[7.17] [ML] Improving empty object creation (#191518) (#191836)

Manual backport of #191518

Also includes changes to server side and common code to use
`Object.hasOwn` rather than `hasOwnProperty`

---------

Co-authored-by: Elastic Machine <elasticmachine@users.noreply.github.com>

x-pack/plugins/ml/common/types/alerts.ts

@@ -75,15 +75,15 @@ export interface InfluencerAnomalyAlertDoc extends BaseAnomalyAlertDoc {
 export type AlertHitDoc = RecordAnomalyAlertDoc | BucketAnomalyAlertDoc | InfluencerAnomalyAlertDoc;
 
 export function isRecordAnomalyAlertDoc(arg: any): arg is RecordAnomalyAlertDoc {
-  return arg.hasOwnProperty('result_type') && arg.result_type === ANOMALY_RESULT_TYPE.RECORD;
+  return Object.hasOwn(arg, 'result_type') && arg.result_type === ANOMALY_RESULT_TYPE.RECORD;
 }
 
 export function isBucketAnomalyAlertDoc(arg: any): arg is BucketAnomalyAlertDoc {
-  return arg.hasOwnProperty('result_type') && arg.result_type === ANOMALY_RESULT_TYPE.BUCKET;
+  return Object.hasOwn(arg, 'result_type') && arg.result_type === ANOMALY_RESULT_TYPE.BUCKET;
 }
 
 export function isInfluencerAnomalyAlertDoc(arg: any): arg is InfluencerAnomalyAlertDoc {
-  return arg.hasOwnProperty('result_type') && arg.result_type === ANOMALY_RESULT_TYPE.INFLUENCER;
+  return Object.hasOwn(arg, 'result_type') && arg.result_type === ANOMALY_RESULT_TYPE.INFLUENCER;
 }
 
 export type MlAnomalyDetectionAlertParams = {

x-pack/plugins/ml/common/types/field_histograms.ts

@@ -22,11 +22,11 @@ export interface NumericChartData {
 export const isNumericChartData = (arg: any): arg is NumericChartData => {
   return (
     typeof arg === 'object' &&
-    arg.hasOwnProperty('data') &&
-    arg.hasOwnProperty('id') &&
-    arg.hasOwnProperty('interval') &&
-    arg.hasOwnProperty('stats') &&
-    arg.hasOwnProperty('type') &&
+    Object.hasOwn(arg, 'data') &&
+    Object.hasOwn(arg, 'id') &&
+    Object.hasOwn(arg, 'interval') &&
+    Object.hasOwn(arg, 'stats') &&
+    Object.hasOwn(arg, 'type') &&
     arg.type === 'numeric'
   );
 };
@@ -47,10 +47,10 @@ export interface OrdinalChartData {
 export const isOrdinalChartData = (arg: any): arg is OrdinalChartData => {
   return (
     typeof arg === 'object' &&
-    arg.hasOwnProperty('data') &&
-    arg.hasOwnProperty('cardinality') &&
-    arg.hasOwnProperty('id') &&
-    arg.hasOwnProperty('type') &&
+    Object.hasOwn(arg, 'data') &&
+    Object.hasOwn(arg, 'cardinality') &&
+    Object.hasOwn(arg, 'id') &&
+    Object.hasOwn(arg, 'type') &&
     (arg.type === 'ordinal' || arg.type === 'boolean')
   );
 };
@@ -61,7 +61,7 @@ export interface UnsupportedChartData {
 }
 
 export const isUnsupportedChartData = (arg: any): arg is UnsupportedChartData => {
-  return typeof arg === 'object' && arg.hasOwnProperty('type') && arg.type === 'unsupported';
+  return typeof arg === 'object' && Object.hasOwn(arg, 'type') && arg.type === 'unsupported';
 };
 
 export type ChartDataItem = NumericDataItem | OrdinalDataItem;

x-pack/plugins/ml/common/util/group_color_utils.ts

@@ -24,7 +24,7 @@ const COLORS = [
   euiVars.euiColorPrimary,
 ];
 
