From 87a754001533bf94620e62767e6b7bcb9ebfe12a Mon Sep 17 00:00:00 2001 From: Melissa Alvarez Date: Fri, 17 Apr 2020 13:55:46 -0400 Subject: [PATCH] [ML] Migrate Mocha unit tests to Jest: migrate job utils and query utils tests (#63775) * migrate mocha tests to jest for query utils * update jobUtils test to jest from mocha * update tests to use jest syntax --- .../job_utils.js => job_utils.test.js} | 351 +++++++++--------- .../query_utils.js => query_utils.test.ts} | 27 +- 2 files changed, 183 insertions(+), 195 deletions(-) rename x-pack/plugins/ml/common/util/{__tests__/job_utils.js => job_utils.test.js} (50%) rename x-pack/plugins/ml/server/lib/{__tests__/query_utils.js => query_utils.test.ts} (67%) diff --git a/x-pack/plugins/ml/common/util/__tests__/job_utils.js b/x-pack/plugins/ml/common/util/job_utils.test.js similarity index 50% rename from x-pack/plugins/ml/common/util/__tests__/job_utils.js rename to x-pack/plugins/ml/common/util/job_utils.test.js index 60270fd438846..a5df160bdf5ca 100644 --- a/x-pack/plugins/ml/common/util/__tests__/job_utils.js +++ b/x-pack/plugins/ml/common/util/job_utils.test.js @@ -4,7 +4,6 @@ * you may not use this file except in compliance with the Elastic License. */ -import expect from '@kbn/expect'; import { calculateDatafeedFrequencyDefaultSeconds, isTimeSeriesViewJob, @@ -20,38 +19,38 @@ import { prefixDatafeedId, getSafeAggregationName, getLatestDataOrBucketTimestamp, -} from '../job_utils'; +} from './job_utils'; describe('ML - job utils', () => { describe('calculateDatafeedFrequencyDefaultSeconds', () => { - it('returns correct frequency for 119', () => { + test('returns correct frequency for 119', () => { const result = calculateDatafeedFrequencyDefaultSeconds(119); - expect(result).to.be(60); + expect(result).toBe(60); }); - it('returns correct frequency for 120', () => { + test('returns correct frequency for 120', () => { const result = calculateDatafeedFrequencyDefaultSeconds(120); - expect(result).to.be(60); + expect(result).toBe(60); }); - it('returns correct frequency for 300', () => { + test('returns correct frequency for 300', () => { const result = calculateDatafeedFrequencyDefaultSeconds(300); - expect(result).to.be(150); + expect(result).toBe(150); }); - it('returns correct frequency for 601', () => { + test('returns correct frequency for 601', () => { const result = calculateDatafeedFrequencyDefaultSeconds(601); - expect(result).to.be(300); + expect(result).toBe(300); }); - it('returns correct frequency for 43200', () => { + test('returns correct frequency for 43200', () => { const result = calculateDatafeedFrequencyDefaultSeconds(43200); - expect(result).to.be(600); + expect(result).toBe(600); }); - it('returns correct frequency for 43201', () => { + test('returns correct frequency for 43201', () => { const result = calculateDatafeedFrequencyDefaultSeconds(43201); - expect(result).to.be(3600); + expect(result).toBe(3600); }); }); describe('isTimeSeriesViewJob', () => { - it('returns true when job has a single detector with a metric function', () => { + test('returns true when job has a single detector with a metric function', () => { const job = { analysis_config: { detectors: [ @@ -64,10 +63,10 @@ describe('ML - job utils', () => { }, }; - expect(isTimeSeriesViewJob(job)).to.be(true); + expect(isTimeSeriesViewJob(job)).toBe(true); }); - it('returns true when job has at least one detector with a metric function', () => { + test('returns true when job has at least one detector with a metric