Giedrius Statkevičius

github.com/prometheus/client_golang has a nice feature where a histogram can simultaneously be both an exponential (native) histogram and a classic (explicit bucket) one. Then, depending on what formats are accepted by the scraper, either both the native histogram version and the classical one are exposed, or just the classical one (text format). There has been an initiative to expose native histograms in text format, but it hasn’t yet solidified.

Unfortunately, in the OpenTelemetry standards, a histogram at any point can only have either explicit buckets or exponential buckets. From https://opentelemetry.io/docs/specs/otel/metrics/data-model/#model-details:

In this low-level metrics data model, a Timeseries is defined by an entity consisting of several metadata properties:

• Metric name
• Attributes (dimensions)
• Value type of the point (integer, floating point, etc)
• Unit of measurement

The primary data of each timeseries are ordered (timestamp, value) points, with one of the following value types:

• Counter (Monotonic, Cumulative)
• Gauge
• Histogram
• Exponential Histogram

Unfortunately, native (exponential) and classic (explicit bucket) expose different metric names even though they have the same metric family. An explicit bucket histogram exposes these extra metrics for each histogram:

• METRICNAME_bucket
• METRICNAME_sum
• METRICNAME_count

Whereas with a native histogram, you would only get METRICNAME. This means that when switching to exponential histograms, you will have to create what is called “a view” of each histogram that you want to convert. The code looks as follows:

package main

import (
	"go.opentelemetry.io/otel/sdk/instrumentation"
	"go.opentelemetry.io/otel/sdk/metric"
)

func main() {
	exponentialView := metric.NewView(
		metric.Instrument{
			Name:  "latency",
			Scope: instrumentation.Scope{Name: "http"},
		},
		metric.Stream{
		        Name: "exponentiallatency",
			Aggregation: metric.AggregationBase2ExponentialHistogram{
				MaxSize:  160,
				MaxScale: 20,
			},
		},
	)
	explicitView := metric.NewView(
		metric.Instrument{
			Name:  "latency",
			Scope: instrumentation.Scope{Name: "http"},
		},
		metric.Stream{
		        Name: "explicit",
			Aggregation: metric.AggregationExplicitBucketHistogram{
					Boundaries: []float64{0, 1, 2, 5, 10},
			},
		},
	)
	

	// The created view can then be registered with the OpenTelemetry metric
	// SDK using the WithView option.
	_ = metric.NewMeterProvider(
		metric.WithView(exponentialView, explicitView),
	)
}

(from https://pkg.go.dev/go.opentelemetry.io/otel/sdk/metric)

What is worse is that this API forces you to redefine the boundaries in the view, even though they could be (and are) defined near the histogram call, where they are created.

So, it seems to me like the suggestion would be to use just AggregationBase2ExponentialHistogram and then after deploying these changes, go rush to change all your alerting/recording rules + dashboards.