[ML] AIOps: Functional/API integration tests for text field support f…

…or log rate analysis (#168177)

This updates the artificial dataset generator for log rate analysis to
allow to create variants including text fields.
The artificial dataset is now used for 4 variants of functional and API
integration tests: Testing spike and dip with both with and without a
text field.

The new tests surfaced some issues that were fixed as part of this PR:

- Getting the counts of log patterns in combination with individual
significant terms ended up with to granular groups. This PR adds
additional queries to get counts for log patterns in combination with
item sets already derived from significant terms.
- The `support` value is returned by the frequent item sets agg and is
used as a threshold whether to include an item set for grouping. This
was missing from significant log patterns and is fixed by this PR.
- Adds a check to not get frequent item sets for log patterns if there
are no significant terms.
- The way we fetched log patterns using a time filter that spans the
whole of the baseline start to the deviation end caused problems with
analysing dips. This PR updates those queries to only fetch the actual
baseline and deviation time range.
- The integration tests caught an issue where we'd still fetch the
histogram for log patterns even if we'd request grouping information
only.

x-pack/plugins/aiops/common/__mocks__/artificial_logs/filtered_frequent_item_sets.ts

@@ -5,9 +5,9 @@
  * 2.0.
  */
 
-import type { ItemsetResult } from '../../types';
+import type { ItemSet } from '../../types';
 
-export const filteredFrequentItemSets: ItemsetResult[] = [
+export const filteredFrequentItemSets: ItemSet[] = [
   {
     set: { response_code: '500', url: 'home.php' },
     size: 2,

x-pack/plugins/aiops/common/__mocks__/artificial_logs/final_significant_term_groups_textfield.ts

@@ -0,0 +1,129 @@
+/*
+ * Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
+ * or more contributor license agreements. Licensed under the Elastic License
+ * 2.0; you may not use this file except in compliance with the Elastic License
+ * 2.0.
+ */
+
+import type { SignificantTermGroup } from '@kbn/ml-agg-utils';
+
+export const finalSignificantTermGroupsTextfield: SignificantTermGroup[] = [
+  {
+    docCount: 636,
+    group: [
+      {
+        docCount: 792,
+        duplicate: 2,
+        fieldName: 'url',
+        fieldValue: 'home.php',
+        key: 'url:home.php',
+        pValue: 0.00974308761016614,
+        type: 'keyword',
+      },
+      {
+        docCount: 636,
+        duplicate: 2,
+        fieldName: 'user',
+        fieldValue: 'Peter',
+        key: 'user:Peter',
+        pValue: 0.00974308761016614,
+        type: 'keyword',
+      },
+    ],
+    id: '2091742187',
+    pValue: 0.00974308761016614,
+  },
+  {
+    docCount: 634,
+    group: [
+      {
+        docCount: 1266,
+        duplicate: 2,
+        fieldName: 'response_code',
+        fieldValue: '500',
+        key: 'response_code:500',
+        pValue: 0.012783309213417932,
+        type: 'keyword',
+      },
+      {
+        docCount: 792,
+        duplicate: 2,
+        fieldName: 'url',
+        fieldValue: 'home.php',
+        key: 'url:home.php',
+        pValue: 0.00974308761016614,
+        type: 'keyword',
+      },
+      {
+        docCount: 634,
+        duplicate: 2,
+        fieldName: 'message',
+        fieldValue: 'an unexpected error occured',
+        key: 'an unexpected error occured',
+        pValue: 0.00974308761016614,
+        type: 'log_pattern',
+      },
+    ],
+    id: '1528268618',
+    pValue: 0.00974308761016614,
+  },
+  {
+    docCount: 632,
+    group: [
+      {
+        docCount: 1266,
+        duplicate: 2,
+        fieldName: 'response_code',
+        fieldValue: '500',
+        key: 'response_code:500',
+        pValue: 0.012783309213417932,
+        type: 'keyword',
+      },
+      {
+        docCount: 790,
+        duplicate: 2,
+        fieldName: 'url',
+        fieldValue: 'login.php',
+        key: 'url:login.php',
+        pValue: 0.012783309213417932,
+        type: 'keyword',
+      },
+      {
+        docCount: 632,
+        duplicate: 2,
+        fieldName: 'message',
+        fieldValue: 'an unexpected error occured',
+        key: 'an unexpected error occured',
+        pValue: 0.012783309213417932,
+        type: 'log_pattern',
+      },
+    ],
+    id: '2619569380',
+    pValue: 0.012783309213417932,
+  },
+  {
+    docCount: 632,
+    group: [
+      {
+        docCount: 790,
+        duplicate: 2,
+        fieldName: 'url',
+        fieldValue: 'login.php',
+        key: 'url:login.php',
+        pValue: 0.012783309213417932,
+        type: 'keyword',
+      },
+      {
+        docCount: 632,
+        duplicate: 2,
+        fieldName: 'user',
+        fieldValue: 'Peter',
+        key: 'user:Peter',
+        pValue: 0.012783309213417932,
+        type: 'keyword',
+      },
+    ],
+    id: '1937394803',
+    pValue: 0.012783309213417932,
+  },
+];

x-pack/plugins/aiops/common/__mocks__/artificial_logs/frequent_item_sets.ts

@@ -5,9 +5,9 @@
  * 2.0.
  */
 
-import type { ItemsetResult } from '../../types';
+import type { ItemSet } from '../../types';
 
