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Prometheus: refactor to DataFrame (#27737)
* Add typing to prometheus response * Refactor result_transformer to return DataFrame * Refactor + test fixes * Fix Prometheus data source test * Modify heatmap function + add back tests * Update performInstantQuery return type * Remove duplicate code from result_transformer * Address review comments * Update metric labels retrival logic to be safer
This commit is contained in:
Zoltán Bedi
GitHub
parent
f97f12f69d
commit
a230aa1031
@ -108,7 +108,7 @@ export interface FetchErrorDataProps {
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export interface FetchError<T extends FetchErrorDataProps = any> {
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status: number;
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statusText?: string;
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data: T | string;
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data: T;
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cancelled?: boolean;
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isHandled?: boolean;
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config: BackendSrvRequest;
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@ -681,32 +681,32 @@ describe('PrometheusDatasource', () => {
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it('should be same length', () => {
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expect(results.data.length).toBe(2);
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expect(results.data[0].datapoints.length).toBe((end - start) / step + 1);
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expect(results.data[1].datapoints.length).toBe((end - start) / step + 1);
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expect(results.data[0].length).toBe((end - start) / step + 1);
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expect(results.data[1].length).toBe((end - start) / step + 1);
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});
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it('should fill null until first datapoint in response', () => {
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expect(results.data[0].datapoints[0][1]).toBe(start * 1000);
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expect(results.data[0].datapoints[0][0]).toBe(null);
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expect(results.data[0].datapoints[1][1]).toBe((start + step * 1) * 1000);
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expect(results.data[0].datapoints[1][0]).toBe(3846);
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expect(results.data[0].fields[0].values.get(0)).toBe(start * 1000);
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expect(results.data[0].fields[1].values.get(0)).toBe(null);
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expect(results.data[0].fields[0].values.get(1)).toBe((start + step * 1) * 1000);
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expect(results.data[0].fields[1].values.get(1)).toBe(3846);
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});
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it('should fill null after last datapoint in response', () => {
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const length = (end - start) / step + 1;
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expect(results.data[0].datapoints[length - 2][1]).toBe((end - step * 1) * 1000);
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expect(results.data[0].datapoints[length - 2][0]).toBe(3848);
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expect(results.data[0].datapoints[length - 1][1]).toBe(end * 1000);
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expect(results.data[0].datapoints[length - 1][0]).toBe(null);
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expect(results.data[0].fields[0].values.get(length - 2)).toBe((end - step * 1) * 1000);
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expect(results.data[0].fields[1].values.get(length - 2)).toBe(3848);
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expect(results.data[0].fields[0].values.get(length - 1)).toBe(end * 1000);
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expect(results.data[0].fields[1].values.get(length - 1)).toBe(null);
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});
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it('should fill null at gap between series', () => {
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expect(results.data[0].datapoints[2][1]).toBe((start + step * 2) * 1000);
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expect(results.data[0].datapoints[2][0]).toBe(null);
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expect(results.data[1].datapoints[1][1]).toBe((start + step * 1) * 1000);
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expect(results.data[1].datapoints[1][0]).toBe(null);
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expect(results.data[1].datapoints[3][1]).toBe((start + step * 3) * 1000);
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expect(results.data[1].datapoints[3][0]).toBe(null);
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expect(results.data[0].fields[0].values.get(2)).toBe((start + step * 2) * 1000);
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expect(results.data[0].fields[1].values.get(2)).toBe(null);
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expect(results.data[1].fields[0].values.get(1)).toBe((start + step * 1) * 1000);
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expect(results.data[1].fields[1].values.get(1)).toBe(null);
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expect(results.data[1].fields[0].values.get(3)).toBe((start + step * 3) * 1000);
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expect(results.data[1].fields[1].values.get(3)).toBe(null);
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});
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});
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@ -1,64 +1,47 @@
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// Libraries
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import cloneDeep from 'lodash/cloneDeep';
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import LRU from 'lru-cache';
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// Services & Utils
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import {
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AnnotationEvent,
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CoreApp,
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DataQueryError,
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DataQueryRequest,
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DataQueryResponse,
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DataQueryResponseData,
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DataSourceApi,
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DataSourceInstanceSettings,
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dateMath,
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DateTime,
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LoadingState,
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rangeUtil,
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ScopedVars,
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TimeRange,
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TimeSeries,
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rangeUtil,
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} from '@grafana/data';
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import { forkJoin, merge, Observable, of, throwError } from 'rxjs';
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import { catchError, filter, map, tap } from 'rxjs/operators';
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import PrometheusMetricFindQuery from './metric_find_query';
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import { ResultTransformer } from './result_transformer';
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import PrometheusLanguageProvider from './language_provider';
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import { BackendSrvRequest, getBackendSrv } from '@grafana/runtime';
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import addLabelToQuery from './add_label_to_query';
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import { getQueryHints } from './query_hints';
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import { expandRecordingRules } from './language_utils';
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// Types
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import { PromOptions, PromQuery, PromQueryRequest } from './types';
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import { BackendSrvRequest, FetchError, getBackendSrv } from '@grafana/runtime';
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import { safeStringifyValue } from 'app/core/utils/explore';
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import templateSrv from 'app/features/templating/template_srv';
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import { getTimeSrv } from 'app/features/dashboard/services/TimeSrv';
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import TableModel from 'app/core/table_model';
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import { defaults } from 'lodash';
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import templateSrv from 'app/features/templating/template_srv';
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import cloneDeep from 'lodash/cloneDeep';
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import defaults from 'lodash/defaults';
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import LRU from 'lru-cache';
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import { forkJoin, merge, Observable, of, pipe, throwError } from 'rxjs';
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import { catchError, filter, map, tap } from 'rxjs/operators';
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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 PrometheusMetricFindQuery from './metric_find_query';
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import { getQueryHints } from './query_hints';
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import { getOriginalMetricName, renderTemplate, transform } from './result_transformer';
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import {
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isFetchErrorResponse,
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PromDataErrorResponse,
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PromDataSuccessResponse,
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PromMatrixData,
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PromOptions,
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PromQuery,
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PromQueryRequest,
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PromScalarData,
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PromVectorData,
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} from './types';
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export const ANNOTATION_QUERY_STEP_DEFAULT = '60s';
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export interface PromDataQueryResponse {
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data: {
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status: string;
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data: {
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resultType: string;
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results?: DataQueryResponseData[];
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result?: DataQueryResponseData[];
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};
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};
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cancelled?: boolean;
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}
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export interface PromLabelQueryResponse {
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data: {
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status: string;
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data: string[];
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};
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cancelled?: boolean;
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}
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export class PrometheusDatasource extends DataSourceApi<PromQuery, PromOptions> {
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type: string;
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editorSrc: string;
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@ -73,7 +56,6 @@ export class PrometheusDatasource extends DataSourceApi<PromQuery, PromOptions>
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httpMethod: string;
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languageProvider: PrometheusLanguageProvider;
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lookupsDisabled: boolean;
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resultTransformer: ResultTransformer;
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customQueryParameters: any;
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constructor(instanceSettings: DataSourceInstanceSettings<PromOptions>) {
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@ -88,7 +70,6 @@ export class PrometheusDatasource extends DataSourceApi<PromQuery, PromOptions>
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this.queryTimeout = instanceSettings.jsonData.queryTimeout;
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this.httpMethod = instanceSettings.jsonData.httpMethod || 'GET';
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this.directUrl = instanceSettings.jsonData.directUrl;
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this.resultTransformer = new ResultTransformer(templateSrv);
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this.ruleMappings = {};
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this.languageProvider = new PrometheusLanguageProvider(this);
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this.lookupsDisabled = instanceSettings.jsonData.disableMetricsLookup ?? false;
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@ -172,38 +153,6 @@ export class PrometheusDatasource extends DataSourceApi<PromQuery, PromOptions>
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return templateSrv.variableExists(target.expr);
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}
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processResult = (
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response: any,
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query: PromQueryRequest,
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target: PromQuery,
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responseListLength: number,
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scopedVars?: ScopedVars,
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mixedQueries?: boolean
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) => {
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// Keeping original start/end for transformers
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const transformerOptions = {
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format: target.format,
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step: query.step,
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legendFormat: target.legendFormat,
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start: query.start,
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end: query.end,
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query: query.expr,
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responseListLength,
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scopedVars,
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refId: target.refId,
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valueWithRefId: target.valueWithRefId,
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meta: {
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/** Fix for showing of Prometheus results in Explore table.
