mirror of
https://github.com/grafana/grafana.git
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804 lines
25 KiB
TypeScript
804 lines
25 KiB
TypeScript
import { clone, filter, find, identity, isArray, keys, map, uniq, values as _values } from 'lodash';
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import {
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DataQueryResponse,
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DataFrame,
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toDataFrame,
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FieldType,
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MutableDataFrame,
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PreferredVisualisationType,
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} from '@grafana/data';
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import { convertFieldType } from '@grafana/data/src/transformations/transformers/convertFieldType';
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import TableModel from 'app/core/TableModel';
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import flatten from 'app/core/utils/flatten';
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import { isMetricAggregationWithField } from './components/QueryEditor/MetricAggregationsEditor/aggregations';
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import { metricAggregationConfig } from './components/QueryEditor/MetricAggregationsEditor/utils';
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import * as queryDef from './queryDef';
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import { ElasticsearchAggregation, ElasticsearchQuery, TopMetrics, ExtendedStatMetaType } from './types';
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import { describeMetric, getScriptValue } from './utils';
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const HIGHLIGHT_TAGS_EXP = `${queryDef.highlightTags.pre}([^@]+)${queryDef.highlightTags.post}`;
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type TopMetricMetric = Record<string, number>;
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interface TopMetricBucket {
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top: Array<{
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metrics: TopMetricMetric;
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}>;
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}
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export class ElasticResponse {
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constructor(private targets: ElasticsearchQuery[], private response: any) {
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this.targets = targets;
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this.response = response;
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}
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processMetrics(esAgg: any, target: ElasticsearchQuery, seriesList: any, props: any) {
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let newSeries: any;
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for (let y = 0; y < target.metrics!.length; y++) {
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const metric = target.metrics![y];
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if (metric.hide) {
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continue;
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}
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switch (metric.type) {
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case 'count': {
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newSeries = { datapoints: [], metric: 'count', props, refId: target.refId };
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for (let i = 0; i < esAgg.buckets.length; i++) {
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const bucket = esAgg.buckets[i];
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const value = bucket.doc_count;
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newSeries.datapoints.push([value, bucket.key]);
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}
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seriesList.push(newSeries);
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break;
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}
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case 'percentiles': {
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if (esAgg.buckets.length === 0) {
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break;
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}
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const firstBucket = esAgg.buckets[0];
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const percentiles = firstBucket[metric.id].values;
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for (const percentileName in percentiles) {
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newSeries = {
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datapoints: [],
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metric: 'p' + percentileName,
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props: props,
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field: metric.field,
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refId: target.refId,
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};
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for (let i = 0; i < esAgg.buckets.length; i++) {
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const bucket = esAgg.buckets[i];
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const values = bucket[metric.id].values;
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newSeries.datapoints.push([values[percentileName], bucket.key]);
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}
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seriesList.push(newSeries);
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}
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break;
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}
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case 'extended_stats': {
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for (const statName in metric.meta) {
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if (!metric.meta[statName as ExtendedStatMetaType]) {
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continue;
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}
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newSeries = {
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datapoints: [],
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metric: statName,
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props: props,
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field: metric.field,
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refId: target.refId,
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};
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for (let i = 0; i < esAgg.buckets.length; i++) {
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const bucket = esAgg.buckets[i];
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const stats = bucket[metric.id];
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// add stats that are in nested obj to top level obj
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stats.std_deviation_bounds_upper = stats.std_deviation_bounds.upper;
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stats.std_deviation_bounds_lower = stats.std_deviation_bounds.lower;
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newSeries.datapoints.push([stats[statName], bucket.key]);
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}
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seriesList.push(newSeries);
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}
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break;
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}
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case 'top_metrics': {
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if (metric.settings?.metrics?.length) {
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for (const metricField of metric.settings?.metrics) {
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newSeries = {
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datapoints: [],
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metric: metric.type,
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props: props,
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refId: target.refId,
