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Send sample arrays instead of single samples over channels.

pull/205/head
Julius Volz 12 years ago
parent
aaf15fe625
commit
d8110fcd9c
12 changed files with 207 additions and 306 deletions
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  1. 6
      main.go
  2. 25
      retrieval/format/processor0_0_1.go
  3. 193
      retrieval/format/processor0_0_1_test.go
  4. 29
      retrieval/format/processor0_0_2.go
  5. 183
      retrieval/format/processor0_0_2_test.go
  6. 7
      retrieval/format/result.go
  7. 4
      retrieval/target.go
  8. 7
      retrieval/target_test.go
  9. 10
      rules/manager.go
  10. 11
      rules/testdata.go
  11. 30
      storage/metric/tiered.go
  12. 8
      storage/metric/tiered_test.go

6
main.go
Unescape View File

@ -131,14 +131,12 @@ func main() {
select {
case scrapeResult := <-scrapeResults:
if scrapeResult.Err == nil {
ts.AppendSample(scrapeResult.Sample)
ts.AppendSamples(scrapeResult.Samples)
}
case ruleResult := <-ruleResults:
if ruleResult.Err == nil {
for _, sample := range ruleResult.Samples {
ts.AppendSample(sample)
}
ts.AppendSamples(ruleResult.Samples)
}
}
}

25
retrieval/format/processor0_0_1.go
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@ -75,6 +75,7 @@ func (p *processor001) Process(stream io.ReadCloser, timestamp time.Time, baseLa
}
// TODO(matt): This outer loop is a great basis for parallelization.
pendingSamples := model.Samples{}
for _, entity := range entities {
for _, value := range entity.Metric.Value {
metric := model.Metric{}
@ -95,19 +96,15 @@ func (p *processor001) Process(stream io.ReadCloser, timestamp time.Time, baseLa
sampleValue, ok := value.Value.(float64)
if !ok {
err = fmt.Errorf("Could not convert value from %s %s to float64.", entity, value)
results <- Result{Err: err}
continue
}
sample := model.Sample{
pendingSamples = append(pendingSamples, model.Sample{
Metric: metric,
Timestamp: timestamp,
Value: model.SampleValue(sampleValue),
}
results <- Result{
Err: err,
Sample: sample,
}
})
break
@ -115,6 +112,7 @@ func (p *processor001) Process(stream io.ReadCloser, timestamp time.Time, baseLa
sampleValue, ok := value.Value.(map[string]interface{})
if !ok {
err = fmt.Errorf("Could not convert value from %q to a map[string]interface{}.", value.Value)
results <- Result{Err: err}
continue
}
@ -122,6 +120,7 @@ func (p *processor001) Process(stream io.ReadCloser, timestamp time.Time, baseLa
individualValue, ok := percentileValue.(float64)
if !ok {
err = fmt.Errorf("Could not convert value from %q to a float64.", percentileValue)
results <- Result{Err: err}
continue
}
@ -133,16 +132,11 @@ func (p *processor001) Process(stream io.ReadCloser, timestamp time.Time, baseLa
childMetric[model.LabelName(percentile001)] = model.LabelValue(percentile)
sample := model.Sample{
pendingSamples = append(pendingSamples, model.Sample{
Metric: childMetric,
Timestamp: timestamp,
Value: model.SampleValue(individualValue),
}
results <- Result{
Err: err,
Sample: sample,
}
})
}
break
@ -150,6 +144,9 @@ func (p *processor001) Process(stream io.ReadCloser, timestamp time.Time, baseLa
}
}
}
if len(pendingSamples) > 0 {
results <- Result{Samples: pendingSamples}
}
return
}