function', () => { const job = { analysis_config: { detectors: [ @@ -86,10 +85,10 @@ describe('ML - job utils', () => { }, }; - expect(isTimeSeriesViewJob(job)).to.be(true); + expect(isTimeSeriesViewJob(job)).toBe(true); }); - it('returns false when job does not have at least one detector with a metric function', () => { + test('returns false when job does not have at least one detector with a metric function', () => { const job = { analysis_config: { detectors: [ @@ -108,10 +107,10 @@ describe('ML - job utils', () => { }, }; - expect(isTimeSeriesViewJob(job)).to.be(false); + expect(isTimeSeriesViewJob(job)).toBe(false); }); - it('returns false when job has a single count by category detector', () => { + test('returns false when job has a single count by category detector', () => { const job = { analysis_config: { detectors: [ @@ -124,7 +123,7 @@ describe('ML - job utils', () => { }, }; - expect(isTimeSeriesViewJob(job)).to.be(false); + expect(isTimeSeriesViewJob(job)).toBe(false); }); }); @@ -171,24 +170,24 @@ describe('ML - job utils', () => { }, }; - it('returns true for a detector with a metric function', () => { - expect(isTimeSeriesViewDetector(job, 0)).to.be(true); + test('returns true for a detector with a metric function', () => { + expect(isTimeSeriesViewDetector(job, 0)).toBe(true); }); - it('returns false for a detector with a non-metric function', () => { - expect(isTimeSeriesViewDetector(job, 1)).to.be(false); + test('returns false for a detector with a non-metric function', () => { + expect(isTimeSeriesViewDetector(job, 1)).toBe(false); }); - it('returns false for a detector using count on an mlcategory field', () => { - expect(isTimeSeriesViewDetector(job, 2)).to.be(false); + test('returns false for a detector using count on an mlcategory field', () => { + expect(isTimeSeriesViewDetector(job, 2)).toBe(false); }); - it('returns false for a detector using a script field as a by field', () => { - expect(isTimeSeriesViewDetector(job, 3)).to.be(false); + test('returns false for a detector using a script field as a by field', () => { + expect(isTimeSeriesViewDetector(job, 3)).toBe(false); }); - it('returns false for a detector using a script field as a metric field_name', () => { - expect(isTimeSeriesViewDetector(job, 4)).to.be(false); + test('returns false for a detector using a script field as a metric field_name', () => { + expect(isTimeSeriesViewDetector(job, 4)).toBe(false); }); }); @@ -233,7 +232,7 @@ describe('ML - job utils', () => { { function: 'time_of_day' }, // 34 { function: 'time_of_week' }, // 35 { function: 'lat_long' }, // 36 - { function: 'mean', field_name: 'NetworkDiff' }, //37 + { function: 'mean', field_name: 'NetworkDiff' }, // 37 ], }, datafeed_config: { @@ -254,48 +253,48 @@ describe('ML - job utils', () => { }, }; - it('returns true for expected detectors', () => { - expect(isSourceDataChartableForDetector(job, 0)).to.be(true); - expect(isSourceDataChartableForDetector(job, 1)).to.be(true); - expect(isSourceDataChartableForDetector(job, 2)).to.be(true); - expect(isSourceDataChartableForDetector(job, 3)).to.be(true); - expect(isSourceDataChartableForDetector(job, 4)).to.be(true); - expect(isSourceDataChartableForDetector(job, 5)).to.be(true); - expect(isSourceDataChartableForDetector(job, 6)).to.be(true); - expect(isSourceDataChartableForDetector(job, 7)).to.be(true); - expect(isSourceDataChartableForDetector(job, 8)).to.be(true); - expect(isSourceDataChartableForDetector(job, 9)).to.be(true); - expect(isSourceDataChartableForDetector(job, 