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field: metricField,
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};
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for (let i = 0; i < esAgg.buckets.length; i++) {
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const bucket = esAgg.buckets[i];
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const stats = bucket[metric.id] as TopMetricBucket;
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const values = stats.top.map((hit) => {
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if (hit.metrics[metricField]) {
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return hit.metrics[metricField];
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}
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return null;
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});
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const point = [values[values.length - 1], bucket.key];
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newSeries.datapoints.push(point);
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}
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seriesList.push(newSeries);
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}
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}
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break;
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}
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default: {
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newSeries = {
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datapoints: [],
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metric: metric.type,
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metricId: metric.id,
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props: props,
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refId: target.refId,
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};
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if (isMetricAggregationWithField(metric)) {
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newSeries.field = metric.field;
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}
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for (let i = 0; i < esAgg.buckets.length; i++) {
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const bucket = esAgg.buckets[i];
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const value = bucket[metric.id];
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if (value !== undefined) {
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if (value.normalized_value) {
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newSeries.datapoints.push([value.normalized_value, bucket.key]);
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} else {
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newSeries.datapoints.push([value.value, bucket.key]);
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}
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}
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}
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seriesList.push(newSeries);
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break;
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}
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}
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}
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}
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processAggregationDocs(
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esAgg: any,
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aggDef: ElasticsearchAggregation,
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target: ElasticsearchQuery,
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table: any,
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props: any
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) {
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// add columns
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if (table.columns.length === 0) {
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for (const propKey of keys(props)) {
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table.addColumn({ text: propKey, filterable: true });
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}
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table.addColumn({ text: aggDef.field, filterable: true });
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}
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// helper func to add values to value array
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const addMetricValue = (values: any[], metricName: string, value: any) => {
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table.addColumn({ text: metricName });
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values.push(value);
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};
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const buckets = isArray(esAgg.buckets) ? esAgg.buckets : [esAgg.buckets];
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for (const bucket of buckets) {
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const values = [];
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for (const propValues of _values(props)) {
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values.push(propValues);
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}
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// add bucket key (value)
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values.push(bucket.key);
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for (const metric of target.metrics || []) {
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switch (metric.type) {
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case 'count': {
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addMetricValue(values, this.getMetricName(metric.type), bucket.doc_count);
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break;
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}
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case 'extended_stats': {
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for (const statName in metric.meta) {
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if (!metric.meta[statName as ExtendedStatMetaType]) {
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continue;
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}
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const stats = bucket[metric.id];
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// add stats that are in nested obj to top level obj
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stats.std_deviation_bounds_upper = stats.std_deviation_bounds.upper;
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stats.std_deviation_bounds_lower = stats.std_deviation_bounds.lower;
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addMetricValue(values, this.getMetricName(statName as ExtendedStatMetaType), stats[statName]);
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}
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break;
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}
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case 'percentiles': {
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const percentiles = bucket[metric.id].values;
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for (const percentileName in percentiles) {
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addMetricValue(values, `p${percentileName} ${metric.field}`, percentiles[percentileName]);
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}
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break;
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}
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case 'top_metrics': {
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const baseName = this.getMetricName(metric.type);
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if (metric.settings?.metrics) {
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for (const metricField of metric.settings.metrics) {
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// If we selected more than one metric we also add each metric name
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const metricName = metric.settings.metrics.length > 1 ? `${baseName} ${metricField}` : baseName;
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const stats = bucket[metric.id] as TopMetricBucket;
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// Size of top_metrics is fixed to 1.
