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2 Commits

Author SHA1 Message Date
Nikhil Soni
5480aaef6d fix: add missing filtering for ip address for scalar data
In domain listing api for external api monitoring,
we have option to filter out the IP address but
it only handles timeseries and raw type data while
domain list handler returns scalar data.
2026-02-10 17:53:02 +05:30
Vikrant Gupta
e699ad8122 fix(meter): custom step intervals for meter aggregations (#10255)
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* fix(meter): custom step interval support

* fix(meter): custom step interval support

* fix(meter): custom step interval support

* fix(meter): custom step interval support

* fix(meter): remove frontend harcoding for step interval

* fix(meter): remove frontend harcoding for step interval
2026-02-10 16:54:14 +05:30
8 changed files with 123 additions and 308 deletions

View File

@@ -103,7 +103,7 @@ export const getTotalLogSizeWidgetData = (): Widgets =>
queryName: 'A',
reduceTo: ReduceOperators.SUM,
spaceAggregation: 'sum',
stepInterval: 60,
stepInterval: null,
timeAggregation: 'increase',
},
],
@@ -140,7 +140,7 @@ export const getTotalTraceSizeWidgetData = (): Widgets =>
queryName: 'A',
reduceTo: ReduceOperators.SUM,
spaceAggregation: 'sum',
stepInterval: 60,
stepInterval: null,
timeAggregation: 'increase',
},
],
@@ -177,7 +177,7 @@ export const getTotalMetricDatapointCountWidgetData = (): Widgets =>
queryName: 'A',
reduceTo: ReduceOperators.SUM,
spaceAggregation: 'sum',
stepInterval: 60,
stepInterval: null,
timeAggregation: 'increase',
},
],
@@ -214,7 +214,7 @@ export const getLogCountWidgetData = (): Widgets =>
queryName: 'A',
reduceTo: ReduceOperators.AVG,
spaceAggregation: 'sum',
stepInterval: 60,
stepInterval: null,
timeAggregation: 'increase',
},
],
@@ -251,7 +251,7 @@ export const getLogSizeWidgetData = (): Widgets =>
queryName: 'A',
reduceTo: ReduceOperators.AVG,
spaceAggregation: 'sum',
stepInterval: 60,
stepInterval: null,
timeAggregation: 'increase',
},
],
@@ -288,7 +288,7 @@ export const getSpanCountWidgetData = (): Widgets =>
queryName: 'A',
reduceTo: ReduceOperators.AVG,
spaceAggregation: 'sum',
stepInterval: 60,
stepInterval: null,
timeAggregation: 'increase',
},
],
@@ -325,7 +325,7 @@ export const getSpanSizeWidgetData = (): Widgets =>
queryName: 'A',
reduceTo: ReduceOperators.AVG,
spaceAggregation: 'sum',
stepInterval: 60,
stepInterval: null,
timeAggregation: 'increase',
},
],
@@ -362,7 +362,7 @@ export const getMetricCountWidgetData = (): Widgets =>
queryName: 'A',
reduceTo: ReduceOperators.AVG,
spaceAggregation: 'sum',
stepInterval: 60,
stepInterval: null,
timeAggregation: 'increase',
},
],