10)).to.be(true); - expect(isSourceDataChartableForDetector(job, 11)).to.be(true); - expect(isSourceDataChartableForDetector(job, 12)).to.be(true); - expect(isSourceDataChartableForDetector(job, 13)).to.be(true); - expect(isSourceDataChartableForDetector(job, 14)).to.be(true); - expect(isSourceDataChartableForDetector(job, 15)).to.be(true); - expect(isSourceDataChartableForDetector(job, 16)).to.be(true); - expect(isSourceDataChartableForDetector(job, 17)).to.be(true); - expect(isSourceDataChartableForDetector(job, 18)).to.be(true); - expect(isSourceDataChartableForDetector(job, 19)).to.be(true); - expect(isSourceDataChartableForDetector(job, 20)).to.be(true); - expect(isSourceDataChartableForDetector(job, 21)).to.be(true); - expect(isSourceDataChartableForDetector(job, 22)).to.be(true); - expect(isSourceDataChartableForDetector(job, 23)).to.be(true); - expect(isSourceDataChartableForDetector(job, 24)).to.be(true); - }); - - it('returns false for expected detectors', () => { - expect(isSourceDataChartableForDetector(job, 25)).to.be(false); - expect(isSourceDataChartableForDetector(job, 26)).to.be(false); - expect(isSourceDataChartableForDetector(job, 27)).to.be(false); - expect(isSourceDataChartableForDetector(job, 28)).to.be(false); - expect(isSourceDataChartableForDetector(job, 29)).to.be(false); - expect(isSourceDataChartableForDetector(job, 30)).to.be(false); - expect(isSourceDataChartableForDetector(job, 31)).to.be(false); - expect(isSourceDataChartableForDetector(job, 32)).to.be(false); - expect(isSourceDataChartableForDetector(job, 33)).to.be(false); - expect(isSourceDataChartableForDetector(job, 34)).to.be(false); - expect(isSourceDataChartableForDetector(job, 35)).to.be(false); - expect(isSourceDataChartableForDetector(job, 36)).to.be(false); - expect(isSourceDataChartableForDetector(job, 37)).to.be(false); + test('returns true for expected detectors', () => { + expect(isSourceDataChartableForDetector(job, 0)).toBe(true); + expect(isSourceDataChartableForDetector(job, 1)).toBe(true); + expect(isSourceDataChartableForDetector(job, 2)).toBe(true); + expect(isSourceDataChartableForDetector(job, 3)).toBe(true); + expect(isSourceDataChartableForDetector(job, 4)).toBe(true); + expect(isSourceDataChartableForDetector(job, 5)).toBe(true); + expect(isSourceDataChartableForDetector(job, 6)).toBe(true); + expect(isSourceDataChartableForDetector(job, 7)).toBe(true); + expect(isSourceDataChartableForDetector(job, 8)).toBe(true); + expect(isSourceDataChartableForDetector(job, 9)).toBe(true); + expect(isSourceDataChartableForDetector(job, 10)).toBe(true); + expect(isSourceDataChartableForDetector(job, 11)).toBe(true); + expect(isSourceDataChartableForDetector(job, 12)).toBe(true); + expect(isSourceDataChartableForDetector(job, 13)).toBe(true); + expect(isSourceDataChartableForDetector(job, 14)).toBe(true); + expect(isSourceDataChartableForDetector(job, 15)).toBe(true); + expect(isSourceDataChartableForDetector(job, 16)).toBe(true); + expect(isSourceDataChartableForDetector(job, 17)).toBe(true); + expect(isSourceDataChartableForDetector(job, 18)).toBe(true); + expect(isSourceDataChartableForDetector(job, 19)).toBe(true); + expect(isSourceDataChartableForDetector(job, 20)).toBe(true); + expect(isSourceDataChartableForDetector(job, 21)).toBe(true); + expect(isSourceDataChartableForDetector(job, 22)).toBe(true); + expect(isSourceDataChartableForDetector(job, 23)).toBe(true); + expect(isSourceDataChartableForDetector(job, 24)).toBe(true); + }); + + test('returns