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addMetricValue(values, metricName, stats.top[0].metrics[metricField]);
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}
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}
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break;
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}
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default: {
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let metricName = this.getMetricName(metric.type);
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const otherMetrics = filter(target.metrics, { type: metric.type });
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// if more of the same metric type include field field name in property
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if (otherMetrics.length > 1) {
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if (isMetricAggregationWithField(metric)) {
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metricName += ' ' + metric.field;
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}
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if (metric.type === 'bucket_script') {
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//Use the formula in the column name
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metricName = getScriptValue(metric);
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}
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}
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addMetricValue(values, metricName, bucket[metric.id].value);
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break;
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}
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}
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}
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table.rows.push(values);
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}
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}
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// This is quite complex
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// need to recurse down the nested buckets to build series
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processBuckets(aggs: any, target: ElasticsearchQuery, seriesList: any, table: TableModel, props: any, depth: number) {
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let bucket, aggDef: any, esAgg, aggId;
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const maxDepth = target.bucketAggs!.length - 1;
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for (aggId in aggs) {
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aggDef = find(target.bucketAggs, { id: aggId });
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esAgg = aggs[aggId];
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if (!aggDef) {
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continue;
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}
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if (aggDef.type === 'nested') {
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this.processBuckets(esAgg, target, seriesList, table, props, depth + 1);
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continue;
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}
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if (depth === maxDepth) {
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if (aggDef.type === 'date_histogram') {
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this.processMetrics(esAgg, target, seriesList, props);
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} else {
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this.processAggregationDocs(esAgg, aggDef, target, table, props);
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}
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} else {
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for (const nameIndex in esAgg.buckets) {
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bucket = esAgg.buckets[nameIndex];
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props = clone(props);
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if (bucket.key !== void 0) {
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props[aggDef.field] = bucket.key;
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} else {
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props['filter'] = nameIndex;
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}
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if (bucket.key_as_string) {
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props[aggDef.field] = bucket.key_as_string;
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}
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this.processBuckets(bucket, target, seriesList, table, props, depth + 1);
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}
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}
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}
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}
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private getMetricName(metric: string): string {
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const metricDef = Object.entries(metricAggregationConfig)
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.filter(([key]) => key === metric)
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.map(([_, value]) => value)[0];
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if (metricDef) {
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return metricDef.label;
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}
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const extendedStat = queryDef.extendedStats.find((e) => e.value === metric);
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if (extendedStat) {
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return extendedStat.label;
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}
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return metric;
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}
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private getSeriesName(series: any, target: ElasticsearchQuery, dedup: boolean) {
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let metricName = this.getMetricName(series.metric);
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if (target.alias) {
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const regex = /\{\{([\s\S]+?)\}\}/g;
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return target.alias.replace(regex, (match: any, g1: any, g2: any) => {
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const group = g1 || g2;
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if (group.indexOf('term ') === 0) {
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return series.props[group.substring(5)];
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}
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if (series.props[group] !== void 0) {
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return series.props[group];
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}
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if (group === 'metric') {
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return metricName;
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}
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if (group === 'field') {
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return series.field || '';
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}
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return match;
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});
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}
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if (queryDef.isPipelineAgg(series.metric)) {
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if (series.metric && queryDef.isPipelineAggWithMultipleBucketPaths(series.metric)) {
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const agg: any = find(target.metrics, { id: series.metricId });
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if (agg && agg.settings.script) {
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metricName = getScriptValue(agg);
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for (const pv of agg.pipelineVariables) {
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const appliedAgg: any = find(target.metrics, { id: pv.pipelineAgg });
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if (appliedAgg) {
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metricName = metricName.replace('params.' + pv.name, describeMetric(appliedAgg));
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}
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}
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} else {
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metricName = 'Unset';
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}
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} else {
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const appliedAgg: any = find(target.metrics, { id: series.field });
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if (appliedAgg) {
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metricName += ' ' + describeMetric(appliedAgg);
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} else {
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metricName = 'Unset';
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}
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}
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} else if (series.field) {
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metricName += ' ' + series.field;
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}
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const propKeys = keys(series.props);
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if (propKeys.length === 0) {
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return metricName;
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}
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let name = '';
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for (const propName in series.props) {
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name += series.props[propName] + ' ';
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}
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if (dedup) {
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return name.trim() + ' ' + metricName;
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}
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return name.trim();
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}
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nameSeries(seriesList: any, target: ElasticsearchQuery) {