-const colorMap: Record<string, string> = {};
+const colorMap: Record<string, string> = Object.create(null);
 
 export function tabColor(name: string): string {
   if (colorMap[name] === undefined) {

x-pack/plugins/ml/common/util/job_utils.ts

@@ -280,8 +280,8 @@ export function isModelPlotEnabled(
       // 'partition' field values even though this is supported on the back-end.
       // If supplied, check both the by and partition entities are in the terms.
       const detector = job.analysis_config.detectors[detectorIndex];
-      const detectorHasPartitionField = detector.hasOwnProperty('partition_field_name');
-      const detectorHasByField = detector.hasOwnProperty('by_field_name');
+      const detectorHasPartitionField = Object.hasOwn(detector, 'partition_field_name');
+      const detectorHasByField = Object.hasOwn(detector, 'by_field_name');
       const terms = termsStr.split(',');
 
       if (detectorHasPartitionField) {

x-pack/plugins/ml/common/util/validation_utils.ts

@@ -45,7 +45,7 @@ export function findAggField(
       value = returnParent === true ? aggs : aggs[k];
       return true;
     }
-    if (aggs.hasOwnProperty(k) && aggs[k] !== null && typeof aggs[k] === 'object') {
+    if (Object.hasOwn(aggs, k) && aggs[k] !== null && typeof aggs[k] === 'object') {
       value = findAggField(aggs[k], fieldName, returnParent);
       return value !== undefined;
     }

x-pack/plugins/ml/common/util/validators.ts

@@ -116,7 +116,7 @@ export function numberValidator(conditions?: { min?: number; max?: number }) {
   }
 
   return (value: number): NumberValidationResult | null => {
-    const result = {} as NumberValidationResult;
+    const result = Object.create(null) as NumberValidationResult;
     if (conditions?.min !== undefined && value < conditions.min) {
       result.min = true;
     }

x-pack/plugins/ml/server/lib/alerts/register_anomaly_detection_alert_type.ts

@@ -130,7 +130,7 @@ export function registerAnomalyDetectionAlertType({
     minimumLicenseRequired: MINIMUM_FULL_LICENSE,
     isExportable: true,
     async executor({ services, params, alertId, state, previousStartedAt, startedAt }) {
-      const fakeRequest = {} as KibanaRequest;
+      const fakeRequest = Object.create(null) as KibanaRequest;
       const { execute } = mlSharedServices.alertingServiceProvider(
         services.savedObjectsClient,
         fakeRequest

x-pack/plugins/ml/server/lib/alerts/register_jobs_monitoring_rule_type.ts

@@ -139,7 +139,7 @@ export function registerJobsMonitoringRuleType({
     async executor(options) {
       const { services, name } = options;
 
-      const fakeRequest = {} as KibanaRequest;
+      const fakeRequest = Object.create(null) as KibanaRequest;
       const { getTestsResults } = mlServicesProviders.jobsHealthServiceProvider(
         services.savedObjectsClient,
         fakeRequest,

x-pack/plugins/ml/server/lib/capabilities/__mocks__/ml_capabilities.ts

@@ -13,15 +13,15 @@ import {
 } from '../../../../common/types/capabilities';
 
 export function getAdminCapabilities() {
-  const caps: any = {};
+  const caps: any = Object.create(null);
   Object.keys(adminMlCapabilities).forEach((k) => {
     caps[k] = true;
   });
   return { ...getUserCapabilities(), ...caps } as MlCapabilities;
 }
 
 export function getUserCapabilities() {
-  const caps: any = {};
+  const caps: any = Object.create(null);
   Object.keys(userMlCapabilities).forEach((k) => {
     caps[k] = true;
   });

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

@@ -66,8 +66,8 @@ const cardinalityCheckProvider = (client: IScopedClusterClient) => {
 
     const { detectors, influencers, bucket_span: bucketSpan } = analysisConfig;
 
-    let overallCardinality = {};
-    let maxBucketCardinality = {};
+    let overallCardinality = Object.create(null);
+    let maxBucketCardinality = Object.create(null);
 
