297 lines
9.8 KiB
TypeScript
297 lines
9.8 KiB
TypeScript
import { toDataFrame, FieldType, Labels, DataFrame, Field, ReducerID } from '@grafana/data';
|
|
|
|
import { timeSeriesToTableTransform } from './timeSeriesTableTransformer';
|
|
|
|
describe('timeSeriesTableTransformer', () => {
|
|
it('Will transform a single query', () => {
|
|
const series = [
|
|
getTimeSeries('A', { instance: 'A', pod: 'B' }),
|
|
getTimeSeries('A', { instance: 'A', pod: 'C' }),
|
|
getTimeSeries('A', { instance: 'A', pod: 'D' }),
|
|
];
|
|
|
|
const results = timeSeriesToTableTransform({}, series);
|
|
expect(results).toHaveLength(1);
|
|
const result = results[0];
|
|
expect(result.refId).toBe('A');
|
|
expect(result.fields).toHaveLength(3);
|
|
expect(result.fields[0].values).toEqual(['A', 'A', 'A']);
|
|
expect(result.fields[1].values).toEqual(['B', 'C', 'D']);
|
|
assertDataFrameField(result.fields[2], series);
|
|
});
|
|
|
|
it('Will pass through non time series frames', () => {
|
|
const series = [
|
|
getTable('B', ['foo', 'bar']),
|
|
getTimeSeries('A', { instance: 'A', pod: 'B' }),
|
|
getTimeSeries('A', { instance: 'A', pod: 'C' }),
|
|
getTable('C', ['bar', 'baz', 'bad']),
|
|
];
|
|
|
|
const results = timeSeriesToTableTransform({}, series);
|
|
expect(results).toHaveLength(3);
|
|
expect(results[0]).toEqual(series[0]);
|
|
expect(results[2].refId).toBe('A');
|
|
expect(results[2].fields).toHaveLength(3);
|
|
expect(results[2].fields[0].values).toEqual(['A', 'A']);
|
|
expect(results[2].fields[1].values).toEqual(['B', 'C']);
|
|
expect(results[1]).toEqual(series[3]);
|
|
});
|
|
|
|
it('Will group by refId', () => {
|
|
const series = [
|
|
getTimeSeries('A', { instance: 'A', pod: 'B' }),
|
|
getTimeSeries('A', { instance: 'A', pod: 'C' }),
|
|
getTimeSeries('A', { instance: 'A', pod: 'D' }),
|
|
getTimeSeries('B', { instance: 'B', pod: 'F', cluster: 'A' }),
|
|
getTimeSeries('B', { instance: 'B', pod: 'G', cluster: 'B' }),
|
|
];
|
|
|
|
const results = timeSeriesToTableTransform({}, series);
|
|
|
|
expect(results).toHaveLength(2);
|
|
expect(results[0].refId).toBe('A');
|
|
expect(results[0].fields).toHaveLength(3);
|
|
expect(results[0].fields[0].values).toEqual(['A', 'A', 'A']);
|
|
expect(results[0].fields[1].values).toEqual(['B', 'C', 'D']);
|
|
assertDataFrameField(results[0].fields[2], series.slice(0, 3));
|
|
expect(results[1].refId).toBe('B');
|
|
expect(results[1].fields).toHaveLength(4);
|
|
expect(results[1].fields[0].values).toEqual(['B', 'B']);
|
|
expect(results[1].fields[1].values).toEqual(['F', 'G']);
|
|
expect(results[1].fields[2].values).toEqual(['A', 'B']);
|
|
assertDataFrameField(results[1].fields[3], series.slice(3, 5));
|
|
});
|
|
|
|
it('Will include last value by deault', () => {
|
|
const series = [
|
|
getTimeSeries('A', { instance: 'A', pod: 'B' }, [4, 2, 3]),
|
|
getTimeSeries('A', { instance: 'A', pod: 'C' }, [3, 4, 5]),
|
|
];
|
|
|
|
const results = timeSeriesToTableTransform({}, series);
|
|
expect(results[0].fields[2].values[0].value).toEqual(3);
|
|
expect(results[0].fields[2].values[1].value).toEqual(5);
|
|
});
|
|
|
|
it('Will calculate average value if configured', () => {
|
|
const series = [
|
|
getTimeSeries('A', { instance: 'A', pod: 'B' }, [4, 2, 3]),
|
|
getTimeSeries('B', { instance: 'A', pod: 'C' }, [3, 4, 5]),
|
|
getTimeSeries('C', { instance: 'B', pod: 'X' }, [4, 2, 0]),