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* We want to show result of instant query always in table and result of range query based on target.runAll;
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*/
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preferredVisualisationType: target.instant ? 'table' : mixedQueries ? 'graph' : undefined,
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},
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};
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const series = this.resultTransformer.transform(response, transformerOptions);
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return series;
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};
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prepareTargets = (options: DataQueryRequest<PromQuery>, start: number, end: number) => {
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const queries: PromQueryRequest[] = [];
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const activeTargets: PromQuery[] = [];
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@ -283,17 +232,13 @@ export class PrometheusDatasource extends DataSourceApi<PromQuery, PromOptions>
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const subQueries = queries.map((query, index) => {
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const target = activeTargets[index];
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let observable = query.instant
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? this.performInstantQuery(query, end)
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: this.performTimeSeriesQuery(query, query.start, query.end);
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return observable.pipe(
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const filterAndMapResponse = pipe(
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// Decrease the counter here. We assume that each request returns only single value and then completes
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// (should hold until there is some streaming requests involved).
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tap(() => runningQueriesCount--),
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filter((response: any) => (response.cancelled ? false : true)),
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map((response: any) => {
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const data = this.processResult(response, query, target, queries.length, undefined, mixedQueries);
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const data = transform(response, { query, target, responseListLength: queries.length, mixedQueries });
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return {
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data,
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key: query.requestId,
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@ -301,6 +246,12 @@ export class PrometheusDatasource extends DataSourceApi<PromQuery, PromOptions>
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} as DataQueryResponse;
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})
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);
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if (query.instant) {
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return this.performInstantQuery(query, end).pipe(filterAndMapResponse);
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}
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return this.performTimeSeriesQuery(query, query.start, query.end).pipe(filterAndMapResponse);
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});
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return merge(...subQueries);
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@ -313,24 +264,26 @@ export class PrometheusDatasource extends DataSourceApi<PromQuery, PromOptions>
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requestId: string,
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scopedVars: ScopedVars
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) {
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const observables: Array<Observable<Array<TableModel | TimeSeries>>> = queries.map((query, index) => {
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const observables = queries.map((query, index) => {
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const target = activeTargets[index];
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let observable = query.instant
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? this.performInstantQuery(query, end)
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: this.performTimeSeriesQuery(query, query.start, query.end);
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return observable.pipe(
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const filterAndMapResponse = pipe(
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filter((response: any) => (response.cancelled ? false : true)),
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map((response: any) => {
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const data = this.processResult(response, query, target, queries.length, scopedVars);
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const data = transform(response, { query, target, responseListLength: queries.length, scopedVars });
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return data;
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})
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);
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if (query.instant) {
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return this.performInstantQuery(query, end).pipe(filterAndMapResponse);
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}
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return this.performTimeSeriesQuery(query, query.start, query.end).pipe(filterAndMapResponse);
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});
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return forkJoin(observables).pipe(
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map((results: Array<Array<TableModel | TimeSeries>>) => {
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map(results => {
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const data = results.reduce((result, current) => {
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return [...result, ...current];
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}, []);
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@ -465,8 +418,11 @@ export class PrometheusDatasource extends DataSourceApi<PromQuery, PromOptions>
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}
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}
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return this._request(url, data, { requestId: query.requestId, headers: query.headers }).pipe(
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catchError(err => {
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return this._request<PromDataSuccessResponse<PromMatrixData>>(url, data, {
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requestId: query.requestId,
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headers: query.headers,
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}).pipe(
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catchError((err: FetchError<PromDataErrorResponse<PromMatrixData>>) => {
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if (err.cancelled) {
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return of(err);
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}
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@ -493,8 +449,11 @@ export class PrometheusDatasource extends DataSourceApi<PromQuery, PromOptions>
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}
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}
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return this._request(url, data, { requestId: query.requestId, headers: query.headers }).pipe(
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catchError(err => {
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return this._request<PromDataSuccessResponse<PromVectorData | PromScalarData>>(url, data, {
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requestId: query.requestId,
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headers: query.headers,
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}).pipe(
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catchError((err: FetchError<PromDataErrorResponse<PromVectorData | PromScalarData>>) => {
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if (err.cancelled) {
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return of(err);
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}
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@ -587,17 +546,11 @@ export class PrometheusDatasource extends DataSourceApi<PromQuery, PromOptions>
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};
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const query = this.createQuery(queryModel, queryOptions, start, end);
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const self = this;
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const response: PromDataQueryResponse = await this.performTimeSeriesQuery(
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query,
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query.start,
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query.end
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).toPromise();
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const response = await this.performTimeSeriesQuery(query, query.start, query.end).toPromise();
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const eventList: AnnotationEvent[] = [];
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const splitKeys = tagKeys.split(',');
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if (response.cancelled) {
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if (isFetchErrorResponse(response) && response.cancelled) {
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return [];
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}
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@ -620,8 +573,8 @@ export class PrometheusDatasource extends DataSourceApi<PromQuery, PromOptions>
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value[0] = timestampValue;
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});
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const activeValues = series.values.filter((value: Record<number, string>) => parseFloat(value[1]) >= 1);
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const activeValuesTimestamps: number[] = activeValues.map((value: number[]) => value[0]);
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const activeValues = series.values.filter(value => parseFloat(value[1]) >= 1);
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const activeValuesTimestamps = activeValues.map(value => value[0]);
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// Instead of creating singular annotation for each active event we group events into region if they are less
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// then `step` apart.