     // Get fields required for the model memory estimation
     const overallCardinalityFields: Set<string> = detectors.reduce(

x-pack/plugins/ml/server/models/data_frame_analytics/analytics_manager.ts

@@ -268,7 +268,7 @@ export class AnalyticsManager {
   async getInitialElementsModelRoot(modelId: string): Promise<InitialElementsReturnType> {
     const resultElements = [];
     const modelElements = [];
-    const details: any = {};
+    const details: any = Object.create(null);
     // fetch model data and create model elements
     let data = await this.getAnalyticsModelData(modelId);
     const modelNodeId = `${data.model_id}-${JOB_MAP_NODE_TYPES.TRAINED_MODEL}`;
@@ -338,7 +338,7 @@ export class AnalyticsManager {
   async getInitialElementsJobRoot(jobId: string): Promise<InitialElementsReturnType> {
     const resultElements = [];
     const modelElements = [];
-    const details: any = {};
+    const details: any = Object.create(null);
     const data = await this.getAnalyticsData(jobId);
     // @ts-expect-error @elastic-elasticsearch Data frame types incomplete
     const nextLinkId = data?.source?.index[0];
@@ -389,7 +389,7 @@ export class AnalyticsManager {
     try {
       await Promise.all([this.setInferenceModels(), this.setJobStats()]);
       // Create first node for incoming analyticsId or modelId
-      let initialData: InitialElementsReturnType = {} as InitialElementsReturnType;
+      let initialData: InitialElementsReturnType = Object.create(null) as InitialElementsReturnType;
       if (analyticsId !== undefined) {
         initialData = await this.getInitialElementsJobRoot(analyticsId);
       } else if (modelId !== undefined) {

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

@@ -333,7 +333,7 @@ export class DataRecognizer {
 
     const jobs: ModuleJob[] = [];
     const datafeeds: ModuleDatafeed[] = [];
-    const kibana: KibanaObjects = {};
+    const kibana: KibanaObjects = Object.create(null);
     // load all of the job configs
     if (isModule(module)) {
       const tempJobs: ModuleJob[] = module.jobs.map((j) => ({
@@ -573,7 +573,7 @@ export class DataRecognizer {
   }
 
   public async dataRecognizerJobsExist(moduleId: string): Promise<JobExistResult> {
-    const results = {} as JobExistResult;
+    const results = Object.create(null) as JobExistResult;
 
     // Load the module with the specified ID and check if the jobs
     // in the module have been created.
@@ -828,7 +828,7 @@ export class DataRecognizer {
     start?: number,
     end?: number
   ): Promise<{ [key: string]: DatafeedResponse }> {
-    const results = {} as { [key: string]: DatafeedResponse };
+    const results = Object.create(null) as { [key: string]: DatafeedResponse };
     for (const datafeed of datafeeds) {
       results[datafeed.id] = await this._startDatafeed(datafeed, start, end);
     }
@@ -939,7 +939,9 @@ export class DataRecognizer {
   // creates an empty results object,
   // listing each job/datafeed/savedObject with a save success boolean
   private _createResultsTemplate(moduleConfig: Module): DataRecognizerConfigResponse {
-    const results: DataRecognizerConfigResponse = {} as DataRecognizerConfigResponse;
+    const results: DataRecognizerConfigResponse = Object.create(
+      null
+    ) as DataRecognizerConfigResponse;
     const reducedConfig = {
       jobs: moduleConfig.jobs,
       datafeeds: moduleConfig.datafeeds,
@@ -964,7 +966,7 @@ export class DataRecognizer {
       if (Array.isArray(reducedConfig[i])) {
         createResultsItems(reducedConfig[i] as any[], results, i);
       } else {
-        results[i] = {} as any;
+        results[i] = Object.create(null) as any;
         Object.keys(reducedConfig[i]).forEach((k) => {
           createResultsItems((reducedConfig[i] as Module['kibana'])[k] as any[], results[i], k);
         });
@@ -1156,7 +1158,7 @@ export class DataRecognizer {
           );
 
           if (!job.config.analysis_limits) {
-            job.config.analysis_limits = {} as AnalysisLimits;
+            job.config.analysis_limits = Object.create(null) as AnalysisLimits;
           }
 
           job.config.analysis_limits.model_memory_limit = modelMemoryLimit;
@@ -1190,7 +1192,7 @@ export class DataRecognizer {
           // so set the jobs mml to be the max
 
           if (!job.config.analysis_limits) {
-            job.config.analysis_limits = {} as AnalysisLimits;
+            job.config.analysis_limits = Object.create(null) as AnalysisLimits;
           }
 
           job.config.analysis_limits.model_memory_limit = maxMml;