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const metricTypeCount = uniq(map(seriesList, 'metric')).length;
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const hasTopMetricWithMultipleMetrics = (
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target.metrics?.filter((m) => m.type === 'top_metrics') as TopMetrics[]
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).some((m) => (m?.settings?.metrics?.length || 0) > 1);
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for (let i = 0; i < seriesList.length; i++) {
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const series = seriesList[i];
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series.target = this.getSeriesName(series, target, metricTypeCount > 1 || hasTopMetricWithMultipleMetrics);
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}
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}
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processHits(hits: { total: { value: any }; hits: any[] }, seriesList: any[], target: ElasticsearchQuery) {
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const hitsTotal = typeof hits.total === 'number' ? hits.total : hits.total.value; // <- Works with Elasticsearch 7.0+
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const series: any = {
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target: target.refId,
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type: 'docs',
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refId: target.refId,
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datapoints: [],
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total: hitsTotal,
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filterable: true,
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};
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let propName, hit, doc: any, i;
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for (i = 0; i < hits.hits.length; i++) {
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hit = hits.hits[i];
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doc = {
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_id: hit._id,
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_type: hit._type,
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_index: hit._index,
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sort: hit.sort,
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highlight: hit.highlight,
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};
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if (hit._source) {
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for (propName in hit._source) {
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doc[propName] = hit._source[propName];
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}
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}
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for (propName in hit.fields) {
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doc[propName] = hit.fields[propName];
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}
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series.datapoints.push(doc);
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}
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seriesList.push(series);
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}
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trimDatapoints(aggregations: any, target: ElasticsearchQuery) {
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const histogram: any = find(target.bucketAggs, { type: 'date_histogram' });
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const shouldDropFirstAndLast = histogram && histogram.settings && histogram.settings.trimEdges;
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if (shouldDropFirstAndLast) {
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const trim = histogram.settings.trimEdges;
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for (const prop in aggregations) {
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const points = aggregations[prop];
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if (points.datapoints.length > trim * 2) {
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points.datapoints = points.datapoints.slice(trim, points.datapoints.length - trim);
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}
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}
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}
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}
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getErrorFromElasticResponse(response: any, err: any) {
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const result: any = {};
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result.data = JSON.stringify(err, null, 4);
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if (err.root_cause && err.root_cause.length > 0 && err.root_cause[0].reason) {
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result.message = err.root_cause[0].reason;
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} else {
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result.message = err.reason || 'Unknown elastic error response';
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}
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if (response.$$config) {
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result.config = response.$$config;
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}
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return result;
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}
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getTimeSeries() {
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if (this.targets.some((target) => queryDef.hasMetricOfType(target, 'raw_data'))) {
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return this.processResponseToDataFrames(false);
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}
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const result = this.processResponseToSeries();
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return {
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...result,
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data: result.data.map((item) => toDataFrame(item)),
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};
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}
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getLogs(logMessageField?: string, logLevelField?: string): DataQueryResponse {
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return this.processResponseToDataFrames(true, logMessageField, logLevelField);
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}
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private processResponseToDataFrames(
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isLogsRequest: boolean,
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logMessageField?: string,
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logLevelField?: string
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): DataQueryResponse {
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const dataFrame: DataFrame[] = [];
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for (let n = 0; n < this.response.responses.length; n++) {
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const response = this.response.responses[n];
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if (response.error) {
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throw this.getErrorFromElasticResponse(this.response, response.error);
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}
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if (response.hits) {
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const { propNames, docs } = flattenHits(response.hits.hits);
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const series = docs.length
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? createEmptyDataFrame(
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propNames.map(toNameTypePair(docs)),
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isLogsRequest,
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this.targets[0].timeField,
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logMessageField,
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logLevelField
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)
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: createEmptyDataFrame([], isLogsRequest);
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if (isLogsRequest) {
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addPreferredVisualisationType(series, 'logs');
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}
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// Add a row for each document
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for (const doc of docs) {
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if (logLevelField) {
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// Remap level field based on the datasource config. This field is
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// then used in explore to figure out the log level. We may rewrite
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// some actual data in the level field if they are different.
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doc['level'] = doc[logLevelField];
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}
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// When highlighting exists, we need to collect all the highlighted
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// phrases and add them to the DataFrame's meta.searchWords array.
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if (doc.highlight) {
|
|
// There might be multiple words so we need two versions of the
|
|
// regular expression. One to match gobally, when used with part.match,
|
|
// it returns and array of matches. The second one is used to capture the
|
|
// values between the tags.
|
|
const globalHighlightWordRegex = new RegExp(HIGHLIGHT_TAGS_EXP, 'g');
|
|
const highlightWordRegex = new RegExp(HIGHLIGHT_TAGS_EXP);
|
|
const newSearchWords = Object.keys(doc.highlight)
|
|
.flatMap((key) => {
|
|
return doc.highlight[key].flatMap((line: string) => {
|
|
const matchedPhrases = line.match(globalHighlightWordRegex);
|
|
if (!matchedPhrases) {
|
|
return [];
|
|
}
|
|
return matchedPhrases.map((part) => {
|
|
const matches = part.match(highlightWordRegex);
|
|
return (matches && matches[1]) || null;
|
|
});
|
|
});
|
|
})
|
|
.filter(identity);
|
|
// If meta and searchWords already exists, add the words and
|
|
// deduplicate otherwise create a new set of search words.
