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ElasticSearch: Remove frontend response parsing (#104148)

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* chore: Remove ElasticReponse

* Ran betterer
This commit is contained in:
Florian Verdonck
2025-06-17 21:20:28 +02:00
committed by GitHub
parent 18fc0bc878
commit 45bd08baf5
3 changed files with 0 additions and 2399 deletions
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34
.betterer.results
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@@ -3383,40 +3383,6 @@ exports[`better eslint`] = {
[0, 0, 0, "Do not use any type assertions.", "0"],
[0, 0, 0, "Do not use any type assertions.", "1"]
],
"public/app/plugins/datasource/elasticsearch/ElasticResponse.ts:5381": [
[0, 0, 0, "Do not use any type assertions.", "0"],
[0, 0, 0, "Do not use any type assertions.", "1"],
[0, 0, 0, "Unexpected any. Specify a different type.", "2"],
[0, 0, 0, "Unexpected any. Specify a different type.", "3"],
[0, 0, 0, "Unexpected any. Specify a different type.", "4"],
[0, 0, 0, "Unexpected any. Specify a different type.", "5"],
[0, 0, 0, "Unexpected any. Specify a different type.", "6"],
[0, 0, 0, "Unexpected any. Specify a different type.", "7"],
[0, 0, 0, "Unexpected any. Specify a different type.", "8"],
[0, 0, 0, "Unexpected any. Specify a different type.", "9"],
[0, 0, 0, "Unexpected any. Specify a different type.", "10"],
[0, 0, 0, "Unexpected any. Specify a different type.", "11"],
[0, 0, 0, "Unexpected any. Specify a different type.", "12"],
[0, 0, 0, "Unexpected any. Specify a different type.", "13"],
[0, 0, 0, "Unexpected any. Specify a different type.", "14"],
[0, 0, 0, "Unexpected any. Specify a different type.", "15"],
[0, 0, 0, "Unexpected any. Specify a different type.", "16"],
[0, 0, 0, "Unexpected any. Specify a different type.", "17"],
[0, 0, 0, "Unexpected any. Specify a different type.", "18"],
[0, 0, 0, "Unexpected any. Specify a different type.", "19"],
[0, 0, 0, "Unexpected any. Specify a different type.", "20"],
[0, 0, 0, "Unexpected any. Specify a different type.", "21"],
[0, 0, 0, "Unexpected any. Specify a different type.", "22"],
[0, 0, 0, "Unexpected any. Specify a different type.", "23"],
[0, 0, 0, "Unexpected any. Specify a different type.", "24"],
[0, 0, 0, "Unexpected any. Specify a different type.", "25"],
[0, 0, 0, "Unexpected any. Specify a different type.", "26"],
[0, 0, 0, "Unexpected any. Specify a different type.", "27"],
[0, 0, 0, "Unexpected any. Specify a different type.", "28"],
[0, 0, 0, "Unexpected any. Specify a different type.", "29"],
[0, 0, 0, "Unexpected any. Specify a different type.", "30"],
[0, 0, 0, "Unexpected any. Specify a different type.", "31"]
],
"public/app/plugins/datasource/elasticsearch/LanguageProvider.ts:5381": [
[0, 0, 0, "Unexpected any. Specify a different type.", "0"],
[0, 0, 0, "Unexpected any. Specify a different type.", "1"],

1558
public/app/plugins/datasource/elasticsearch/ElasticResponse.test.ts
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807
public/app/plugins/datasource/elasticsearch/ElasticResponse.ts
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@@ -1,807 +0,0 @@
import { clone, filter, find, identity, isArray, keys, map, uniq, values as _values } from 'lodash';
import {
DataQueryResponse,
DataFrame,
toDataFrame,
FieldType,
MutableDataFrame,
PreferredVisualisationType,
} from '@grafana/data';
import { convertFieldType } from '@grafana/data/internal';
import TableModel from 'app/core/TableModel';
import { isMetricAggregationWithField } from './components/QueryEditor/MetricAggregationsEditor/aggregations';
import { metricAggregationConfig } from './components/QueryEditor/MetricAggregationsEditor/utils';
import * as queryDef from './queryDef';
import { ElasticsearchAggregation, ElasticsearchQuery, TopMetrics, ExtendedStatMetaType } from './types';
import { describeMetric, flattenObject, getScriptValue } from './utils';
const HIGHLIGHT_TAGS_EXP = `${queryDef.highlightTags.pre}([^@]+)${queryDef.highlightTags.post}`;
type TopMetricMetric = Record<string, number>;
interface TopMetricBucket {
top: Array<{
metrics: TopMetricMetric;
}>;
}
export class ElasticResponse {
constructor(
private targets: ElasticsearchQuery[],
private response: any
) {
this.targets = targets;
this.response = response;
}
processMetrics(esAgg: any, target: ElasticsearchQuery, seriesList: any, props: any) {
let newSeries: any;
for (let y = 0; y < target.metrics!.length; y++) {
const metric = target.metrics![y];
if (metric.hide) {
continue;
}
switch (metric.type) {
case 'count': {
