mirror of https://github.com/grafana/grafana.git
Prometheus: Run Explore range queries trough backend (#39133)
* Run Explore range queries trough backend * Remove trailing comma * Add timeRange step alignment to backend * Remove creation of instant query on backend as it is not supported ATM * Remove non-related frontend changes * Pass offset to calculate aligned range trough prom query * Update order in query error message * tableRefIds shouldn't contain undefined refIds * Remove cloning of dataframes when processing * Don't mutate response * Remove ordering of processed frames * Remove df because not needed
This commit is contained in:
Ivana Huckova
GitHub
parent
0606618d4a
commit
81756cd702
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@ -51,6 +51,7 @@ type QueryModel struct {
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RangeQuery bool `json:"range"`
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InstantQuery bool `json:"instant"`
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IntervalFactor int64 `json:"intervalFactor"`
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OffsetSec int64 `json:"offsetSec"`
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}
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type Service struct {
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@ -162,13 +163,20 @@ func (s *Service) QueryData(ctx context.Context, req *backend.QueryDataRequest)
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span.SetTag("stop_unixnano", query.End.UnixNano())
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defer span.Finish()
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value, _, err := client.QueryRange(ctx, query.Expr, timeRange)
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var results model.Value
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switch query.QueryType {
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case Range:
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results, _, err = client.QueryRange(ctx, query.Expr, timeRange)
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if err != nil {
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return &result, err
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return &result, fmt.Errorf("query: %s failed with: %v", query.Expr, err)
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}
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frame, err := parseResponse(value, query)
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default:
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return &result, fmt.Errorf("unknown Query type detected %#v", query.QueryType)
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}
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frame, err := parseResponse(results, query)
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if err != nil {
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return &result, err
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}
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@ -283,13 +291,20 @@ func (s *Service) parseQuery(queryContext *backend.QueryDataRequest, dsInfo *Dat
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expr = strings.ReplaceAll(expr, "$__range", strconv.FormatInt(rangeS, 10)+"s")
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expr = strings.ReplaceAll(expr, "$__rate_interval", intervalv2.FormatDuration(calculateRateInterval(interval, dsInfo.TimeInterval, s.intervalCalculator)))
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queryType := Range
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// Align query range to step. It rounds start and end down to a multiple of step.
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start := int64(math.Floor((float64(query.TimeRange.From.Unix()+model.OffsetSec)/interval.Seconds()))*interval.Seconds() - float64(model.OffsetSec))
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end := int64(math.Floor((float64(query.TimeRange.To.Unix()+model.OffsetSec)/interval.Seconds()))*interval.Seconds() - float64(model.OffsetSec))
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qs = append(qs, &PrometheusQuery{
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Expr: expr,
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Step: interval,
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LegendFormat: model.LegendFormat,
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Start: query.TimeRange.From,
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End: query.TimeRange.To,
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Start: time.Unix(start, 0),
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End: time.Unix(end, 0),
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RefId: query.RefID,
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QueryType: queryType,
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})
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}
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@ -275,6 +275,45 @@ func TestPrometheus_parseQuery(t *testing.T) {
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require.NoError(t, err)
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require.Equal(t, "rate(ALERTS{job=\"test\" [1m]})", models[0].Expr)
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})
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t.Run("parsing query model of range query", func(t *testing.T) {
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timeRange := backend.TimeRange{
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From: now,
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To: now.Add(48 * time.Hour),
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}
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query := queryContext(`{
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"expr": "go_goroutines",
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"format": "time_series",
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"intervalFactor": 1,
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"refId": "A",
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"range": true
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}`, timeRange)
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dsInfo := &DatasourceInfo{}
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models, err := service.parseQuery(query, dsInfo)
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require.NoError(t, err)
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require.Equal(t, Range, models[0].QueryType)
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})
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t.Run("parsing query model of with default query type", func(t *testing.T) {
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timeRange := backend.TimeRange{
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From: now,
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To: now.Add(48 * time.Hour),
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}
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query := queryContext(`{
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"expr": "go_goroutines",
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"format": "time_series",
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"intervalFactor": 1,
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"refId": "A"
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}`, timeRange)
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dsInfo := &DatasourceInfo{}
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models, err := service.parseQuery(query, dsInfo)
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require.NoError(t, err)
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require.Equal(t, Range, models[0].QueryType)
