Merge pull request #13216 from prometheus/beorn7/doc

Update “conventional histogram” → “classic histogram”

docs/feature_flags.md

@@ -119,20 +119,19 @@ also experimental) protobuf parser, through which _all_ metrics are ingested
 (i.e. not only native histograms). Prometheus will try to negotiate the
 protobuf format first. The instrumented target needs to support the protobuf
 format, too, _and_ it needs to expose native histograms. The protobuf format
-allows to expose conventional and native histograms side by side. With this
-feature flag disabled, Prometheus will continue to parse the conventional
-histogram (albeit via the text format). With this flag enabled, Prometheus will
-still ingest those conventional histograms that do not come with a
-corresponding native histogram. However, if a native histogram is present,
-Prometheus will ignore the corresponding conventional histogram, with the
-notable exception of exemplars, which are always ingested. To keep the
-conventional histograms as well, enable `scrape_classic_histograms` in the
-scrape job.
+allows to expose classic and native histograms side by side. With this feature
+flag disabled, Prometheus will continue to parse the classic histogram (albeit
+via the text format). With this flag enabled, Prometheus will still ingest
+those classic histograms that do not come with a corresponding native
+histogram. However, if a native histogram is present, Prometheus will ignore
+the corresponding classic histogram, with the notable exception of exemplars,
+which are always ingested. To keep the classic histograms as well, enable
+`scrape_classic_histograms` in the scrape job.
 
 _Note about the format of `le` and `quantile` label values:_
 
 In certain situations, the protobuf parsing changes the number formatting of
-the `le` labels of conventional histograms and the `quantile` labels of
+the `le` labels of classic histograms and the `quantile` labels of
 summaries. Typically, this happens if the scraped target is instrumented with
 [client_golang](https://github.com/prometheus/client_golang) provided that
 [promhttp.HandlerOpts.EnableOpenMetrics](https://pkg.go.dev/github.com/prometheus/client_golang/prometheus/promhttp#HandlerOpts)

docs/querying/functions.md

@@ -238,41 +238,41 @@ boundaries are inclusive or exclusive.
 ## `histogram_quantile()`
 
 `histogram_quantile(φ scalar, b instant-vector)` calculates the φ-quantile (0 ≤
-φ ≤ 1) from a [conventional
+φ ≤ 1) from a [classic
 histogram](https://prometheus.io/docs/concepts/metric_types/#histogram) or from
 a native histogram. (See [histograms and
 summaries](https://prometheus.io/docs/practices/histograms) for a detailed
-explanation of φ-quantiles and the usage of the (conventional) histogram metric
+explanation of φ-quantiles and the usage of the (classic) histogram metric
 type in general.)
 
 _Note that native histograms are an experimental feature. The behavior of this
 function when dealing with native histograms may change in future versions of
 Prometheus._
 
-The conventional float samples in `b` are considered the counts of observations
-in each bucket of one or more conventional histograms. Each float sample must
-have a label `le` where the label value denotes the inclusive upper bound of
-the bucket. (Float samples without such a label are silently ignored.) The
-other labels and the metric name are used to identify the buckets belonging to
-each conventional histogram. The [histogram metric
+The float samples in `b` are considered the counts of observations in each
+bucket of one or more classic histograms. Each float sample must have a label
+`le` where the label value denotes the inclusive upper bound of the bucket.
+(Float samples without such a label are silently ignored.) The other labels and
+the metric name are used to identify the buckets belonging to each classic
+histogram. The [histogram metric
 type](https://prometheus.io/docs/concepts/metric_types/#histogram)
 automatically provides time series with the `_bucket` suffix and the
 appropriate labels.
 
 The native histogram samples in `b` are treated each individually as a separate
 histogram to calculate the quantile from.
 
-As long as no naming collisions arise, `b` may contain a mix of conventional
+As long as no naming collisions arise, `b` may contain a mix of classic
 and native histograms.
 
 Use the `rate()` function to specify the time window for the quantile
 calculation.
 