false for expected detectors', () => { + expect(isSourceDataChartableForDetector(job, 25)).toBe(false); + expect(isSourceDataChartableForDetector(job, 26)).toBe(false); + expect(isSourceDataChartableForDetector(job, 27)).toBe(false); + expect(isSourceDataChartableForDetector(job, 28)).toBe(false); + expect(isSourceDataChartableForDetector(job, 29)).toBe(false); + expect(isSourceDataChartableForDetector(job, 30)).toBe(false); + expect(isSourceDataChartableForDetector(job, 31)).toBe(false); + expect(isSourceDataChartableForDetector(job, 32)).toBe(false); + expect(isSourceDataChartableForDetector(job, 33)).toBe(false); + expect(isSourceDataChartableForDetector(job, 34)).toBe(false); + expect(isSourceDataChartableForDetector(job, 35)).toBe(false); + expect(isSourceDataChartableForDetector(job, 36)).toBe(false); + expect(isSourceDataChartableForDetector(job, 37)).toBe(false); }); }); @@ -315,16 +314,16 @@ describe('ML - job utils', () => { }, }; - it('returns false when model plot is not enabled', () => { - expect(isModelPlotChartableForDetector(job1, 0)).to.be(false); + test('returns false when model plot is not enabled', () => { + expect(isModelPlotChartableForDetector(job1, 0)).toBe(false); }); - it('returns true for count detector when model plot is enabled', () => { - expect(isModelPlotChartableForDetector(job2, 0)).to.be(true); + test('returns true for count detector when model plot is enabled', () => { + expect(isModelPlotChartableForDetector(job2, 0)).toBe(true); }); - it('returns true for info_content detector when model plot is enabled', () => { - expect(isModelPlotChartableForDetector(job2, 1)).to.be(true); + test('returns true for info_content detector when model plot is enabled', () => { + expect(isModelPlotChartableForDetector(job2, 1)).toBe(true); }); }); @@ -359,39 +358,29 @@ describe('ML - job utils', () => { }, }; - it('returns empty array for a detector with no partitioning fields', () => { + test('returns empty array for a detector with no partitioning fields', () => { const resp = getPartitioningFieldNames(job, 0); - expect(resp).to.be.an('array'); - expect(resp).to.be.empty(); + expect(resp).toEqual([]); }); - it('returns expected array for a detector with a partition field', () => { + test('returns expected array for a detector with a partition field', () => { const resp = getPartitioningFieldNames(job, 1); - expect(resp).to.be.an('array'); - expect(resp).to.have.length(1); - expect(resp).to.contain('clientip'); + expect(resp).toEqual(['clientip']); }); - it('returns expected array for a detector with by and over fields', () => { + test('returns expected array for a detector with by and over fields', () => { const resp = getPartitioningFieldNames(job, 2); - expect(resp).to.be.an('array'); - expect(resp).to.have.length(2); - expect(resp).to.contain('uri'); - expect(resp).to.contain('clientip'); + expect(resp).toEqual(['uri', 'clientip']); }); - it('returns expected array for a detector with partition, by and over fields', () => { + test('returns expected array for a detector with partition, by and over fields', () => { const resp = getPartitioningFieldNames(job, 3); - expect(resp).to.be.an('array'); - expect(resp).to.have.length(3); - expect(resp).to.contain('uri'); - expect(resp).to.contain('clientip'); - expect(resp).to.contain('method'); + expect(resp).toEqual(['method', 'uri', 'clientip']); }); }); describe('isModelPlotEnabled', () => { - it('returns true for a job in which model plot has been enabled', () => { + test('returns true