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@ -644,9 +597,9 @@ export class PrometheusDatasource extends DataSourceApi<PromQuery, PromOptions>
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time: timestamp,
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timeEnd: timestamp,
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annotation,
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title: self.resultTransformer.renderTemplate(titleFormat, series.metric),
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title: renderTemplate(titleFormat, series.metric),
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tags,
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text: self.resultTransformer.renderTemplate(textFormat, series.metric),
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text: renderTemplate(textFormat, series.metric),
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};
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}
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@ -676,7 +629,7 @@ export class PrometheusDatasource extends DataSourceApi<PromQuery, PromOptions>
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const response = await this.performInstantQuery(query, now / 1000).toPromise();
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return response.data.status === 'success'
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? { status: 'success', message: 'Data source is working' }
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: { status: 'error', message: response.error };
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: { status: 'error', message: response.data.error };
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}
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interpolateVariablesInQueries(queries: PromQuery[], scopedVars: ScopedVars): PromQuery[] {
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@ -764,7 +717,7 @@ export class PrometheusDatasource extends DataSourceApi<PromQuery, PromOptions>
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}
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getOriginalMetricName(labelData: { [key: string]: string }) {
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return this.resultTransformer.getOriginalMetricName(labelData);
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return getOriginalMetricName(labelData);
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}
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}
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@ -1,2 +0,0 @@
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declare var test: any;
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export default test;
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@ -1,7 +1,7 @@
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import _ from 'lodash';
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import { map } from 'rxjs/operators';
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import { MetricFindValue, TimeRange } from '@grafana/data';
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import { PromDataQueryResponse, PrometheusDatasource } from './datasource';
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import { PrometheusDatasource } from './datasource';
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import { PromQueryRequest } from './types';
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import { getTimeSrv } from 'app/features/dashboard/services/TimeSrv';
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@ -137,7 +137,7 @@ export default class PrometheusMetricFindQuery {
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const end = this.datasource.getPrometheusTime(this.range.to, true);
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const instantQuery: PromQueryRequest = { expr: query } as PromQueryRequest;
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return this.datasource.performInstantQuery(instantQuery, end).pipe(
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map((result: PromDataQueryResponse) => {
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map(result => {
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return _.map(result.data.data.result, metricData => {
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let text = metricData.metric.__name__ || '';
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delete metricData.metric.__name__;
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@ -1,38 +1,30 @@
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import { ResultTransformer } from './result_transformer';
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import { DataQueryResponseData } from '@grafana/data';
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import { DataFrame } from '@grafana/data';
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import { transform } from './result_transformer';
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describe('Prometheus Result Transformer', () => {
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const ctx: any = {};
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beforeEach(() => {
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ctx.templateSrv = {
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replace: (str: string) => str,
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};
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ctx.resultTransformer = new ResultTransformer(ctx.templateSrv);
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});
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const options: any = { target: {}, query: {} };
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describe('When nothing is returned', () => {
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test('should return empty series', () => {
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it('should return empty array', () => {
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const response = {
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status: 'success',
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data: {
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resultType: '',
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result: (null as unknown) as DataQueryResponseData[],
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result: null,
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},
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};
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const series = ctx.resultTransformer.transform({ data: response }, {});
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const series = transform({ data: response } as any, options);
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expect(series).toEqual([]);
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});
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test('should return empty table', () => {
