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[ML] Functional tests - reduce job run time in date nanos and categorization tests (#123899) (#123931)
This PR stabilizes the date nanos job and categorization job tests for cloud execution by reducing the job run time.
(cherry picked from commit 4f1d97a908)
Co-authored-by: Robert Oskamp <robert.oskamp@elastic.co>
This commit is contained in:
Kibana Machine
GitHub
parent
60a9838d21
commit
59d5addac5
3 changed files with 25 additions and 25 deletions
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@ -21,7 +21,7 @@ export default function ({ getService }: FtrProviderContext) {
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const detectorTypeIdentifier = 'Rare';
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const categorizationFieldIdentifier = 'field1';
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const categorizationExampleCount = 5;
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const bucketSpan = '15m';
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const bucketSpan = '1d';
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const memoryLimit = '15mb';
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function getExpectedRow(expectedJobId: string, expectedJobGroups: string[]) {
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@ -29,32 +29,32 @@ export default function ({ getService }: FtrProviderContext) {
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id: expectedJobId,
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description: jobDescription,
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jobGroups: [...new Set(expectedJobGroups)].sort(),
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recordCount: '1,501',
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recordCount: '1,000',
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memoryStatus: 'ok',
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jobState: 'closed',
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datafeedState: 'stopped',
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latestTimestamp: '2019-11-21 06:01:13',
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latestTimestamp: '2019-11-21 00:01:13',
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};
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}
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function getExpectedCounts(expectedJobId: string) {
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return {
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job_id: expectedJobId,
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processed_record_count: '1,501',
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processed_field_count: '1,501',
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input_bytes: '335.4 KB',
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input_field_count: '1,501',
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processed_record_count: '1,000',
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processed_field_count: '1,000',
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input_bytes: '148.8 KB',
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input_field_count: '1,000',
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invalid_date_count: '0',
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missing_field_count: '0',
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out_of_order_timestamp_count: '0',
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empty_bucket_count: '21,428',
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empty_bucket_count: '23',
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sparse_bucket_count: '0',
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bucket_count: '22,059',
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bucket_count: '230',
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earliest_record_timestamp: '2019-04-05 11:25:35',
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latest_record_timestamp: '2019-11-21 06:01:13',
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input_record_count: '1,501',
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latest_bucket_timestamp: '2019-11-21 06:00:00',
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latest_empty_bucket_timestamp: '2019-11-21 05:45:00',
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latest_record_timestamp: '2019-11-21 00:01:13',
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input_record_count: '1,000',
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latest_bucket_timestamp: '2019-11-21 00:00:00',
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latest_empty_bucket_timestamp: '2019-11-17 00:00:00',
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};
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}
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@ -68,7 +68,7 @@ export default function ({ getService }: FtrProviderContext) {
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total_partition_field_count: '2',
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bucket_allocation_failures_count: '0',
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memory_status: 'ok',
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timestamp: '2019-11-21 05:45:00',
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timestamp: '2019-11-20 00:00:00',
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};
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}
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@ -77,8 +77,8 @@ export default function ({ getService }: FtrProviderContext) {
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describe('categorization', function () {
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this.tags(['mlqa']);
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before(async () => {
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await esArchiver.loadIfNeeded('x-pack/test/functional/es_archives/ml/categorization');
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await ml.testResources.createIndexPatternIfNeeded('ft_categorization', '@timestamp');
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await esArchiver.loadIfNeeded('x-pack/test/functional/es_archives/ml/categorization_small');
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await ml.testResources.createIndexPatternIfNeeded('ft_categorization_small', '@timestamp');
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await ml.testResources.setKibanaTimeZoneToUTC();
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await ml.api.createCalendar(calendarId);
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@ -87,7 +87,7 @@ export default function ({ getService }: FtrProviderContext) {
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after(async () => {
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await ml.api.cleanMlIndices();
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await ml.testResources.deleteIndexPatternByTitle('ft_categorization');