for a job in which model plot has been enabled', () => { const job = { analysis_config: { detectors: [ @@ -407,10 +396,10 @@ describe('ML - job utils', () => { }, }; - expect(isModelPlotEnabled(job, 0)).to.be(true); + expect(isModelPlotEnabled(job, 0)).toBe(true); }); - it('returns expected values for a job in which model plot has been enabled with terms', () => { + test('returns expected values for a job in which model plot has been enabled with terms', () => { const job = { analysis_config: { detectors: [ @@ -433,23 +422,23 @@ describe('ML - job utils', () => { { fieldName: 'country', fieldValue: 'US' }, { fieldName: 'airline', fieldValue: 'AAL' }, ]) - ).to.be(true); - expect(isModelPlotEnabled(job, 0, [{ fieldName: 'country', fieldValue: 'US' }])).to.be(false); + ).toBe(true); + expect(isModelPlotEnabled(job, 0, [{ fieldName: 'country', fieldValue: 'US' }])).toBe(false); expect( isModelPlotEnabled(job, 0, [ { fieldName: 'country', fieldValue: 'GB' }, { fieldName: 'airline', fieldValue: 'AAL' }, ]) - ).to.be(false); + ).toBe(false); expect( isModelPlotEnabled(job, 0, [ { fieldName: 'country', fieldValue: 'JP' }, { fieldName: 'airline', fieldValue: 'JAL' }, ]) - ).to.be(false); + ).toBe(false); }); - it('returns true for jobs in which model plot has not been enabled', () => { + test('returns true for jobs in which model plot has not been enabled', () => { const job1 = { analysis_config: { detectors: [ @@ -466,8 +455,8 @@ describe('ML - job utils', () => { }; const job2 = {}; - expect(isModelPlotEnabled(job1, 0)).to.be(false); - expect(isModelPlotEnabled(job2, 0)).to.be(false); + expect(isModelPlotEnabled(job1, 0)).toBe(false); + expect(isModelPlotEnabled(job2, 0)).toBe(false); }); }); @@ -476,115 +465,115 @@ describe('ML - job utils', () => { job_version: '6.1.1', }; - it('returns true for later job version', () => { - expect(isJobVersionGte(job, '6.1.0')).to.be(true); + test('returns true for later job version', () => { + expect(isJobVersionGte(job, '6.1.0')).toBe(true); }); - it('returns true for equal job version', () => { - expect(isJobVersionGte(job, '6.1.1')).to.be(true); + test('returns true for equal job version', () => { + expect(isJobVersionGte(job, '6.1.1')).toBe(true); }); - it('returns false for earlier job version', () => { - expect(isJobVersionGte(job, '6.1.2')).to.be(false); + test('returns false for earlier job version', () => { + expect(isJobVersionGte(job, '6.1.2')).toBe(false); }); }); describe('mlFunctionToESAggregation', () => { - it('returns correct ES aggregation type for ML function', () => { - expect(mlFunctionToESAggregation('count')).to.be('count'); - expect(mlFunctionToESAggregation('low_count')).to.be('count'); - expect(mlFunctionToESAggregation('high_count')).to.be('count'); - expect(mlFunctionToESAggregation('non_zero_count')).to.be('count'); - expect(mlFunctionToESAggregation('low_non_zero_count')).to.be('count'); - expect(mlFunctionToESAggregation('high_non_zero_count')).to.be('count'); - expect(mlFunctionToESAggregation('distinct_count')).to.be('cardinality'); - expect(mlFunctionToESAggregation('low_distinct_count')).to.be('cardinality'); - expect(mlFunctionToESAggregation('high_distinct_count')).to.be('cardinality'); - expect(mlFunctionToESAggregation('metric')).to.be('avg'); - expect(mlFunctionToESAggregation('mean')).to.be('avg'); - expect(mlFunctionToESAggregation('low_mean')).to.be('avg'); - expect(mlFunctionToESAggregation('high_mean')).to.be('avg'); - expect(mlFunctionToESAggregation('min')).to.be('min'); - expect