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Prepend "exporter_" to labels that already exist in exported metrics. 

If the metrics exported by a process already contain any of a target's
base labels (such as "job" or "instance", but also any manually assigned
target-group label), don't overwrite that label, but instead add a new
label consisting of the original label name prepended with "exporter_".
This is to accomodate intermediate exporter jobs, which might indicate
e.g. the jobs and instances for which they are exporting data.
pull/277/head
Julius Volz 12 years ago
parent
d828b9c835
commit
dcfd09c801
8 changed files with 182 additions and 73 deletions
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  1. 3
      model/labelname.go
  2. 0
      retrieval/format/fixtures/empty.json
  3. 79
      retrieval/format/fixtures/test0_0_1-0_0_2.json
  4. 21
      retrieval/format/processor.go
  5. 22
      retrieval/format/processor0_0_1.go
  6. 60
      retrieval/format/processor0_0_1_test.go
  7. 10
      retrieval/format/processor0_0_2.go
  8. 60
      retrieval/format/processor0_0_2_test.go

3
model/labelname.go
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@ -20,6 +20,9 @@ const (
JobLabel = LabelName("job")
// The label name indicating the instance from which a timeseries was scraped.
InstanceLabel = LabelName("instance")
// The label name prefix to prepend if a synthetic label is already present
// in the exported metrics.
ExporterLabelPrefix = LabelName("exporter_")
// The metric name for the synthetic health variable.
ScrapeHealthMetricName = LabelValue("up")
// The metric name for synthetic alert timeseries.

0
retrieval/format/fixtures/empty.json
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79
retrieval/format/fixtures/test0_0_1-0_0_2.json
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@ -0,0 +1,79 @@
[
{
"baseLabels": {
"name": "rpc_calls_total",
"job": "batch_job"
},
"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
}
}
]
}
}
]

21
retrieval/format/processor.go
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@ -47,3 +47,24 @@ func LabelSet(labels map[string]string) model.LabelSet {
return labelset
}
// Helper function to merge a target's base labels ontop of the labels of an
// exported sample. If a label is already defined in the exported sample, we
// assume that we are scraping an intermediate exporter and attach
// "exporter_"-prefixes to Prometheus' own base labels.
func mergeTargetLabels(entityLabels, targetLabels model.LabelSet) model.LabelSet {
result := model.LabelSet{}
for label, value := range entityLabels {
result[label] = value
}
for label, labelValue := range targetLabels {
if _, exists := result[label]; exists {
result[model.ExporterLabelPrefix+label] = labelValue
} else {
result[label] = labelValue
}
}
return result
}

22
retrieval/format/processor0_0_1.go
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@ -77,18 +77,8 @@ func (p *processor001) Process(stream io.ReadCloser, timestamp time.Time, baseLa
pendingSamples := model.Samples{}
for _, entity := range entities {
for _, value := range entity.Metric.Value {
metric := model.Metric{}
for label, labelValue := range baseLabels {
metric[label] = labelValue
}
for label, labelValue := range entity.BaseLabels {
metric[model.LabelName(label)] = model.LabelValue(labelValue)
}
for label, labelValue := range value.Labels {
metric[model.LabelName(label)] = model.LabelValue(labelValue)
}
entityLabels := LabelSet(entity.BaseLabels).Merge(LabelSet(value.Labels))
labels := mergeTargetLabels(entityLabels, baseLabels)
switch entity.Metric.MetricType {
case gauge001, counter001:
@ -100,7 +90,7 @@ func (p *processor001) Process(stream io.ReadCloser, timestamp time.Time, baseLa
}
pendingSamples = append(pendingSamples, model.Sample{
Metric: metric,
Metric: model.Metric(labels),
Timestamp: timestamp,
Value: model.SampleValue(sampleValue),
})
@ -123,16 +113,16 @@ func (p *processor001) Process(stream io.ReadCloser, timestamp time.Time, baseLa
continue
}
childMetric := make(map[model.LabelName]model.LabelValue, len(metric)+1)
childMetric := make(map[model.LabelName]model.LabelValue, len(labels)+1)
for k, v := range metric {
for k, v := range labels {
childMetric[k] = v
}
childMetric[model.LabelName(percentile001)] = model.LabelValue(percentile)
pendingSamples = append(pendingSamples, model.Sample{
Metric: childMetric,
Metric: model.Metric(childMetric),
Timestamp: timestamp,
Value: model.SampleValue(individualValue),
})

