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[ML] Improving empty object creation (#191518)

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Replacing instances of empty object creation with `Object.create(null)`
to remove any risk of prototype pollution.
Only touching server and common code.

Follows on from https://github.com/elastic/kibana/pull/186821
This commit is contained in:
James Gowdy 2024-08-28 16:41:34 +01:00 • committed by GitHub
parent ac4a786c38
commit 5ea65f9ed7
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
25 changed files with 94 additions and 88 deletions
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2
x-pack/plugins/ml/common/util/group_color_utils.ts
View file

@ -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) {

2
x-pack/plugins/ml/server/lib/alerts/register_anomaly_detection_alert_type.ts
View file

@ -255,7 +255,7 @@ export function registerAnomalyDetectionAlertType({
spaceId,
rule,
}: ExecutorOptions<MlAnomalyDetectionAlertParams>) => {
const fakeRequest = {} as KibanaRequest;
const fakeRequest = Object.create(null) as KibanaRequest;
const alertingService = mlSharedServices.alertingServiceProvider(
services.savedObjectsClient,
fakeRequest

2
x-pack/plugins/ml/server/lib/alerts/register_jobs_monitoring_rule_type.ts
View file

@ -266,7 +266,7 @@ export function registerJobsMonitoringRuleType({
throw new AlertsClientError();
}
const fakeRequest = {} as KibanaRequest;
const fakeRequest = Object.create(null) as KibanaRequest;
const { getTestsResults } = mlServicesProviders.jobsHealthServiceProvider(
services.savedObjectsClient,
fakeRequest,

4
x-pack/plugins/ml/server/lib/capabilities/__mocks__/ml_capabilities.ts
View file

@ -13,7 +13,7 @@ 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;
});
@ -21,7 +21,7 @@ export function getAdminCapabilities() {
}
export function getUserCapabilities() {
const caps: any = {};
const caps: any = Object.create(null);
Object.keys(userMlCapabilities).forEach((k) => {
caps[k] = true;
});

4
x-pack/plugins/ml/server/models/calculate_model_memory_limit/calculate_model_memory_limit.ts
View file

@ -65,8 +65,8 @@ 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(

6
x-pack/plugins/ml/server/models/data_frame_analytics/analytics_manager.ts
View file

@ -228,7 +228,7 @@ export class AnalyticsManager {
private async getInitialElementsModelRoot(modelId: string): Promise<InitialElementsReturnType> {
const resultElements = [];
const modelElements = [];
const details: any = {};
const details: any = Object.create(null);
let data: estypes.MlTrainedModelConfig | estypes.MlDataframeAnalyticsSummary | undefined;
// fetch model data and create model elements
data = this.findTrainedModel(modelId);
@ -296,7 +296,7 @@ export class AnalyticsManager {
): Promise<InitialElementsReturnType> {
const resultElements = [];
const modelElements = [];
const details: any = {};
const details: any = Object.create(null);
const data = this.findJob(jobId);
const nextLinkId = data?.source?.index[0];
@ -351,7 +351,7 @@ export class AnalyticsManager {
try {
await this.initData();
// Create first node for incoming analyticsId or modelId
let initialData: InitialElementsReturnType = {} as InitialElementsReturnType;
let initialData: InitialElementsReturnType = Object.create(null) as InitialElementsReturnType;
const job = analyticsId === undefined ? undefined : this.findJob(analyticsId);
if (analyticsId !== undefined && job !== undefined) {
const jobCreateTime = job.create_time!;

16
x-pack/plugins/ml/server/models/data_recognizer/data_recognizer.ts
View file

@ -355,7 +355,7 @@ 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) => ({
@ -595,7 +595,7 @@ 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.
@ -851,7 +851,7 @@ 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);
}
@ -957,7 +957,9 @@ 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,
@ -982,7 +984,7 @@ 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);
Object.keys(reducedConfig[i]).forEach((k) => {
createResultsItems((reducedConfig[i] as Module['kibana'])[k] as any[], results[i], k);
});
@ -1174,7 +1176,7 @@ 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;
@ -1206,7 +1208,7 @@ 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;