193
retrieval/format/processor0_0_1_test.go
Unescape View File

@ -27,138 +27,91 @@ import (
func testProcessor001Process(t test.Tester) {
var scenarios = []struct {
in string
out []Result
out model.Samples
err error
}{
{
err: fmt.Errorf("unexpected end of JSON input"),
},
{
in: "[{\"baseLabels\":{\"name\":\"rpc_calls_total\"},\"docstring\":\"RPC calls.\",\"metric\":{\"type\":\"counter\",\"value\":[{\"labels\":{\"service\":\"zed\"},\"value\":25},{\"labels\":{\"service\":\"bar\"},\"value\":25},{\"labels\":{\"service\":\"foo\"},\"value\":25}]}},{\"baseLabels\":{\"name\":\"rpc_latency_microseconds\"},\"docstring\":\"RPC latency.\",\"metric\":{\"type\":\"histogram\",\"value\":[{\"labels\":{\"service\":\"foo\"},\"value\":{\"0.010000\":15.890724674774395,\"0.050000\":15.890724674774395,\"0.500000\":84.63044031436561,\"0.900000\":160.21100853053224,\"0.990000\":172.49828748957728}},{\"labels\":{\"service\":\"zed\"},\"value\":{\"0.010000\":0.0459814091918713,\"0.050000\":0.0459814091918713,\"0.500000\":0.6120456642749681,\"0.900000\":1.355915069887731,\"0.990000\":1.772733213161236}},{\"labels\":{\"service\":\"bar\"},\"value\":{\"0.010000\":78.48563317257356,\"0.050000\":78.48563317257356,\"0.500000\":97.31798360385088,\"0.900000\":109.89202084295582,\"0.990000\":109.99626121011262}}]}}]",
out: []Result{
{
Sample: model.Sample{
Metric: model.Metric{"service": "zed", model.MetricNameLabel: "rpc_calls_total"},
Value: 25,
},
in: `[{"baseLabels":{"name":"rpc_calls_total"},"docstring":"RPC calls.","metric":{"type":"counter","value":[{"labels":{"service":"zed"},"value":25},{"labels":{"service":"bar"},"value":25},{"labels":{"service":"foo"},"value":25}]}},{"baseLabels":{"name":"rpc_latency_microseconds"},"docstring":"RPC latency.","metric":{"type":"histogram","value":[{"labels":{"service":"foo"},"value":{"0.010000":15.890724674774395,"0.050000":15.890724674774395,"0.500000":84.63044031436561,"0.900000":160.21100853053224,"0.990000":172.49828748957728}},{"labels":{"service":"zed"},"value":{"0.010000":0.0459814091918713,"0.050000":0.0459814091918713,"0.500000":0.6120456642749681,"0.900000":1.355915069887731,"0.990000":1.772733213161236}},{"labels":{"service":"bar"},"value":{"0.010000":78.48563317257356,"0.050000":78.48563317257356,"0.500000":97.31798360385088,"0.900000":109.89202084295582,"0.990000":109.99626121011262}}]}}]`,
out: model.Samples{
model.Sample{
Metric: model.Metric{"service": "zed", model.MetricNameLabel: "rpc_calls_total"},
Value: 25,
},
{
Sample: model.Sample{
Metric: model.Metric{"service": "bar", model.MetricNameLabel: "rpc_calls_total"},
Value: 25,
},
model.Sample{
Metric: model.Metric{"service": "bar", model.MetricNameLabel: "rpc_calls_total"},
Value: 25,
},
{
Sample: model.Sample{
Metric: model.Metric{"service": "foo", model.MetricNameLabel: "rpc_calls_total"},
Value: 25,
},
model.Sample{
Metric: model.Metric{"service": "foo", model.MetricNameLabel: "rpc_calls_total"},
Value: 25,
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.010000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Value: 0.0459814091918713,
},
model.Sample{
Metric: model.Metric{"percentile": "0.010000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Value: 0.0459814091918713,
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.010000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Value: 78.48563317257356,
},
model.Sample{
Metric: model.Metric{"percentile": "0.010000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Value: 78.48563317257356,
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.010000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Value: 15.890724674774395,
},
model.Sample{
Metric: model.Metric{"percentile": "0.010000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Value: 15.890724674774395,
},
{
Sample: model.Sample{
model.Sample{
Metric: model.Metric{"percentile": "0.050000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Value: 0.0459814091918713,
},
Metric: model.Metric{"percentile": "0.050000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Value: 0.0459814091918713,
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.050000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Value: 78.48563317257356,
},
model.Sample{
Metric: model.Metric{"percentile": "0.050000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Value: 78.48563317257356,
},
{
Sample: model.Sample{
model.Sample{