60
retrieval/format/processor0_0_1_test.go
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@ -18,99 +18,104 @@ import (
"fmt"
"github.com/prometheus/prometheus/model"
"github.com/prometheus/prometheus/utility/test"
"io/ioutil"
"strings"
"os"
"path"
"testing"
"time"
)
func testProcessor001Process(t test.Tester) {
var scenarios = []struct {
in string
out model.Samples
err error
in string
baseLabels model.LabelSet
out model.Samples
err error
}{
{
in: "empty.json",
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}}]}}]`,
in: "test0_0_1-0_0_2.json",
baseLabels: model.LabelSet{
model.JobLabel: "batch_exporter",
},
out: model.Samples{
model.Sample{
Metric: model.Metric{"service": "zed", model.MetricNameLabel: "rpc_calls_total"},
Metric: model.Metric{"service": "zed", model.MetricNameLabel: "rpc_calls_total", "job": "batch_job", "exporter_job": "batch_exporter"},
Value: 25,
},
model.Sample{
Metric: model.Metric{"service": "bar", model.MetricNameLabel: "rpc_calls_total"},
Metric: model.Metric{"service": "bar", model.MetricNameLabel: "rpc_calls_total", "job": "batch_job", "exporter_job": "batch_exporter"},
Value: 25,
},
model.Sample{
Metric: model.Metric{"service": "foo", model.MetricNameLabel: "rpc_calls_total"},
Metric: model.Metric{"service": "foo", model.MetricNameLabel: "rpc_calls_total", "job": "batch_job", "exporter_job": "batch_exporter"},
Value: 25,
},
model.Sample{
Metric: model.Metric{"percentile": "0.010000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Metric: model.Metric{"percentile": "0.010000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed", "job": "batch_exporter"},
Value: 0.0459814091918713,
},
model.Sample{
Metric: model.Metric{"percentile": "0.010000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Metric: model.Metric{"percentile": "0.010000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar", "job": "batch_exporter"},
Value: 78.48563317257356,
},
model.Sample{
Metric: model.Metric{"percentile": "0.010000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Metric: model.Metric{"percentile": "0.010000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo", "job": "batch_exporter"},
Value: 15.890724674774395,
},
model.Sample{
Metric: model.Metric{"percentile": "0.050000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Metric: model.Metric{"percentile": "0.050000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed", "job": "batch_exporter"},
Value: 0.0459814091918713,
},
model.Sample{
Metric: model.Metric{"percentile": "0.050000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Metric: model.Metric{"percentile": "0.050000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar", "job": "batch_exporter"},
Value: 78.48563317257356,
},
model.Sample{
Metric: model.Metric{"percentile": "0.050000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Metric: model.Metric{"percentile": "0.050000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo", "job": "batch_exporter"},
Value: 15.890724674774395,
},
model.Sample{
Metric: model.Metric{"percentile": "0.500000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Metric: model.Metric{"percentile": "0.500000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed", "job": "batch_exporter"},
Value: 0.6120456642749681,
},
model.Sample{
Metric: model.Metric{"percentile": "0.500000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Metric: model.Metric{"percentile": "0.500000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar", "job": "batch_exporter"},
Value: 97.31798360385088,
},
model.Sample{
Metric: model.Metric{"percentile": "0.500000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Metric: model.Metric{"percentile": "0.500000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo", "job": "batch_exporter"},
Value: 84.63044031436561,
},
model.Sample{
Metric: model.Metric{"percentile": "0.900000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Metric: model.Metric{"percentile": "0.900000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed", "job": "batch_exporter"},
Value: 1.355915069887731,
},
model.Sample{
Metric: model.Metric{"percentile": "0.900000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Metric: model.Metric{"percentile": "0.900000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar", "job": "batch_exporter"},
Value: 109.89202084295582,
},
model.Sample{
Metric: model.Metric{"percentile": "0.900000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Metric: model.Metric{"percentile": "0.900000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo", "job": "batch_exporter"},
Value: 160.21100853053224,
},
model.Sample{
Metric: model.Metric{"percentile": "0.990000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Metric: model.Metric{"percentile": "0.990000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed", "job": "batch_exporter"},
Value: 1.772733213161236,
},
model.Sample{
Metric: model.Metric{"percentile": "0.990000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Metric: model.Metric{"percentile": "0.990000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar", "job": "batch_exporter"},
Value: 109.99626121011262,
},
model.Sample{
Metric: model.Metric{"percentile": "0.990000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Metric: model.Metric{"percentile": "0.990000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo", "job": "batch_exporter"},
Value: 172.49828748957728,
},
},
@ -124,9 +129,12 @@ func testProcessor001Process(t test.Tester) {
close(c)
}(inputChannel)
reader := strings.NewReader(scenario.in)
reader, err := os.Open(path.Join("fixtures", scenario.in))
if err != nil {
t.Fatalf("%d. couldn't open scenario input file %s: %s", scenario.in, err)
}
err := Processor001.Process(ioutil.NopCloser(reader), time.Now(), model.LabelSet{}, inputChannel)
err = Processor001.Process(reader, time.Now(), scenario.baseLabels, inputChannel)
if !test.ErrorEqual(scenario.err, err) {
t.Errorf("%d. expected err of %s, got %s", i, scenario.err, err)
continue