newSeries = { datapoints: [], metric: 'count', props, refId: target.refId };
for (let i = 0; i < esAgg.buckets.length; i++) {
const bucket = esAgg.buckets[i];
const value = bucket.doc_count;
newSeries.datapoints.push([value, bucket.key]);
}
seriesList.push(newSeries);
break;
}
case 'percentiles': {
if (esAgg.buckets.length === 0) {
break;
}
const firstBucket = esAgg.buckets[0];
const percentiles = firstBucket[metric.id].values;
for (const percentileName in percentiles) {
newSeries = {
datapoints: [],
metric: 'p' + percentileName,
props: props,
field: metric.field,
refId: target.refId,
};
for (let i = 0; i < esAgg.buckets.length; i++) {
const bucket = esAgg.buckets[i];
const values = bucket[metric.id].values;
newSeries.datapoints.push([values[percentileName], bucket.key]);
}
seriesList.push(newSeries);
}
break;
}
case 'extended_stats': {
for (const statName in metric.meta) {
if (!metric.meta[statName]) {
continue;
}
newSeries = {
datapoints: [],
metric: statName,
props: props,
field: metric.field,
refId: target.refId,
};
for (let i = 0; i < esAgg.buckets.length; i++) {
const bucket = esAgg.buckets[i];
const stats = bucket[metric.id];
// add stats that are in nested obj to top level obj
stats.std_deviation_bounds_upper = stats.std_deviation_bounds.upper;
stats.std_deviation_bounds_lower = stats.std_deviation_bounds.lower;
newSeries.datapoints.push([stats[statName], bucket.key]);
}
seriesList.push(newSeries);
}
break;
}
case 'top_metrics': {
if (metric.settings?.metrics?.length) {
for (const metricField of metric.settings?.metrics) {
newSeries = {
datapoints: [],
metric: metric.type,
props: props,
refId: target.refId,
field: metricField,
};
for (let i = 0; i < esAgg.buckets.length; i++) {
const bucket = esAgg.buckets[i];
const stats: TopMetricBucket = bucket[metric.id];
const values = stats.top.map((hit) => {
if (hit.metrics[metricField]) {
return hit.metrics[metricField];
}
return null;
});
const point = [values[values.length - 1], bucket.key];
newSeries.datapoints.push(point);
}
seriesList.push(newSeries);
}
}
break;
}
default: {
newSeries = {
datapoints: [],
metric: metric.type,
metricId: metric.id,
props: props,
refId: target.refId,
};
if (isMetricAggregationWithField(metric)) {
newSeries.field = metric.field;
}
for (let i = 0; i < esAgg.buckets.length; i++) {
const bucket = esAgg.buckets[i];
const value = bucket[metric.id];
if (value !== undefined) {
if (value.normalized_value) {
newSeries.datapoints.push([value.normalized_value, bucket.key]);
} else {
newSeries.datapoints.push([value.value, bucket.key]);
}
}
}
seriesList.push(newSeries);
break;
}
}
}
}
processAggregationDocs(
esAgg: any,
aggDef: ElasticsearchAggregation,
target: ElasticsearchQuery,
table: any,
props: any
) {
// add columns
if (table.columns.length === 0) {
for (const propKey of keys(props)) {
table.addColumn({ text: propKey, filterable: true });
}
table.addColumn({ text: aggDef.field, filterable: true });
}
// helper func to add values to value array
const addMetricValue = (values: unknown[], metricName: string, value: unknown) => {
table.addColumn({ text: metricName });
values.push(value);
};
const buckets = isArray(esAgg.buckets) ? esAgg.buckets : [esAgg.buckets];
for (const bucket of buckets) {
const values = [];
for (const propValues of _values(props)) {
values.push(propValues);
}
// add bucket key (value)
values.push(bucket.key);
for (const metric of target.metrics || []) {
switch (metric.type) {
case 'count': {
addMetricValue(values, this.getMetricName(metric.type), bucket.doc_count);
break;
}
case 'extended_stats': {
for (const statName in metric.meta) {
if (!metric.meta[statName]) {
continue;
}
const stats = bucket[metric.id];
// add stats that are in nested obj to top level obj
stats.std_deviation_bounds_upper = stats.std_deviation_bounds.upper;
stats.std_deviation_bounds_lower = stats.std_deviation_bounds.lower;
addMetricValue(values, this.getMetricName(statName as ExtendedStatMetaType), stats[statName]);
}
break;
}
case 'percentiles': {
const percentiles = bucket[metric.id].values;
for (const percentileName in percentiles) {
addMetricValue(values, `p${percentileName} ${metric.field}`, percentiles[percentileName]);
}
break;
}
case 'top_metrics': {
const baseName = this.getMetricName(metric.type);
if (metric.settings?.metrics) {
for (const metricField of metric.settings.metrics) {
// If we selected more than one metric we also add each metric name
const metricName = metric.settings.metrics.length > 1 ? `${baseName} ${metricField}` : baseName;
const stats: TopMetricBucket = bucket[metric.id];
// Size of top_metrics is fixed to 1.