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})
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}
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func queryContext(json string, timeRange backend.TimeRange) *backend.QueryDataRequest {
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@ -9,4 +9,13 @@ type PrometheusQuery struct {
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Start time.Time
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End time.Time
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RefId string
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QueryType PrometheusQueryType
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}
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type PrometheusQueryType string
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const (
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Range PrometheusQueryType = "range"
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//This is currently not used, but we will use it in next iteration
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Instant PrometheusQueryType = "instant"
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)
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@ -8,7 +8,6 @@ import {
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DataQueryError,
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DataQueryRequest,
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DataQueryResponse,
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DataSourceApi,
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DataSourceInstanceSettings,
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dateMath,
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DateTime,
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@ -17,7 +16,7 @@ import {
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ScopedVars,
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TimeRange,
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} from '@grafana/data';
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import { BackendSrvRequest, FetchError, FetchResponse, getBackendSrv } from '@grafana/runtime';
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import { BackendSrvRequest, FetchError, FetchResponse, getBackendSrv, DataSourceWithBackend } from '@grafana/runtime';
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import { safeStringifyValue } from 'app/core/utils/explore';
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import { getTimeSrv, TimeSrv } from 'app/features/dashboard/services/TimeSrv';
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@ -26,7 +25,7 @@ import addLabelToQuery from './add_label_to_query';
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import PrometheusLanguageProvider from './language_provider';
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import { expandRecordingRules } from './language_utils';
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import { getInitHints, getQueryHints } from './query_hints';
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import { getOriginalMetricName, renderTemplate, transform } from './result_transformer';
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import { getOriginalMetricName, renderTemplate, transform, transformV2 } from './result_transformer';
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import {
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ExemplarTraceIdDestination,
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isFetchErrorResponse,
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@ -47,12 +46,13 @@ export const ANNOTATION_QUERY_STEP_DEFAULT = '60s';
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const EXEMPLARS_NOT_AVAILABLE = 'Exemplars for this query are not available.';
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const GET_AND_POST_METADATA_ENDPOINTS = ['api/v1/query', 'api/v1/query_range', 'api/v1/series', 'api/v1/labels'];
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export class PrometheusDatasource extends DataSourceApi<PromQuery, PromOptions> {
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export class PrometheusDatasource extends DataSourceWithBackend<PromQuery, PromOptions> {
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type: string;
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editorSrc: string;
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ruleMappings: { [index: string]: string };
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url: string;
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directUrl: string;
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access: 'direct' | 'proxy';
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basicAuth: any;
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withCredentials: any;
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metricsNameCache = new LRU<string, string[]>(10);
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@ -75,6 +75,7 @@ export class PrometheusDatasource extends DataSourceApi<PromQuery, PromOptions>
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this.type = 'prometheus';
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this.editorSrc = 'app/features/prometheus/partials/query.editor.html';
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this.url = instanceSettings.url!;
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this.access = instanceSettings.access;
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this.basicAuth = instanceSettings.basicAuth;
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this.withCredentials = instanceSettings.withCredentials;
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this.interval = instanceSettings.jsonData.timeInterval || '15s';
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@ -288,7 +289,34 @@ export class PrometheusDatasource extends DataSourceApi<PromQuery, PromOptions>
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};
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};
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prepareOptionsV2 = (options: DataQueryRequest<PromQuery>) => {
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const targets = options.targets.map((target) => {
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// We want to format Explore + range queries as time_series
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return {
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...target,
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instant: false,
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range: true,
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format: 'time_series',
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offsetSec: this.timeSrv.timeRange().to.utcOffset() * 60,
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};
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});
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return { ...options, targets };
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};
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query(options: DataQueryRequest<PromQuery>): Observable<DataQueryResponse> {
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// WIP - currently we want to run trough backend only if all queries are explore + range queries
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const shouldRunBackendQuery =
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this.access === 'proxy' &&
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options.app === CoreApp.Explore &&
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!options.targets.some((query) => query.exemplar) &&
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!options.targets.some((query) => query.instant);
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if (shouldRunBackendQuery) {