(mlFunctionToESAggregation('max')).to.be('max'); - expect(mlFunctionToESAggregation('sum')).to.be('sum'); - expect(mlFunctionToESAggregation('low_sum')).to.be('sum'); - expect(mlFunctionToESAggregation('high_sum')).to.be('sum'); - expect(mlFunctionToESAggregation('non_null_sum')).to.be('sum'); - expect(mlFunctionToESAggregation('low_non_null_sum')).to.be('sum'); - expect(mlFunctionToESAggregation('high_non_null_sum')).to.be('sum'); - expect(mlFunctionToESAggregation('rare')).to.be('count'); - expect(mlFunctionToESAggregation('freq_rare')).to.be(null); - expect(mlFunctionToESAggregation('info_content')).to.be(null); - expect(mlFunctionToESAggregation('low_info_content')).to.be(null); - expect(mlFunctionToESAggregation('high_info_content')).to.be(null); - expect(mlFunctionToESAggregation('median')).to.be('percentiles'); - expect(mlFunctionToESAggregation('low_median')).to.be('percentiles'); - expect(mlFunctionToESAggregation('high_median')).to.be('percentiles'); - expect(mlFunctionToESAggregation('varp')).to.be(null); - expect(mlFunctionToESAggregation('low_varp')).to.be(null); - expect(mlFunctionToESAggregation('high_varp')).to.be(null); - expect(mlFunctionToESAggregation('time_of_day')).to.be(null); - expect(mlFunctionToESAggregation('time_of_week')).to.be(null); - expect(mlFunctionToESAggregation('lat_long')).to.be(null); + test('returns correct ES aggregation type for ML function', () => { + expect(mlFunctionToESAggregation('count')).toBe('count'); + expect(mlFunctionToESAggregation('low_count')).toBe('count'); + expect(mlFunctionToESAggregation('high_count')).toBe('count'); + expect(mlFunctionToESAggregation('non_zero_count')).toBe('count'); + expect(mlFunctionToESAggregation('low_non_zero_count')).toBe('count'); + expect(mlFunctionToESAggregation('high_non_zero_count')).toBe('count'); + expect(mlFunctionToESAggregation('distinct_count')).toBe('cardinality'); + expect(mlFunctionToESAggregation('low_distinct_count')).toBe('cardinality'); + expect(mlFunctionToESAggregation('high_distinct_count')).toBe('cardinality'); + expect(mlFunctionToESAggregation('metric')).toBe('avg'); + expect(mlFunctionToESAggregation('mean')).toBe('avg'); + expect(mlFunctionToESAggregation('low_mean')).toBe('avg'); + expect(mlFunctionToESAggregation('high_mean')).toBe('avg'); + expect(mlFunctionToESAggregation('min')).toBe('min'); + expect(mlFunctionToESAggregation('max')).toBe('max'); + expect(mlFunctionToESAggregation('sum')).toBe('sum'); + expect(mlFunctionToESAggregation('low_sum')).toBe('sum'); + expect(mlFunctionToESAggregation('high_sum')).toBe('sum'); + expect(mlFunctionToESAggregation('non_null_sum')).toBe('sum'); + expect(mlFunctionToESAggregation('low_non_null_sum')).toBe('sum'); + expect(mlFunctionToESAggregation('high_non_null_sum')).toBe('sum'); + expect(mlFunctionToESAggregation('rare')).toBe('count'); + expect(mlFunctionToESAggregation('freq_rare')).toBe(null); + expect(mlFunctionToESAggregation('info_content')).toBe(null); + expect(mlFunctionToESAggregation('low_info_content')).toBe(null); + expect(mlFunctionToESAggregation('high_info_content')).toBe(null); + expect(mlFunctionToESAggregation('median')).toBe('percentiles'); + expect(mlFunctionToESAggregation('low_median')).toBe('percentiles'); + expect(mlFunctionToESAggregation('high_median')).toBe('percentiles'); + expect(mlFunctionToESAggregation('varp')).toBe(null); + expect(mlFunctionToESAggregation('low_varp')).toBe(null); + expect(mlFunctionToESAggregation('high_varp')).toBe(null); + expect(mlFunctionToESAggregation('time_of_day')).toBe(null); + expect(mlFunctionToESAggregation('time_of_week')).toBe(null); + expect(mlFunctionToESAggregation('lat_long')).toBe(null); }); }); describe('isJobIdValid', () => { - it('returns