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PR for request #562

Browse Source 
https://github.com/alexanderzobnin/grafana-zabbix/issues/562
This commit is contained in:
Бунин Олег Александрович
2018-04-19 17:40:08 +03:00
parent f0a5e7de8d
commit e78387fcb0
72 changed files with 4110 additions and 4005 deletions
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102
dist/datasource-zabbix/specs/dataProcessor.spec.js vendored
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@@ -1,51 +1,51 @@
import _ from 'lodash';
import dataProcessor from '../dataProcessor';
describe('dataProcessor', () => {
let ctx = {};
beforeEach(() => {
ctx.datapoints = [
[[10, 1500000000000], [2, 1500000001000], [7, 1500000002000], [1, 1500000003000]],
[[9, 1500000000000], [3, 1500000001000], [4, 1500000002000], [8, 1500000003000]],
];
});
describe('When apply groupBy() functions', () => {
it('should return series average', () => {
let aggregateBy = dataProcessor.metricFunctions['groupBy'];
const avg2s = _.map(ctx.datapoints, (dp) => aggregateBy('2s', 'avg', dp));
expect(avg2s).toEqual([
[[6, 1500000000000], [4, 1500000002000]],
[[6, 1500000000000], [6, 1500000002000]],
]);
const avg10s = _.map(ctx.datapoints, (dp) => aggregateBy('10s', 'avg', dp));
expect(avg10s).toEqual([
[[5, 1500000000000]],
[[6, 1500000000000]],
]);
// not aligned
const dp = [[10, 1500000001000], [2, 1500000002000], [7, 1500000003000], [1, 1500000004000]];
expect(aggregateBy('2s', 'avg', dp)).toEqual([
[10, 1500000000000], [4.5, 1500000002000], [1, 1500000004000]
]);
});
});
describe('When apply aggregateBy() functions', () => {
it('should return series average', () => {
let aggregateBy = dataProcessor.metricFunctions['aggregateBy'];
const avg1s = aggregateBy('1s', 'avg', ctx.datapoints);
expect(avg1s).toEqual([
[9.5, 1500000000000], [2.5, 1500000001000], [5.5, 1500000002000], [4.5, 1500000003000]
]);
const avg10s = aggregateBy('10s', 'avg', ctx.datapoints);
expect(avg10s).toEqual([
[5.5, 1500000000000]
]);
});
});
});
import _ from 'lodash';
import dataProcessor from '../dataProcessor';
describe('dataProcessor', () => {
let ctx = {};
beforeEach(() => {
ctx.datapoints = [
[[10, 1500000000000], [2, 1500000001000], [7, 1500000002000], [1, 1500000003000]],
[[9, 1500000000000], [3, 1500000001000], [4, 1500000002000], [8, 1500000003000]],
];
});
describe('When apply groupBy() functions', () => {
it('should return series average', () => {
let aggregateBy = dataProcessor.metricFunctions['groupBy'];
const avg2s = _.map(ctx.datapoints, (dp) => aggregateBy('2s', 'avg', dp));
expect(avg2s).toEqual([
[[6, 1500000000000], [4, 1500000002000]],
[[6, 1500000000000], [6, 1500000002000]],
]);
const avg10s = _.map(ctx.datapoints, (dp) => aggregateBy('10s', 'avg', dp));
expect(avg10s).toEqual([
[[5, 1500000000000]],
[[6, 1500000000000]],
]);
// not aligned
const dp = [[10, 1500000001000], [2, 1500000002000], [7, 1500000003000], [1, 1500000004000]];
expect(aggregateBy('2s', 'avg', dp)).toEqual([
[10, 1500000000000], [4.5, 1500000002000], [1, 1500000004000]
]);
});
});
describe('When apply aggregateBy() functions', () => {
it('should return series average', () => {
let aggregateBy = dataProcessor.metricFunctions['aggregateBy'];
const avg1s = aggregateBy('1s', 'avg', ctx.datapoints);
expect(avg1s).toEqual([
[9.5, 1500000000000], [2.5, 1500000001000], [5.5, 1500000002000], [4.5, 1500000003000]
]);
const avg10s = aggregateBy('10s', 'avg', ctx.datapoints);
expect(avg10s).toEqual([
[5.5, 1500000000000]
]);
});
});
});