|
|
getTimeSeries('D', { instance: 'B', pod: 'Y' }, [0, 0, 0]),
|
|
];
|
|
|
|
const results = timeSeriesToTableTransform(
|
|
{
|
|
B: {
|
|
stat: ReducerID.mean,
|
|
},
|
|
D: {
|
|
stat: ReducerID.mean,
|
|
},
|
|
},
|
|
series
|
|
);
|
|
|
|
expect(results[0].fields[2].values[0].value).toEqual(3);
|
|
expect(results[1].fields[2].values[0].value).toEqual(4);
|
|
expect(results[2].fields[2].values[0].value).toEqual(0);
|
|
expect(results[3].fields[2].values[0].value).toEqual(0);
|
|
});
|
|
|
|
it('calculate the value for an empty series to null', () => {
|
|
const series = [getTimeSeries('D', { instance: 'B', pod: 'Y' }, [])];
|
|
|
|
const results = timeSeriesToTableTransform(
|
|
{
|
|
B: {
|
|
stat: ReducerID.mean,
|
|
},
|
|
D: {
|
|
stat: ReducerID.mean,
|
|
},
|
|
},
|
|
series
|
|
);
|
|
|
|
expect(results[0].fields[2].values[0].value).toEqual(null);
|
|
});
|
|
|
|
it('Will transform multiple data series with the same label', () => {
|
|
const series = [
|
|
getTimeSeries('A', { instance: 'A', pod: 'B' }, [4, 2, 3]),
|
|
getTimeSeries('B', { instance: 'A', pod: 'B' }, [3, 4, 5]),
|
|
getTimeSeries('C', { instance: 'A', pod: 'B' }, [3, 4, 5]),
|
|
];
|
|
|
|
const results = timeSeriesToTableTransform({}, series);
|
|
|
|
// Check series A
|
|
expect(results[0].fields).toHaveLength(3);
|
|
expect(results[0].fields[0].values[0]).toBe('A');
|
|
expect(results[0].fields[1].values[0]).toBe('B');
|
|
|
|
// Check series B
|
|
expect(results[1].fields).toHaveLength(3);
|
|
expect(results[1].fields[0].values[0]).toBe('A');
|
|
expect(results[1].fields[1].values[0]).toBe('B');
|
|
|
|
// Check series C
|
|
expect(results[2].fields).toHaveLength(3);
|
|
expect(results[2].fields[0].values[0]).toBe('A');
|
|
expect(results[2].fields[1].values[0]).toBe('B');
|
|
});
|
|
|
|
it('will not transform frames with time fields that are non-timeseries', () => {
|
|
const series = [
|
|
getTimeSeries('A', { instance: 'A', pod: 'B' }, [4, 2, 3]),
|
|
getNonTimeSeries('B', { instance: 'A', pod: 'B' }, [3, 4, 5]),
|
|
];
|
|
|
|
const results = timeSeriesToTableTransform({}, series);
|
|
|
|
// Expect the timeseries to be transformed
|
|
// Having a trend field will show this
|
|
expect(results[1].fields[2].name).toBe('Trend #A');
|
|
|
|
// We should expect the field length to remain at
|
|
// 2 with a time field and a value field
|
|
expect(results[0].fields.length).toBe(2);
|
|
});
|
|
|
|
it('will not transform series that have the same value for all times', () => {
|
|
const series = [getNonTimeSeries('A', { instance: 'A' }, [4, 2, 5], [1699476339, 1699476339, 1699476339])];
|
|
|
|
const results = timeSeriesToTableTransform({}, series);
|
|
|
|
expect(results[0].fields[0].values[0]).toBe(1699476339);
|
|
expect(results[0].fields[1].values[0]).toBe(4);
|
|
});
|
|
|
|
it('will transform a series with two time fields', () => {
|
|
const frame = toDataFrame({
|
|
refId: 'A',
|
|
fields: [
|
|
{ name: 'Time', type: FieldType.time, values: [0, 50, 90] },
|
|
{ name: 'UpdateTime', type: FieldType.time, values: [10, 100, 100] },
|
|
{
|
|
name: 'Value',
|
|
type: FieldType.number,
|
|
values: [2, 3, 4],
|
|
},
|
|
],
|
|
});
|
|
|
|
const results = timeSeriesToTableTransform({}, [frame]);
|
|
|
|
// We should have a created trend field
|
|
// with the first time field used as a time
|
|
// and the values coming along with that
|