View File

@@ -1,62 +0,0 @@
import { isInvalidPlotValue, normalizePlotValue } from '../dataUtils';
describe('dataUtils', () => {
describe('isInvalidPlotValue', () => {
it('treats null and undefined as invalid', () => {
expect(isInvalidPlotValue(null)).toBe(true);
expect(isInvalidPlotValue(undefined)).toBe(true);
});
it('treats finite numbers as valid and non-finite as invalid', () => {
expect(isInvalidPlotValue(0)).toBe(false);
expect(isInvalidPlotValue(123.45)).toBe(false);
expect(isInvalidPlotValue(Number.NaN)).toBe(true);
expect(isInvalidPlotValue(Infinity)).toBe(true);
expect(isInvalidPlotValue(-Infinity)).toBe(true);
});
it('treats well-formed numeric strings as valid', () => {
expect(isInvalidPlotValue('0')).toBe(false);
expect(isInvalidPlotValue('123.45')).toBe(false);
expect(isInvalidPlotValue('-1')).toBe(false);
});
it('treats Infinity/NaN string variants and non-numeric strings as invalid', () => {
expect(isInvalidPlotValue('+Inf')).toBe(true);
expect(isInvalidPlotValue('-Inf')).toBe(true);
expect(isInvalidPlotValue('Infinity')).toBe(true);
expect(isInvalidPlotValue('-Infinity')).toBe(true);
expect(isInvalidPlotValue('NaN')).toBe(true);
expect(isInvalidPlotValue('not-a-number')).toBe(true);
});
it('treats non-number, non-string values as valid (left to caller)', () => {
expect(isInvalidPlotValue({})).toBe(false);
expect(isInvalidPlotValue([])).toBe(false);
expect(isInvalidPlotValue(true)).toBe(false);
});
});
describe('normalizePlotValue', () => {
it('returns null for invalid values detected by isInvalidPlotValue', () => {
expect(normalizePlotValue(null)).toBeNull();
expect(normalizePlotValue(undefined)).toBeNull();
expect(normalizePlotValue(NaN)).toBeNull();
expect(normalizePlotValue(Infinity)).toBeNull();
expect(normalizePlotValue('-Infinity')).toBeNull();
expect(normalizePlotValue('not-a-number')).toBeNull();
});
it('parses valid numeric strings into numbers', () => {
expect(normalizePlotValue('0')).toBe(0);
expect(normalizePlotValue('123.45')).toBe(123.45);
expect(normalizePlotValue('-1')).toBe(-1);
});
it('passes through valid numbers unchanged', () => {
expect(normalizePlotValue(0)).toBe(0);
expect(normalizePlotValue(123)).toBe(123);
expect(normalizePlotValue(42.5)).toBe(42.5);
});
});
});

View File

@@ -1,201 +0,0 @@
import uPlot from 'uplot';
import { DistributionType } from '../../config/types';
import * as scaleUtils from '../scale';
describe('scale utils', () => {
describe('normalizeLogScaleLimits', () => {
it('returns limits unchanged when distribution is not logarithmic', () => {
const limits = {
min: 1,
max: 100,
softMin: 5,
softMax: 50,
};
const result = scaleUtils.normalizeLogScaleLimits({
distr: DistributionType.Linear,
logBase: 10,
limits,
});
expect(result).toEqual(limits);
});
it('snaps positive limits to powers of the log base when distribution is logarithmic', () => {
const result = scaleUtils.normalizeLogScaleLimits({
distr: DistributionType.Logarithmic,
logBase: 10,
limits: {
min: 3,
max: 900,
softMin: 12,
softMax: 85,
},
});
expect(result.min).toBe(1); // 10^0
expect(result.max).toBe(1000); // 10^3
expect(result.softMin).toBe(10); // 10^1
expect(result.softMax).toBe(100); // 10^2
});
});
describe('getDistributionConfig', () => {
it('returns empty config for time scales', () => {
const config = scaleUtils.getDistributionConfig({
time: true,
