[ML] Cardinality validation API integration tests

x-pack/plugins/ml/server/models/data_visualizer/data_visualizer.ts

@@ -342,8 +342,8 @@ export class DataVisualizer {
     aggregatableFields: string[],
     samplerShardSize: number,
     timeFieldName: string,
-    earliestMs: number,
-    latestMs: number
+    earliestMs?: number,
+    latestMs?: number
   ) {
     const index = indexPatternTitle;
     const size = 0;

x-pack/plugins/ml/server/models/job_validation/validate_cardinality.js → x-pack/plugins/ml/server/models/job_validation/validate_cardinality.ts

@@ -4,21 +4,22 @@
  * you may not use this file except in compliance with the Elastic License.
  */
 
-import _ from 'lodash';
-
+import { APICaller } from 'kibana/server';
 import { DataVisualizer } from '../data_visualizer';
 
 import { validateJobObject } from './validate_job_object';
+import { CombinedJob } from '../../../common/types/anomaly_detection_jobs';
+import { Detector } from '../../../common/types/anomaly_detection_jobs';
 
-function isValidCategorizationConfig(job, fieldName) {
+function isValidCategorizationConfig(job: CombinedJob, fieldName: string): boolean {
   return (
     typeof job.analysis_config.categorization_field_name !== 'undefined' &&
     fieldName === 'mlcategory'
   );
 }
 
-function isScriptField(job, fieldName) {
-  const scriptFields = Object.keys(_.get(job, 'datafeed_config.script_fields', {}));
+function isScriptField(job: CombinedJob, fieldName: string): boolean {
+  const scriptFields = Object.keys(job.datafeed_config.script_fields ?? {});
   return scriptFields.includes(fieldName);
 }
 
@@ -30,10 +31,21 @@ const PARTITION_FIELD_CARDINALITY_THRESHOLD = 1000;
 const BY_FIELD_CARDINALITY_THRESHOLD = 1000;
 const MODEL_PLOT_THRESHOLD_HIGH = 100;
 
-const validateFactory = (callWithRequest, job) => {
+type Messages = Array<{ id: string; fieldName?: string }>;
+
+type Validator = (obj: {
+  type: string;
+  isInvalid: (cardinality: number) => boolean;
+  messageId?: string;
+}) => Promise<{
+  modelPlotCardinality: number;
+  messages: Messages;
+}>;
+
+const validateFactory = (callWithRequest: APICaller, job: CombinedJob): Validator => {
   const dv = new DataVisualizer(callWithRequest);
 
-  const modelPlotConfigTerms = _.get(job, ['model_plot_config', 'terms'], '');
+  const modelPlotConfigTerms = job?.model_plot_config?.terms ?? '';
   const modelPlotConfigFieldCount =
     modelPlotConfigTerms.length > 0 ? modelPlotConfigTerms.split(',').length : 0;
 
@@ -42,8 +54,11 @@ const validateFactory = (callWithRequest, job) => {
     // if model_plot_config.terms is used, it doesn't count the real cardinality of the field
     // but adds only the count of fields used in model_plot_config.terms
     let modelPlotCardinality = 0;
-    const messages = [];
-    const fieldName = `${type}_field_name`;
+    const messages: Messages = [];
+    const fieldName = `${type}_field_name` as keyof Pick<
+      Detector,
+      'by_field_name' | 'over_field_name' | 'partition_field_name'
+    >;
 
     const detectors = job.analysis_config.detectors;
     const relevantDetectors = detectors.filter(detector => {
@@ -52,15 +67,15 @@ const validateFactory = (callWithRequest, job) => {
 
     if (relevantDetectors.length > 0) {
       try {
-        const uniqueFieldNames = _.uniq(relevantDetectors.map(f => f[fieldName]));
+        const uniqueFieldNames = [...new Set(relevantDetectors.map(f => f[fieldName]))] as string[];
 
         // use fieldCaps endpoint to get data about whether fields are aggregatable
         const fieldCaps = await callWithRequest('fieldCaps', {
           index: job.datafeed_config.indices.join(','),
           fields: uniqueFieldNames,
         });
 
-        let aggregatableFieldNames = [];
+        let aggregatableFieldNames: string[] = [];
         // parse fieldCaps to return an array of just the fields which are aggregatable
         if (typeof fieldCaps === 'object' && typeof fieldCaps.fields === 'object') {
           aggregatableFieldNames = uniqueFieldNames.filter(field => {
@@ -81,12 +96,14 @@ const validateFactory = (callWithRequest, job) => {
         );
 
         uniqueFieldNames.forEach(uniqueFieldName => {
-          const field = _.find(stats.aggregatableExistsFields, { fieldName: uniqueFieldName });
-          if (typeof field === 'object') {
+          const field = stats.aggregatableExistsFields.find(
+            fieldData => fieldData.fieldName === uniqueFieldName
+          );
+          if (field !== undefined && typeof field === 'object' && field.stats) {
             modelPlotCardinality +=
-              modelPlotConfigFieldCount > 0 ? modelPlotConfigFieldCount : field.stats.cardinality;
+              modelPlotConfigFieldCount > 0 ? modelPlotConfigFieldCount : field.stats.cardinality!;
 
