// === delta(matrix MatrixNode, isCounter ScalarNode) ===
func deltaImpl(timestamp *time.Time, args []Node) interface{} {
matrixNode := args[0].(MatrixNode)
isCounter := int(args[1].(ScalarNode).Eval(timestamp))
resultVector := Vector{}
// If we treat these metrics as counters, we need to fetch all values
// in the interval to find breaks in the timeseries' monotonicity.
// I.e. if a counter resets, we want to ignore that reset.
var matrixValue Matrix
if isCounter > 0 {
matrixValue = matrixNode.Eval(timestamp)
} else {
matrixValue = matrixNode.EvalBoundaries(timestamp)
}
for _, samples := range matrixValue {
counterCorrection := model.SampleValue(0)
lastValue := model.SampleValue(0)
for _, sample := range samples.Values {
currentValue := sample.Value
if currentValue < lastValue {
counterCorrection += lastValue - currentValue
}
lastValue = currentValue
}
resultValue := lastValue - samples.Values[0].Value + counterCorrection
resultSample := &model.Sample{
Metric: samples.Metric,
Value: resultValue,
Timestamp: *timestamp,
}
resultVector = append(resultVector, resultSample)
}
return resultVector
}
func TestGetFingerprintsForLabelSetUsesAnd(t *testing.T) {
temporaryDirectory, _ := ioutil.TempDir("", "test_get_fingerprints_for_label_set_uses_and")
defer func() {
err := os.RemoveAll(temporaryDirectory)
if err != nil {
t.Errorf("could not remove temporary directory: %f", err)
}
}()
persistence, _ := NewLevelDBMetricPersistence(temporaryDirectory)
defer persistence.Close()
metrics := []map[string]string{
{"name": "request_metrics_latency_equal_tallying_microseconds", "instance": "http://localhost:9090/metrics.json", "percentile": "0.010000"},
{"name": "requests_metrics_latency_equal_accumulating_microseconds", "instance": "http://localhost:9090/metrics.json", "percentile": "0.010000"},
{"name": "requests_metrics_latency_logarithmic_accumulating_microseconds", "instance": "http://localhost:9090/metrics.json", "percentile": "0.010000"},
{"name": "requests_metrics_latency_logarithmic_tallying_microseconds", "instance": "http://localhost:9090/metrics.json", "percentile": "0.010000"},
{"name": "targets_healthy_scrape_latency_ms", "instance": "http://localhost:9090/metrics.json", "percentile": "0.010000"},
}
for _, metric := range metrics {
m := model.Metric{}
for k, v := range metric {
m[model.LabelName(k)] = model.LabelValue(v)
}
err := persistence.AppendSample(&model.Sample{
Value: model.SampleValue(0.0),
Timestamp: time.Now(),
Metric: m,
})
if err != nil {
t.Errorf("could not create base sample: %s", err)
}
}
labelSet := model.LabelSet{
"name": "targets_healthy_scrape_latency_ms",
"percentile": "0.010000",
}
fingerprints, err := persistence.GetFingerprintsForLabelSet(&labelSet)
if err != nil {
t.Errorf("could not get labels: %s", err)
}
if len(fingerprints) != 1 {
t.Errorf("did not get a single metric as is expected, got %s", fingerprints)
}
}
// === rate(node *MatrixNode) ===
func rateImpl(timestamp *time.Time, args []Node) interface{} {
args = append(args, &ScalarLiteral{value: 1})
vector := deltaImpl(timestamp, args).(Vector)
// TODO: could be other type of MatrixNode in the future (right now, only
// MatrixLiteral exists). Find a better way of getting the duration of a
// matrix, such as looking at the samples themselves.