12
x-pack/plugins/ml/server/models/data_visualizer/data_visualizer.ts
View file

@ -246,7 +246,7 @@ 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.
@ -584,7 +584,7 @@ export class DataVisualizer {
{ maxRetries: 0 }
);
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'],
@ -628,7 +628,7 @@ 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[`${safeFieldName}_field_stats`] = {
@ -757,7 +757,7 @@ export class DataVisualizer {
const size = 0;
const filterCriteria = buildBaseFilterCriteria(timeFieldName, earliestMs, latestMs, query);
const aggs: Aggs = {};
const aggs: Aggs = Object.create(null);
fields.forEach((field, i) => {
const safeFieldName = getSafeAggregationName(field.fieldName, i);
const top = {
@ -839,7 +839,7 @@ export class DataVisualizer {
const size = 0;
const filterCriteria = buildBaseFilterCriteria(timeFieldName, earliestMs, latestMs, query);
const aggs: Aggs = {};
const aggs: Aggs = Object.create(null);
fields.forEach((field, i) => {
const safeFieldName = getSafeAggregationName(field.fieldName, i);
aggs[`${safeFieldName}_field_stats`] = {
@ -910,7 +910,7 @@ 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`] = {

2
x-pack/plugins/ml/server/models/fields_service/fields_service.ts
View file

@ -146,7 +146,7 @@ export function fieldsServiceProvider({ asCurrentUser }: IScopedClusterClient) {
mustCriteria.push(query);
}
const runtimeMappings: any = {};
const runtimeMappings: any = Object.create(null);
const aggs = fieldsToAgg.reduce(
(obj, field) => {
if (

4
x-pack/plugins/ml/server/models/filter/filter_manager.ts
View file

@ -77,7 +77,7 @@ 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] as estypes.MlGetJobsResponse).jobs) {
filtersInUse = this.buildFiltersInUse((results[JOBS] as estypes.MlGetJobsResponse).jobs);
}
@ -136,7 +136,7 @@ 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) => {

2
x-pack/plugins/ml/server/models/job_audit_messages/job_audit_messages.ts
View file

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

4
x-pack/plugins/ml/server/models/job_service/datafeeds.ts
View file

@ -41,7 +41,7 @@ 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) {
@ -114,7 +114,7 @@ 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 {

6
x-pack/plugins/ml/server/models/job_service/groups.ts
View file

@ -22,8 +22,8 @@ export function groupsProvider(mlClient: MlClient) {
const calMngr = new CalendarManager(mlClient);
async function getAllGroups() {
const groups: { [id: string]: Group } = {};
const jobIds: { [id: string]: undefined | null } = {};
const groups: { [id: string]: Group } = Object.create(null);
const jobIds: { [id: string]: undefined | null } = Object.create(null);
const [body, calendars] = await Promise.all([mlClient.getJobs(), calMngr.getAllCalendars()]);
const { jobs } = body;
@ -70,7 +70,7 @@ export function groupsProvider(mlClient: MlClient) {
}
async function updateGroups(jobs: UpdateGroupsRequest['jobs']) {
const results: Results = {};
const results: Results = Object.create(null);
for (const job of jobs) {
const { jobId, groups } = job;
try {