|
|
const searchWords = series.meta?.searchWords
|
|
? uniq([...series.meta.searchWords, ...newSearchWords])
|
|
: [...newSearchWords];
|
|
series.meta = series.meta ? { ...series.meta, searchWords } : { searchWords };
|
|
}
|
|
series.add(doc);
|
|
}
|
|
|
|
const target = this.targets[n];
|
|
series.refId = target.refId;
|
|
dataFrame.push(series);
|
|
}
|
|
|
|
if (response.aggregations) {
|
|
const aggregations = response.aggregations;
|
|
const target = this.targets[n];
|
|
const tmpSeriesList: any[] = [];
|
|
const table = new TableModel();
|
|
|
|
this.processBuckets(aggregations, target, tmpSeriesList, table, {}, 0);
|
|
this.trimDatapoints(tmpSeriesList, target);
|
|
this.nameSeries(tmpSeriesList, target);
|
|
|
|
if (table.rows.length > 0) {
|
|
const series = toDataFrame(table);
|
|
series.refId = target.refId;
|
|
dataFrame.push(series);
|
|
}
|
|
|
|
for (let y = 0; y < tmpSeriesList.length; y++) {
|
|
let series = toDataFrame(tmpSeriesList[y]);
|
|
|
|
// When log results, show aggregations only in graph. Log fields are then going to be shown in table.
|
|
if (isLogsRequest) {
|
|
addPreferredVisualisationType(series, 'graph');
|
|
}
|
|
|
|
series.refId = target.refId;
|
|
dataFrame.push(series);
|
|
}
|
|
}
|
|
}
|
|
|
|
for (let frame of dataFrame) {
|
|
for (let field of frame.fields) {
|
|
if (field.type === FieldType.time && typeof field.values[0] !== 'number') {
|
|
field.values = convertFieldType(field, { destinationType: FieldType.time }).values;
|
|
}
|
|
}
|
|
}
|
|
|
|
return { data: dataFrame };
|
|
}
|
|
|
|
processResponseToSeries = () => {
|
|
const seriesList = [];
|
|
|
|
for (let i = 0; i < this.response.responses.length; i++) {
|
|
const response = this.response.responses[i];
|
|
const target = this.targets[i];
|
|
|
|
if (response.error) {
|
|
throw this.getErrorFromElasticResponse(this.response, response.error);
|
|
}
|
|
|
|
if (response.hits && response.hits.hits.length > 0) {
|
|
this.processHits(response.hits, seriesList, target);
|
|
}
|
|
|
|
if (response.aggregations) {
|
|
const aggregations = response.aggregations;
|
|
const target = this.targets[i];
|
|
const tmpSeriesList: any[] = [];
|
|
const table = new TableModel();
|
|
table.refId = target.refId;
|
|
|
|
this.processBuckets(aggregations, target, tmpSeriesList, table, {}, 0);
|
|
this.trimDatapoints(tmpSeriesList, target);
|
|
this.nameSeries(tmpSeriesList, target);
|
|
|
|
for (let y = 0; y < tmpSeriesList.length; y++) {
|
|
seriesList.push(tmpSeriesList[y]);
|
|
}
|
|
|
|
if (table.rows.length > 0) {
|
|
seriesList.push(table);
|
|
}
|
|
}
|
|
}
|
|
|
|
return { data: seriesList };
|
|
};
|
|
}
|
|
|
|
type Doc = {
|
|
_id: string;
|
|
_type: string;
|
|
_index: string;
|
|
_source?: any;
|
|
sort?: Array<string | number>;
|
|
highlight?: Record<string, string[]>;
|
|
};
|
|
|
|
/**
|
|
* Flatten the docs from response mainly the _source part which can be nested. This flattens it so that it is one level
|
|
* deep and the keys are: `level1Name.level2Name...`. Also returns list of all properties from all the docs (not all
|
|
* docs have to have the same keys).