Metric: model.Metric{"percentile": "0.050000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Value: 15.890724674774395,
},
Metric: model.Metric{"percentile": "0.050000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Value: 15.890724674774395,
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.500000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Value: 0.6120456642749681,
},
model.Sample{
Metric: model.Metric{"percentile": "0.500000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Value: 0.6120456642749681,
},
{
Sample: model.Sample{
model.Sample{
Metric: model.Metric{"percentile": "0.500000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Value: 97.31798360385088,
},
Metric: model.Metric{"percentile": "0.500000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Value: 97.31798360385088,
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.500000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Value: 84.63044031436561,
},
model.Sample{
Metric: model.Metric{"percentile": "0.500000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Value: 84.63044031436561,
},
{
Sample: model.Sample{
model.Sample{
Metric: model.Metric{"percentile": "0.900000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Value: 1.355915069887731,
},
Metric: model.Metric{"percentile": "0.900000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Value: 1.355915069887731,
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.900000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Value: 109.89202084295582,
},
model.Sample{
Metric: model.Metric{"percentile": "0.900000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Value: 109.89202084295582,
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.900000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Value: 160.21100853053224,
},
model.Sample{
Metric: model.Metric{"percentile": "0.900000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Value: 160.21100853053224,
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.990000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Value: 1.772733213161236,
},
model.Sample{
Metric: model.Metric{"percentile": "0.990000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Value: 1.772733213161236,
},
{
Sample: model.Sample{
model.Sample{
Metric: model.Metric{"percentile": "0.990000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Value: 109.99626121011262,
},
Metric: model.Metric{"percentile": "0.990000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Value: 109.99626121011262,
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.990000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Value: 172.49828748957728,
},
model.Sample{
Metric: model.Metric{"percentile": "0.990000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Value: 172.49828748957728,
},
},
},
@ -179,18 +132,14 @@ func testProcessor001Process(t test.Tester) {
continue
}
if scenario.err != nil && err != nil {
if scenario.err.Error() != err.Error() {
t.Errorf("%d. expected err of %s, got %s", i, scenario.err, err)
}
} else if scenario.err != err {
t.Errorf("%d. expected err of %s, got %s", i, scenario.err, err)
}
delivered := make([]Result, 0)
delivered := model.Samples{}
for len(inputChannel) != 0 {
delivered = append(delivered, <-inputChannel)
result := <-inputChannel
if result.Err != nil {
t.Fatalf("%d. expected no error, got: %s", i, result.Err)
}
delivered = append(delivered, result.Samples...)
}
if len(delivered) != len(scenario.out) {
@ -209,24 +158,20 @@ func testProcessor001Process(t test.Tester) {
found := false
for element := expectedElements.Front(); element != nil && found == false; element = element.Next() {
candidate := element.Value.(Result)
if !test.ErrorEqual(candidate.Err, actual.Err) {
continue
}
candidate := element.Value.(model.Sample)
if candidate.Sample.Value != actual.Sample.Value {
if candidate.Value != actual.Value {
continue
}
if len(candidate.Sample.Metric) != len(actual.Sample.Metric) {
if len(candidate.Metric) != len(actual.Metric) {
continue
}
labelsMatch := false
for key, value := range candidate.Sample.Metric {
actualValue, ok := actual.Sample.Metric[key]
for key, value := range candidate.Metric {
actualValue, ok := actual.Metric[key]
if !ok {
break
}
@ -246,7 +191,7 @@ func testProcessor001Process(t test.Tester) {
}
if !found {
t.Errorf("%d.%d. expected to find %s among candidate, absent", i, j, actual.Sample)
t.Errorf("%d.%d. expected to find %s among candidate, absent", i, j, actual)
}
}
}