22
x-pack/plugins/ml/server/models/job_service/jobs.ts
View file

@ -89,7 +89,7 @@ export function jobsProvider(
deleteUserAnnotations = false,
deleteAlertingRules = false
) {
const results: Results = {};
const results: Results = Object.create(null);
const datafeedIds = await getDatafeedIdsByJobId();
if (deleteAlertingRules && rulesClient) {
@ -149,7 +149,7 @@ export function jobsProvider(
}
async function closeJobs(jobIds: string[]) {
const results: Results = {};
const results: Results = Object.create(null);
for (const jobId of jobIds) {
try {
await mlClient.closeJob({ job_id: jobId });
@ -185,7 +185,7 @@ export function jobsProvider(
}
async function resetJobs(jobIds: string[], deleteUserAnnotations = false) {
const results: ResetJobsResponse = {};
const results: ResetJobsResponse = Object.create(null);
for (const jobId of jobIds) {
try {
// @ts-expect-error @elastic-elasticsearch resetJob response incorrect, missing task
@ -229,7 +229,7 @@ export function jobsProvider(
async function jobsSummary(jobIds: string[] = []) {
const fullJobsList: CombinedJobWithStats[] = await createFullJobsList();
const fullJobsIds = fullJobsList.map((job) => job.job_id);
let auditMessagesByJob: { [id: string]: AuditMessage } = {};
let auditMessagesByJob: { [id: string]: AuditMessage } = Object.create(null);
// even if there are errors getting the audit messages, we still want to show the full list
try {
@ -309,12 +309,12 @@ export function jobsProvider(
async function jobsWithTimerange() {
const fullJobsList = await createFullJobsList();
const jobsMap: { [id: string]: string[] } = {};
const jobsMap: { [id: string]: string[] } = Object.create(null);
const jobs = fullJobsList.map((job) => {
jobsMap[job.job_id] = job.groups || [];
const hasDatafeed = isPopulatedObject(job.datafeed_config);
const timeRange: { to?: number; from?: number } = {};
const timeRange: { to?: number; from?: number } = Object.create(null);
const dataCounts = job.data_counts;
if (dataCounts !== undefined) {
@ -378,9 +378,9 @@ export function jobsProvider(
async function createFullJobsList(jobIds: string[] = []) {
const jobs: CombinedJobWithStats[] = [];
const groups: { [jobId: string]: string[] } = {};
const datafeeds: { [id: string]: DatafeedWithStats } = {};
const calendarsByJobId: { [jobId: string]: string[] } = {};
const groups: { [jobId: string]: string[] } = Object.create(null);
const datafeeds: { [id: string]: DatafeedWithStats } = Object.create(null);
const calendarsByJobId: { [jobId: string]: string[] } = Object.create(null);
const globalCalendars: string[] = [];
const jobIdsString = jobIds.join();
@ -583,7 +583,7 @@ export function jobsProvider(
jobIds: string[] = [],
allSpaces: boolean = false
): Promise<JobsExistResponse> {
const results: JobsExistResponse = {};
const results: JobsExistResponse = Object.create(null);
for (const jobId of jobIds) {
try {
if (jobId === '') {
@ -669,7 +669,7 @@ export function jobsProvider(
jobs: Array<{ job: Job; datafeed: Datafeed }>,
authHeader: AuthorizationHeader
) {
const results: BulkCreateResults = {};
const results: BulkCreateResults = Object.create(null);
await Promise.all(
jobs.map(async ({ job, datafeed }) => {
results[job.job_id] = { job: { success: false }, datafeed: { success: false } };

7
x-pack/plugins/ml/server/models/job_service/new_job/line_chart.ts
View file

@ -69,7 +69,7 @@ export function newJobLineChartProvider({ asCurrentUser }: IScopedClusterClient)
function processSearchResults(resp: any, fields: string[]): ProcessedResults {
const aggregationsByTime = get(resp, ['aggregations', 'times', 'buckets'], []);
const tempResults: Record<DtrIndex, Result[]> = {};
const tempResults: Record<DtrIndex, Result[]> = Object.create(null);
fields.forEach((f, i) => (tempResults[i] = []));
aggregationsByTime.forEach((dataForTime: any) => {
@ -166,7 +166,10 @@ function getSearchJsonFromConfig(
json.body.query = query;
const aggs: Record<number, Record<string, { field: string; percents?: string[] }>> = {};
const aggs: Record<
number,
Record<string, { field: string; percents?: string[] }>
> = Object.create(null);
aggFieldNamePairs.forEach(({ agg, field }, i) => {
if (field !== null && field !== EVENT_RATE_FIELD_ID) {

4
x-pack/plugins/ml/server/models/job_service/new_job/population_chart.ts
View file

