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[ML] Add multi metric job wizard test #45279
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0f76640
Add data-test-subj attributes
pheyos b229cfb
Add multi metric wizard methods to services
pheyos 615b5ed
Move job list validation logic to job table service
pheyos 3791c51
Add 'create multi metric job' test
pheyos d65e591
Reword test steps
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214 changes: 214 additions & 0 deletions
214
x-pack/test/functional/apps/machine_learning/create_multi_metric_job.ts
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,214 @@ | ||
| /* | ||
| * 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'; | ||
|
|
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| // eslint-disable-next-line import/no-default-export | ||
| export default function({ getService }: FtrProviderContext) { | ||
| const esArchiver = getService('esArchiver'); | ||
| const ml = getService('ml'); | ||
|
|
||
| const jobId = `fq_multi_1_${Date.now()}`; | ||
| const jobDescription = | ||
| 'Create multi metric job based on the farequote dataset with 15m bucketspan and min/max/mean(responsetime) split by airline'; | ||
| const jobGroups = ['automated', 'farequote', 'multi-metric']; | ||
| const aggAndFieldIdentifiers = ['Min(responsetime)', 'Max(responsetime)', 'Mean(responsetime)']; | ||
| const splitField = 'airline'; | ||
| const bucketSpan = '15m'; | ||
| const memoryLimit = '20MB'; | ||
|
|
||
| describe('multi metric job creation', function() { | ||
| this.tags(['smoke', 'mlqa']); | ||
| before(async () => { | ||
| await esArchiver.loadIfNeeded('ml/farequote'); | ||
| }); | ||
|
|
||
| after(async () => { | ||
| await esArchiver.unload('ml/farequote'); | ||
| await ml.api.cleanMlIndices(); | ||
| await ml.api.cleanDataframeIndices(); | ||
| }); | ||
|
|
||
| it('loads the job management page', async () => { | ||
| await ml.navigation.navigateToMl(); | ||
| await ml.navigation.navigateToJobManagement(); | ||
| }); | ||
|
|
||
| it('loads the new job source selection page', async () => { | ||
| await ml.jobManagement.navigateToNewJobSourceSelection(); | ||
| }); | ||
|
|
||
| it('loads the job type selection page', async () => { | ||
| await ml.jobSourceSelection.selectSourceIndexPattern('farequote'); | ||
| }); | ||
|
|
||
| it('loads the single metric job wizard page', async () => { | ||
| await ml.jobTypeSelection.selectMultiMetricJob(); | ||
| }); | ||
|
|
||
| it('displays the time range step', async () => { | ||
| await ml.jobWizardCommon.assertTimeRangeSectionExists(); | ||
| }); | ||
|
|
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| it('displays the event rate chart', async () => { | ||
| await ml.jobWizardCommon.clickUseFullDataButton(); | ||
| await ml.jobWizardCommon.assertEventRateChartExists(); | ||
| }); | ||
|
|
||
| it('displays the pick fields step', async () => { | ||
| await ml.jobWizardCommon.clickNextButton(); | ||
| await ml.jobWizardCommon.assertPickFieldsSectionExists(); | ||
| }); | ||
|
|
||
| it('selects detectors and displays detector previews', async () => { | ||
| for (const [index, aggAndFieldIdentifier] of aggAndFieldIdentifiers.entries()) { | ||
| await ml.jobWizardCommon.assertAggAndFieldInputExists(); | ||
| await ml.jobWizardCommon.selectAggAndField(aggAndFieldIdentifier); | ||
| await ml.jobWizardCommon.assertDetectorPreviewExists(aggAndFieldIdentifier, index, 'LINE'); | ||
| } | ||
| }); | ||
|
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| it('inputs the split field and displays split cards', async () => { | ||
| await ml.jobWizardCommon.assertMultiMetricSplitFieldInputExists(); | ||
| await ml.jobWizardCommon.selectMultiMetricSplitField(splitField); | ||
| await ml.jobWizardCommon.assertMultiMetricSplitFieldSelection(splitField); | ||
|
|
||
| await ml.jobWizardCommon.assertDetectorSplitExists(splitField); | ||
| await ml.jobWizardCommon.assertDetectorSplitFrontCardTitle('AAL'); | ||
| await ml.jobWizardCommon.assertDetectorSplitNumberOfBackCards(9); | ||
|
|
||
| await ml.jobWizardCommon.assertInfluencerSelection([splitField]); | ||
| }); | ||
|
|
||
| it('displays the influencer field', async () => { | ||
| await ml.jobWizardCommon.assertInfluencerInputExists(); | ||
| await ml.jobWizardCommon.assertInfluencerSelection([splitField]); | ||
| }); | ||
|
|
||
| it('inputs the bucket span', async () => { | ||
| await ml.jobWizardCommon.assertBucketSpanInputExists(); | ||
| await ml.jobWizardCommon.setBucketSpan(bucketSpan); | ||
| await ml.jobWizardCommon.assertBucketSpanValue(bucketSpan); | ||
| }); | ||
|
|
||
| it('displays the job details step', async () => { | ||
| await ml.jobWizardCommon.clickNextButton(); | ||
| await ml.jobWizardCommon.assertJobDetailsSectionExists(); | ||
| }); | ||
|
|
||
| it('inputs the job id', async () => { | ||
| await ml.jobWizardCommon.assertJobIdInputExists(); | ||