-export const frequentItemSets: ItemsetResult[] = [
+export const frequentItemSets: ItemSet[] = [
   {
     set: { response_code: '500', url: 'home.php' },
     size: 2,

x-pack/plugins/aiops/common/__mocks__/artificial_logs/significant_log_patterns.ts

@@ -0,0 +1,24 @@
+/*
+ * Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
+ * or more contributor license agreements. Licensed under the Elastic License
+ * 2.0; you may not use this file except in compliance with the Elastic License
+ * 2.0.
+ */
+
+import type { SignificantTerm } from '@kbn/ml-agg-utils';
+
+export const significantLogPatterns: SignificantTerm[] = [
+  {
+    bg_count: 0,
+    doc_count: 1266,
+    fieldName: 'message',
+    fieldValue: 'an unexpected error occured',
+    key: 'an unexpected error occured',
+    normalizedScore: 0,
+    pValue: 0.000001,
+    score: -13.815510557964274,
+    total_bg_count: 1975,
+    total_doc_count: 4669,
+    type: 'log_pattern',
+  },
+];

x-pack/plugins/aiops/common/constants.ts

@@ -5,10 +5,16 @@
  * 2.0.
  */
 
-/**
- * The p-value threshold to be used for statistically significant items.
- */
-export const LOG_RATE_ANALYSIS_P_VALUE_THRESHOLD = 0.02;
+export const LOG_RATE_ANALYSIS_SETTINGS = {
+  /**
+   * The p-value threshold to be used for statistically significant items.
+   */
+  P_VALUE_THRESHOLD: 0.02,
+  /**
+   * The minimum support value to be used for the frequent item sets aggration.
+   */
+  FREQUENT_ITEMS_SETS_MINIMUM_SUPPORT: 0.001,
+} as const;
 
 /**
  * For the technical preview of Log Rate Analysis we use a hard coded seed.

x-pack/plugins/aiops/common/types.ts

@@ -14,7 +14,7 @@ export interface SignificantTermDuplicateGroup {
 
 export type FieldValuePairCounts = Record<string, Record<string, number>>;
 
-export interface ItemsetResult {
+export interface ItemSet {
   set: Record<FieldValuePair['fieldName'], FieldValuePair['fieldValue']>;
   size: number;
   maxPValue: number;
@@ -23,6 +23,12 @@ export interface ItemsetResult {
   total_doc_count: number;
 }
 
+export interface FetchFrequentItemSetsResponse {
+  fields: string[];
+  itemSets: ItemSet[];
+  totalDocCount: number;
+}
+
 interface SimpleHierarchicalTreeNodeSet extends FieldValuePair {
   key: string;
   type: SignificantTermType;

x-pack/plugins/aiops/server/routes/log_rate_analysis.ts

@@ -506,7 +506,7 @@ export const defineLogRateAnalysisRoute = (
                 );
 
                 try {
-                  const { fields, df } = await fetchFrequentItemSets(
+                  const { fields, itemSets } = await fetchFrequentItemSets(
                     client,
                     request.body.index,
                     JSON.parse(request.body.searchQuery) as estypes.QueryDslQueryContainer,
@@ -520,23 +520,26 @@ export const defineLogRateAnalysisRoute = (
                     abortSignal
                   );
 
-                  if (significantCategories.length > 0) {
-                    const { fields: significantCategoriesFields, df: significantCategoriesDf } =
-                      await fetchTerms2CategoriesCounts(
-                        client,
-                        request.body,
-                        JSON.parse(request.body.searchQuery) as estypes.QueryDslQueryContainer,
-                        significantTerms,
-                        significantCategories,
-                        request.body.deviationMin,
-                        request.body.deviationMax,
-                        logger,
-                        pushError,
-                        abortSignal
-                      );
+                  if (significantCategories.length > 0 && significantTerms.length > 0) {
+                    const {
+                      fields: significantCategoriesFields,
+                      itemSets: significantCategoriesItemSets,
+                    } = await fetchTerms2CategoriesCounts(
+                      client,
+                      request.body,
+                      JSON.parse(request.body.searchQuery) as estypes.QueryDslQueryContainer,
+                      significantTerms,
+                      itemSets,
+                      significantCategories,
+                      request.body.deviationMin,
+                      request.body.deviationMax,
+                      logger,
+                      pushError,
+                      abortSignal
+                    );
 
                     fields.push(...significantCategoriesFields);
-                    df.push(...significantCategoriesDf);
+                    itemSets.push(...significantCategoriesItemSets);
                   }
 
                   if (shouldStop) {
@@ -545,9 +548,9 @@ export const defineLogRateAnalysisRoute = (
                     return;
                   }
 