addMetricValue(values, metricName, stats.top[0].metrics[metricField]);
}
}
break;
}
default: {
let metricName = this.getMetricName(metric.type);
const otherMetrics = filter(target.metrics, { type: metric.type });
// if more of the same metric type include field field name in property
if (otherMetrics.length > 1) {
if (isMetricAggregationWithField(metric)) {
metricName += ' ' + metric.field;
}
if (metric.type === 'bucket_script') {
//Use the formula in the column name
metricName = getScriptValue(metric);
}
}
addMetricValue(values, metricName, bucket[metric.id].value);
break;
}
}
}
table.rows.push(values);
}
}
// This is quite complex
// need to recurse down the nested buckets to build series
processBuckets(aggs: any, target: ElasticsearchQuery, seriesList: any, table: TableModel, props: any, depth: number) {
let bucket, aggDef: any, esAgg, aggId;
const maxDepth = target.bucketAggs!.length - 1;
for (aggId in aggs) {
aggDef = find(target.bucketAggs, { id: aggId });
esAgg = aggs[aggId];
if (!aggDef) {
continue;
}
if (aggDef.type === 'nested') {
this.processBuckets(esAgg, target, seriesList, table, props, depth + 1);
continue;
}
if (depth === maxDepth) {
if (aggDef.type === 'date_histogram') {
this.processMetrics(esAgg, target, seriesList, props);
} else {
this.processAggregationDocs(esAgg, aggDef, target, table, props);
}
} else {
for (const nameIndex in esAgg.buckets) {
bucket = esAgg.buckets[nameIndex];
props = clone(props);
if (bucket.key !== void 0) {
props[aggDef.field] = bucket.key;
} else {
props['filter'] = nameIndex;
}
if (bucket.key_as_string) {
props[aggDef.field] = bucket.key_as_string;
}
this.processBuckets(bucket, target, seriesList, table, props, depth + 1);
}
}
}
}
private getMetricName(metric: string): string {
const metricDef = Object.entries(metricAggregationConfig)
.filter(([key]) => key === metric)
.map(([_, value]) => value)[0];
if (metricDef) {
return metricDef.label;
}
const extendedStat = queryDef.extendedStats.find((e) => e.value === metric);
if (extendedStat) {
return extendedStat.label;
}
return metric;
}
private getSeriesName(series: any, target: ElasticsearchQuery, dedup: boolean) {
let metricName = this.getMetricName(series.metric);
if (target.alias) {
const regex = /\{\{([\s\S]+?)\}\}/g;
return target.alias.replace(regex, (match, g1, g2) => {
const group = g1 || g2;
if (group.indexOf('term ') === 0) {
return series.props[group.substring(5)];
}
if (series.props[group] !== void 0) {
return series.props[group];
}
if (group === 'metric') {
return metricName;
}
if (group === 'field') {
return series.field || '';
}
return match;
});
}
if (queryDef.isPipelineAgg(series.metric)) {
if (series.metric && queryDef.isPipelineAggWithMultipleBucketPaths(series.metric)) {
const agg: any = find(target.metrics, { id: series.metricId });
if (agg && agg.settings.script) {
metricName = getScriptValue(agg);
for (const pv of agg.pipelineVariables) {
const appliedAgg = find(target.metrics, { id: pv.pipelineAgg });
if (appliedAgg) {
metricName = metricName.replace('params.' + pv.name, describeMetric(appliedAgg));
}
}
} else {
metricName = 'Unset';
}
} else {
const appliedAgg = find(target.metrics, { id: series.field });
if (appliedAgg) {
metricName += ' ' + describeMetric(appliedAgg);
} else {
metricName = 'Unset';
}
}
} else if (series.field) {
metricName += ' ' + series.field;
}
const propKeys = keys(series.props);
if (propKeys.length === 0) {
return metricName;
}
let name = '';
for (const propName in series.props) {
name += series.props[propName] + ' ';
}
if (dedup) {
return name.trim() + ' ' + metricName;
}
return name.trim();
}
nameSeries(seriesList: any, target: ElasticsearchQuery) {
const metricTypeCount = uniq(map(seriesList, 'metric')).length;
const hasTopMetricWithMultipleMetrics = (
target.metrics?.filter((m) => m.type === 'top_metrics') as TopMetrics[]
).some((m) => (m?.settings?.metrics?.length || 0) > 1);
for (let i = 0; i < seriesList.length; i++) {
const series = seriesList[i];
series.target = this.getSeriesName(series, target, metricTypeCount > 1 || hasTopMetricWithMultipleMetrics);