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it('should return empty array', () => {
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const response = {
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status: 'success',
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data: {
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resultType: '',
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result: (null as unknown) as DataQueryResponseData[],
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result: null,
|
||||
},
|
||||
};
|
||||
const table = ctx.resultTransformer.transform({ data: response }, { format: 'table' });
|
||||
expect(table).toMatchObject([{ type: 'table', rows: [] }]);
|
||||
const result = transform({ data: response } as any, { ...options, target: { format: 'table' } });
|
||||
expect(result).toHaveLength(0);
|
||||
});
|
||||
});
|
||||
|
||||
@ -44,48 +36,65 @@ describe('Prometheus Result Transformer', () => {
|
||||
result: [
|
||||
{
|
||||
metric: { __name__: 'test', job: 'testjob' },
|
||||
values: [[1443454528, '3846']],
|
||||
values: [
|
||||
[1443454528, '3846'],
|
||||
[1443454530, '3848'],
|
||||
],
|
||||
},
|
||||
{
|
||||
metric: {
|
||||
__name__: 'test',
|
||||
__name__: 'test2',
|
||||
instance: 'localhost:8080',
|
||||
job: 'otherjob',
|
||||
},
|
||||
values: [[1443454529, '3847']],
|
||||
values: [
|
||||
[1443454529, '3847'],
|
||||
[1443454531, '3849'],
|
||||
],
|
||||
},
|
||||
],
|
||||
},
|
||||
};
|
||||
|
||||
it('should return table model', () => {
|
||||
const table = ctx.resultTransformer.transformMetricDataToTable(response.data.result, 0, 'A');
|
||||
expect(table.type).toBe('table');
|
||||
expect(table.rows).toEqual([
|
||||
[1443454528000, 'test', '', 'testjob', 3846],
|
||||
[1443454529000, 'test', 'localhost:8080', 'otherjob', 3847],
|
||||
it('should return data frame', () => {
|
||||
const result = transform({ data: response } as any, {
|
||||
...options,
|
||||
target: {
|
||||
responseListLength: 0,
|
||||
refId: 'A',
|
||||
format: 'table',
|
||||
},
|
||||
});
|
||||
expect(result[0].fields[0].values.toArray()).toEqual([
|
||||
1443454528000,
|
||||
1443454530000,
|
||||
1443454529000,
|
||||
1443454531000,
|
||||
]);
|
||||
expect(table.columns).toMatchObject([
|
||||
{ text: 'Time', type: 'time' },
|
||||
{ text: '__name__', filterable: true },
|
||||
{ text: 'instance', filterable: true },
|
||||
{ text: 'job' },
|
||||
{ text: 'Value' },
|
||||
]);
|
||||
expect(table.columns[4].filterable).toBeUndefined();
|
||||
expect(table.refId).toBe('A');
|
||||
expect(result[0].fields[0].name).toBe('Time');
|
||||
expect(result[0].fields[1].values.toArray()).toEqual(['test', 'test', 'test2', 'test2']);
|
||||
expect(result[0].fields[1].name).toBe('__name__');
|
||||
expect(result[0].fields[1].config.filterable).toBe(true);
|
||||
expect(result[0].fields[2].values.toArray()).toEqual(['', '', 'localhost:8080', 'localhost:8080']);
|
||||
expect(result[0].fields[2].name).toBe('instance');
|
||||
expect(result[0].fields[3].values.toArray()).toEqual(['testjob', 'testjob', 'otherjob', 'otherjob']);
|
||||
expect(result[0].fields[3].name).toBe('job');
|
||||
expect(result[0].fields[4].values.toArray()).toEqual([3846, 3848, 3847, 3849]);
|
||||
expect(result[0].fields[4].name).toEqual('Value');
|
||||
expect(result[0].refId).toBe('A');
|
||||
});
|
||||
|
||||
it('should column title include refId if response count is more than 2', () => {
|
||||
const table = ctx.resultTransformer.transformMetricDataToTable(response.data.result, 2, 'B');
|
||||
expect(table.type).toBe('table');
|
||||
expect(table.columns).toMatchObject([
|
||||
{ text: 'Time', type: 'time' },
|
||||
{ text: '__name__' },
|
||||
{ text: 'instance' },
|
||||
{ text: 'job' },
|
||||
{ text: 'Value #B' },
|
||||
]);
|
||||
it('should include refId if response count is more than 2', () => {
|
||||
const result = transform({ data: response } as any, {
|
||||
...options,
|
||||
target: {
|
||||
refId: 'B',
|
||||
format: 'table',
|
||||
},
|
||||
responseListLength: 2,
|
||||
});
|
||||
|
||||
expect(result[0].fields[4].name).toEqual('Value #B');
|
||||
});
|
||||
});
|
||||
|
||||
@ -103,31 +112,37 @@ describe('Prometheus Result Transformer', () => {
|
||||
},
|
||||
};
|
||||
|
||||
it('should return table model', () => {
|
||||
const table = ctx.resultTransformer.transformMetricDataToTable(response.data.result);
|
||||
expect(table.type).toBe('table');
|
||||
expect(table.rows).toEqual([[1443454528000, 'test', 'testjob', 3846]]);
|
||||
expect(table.columns).toMatchObject([
|
||||
{ text: 'Time', type: 'time' },
|
||||
{ text: '__name__' },
|
||||
{ text: 'job' },
|
||||
{ text: 'Value' },
|
||||
]);
|
||||
it('should return data frame', () => {
|
||||
const result = transform({ data: response } as any, { ...options, target: { format: 'table' } });
|
||||
expect(result[0].fields[0].values.toArray()).toEqual([1443454528000]);
|
||||
expect(result[0].fields[0].name).toBe('Time');
|
||||
expect(result[0].fields[1].values.toArray()).toEqual(['test']);
|
||||
expect(result[0].fields[1].name).toBe('__name__');
|
||||
expect(result[0].fields[2].values.toArray()).toEqual(['testjob']);
|
||||
expect(result[0].fields[2].name).toBe('job');
|
||||
expect(result[0].fields[3].values.toArray()).toEqual([3846]);
|
||||
expect(result[0].fields[3].name).toEqual('Value');
|
||||
});
|
||||
|
||||
it('should return table model with le label values parsed as numbers', () => {
|
||||
const table = ctx.resultTransformer.transformMetricDataToTable([
|
||||
it('should return le label values parsed as numbers', () => {
|
||||
const response = {
|
||||
status: 'success',
|
||||
data: {
|
||||
resultType: 'vector',
|
||||
result: [
|
||||
{
|
||||
metric: { le: '102' },
|
||||
value: [1594908838, '0'],
|
||||
},
|
||||
]);
|
||||
expect(table.type).toBe('table');
|
||||
expect(table.rows).toEqual([[1594908838000, 102, 0]]);
|
||||
],
|
||||
},
|
||||
};
|
||||
const result = transform({ data: response } as any, { ...options, target: { format: 'table' } });
|
||||
expect(result[0].fields[1].values.toArray()).toEqual([102]);
|
||||
});
|
||||
});
|
||||
|
||||
describe('When resultFormat is time series and instant = true', () => {
|
||||
describe('When instant = true', () => {
|
||||
const response = {
|
||||
status: 'success',
|
||||
data: {
|
||||
@ -141,19 +156,30 @@ describe('Prometheus Result Transformer', () => {
|
||||
},
|
||||
};
|
||||
|
||||
it('should return time series', () => {
|
||||
const timeSeries = ctx.resultTransformer.transform({ data: response }, {});
|
||||
expect(timeSeries[0].target).toBe('test{job="testjob"}');
|
||||
expect(timeSeries[0].title).toBe('test{job="testjob"}');
|
||||
it('should return data frame', () => {
|
||||
const result: DataFrame[] = transform({ data: response } as any, { ...options, query: { instant: true } });
|
||||
expect(result[0].name).toBe('test{job="testjob"}');
|
||||
});
|
||||
});
|
||||
|
||||
describe('When resultFormat is heatmap', () => {
|
||||
const response = {
|
||||