interval := args[0].(*MatrixLiteral).interval
for _, sample := range vector {
sample.Value /= model.SampleValue(interval / time.Second)
}
return vector
}
func (node *VectorAggregation) groupedAggregationsToVector(aggregations map[string]*groupedAggregation, timestamp *time.Time) Vector {
vector := Vector{}
for _, aggregation := range aggregations {
if node.aggrType == AVG {
aggregation.value = aggregation.value / model.SampleValue(aggregation.groupCount)
}
sample := &model.Sample{
Metric: aggregation.labels,
Value: aggregation.value,
Timestamp: *timestamp,
}
vector = append(vector, sample)
}
return vector
}
}
}()
persistence, _ := NewLevelDBMetricPersistence(temporaryDirectory)
defer func() {
persistence.Close()
}()
m := model.Metric{
"name": "age_in_years",
}
for j, value := range context.values {
err := persistence.AppendSample(&model.Sample{
Value: model.SampleValue(value.value),
Timestamp: time.Date(value.year, value.month, value.day, value.hour, 0, 0, 0, time.UTC),
Metric: m,
})
if err != nil {
t.Errorf("%d.%d(%s). Could not create sample: %q\n", i, j, context.name, err)
}
}
for j, behavior := range context.behaviors {
input := behavior.input
time := time.Date(input.year, input.month, input.day, input.hour, 0, 0, 0, time.UTC)
p := metric.StalenessPolicy{
DeltaAllowance: input.staleness,
}
return yyactionreturn{0, yyRT_FALLTHROUGH}
}}, {regexp.MustCompile("\\-?([0-9])+(\\.([0-9])*)?"), nil, []yystartcondition{}, false, func() (yyar yyactionreturn) {
defer func() {
if r := recover(); r != nil {
if r != "yyREJECT" {
panic(r)
}
yyar.returnType = yyRT_REJECT
}
}()
{
num, err := strconv.ParseFloat(yytext, 32)
if err != nil {
rulesError("Invalid float %v", yytext)
}
yylval.num = model.SampleValue(num)
return yyactionreturn{NUMBER, yyRT_USER_RETURN}
}
return yyactionreturn{0, yyRT_FALLTHROUGH}
}}, {regexp.MustCompile("\\\"(\\\\[^\\n]|[^\\\\\"])*\\\""), nil, []yystartcondition{}, false, func() (yyar yyactionreturn) {
defer func() {
if r := recover(); r != nil {
if r != "yyREJECT" {
panic(r)
}
yyar.returnType = yyRT_REJECT
}
}()
{
yylval.str = yytext[1 : len(yytext)-1]
func (t *target) Scrape(earliest time.Time, results chan Result) (err error) {
result := Result{}
defer func() {
futureState := t.state
switch err {
case nil:
futureState = ALIVE
default:
futureState = UNREACHABLE
}
t.scheduler.Reschedule(earliest, futureState)
result.Err = err
results <- result
}()
done := make(chan bool)
request := func() {
ti := time.Now()
resp, err := http.Get(t.Address())
if err != nil {
return
}
defer resp.Body.Close()
raw, err := ioutil.ReadAll(resp.Body)
if err != nil {
return
}
intermediate := make(map[string]interface{})
err = json.Unmarshal(raw, &intermediate)
if err != nil {
return
}
baseLabels := model.LabelSet{"instance": model.LabelValue(t.Address())}
for baseK, baseV := range t.BaseLabels {
baseLabels[baseK] = baseV
}
for name, v := range intermediate {
asMap, ok := v.(map[string]interface{})
if !ok {
continue
}
switch asMap["type"] {
case "counter":
m := model.Metric{}
m["name"] = model.LabelValue(name)
asFloat, ok := asMap["value"].(float64)
if !ok {
continue
}
s := model.Sample{
Metric: m,
Value: model.SampleValue(asFloat),
Timestamp: ti,
}
for baseK, baseV := range baseLabels {
m[baseK] = baseV
}
result.Samples = append(result.Samples, s)
case "histogram":
values, ok := asMap["value"].(map[string]interface{})
if !ok {
continue
}
for p, pValue := range values {
asString, ok := pValue.(string)
if !ok {
continue
}
float, err := strconv.ParseFloat(asString, 64)
if err != nil {
continue
}
m := model.Metric{}
m["name"] = model.LabelValue(name)
m["percentile"] = model.LabelValue(p)
s := model.Sample{
Metric: m,
Value: model.SampleValue(float),
Timestamp: ti,
}
for baseK, baseV := range baseLabels {
m[baseK] = baseV
}
result.Samples = append(result.Samples, s)
}
}
}
done <- true
}
accumulator := func(d time.Duration) {
ms := float64(d) / float64(time.Millisecond)
labels := map[string]string{address: t.Address(), outcome: success}
if err != nil {
labels[outcome] = failure
}
targetOperationLatencies.Add(labels, ms)
targetOperations.Increment(labels)
}
go metrics.InstrumentCall(request, accumulator)
select {
case <-done:
break
case <-time.After(t.Deadline):
err = fmt.Errorf("Target %s exceeded %s deadline.", t, t.Deadline)
}
return
}
// === count(vector VectorNode) ===
func countImpl(timestamp *time.Time, args []Node) interface{} {
return model.SampleValue(len(args[0].(VectorNode).Eval(timestamp)))
}
// === time() ===
func timeImpl(timestamp *time.Time, args []Node) interface{} {
return model.SampleValue(time.Now().Unix())
}
func (l *LevelDBMetricPersistence) GetSamplesForMetric(metric model.Metric, interval model.Interval) ([]model.Samples, error) {