29
retrieval/format/processor0_0_2.go
Unescape View File

@ -51,6 +51,7 @@ var Processor002 ProcessorFunc = func(stream io.ReadCloser, timestamp time.Time,
return err
}
pendingSamples := model.Samples{}
for _, entity := range entities {
entityLabels := baseLabels.Merge(LabelSet(entity.BaseLabels))
@ -68,13 +69,11 @@ var Processor002 ProcessorFunc = func(stream io.ReadCloser, timestamp time.Time,
for _, counter := range values {
labels := entityLabels.Merge(LabelSet(counter.Labels))
results <- Result{
Sample: model.Sample{
Metric: model.Metric(labels),
Timestamp: timestamp,
Value: counter.Value,
},
}
pendingSamples = append(pendingSamples, model.Sample{
Metric: model.Metric(labels),
Timestamp: timestamp,
Value: counter.Value,
})
}
case "histogram":
@ -92,13 +91,11 @@ var Processor002 ProcessorFunc = func(stream io.ReadCloser, timestamp time.Time,
labels := entityLabels.Merge(LabelSet(histogram.Labels))
labels[model.LabelName("percentile")] = model.LabelValue(percentile)
results <- Result{
Sample: model.Sample{
Metric: model.Metric(labels),
Timestamp: timestamp,
Value: value,
},
}
pendingSamples = append(pendingSamples, model.Sample{
Metric: model.Metric(labels),
Timestamp: timestamp,
Value: value,
})
}
}
@ -109,5 +106,9 @@ var Processor002 ProcessorFunc = func(stream io.ReadCloser, timestamp time.Time,
}
}
if len(pendingSamples) > 0 {
results <- Result{Samples: pendingSamples}
}
return nil
}