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const newOptions = this.prepareOptionsV2(options);
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return super.query(newOptions).pipe(map((response) => transformV2(response, newOptions)));
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// Run queries trough browser/proxy
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} else {
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const start = this.getPrometheusTime(options.range.from, false);
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const end = this.getPrometheusTime(options.range.to, true);
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const { queries, activeTargets } = this.prepareTargets(options, start, end);
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@ -307,6 +335,7 @@ export class PrometheusDatasource extends DataSourceApi<PromQuery, PromOptions>
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return this.panelsQuery(queries, activeTargets, end, options.requestId, options.scopedVars);
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}
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}
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private exploreQuery(queries: PromQueryRequest[], activeTargets: PromQuery[], end: number) {
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let runningQueriesCount = queries.length;
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@ -824,6 +853,27 @@ export class PrometheusDatasource extends DataSourceApi<PromQuery, PromOptions>
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getOriginalMetricName(labelData: { [key: string]: string }) {
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return getOriginalMetricName(labelData);
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}
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// Used when running queries trough backend
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filterQuery(query: PromQuery): boolean {
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if (query.hide || !query.expr) {
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return false;
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}
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return true;
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}
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// Used when running queries trough backend
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applyTemplateVariables(target: PromQuery, scopedVars: ScopedVars): Record<string, any> {
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const variables = cloneDeep(scopedVars);
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// We want to interpolate these variables on backend
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delete variables.__interval;
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delete variables.__interval_ms;
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return {
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...target,
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expr: this.templateSrv.replace(target.expr, variables, this.interpolateQueryExpr),
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};
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}
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}
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/**
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@ -1,5 +1,6 @@
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import { DataFrame, FieldType } from '@grafana/data';
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import { transform } from './result_transformer';
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import { DataFrame, FieldType, DataQueryRequest, DataQueryResponse, MutableDataFrame } from '@grafana/data';
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import { transform, transformV2, transformDFoTable } from './result_transformer';
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import { PromQuery } from './types';
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jest.mock('@grafana/runtime', () => ({
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getTemplateSrv: () => ({
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@ -31,6 +32,169 @@ const matrixResponse = {
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};
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describe('Prometheus Result Transformer', () => {
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describe('transformV2', () => {
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it('results with time_series format should be enriched with preferredVisualisationType', () => {
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const options = ({
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targets: [
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{
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format: 'time_series',
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refId: 'A',
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},
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],
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} as unknown) as DataQueryRequest<PromQuery>;
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const response = ({
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state: 'Done',
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data: [
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{
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fields: [],
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length: 2,
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name: 'ALERTS',
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refId: 'A',
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},
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],
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} as unknown) as DataQueryResponse;
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const series = transformV2(response, options);
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expect(series).toEqual({
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data: [{ fields: [], length: 2, meta: { preferredVisualisationType: 'graph' }, name: 'ALERTS', refId: 'A' }],
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state: 'Done',
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});
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});
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it('results with table format should be transformed to table dataFrames', () => {
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const options = ({
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targets: [
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{
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format: 'table',
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refId: 'A',
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},
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],
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} as unknown) as DataQueryRequest<PromQuery>;
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const response = ({
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state: 'Done',
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data: [
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new MutableDataFrame({
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refId: 'A',
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fields: [
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{ name: 'time', type: FieldType.time, values: [6, 5, 4] },
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{
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name: 'value',
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type: FieldType.number,
