}
} else if query.LegendFormat != "" {
result := legendFormat.ReplaceAllFunc([]byte(query.LegendFormat), func(in []byte) []byte {
labelName := strings.Replace(string(in), "{{", "", 1)
labelName = strings.Replace(labelName, "}}", "", 1)
labelName = strings.TrimSpace(labelName)
if val, exists := metric[model.LabelName(labelName)]; exists {
return []byte(val)
}
return []byte{}
})
legend = string(result)
}
// If legend is empty brackets, use query expression
if legend == "{}" {
legend = query.Expr
}
return legend
}
func (b *Buffered) parseTimeSeriesQuery(req *backend.QueryDataRequest) ([]*PrometheusQuery, error) {
qs := []*PrometheusQuery{}
for _, query := range req.Queries {
model := &QueryModel{}
err := json.Unmarshal(query.JSON, model)
if err != nil {
return nil, fmt.Errorf("error unmarshaling query model: %v", err)
}
//Final interval value
interval, err := calculatePrometheusInterval(model, b.TimeInterval, query, b.intervalCalculator)
if err != nil {
return nil, fmt.Errorf("error calculating interval: %v", err)
}
// Interpolate variables in expr
timeRange := query.TimeRange.To.Sub(query.TimeRange.From)
expr := interpolateVariables(model, interval, timeRange, b.intervalCalculator, b.TimeInterval)
rangeQuery := model.RangeQuery
if !model.InstantQuery && !model.RangeQuery {
// In older dashboards, we were not setting range query param and !range && !instant was run as range query
rangeQuery = true
}
// We never want to run exemplar query for alerting
exemplarQuery := model.ExemplarQuery
if req.Headers["FromAlert"] == "true" {
exemplarQuery = false
}
qs = append(qs, &PrometheusQuery{
Expr: expr,
Step: interval,
LegendFormat: model.LegendFormat,
Start: query.TimeRange.From,
End: query.TimeRange.To,
RefId: query.RefID,
InstantQuery: model.InstantQuery,
RangeQuery: rangeQuery,
ExemplarQuery: exemplarQuery,
UtcOffsetSec: model.UtcOffsetSec,
})
}
return qs, nil
}
func parseTimeSeriesResponse(value map[TimeSeriesQueryType]interface{}, query *PrometheusQuery) (data.Frames, error) {
var (
frames = data.Frames{}
nextFrames = data.Frames{}
)
for _, value := range value {
// Zero out the slice to prevent data corruption.
nextFrames = nextFrames[:0]
switch v := value.(type) {
case model.Matrix:
nextFrames = matrixToDataFrames(v, query, nextFrames)
case model.Vector:
nextFrames = vectorToDataFrames(v, query, nextFrames)
case *model.Scalar:
nextFrames = scalarToDataFrames(v, query, nextFrames)
case []apiv1.ExemplarQueryResult:
nextFrames = exemplarToDataFrames(v, query, nextFrames)
default:
return nil, fmt.Errorf("unexpected result type: %s query: %s", v, query.Expr)
}
frames = append(frames, nextFrames...)