10
retrieval/format/processor0_0_2.go
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@ -53,8 +53,6 @@ var Processor002 ProcessorFunc = func(stream io.ReadCloser, timestamp time.Time,
pendingSamples := model.Samples{}
for _, entity := range entities {
entityLabels := baseLabels.Merge(LabelSet(entity.BaseLabels))
switch entity.Metric.Type {
case "counter", "gauge":
var values []counter
@ -67,7 +65,8 @@ var Processor002 ProcessorFunc = func(stream io.ReadCloser, timestamp time.Time,
}
for _, counter := range values {
labels := entityLabels.Merge(LabelSet(counter.Labels))
entityLabels := LabelSet(entity.BaseLabels).Merge(LabelSet(counter.Labels))
labels := mergeTargetLabels(entityLabels, baseLabels)
pendingSamples = append(pendingSamples, model.Sample{
Metric: model.Metric(labels),
@ -88,8 +87,9 @@ var Processor002 ProcessorFunc = func(stream io.ReadCloser, timestamp time.Time,
for _, histogram := range values {
for percentile, value := range histogram.Values {
labels := entityLabels.Merge(LabelSet(histogram.Labels))
labels[model.LabelName("percentile")] = model.LabelValue(percentile)
entityLabels := LabelSet(entity.BaseLabels).Merge(LabelSet(histogram.Labels))
entityLabels[model.LabelName("percentile")] = model.LabelValue(percentile)
labels := mergeTargetLabels(entityLabels, baseLabels)
pendingSamples = append(pendingSamples, model.Sample{
Metric: model.Metric(labels),