|
expect(results[0].fields[0].name).toBe('Trend #A');
|
|
expect(results[0].fields[0].values[0].fields[0].values[0]).toBe(0);
|
|
expect(results[0].fields[0].values[0].fields[1].values[0]).toBe(2);
|
|
});
|
|
|
|
it('will transform a series with two time fields and a time field configured', () => {
|
|
const frame = toDataFrame({
|
|
refId: 'A',
|
|
fields: [
|
|
{ name: 'Time', type: FieldType.time, values: [0, 50, 90] },
|
|
{ name: 'UpdateTime', type: FieldType.time, values: [10, 100, 100] },
|
|
{
|
|
name: 'Value',
|
|
type: FieldType.number,
|
|
values: [2, 3, 4],
|
|
},
|
|
],
|
|
});
|
|
|
|
const results = timeSeriesToTableTransform({ A: { timeField: 'UpdateTime' } }, [frame]);
|
|
|
|
// We should have a created trend field
|
|
// with the "UpdateTime" time field used as a time
|
|
// and the values coming along with that
|
|
expect(results[0].fields[0].name).toBe('Trend #A');
|
|
expect(results[0].fields[0].values[0].fields[0].values[0]).toBe(10);
|
|
expect(results[0].fields[0].values[0].fields[1].values[0]).toBe(2);
|
|
});
|
|
|
|
it('Will correctly fill in gaps in labels', () => {
|
|
const series = [
|
|
getTimeSeries('A', { instance: 'A', pod: 'AA' }),
|
|
getTimeSeries('A', { instance: 'B' }),
|
|
getTimeSeries('A', { instance: 'C', pod: 'CC' }),
|
|
];
|
|
|
|
const results = timeSeriesToTableTransform({}, series);
|
|
expect(results).toHaveLength(1);
|
|
const result = results[0];
|
|
expect(result.refId).toBe('A');
|
|
expect(result.fields).toHaveLength(3);
|
|
expect(result.fields[0].values).toEqual(['A', 'B', 'C']);
|
|
expect(result.fields[1].values).toEqual(['AA', '', 'CC']);
|
|
});
|
|
});
|
|
|
|
function assertFieldsEqual(field1: Field, field2: Field) {
|
|
expect(field1.type).toEqual(field2.type);
|
|
expect(field1.name).toEqual(field2.name);
|
|
expect(field1.values).toEqual(field2.values);
|
|
expect(field1.labels ?? {}).toEqual(field2.labels ?? {});
|
|
}
|
|
|
|
function assertDataFrameField(field: Field, matchesFrames: DataFrame[]) {
|
|
const frames: DataFrame[] = field.values;
|
|
expect(frames).toHaveLength(matchesFrames.length);
|
|
frames.forEach((frame, idx) => {
|
|
const matchingFrame = matchesFrames[idx];
|
|
expect(frame.fields).toHaveLength(matchingFrame.fields.length);
|
|
frame.fields.forEach((field, fidx) => assertFieldsEqual(field, matchingFrame.fields[fidx]));
|
|
});
|
|
}
|
|
|
|
function getTimeSeries(refId: string, labels: Labels, values: number[] = [10]) {
|
|
return toDataFrame({
|
|
refId,
|
|
fields: [
|
|
{ name: 'Time', type: FieldType.time, values: [10] },
|
|
{
|
|
name: 'Value',
|
|
type: FieldType.number,
|
|
values,
|
|
labels,
|
|
},
|
|
],
|
|
});
|
|
}
|
|
|
|
function getNonTimeSeries(refId: string, labels: Labels, values: number[], times?: number[]) {
|
|
if (times === undefined) {
|
|
times = [1699476339, 1699475339, 1699476300];
|
|
}
|
|
|
|
return toDataFrame({
|
|
refId,
|
|
fields: [
|
|
// These times are in non-ascending order
|
|
// and thus this isn't a timeseries
|
|
{ name: 'Time', type: FieldType.time, values: times },
|
|
{
|
|
name: 'Value',
|
|
type: FieldType.number,
|
|
values,
|
|
labels,
|
|
},
|
|
],
|
|
});
|
|
}
|
|
|
|
function getTable(refId: string, fields: string[]) {
|
|
return toDataFrame({
|
|
refId,
|
|
fields: fields.map((f) => ({ name: f, type: FieldType.string, values: ['value'] })),
|
|
labels: {},
|
|
});
|
|
}
|