183
retrieval/format/processor0_0_2_test.go
Unescape View File

@ -27,7 +27,7 @@ import (
func testProcessor002Process(t test.Tester) {
var scenarios = []struct {
in string
out []Result
out model.Samples
err error
}{
{
@ -35,130 +35,83 @@ func testProcessor002Process(t test.Tester) {
},
{
in: `[{"baseLabels":{"name":"rpc_calls_total"},"docstring":"RPC calls.","metric":{"type":"counter","value":[{"labels":{"service":"zed"},"value":25},{"labels":{"service":"bar"},"value":25},{"labels":{"service":"foo"},"value":25}]}},{"baseLabels":{"name":"rpc_latency_microseconds"},"docstring":"RPC latency.","metric":{"type":"histogram","value":[{"labels":{"service":"foo"},"value":{"0.010000":15.890724674774395,"0.050000":15.890724674774395,"0.500000":84.63044031436561,"0.900000":160.21100853053224,"0.990000":172.49828748957728}},{"labels":{"service":"zed"},"value":{"0.010000":0.0459814091918713,"0.050000":0.0459814091918713,"0.500000":0.6120456642749681,"0.900000":1.355915069887731,"0.990000":1.772733213161236}},{"labels":{"service":"bar"},"value":{"0.010000":78.48563317257356,"0.050000":78.48563317257356,"0.500000":97.31798360385088,"0.900000":109.89202084295582,"0.990000":109.99626121011262}}]}}]`,
out: []Result{
{
Sample: model.Sample{
Metric: model.Metric{"service": "zed", model.MetricNameLabel: "rpc_calls_total"},
Value: 25,
},
out: model.Samples{
model.Sample{
Metric: model.Metric{"service": "zed", model.MetricNameLabel: "rpc_calls_total"},
Value: 25,
},
{
Sample: model.Sample{
Metric: model.Metric{"service": "bar", model.MetricNameLabel: "rpc_calls_total"},
Value: 25,
},
model.Sample{
Metric: model.Metric{"service": "bar", model.MetricNameLabel: "rpc_calls_total"},
Value: 25,
},
{
Sample: model.Sample{
Metric: model.Metric{"service": "foo", model.MetricNameLabel: "rpc_calls_total"},
Value: 25,
},
model.Sample{
Metric: model.Metric{"service": "foo", model.MetricNameLabel: "rpc_calls_total"},
Value: 25,
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.010000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Value: 0.0459814091918713,
},
model.Sample{
Metric: model.Metric{"percentile": "0.010000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Value: 0.0459814091918713,
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.010000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Value: 78.48563317257356,
},
model.Sample{
Metric: model.Metric{"percentile": "0.010000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Value: 78.48563317257356,
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.010000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Value: 15.890724674774395,
},
model.Sample{
Metric: model.Metric{"percentile": "0.010000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Value: 15.890724674774395,
},
{
Sample: model.Sample{
model.Sample{
Metric: model.Metric{"percentile": "0.050000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Value: 0.0459814091918713,
},
Metric: model.Metric{"percentile": "0.050000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Value: 0.0459814091918713,
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.050000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Value: 78.48563317257356,
},
model.Sample{
Metric: model.Metric{"percentile": "0.050000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Value: 78.48563317257356,
},
{
Sample: model.Sample{
model.Sample{
Metric: model.Metric{"percentile": "0.050000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Value: 15.890724674774395,
},
Metric: model.Metric{"percentile": "0.050000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Value: 15.890724674774395,
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.500000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Value: 0.6120456642749681,
},
model.Sample{
Metric: model.Metric{"percentile": "0.500000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Value: 0.6120456642749681,
},
{
Sample: model.Sample{
model.Sample{
Metric: model.Metric{"percentile": "0.500000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Value: 97.31798360385088,
},
Metric: model.Metric{"percentile": "0.500000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Value: 97.31798360385088,
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.500000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Value: 84.63044031436561,
},
model.Sample{
Metric: model.Metric{"percentile": "0.500000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Value: 84.63044031436561,
},
{
Sample: model.Sample{
model.Sample{
Metric: model.Metric{"percentile": "0.900000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Value: 1.355915069887731,
},
Metric: model.Metric{"percentile": "0.900000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Value: 1.355915069887731,
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.900000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Value: 109.89202084295582,
},
model.Sample{
Metric: model.Metric{"percentile": "0.900000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Value: 109.89202084295582,
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.900000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Value: 160.21100853053224,
},
model.Sample{
Metric: model.Metric{"percentile": "0.900000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Value: 160.21100853053224,
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.990000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Value: 1.772733213161236,
},
model.Sample{
Metric: model.Metric{"percentile": "0.990000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Value: 1.772733213161236,
},
{
Sample: model.Sample{
model.Sample{
Metric: model.Metric{"percentile": "0.990000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Value: 109.99626121011262,
},
Metric: model.Metric{"percentile": "0.990000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Value: 109.99626121011262,
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.990000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Value: 172.49828748957728,
},
model.Sample{
Metric: model.Metric{"percentile": "0.990000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Value: 172.49828748957728,
},
},
},
@ -179,10 +132,14 @@ func testProcessor002Process(t test.Tester) {
continue
}
delivered := make([]Result, 0)
delivered := model.Samples{}
for len(inputChannel) != 0 {
delivered = append(delivered, <-inputChannel)
result := <-inputChannel
if result.Err != nil {
t.Fatalf("%d. expected no error, got: %s", i, result.Err)
}
delivered = append(delivered, result.Samples...)
}
if len(delivered) != len(scenario.out) {
@ -201,24 +158,20 @@ func testProcessor002Process(t test.Tester) {
found := false
for element := expectedElements.Front(); element != nil && found == false; element = element.Next() {
candidate := element.Value.(Result)
if !test.ErrorEqual(candidate.Err, actual.Err) {
continue
}
candidate := element.Value.(model.Sample)
if candidate.Sample.Value != actual.Sample.Value {
if candidate.Value != actual.Value {
continue
}
if len(candidate.Sample.Metric) != len(actual.Sample.Metric) {
if len(candidate.Metric) != len(actual.Metric) {
continue
}
labelsMatch := false
for key, value := range candidate.Sample.Metric {
actualValue, ok := actual.Sample.Metric[key]
for key, value := range candidate.Metric {
actualValue, ok := actual.Metric[key]
if !ok {
break
}
@ -238,7 +191,7 @@ func testProcessor002Process(t test.Tester) {
}
if !found {
t.Errorf("%d.%d. expected to find %s among candidate, absent", i, j, actual.Sample)
t.Errorf("%d.%d. expected to find %s among candidate, absent", i, j, actual)
}
}
}