true for job id: "good_job-name"', () => { - expect(isJobIdValid('good_job-name')).to.be(true); + test('returns true for job id: "good_job-name"', () => { + expect(isJobIdValid('good_job-name')).toBe(true); }); - it('returns false for job id: "_bad_job-name"', () => { - expect(isJobIdValid('_bad_job-name')).to.be(false); + test('returns false for job id: "_bad_job-name"', () => { + expect(isJobIdValid('_bad_job-name')).toBe(false); }); - it('returns false for job id: "bad_job-name_"', () => { - expect(isJobIdValid('bad_job-name_')).to.be(false); + test('returns false for job id: "bad_job-name_"', () => { + expect(isJobIdValid('bad_job-name_')).toBe(false); }); - it('returns false for job id: "-bad_job-name"', () => { - expect(isJobIdValid('-bad_job-name')).to.be(false); + test('returns false for job id: "-bad_job-name"', () => { + expect(isJobIdValid('-bad_job-name')).toBe(false); }); - it('returns false for job id: "bad_job-name-"', () => { - expect(isJobIdValid('bad_job-name-')).to.be(false); + test('returns false for job id: "bad_job-name-"', () => { + expect(isJobIdValid('bad_job-name-')).toBe(false); }); - it('returns false for job id: "bad&job-name"', () => { - expect(isJobIdValid('bad&job-name')).to.be(false); + test('returns false for job id: "bad&job-name"', () => { + expect(isJobIdValid('bad&job-name')).toBe(false); }); }); describe('ML_MEDIAN_PERCENTS', () => { - it("is '50.0'", () => { - expect(ML_MEDIAN_PERCENTS).to.be('50.0'); + test("is '50.0'", () => { + expect(ML_MEDIAN_PERCENTS).toBe('50.0'); }); }); describe('prefixDatafeedId', () => { - it('returns datafeed-prefix-job from datafeed-job"', () => { - expect(prefixDatafeedId('datafeed-job', 'prefix-')).to.be('datafeed-prefix-job'); + test('returns datafeed-prefix-job from datafeed-job"', () => { + expect(prefixDatafeedId('datafeed-job', 'prefix-')).toBe('datafeed-prefix-job'); }); - it('returns datafeed-prefix-job from job"', () => { - expect(prefixDatafeedId('job', 'prefix-')).to.be('datafeed-prefix-job'); + test('returns datafeed-prefix-job from job"', () => { + expect(prefixDatafeedId('job', 'prefix-')).toBe('datafeed-prefix-job'); }); }); describe('getSafeAggregationName', () => { - it('"foo" should be "foo"', () => { - expect(getSafeAggregationName('foo', 0)).to.be('foo'); + test('"foo" should be "foo"', () => { + expect(getSafeAggregationName('foo', 0)).toBe('foo'); }); - it('"foo.bar" should be "foo.bar"', () => { - expect(getSafeAggregationName('foo.bar', 0)).to.be('foo.bar'); + test('"foo.bar" should be "foo.bar"', () => { + expect(getSafeAggregationName('foo.bar', 0)).toBe('foo.bar'); }); - it('"foo&bar" should be "field_0"', () => { - expect(getSafeAggregationName('foo&bar', 0)).to.be('field_0'); + test('"foo&bar" should be "field_0"', () => { + expect(getSafeAggregationName('foo&bar', 0)).toBe('field_0'); }); }); describe('getLatestDataOrBucketTimestamp', () => { - it('returns expected value when no gap in data at end of bucket processing', () => { - expect(getLatestDataOrBucketTimestamp(1549929594000, 1549928700000)).to.be(1549929594000); + test('returns expected value when no gap in data at end of bucket processing', () => { + expect(getLatestDataOrBucketTimestamp(1549929594000, 1549928700000)).toBe(1549929594000); }); - it('returns expected value when there is