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values: [6, 5, 4],
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labels: { label1: 'value1', label2: 'value2' },
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},
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],
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}),
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],
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} as unknown) as DataQueryResponse;
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const series = transformV2(response, options);
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// expect(series.data[0]).toBe({});
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expect(series.data[0].fields[0].name).toEqual('time');
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expect(series.data[0].fields[1].name).toEqual('label1');
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expect(series.data[0].fields[2].name).toEqual('label2');
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expect(series.data[0].fields[3].name).toEqual('Value');
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expect(series.data[0].meta?.preferredVisualisationType).toEqual('table');
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});
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it('results with table and time_series format should be correctly transformed', () => {
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const options = ({
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targets: [
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{
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format: 'table',
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refId: 'A',
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},
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{
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format: 'time_series',
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refId: 'B',
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},
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],
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} as unknown) as DataQueryRequest<PromQuery>;
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const response = ({
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state: 'Done',
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data: [
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new MutableDataFrame({
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refId: 'A',
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fields: [
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{ name: 'time', type: FieldType.time, values: [6, 5, 4] },
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{
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name: 'value',
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type: FieldType.number,
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values: [6, 5, 4],
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labels: { label1: 'value1', label2: 'value2' },
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},
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],
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}),
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new MutableDataFrame({
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refId: 'B',
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fields: [
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{ name: 'time', type: FieldType.time, values: [6, 5, 4] },
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{
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name: 'value',
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type: FieldType.number,
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values: [6, 5, 4],
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labels: { label1: 'value1', label2: 'value2' },
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},
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],
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}),
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],
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} as unknown) as DataQueryResponse;
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const series = transformV2(response, options);
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expect(series.data[0].fields.length).toEqual(2);
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expect(series.data[0].meta?.preferredVisualisationType).toEqual('graph');
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expect(series.data[1].fields.length).toEqual(4);
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expect(series.data[1].meta?.preferredVisualisationType).toEqual('table');
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});
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});
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describe('transformDFoTable', () => {
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it('transforms dataFrame with response length 1 to table dataFrame', () => {
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const df = new MutableDataFrame({
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refId: 'A',
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fields: [
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{ name: 'time', type: FieldType.time, values: [6, 5, 4] },
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{
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name: 'value',
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type: FieldType.number,
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values: [6, 5, 4],
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labels: { label1: 'value1', label2: 'value2' },
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},
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],
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});
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const tableDf = transformDFoTable(df, 1);
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expect(tableDf.fields.length).toBe(4);
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expect(tableDf.fields[0].name).toBe('time');
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expect(tableDf.fields[1].name).toBe('label1');
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expect(tableDf.fields[1].values.get(0)).toBe('value1');
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expect(tableDf.fields[2].name).toBe('label2');
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expect(tableDf.fields[2].values.get(0)).toBe('value2');
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expect(tableDf.fields[3].name).toBe('Value');
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});
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it('transforms dataFrame with response length 2 to table dataFrame', () => {
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const df = new MutableDataFrame({
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refId: 'A',
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fields: [
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{ name: 'time', type: FieldType.time, values: [6, 5, 4] },
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{
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name: 'value',
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type: FieldType.number,
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values: [6, 5, 4],
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labels: { label1: 'value1', label2: 'value2' },
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},
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],
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});
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const tableDf = transformDFoTable(df, 3);