}
return frames, nil
}
func calculatePrometheusInterval(model *QueryModel, timeInterval string, query backend.DataQuery, intervalCalculator intervalv2.Calculator) (time.Duration, error) {
queryInterval := model.Interval
//If we are using variable for interval/step, we will replace it with calculated interval
if isVariableInterval(queryInterval) {
queryInterval = ""
}
minInterval, err := intervalv2.GetIntervalFrom(timeInterval, queryInterval, model.IntervalMS, 15*time.Second)
if err != nil {
return time.Duration(0), err
}
calculatedInterval := intervalCalculator.Calculate(query.TimeRange, minInterval, query.MaxDataPoints)
safeInterval := intervalCalculator.CalculateSafeInterval(query.TimeRange, int64(safeRes))
adjustedInterval := safeInterval.Value
if calculatedInterval.Value > safeInterval.Value {
adjustedInterval = calculatedInterval.Value
}
if model.Interval == varRateInterval || model.Interval == varRateIntervalAlt {
// Rate interval is final and is not affected by resolution
return calculateRateInterval(adjustedInterval, timeInterval, intervalCalculator), nil
} else {
intervalFactor := model.IntervalFactor
if intervalFactor == 0 {
intervalFactor = 1
}
return time.Duration(int64(adjustedInterval) * intervalFactor), nil
}
}
func calculateRateInterval(interval time.Duration, scrapeInterval string, intervalCalculator intervalv2.Calculator) time.Duration {
scrape := scrapeInterval
if scrape == "" {
scrape = "15s"
}
scrapeIntervalDuration, err := intervalv2.ParseIntervalStringToTimeDuration(scrape)
if err != nil {
return time.Duration(0)
}
rateInterval := time.Duration(int64(math.Max(float64(interval+scrapeIntervalDuration), float64(4)*float64(scrapeIntervalDuration))))
return rateInterval
}
func interpolateVariables(model *QueryModel, interval time.Duration, timeRange time.Duration, intervalCalculator intervalv2.Calculator, timeInterval string) string {
expr := model.Expr
rangeMs := timeRange.Milliseconds()
rangeSRounded := int64(math.Round(float64(rangeMs) / 1000.0))
var rateInterval time.Duration
if model.Interval == varRateInterval || model.Interval == varRateIntervalAlt {
rateInterval = interval
} else {
rateInterval = calculateRateInterval(interval, timeInterval, intervalCalculator)
}
expr = strings.ReplaceAll(expr, varIntervalMs, strconv.FormatInt(int64(interval/time.Millisecond), 10))
expr = strings.ReplaceAll(expr, varInterval, intervalv2.FormatDuration(interval))
expr = strings.ReplaceAll(expr, varRangeMs, strconv.FormatInt(rangeMs, 10))
expr = strings.ReplaceAll(expr, varRangeS, strconv.FormatInt(rangeSRounded, 10))
expr = strings.ReplaceAll(expr, varRange, strconv.FormatInt(rangeSRounded, 10)+"s")
expr = strings.ReplaceAll(expr, varRateInterval, rateInterval.String())
// Repetitive code, we should have functionality to unify these
expr = strings.ReplaceAll(expr, varIntervalMsAlt, strconv.FormatInt(int64(interval/time.Millisecond), 10))
expr = strings.ReplaceAll(expr, varIntervalAlt, intervalv2.FormatDuration(interval))
expr = strings.ReplaceAll(expr, varRangeMsAlt, strconv.FormatInt(rangeMs, 10))
expr = strings.ReplaceAll(expr, varRangeSAlt, strconv.FormatInt(rangeSRounded, 10))
expr = strings.ReplaceAll(expr, varRangeAlt, strconv.FormatInt(rangeSRounded, 10)+"s")
expr = strings.ReplaceAll(expr, varRateIntervalAlt, rateInterval.String())
return expr
}
func matrixToDataFrames(matrix model.Matrix, query *PrometheusQuery, frames data.Frames) data.Frames {
for _, v := range matrix {
tags := make(map[string]string, len(v.Metric))
for k, v := range v.Metric {
tags[string(k)] = string(v)
}
timeField := data.NewFieldFromFieldType(data.FieldTypeTime, len(v.Values))
valueField := data.NewFieldFromFieldType(data.FieldTypeFloat64, len(v.Values))
for i, k := range v.Values {
timeField.Set(i, k.Timestamp.Time().UTC())
value := float64(k.Value)
if !math.IsNaN(value) {
valueField.Set(i, value)
}
}
name := formatLegend(v.Metric, query)
timeField.Name = data.TimeSeriesTimeFieldName
timeField.Config = &data.FieldConfig{Interval: float64(query.Step.Milliseconds())}
valueField.Name = data.TimeSeriesValueFieldName
valueField.Labels = tags
if name != "" {
valueField.Config = &data.FieldConfig{DisplayNameFromDS: name}
}
frames = append(frames, newDataFrame(name, "matrix", timeField, valueField))
}
return frames
}
func scalarToDataFrames(scalar *model.Scalar, query *PrometheusQuery, frames data.Frames) data.Frames {
timeVector := []time.Time{scalar.Timestamp.Time().UTC()}
values := []float64{float64(scalar.Value)}
name := fmt.Sprintf("%g", values[0])
return append(
frames,
newDataFrame(
name,
"scalar",
data.NewField("Time", nil, timeVector),
data.NewField("Value", nil, values).SetConfig(&data.FieldConfig{DisplayNameFromDS: name}),
),
)
}
func vectorToDataFrames(vector model.Vector, query *PrometheusQuery, frames data.Frames) data.Frames {
for _, v := range vector {
name := formatLegend(v.Metric, query)
tags := make(map[string]string, len(v.Metric))
timeVector := []time.Time{v.Timestamp.Time().UTC()}
values := []float64{float64(v.Value)}
for k, v := range v.Metric {
tags[string(k)] = string(v)
}
frames = append(
frames,
newDataFrame(
name,
"vector",
data.NewField("Time", nil, timeVector),
data.NewField("Value", tags, values).SetConfig(&data.FieldConfig{DisplayNameFromDS: name}),
),
)
}
return frames
}
// normalizeExemplars transforms the exemplar results into a single list of events. At the same time we make sure
// that all exemplar events have the same labels which is important when converting to dataFrames so that we have
// the same length of each field (each label will be a separate field). Exemplars can have different label either
// because the exemplar event have different labels or because they are from different series.