const getResponse = (result: any) => ({
|
||||
status: 'success',
|
||||
data: {
|
||||
resultType: 'matrix',
|
||||
result: [
|
||||
result,
|
||||
},
|
||||
});
|
||||
|
||||
const options = {
|
||||
format: 'heatmap',
|
||||
start: 1445000010,
|
||||
end: 1445000030,
|
||||
legendFormat: '{{le}}',
|
||||
};
|
||||
|
||||
it('should convert cumulative histogram to regular', () => {
|
||||
const response = getResponse([
|
||||
{
|
||||
metric: { __name__: 'test', job: 'testjob', le: '1' },
|
||||
values: [
|
||||
@ -178,121 +204,51 @@ describe('Prometheus Result Transformer', () => {
|
||||
[1445000030, '40'],
|
||||
],
|
||||
},
|
||||
],
|
||||
},
|
||||
};
|
||||
|
||||
it('should convert cumulative histogram to regular', () => {
|
||||
const options = {
|
||||
format: 'heatmap',
|
||||
start: 1445000010,
|
||||
end: 1445000030,
|
||||
legendFormat: '{{le}}',
|
||||
};
|
||||
|
||||
const result = ctx.resultTransformer.transform({ data: response }, options);
|
||||
expect(result).toEqual([
|
||||
{
|
||||
target: '1',
|
||||
title: '1',
|
||||
query: undefined,
|
||||
datapoints: [
|
||||
[10, 1445000010000],
|
||||
[10, 1445000020000],
|
||||
[0, 1445000030000],
|
||||
],
|
||||
tags: { __name__: 'test', job: 'testjob', le: '1' },
|
||||
},
|
||||
{
|
||||
target: '2',
|
||||
title: '2',
|
||||
query: undefined,
|
||||
datapoints: [
|
||||
[10, 1445000010000],
|
||||
[0, 1445000020000],
|
||||
[30, 1445000030000],
|
||||
],
|
||||
tags: { __name__: 'test', job: 'testjob', le: '2' },
|
||||
},
|
||||
{
|
||||
target: '3',
|
||||
title: '3',
|
||||
query: undefined,
|
||||
datapoints: [
|
||||
[10, 1445000010000],
|
||||
[0, 1445000020000],
|
||||
[10, 1445000030000],
|
||||
],
|
||||
tags: { __name__: 'test', job: 'testjob', le: '3' },
|
||||
},
|
||||
]);
|
||||
|
||||
const result = transform({ data: response } as any, { query: options, target: options } as any);
|
||||
expect(result[0].fields[0].values.toArray()).toEqual([1445000010000, 1445000020000, 1445000030000]);
|
||||
expect(result[0].fields[1].values.toArray()).toEqual([10, 10, 0]);
|
||||
expect(result[1].fields[0].values.toArray()).toEqual([1445000010000, 1445000020000, 1445000030000]);
|
||||
expect(result[1].fields[1].values.toArray()).toEqual([10, 0, 30]);
|
||||
expect(result[2].fields[0].values.toArray()).toEqual([1445000010000, 1445000020000, 1445000030000]);
|
||||
expect(result[2].fields[1].values.toArray()).toEqual([10, 0, 10]);
|
||||
});
|
||||
|
||||
it('should handle missing datapoints', () => {
|
||||
const seriesList = [
|
||||
const response = getResponse([
|
||||
{
|
||||
datapoints: [
|
||||
[1, 1000],
|
||||
[2, 2000],
|
||||
metric: { __name__: 'test', job: 'testjob', le: '1' },
|
||||
values: [
|
||||
[1445000010, '1'],
|
||||
[1445000020, '2'],
|
||||
],
|
||||
},
|
||||
{
|
||||
datapoints: [
|
||||
[2, 1000],
|
||||
[5, 2000],
|
||||
[1, 3000],
|
||||
metric: { __name__: 'test', job: 'testjob', le: '2' },
|
||||
values: [
|
||||
[1445000010, '2'],
|
||||
[1445000020, '5'],
|
||||
[1445000030, '1'],
|
||||
],
|
||||
},
|
||||
{
|
||||
datapoints: [
|
||||
[3, 1000],
|
||||
[7, 2000],
|
||||
metric: { __name__: 'test', job: 'testjob', le: '3' },
|
||||
values: [
|
||||
[1445000010, '3'],
|
||||
[1445000020, '7'],
|
||||
],
|
||||
},
|
||||
];
|
||||
const expected = [
|
||||
{
|
||||
datapoints: [
|
||||
[1, 1000],
|
||||
[2, 2000],
|
||||
],
|
||||
},
|
||||
{
|
||||
datapoints: [
|
||||
[1, 1000],
|
||||
[3, 2000],
|
||||
[1, 3000],
|
||||
],
|
||||
},
|
||||
{
|
||||
datapoints: [
|
||||
[1, 1000],
|
||||
[2, 2000],
|
||||
],
|
||||
},
|
||||
];
|
||||
const result = ctx.resultTransformer.transformToHistogramOverTime(seriesList);
|
||||
expect(result).toEqual(expected);
|
||||
});
|
||||
|
||||
it('should throw error when data in wrong format', () => {
|
||||
const seriesList = [{ rows: [] as any[] }, { datapoints: [] as any[] }];
|
||||
expect(() => {
|
||||
ctx.resultTransformer.transformToHistogramOverTime(seriesList);
|
||||
}).toThrow();
|
||||
});
|
||||
|
||||
it('should throw error when prometheus returned non-timeseries', () => {
|
||||
// should be { metric: {}, values: [] } for timeseries
|
||||
const metricData = { metric: {}, value: [] as any[] };
|
||||
expect(() => {
|
||||
ctx.resultTransformer.transformMetricData(metricData, { step: 1 }, 1000, 2000);
|
||||
}).toThrow();
|
||||
]);
|
||||
const result = transform({ data: response } as any, { query: options, target: options } as any);
|
||||
expect(result[0].fields[1].values.toArray()).toEqual([1, 2]);
|
||||
expect(result[1].fields[1].values.toArray()).toEqual([1, 3, 1]);
|
||||
expect(result[2].fields[1].values.toArray()).toEqual([1, 2]);
|
||||
});
|
||||
});
|
||||
|
||||
describe('When resultFormat is time series', () => {
|
||||
it('should transform matrix into timeseries', () => {
|
||||
describe('When the response is a matrix', () => {
|
||||
it('should transform into a data frame', () => {
|
||||
const response = {
|
||||
status: 'success',
|
||||
data: {
|
||||
@ -309,31 +265,20 @@ describe('Prometheus Result Transformer', () => {
|
||||
],
|
||||
},
|
||||
};
|
||||
const options = {
|
||||
format: 'timeseries',
|
||||
|
||||
const result: DataFrame[] = transform({ data: response } as any, {
|
||||
...options,
|
||||
query: {
|
||||
start: 0,
|
||||
end: 2,
|
||||
refId: 'B',
|
||||
};
|
||||
|
||||
const result = ctx.resultTransformer.transform({ data: response }, options);
|
||||
expect(result).toEqual([
|
||||
{
|
||||
target: 'test{job="testjob"}',
|
||||
title: 'test{job="testjob"}',
|
||||
query: undefined,
|
||||
datapoints: [
|
||||
[10, 0],
|
||||
[10, 1000],
|
||||
[0, 2000],
|
||||
],
|
||||
tags: { job: 'testjob' },
|
||||
refId: 'B',
|
||||
},
|
||||
]);
|
||||
});
|
||||
expect(result[0].fields[0].values.toArray()).toEqual([0, 1000, 2000]);
|
||||
expect(result[0].fields[1].values.toArray()).toEqual([10, 10, 0]);
|
||||
expect(result[0].name).toBe('test{job="testjob"}');
|
||||
});
|
||||
|
||||
it('should fill timeseries with null values', () => {
|
||||
it('should fill null values', () => {
|
||||
const response = {
|
||||
status: 'success',
|
||||
data: {
|
||||
@ -349,27 +294,11 @@ describe('Prometheus Result Transformer', () => {
|
||||
],
|
||||
},
|
||||
};
|
||||
const options = {
|
||||
format: 'timeseries',
|
||||
step: 1,
|
||||
start: 0,
|
||||
end: 2,
|
||||
};
|
||||
|
||||
const result = ctx.resultTransformer.transform({ data: response }, options);
|
||||
expect(result).toEqual([
|
||||
{
|
||||
target: 'test{job="testjob"}',
|
||||
title: 'test{job="testjob"}',
|
||||
query: undefined,
|
||||
datapoints: [
|
||||
[null, 0],
|
||||
[10, 1000],
|
||||
[0, 2000],
|
||||
],
|
||||
tags: { job: 'testjob' },
|
||||
},
|
||||
]);
|
||||
const result = transform({ data: response } as any, { ...options, query: { step: 1, start: 0, end: 2 } });
|
||||
|
||||
expect(result[0].fields[0].values.toArray()).toEqual([0, 1000, 2000]);
|
||||
expect(result[0].fields[1].values.toArray()).toEqual([null, 10, 0]);
|
||||
});
|
||||
|
||||