a gap in data at end of bucket processing', () => { - expect(getLatestDataOrBucketTimestamp(1549929594000, 1562256600000)).to.be(1562256600000); + test('returns expected value when there is a gap in data at end of bucket processing', () => { + expect(getLatestDataOrBucketTimestamp(1549929594000, 1562256600000)).toBe(1562256600000); }); - it('returns expected value when job has not run', () => { - expect(getLatestDataOrBucketTimestamp(undefined, undefined)).to.be(undefined); + test('returns expected value when job has not run', () => { + expect(getLatestDataOrBucketTimestamp(undefined, undefined)).toBe(undefined); }); }); }); diff --git a/x-pack/plugins/ml/server/lib/__tests__/query_utils.js b/x-pack/plugins/ml/server/lib/query_utils.test.ts similarity index 67% rename from x-pack/plugins/ml/server/lib/__tests__/query_utils.js rename to x-pack/plugins/ml/server/lib/query_utils.test.ts index 05292abb36b25..c2f5e814da332 100644 --- a/x-pack/plugins/ml/server/lib/__tests__/query_utils.js +++ b/x-pack/plugins/ml/server/lib/query_utils.test.ts @@ -4,12 +4,11 @@ * you may not use this file except in compliance with the Elastic License. */ -import expect from '@kbn/expect'; import { buildBaseFilterCriteria, buildSamplerAggregation, getSamplerAggregationsResponsePath, -} from '../query_utils'; +} from './query_utils'; describe('ML - query utils', () => { describe('buildBaseFilterCriteria', () => { @@ -23,8 +22,8 @@ describe('ML - query utils', () => { }, }; - it('returns correct criteria for time range', () => { - expect(buildBaseFilterCriteria('timestamp', earliestMs, latestMs)).to.eql([ + test('returns correct criteria for time range', () => { + expect(buildBaseFilterCriteria('timestamp', earliestMs, latestMs)).toEqual([ { range: { timestamp: { @@ -37,8 +36,8 @@ describe('ML - query utils', () => { ]); }); - it('returns correct criteria for time range and query', () => { - expect(buildBaseFilterCriteria('timestamp', earliestMs, latestMs, query)).to.eql([ + test('returns correct criteria for time range and query', () => { + expect(buildBaseFilterCriteria('timestamp', earliestMs, latestMs, query)).toEqual([ { range: { timestamp: { @@ -60,8 +59,8 @@ describe('ML - query utils', () => { }, }; - it('returns wrapped sampler aggregation for sampler shard size of 1000', () => { - expect(buildSamplerAggregation(testAggs, 1000)).to.eql({ + test('returns wrapped sampler aggregation for sampler shard size of 1000', () => { + expect(buildSamplerAggregation(testAggs, 1000)).toEqual({ sample: { sampler: { shard_size: 1000, @@ -71,18 +70,18 @@ describe('ML - query utils', () => { }); }); - it('returns un-sampled aggregation as-is for sampler shard size of 0', () => { - expect(buildSamplerAggregation(testAggs, 0)).to.eql(testAggs); + test('returns un-sampled aggregation as-is for sampler shard size of 0', () => { + expect(buildSamplerAggregation(testAggs, 0)).toEqual(testAggs); }); }); describe('getSamplerAggregationsResponsePath', () => { - it('returns correct path for sampler shard size of 1000', () => { - expect(getSamplerAggregationsResponsePath(1000)).to.eql(['sample']); + test('returns correct path for sampler shard size of 1000', () => { + expect(getSamplerAggregationsResponsePath(1000)).toEqual(['sample']); }); - it('returns correct path for sampler shard size of 0', () => { - expect(getSamplerAggregationsResponsePath(0)).to.eql([]); + test('returns correct path for sampler shard size of 0', () => { + expect(getSamplerAggregationsResponsePath(0)).toEqual([]); }); }); });