@ -75,7 +75,7 @@ export function newJobPopulationChartProvider({ asCurrentUser }: IScopedClusterC
function processSearchResults(resp: any, fields: string[]): ProcessedResults {
const aggregationsByTime = get(resp, ['aggregations', 'times', 'buckets'], []);
const tempResults: Record<DtrIndex, Result[]> = {};
const tempResults: Record<DtrIndex, Result[]> = Object.create(null);
fields.forEach((f, i) => (tempResults[i] = []));
aggregationsByTime.forEach((dataForTime: any) => {
@ -188,7 +188,7 @@ function getPopulationSearchJsonFromConfig(
json.body.query = query;
const aggs: any = {};
const aggs: any = Object.create(null);
aggFieldNamePairs.forEach(({ agg, field, by }, i) => {
if (field === EVENT_RATE_FIELD_ID) {

4
x-pack/plugins/ml/server/models/job_service/new_job_caps/field_service.ts
View file

@ -125,7 +125,7 @@ class FieldsService {
// the _indexPattern will be replaced with a comma separated list
// of index patterns from all of the rollup jobs
public async getData(includeNested: boolean = false): Promise<NewJobCaps> {
let rollupFields: RollupFields = {};
let rollupFields: RollupFields = Object.create(null);
if (this._isRollup) {
const rollupService = await rollupServiceProvider(
@ -159,7 +159,7 @@ class FieldsService {
function combineAllRollupFields(
rollupConfigs: estypes.RollupGetRollupCapsRollupCapabilitySummary[]
): RollupFields {
const rollupFields: RollupFields = {};
const rollupFields: RollupFields = Object.create(null);
rollupConfigs.forEach((conf) => {
Object.keys(conf.fields).forEach((fieldName) => {
if (rollupFields[fieldName] === undefined) {

2
x-pack/plugins/ml/server/models/job_validation/job_validation.test.ts
View file

@ -23,7 +23,7 @@ callAs.search.mockResponse({
hits: { total: { value: 1, relation: ES_CLIENT_TOTAL_HITS_RELATION.EQ } },
});
const authHeader: AuthorizationHeader = {};
const authHeader: AuthorizationHeader = Object.create(null);
const mlClusterClient = {
asCurrentUser: callAs,