distr: DistributionType.Linear,
logBase: 2,
});
expect(config).toEqual({});
});
it('returns linear distribution settings for non-time scales', () => {
const config = scaleUtils.getDistributionConfig({
time: false,
distr: DistributionType.Linear,
logBase: 2,
});
expect(config.distr).toBe(1);
expect(config.log).toBe(2);
});
it('returns log distribution settings for non-time scales', () => {
const config = scaleUtils.getDistributionConfig({
time: false,
distr: DistributionType.Logarithmic,
logBase: 10,
});
expect(config.distr).toBe(3);
expect(config.log).toBe(10);
});
});
describe('getRangeConfig', () => {
it('computes range config and fixed range flags correctly', () => {
const {
rangeConfig,
hardMinOnly,
hardMaxOnly,
hasFixedRange,
} = scaleUtils.getRangeConfig(0, 100, null, null, 0.1, 0.2);
expect(rangeConfig.min).toEqual({
pad: 0.1,
hard: 0,
soft: undefined,
mode: 3,
});
expect(rangeConfig.max).toEqual({
pad: 0.2,
hard: 100,
soft: undefined,
mode: 3,
});
expect(hardMinOnly).toBe(true);
expect(hardMaxOnly).toBe(true);
expect(hasFixedRange).toBe(true);
});
});
describe('createRangeFunction', () => {
it('returns [dataMin, dataMax] when no fixed range and no data', () => {
const params = {
rangeConfig: {} as uPlot.Range.Config,
hardMinOnly: false,
hardMaxOnly: false,
hasFixedRange: false,
min: null,
max: null,
};
const rangeFn = scaleUtils.createRangeFunction(params);
const u = ({
scales: {
y: {
distr: 1,
log: 10,
},
},
} as unknown) as uPlot;
const result = rangeFn(
u,
(null as unknown) as number,
(null as unknown) as number,
'y',
);
expect(result).toEqual([null, null]);
});
it('applies hard min/max for linear scale when only hard limits are set', () => {
const params = {
rangeConfig: {} as uPlot.Range.Config,
hardMinOnly: true,
hardMaxOnly: true,
hasFixedRange: true,
min: 0,
max: 100,
};
const rangeFn = scaleUtils.createRangeFunction(params);
// Use an undefined distr so the range function skips calling uPlot.rangeNum
// and we can focus on the behavior of applyHardLimits.
const u = ({
scales: {
y: {
distr: undefined,
log: 10,
},
},
} as unknown) as uPlot;
const result = rangeFn(u, 10, 20, 'y');
// After applyHardLimits, the returned range should respect configured min/max
expect(result).toEqual([0, 100]);
});
});
describe('adjustSoftLimitsWithThresholds', () => {
it('returns original soft limits when there are no thresholds', () => {
const result = scaleUtils.adjustSoftLimitsWithThresholds(1, 5, [], 'ms');
expect(result).toEqual({ softMin: 1, softMax: 5 });
});
it('expands soft limits to include threshold min/max values', () => {
const result = scaleUtils.adjustSoftLimitsWithThresholds(
3,
6,
[{ thresholdValue: 2 }, { thresholdValue: 8 }],
'ms',
);
// min should be pulled down to the smallest threshold value
expect(result.softMin).toBe(2);
// max should be pushed up to the largest threshold value
expect(result.softMax).toBe(8);
});
});
describe('getFallbackMinMaxTimeStamp', () => {
it('returns a 24-hour window ending at approximately now', () => {
const { fallbackMin, fallbackMax } = scaleUtils.getFallbackMinMaxTimeStamp();
// Difference should be exactly one day in seconds
expect(fallbackMax - fallbackMin).toBe(86400);
// Both should be reasonable timestamps (not NaN or negative)
expect(fallbackMin).toBeGreaterThan(0);
expect(fallbackMax).toBeGreaterThan(fallbackMin);
});
});
});