 Example: A histogram metric is called `http_request_duration_seconds` (and
-therefore the metric name for the buckets of a conventional histogram is
+therefore the metric name for the buckets of a classic histogram is
 `http_request_duration_seconds_bucket`). To calculate the 90th percentile of request
 durations over the last 10m, use the following expression in case
-`http_request_duration_seconds` is a conventional histogram:
+`http_request_duration_seconds` is a classic histogram:
 
     histogram_quantile(0.9, rate(http_request_duration_seconds_bucket[10m]))
 
@@ -283,17 +283,17 @@ For a native histogram, use the following expression instead:
 The quantile is calculated for each label combination in
 `http_request_duration_seconds`. To aggregate, use the `sum()` aggregator
 around the `rate()` function. Since the `le` label is required by
-`histogram_quantile()` to deal with conventional histograms, it has to be
+`histogram_quantile()` to deal with classic histograms, it has to be
 included in the `by` clause. The following expression aggregates the 90th
-percentile by `job` for conventional histograms:
+percentile by `job` for classic histograms:
 
     histogram_quantile(0.9, sum by (job, le) (rate(http_request_duration_seconds_bucket[10m])))
 
 When aggregating native histograms, the expression simplifies to:
 
     histogram_quantile(0.9, sum by (job) (rate(http_request_duration_seconds[10m])))
 
-To aggregate all conventional histograms, specify only the `le` label:
+To aggregate all classic histograms, specify only the `le` label:
 
     histogram_quantile(0.9, sum by (le) (rate(http_request_duration_seconds_bucket[10m])))
 
@@ -307,7 +307,7 @@ assuming a linear distribution within a bucket.
 If `b` has 0 observations, `NaN` is returned. For φ < 0, `-Inf` is
 returned. For φ > 1, `+Inf` is returned. For φ = `NaN`, `NaN` is returned.
 
-The following is only relevant for conventional histograms: If `b` contains
+The following is only relevant for classic histograms: If `b` contains
 fewer than two buckets, `NaN` is returned. The highest bucket must have an
 upper bound of `+Inf`. (Otherwise, `NaN` is returned.) If a quantile is located
 in the highest bucket, the upper bound of the second highest bucket is

prompb/io/prometheus/client/metrics.proto

@@ -76,7 +76,7 @@ message Histogram {
   uint64 sample_count       = 1;
   double sample_count_float = 4; // Overrides sample_count if > 0.
   double sample_sum         = 2;
-  // Buckets for the conventional histogram.
+  // Buckets for the classic histogram.
   repeated Bucket bucket = 3 [(gogoproto.nullable) = false]; // Ordered in increasing order of upper_bound, +Inf bucket is optional.
 
   google.protobuf.Timestamp created_timestamp = 15;

promql/engine.go

@@ -1074,7 +1074,7 @@ type EvalNodeHelper struct {
 	// Caches.
 	// DropMetricName and label_*.
 	Dmn map[uint64]labels.Labels
-	// funcHistogramQuantile for conventional histograms.
+	// funcHistogramQuantile for classic histograms.
 	signatureToMetricWithBuckets map[string]*metricWithBuckets
 	// label_replace.
 	regex *regexp.Regexp

promql/functions.go

@@ -1176,7 +1176,7 @@ func funcHistogramQuantile(vals []parser.Value, args parser.Expressions, enh *Ev
 	var histogramSamples []Sample
 
 	for _, sample := range inVec {
-		// We are only looking for conventional buckets here. Remember
+		// We are only looking for classic buckets here. Remember
 		// the histograms for later treatment.
 		if sample.H != nil {
 			histogramSamples = append(histogramSamples, sample)
@@ -1207,10 +1207,10 @@ func funcHistogramQuantile(vals []parser.Value, args parser.Expressions, enh *Ev
 	// Now deal with the histograms.
 	for _, sample := range histogramSamples {
 		// We have to reconstruct the exact same signature as above for
-		// a conventional histogram, just ignoring any le label.
+		// a classic histogram, just ignoring any le label.
 		enh.lblBuf = sample.Metric.Bytes(enh.lblBuf)
 		if mb, ok := enh.signatureToMetricWithBuckets[string(enh.lblBuf)]; ok && len(mb.buckets) > 0 {
-			// At this data point, we have conventional histogram
+			// At this data point, we have classic histogram
 			// buckets and a native histogram with the same name and
 			// labels. Do not evaluate anything.
 			annos.Add(annotations.NewMixedClassicNativeHistogramsWarning(sample.Metric.Get(labels.MetricName), args[1].PositionRange()))