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

@@ -479,7 +479,7 @@ export class DataVisualizer {
   ): Promise<BatchStats[]> {
     // Batch up fields by type, getting stats for multiple fields at a time.
     const batches: Field[][] = [];
-    const batchedFields: { [key: string]: Field[][] } = {};
+    const batchedFields: { [key: string]: Field[][] } = Object.create(null);
     each(fields, (field) => {
       if (field.fieldName === undefined) {
         // undefined fieldName is used for a document count request.
@@ -808,7 +808,7 @@ export class DataVisualizer {
       body: searchBody,
     });
 
-    const buckets: { [key: string]: number } = {};
+    const buckets: { [key: string]: number } = Object.create(null);
     const dataByTimeBucket: Array<{ key: string; doc_count: number }> = get(
       body,
       ['aggregations', 'eventRate', 'buckets'],
@@ -852,7 +852,7 @@ export class DataVisualizer {
       () => (count += PERCENTILE_SPACING)
     );
 
-    const aggs: { [key: string]: any } = {};
+    const aggs: { [key: string]: any } = Object.create(null);
     fields.forEach((field, i) => {
       const safeFieldName = getSafeAggregationName(field.fieldName, i);
       aggs[`${safeFieldName}_field_stats`] = {
@@ -991,7 +991,7 @@ export class DataVisualizer {
     const size = 0;
     const filterCriteria = buildBaseFilterCriteria(timeFieldName, earliestMs, latestMs, query);
 
-    const aggs: Aggs = {};
+    const aggs: Aggs = Object.create(null);
     fields.forEach((field, i) => {
       const safeFieldName = getSafeAggregationName(field.fieldName, i);
       const top = {
@@ -1083,7 +1083,7 @@ export class DataVisualizer {
     const size = 0;
     const filterCriteria = buildBaseFilterCriteria(timeFieldName, earliestMs, latestMs, query);
 
-    const aggs: Aggs = {};
+    const aggs: Aggs = Object.create(null);
     fields.forEach((field, i) => {
       const safeFieldName = getSafeAggregationName(field.fieldName, i);
       aggs[`${safeFieldName}_field_stats`] = {
@@ -1151,7 +1151,7 @@ export class DataVisualizer {
     const size = 0;
     const filterCriteria = buildBaseFilterCriteria(timeFieldName, earliestMs, latestMs, query);
 
-    const aggs: Aggs = {};
+    const aggs: Aggs = Object.create(null);
     fields.forEach((field, i) => {
       const safeFieldName = getSafeAggregationName(field.fieldName, i);
       aggs[`${safeFieldName}_value_count`] = {

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

@@ -55,13 +55,13 @@ export function fieldsServiceProvider({ asCurrentUser }: IScopedClusterClient) {
     fieldNames.forEach((fieldName) => {
       if (
         typeof datafeedConfig?.script_fields === 'object' &&
-        datafeedConfig.script_fields.hasOwnProperty(fieldName)
+        Object.hasOwn(datafeedConfig.script_fields, fieldName)
       ) {
         aggregatableFields.push(fieldName);
       }
       if (
         typeof datafeedConfig?.runtime_mappings === 'object' &&
-        datafeedConfig.runtime_mappings.hasOwnProperty(fieldName)
+        Object.hasOwn(datafeedConfig.runtime_mappings, fieldName)
       ) {
         aggregatableFields.push(fieldName);
       }
@@ -118,7 +118,7 @@ export function fieldsServiceProvider({ asCurrentUser }: IScopedClusterClient) {
       ) ?? {};
 
     // No need to perform aggregation over the cached fields
-    const fieldsToAgg = aggregatableFields.filter((field) => !cachedValues.hasOwnProperty(field));
+    const fieldsToAgg = aggregatableFields.filter((field) => !Object.hasOwn(cachedValues, field));
 
     if (fieldsToAgg.length === 0) {
       return cachedValues;
@@ -142,17 +142,17 @@ export function fieldsServiceProvider({ asCurrentUser }: IScopedClusterClient) {
       mustCriteria.push(query);
     }
 