View File

@@ -1,36 +0,0 @@
import { findMinMaxThresholdValues } from '../threshold';
describe('findMinMaxThresholdValues', () => {
it('returns [null, null] when thresholds array is empty or missing', () => {
expect(findMinMaxThresholdValues([], 'ms')).toEqual([null, null]);
// @ts-expect-error intentional undefined to cover defensive branch
expect(findMinMaxThresholdValues(undefined, 'ms')).toEqual([null, null]);
});
it('returns min and max from thresholdValue when units are not provided', () => {
const thresholds = [
{ thresholdValue: 5 },
{ thresholdValue: 1 },
{ thresholdValue: 10 },
];
const [min, max] = findMinMaxThresholdValues(thresholds);
expect(min).toBe(1);
expect(max).toBe(10);
});
it('ignores thresholds without a value or with unconvertible units', () => {
const thresholds = [
// Should be ignored: convertValue returns null for unknown unit
{ thresholdValue: 100, thresholdUnit: 'unknown-unit' },
// Should be used
{ thresholdValue: 4 },
];
const [min, max] = findMinMaxThresholdValues(thresholds, 'ms');
expect(min).toBe(4);
expect(max).toBe(4);
});
});

View File

@@ -247,6 +247,8 @@ func FilterResponse(results []*qbtypes.QueryRangeResponse) []*qbtypes.QueryRange
}
}
resultData.Rows = filteredRows
case *qbtypes.ScalarData:
resultData.Data = filterScalarDataIPs(resultData.Columns, resultData.Data)
}
filteredData = append(filteredData, result)
@@ -272,6 +274,39 @@ func shouldIncludeSeries(series *qbtypes.TimeSeries) bool {
return true
}
func filterScalarDataIPs(columns []*qbtypes.ColumnDescriptor, data [][]any) [][]any {
// Find column indices for server address fields
serverColIndices := make([]int, 0)
for i, col := range columns {
if col.Name == serverAddressKeyLegacy || col.Name == serverAddressKey {
serverColIndices = append(serverColIndices, i)
}
}
if len(serverColIndices) == 0 {
return data
}
filtered := make([][]any, 0, len(data))
for _, row := range data {
includeRow := true
for _, colIdx := range serverColIndices {
if colIdx < len(row) {
if strVal, ok := row[colIdx].(string); ok {
if net.ParseIP(strVal) != nil {
includeRow = false
break
}
}
}
}
if includeRow {
filtered = append(filtered, row)
}
}
return filtered
}
func shouldIncludeRow(row *qbtypes.RawRow) bool {
if row.Data != nil {
for _, key := range []string{serverAddressKeyLegacy, serverAddressKey} {

View File

@@ -116,6 +116,59 @@ func TestFilterResponse(t *testing.T) {
},
},
},
{
name: "should filter out IP addresses from scalar data",
input: []*qbtypes.QueryRangeResponse{
{
Data: qbtypes.QueryData{
Results: []any{
&qbtypes.ScalarData{
QueryName: "endpoints",
Columns: []*qbtypes.ColumnDescriptor{
{
TelemetryFieldKey: telemetrytypes.TelemetryFieldKey{Name: "net.peer.name"},
Type: qbtypes.ColumnTypeGroup,
},
{
TelemetryFieldKey: telemetrytypes.TelemetryFieldKey{Name: "endpoints"},
Type: qbtypes.ColumnTypeAggregation,
},
},
Data: [][]any{
{"192.168.1.1", 10},
{"example.com", 20},
{"10.0.0.1", 5},
},
},
},
},
},
},
expected: []*qbtypes.QueryRangeResponse{
{
Data: qbtypes.QueryData{
Results: []any{
&qbtypes.ScalarData{
QueryName: "endpoints",
Columns: []*qbtypes.ColumnDescriptor{
{
TelemetryFieldKey: telemetrytypes.TelemetryFieldKey{Name: "net.peer.name"},
Type: qbtypes.ColumnTypeGroup,
},
{
TelemetryFieldKey: telemetrytypes.TelemetryFieldKey{Name: "endpoints"},
Type: qbtypes.ColumnTypeAggregation,
},
},
Data: [][]any{
{"example.com", 20},
},
},
},
},
},
},
},
}
for _, tt := range tests {

View File

@@ -208,7 +208,16 @@ func (q *querier) QueryRange(ctx context.Context, orgID valuer.UUID, req *qbtype
event.GroupByApplied = len(spec.GroupBy) > 0
if spec.Source == telemetrytypes.SourceMeter {
spec.StepInterval = qbtypes.Step{Duration: time.Second * time.Duration(querybuilder.RecommendedStepIntervalForMeter(req.Start, req.End))}
if spec.StepInterval.Seconds() == 0 {
spec.StepInterval = qbtypes.Step{Duration: time.Second * time.Duration(querybuilder.RecommendedStepIntervalForMeter(req.Start, req.End))}
}
if spec.StepInterval.Seconds() < float64(querybuilder.MinAllowedStepIntervalForMeter(req.Start, req.End)) {
newStep := qbtypes.Step{
Duration: time.Second * time.Duration(querybuilder.MinAllowedStepIntervalForMeter(req.Start, req.End)),
}
spec.StepInterval = newStep
}
} else {
if spec.StepInterval.Seconds() == 0 {
spec.StepInterval = qbtypes.Step{

View File

@@ -89,6 +89,23 @@ func RecommendedStepIntervalForMeter(start, end uint64) uint64 {
return recommended
}
func MinAllowedStepIntervalForMeter(start, end uint64) uint64 {
start = ToNanoSecs(start)
end = ToNanoSecs(end)
step := (end - start) / RecommendedNumberOfPoints / 1e9
// for meter queries the minimum step interval allowed is 1 hour as this is our granularity
if step < 3600 {
return 3600
}
// return the nearest lower multiple of 3600 ( 1 hour )
minAllowed := step - step%3600
return minAllowed
}
func RecommendedStepIntervalForMetric(start, end uint64) uint64 {
start = ToNanoSecs(start)
end = ToNanoSecs(end)