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await ml.testResources.deleteIndexPatternByTitle('ft_categorization_small');
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});
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it('job creation loads the categorization wizard for the source data', async () => {
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@ -100,7 +100,7 @@ export default function ({ getService }: FtrProviderContext) {
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await ml.jobManagement.navigateToNewJobSourceSelection();
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await ml.testExecution.logTestStep('job creation loads the job type selection page');
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await ml.jobSourceSelection.selectSourceForAnomalyDetectionJob('ft_categorization');
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await ml.jobSourceSelection.selectSourceForAnomalyDetectionJob('ft_categorization_small');
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await ml.testExecution.logTestStep('job creation loads the categorization job wizard page');
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await ml.jobTypeSelection.selectCategorizationJob();
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@ -113,7 +113,7 @@ export default function ({ getService }: FtrProviderContext) {
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await ml.testExecution.logTestStep('job creation sets the time range');
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await ml.jobWizardCommon.clickUseFullDataButton(
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'Apr 5, 2019 @ 11:25:35.770',
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'Nov 21, 2019 @ 06:01:13.914'
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'Nov 21, 2019 @ 00:01:13.923'
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);
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await ml.testExecution.logTestStep('job creation displays the event rate chart');
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@ -235,7 +235,7 @@ export default function ({ getService }: FtrProviderContext) {
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await ml.testExecution.logTestStep('job cloning sets the time range');
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await ml.jobWizardCommon.clickUseFullDataButton(
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'Apr 5, 2019 @ 11:25:35.770',
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'Nov 21, 2019 @ 06:01:13.914'
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'Nov 21, 2019 @ 00:01:13.923'
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);
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await ml.testExecution.logTestStep('job cloning displays the event rate chart');
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@ -57,7 +57,7 @@ export default function ({ getService }: FtrProviderContext) {
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jobSource: 'ft_event_rate_gen_trend_nanos',
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jobId: `event_rate_nanos_count_1_${Date.now()}`,
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jobDescription:
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'Create advanced job based on the event rate dataset with a date_nanos time field, 30m bucketspan and count',
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'Create advanced job based on the event rate dataset with a date_nanos time field, 1d bucketspan and count',
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jobGroups: ['automated', 'event-rate', 'date-nanos'],
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pickFieldsConfig: {
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detectors: [
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@ -69,7 +69,7 @@ export default function ({ getService }: FtrProviderContext) {
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],
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summaryCountField: 'count',
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influencers: [],
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bucketSpan: '30m',
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bucketSpan: '1d',
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memoryLimit: '10mb',
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} as PickFieldsConfig,
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datafeedConfig: {} as DatafeedConfig,
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@ -94,7 +94,7 @@ export default function ({ getService }: FtrProviderContext) {
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out_of_order_timestamp_count: '0',
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empty_bucket_count: '0',
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sparse_bucket_count: '0',
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bucket_count: '17,520',
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bucket_count: '365',
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earliest_record_timestamp: '2015-01-01 00:10:00',
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latest_record_timestamp: '2016-01-01 00:00:00',
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input_record_count: '105,120',
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@ -108,7 +108,7 @@ export default function ({ getService }: FtrProviderContext) {
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total_partition_field_count: '2',
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bucket_allocation_failures_count: '0',
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memory_status: 'ok',
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timestamp: '2015-12-31 23:30:00',
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timestamp: '2015-12-31 00:00:00',
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},
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},
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},
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@ -26,7 +26,7 @@ export default function ({ getService, loadTestFile }: FtrProviderContext) {
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await esArchiver.unload('x-pack/test/functional/es_archives/ml/farequote');
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await esArchiver.unload('x-pack/test/functional/es_archives/ml/ecommerce');
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await esArchiver.unload('x-pack/test/functional/es_archives/ml/categorization');
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await esArchiver.unload('x-pack/test/functional/es_archives/ml/categorization_small');
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await esArchiver.unload('x-pack/test/functional/es_archives/ml/event_rate_nanos');
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await esArchiver.unload('x-pack/test/functional/es_archives/ml/bm_classification');
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await esArchiver.unload('x-pack/test/functional/es_archives/ml/ihp_outlier');
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