7
retrieval/format/result.go
Unescape View File

@ -17,9 +17,8 @@ import (
"github.com/prometheus/prometheus/model"
)
// Result encapsulates the outcome from processing a given sample from a
// source.
// Result encapsulates the outcome from processing samples from a source.
type Result struct {
Err error
Sample model.Sample
Err error
Samples model.Samples
}

4
retrieval/target.go
Unescape View File

@ -157,8 +157,8 @@ func (t *target) recordScrapeHealth(results chan format.Result, timestamp time.T
}
results <- format.Result{
Err: nil,
Sample: sample,
Err: nil,
Samples: model.Samples{sample},
}
}

7
retrieval/target_test.go
Unescape View File

@ -46,7 +46,12 @@ func TestTargetRecordScrapeHealth(t *testing.T) {
go testTarget.recordScrapeHealth(results, now, true)
result := <-results
actual := result.Sample
if len(result.Samples) != 1 {
t.Fatalf("Expected one sample, got %d", len(result.Samples))
}
actual := result.Samples[0]
expected := model.Sample{
Metric: model.Metric{
model.MetricNameLabel: model.ScrapeHealthMetricName,

10
rules/manager.go
Unescape View File

@ -15,7 +15,7 @@ package rules
import (
"github.com/prometheus/prometheus/config"
"github.com/prometheus/prometheus/rules/ast"
"github.com/prometheus/prometheus/model"
"log"
"sync"
"time"
@ -23,7 +23,7 @@ import (
type Result struct {
Err error // TODO propagate errors from rule evaluation.
Samples ast.Vector
Samples model.Samples
}
type RuleManager interface {
@ -73,8 +73,12 @@ func (m *ruleManager) runIteration(results chan *Result) {
wg.Add(1)
go func(rule Rule) {
vector, err := rule.Eval(now)
samples := model.Samples{}
for _, sample := range vector {
samples = append(samples, sample)
}
m.results <- &Result{
Samples: vector,
Samples: samples,
Err: err,
}
wg.Done()

11
rules/testdata.go
Unescape View File

@ -51,20 +51,19 @@ func getTestVectorFromTestMatrix(matrix ast.Matrix) ast.Vector {
return vector
}
func storeMatrix(storage metric.Storage, matrix ast.Matrix) error {
func storeMatrix(storage metric.Storage, matrix ast.Matrix) (err error) {
pendingSamples := model.Samples{}
for _, sampleSet := range matrix {
for _, sample := range sampleSet.Values {
err := storage.AppendSample(model.Sample{
pendingSamples = append(pendingSamples, model.Sample{
Metric: sampleSet.Metric,
Value: sample.Value,
Timestamp: sample.Timestamp,
})
if err != nil {
return err
}
}
}
return nil
err = storage.AppendSamples(pendingSamples)
return
}
var testMatrix = ast.Matrix{