-    const runtimeMappings: any = {};
+    const runtimeMappings: any = Object.create(null);
     const aggs = fieldsToAgg.reduce(
       (obj, field) => {
         if (
           typeof datafeedConfig?.script_fields === 'object' &&
-          datafeedConfig.script_fields.hasOwnProperty(field)
+          Object.hasOwn(datafeedConfig.script_fields, field)
         ) {
           obj[field] = { cardinality: { script: datafeedConfig.script_fields[field].script } };
         } else if (
           typeof datafeedConfig?.runtime_mappings === 'object' &&
-          datafeedConfig.runtime_mappings.hasOwnProperty(field)
+          Object.hasOwn(datafeedConfig.runtime_mappings, field)
         ) {
           obj[field] = { cardinality: { field } };
           runtimeMappings.runtime_mappings = datafeedConfig.runtime_mappings;
@@ -339,7 +339,7 @@ export function fieldsServiceProvider({ asCurrentUser }: IScopedClusterClient) {
       ) ?? {};
 
     // No need to perform aggregation over the cached fields
-    const fieldsToAgg = aggregatableFields.filter((field) => !cachedValues.hasOwnProperty(field));
+    const fieldsToAgg = aggregatableFields.filter((field) => !Object.hasOwn(cachedValues, field));
 
     if (fieldsToAgg.length === 0) {
       return cachedValues;

x-pack/plugins/ml/server/models/filter/filter_manager.ts

@@ -68,7 +68,7 @@ export class FilterManager {
         results[FILTERS] &&
         (results[FILTERS].body as estypes.MlGetFiltersResponse).filters.length
       ) {
-        let filtersInUse: FiltersInUse = {};
+        let filtersInUse: FiltersInUse = Object.create(null);
         if (results[JOBS] && (results[JOBS].body as estypes.MlGetJobsResponse).jobs) {
           filtersInUse = this.buildFiltersInUse(
             (results[JOBS].body as estypes.MlGetJobsResponse).jobs
@@ -107,7 +107,7 @@ export class FilterManager {
       ]);
 
       // Build a map of filter_ids against jobs and detectors using that filter.
-      let filtersInUse: FiltersInUse = {};
+      let filtersInUse: FiltersInUse = Object.create(null);
       if (results[JOBS] && (results[JOBS].body as estypes.MlGetJobsResponse).jobs) {
         filtersInUse = this.buildFiltersInUse(
           (results[JOBS].body as estypes.MlGetJobsResponse).jobs
@@ -182,7 +182,7 @@ export class FilterManager {
 
   buildFiltersInUse(jobsList: Job[]) {
     // Build a map of filter_ids against jobs and detectors using that filter.
-    const filtersInUse: FiltersInUse = {};
+    const filtersInUse: FiltersInUse = Object.create(null);
     jobsList.forEach((job) => {
       const detectors = job.analysis_config.detectors;
       detectors.forEach((detector) => {

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

@@ -89,7 +89,7 @@ export function jobAuditMessagesProvider(
       gte = `now-${from}`;
     }
 
-    let timeFilter = {};
+    let timeFilter = Object.create(null);
     if (from !== null) {
       timeFilter = {
         range: {

x-pack/plugins/ml/server/models/job_service/datafeeds.ts

@@ -40,7 +40,7 @@ export function datafeedsProvider(client: IScopedClusterClient, mlClient: MlClie
       return acc;
     }, {} as { [id: string]: boolean });
 
-    const results: Results = {};
+    const results: Results = Object.create(null);
 
     async function doStart(datafeedId: string): Promise<{ started: boolean; error?: string }> {
       if (doStartsCalled[datafeedId] === false) {
@@ -113,7 +113,7 @@ export function datafeedsProvider(client: IScopedClusterClient, mlClient: MlClie
   }
 
   async function stopDatafeeds(datafeedIds: string[]) {
-    const results: Results = {};
+    const results: Results = Object.create(null);
 
     for (const datafeedId of datafeedIds) {
       try {