}
}
processHits(hits: { total: { value: any }; hits: any[] }, seriesList: any[], target: ElasticsearchQuery) {
const hitsTotal = typeof hits.total === 'number' ? hits.total : hits.total.value; // <- Works with Elasticsearch 7.0+
const series: any = {
target: target.refId,
type: 'docs',
refId: target.refId,
datapoints: [],
total: hitsTotal,
filterable: true,
};
let propName, hit, doc: any, i;
for (i = 0; i < hits.hits.length; i++) {
hit = hits.hits[i];
doc = {
_id: hit._id,
_type: hit._type,
_index: hit._index,
sort: hit.sort,
highlight: hit.highlight,
};
if (hit._source) {
for (propName in hit._source) {
doc[propName] = hit._source[propName];
}
}
for (propName in hit.fields) {
doc[propName] = hit.fields[propName];
}
series.datapoints.push(doc);
}
seriesList.push(series);
}
trimDatapoints(aggregations: any, target: ElasticsearchQuery) {
const histogram: any = find(target.bucketAggs, { type: 'date_histogram' });
const shouldDropFirstAndLast = histogram && histogram.settings && histogram.settings.trimEdges;
if (shouldDropFirstAndLast) {
const trim = histogram.settings.trimEdges;
for (const prop in aggregations) {
const points = aggregations[prop];
if (points.datapoints.length > trim * 2) {
points.datapoints = points.datapoints.slice(trim, points.datapoints.length - trim);
}
}
}
}
getErrorFromElasticResponse(response: any, err: any) {
const result: any = {};
result.data = JSON.stringify(err, null, 4);
if (err.root_cause && err.root_cause.length > 0 && err.root_cause[0].reason) {
result.message = err.root_cause[0].reason;
} else {
result.message = err.reason || 'Unknown elastic error response';
}
if (response.$$config) {
result.config = response.$$config;
}
return result;
}
getTimeSeries() {
if (this.targets.some((target) => queryDef.hasMetricOfType(target, 'raw_data'))) {
return this.processResponseToDataFrames(false);
}
const result = this.processResponseToSeries();
return {
...result,
data: result.data.map((item) => toDataFrame(item)),
};
}
getLogs(logMessageField?: string, logLevelField?: string): DataQueryResponse {
return this.processResponseToDataFrames(true, logMessageField, logLevelField);
}
private processResponseToDataFrames(
isLogsRequest: boolean,
logMessageField?: string,
logLevelField?: string
): DataQueryResponse {
const dataFrame: DataFrame[] = [];
for (let n = 0; n < this.response.responses.length; n++) {
const response = this.response.responses[n];
if (response.error) {
throw this.getErrorFromElasticResponse(this.response, response.error);
}
if (response.hits) {
const { propNames, docs } = flattenHits(response.hits.hits);
const series = docs.length
? createEmptyDataFrame(
propNames.map(toNameTypePair(docs)),
isLogsRequest,
this.targets[0].timeField,
logMessageField,
logLevelField
)
: createEmptyDataFrame([], isLogsRequest);
if (isLogsRequest) {
addPreferredVisualisationType(series, 'logs');
}
// Add a row for each document
for (const doc of docs) {
if (logLevelField) {
// Remap level field based on the datasource config. This field is
// then used in explore to figure out the log level. We may rewrite
// some actual data in the level field if they are different.
doc['level'] = doc[logLevelField];
}
// When highlighting exists, we need to collect all the highlighted
// phrases and add them to the DataFrame's meta.searchWords array.
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 ? flattenObject(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) => {
return v || '';
});
}
if (logLevelField) {
const f = series.addField({
name: 'level',
type: FieldType.string,
});
series.setParser(f, (v) => {
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) => {
return v || '';
});
}
return series;
};
const addPreferredVisualisationType = (series: DataFrame, type: PreferredVisualisationType) => {
let s = series;
s.meta
? (s.meta.preferredVisualisationType = type)
: (s.meta = {
preferredVisualisationType: type,
});
};
const toNameTypePair =
(docs: Array<Record<string, unknown>>) =>
(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;
}
};