30
storage/metric/tiered.go
Unescape View File

@ -30,8 +30,8 @@ import (
// tieredStorage both persists samples and generates materialized views for
// queries.
type tieredStorage struct {
appendToDiskQueue chan model.Sample
appendToMemoryQueue chan model.Sample
appendToDiskQueue chan model.Samples
appendToMemoryQueue chan model.Samples
diskFrontier *diskFrontier
diskStorage *LevelDBMetricPersistence
draining chan chan bool
@ -54,8 +54,8 @@ type viewJob struct {
// Provides a unified means for batch appending values into the datastore along
// with querying for values in an efficient way.
type Storage interface {
// Enqueues a Sample for storage.
AppendSample(model.Sample) error
// Enqueues Samples for storage.
AppendSamples(model.Samples) error
// Enqueus a ViewRequestBuilder for materialization, subject to a timeout.
MakeView(request ViewRequestBuilder, timeout time.Duration) (View, error)
// Starts serving requests.
@ -81,8 +81,8 @@ func NewTieredStorage(appendToMemoryQueueDepth, appendToDiskQueueDepth, viewQueu
}
storage = &tieredStorage{
appendToDiskQueue: make(chan model.Sample, appendToDiskQueueDepth),
appendToMemoryQueue: make(chan model.Sample, appendToMemoryQueueDepth),
appendToDiskQueue: make(chan model.Samples, appendToDiskQueueDepth),
appendToMemoryQueue: make(chan model.Samples, appendToMemoryQueueDepth),
diskStorage: diskStorage,
draining: make(chan chan bool),
flushMemoryInterval: flushMemoryInterval,
@ -94,7 +94,7 @@ func NewTieredStorage(appendToMemoryQueueDepth, appendToDiskQueueDepth, viewQueu
return
}
func (t tieredStorage) AppendSample(s model.Sample) (err error) {
func (t tieredStorage) AppendSamples(s model.Samples) (err error) {
if len(t.draining) > 0 {
return fmt.Errorf("Storage is in the process of draining.")
}
@ -218,7 +218,7 @@ func (t *tieredStorage) writeMemory() {
pendingLength := len(t.appendToMemoryQueue)
for i := 0; i < pendingLength; i++ {
t.memoryArena.AppendSample(<-t.appendToMemoryQueue)
t.memoryArena.AppendSamples(<-t.appendToMemoryQueue)
}
}
@ -248,7 +248,7 @@ func (t tieredStorage) flush() (err error) {
}
type memoryToDiskFlusher struct {
toDiskQueue chan model.Sample
toDiskQueue chan model.Samples
disk MetricPersistence
olderThan time.Time
valuesAccepted int
@ -294,10 +294,12 @@ func (f memoryToDiskFlusherVisitor) Operate(key, value interface{}) (err *storag
f.flusher.Flush()
}
f.flusher.toDiskQueue <- model.Sample{
Metric: f.stream.metric,
Timestamp: recordTime,
Value: recordValue,
f.flusher.toDiskQueue <- model.Samples{
model.Sample{
Metric: f.stream.metric,
Timestamp: recordTime,
Value: recordValue,
},
}
f.stream.values.Delete(skipListTime(recordTime))
@ -318,7 +320,7 @@ func (f *memoryToDiskFlusher) Flush() {
length := len(f.toDiskQueue)
samples := model.Samples{}
for i := 0; i < length; i++ {
samples = append(samples, <-f.toDiskQueue)
samples = append(samples, <-f.toDiskQueue...)
}
f.disk.AppendSamples(samples)
}

8
storage/metric/tiered_test.go
Unescape View File

@ -340,11 +340,9 @@ func testMakeView(t test.Tester, flushToDisk bool) {
for i, scenario := range scenarios {
tiered, closer := NewTestTieredStorage(t)
for j, datum := range scenario.data {
err := tiered.AppendSample(datum)
if err != nil {
t.Fatalf("%d.%d. failed to add fixture data: %s", i, j, err)
}
err := tiered.AppendSamples(scenario.data)
if err != nil {
t.Fatalf("%d. failed to add fixture data: %s", i, err)
}
if flushToDisk {

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