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101 lines
2.8 KiB
TypeScript
101 lines
2.8 KiB
TypeScript
/**
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* Licensed to the Apache Software Foundation (ASF) under one
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* or more contributor license agreements. See the NOTICE file
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* distributed with this work for additional information
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* regarding copyright ownership. The ASF licenses this file
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* to you under the Apache License, Version 2.0 (the
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* "License"); you may not use this file except in compliance
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* with the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing,
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* software distributed under the License is distributed on an
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* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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* KIND, either express or implied. See the License for the
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* specific language governing permissions and limitations
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* under the License.
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*/
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import transformData from '../src/transformData';
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const t1 = 1704067200000;
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const t2 = 1704153600000;
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test('builds one series per group, dropping the single metric from the key', () => {
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const data = transformData(
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[
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{ __timestamp: t1, gender: 'boy', sum__num: 10 },
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{ __timestamp: t2, gender: 'boy', sum__num: 20 },
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{ __timestamp: t1, gender: 'girl', sum__num: 30 },
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],
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['gender'],
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['sum__num'],
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);
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expect(data).toEqual([
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{
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key: ['boy'],
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values: [
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{ x: t1, y: 10 },
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{ x: t2, y: 20 },
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],
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},
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{
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key: ['girl'],
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values: [
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{ x: t1, y: 30 },
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{ x: t2, y: null },
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],
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},
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]);
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});
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test('keys ungrouped series by the metric name', () => {
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const data = transformData(
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[
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{ __timestamp: t1, sum__num: 1, count: 2 },
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{ __timestamp: t2, sum__num: 3, count: 4 },
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],
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[],
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['sum__num', 'count'],
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);
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expect(data.map(series => series.key)).toEqual(['count', 'sum__num']);
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});
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test('keeps the metric in multi-metric grouped keys', () => {
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const data = transformData(
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[{ __timestamp: t1, gender: 'boy', sum__num: 1, count: 2 }],
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['gender'],
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['sum__num', 'count'],
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);
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expect(data.map(series => series.key)).toEqual([
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['count', 'boy'],
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['sum__num', 'boy'],
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]);
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});
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test('normalizes rows when contribution is on', () => {
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const data = transformData(
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[
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{ __timestamp: t1, gender: 'boy', sum__num: 30 },
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{ __timestamp: t1, gender: 'girl', sum__num: 10 },
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],
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['gender'],
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['sum__num'],
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true,
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);
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expect(data[0].values[0].y).toBeCloseTo(0.75);
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expect(data[1].values[0].y).toBeCloseTo(0.25);
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});
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test('drops series whose values are all null', () => {
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const data = transformData(
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[
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{ __timestamp: t1, gender: 'boy', sum__num: 5 },
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{ __timestamp: t1, gender: 'girl', sum__num: null },
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],
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['gender'],
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['sum__num'],
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);
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expect(data.map(series => series.key)).toEqual([['boy']]);
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});
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