it('should use __name__ label as series name', () => {
|
||||
@ -389,15 +318,15 @@ describe('Prometheus Result Transformer', () => {
|
||||
},
|
||||
};
|
||||
|
||||
const options = {
|
||||
format: 'timeseries',
|
||||
const result = transform({ data: response } as any, {
|
||||
...options,
|
||||
query: {
|
||||
step: 1,
|
||||
start: 0,
|
||||
end: 2,
|
||||
};
|
||||
|
||||
const result = ctx.resultTransformer.transform({ data: response }, options);
|
||||
expect(result[0].target).toEqual('test{job="testjob"}');
|
||||
},
|
||||
});
|
||||
expect(result[0].name).toEqual('test{job="testjob"}');
|
||||
});
|
||||
|
||||
it('should set frame name to undefined if no __name__ label but there are other labels', () => {
|
||||
@ -417,17 +346,15 @@ describe('Prometheus Result Transformer', () => {
|
||||
},
|
||||
};
|
||||
|
||||
const options = {
|
||||
format: 'timeseries',
|
||||
const result = transform({ data: response } as any, {
|
||||
...options,
|
||||
query: {
|
||||
step: 1,
|
||||
query: 'Some query',
|
||||
start: 0,
|
||||
end: 2,
|
||||
};
|
||||
|
||||
const result = ctx.resultTransformer.transform({ data: response }, options);
|
||||
expect(result[0].target).toBe('{job="testjob"}');
|
||||
expect(result[0].tags.job).toEqual('testjob');
|
||||
},
|
||||
});
|
||||
expect(result[0].name).toBe('{job="testjob"}');
|
||||
});
|
||||
|
||||
it('should align null values with step', () => {
|
||||
@ -446,35 +373,10 @@ describe('Prometheus Result Transformer', () => {
|
||||
],
|
||||
},
|
||||
};
|
||||
const options = {
|
||||
format: 'timeseries',
|
||||
step: 2,
|
||||
start: 0,
|
||||
end: 8,
|
||||
refId: 'A',
|
||||
meta: { custom: { hello: '1' } },
|
||||
};
|
||||
|
||||
const result = ctx.resultTransformer.transform({ data: response }, options);
|
||||
expect(result).toEqual([
|
||||
{
|
||||
target: 'test{job="testjob"}',
|
||||
title: 'test{job="testjob"}',
|
||||
meta: {
|
||||
custom: { hello: '1' },
|
||||
},
|
||||
query: undefined,
|
||||
refId: 'A',
|
||||
datapoints: [
|
||||
[null, 0],
|
||||
[null, 2000],
|
||||
[10, 4000],
|
||||
[null, 6000],
|
||||
[10, 8000],
|
||||
],
|
||||
tags: { job: 'testjob' },
|
||||
},
|
||||
]);
|
||||
const result = transform({ data: response } as any, { ...options, query: { step: 2, start: 0, end: 8 } });
|
||||
expect(result[0].fields[0].values.toArray()).toEqual([0, 2000, 4000, 6000, 8000]);
|
||||
expect(result[0].fields[1].values.toArray()).toEqual([null, null, 10, null, 10]);
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
@ -1,204 +1,267 @@
|
||||
import _ from 'lodash';
|
||||
import TableModel from 'app/core/table_model';
|
||||
import { TimeSeries, FieldType, Labels, formatLabels, QueryResultMeta } from '@grafana/data';
|
||||
import { TemplateSrv } from 'app/features/templating/template_srv';
|
||||
import {
|
||||
ArrayVector,
|
||||
DataFrame,
|
||||
Field,
|
||||
FieldType,
|
||||
formatLabels,
|
||||
MutableField,
|
||||
ScopedVars,
|
||||
TIME_SERIES_TIME_FIELD_NAME,
|
||||
TIME_SERIES_VALUE_FIELD_NAME,
|
||||
} from '@grafana/data';
|
||||
import { FetchResponse } from '@grafana/runtime';
|
||||
import templateSrv from 'app/features/templating/template_srv';
|
||||
import {
|
||||
isMatrixData,
|
||||
MatrixOrVectorResult,
|
||||
PromDataSuccessResponse,
|
||||
PromMetric,
|
||||
PromQuery,
|
||||
PromQueryRequest,
|
||||
PromValue,
|
||||
TransformOptions,
|
||||
} from './types';
|
||||
|
||||
export class ResultTransformer {
|
||||
constructor(private templateSrv: TemplateSrv) {}
|
||||
|
||||
transform(response: any, options: any): Array<TableModel | TimeSeries> {
|
||||
const prometheusResult = response.data.data.result;
|
||||
|
||||
if (options.format === 'table') {
|
||||
return [
|
||||
this.transformMetricDataToTable(
|
||||
prometheusResult,
|
||||
options.responseListLength,
|
||||
options.refId,
|
||||
options.meta,
|
||||
options.valueWithRefId
|
||||
export function transform(
|
||||
response: FetchResponse<PromDataSuccessResponse>,
|
||||
transformOptions: {
|
||||
query: PromQueryRequest;
|
||||
target: PromQuery;
|
||||
responseListLength: number;
|
||||
scopedVars?: ScopedVars;
|
||||
mixedQueries?: boolean;
|
||||
}
|
||||
) {
|
||||
// Create options object from transformOptions
|
||||
const options: TransformOptions = {
|
||||
format: transformOptions.target.format,
|
||||
step: transformOptions.query.step,
|
||||
legendFormat: transformOptions.target.legendFormat,
|
||||
start: transformOptions.query.start,
|
||||
end: transformOptions.query.end,
|
||||
query: transformOptions.query.expr,
|
||||
responseListLength: transformOptions.responseListLength,
|
||||
scopedVars: transformOptions.scopedVars,
|
||||
refId: transformOptions.target.refId,
|
||||
valueWithRefId: transformOptions.target.valueWithRefId,
|
||||
meta: {
|
||||
/**
|
||||
* Fix for showing of Prometheus results in Explore table.
|
||||
* We want to show result of instant query always in table and result of range query based on target.runAll;
|
||||
*/
|
||||
preferredVisualisationType: getPreferredVisualisationType(
|
||||
transformOptions.query.instant,
|
||||
transformOptions.mixedQueries
|
||||
),
|
||||
];
|
||||
} else if (prometheusResult && options.format === 'heatmap') {
|
||||
let seriesList: TimeSeries[] = [];
|
||||
for (const metricData of prometheusResult) {
|
||||
seriesList.push(this.transformMetricData(metricData, options, options.start, options.end));
|
||||
}
|
||||
seriesList.sort(sortSeriesByLabel);
|
||||
seriesList = this.transformToHistogramOverTime(seriesList);
|
||||
return seriesList;
|
||||
} else if (prometheusResult) {
|
||||
const seriesList: TimeSeries[] = [];
|
||||
for (const metricData of prometheusResult) {
|
||||
if (response.data.data.resultType === 'matrix') {
|
||||
seriesList.push(this.transformMetricData(metricData, options, options.start, options.end));
|
||||
} else if (response.data.data.resultType === 'vector') {
|
||||
seriesList.push(this.transformInstantMetricData(metricData, options));
|
||||
}
|
||||
}
|
||||
return seriesList;
|
||||
}
|
||||
},
|
||||
};
|
||||
const prometheusResult = response.data.data;
|
||||
|
||||
if (!prometheusResult.result) {
|
||||
return [];
|
||||
}
|
||||
|
||||
transformMetricData(metricData: any, options: any, start: number, end: number): TimeSeries {
|
||||
const dps = [];
|
||||
const { name, labels, title } = this.createLabelInfo(metricData.metric, options);
|
||||
|
||||
const stepMs = parseFloat(options.step) * 1000;
|
||||
let baseTimestamp = start * 1000;
|
||||
|
||||
if (metricData.values === undefined) {
|
||||
throw new Error('Prometheus heatmap error: data should be a time series');
|
||||
// Return early if result type is scalar
|
||||
if (prometheusResult.resultType === 'scalar') {
|
||||
return [
|
||||
{
|
||||
meta: options.meta,
|
||||
refId: options.refId,
|
||||
length: 1,
|
||||
fields: [getTimeField([prometheusResult.result]), getValueField([prometheusResult.result])],
|
||||
},
|
||||
];
|
||||
}
|
||||
|
||||
for (const value of metricData.values) {
|
||||