// Reason why we merge exemplars into single list even if they are from different series is that for example in case
// of a histogram query, like histogram_quantile(0.99, sum(rate(traces_spanmetrics_duration_seconds_bucket[15s])) by (le))
// Prometheus still returns all the exemplars for all the series of metric traces_spanmetrics_duration_seconds_bucket.
// Which makes sense because each histogram bucket is separate series but we still want to show all the exemplars for
// the metric and we don't specifically care which buckets they are from.
// For non histogram queries or if you split by some label it would probably be nicer to then split also exemplars to
// multiple frames (so they will have different symbols in the UI) but that would require understanding the query so it
// is not implemented now.
func normalizeExemplars(response []apiv1.ExemplarQueryResult) []ExemplarEvent {
// TODO: this preallocation is very naive.
// We should figure out a better approximation here.
events := make([]ExemplarEvent, 0, len(response)*2)
// Get all the labels across all exemplars both from the examplars and their series labels. We will use this to make
// sure the resulting data frame has consistent number of values in each column.
eventLabels := make(map[string]struct{})
for _, exemplarData := range response {
// Check each exemplar labels as there isn't a guarantee they are consistent
for _, exemplar := range exemplarData.Exemplars {
for label := range exemplar.Labels {
eventLabels[string(label)] = struct{}{}
}
}
for label := range exemplarData.SeriesLabels {
eventLabels[string(label)] = struct{}{}
}
}
for _, exemplarData := range response {
for _, exemplar := range exemplarData.Exemplars {
event := ExemplarEvent{}
exemplarTime := exemplar.Timestamp.Time().UTC()
event.Time = exemplarTime
event.Value = float64(exemplar.Value)
event.Labels = make(map[string]string)
// Fill in all the labels from eventLabels with values from exemplar labels or series labels or fill with
// empty string
for label := range eventLabels {
if _, ok := exemplar.Labels[model.LabelName(label)]; ok {
event.Labels[label] = string(exemplar.Labels[model.LabelName(label)])
} else if _, ok := exemplarData.SeriesLabels[model.LabelName(label)]; ok {
event.Labels[label] = string(exemplarData.SeriesLabels[model.LabelName(label)])
} else {
event.Labels[label] = ""
}
}
events = append(events, event)
}
}
return events
}
func exemplarToDataFrames(response []apiv1.ExemplarQueryResult, query *PrometheusQuery, frames data.Frames) data.Frames {
events := normalizeExemplars(response)
// Sampling of exemplars
bucketedExemplars := make(map[string][]ExemplarEvent)
values := make([]float64, 0, len(events))
// Create bucketed exemplars based on aligned timestamp
for _, event := range events {
alignedTs := fmt.Sprintf("%.0f", math.Floor(float64(event.Time.Unix())/query.Step.Seconds())*query.Step.Seconds())
_, ok := bucketedExemplars[alignedTs]
if !ok {
bucketedExemplars[alignedTs] = make([]ExemplarEvent, 0)
}
bucketedExemplars[alignedTs] = append(bucketedExemplars[alignedTs], event)
values = append(values, event.Value)
}
// Calculate standard deviation
standardDeviation := deviation(values)
// Create slice with all of the bucketed exemplars
sampledBuckets := make([]string, len(bucketedExemplars))
for bucketTimes := range bucketedExemplars {
sampledBuckets = append(sampledBuckets, bucketTimes)
}
sort.Strings(sampledBuckets)
// Sample exemplars based ona value, so we are not showing too many of them
sampleExemplars := make([]ExemplarEvent, 0, len(sampledBuckets))