|
|
* @param hits
|
|
*/
|
|
const flattenHits = (hits: Doc[]): { docs: Array<Record<string, any>>; propNames: string[] } => {
|
|
const docs: any[] = [];
|
|
// We keep a list of all props so that we can create all the fields in the dataFrame, this can lead
|
|
// to wide sparse dataframes in case the scheme is different per document.
|
|
let propNames: string[] = [];
|
|
|
|
for (const hit of hits) {
|
|
const flattened = hit._source ? flatten(hit._source) : {};
|
|
const doc = {
|
|
_id: hit._id,
|
|
_type: hit._type,
|
|
_index: hit._index,
|
|
sort: hit.sort,
|
|
highlight: hit.highlight,
|
|
_source: { ...flattened },
|
|
...flattened,
|
|
};
|
|
|
|
for (const propName of Object.keys(doc)) {
|
|
if (propNames.indexOf(propName) === -1) {
|
|
propNames.push(propName);
|
|
}
|
|
}
|
|
|
|
docs.push(doc);
|
|
}
|
|
|
|
propNames.sort();
|
|
return { docs, propNames };
|
|
};
|
|
|
|
/**
|
|
* Create empty dataframe but with created fields. Fields are based from propNames (should be from the response) and
|
|
* also from configuration specified fields for message, time, and level.
|
|
* @param propNames
|
|
* @param timeField
|
|
* @param logMessageField
|
|
* @param logLevelField
|
|
*/
|
|
const createEmptyDataFrame = (
|
|
props: Array<[string, FieldType]>,
|
|
isLogsRequest: boolean,
|
|
timeField?: string,
|
|
logMessageField?: string,
|
|
logLevelField?: string
|
|
): MutableDataFrame => {
|
|
const series = new MutableDataFrame({ fields: [] });
|
|
|
|
if (timeField) {
|
|
series.addField({
|
|
config: {
|
|
filterable: true,
|
|
},
|
|
name: timeField,
|
|
type: FieldType.time,
|
|
});
|
|
}
|
|
|
|
if (logMessageField) {
|
|
const f = series.addField({
|
|
name: logMessageField,
|
|
type: FieldType.string,
|
|
});
|
|
series.setParser(f, (v: any) => {
|
|
return v || '';
|
|
});
|
|
}
|
|
|
|
if (logLevelField) {
|
|
const f = series.addField({
|
|
name: 'level',
|
|
type: FieldType.string,
|
|
});
|
|
series.setParser(f, (v: any) => {
|
|
return v || '';
|
|
});
|
|
}
|
|
|
|
const fieldNames = series.fields.map((field) => field.name);
|
|
|
|
for (const [name, type] of props) {
|
|
// Do not duplicate fields. This can mean that we will shadow some fields.
|
|
if (fieldNames.includes(name)) {
|
|
continue;
|
|
}
|
|
// Do not add _source field (besides logs) as we are showing each _source field in table instead.
|
|
if (!isLogsRequest && name === '_source') {
|
|
continue;
|
|
}
|
|
|
|
const f = series.addField({
|
|
config: {
|
|
filterable: true,
|
|
},
|
|
name,
|
|
type,
|
|
});
|
|
series.setParser(f, (v: any) => {
|
|
return v || '';
|
|
});
|
|
}
|
|
|
|
return series;
|
|
};
|
|
|
|
const addPreferredVisualisationType = (series: any, type: PreferredVisualisationType) => {
|
|
let s = series;
|
|
s.meta
|
|
? (s.meta.preferredVisualisationType = type)
|
|
: (s.meta = {
|
|
preferredVisualisationType: type,
|
|
});
|
|
};
|
|
|
|
const toNameTypePair =
|
|
(docs: Array<Record<string, any>>) =>
|
|
(propName: string): [string, FieldType] =>
|
|
[propName, guessType(docs.find((doc) => doc[propName] !== undefined)?.[propName])];
|
|
|
|
/**
|
|
* Trying to guess data type from its value. This is far from perfect, as in order to have accurate guess
|
|
* we should have access to the elasticsearch mapping, but it covers the most common use cases for numbers, strings & arrays.
|
|
*/
|
|
const guessType = (value: unknown): FieldType => {
|
|
switch (typeof value) {
|
|
case 'number':
|
|
return FieldType.number;
|
|
case 'string':
|
|
return FieldType.string;
|
|
default:
|
|
return FieldType.other;
|
|
}
|
|
};
|