// Return early again if the format is table, this needs special transformation.
|
||||
if (options.format === 'table') {
|
||||
const tableData = transformMetricDataToTable(prometheusResult.result, options);
|
||||
return [tableData];
|
||||
}
|
||||
|
||||
// Process matrix and vector results to DataFrame
|
||||
const dataFrame: DataFrame[] = [];
|
||||
prometheusResult.result.forEach((data: MatrixOrVectorResult) => dataFrame.push(transformToDataFrame(data, options)));
|
||||
|
||||
// When format is heatmap use the already created data frames and transform it more
|
||||
if (options.format === 'heatmap') {
|
||||
dataFrame.sort(sortSeriesByLabel);
|
||||
const seriesList = transformToHistogramOverTime(dataFrame);
|
||||
return seriesList;
|
||||
}
|
||||
|
||||
// Return matrix or vector result as DataFrame[]
|
||||
return dataFrame;
|
||||
}
|
||||
|
||||
function getPreferredVisualisationType(isInstantQuery?: boolean, mixedQueries?: boolean) {
|
||||
if (isInstantQuery) {
|
||||
return 'table';
|
||||
}
|
||||
|
||||
return mixedQueries ? 'graph' : undefined;
|
||||
}
|
||||
|
||||
/**
|
||||
* Transforms matrix and vector result from Prometheus result to DataFrame
|
||||
*/
|
||||
function transformToDataFrame(data: MatrixOrVectorResult, options: TransformOptions): DataFrame {
|
||||
const { name } = createLabelInfo(data.metric, options);
|
||||
|
||||
const fields: Field[] = [];
|
||||
|
||||
if (isMatrixData(data)) {
|
||||
const stepMs = options.step ? options.step * 1000 : NaN;
|
||||
let baseTimestamp = options.start * 1000;
|
||||
const dps: PromValue[] = [];
|
||||
|
||||
for (const value of data.values) {
|
||||
let dpValue: number | null = parseFloat(value[1]);
|
||||
|
||||
if (_.isNaN(dpValue)) {
|
||||
if (isNaN(dpValue)) {
|
||||
dpValue = null;
|
||||
}
|
||||
|
||||
const timestamp = parseFloat(value[0]) * 1000;
|
||||
const timestamp = value[0] * 1000;
|
||||
for (let t = baseTimestamp; t < timestamp; t += stepMs) {
|
||||
dps.push([null, t]);
|
||||
dps.push([t, null]);
|
||||
}
|
||||
baseTimestamp = timestamp + stepMs;
|
||||
dps.push([dpValue, timestamp]);
|
||||
dps.push([timestamp, dpValue]);
|
||||
}
|
||||
|
||||
const endTimestamp = end * 1000;
|
||||
const endTimestamp = options.end * 1000;
|
||||
for (let t = baseTimestamp; t <= endTimestamp; t += stepMs) {
|
||||
dps.push([null, t]);
|
||||
dps.push([t, null]);
|
||||
}
|
||||
fields.push(getTimeField(dps, true));
|
||||
fields.push(getValueField(dps, undefined, false));
|
||||
} else {
|
||||
fields.push(getTimeField([data.value]));
|
||||
fields.push(getValueField([data.value]));
|
||||
}
|
||||
|
||||
return {
|
||||
datapoints: dps,
|
||||
refId: options.refId,
|
||||
target: name ?? '',
|
||||
tags: labels,
|
||||
title,
|
||||
meta: options.meta,
|
||||
refId: options.refId,
|
||||
length: fields[0].values.length,
|
||||
fields,
|
||||
name,
|
||||
};
|
||||
}
|
||||
|
||||
function transformMetricDataToTable(md: MatrixOrVectorResult[], options: TransformOptions): DataFrame {
|
||||
if (!md || md.length === 0) {
|
||||
return {
|
||||
meta: options.meta,
|
||||
refId: options.refId,
|
||||
length: 0,
|
||||
fields: [],
|
||||
};
|
||||
}
|
||||
|
||||
transformMetricDataToTable(
|
||||
md: any,
|
||||
resultCount: number,
|
||||
refId: string,
|
||||
meta: QueryResultMeta,
|
||||
valueWithRefId?: boolean
|
||||
): TableModel {
|
||||
const table = new TableModel();
|
||||
table.refId = refId;
|
||||
table.meta = meta;
|
||||
const valueText = options.responseListLength > 1 || options.valueWithRefId ? `Value #${options.refId}` : 'Value';
|
||||
|
||||
let i: number, j: number;
|
||||
const metricLabels: { [key: string]: number } = {};
|
||||
const timeField = getTimeField([]);
|
||||
const metricFields = Object.keys(md.reduce((acc, series) => ({ ...acc, ...series.metric }), {}))
|
||||
.sort()
|
||||
.map(label => {
|
||||
return {
|
||||
name: label,
|
||||
config: { filterable: true },
|
||||
type: FieldType.other,
|
||||
values: new ArrayVector(),
|
||||
};
|
||||
});
|
||||
const valueField = getValueField([], valueText);
|
||||
|
||||
if (!md || md.length === 0) {
|
||||
return table;
|
||||
}
|
||||
|
||||
// Collect all labels across all metrics
|
||||
_.each(md, series => {
|
||||
for (const label in series.metric) {
|
||||
if (!metricLabels.hasOwnProperty(label)) {
|
||||
metricLabels[label] = 1;
|
||||
}
|
||||
md.forEach(d => {
|
||||
if (isMatrixData(d)) {
|
||||
d.values.forEach(val => {
|
||||
timeField.values.add(val[0] * 1000);
|
||||
metricFields.forEach(metricField => metricField.values.add(getLabelValue(d.metric, metricField.name)));
|
||||
valueField.values.add(parseFloat(val[1]));
|
||||
});
|
||||
} else {
|
||||
timeField.values.add(d.value[0] * 1000);
|
||||
metricFields.forEach(metricField => metricField.values.add(getLabelValue(d.metric, metricField.name)));
|
||||
valueField.values.add(parseFloat(d.value[1]));
|
||||
}
|
||||
});
|
||||
|
||||
// Sort metric labels, create columns for them and record their index
|
||||
const sortedLabels = _.keys(metricLabels).sort();
|
||||
table.columns.push({ text: 'Time', type: FieldType.time });
|
||||
_.each(sortedLabels, (label, labelIndex) => {
|
||||
metricLabels[label] = labelIndex + 1;
|
||||
table.columns.push({ text: label, filterable: true });
|
||||
});
|
||||
const valueText = resultCount > 1 || valueWithRefId ? `Value #${refId}` : 'Value';
|
||||
table.columns.push({ text: valueText });
|
||||
return {
|
||||
meta: options.meta,
|
||||
refId: options.refId,
|
||||
length: timeField.values.length,
|
||||
fields: [timeField, ...metricFields, valueField],
|
||||
};
|
||||
}
|
||||
|
||||
// Populate rows, set value to empty string when label not present.
|
||||
_.each(md, series => {
|
||||
if (series.value) {
|
||||
series.values = [series.value];
|
||||
}
|
||||
if (series.values) {
|
||||
for (i = 0; i < series.values.length; i++) {
|
||||
const values = series.values[i];
|
||||
const reordered: any = [values[0] * 1000];
|
||||
if (series.metric) {
|
||||
for (j = 0; j < sortedLabels.length; j++) {
|
||||
const label = sortedLabels[j];
|
||||
if (series.metric.hasOwnProperty(label)) {
|
||||
function getLabelValue(metric: PromMetric, label: string): string | number {
|
||||
if (metric.hasOwnProperty(label)) {
|
||||
if (label === 'le') {
|
||||
reordered.push(parseHistogramLabel(series.metric[label]));
|
||||
} else {
|
||||
reordered.push(series.metric[label]);
|
||||
return parseHistogramLabel(metric[label]);
|
||||
}
|
||||
} else {
|
||||
reordered.push('');
|
||||
return metric[label];
|
||||
}
|
||||
}
|
||||
}
|
||||
reordered.push(parseFloat(values[1]));
|
||||
table.rows.push(reordered);
|
||||
}
|
||||
}
|
||||
});
|
||||
return '';
|
||||
}
|
||||
|
||||
return table;
|
||||
}
|
||||
function getTimeField(data: PromValue[], isMs = false): MutableField {
|
||||
return {
|
||||
name: TIME_SERIES_TIME_FIELD_NAME,
|
||||
type: FieldType.time,
|
||||
config: {},
|
||||
values: new ArrayVector<number>(data.map(val => (isMs ? val[0] : val[0] * 1000))),
|
||||
};
|
||||
}
|
||||
|
