VictoriaMetrics/app/vmalert/rule/recording_test.go
Hui Wang 1f477aba41
vmalert: automatically add exported_ prefix for original evaluation… (#5398)
automatically add `exported_` prefix for original evaluation result label if it's conflicted with external or reserved one,
previously it was overridden.

https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5161

Signed-off-by: hagen1778 <roman@victoriametrics.com>
Co-authored-by: hagen1778 <roman@victoriametrics.com>
2023-12-22 16:07:47 +01:00

262 lines
7.2 KiB
Go

package rule
import (
"context"
"errors"
"strings"
"testing"
"time"
"github.com/VictoriaMetrics/VictoriaMetrics/app/vmalert/datasource"
"github.com/VictoriaMetrics/VictoriaMetrics/app/vmalert/utils"
"github.com/VictoriaMetrics/VictoriaMetrics/lib/prompbmarshal"
)
func TestRecordingRule_Exec(t *testing.T) {
timestamp := time.Now()
testCases := []struct {
rule *RecordingRule
metrics []datasource.Metric
expTS []prompbmarshal.TimeSeries
}{
{
&RecordingRule{Name: "foo"},
[]datasource.Metric{metricWithValueAndLabels(t, 10,
"__name__", "bar",
)},
[]prompbmarshal.TimeSeries{
newTimeSeries([]float64{10}, []int64{timestamp.UnixNano()}, map[string]string{
"__name__": "foo",
}),
},
},
{
&RecordingRule{Name: "foobarbaz"},
[]datasource.Metric{
metricWithValueAndLabels(t, 1, "__name__", "foo", "job", "foo"),
metricWithValueAndLabels(t, 2, "__name__", "bar", "job", "bar"),
metricWithValueAndLabels(t, 3, "__name__", "baz", "job", "baz"),
},
[]prompbmarshal.TimeSeries{
newTimeSeries([]float64{1}, []int64{timestamp.UnixNano()}, map[string]string{
"__name__": "foobarbaz",
"job": "foo",
}),
newTimeSeries([]float64{2}, []int64{timestamp.UnixNano()}, map[string]string{
"__name__": "foobarbaz",
"job": "bar",
}),
newTimeSeries([]float64{3}, []int64{timestamp.UnixNano()}, map[string]string{
"__name__": "foobarbaz",
"job": "baz",
}),
},
},
{
&RecordingRule{
Name: "job:foo",
Labels: map[string]string{
"source": "test",
},
},
[]datasource.Metric{
metricWithValueAndLabels(t, 2, "__name__", "foo", "job", "foo"),
metricWithValueAndLabels(t, 1, "__name__", "bar", "job", "bar", "source", "origin"),
},
[]prompbmarshal.TimeSeries{
newTimeSeries([]float64{2}, []int64{timestamp.UnixNano()}, map[string]string{
"__name__": "job:foo",
"job": "foo",
"source": "test",
}),
newTimeSeries([]float64{1}, []int64{timestamp.UnixNano()}, map[string]string{
"__name__": "job:foo",
"job": "bar",
"source": "test",
"exported_source": "origin",
}),
},
},
}
for _, tc := range testCases {
t.Run(tc.rule.Name, func(t *testing.T) {
fq := &datasource.FakeQuerier{}
fq.Add(tc.metrics...)
tc.rule.q = fq
tc.rule.state = &ruleState{entries: make([]StateEntry, 10)}
tss, err := tc.rule.exec(context.TODO(), time.Now(), 0)
if err != nil {
t.Fatalf("unexpected Exec err: %s", err)
}
if err := compareTimeSeries(t, tc.expTS, tss); err != nil {
t.Fatalf("timeseries missmatch: %s", err)
}
})
}
}
func TestRecordingRule_ExecRange(t *testing.T) {
timestamp := time.Now()
testCases := []struct {
rule *RecordingRule
metrics []datasource.Metric
expTS []prompbmarshal.TimeSeries
}{
{
&RecordingRule{Name: "foo"},
[]datasource.Metric{metricWithValuesAndLabels(t, []float64{10, 20, 30},
"__name__", "bar",
)},
[]prompbmarshal.TimeSeries{
newTimeSeries([]float64{10, 20, 30},
[]int64{timestamp.UnixNano(), timestamp.UnixNano(), timestamp.UnixNano()},
map[string]string{
"__name__": "foo",
}),
},
},
{
&RecordingRule{Name: "foobarbaz"},
[]datasource.Metric{
metricWithValuesAndLabels(t, []float64{1}, "__name__", "foo", "job", "foo"),
metricWithValuesAndLabels(t, []float64{2, 3}, "__name__", "bar", "job", "bar"),