34
x-pack/plugins/ml/server/models/results_service/anomaly_charts.ts
View file

@ -227,7 +227,7 @@ export function anomalyChartsDataProvider(mlClient: MlClient, client: IScopedClu
esSearchRequest.query!.bool!.minimum_should_match = shouldCriteria.length / 2;
}
esSearchRequest.aggs!.byTime.aggs = {};
esSearchRequest.aggs!.byTime.aggs = Object.create(null);
if (metricFieldName !== undefined && metricFieldName !== '' && metricFunction) {
const metricAgg: any = {
@ -258,7 +258,7 @@ export function anomalyChartsDataProvider(mlClient: MlClient, client: IScopedClu
}
esSearchRequest.aggs!.byTime.aggs = tempAggs;
} else {
esSearchRequest.aggs!.byTime.aggs.metric = metricAgg;
esSearchRequest.aggs!.byTime.aggs!.metric = metricAgg;
}
} else {
// if metricFieldName is not defined, it's probably a variation of the non zero count function
@ -465,9 +465,9 @@ export function anomalyChartsDataProvider(mlClient: MlClient, client: IScopedClu
return { records: [], errors: undefined };
}
// Aggregate by job, detector, and analysis fields (partition, by, over).
const aggregatedData: Record<string, any> = {};
const aggregatedData: Record<string, any> = Object.create(null);
const jobsErrorMessage: Record<string, string> = {};
const jobsErrorMessage: Record<string, string> = Object.create(null);
each(anomalyRecords, (record) => {
// Check if we can plot a chart for this record, depending on whether the source data
// is chartable, and if model plot is enabled for the job.
@ -516,13 +516,13 @@ export function anomalyChartsDataProvider(mlClient: MlClient, client: IScopedClu
}
const jobId = record.job_id;
if (aggregatedData[jobId] === undefined) {
aggregatedData[jobId] = {};
aggregatedData[jobId] = Object.create(null);
}
const detectorsForJob = aggregatedData[jobId];
const detectorIndex = record.detector_index;
if (detectorsForJob[detectorIndex] === undefined) {
detectorsForJob[detectorIndex] = {};
detectorsForJob[detectorIndex] = Object.create(null);
}
// TODO - work out how best to display results from detectors with just an over field.
@ -534,11 +534,11 @@ export function anomalyChartsDataProvider(mlClient: MlClient, client: IScopedClu
const groupsForDetector = detectorsForJob[detectorIndex];
if (groupsForDetector[firstFieldName] === undefined) {
groupsForDetector[firstFieldName] = {};
groupsForDetector[firstFieldName] = Object.create(null);
}
const valuesForGroup: Record<string, any> = groupsForDetector[firstFieldName];
if (valuesForGroup[firstFieldValue] === undefined) {
valuesForGroup[firstFieldValue] = {};
valuesForGroup[firstFieldValue] = Object.create(null);
}
const dataForGroupValue = valuesForGroup[firstFieldValue];
@ -568,12 +568,12 @@ export function anomalyChartsDataProvider(mlClient: MlClient, client: IScopedClu
if (secondFieldName !== undefined && secondFieldValue !== undefined) {
if (dataForGroupValue[secondFieldName] === undefined) {
dataForGroupValue[secondFieldName] = {};
dataForGroupValue[secondFieldName] = Object.create(null);
}
const splitsForGroup = dataForGroupValue[secondFieldName];
if (splitsForGroup[secondFieldValue] === undefined) {
splitsForGroup[secondFieldValue] = {};
splitsForGroup[secondFieldValue] = Object.create(null);
}
const dataForSplitValue = splitsForGroup[secondFieldValue];
@ -604,7 +604,7 @@ export function anomalyChartsDataProvider(mlClient: MlClient, client: IScopedClu
});
// Group job id by error message instead of by job:
const errorMessages: Record<string, Set<string>> | undefined = {};
const errorMessages: Record<string, Set<string>> = Object.create(null);
Object.keys(jobsErrorMessage).forEach((jobId) => {
const msg = jobsErrorMessage[jobId];
if (errorMessages[msg] === undefined) {
@ -907,13 +907,13 @@ export function anomalyChartsDataProvider(mlClient: MlClient, client: IScopedClu
handleError = (errorMsg: string, jobId: string) => {
// Group the jobIds by the type of error message
if (!errorMessages) {
errorMessages = {};
errorMessages = Object.create(null);
}
if (errorMessages[errorMsg]) {
errorMessages[errorMsg].add(jobId);
if (errorMessages![errorMsg]) {
errorMessages![errorMsg].add(jobId);
} else {
errorMessages[errorMsg] = new Set([jobId]);
errorMessages![errorMsg] = new Set([jobId]);
}
};
}
@ -1408,7 +1408,7 @@ export function anomalyChartsDataProvider(mlClient: MlClient, client: IScopedClu
const dataByJobId = get(resp, ['aggregations', 'jobs', 'buckets'], []);
each(dataByJobId, (dataForJob: any) => {
const jobId: string = dataForJob.key;
const resultsForTime: Record<string, any> = {};
const resultsForTime: Record<string, any> = Object.create(null);
const dataByTime = get(dataForJob, ['times', 'buckets'], []);
each(dataByTime, (dataForTime: any) => {
const time: string = dataForTime.key;
@ -1543,7 +1543,7 @@ export function anomalyChartsDataProvider(mlClient: MlClient, client: IScopedClu
typeof metricFunction === 'string'
) {
// @ts-ignore
body.aggs.sample.aggs.byTime.aggs.entities.aggs = {};
body.aggs.sample.aggs.byTime.aggs.entities.aggs = Object.create(null);
const metricAgg = {
[metricFunction]: {