-            if (isInvalid(field.stats.cardinality)) {
+            if (isInvalid(field.stats.cardinality!)) {
               messages.push({
                 id: messageId || `cardinality_${type}_field`,
                 fieldName: uniqueFieldName,
@@ -115,7 +132,7 @@ const validateFactory = (callWithRequest, job) => {
         if (relevantDetectors.length === 1) {
           messages.push({
             id: 'field_not_aggregatable',
-            fieldName: relevantDetectors[0][fieldName],
+            fieldName: relevantDetectors[0][fieldName]!,
           });
         } else {
           messages.push({ id: 'fields_not_aggregatable' });
@@ -129,25 +146,30 @@ const validateFactory = (callWithRequest, job) => {
   };
 };
 
-export async function validateCardinality(callWithRequest, job) {
+export async function validateCardinality(
+  callWithRequest: APICaller,
+  job?: CombinedJob
+): Promise<Array<{ id: string; modelPlotCardinality?: number; fieldName?: string }>> | never {
   const messages = [];
 
-  validateJobObject(job);
+  if (!validateJobObject(job)) {
+    // required for TS type casting, validateJobObject throws an error internally.
+    throw new Error();
+  }
 
   // find out if there are any relevant detector field names
   // where cardinality checks could be run against.
   const numDetectorsWithFieldNames = job.analysis_config.detectors.filter(d => {
     return d.by_field_name || d.over_field_name || d.partition_field_name;
   });
   if (numDetectorsWithFieldNames.length === 0) {
-    return Promise.resolve([]);
+    return [];
   }
 
   // validate({ type, isInvalid }) asynchronously returns an array of validation messages
   const validate = validateFactory(callWithRequest, job);
 
-  const modelPlotEnabled =
-    (job.model_plot_config && job.model_plot_config.enabled === true) || false;
+  const modelPlotEnabled = job.model_plot_config?.enabled ?? false;
 
   // check over fields (population analysis)
   const validateOverFieldsLow = validate({

x-pack/plugins/ml/server/models/job_validation/validate_job_object.ts

@@ -7,7 +7,7 @@
 import { i18n } from '@kbn/i18n';
 import { CombinedJob } from '../../../common/types/anomaly_detection_jobs';
 
-export function validateJobObject(job: CombinedJob | null) {
+export function validateJobObject(job: CombinedJob | null | undefined): job is CombinedJob | never {
   if (job === null || typeof job !== 'object') {
     throw new Error(
       i18n.translate('xpack.ml.models.jobValidation.validateJobObject.jobIsNotObjectErrorMessage', {
@@ -93,4 +93,5 @@ export function validateJobObject(job: CombinedJob | null) {
       )
     );
   }
+  return true;
 }