metricDTO := model.MetricToDTO(&metric)
if fingerprintDTO, fingerprintDTOErr := model.MessageToFingerprintDTO(metricDTO); fingerprintDTOErr == nil {
if iterator, closer, iteratorErr := l.metricSamples.GetIterator(); iteratorErr == nil {
defer closer.Close()
start := &dto.SampleKey{
Fingerprint: fingerprintDTO,
Timestamp: indexable.EncodeTime(interval.OldestInclusive),
}
emission := make([]model.Samples, 0)
if encode, encodeErr := coding.NewProtocolBufferEncoder(start).Encode(); encodeErr == nil {
iterator.Seek(encode)
predicate := keyIsAtMostOld(interval.NewestInclusive)
for iterator = iterator; iterator.Valid(); iterator.Next() {
key := &dto.SampleKey{}
value := &dto.SampleValue{}
if keyUnmarshalErr := proto.Unmarshal(iterator.Key(), key); keyUnmarshalErr == nil {
if valueUnmarshalErr := proto.Unmarshal(iterator.Value(), value); valueUnmarshalErr == nil {
if fingerprintsEqual(fingerprintDTO, key.Fingerprint) {
// Wart
if predicate(key) {
emission = append(emission, model.Samples{
Value: model.SampleValue(*value.Value),
Timestamp: indexable.DecodeTime(key.Timestamp),
})
} else {
break
}
} else {
break
}
} else {
return nil, valueUnmarshalErr
}
} else {
return nil, keyUnmarshalErr
}
}
return emission, nil
} else {
log.Printf("Could not encode the start key: %q\n", encodeErr)
return nil, encodeErr
}
} else {
log.Printf("Could not acquire iterator: %q\n", iteratorErr)
return nil, iteratorErr
}
} else {
log.Printf("Could not create fingerprint for the metric: %q\n", fingerprintDTOErr)
return nil, fingerprintDTOErr
}
panic("unreachable")
}
func (l *LevelDBMetricPersistence) GetRangeValues(m *model.Metric, i *model.Interval, s *metric.StalenessPolicy) (v *model.SampleSet, err error) {
begin := time.Now()
defer func() {
duration := time.Now().Sub(begin)
recordOutcome(storageOperations, storageLatency, duration, err, map[string]string{operation: getRangeValues, result: success}, map[string]string{operation: getRangeValues, result: failure})
}()
d := model.MetricToDTO(m)
f, err := model.MessageToFingerprintDTO(d)
if err != nil {
return
}
k := &dto.SampleKey{
Fingerprint: f,
Timestamp: indexable.EncodeTime(i.OldestInclusive),
}
e, err := coding.NewProtocolBufferEncoder(k).Encode()
if err != nil {
return
}
iterator, closer, err := l.metricSamples.GetIterator()
if err != nil {
return
}
defer closer.Close()
iterator.Seek(e)
predicate := keyIsOlderThan(i.NewestInclusive)
for ; iterator.Valid(); iterator.Next() {
retrievedKey := &dto.SampleKey{}
retrievedKey, err = extractSampleKey(iterator)
if err != nil {
return
}
if predicate(retrievedKey) {
break
}
if !fingerprintsEqual(retrievedKey.Fingerprint, k.Fingerprint) {
break
}
retrievedValue, err := extractSampleValue(iterator)
if err != nil {
return nil, err
}
if v == nil {
v = &model.SampleSet{}
}
v.Values = append(v.Values, model.SamplePair{
Value: model.SampleValue(*retrievedValue.Value),
Timestamp: indexable.DecodeTime(retrievedKey.Timestamp),
})
}
return
}
func evalVectorBinop(opType BinOpType,
lhs model.SampleValue,
rhs model.SampleValue) (model.SampleValue, bool) {
switch opType {
case ADD:
return lhs + rhs, true
case SUB:
return lhs - rhs, true
case MUL:
return lhs * rhs, true
case DIV:
if rhs != 0 {
return lhs / rhs, true
} else {
return model.SampleValue(math.Inf(int(rhs))), true
}
case MOD:
if rhs != 0 {
return model.SampleValue(int(lhs) % int(rhs)), true
} else {
return model.SampleValue(math.Inf(int(rhs))), true
}
case EQ:
if lhs == rhs {
return lhs, true
} else {
return 0, false
}
case NE:
if lhs != rhs {
return lhs, true
} else {
return 0, false
}
case GT:
if lhs > rhs {
return lhs, true
} else {
return 0, false
}
case LT:
if lhs < rhs {
return lhs, true
} else {
return 0, false
}
case GE:
if lhs >= rhs {
return lhs, true
} else {
return 0, false
}
case LE:
if lhs <= rhs {
return lhs, true
} else {
return 0, false
}
case AND:
return lhs, true
case OR:
return lhs, true // TODO: implement OR
}
panic("Not all enum values enumerated in switch")
}
func evalScalarBinop(opType BinOpType,
lhs model.SampleValue,
rhs model.SampleValue) model.SampleValue {
switch opType {
case ADD:
return lhs + rhs
case SUB:
return lhs - rhs
case MUL:
return lhs * rhs
case DIV:
if rhs != 0 {
return lhs / rhs
} else {
return model.SampleValue(math.Inf(int(rhs)))
}
case MOD:
if rhs != 0 {
return model.SampleValue(int(lhs) % int(rhs))
} else {
return model.SampleValue(math.Inf(int(rhs)))
}
case EQ:
if lhs == rhs {
return 1
} else {
return 0
}
case NE:
if lhs != rhs {
return 1
} else {
return 0
}
case GT:
if lhs > rhs {
return 1
} else {
return 0
}
case LT:
if lhs < rhs {
return 1
} else {
return 0
}
case GE:
if lhs >= rhs {
return 1
} else {
return 0
}
case LE:
if lhs <= rhs {
return 1
} else {
return 0
}
}
panic("Not all enum values enumerated in switch")
}