10
x-pack/plugins/ml/server/models/results_service/results_service.ts
View file

@ -54,7 +54,7 @@ interface Influencer {
* @param source
*/
export function getTypicalAndActualValues(source: MlAnomalyRecordDoc) {
const result: { actual?: number[]; typical?: number[] } = {};
const result: { actual?: number[]; typical?: number[] } = Object.create(null);
const functionDescription = source.function_description || '';
const causes = source.causes || [];
@ -254,9 +254,9 @@ export function resultsServiceProvider(mlClient: MlClient, client?: IScopedClust
(item: any) => item.entityName === 'mlcategory'
);
if (categoryAnomalies.length > 0) {
tableData.examplesByJobId = {};
tableData.examplesByJobId = Object.create(null);
const categoryIdsByJobId: { [key: string]: any } = {};
const categoryIdsByJobId: { [key: string]: any } = Object.create(null);
categoryAnomalies.forEach((anomaly) => {
if (categoryIdsByJobId[anomaly.jobId] === undefined) {
categoryIdsByJobId[anomaly.jobId] = [];
@ -420,7 +420,7 @@ export function resultsServiceProvider(mlClient: MlClient, client?: IScopedClust
['aggregations', 'byJobId', 'buckets'],
[]
);
const timestampByJobId: { [key: string]: number | undefined } = {};
const timestampByJobId: { [key: string]: number | undefined } = Object.create(null);
bucketsByJobId.forEach((bucket) => {
timestampByJobId[bucket.key] = bucket.maxTimestamp.value;
});
@ -446,7 +446,7 @@ export function resultsServiceProvider(mlClient: MlClient, client?: IScopedClust
[jobId]
);
const examplesByCategoryId: { [key: string]: any } = {};
const examplesByCategoryId: { [key: string]: any } = Object.create(null);
// @ts-expect-error incorrect search response type
if (body.hits.total.value > 0) {
body.hits.hits.forEach((hit: any) => {

2
x-pack/plugins/ml/server/routes/data_frame_analytics.ts
View file

@ -668,7 +668,7 @@ export function dataFrameAnalyticsRoutes(
routeGuard.fullLicenseAPIGuard(async ({ client, mlClient, request, response }) => {
try {
const { analyticsIds, allSpaces } = request.body;
const results: { [id: string]: { exists: boolean } } = {};
const results: { [id: string]: { exists: boolean } } = Object.create(null);
for (const id of analyticsIds) {
try {
const body = allSpaces

3
x-pack/plugins/ml/server/routes/trained_models.ts
View file

@ -617,7 +617,8 @@ export function trainedModelsRoutes(
try {
const { deploymentId, modelId } = request.params;
const results: Record<string, { success: boolean; error?: ErrorType }> = {};
const results: Record<string, { success: boolean; error?: ErrorType }> =
Object.create(null);
for (const id of deploymentId.split(',')) {
try {

2
x-pack/plugins/ml/server/saved_objects/checks.ts
View file

@ -404,7 +404,7 @@ export function checksFactory(
.reduce((acc, cur) => {
const type = cur.type;
if (acc[type] === undefined) {
acc[type] = {};
acc[type] = Object.create(null);
}
acc[type][cur.jobId] = cur.namespaces;
return acc;

10
x-pack/plugins/ml/server/saved_objects/service.ts
View file

@ -75,7 +75,7 @@ export function mlSavedObjectServiceFactory(
currentSpaceOnly: boolean = true
) {
await isMlReady();
const filterObject: JobObjectFilter = {};
const filterObject: JobObjectFilter = Object.create(null);
if (jobType !== undefined) {
filterObject.type = jobType;
@ -225,7 +225,7 @@ export function mlSavedObjectServiceFactory(
async function getAllJobObjectsForAllSpaces(jobType?: JobType, jobId?: string) {
await isMlReady();
const filterObject: JobObjectFilter = {};
const filterObject: JobObjectFilter = Object.create(null);
if (jobType !== undefined) {
filterObject.type = jobType;
@ -359,7 +359,7 @@ export function mlSavedObjectServiceFactory(
return {};
}
const results: SavedObjectResult = {};
const results: SavedObjectResult = Object.create(null);
const jobs = await _getJobObjects(jobType);
const jobObjectIdMap = new Map<string, string>();
const jobObjectsToUpdate: Array<{ type: string; id: string }> = [];
@ -463,7 +463,7 @@ export function mlSavedObjectServiceFactory(
async function _getTrainedModelObjects(modelId?: string, currentSpaceOnly: boolean = true) {
await isMlReady();
const filterObject: TrainedModelObjectFilter = {};
const filterObject: TrainedModelObjectFilter = Object.create(null);
if (modelId !== undefined) {
filterObject.model_id = modelId;
@ -725,7 +725,7 @@ export function mlSavedObjectServiceFactory(
if (modelIds.length === 0 || (spacesToAdd.length === 0 && spacesToRemove.length === 0)) {
return {};
}
const results: SavedObjectResult = {};
const results: SavedObjectResult = Object.create(null);
const models = await _getTrainedModelObjects();
const trainedModelObjectIdMap = new Map<string, string>();
const objectsToUpdate: Array<{ type: string; id: string }> = [];