for _, bucket := range sampledBuckets {
exemplarsInBucket := bucketedExemplars[bucket]
if len(exemplarsInBucket) == 1 {
sampleExemplars = append(sampleExemplars, exemplarsInBucket[0])
} else {
bucketValues := make([]float64, len(exemplarsInBucket))
for _, exemplar := range exemplarsInBucket {
bucketValues = append(bucketValues, exemplar.Value)
}
sort.Slice(bucketValues, func(i, j int) bool {
return bucketValues[i] > bucketValues[j]
})
sampledBucketValues := make([]float64, 0)
for _, value := range bucketValues {
if len(sampledBucketValues) == 0 {
sampledBucketValues = append(sampledBucketValues, value)
} else {
// Then take values only when at least 2 standard deviation distance to previously taken value
prev := sampledBucketValues[len(sampledBucketValues)-1]
if standardDeviation != 0 && prev-value >= float64(2)*standardDeviation {
sampledBucketValues = append(sampledBucketValues, value)
}
}
}
for _, valueBucket := range sampledBucketValues {
for _, exemplar := range exemplarsInBucket {
if exemplar.Value == valueBucket {
sampleExemplars = append(sampleExemplars, exemplar)
}
}
}
}
}
// Create DF from sampled exemplars
timeField := data.NewFieldFromFieldType(data.FieldTypeTime, len(sampleExemplars))
timeField.Name = "Time"
valueField := data.NewFieldFromFieldType(data.FieldTypeFloat64, len(sampleExemplars))
valueField.Name = "Value"
labelsVector := make(map[string][]string, len(sampleExemplars))
for i, exemplar := range sampleExemplars {
timeField.Set(i, exemplar.Time)
valueField.Set(i, exemplar.Value)
for label, value := range exemplar.Labels {
if labelsVector[label] == nil {
labelsVector[label] = make([]string, 0)
}
labelsVector[label] = append(labelsVector[label], value)
}
}
dataFields := make([]*data.Field, 0, len(labelsVector)+2)
dataFields = append(dataFields, timeField, valueField)
// Sort the labels/fields so that it is consistent (mainly for easier testing)
allLabels := sortedLabels(labelsVector)
for _, label := range allLabels {
dataFields = append(dataFields, data.NewField(label, nil, labelsVector[label]))
}
return append(frames, newDataFrame("exemplar", "exemplar", dataFields...))
}
func sortedLabels(labelsVector map[string][]string) []string {
allLabels := make([]string, len(labelsVector))
i := 0
for key := range labelsVector {
allLabels[i] = key
i++
}
sort.Strings(allLabels)
return allLabels
}
func deviation(values []float64) float64 {
var sum, mean, sd float64
valuesLen := float64(len(values))
for _, value := range values {
sum += value
}
mean = sum / valuesLen
for j := 0; j < len(values); j++ {
sd += math.Pow(values[j]-mean, 2)
}
return math.Sqrt(sd / (valuesLen - 1))
}
func newDataFrame(name string, typ string, fields ...*data.Field) *data.Frame {
frame := data.NewFrame(name, fields...)
frame.Meta = &data.FrameMeta{
Type: data.FrameTypeTimeSeriesMany,
Custom: map[string]string{
"resultType": typ, // Note: SSE depends on this property and map type
},
}
return frame
}
func alignTimeRange(t time.Time, step time.Duration, offset int64) time.Time {
offsetNano := float64(offset * 1e9)
stepNano := float64(step.Nanoseconds())
return time.Unix(0, int64(math.Floor((float64(t.UnixNano())+offsetNano)/stepNano)*stepNano-offsetNano))
}
func isVariableInterval(interval string) bool {
if interval == varInterval || interval == varIntervalMs || interval == varRateInterval {
return true
}
//Repetitive code, we should have functionality to unify these
if interval == varIntervalAlt || interval == varIntervalMsAlt || interval == varRateIntervalAlt {
return true
}
return false
}