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expect(tableDf.fields.length).toBe(4);
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expect(tableDf.fields[0].name).toBe('time');
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expect(tableDf.fields[1].name).toBe('label1');
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expect(tableDf.fields[1].values.get(0)).toBe('value1');
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expect(tableDf.fields[2].name).toBe('label2');
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expect(tableDf.fields[2].values.get(0)).toBe('value2');
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expect(tableDf.fields[3].name).toBe('Value #A');
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});
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});
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describe('transform', () => {
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const options: any = { target: {}, query: {} };
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describe('When nothing is returned', () => {
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it('should return empty array', () => {
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@ -341,7 +505,10 @@ describe('Prometheus Result Transformer', () => {
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});
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it('should fill null values', () => {
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const result = transform({ data: matrixResponse } as any, { ...options, query: { step: 1, start: 0, end: 2 } });
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const result = transform({ data: matrixResponse } as any, {
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...options,
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query: { step: 1, start: 0, end: 2 },
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});
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expect(result[0].fields[0].values.toArray()).toEqual([0, 1000, 2000]);
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expect(result[0].fields[1].values.toArray()).toEqual([null, 10, 0]);
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@ -453,7 +620,10 @@ describe('Prometheus Result Transformer', () => {
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};
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it('should correctly parse values', () => {
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const result: DataFrame[] = transform({ data: response } as any, { ...options, target: { format: 'table' } });
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const result: DataFrame[] = transform({ data: response } as any, {
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...options,
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target: { format: 'table' },
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});
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expect(result[0].fields[1].values.toArray()).toEqual([Number.POSITIVE_INFINITY]);
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});
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});
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|
|
@ -478,7 +648,10 @@ describe('Prometheus Result Transformer', () => {
|
|||
|
||||
describe('When format is table', () => {
|
||||
it('should correctly parse values', () => {
|
||||
const result: DataFrame[] = transform({ data: response } as any, { ...options, target: { format: 'table' } });
|
||||
const result: DataFrame[] = transform({ data: response } as any, {
|
||||
...options,
|
||||
target: { format: 'table' },
|
||||
});
|
||||
expect(result[0].fields[3].values.toArray()).toEqual([Number.POSITIVE_INFINITY, Number.NEGATIVE_INFINITY]);
|
||||
});
|
||||
});
|
||||
|
|
@ -585,3 +758,4 @@ describe('Prometheus Result Transformer', () => {
|
|||
});
|
||||
});
|
||||
});
|
||||
});
|
||||
|
|
|
|||
|
|
@ -13,8 +13,12 @@ import {
|
|||
ScopedVars,
|
||||
TIME_SERIES_TIME_FIELD_NAME,
|
||||
TIME_SERIES_VALUE_FIELD_NAME,
|
||||
DataQueryResponse,
|
||||
DataQueryRequest,
|
||||
PreferredVisualisationType,
|
||||
} from '@grafana/data';
|
||||
import { FetchResponse, getDataSourceSrv, getTemplateSrv } from '@grafana/runtime';
|
||||
import { partition } from 'lodash';
|
||||
import { descending, deviation } from 'd3';
|
||||
import {
|
||||
ExemplarTraceIdDestination,
|
||||
|
|
@ -37,6 +41,84 @@ interface TimeAndValue {
|
|||
[TIME_SERIES_VALUE_FIELD_NAME]: number;
|
||||
}
|
||||
|
||||
// V2 result trasnformer used to transform query results from queries that were run trough prometheus backend
|
||||
export function transformV2(response: DataQueryResponse, options: DataQueryRequest<PromQuery>) {
|
||||
// Get refIds that have table format as we need to process those to table reuslts
|
||||
const tableRefIds = options.targets.filter((target) => target.format === 'table').map((target) => target.refId);
|
||||
const [tableResults, otherResults]: [DataFrame[], DataFrame[]] = partition(response.data, (dataFrame) =>
|
||||
dataFrame.refId ? tableRefIds.includes(dataFrame.refId) : false
|
||||
);
|
||||
|
||||
// For table results, we need to transform data frames to table data frames
|
||||
const tableFrames = tableResults.map((dataFrame) => {
|
||||
const df = transformDFoTable(dataFrame, options.targets.length);
|
||||
return df;
|
||||
});
|
||||
|
||||
// Everything else is processed as time_series result and graph preferredVisualisationType
|
||||
const otherFrames = otherResults.map((dataFrame) => {
|
||||
const df = {
|
||||
...dataFrame,
|
||||
meta: {
|
||||
...dataFrame.meta,
|
||||
preferredVisualisationType: 'graph',
|
||||
},
|
||||
} as DataFrame;
|
||||
return df;
|
||||
});
|
||||
|
||||
return { ...response, data: [...otherFrames, ...tableFrames] };
|
||||
}
|
||||
|
||||
export function transformDFoTable(df: DataFrame, responseLength: number): DataFrame {
|
||||
if (df.length === 0) {
|
||||
return df;
|
||||
}
|
||||
|
||||
const timeField = df.fields[0];
|
||||
const valueField = df.fields[1];
|
||||
|
||||
// Create label fields
|
||||
const promLabels: PromMetric = valueField.labels ?? {};
|
||||
const labelFields = Object.keys(promLabels)
|
||||
.sort()
|
||||
.map((label) => {
|
||||
const numberField = label === 'le';
|
||||
return {
|
||||
name: label,
|
||||
config: { filterable: true },
|
||||
type: numberField ? FieldType.number : FieldType.string,
|
||||
values: new ArrayVector(),
|
||||
};
|
||||
});
|
||||
|
||||
// Fill labelFields with label values
|
||||
labelFields.forEach((field) => field.values.add(getLabelValue(promLabels, field.name)));
|
||||
|
||||
const tableDataFrame = {
|
||||
...df,
|
||||
name: undefined,
|
||||
meta: { ...df.meta, preferredVisualisationType: 'table' as PreferredVisualisationType },
|
||||
fields: [
|
||||
timeField,
|
||||
...labelFields,
|
||||
{
|
||||
...valueField,
|
||||
name: getValueText(responseLength, df.refId),
|
||||
labels: undefined,
|
||||
config: { ...valueField.config, displayNameFromDS: undefined },
|
||||
state: { ...valueField.state, displayName: undefined },
|
||||
},
|
||||
],
|
||||
};
|
||||
|
||||
return tableDataFrame;
|
||||
}
|
||||
|
||||
function getValueText(responseLength: number, refId = '') {
|
||||
return responseLength > 1 ? `Value #${refId}` : 'Value';
|
||||
}
|
||||
|
||||
export function transform(
|
||||
response: FetchResponse<PromDataSuccessResponse>,
|
||||
transformOptions: {
|
||||
|
|
|
|||
|
|
@ -10,6 +10,8 @@ export interface PromQuery extends DataQuery {
|
|||
hinting?: boolean;
|
||||
interval?: string;
|
||||
intervalFactor?: number;
|
||||
// Timezone offset to align start & end time on backend
|
||||
offsetSec?: number;
|
||||
legendFormat?: string;
|
||||
valueWithRefId?: boolean;
|
||||
requestId?: string;
|
||||
|
|
|
|||
Loading…
Reference in New Issue