| await ml.jobWizardCommon.setJobId(jobId); | ||
| await ml.jobWizardCommon.assertJobIdValue(jobId); | ||
| }); | ||
|
|
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| it('inputs the job description', async () => { | ||
| await ml.jobWizardCommon.assertJobDescriptionInputExists(); | ||
| await ml.jobWizardCommon.setJobDescription(jobDescription); | ||
| await ml.jobWizardCommon.assertJobDescriptionValue(jobDescription); | ||
| }); | ||
|
|
||
| it('inputs job groups', async () => { | ||
| await ml.jobWizardCommon.assertJobGroupInputExists(); | ||
| for (const jobGroup of jobGroups) { | ||
| await ml.jobWizardCommon.addJobGroup(jobGroup); | ||
| } | ||
| await ml.jobWizardCommon.assertJobGroupSelection(jobGroups); | ||
| }); | ||
|
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||
| it('opens the advanced section', async () => { | ||
| await ml.jobWizardCommon.ensureAdvancedSectionOpen(); | ||
| }); | ||
|
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| it('displays the model plot switch', async () => { | ||
| await ml.jobWizardCommon.assertModelPlotSwitchExists(); | ||
| }); | ||
|
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| it('enables the dedicated index switch', async () => { | ||
| await ml.jobWizardCommon.assertDedicatedIndexSwitchExists(); | ||
| await ml.jobWizardCommon.activateDedicatedIndexSwitch(); | ||
| await ml.jobWizardCommon.assertDedicatedIndexSwitchCheckedState(true); | ||
| }); | ||
|
|
||
| it('inputs the model memory limit', async () => { | ||
| await ml.jobWizardCommon.assertModelMemoryLimitInputExists(); | ||
| await ml.jobWizardCommon.setModelMemoryLimit(memoryLimit); | ||
| await ml.jobWizardCommon.assertModelMemoryLimitValue(memoryLimit); | ||
| }); | ||
|
|
||
| it('displays the validation step', async () => { | ||
| await ml.jobWizardCommon.clickNextButton(); | ||
| await ml.jobWizardCommon.assertValidationSectionExists(); | ||
| }); | ||
|
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| it('displays the summary step', async () => { | ||
| await ml.jobWizardCommon.clickNextButton(); | ||
| await ml.jobWizardCommon.assertSummarySectionExists(); | ||
| }); | ||
|
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||
| it('creates the job and finishes processing', async () => { | ||
| await ml.jobWizardCommon.assertCreateJobButtonExists(); | ||
| await ml.jobWizardCommon.createJobAndWaitForCompletion(); | ||
| }); | ||
|
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| it('displays the created job in the job list', async () => { | ||
| await ml.navigation.navigateToMl(); | ||
| await ml.navigation.navigateToJobManagement(); | ||
|
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||
| await ml.jobTable.waitForJobsToLoad(); | ||
| await ml.jobTable.filterWithSearchString(jobId); | ||
| const rows = await ml.jobTable.parseJobTable(); | ||
| expect(rows.filter(row => row.id === jobId)).to.have.length(1); | ||
| }); | ||
|
|
||
| it('displays details for the created job in the job list', async () => { | ||
| const expectedRow = { | ||
| id: jobId, | ||
| description: jobDescription, | ||
| jobGroups, | ||
| recordCount: '86,274', | ||
| memoryStatus: 'ok', | ||
| jobState: 'closed', | ||
| datafeedState: 'stopped', | ||
| latestTimestamp: '2016-02-11 23:59:54', | ||
| }; | ||
| await ml.jobTable.assertJobRowFields(jobId, expectedRow); | ||
|
|
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| const expectedCounts = { | ||
| job_id: jobId, | ||
| processed_record_count: '86,274', | ||
| processed_field_count: '172,548', | ||
| input_bytes: '6.4 MB', | ||
| input_field_count: '172,548', | ||
| invalid_date_count: '0', | ||
| missing_field_count: '0', | ||
| out_of_order_timestamp_count: '0', | ||
| empty_bucket_count: '0', | ||
| sparse_bucket_count: '0', | ||
| bucket_count: '479', | ||
| earliest_record_timestamp: '2016-02-07 00:00:00', | ||
| latest_record_timestamp: '2016-02-11 23:59:54', | ||
| input_record_count: '86,274', | ||
| latest_bucket_timestamp: '2016-02-11 23:45:00', | ||
| }; | ||
| const expectedModelSizeStats = { | ||
| job_id: jobId, | ||
| result_type: 'model_size_stats', | ||
| model_bytes: '1.8 MB', | ||
| model_bytes_exceeded: '0', | ||
| model_bytes_memory_limit: '20971520', | ||
| total_by_field_count: '59', | ||
| total_over_field_count: '0', | ||
| total_partition_field_count: '58', | ||
| bucket_allocation_failures_count: '0', | ||
| memory_status: 'ok', | ||
| timestamp: '2016-02-11 23:30:00', | ||
| }; | ||
| await ml.jobTable.assertJobRowDetailsCounts(jobId, expectedCounts, expectedModelSizeStats); | ||
| }); | ||
| }); | ||
| } |
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I know that @dmlemeshko is changing the way that test subjects with spaces work, so I want to make sure that this and that PR don't collide for disaster. Can you two coordinate to make sure that your PRs get merged in the right order?