x-pack/test/api_integration/apis/ml/job_validation/cardinality.ts

@@ -0,0 +1,175 @@
+/*
+ * Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
+ * or more contributor license agreements. Licensed under the Elastic License;
+ * you may not use this file except in compliance with the Elastic License.
+ */
+import expect from '@kbn/expect';
+import { FtrProviderContext } from '../../../ftr_provider_context';
+import { USER } from '../../../../functional/services/machine_learning/security_common';
+
+const COMMON_HEADERS = {
+  'kbn-xsrf': 'some-xsrf-token',
+};
+
+// eslint-disable-next-line import/no-default-export
+export default ({ getService }: FtrProviderContext) => {
+  const esArchiver = getService('esArchiver');
+  const supertest = getService('supertestWithoutAuth');
+  const ml = getService('ml');
+
+  describe('ValidateCardinality', function() {
+    before(async () => {
+      await esArchiver.loadIfNeeded('ml/ecommerce');
+      await ml.testResources.setKibanaTimeZoneToUTC();
+    });
+
+    after(async () => {
+      await ml.api.cleanMlIndices();
+    });
+
+    it(`should recognize a valid cardinality`, async () => {
+      const requestBody = {
+        job_id: '',
+        description: '',
+        groups: [],
+        analysis_config: {
+          bucket_span: '10m',
+          detectors: [
+            {
+              function: 'mean',
+              field_name: 'products.base_price',
+              partition_field_name: 'geoip.city_name',
+            },
+          ],
+          influencers: ['geoip.city_name'],
+        },
+        data_description: { time_field: 'order_date' },
+        analysis_limits: { model_memory_limit: '12MB' },
+        model_plot_config: { enabled: true },
+        datafeed_config: {
+          datafeed_id: 'datafeed-',
+          job_id: '',
+          indices: ['ft_ecommerce'],
+          query: { bool: { must: [{ match_all: {} }], filter: [], must_not: [] } },
+        },
+      };
+
+      const { body } = await supertest
+        .post('/api/ml/validate/cardinality')
+        .auth(USER.ML_POWERUSER, ml.securityCommon.getPasswordForUser(USER.ML_POWERUSER))
+        .set(COMMON_HEADERS)
+        .send(requestBody)
+        .expect(200);
+
+      expect(body).to.eql([{ id: 'success_cardinality' }]);
+    });
+
+    it(`should recognize a high model plot cardinality`, async () => {
+      const requestBody = {
+        job_id: '',
+        description: '',
+        groups: [],
+        analysis_config: {
+          bucket_span: '10m',
+          detectors: [
+            {
+              function: 'mean',
+              field_name: 'products.base_price',
+              // some high cardinality field
+              partition_field_name: 'order_id',
+            },
+          ],
+          influencers: ['geoip.city_name'],
+        },
+        data_description: { time_field: 'order_date' },
+        analysis_limits: { model_memory_limit: '11MB' },
+        model_plot_config: { enabled: true },
+        datafeed_config: {
+          datafeed_id: 'datafeed-',
+          job_id: '',
+          indices: ['ft_ecommerce'],
+          query: { bool: { must: [{ match_all: {} }], filter: [], must_not: [] } },
+        },
+      };
+      const { body } = await supertest
+        .post('/api/ml/validate/cardinality')
+        .auth(USER.ML_POWERUSER, ml.securityCommon.getPasswordForUser(USER.ML_POWERUSER))
+        .set(COMMON_HEADERS)
+        .send(requestBody)
+        .expect(200);
+
+      expect(body).to.eql([
+        { id: 'cardinality_model_plot_high', modelPlotCardinality: 4711 },
+        { id: 'cardinality_partition_field', fieldName: 'order_id' },
+      ]);
+    });
+
+    it('should not validate cardinality in case request payload is invalid', async () => {
+      const requestBody = {
+        job_id: '',
+        description: '',
+        groups: [],
+        // missing analysis_config
+        data_description: { time_field: 'order_date' },
+        analysis_limits: { model_memory_limit: '12MB' },
+        model_plot_config: { enabled: true },
+        datafeed_config: {
+          datafeed_id: 'datafeed-',
+          job_id: '',
+          indices: ['ft_ecommerce'],
+          query: { bool: { must: [{ match_all: {} }], filter: [], must_not: [] } },
+        },
+      };
+
+      const { body } = await supertest
+        .post('/api/ml/validate/cardinality')
+        .auth(USER.ML_POWERUSER, ml.securityCommon.getPasswordForUser(USER.ML_POWERUSER))
+        .set(COMMON_HEADERS)
+        .send(requestBody)
+        .expect(400);
+
+      expect(body.error).to.eql('Bad Request');
+      expect(body.message).to.eql(
+        '[request body.analysis_config.detectors]: expected value of type [array] but got [undefined]'
+      );
+    });
+
+    it('should not validate cardinality if the user does not have required permissions', async () => {
+      const requestBody = {
+        job_id: '',
+        description: '',
+        groups: [],
+        analysis_config: {
+          bucket_span: '10m',
+          detectors: [
+            {
+              function: 'mean',
+              field_name: 'products.base_price',
+              partition_field_name: 'geoip.city_name',
+            },
+          ],
+          influencers: ['geoip.city_name'],
+        },
+        data_description: { time_field: 'order_date' },
+        analysis_limits: { model_memory_limit: '12MB' },
+        model_plot_config: { enabled: true },
+        datafeed_config: {
+          datafeed_id: 'datafeed-',
+          job_id: '',
+          indices: ['ft_ecommerce'],
+          query: { bool: { must: [{ match_all: {} }], filter: [], must_not: [] } },
+        },
+      };
+
+      const { body } = await supertest
+        .post('/api/ml/validate/cardinality')
+        .auth(USER.ML_VIEWER, ml.securityCommon.getPasswordForUser(USER.ML_VIEWER))
+        .set(COMMON_HEADERS)
+        .send(requestBody)
+        .expect(404);
+
+      expect(body.error).to.eql('Not Found');
+      expect(body.message).to.eql('Not Found');
+    });
+  });
+};

x-pack/test/api_integration/apis/ml/job_validation/index.ts

@@ -9,5 +9,6 @@ export default function({ loadTestFile }: FtrProviderContext) {
   describe('job validation', function() {
     loadTestFile(require.resolve('./bucket_span_estimator'));
     loadTestFile(require.resolve('./calculate_model_memory_limit'));
+    loadTestFile(require.resolve('./cardinality'));
   });
 }