16
x-pack/plugins/ml/server/saved_objects/sync.ts
View file

@ -61,7 +61,7 @@ export function syncSavedObjectsFactory(
if (job.checks.savedObjectExits === false) {
const type = 'anomaly-detector';
if (results.savedObjectsCreated[type] === undefined) {
results.savedObjectsCreated[type] = {};
results.savedObjectsCreated[type] = Object.create(null);
}
if (simulate === true) {
results.savedObjectsCreated[type]![job.jobId] = { success: true };
@ -88,7 +88,7 @@ export function syncSavedObjectsFactory(
if (job.checks.savedObjectExits === false) {
const type = 'data-frame-analytics';
if (results.savedObjectsCreated[type] === undefined) {
results.savedObjectsCreated[type] = {};
results.savedObjectsCreated[type] = Object.create(null);
}
if (simulate === true) {
results.savedObjectsCreated[type]![job.jobId] = { success: true };
@ -119,7 +119,7 @@ export function syncSavedObjectsFactory(
const { modelId } = model;
const type = 'trained-model';
if (results.savedObjectsCreated[type] === undefined) {
results.savedObjectsCreated[type] = {};
results.savedObjectsCreated[type] = Object.create(null);
}
if (simulate === true) {
results.savedObjectsCreated[type]![modelId] = { success: true };
@ -161,7 +161,7 @@ export function syncSavedObjectsFactory(
if (job.checks.jobExists === false) {
const type = 'anomaly-detector';
if (results.savedObjectsDeleted[type] === undefined) {
results.savedObjectsDeleted[type] = {};
results.savedObjectsDeleted[type] = Object.create(null);
}
if (simulate === true) {
results.savedObjectsDeleted[type]![job.jobId] = { success: true };
@ -191,7 +191,7 @@ export function syncSavedObjectsFactory(
if (job.checks.jobExists === false) {
const type = 'data-frame-analytics';
if (results.savedObjectsDeleted[type] === undefined) {
results.savedObjectsDeleted[type] = {};
results.savedObjectsDeleted[type] = Object.create(null);
}
if (simulate === true) {
results.savedObjectsDeleted[type]![job.jobId] = { success: true };
@ -225,7 +225,7 @@ export function syncSavedObjectsFactory(
const { modelId, namespaces } = model;
const type = 'trained-model';
if (results.savedObjectsDeleted[type] === undefined) {
results.savedObjectsDeleted[type] = {};
results.savedObjectsDeleted[type] = Object.create(null);
}
if (simulate === true) {
@ -265,7 +265,7 @@ export function syncSavedObjectsFactory(
adJobsById[job.jobId].datafeedId !== job.datafeedId)
) {
if (results.datafeedsAdded[type] === undefined) {
results.datafeedsAdded[type] = {};
results.datafeedsAdded[type] = Object.create(null);
}
// add datafeed id for jobs where the datafeed exists but the id is missing from the saved object
// or if the datafeed id in the saved object is not the same as the one attached to the job in es
@ -298,7 +298,7 @@ export function syncSavedObjectsFactory(
job.datafeedId !== undefined
) {
if (results.datafeedsRemoved[type] === undefined) {
results.datafeedsRemoved[type] = {};
results.datafeedsRemoved[type] = Object.create(null);
}
// remove datafeed id for jobs where the datafeed no longer exists but the id is populated in the saved object
if (simulate === true) {