-                  if (fields.length > 0 && df.length > 0) {
+                  if (fields.length > 0 && itemSets.length > 0) {
                     const significantTermGroups = getSignificantTermGroups(
-                      df,
+                      itemSets,
                       [...significantTerms, ...significantCategories],
                       fields
                     );
@@ -757,7 +760,11 @@ export const defineLogRateAnalysisRoute = (
               }
 
               // histograms for text field patterns
-              if (overallTimeSeries !== undefined && significantCategories.length > 0) {
+              if (
+                overallTimeSeries !== undefined &&
+                significantCategories.length > 0 &&
+                !request.body.overrides?.regroupOnly
+              ) {
                 const significantCategoriesHistogramQueries = significantCategories.map((d) => {
                   const histogramQuery = getHistogramQuery(request.body);
                   const categoryQuery = getCategoryQuery(d.fieldName, [

x-pack/plugins/aiops/server/routes/queries/fetch_categories.ts

@@ -33,11 +33,14 @@ export const getCategoryRequest = (
   fieldName: string,
   from: number | undefined,
   to: number | undefined,
+  filter: estypes.QueryDslQueryContainer,
   { wrap }: RandomSamplerWrapper
 ): estypes.SearchRequest => {
   const { index, timeFieldName } = params;
   const query = getQueryWithParams({
     params,
+    termFilters: undefined,
+    filter,
   });
   const { params: request } = createCategoryRequest(
     index,
@@ -63,6 +66,7 @@ export const fetchCategories = async (
   fieldNames: string[],
   from: number | undefined,
   to: number | undefined,
+  filter: estypes.QueryDslQueryContainer,
   logger: Logger,
   // The default value of 1 means no sampling will be used
   sampleProbability: number = 1,
@@ -78,7 +82,7 @@ export const fetchCategories = async (
 
   const settledPromises = await Promise.allSettled(
     fieldNames.map((fieldName) => {
-      const request = getCategoryRequest(params, fieldName, from, to, randomSamplerWrapper);
+      const request = getCategoryRequest(params, fieldName, from, to, filter, randomSamplerWrapper);
       return esClient.search(request, {
         signal: abortSignal,
         maxRetries: 0,

x-pack/plugins/aiops/server/routes/queries/fetch_frequent_item_sets.ts

@@ -15,8 +15,12 @@ import type { Logger } from '@kbn/logging';
 import { type SignificantTerm } from '@kbn/ml-agg-utils';
 import { createRandomSamplerWrapper } from '@kbn/ml-random-sampler-utils';
 
-import { RANDOM_SAMPLER_SEED } from '../../../common/constants';
-import type { SignificantTermDuplicateGroup, ItemsetResult } from '../../../common/types';
+import { RANDOM_SAMPLER_SEED, LOG_RATE_ANALYSIS_SETTINGS } from '../../../common/constants';
+import type {
+  SignificantTermDuplicateGroup,
+  ItemSet,
+  FetchFrequentItemSetsResponse,
+} from '../../../common/types';
 
 interface FrequentItemSetsAggregation extends estypes.AggregationsSamplerAggregation {
   fi: {
@@ -74,7 +78,7 @@ export async function fetchFrequentItemSets(
   sampleProbability: number = 1,
   emitError: (m: string) => void,
   abortSignal?: AbortSignal
-) {
+): Promise<FetchFrequentItemSetsResponse> {
   // Sort significant terms by ascending p-value, necessary to apply the field limit correctly.
   const sortedSignificantTerms = significantTerms.slice().sort((a, b) => {
     return (a.pValue ?? 0) - (b.pValue ?? 0);
@@ -103,7 +107,7 @@ export async function fetchFrequentItemSets(
       frequent_item_sets: {
         minimum_set_size: 2,
         size: 200,
-        minimum_support: 0.001,
+        minimum_support: LOG_RATE_ANALYSIS_SETTINGS.FREQUENT_ITEMS_SETS_MINIMUM_SUPPORT,
         fields: getFrequentItemSetsAggFields(sortedSignificantTerms),
       },
     },
@@ -138,7 +142,7 @@ export async function fetchFrequentItemSets(
     emitError(`Failed to fetch frequent_item_sets.`);
     return {
       fields: [],
-      df: [],
+      itemSets: [],
       totalDocCount: 0,
     };
   }
@@ -158,10 +162,10 @@ export async function fetchFrequentItemSets(
   const fiss = frequentItemSets.fi.buckets;
   fiss.length = maximum;
 
-  const results: ItemsetResult[] = [];
+  const results: ItemSet[] = [];
 
   fiss.forEach((fis) => {
-    const result: ItemsetResult = {
+    const result: ItemSet = {
       set: {},
       size: 0,
       maxPValue: 0,
@@ -203,7 +207,7 @@ export async function fetchFrequentItemSets(
 
   return {
     fields: uniqueFields,
-    df: results,
+    itemSets: results,
     totalDocCount: totalDocCountFi,
   };
 }