metricWithValuesAndLabels(t, []float64{4, 5, 6}, "__name__", "baz", "job", "baz"),
},
[]prompbmarshal.TimeSeries{
newTimeSeries([]float64{1}, []int64{timestamp.UnixNano()}, map[string]string{
"__name__": "foobarbaz",
"job": "foo",
}),
newTimeSeries([]float64{2, 3}, []int64{timestamp.UnixNano(), timestamp.UnixNano()}, map[string]string{
"__name__": "foobarbaz",
"job": "bar",
}),
newTimeSeries([]float64{4, 5, 6},
[]int64{timestamp.UnixNano(), timestamp.UnixNano(), timestamp.UnixNano()},
map[string]string{
"__name__": "foobarbaz",
"job": "baz",
}),
},
},
{
&RecordingRule{Name: "job:foo", Labels: map[string]string{
"source": "test",
}},
[]datasource.Metric{
metricWithValueAndLabels(t, 2, "__name__", "foo", "job", "foo"),
metricWithValueAndLabels(t, 1, "__name__", "bar", "job", "bar"),
},
[]prompbmarshal.TimeSeries{
newTimeSeries([]float64{2}, []int64{timestamp.UnixNano()}, map[string]string{
"__name__": "job:foo",
"job": "foo",
"source": "test",
}),
newTimeSeries([]float64{1}, []int64{timestamp.UnixNano()}, map[string]string{
"__name__": "job:foo",
"job": "bar",
"source": "test",
}),
},
},
}
for _, tc := range testCases {
t.Run(tc.rule.Name, func(t *testing.T) {
fq := &datasource.FakeQuerier{}
fq.Add(tc.metrics...)
tc.rule.q = fq
tss, err := tc.rule.execRange(context.TODO(), time.Now(), time.Now())
if err != nil {
t.Fatalf("unexpected Exec err: %s", err)
}
if err := compareTimeSeries(t, tc.expTS, tss); err != nil {
t.Fatalf("timeseries missmatch: %s", err)
}
})
}
}
func TestRecordingRuleLimit(t *testing.T) {
timestamp := time.Now()
testCases := []struct {
limit int
err string
}{
{
limit: 0,
},
{
limit: -1,
},
{
limit: 1,
err: "exec exceeded limit of 1 with 3 series",
},
{
limit: 2,
err: "exec exceeded limit of 2 with 3 series",
},
}
testMetrics := []datasource.Metric{
metricWithValuesAndLabels(t, []float64{1}, "__name__", "foo", "job", "foo"),
metricWithValuesAndLabels(t, []float64{2, 3}, "__name__", "bar", "job", "bar"),
metricWithValuesAndLabels(t, []float64{4, 5, 6}, "__name__", "baz", "job", "baz"),
}
rule := &RecordingRule{Name: "job:foo",
state: &ruleState{entries: make([]StateEntry, 10)},
Labels: map[string]string{
"source": "test_limit",
},
metrics: &recordingRuleMetrics{
errors: utils.GetOrCreateCounter(`vmalert_recording_rules_errors_total{alertname="job:foo"}`),
},
}
var err error
for _, testCase := range testCases {
fq := &datasource.FakeQuerier{}
fq.Add(testMetrics...)
rule.q = fq
_, err = rule.exec(context.TODO(), timestamp, testCase.limit)
if err != nil && !strings.EqualFold(err.Error(), testCase.err) {
t.Fatal(err)
}
}
}
func TestRecordingRule_ExecNegative(t *testing.T) {
rr := &RecordingRule{
Name: "job:foo",
Labels: map[string]string{
"job": "test",
},
state: &ruleState{entries: make([]StateEntry, 10)},
metrics: &recordingRuleMetrics{
errors: utils.GetOrCreateCounter(`vmalert_recording_rules_errors_total{alertname="job:foo"}`),
},
}
fq := &datasource.FakeQuerier{}
expErr := "connection reset by peer"
fq.SetErr(errors.New(expErr))
rr.q = fq
_, err := rr.exec(context.TODO(), time.Now(), 0)
if err == nil {
t.Fatalf("expected to get err; got nil")
}
if !strings.Contains(err.Error(), expErr) {
t.Fatalf("expected to get err %q; got %q insterad", expErr, err)
}
fq.Reset()
// add metrics which differs only by `job` label
// which will be overridden by rule
fq.Add(metricWithValueAndLabels(t, 1, "__name__", "foo", "job", "foo"))
fq.Add(metricWithValueAndLabels(t, 2, "__name__", "foo", "job", "bar"))
_, err = rr.exec(context.TODO(), time.Now(), 0)
if err != nil {
t.Fatal(err)
}
}