60
retrieval/format/processor0_0_2_test.go
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@ -18,99 +18,104 @@ import (
"fmt"
"github.com/prometheus/prometheus/model"
"github.com/prometheus/prometheus/utility/test"
"io/ioutil"
"strings"
"os"
"path"
"testing"
"time"
)
func testProcessor002Process(t test.Tester) {
var scenarios = []struct {
in string
out model.Samples
err error
in string
baseLabels model.LabelSet
out model.Samples
err error
}{
{
in: "empty.json",
err: fmt.Errorf("EOF"),
},
{
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}}]}}]`,
in: "test0_0_1-0_0_2.json",
baseLabels: model.LabelSet{
model.JobLabel: "batch_exporter",
},
out: model.Samples{
model.Sample{
Metric: model.Metric{"service": "zed", model.MetricNameLabel: "rpc_calls_total"},
Metric: model.Metric{"service": "zed", model.MetricNameLabel: "rpc_calls_total", "job": "batch_job", "exporter_job": "batch_exporter"},
Value: 25,
},
model.Sample{
Metric: model.Metric{"service": "bar", model.MetricNameLabel: "rpc_calls_total"},
Metric: model.Metric{"service": "bar", model.MetricNameLabel: "rpc_calls_total", "job": "batch_job", "exporter_job": "batch_exporter"},
Value: 25,
},
model.Sample{
Metric: model.Metric{"service": "foo", model.MetricNameLabel: "rpc_calls_total"},
Metric: model.Metric{"service": "foo", model.MetricNameLabel: "rpc_calls_total", "job": "batch_job", "exporter_job": "batch_exporter"},
Value: 25,
},
model.Sample{
Metric: model.Metric{"percentile": "0.010000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Metric: model.Metric{"percentile": "0.010000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed", "job": "batch_exporter"},
Value: 0.0459814091918713,
},
model.Sample{
Metric: model.Metric{"percentile": "0.010000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Metric: model.Metric{"percentile": "0.010000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar", "job": "batch_exporter"},
Value: 78.48563317257356,
},
model.Sample{
Metric: model.Metric{"percentile": "0.010000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Metric: model.Metric{"percentile": "0.010000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo", "job": "batch_exporter"},
Value: 15.890724674774395,
},
model.Sample{
Metric: model.Metric{"percentile": "0.050000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Metric: model.Metric{"percentile": "0.050000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed", "job": "batch_exporter"},
Value: 0.0459814091918713,
},
model.Sample{
Metric: model.Metric{"percentile": "0.050000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Metric: model.Metric{"percentile": "0.050000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar", "job": "batch_exporter"},
Value: 78.48563317257356,
},
model.Sample{
Metric: model.Metric{"percentile": "0.050000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Metric: model.Metric{"percentile": "0.050000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo", "job": "batch_exporter"},
Value: 15.890724674774395,
},
model.Sample{
Metric: model.Metric{"percentile": "0.500000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Metric: model.Metric{"percentile": "0.500000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed", "job": "batch_exporter"},
Value: 0.6120456642749681,
},
model.Sample{
Metric: model.Metric{"percentile": "0.500000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Metric: model.Metric{"percentile": "0.500000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar", "job": "batch_exporter"},
Value: 97.31798360385088,
},
model.Sample{
Metric: model.Metric{"percentile": "0.500000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Metric: model.Metric{"percentile": "0.500000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo", "job": "batch_exporter"},
Value: 84.63044031436561,
},
model.Sample{
Metric: model.Metric{"percentile": "0.900000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Metric: model.Metric{"percentile": "0.900000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed", "job": "batch_exporter"},
Value: 1.355915069887731,
},
model.Sample{
Metric: model.Metric{"percentile": "0.900000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Metric: model.Metric{"percentile": "0.900000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar", "job": "batch_exporter"},
Value: 109.89202084295582,
},
model.Sample{
Metric: model.Metric{"percentile": "0.900000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Metric: model.Metric{"percentile": "0.900000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo", "job": "batch_exporter"},
Value: 160.21100853053224,
},
model.Sample{
Metric: model.Metric{"percentile": "0.990000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Metric: model.Metric{"percentile": "0.990000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed", "job": "batch_exporter"},
Value: 1.772733213161236,
},
model.Sample{
Metric: model.Metric{"percentile": "0.990000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Metric: model.Metric{"percentile": "0.990000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar", "job": "batch_exporter"},
Value: 109.99626121011262,
},
model.Sample{
Metric: model.Metric{"percentile": "0.990000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Metric: model.Metric{"percentile": "0.990000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo", "job": "batch_exporter"},
Value: 172.49828748957728,
},
},
@ -124,9 +129,12 @@ func testProcessor002Process(t test.Tester) {
close(c)
}(inputChannel)
reader := strings.NewReader(scenario.in)
reader, err := os.Open(path.Join("fixtures", scenario.in))
if err != nil {
t.Fatalf("%d. couldn't open scenario input file %s: %s", scenario.in, err)
}
err := Processor002.Process(ioutil.NopCloser(reader), time.Now(), model.LabelSet{}, inputChannel)
err = Processor002.Process(reader, time.Now(), scenario.baseLabels, inputChannel)
if !test.ErrorEqual(scenario.err, err) {
t.Errorf("%d. expected err of %s, got %s", i, scenario.err, err)
continue

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