||||
transformInstantMetricData(md: any, options: any): TimeSeries {
|
||||
const dps = [];
|
||||
const { name, labels } = this.createLabelInfo(md.metric, options);
|
||||
dps.push([parseFloat(md.value[1]), md.value[0] * 1000]);
|
||||
return { target: name ?? '', title: name, datapoints: dps, tags: labels, refId: options.refId, meta: options.meta };
|
||||
}
|
||||
function getValueField(
|
||||
data: PromValue[],
|
||||
valueName: string = TIME_SERIES_VALUE_FIELD_NAME,
|
||||
parseValue = true
|
||||
): MutableField {
|
||||
return {
|
||||
name: valueName,
|
||||
type: FieldType.number,
|
||||
config: {},
|
||||
values: new ArrayVector<number | null>(data.map(val => (parseValue ? parseFloat(val[1]) : val[1]))),
|
||||
};
|
||||
}
|
||||
|
||||
createLabelInfo(labels: { [key: string]: string }, options: any): { name?: string; labels: Labels; title?: string } {
|
||||
function createLabelInfo(labels: { [key: string]: string }, options: TransformOptions) {
|
||||
if (options?.legendFormat) {
|
||||
const title = this.renderTemplate(this.templateSrv.replace(options.legendFormat, options?.scopedVars), labels);
|
||||
return { name: title, title, labels };
|
||||
const title = renderTemplate(templateSrv.replace(options.legendFormat, options?.scopedVars), labels);
|
||||
return { name: title, labels };
|
||||
}
|
||||
|
||||
let { __name__, ...labelsWithoutName } = labels;
|
||||
|
||||
let title = __name__ || '';
|
||||
|
||||
const { __name__, ...labelsWithoutName } = labels;
|
||||
const labelPart = formatLabels(labelsWithoutName);
|
||||
const title = `${__name__ ?? ''}${labelPart}`;
|
||||
|
||||
if (!title && !labelPart) {
|
||||
title = options.query;
|
||||
}
|
||||
return { name: title, labels: labelsWithoutName };
|
||||
}
|
||||
|
||||
title = `${__name__ ?? ''}${labelPart}`;
|
||||
|
||||
return { name: title, title, labels: labelsWithoutName };
|
||||
}
|
||||
|
||||
getOriginalMetricName(labelData: { [key: string]: string }) {
|
||||
export function getOriginalMetricName(labelData: { [key: string]: string }) {
|
||||
const metricName = labelData.__name__ || '';
|
||||
delete labelData.__name__;
|
||||
const labelPart = Object.entries(labelData)
|
||||
.map(label => `${label[0]}="${label[1]}"`)
|
||||
.join(',');
|
||||
return `${metricName}{${labelPart}}`;
|
||||
}
|
||||
}
|
||||
|
||||
renderTemplate(aliasPattern: string, aliasData: { [key: string]: string }) {
|
||||
export function renderTemplate(aliasPattern: string, aliasData: { [key: string]: string }) {
|
||||
const aliasRegex = /\{\{\s*(.+?)\s*\}\}/g;
|
||||
return aliasPattern.replace(aliasRegex, (match, g1) => {
|
||||
return aliasPattern.replace(aliasRegex, (_match, g1) => {
|
||||
if (aliasData[g1]) {
|
||||
return aliasData[g1];
|
||||
}
|
||||
return '';
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
transformToHistogramOverTime(seriesList: TimeSeries[]) {
|
||||
function transformToHistogramOverTime(seriesList: DataFrame[]) {
|
||||
/* t1 = timestamp1, t2 = timestamp2 etc.
|
||||
t1 t2 t3 t1 t2 t3
|
||||
le10 10 10 0 => 10 10 0
|
||||
@ -206,29 +269,28 @@ export class ResultTransformer {
|
||||
le30 30 10 35 => 10 0 5
|
||||
*/
|
||||
for (let i = seriesList.length - 1; i > 0; i--) {
|
||||
const topSeries = seriesList[i].datapoints;
|
||||
const bottomSeries = seriesList[i - 1].datapoints;
|
||||
const topSeries = seriesList[i].fields.find(s => s.name === TIME_SERIES_VALUE_FIELD_NAME);
|
||||
const bottomSeries = seriesList[i - 1].fields.find(s => s.name === TIME_SERIES_VALUE_FIELD_NAME);
|
||||
if (!topSeries || !bottomSeries) {
|
||||
throw new Error('Prometheus heatmap transform error: data should be a time series');
|
||||
}
|
||||
|
||||
for (let j = 0; j < topSeries.length; j++) {
|
||||
const bottomPoint = bottomSeries[j] || [0];
|
||||
topSeries[j][0]! -= bottomPoint[0]!;
|
||||
for (let j = 0; j < topSeries.values.length; j++) {
|
||||
const bottomPoint = bottomSeries.values.get(j) || [0];
|
||||
topSeries.values.toArray()[j] -= bottomPoint;
|
||||
}
|
||||
}
|
||||
|
||||
return seriesList;
|
||||
}
|
||||
}
|
||||
|
||||
function sortSeriesByLabel(s1: TimeSeries, s2: TimeSeries): number {
|
||||
function sortSeriesByLabel(s1: DataFrame, s2: DataFrame): number {
|
||||
let le1, le2;
|
||||
|
||||
try {
|
||||
// fail if not integer. might happen with bad queries
|
||||
le1 = parseHistogramLabel(s1.target);
|
||||
le2 = parseHistogramLabel(s2.target);
|
||||
le1 = parseHistogramLabel(s1.name ?? '');
|
||||
le2 = parseHistogramLabel(s2.name ?? '');
|
||||
} catch (err) {
|
||||
console.error(err);
|
||||
return 0;
|
||||
|
||||
@ -1,4 +1,5 @@
|
||||
import { DataQuery, DataSourceJsonData } from '@grafana/data';
|
||||
import { DataQuery, DataSourceJsonData, QueryResultMeta, ScopedVars } from '@grafana/data';
|
||||
import { FetchError } from '@grafana/runtime';
|
||||
|
||||
export interface PromQuery extends DataQuery {
|
||||
expr: string;
|
||||
@ -41,3 +42,77 @@ export interface PromMetricsMetadataItem {
|
||||
export interface PromMetricsMetadata {
|
||||
[metric: string]: PromMetricsMetadataItem[];
|
||||
}
|
||||
|
||||
export interface PromDataSuccessResponse<T = PromData> {
|
||||
status: 'success';
|
||||
data: T;
|
||||
}
|
||||
|
||||
export interface PromDataErrorResponse<T = PromData> {
|
||||
status: 'error';
|
||||
errorType: string;
|
||||
error: string;
|
||||
data: T;
|
||||
}
|
||||
|
||||
export type PromData = PromMatrixData | PromVectorData | PromScalarData;
|
||||
|
||||
export interface PromVectorData {
|
||||
resultType: 'vector';
|
||||
result: Array<{
|
||||
metric: PromMetric;
|
||||
value: PromValue;
|
||||
}>;
|
||||
}
|
||||
|
||||
export interface PromMatrixData {
|
||||
resultType: 'matrix';
|
||||
result: Array<{
|
||||
metric: PromMetric;
|
||||
values: PromValue[];
|
||||
}>;
|
||||
}
|
||||
|
||||
export interface PromScalarData {
|
||||
resultType: 'scalar';
|
||||
result: PromValue;
|
||||
}
|
||||
|
||||
export type PromValue = [number, any];
|
||||
|
||||
export interface PromMetric {
|
||||
__name__?: string;
|
||||
[index: string]: any;
|
||||
}
|
||||
|
||||
export function isFetchErrorResponse(response: any): response is FetchError {
|
||||
return 'cancelled' in response;
|
||||
}
|
||||
|
||||
export function isMatrixData(result: MatrixOrVectorResult): result is PromMatrixData['result'][0] {
|
||||
return 'values' in result;
|
||||
}
|
||||
|
||||
export type MatrixOrVectorResult = PromMatrixData['result'][0] | PromVectorData['result'][0];
|
||||
|
||||
export interface TransformOptions {
|
||||
format?: string;
|
||||
step?: number;
|
||||
legendFormat?: string;
|
||||
start: number;
|
||||
end: number;
|
||||
query: string;
|
||||
responseListLength: number;
|
||||
scopedVars?: ScopedVars;
|
||||
refId: string;
|
||||
valueWithRefId?: boolean;
|
||||
meta: QueryResultMeta;
|
||||
}
|
||||
|
||||
export interface PromLabelQueryResponse {
|
||||
data: {
|
||||
status: string;
|
||||
data: string[];
|
||||
};
|
||||
cancelled?: boolean;
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user