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dcfd09c801
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.
115 lines
3.2 KiB
Go
115 lines
3.2 KiB
Go
// Copyright 2013 Prometheus Team
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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package format
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import (
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"encoding/json"
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"fmt"
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"github.com/prometheus/prometheus/model"
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"io"
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"time"
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)
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// Processor for telemetry schema version 0.0.2.
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var Processor002 ProcessorFunc = func(stream io.ReadCloser, timestamp time.Time, baseLabels model.LabelSet, results chan Result) error {
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// container for telemetry data
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var entities []struct {
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BaseLabels map[string]string `json:"baseLabels"`
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Docstring string `json:"docstring"`
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Metric struct {
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Type string `json:"type"`
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Values json.RawMessage `json:"value"`
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} `json:"metric"`
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}
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// concrete type for histogram values
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type histogram struct {
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Labels map[string]string `json:"labels"`
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Values map[string]model.SampleValue `json:"value"`
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}
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// concrete type for counter and gauge values
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type counter struct {
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Labels map[string]string `json:"labels"`
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Value model.SampleValue `json:"value"`
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}
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defer stream.Close()
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if err := json.NewDecoder(stream).Decode(&entities); err != nil {
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return err
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}
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pendingSamples := model.Samples{}
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for _, entity := range entities {
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switch entity.Metric.Type {
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case "counter", "gauge":
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var values []counter
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if err := json.Unmarshal(entity.Metric.Values, &values); err != nil {
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results <- Result{
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Err: fmt.Errorf("Could not extract %s value: %s", entity.Metric.Type, err),
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}
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continue
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}
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for _, counter := range values {
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entityLabels := LabelSet(entity.BaseLabels).Merge(LabelSet(counter.Labels))
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labels := mergeTargetLabels(entityLabels, baseLabels)
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pendingSamples = append(pendingSamples, model.Sample{
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Metric: model.Metric(labels),
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Timestamp: timestamp,
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Value: counter.Value,
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})
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}
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case "histogram":
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var values []histogram
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if err := json.Unmarshal(entity.Metric.Values, &values); err != nil {
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results <- Result{
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Err: fmt.Errorf("Could not extract %s value: %s", entity.Metric.Type, err),
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}
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continue
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}
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for _, histogram := range values {
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for percentile, value := range histogram.Values {
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entityLabels := LabelSet(entity.BaseLabels).Merge(LabelSet(histogram.Labels))
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entityLabels[model.LabelName("percentile")] = model.LabelValue(percentile)
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labels := mergeTargetLabels(entityLabels, baseLabels)
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pendingSamples = append(pendingSamples, model.Sample{
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Metric: model.Metric(labels),
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Timestamp: timestamp,
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Value: value,
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})
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}
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}
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default:
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results <- Result{
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Err: fmt.Errorf("Unknown metric type %q", entity.Metric.Type),
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}
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}
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}
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if len(pendingSamples) > 0 {
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results <- Result{Samples: pendingSamples}
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}
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return nil
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}
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