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1. Goals & timeline

Goals: Who is this for?

• Platform engineer: install SDKs and colelctors, configure resources, metrics receivers, export pipelines
• Software engineer: use metrics APIs, write instrumentations pkgs
• End user: observe and monitor!

Goals: Vendor-netural

• OpenTelemetry mandates a strong separation of the API, the SDK, and exporters
• Decoupling these avoids vendor “lock-in”

Goals: Open-source & observaibility

• First-class support for open-source ecosystems

• Community devloped.

• Jaeger : Open Metrics

• ZIPKIN : StatsD

• OTEL : OTL

Goals: OpenCensus requirements are met

• SDK has programmable processing interfaces:
  • configurable aggregation
  • high performance
  • label set reduction
  • push and pull controls

Goals: Semantic conventions

• Standard instruments : e.g. http.server.duration
• Standard attribute names shared with tracing : e.g. http.method, http.host
• General naming patterns : e.g. .usage, .limit, .time, .io, and .duration
• Shared resources attributes : e.g. service.name, telemetry.sdk.language

2. Data & semantic models

Data model: Count timeseries

• measurements are sum totals : cumulative

Data model: Rate timeseries

• measurements are sum changes : delta

Data model: Gauge timeseries

• individual measurements : last value reporting

Data model: Histogram timeseries

• individual measurmenets : value and count are independent

Data model: Resources

• Resources: attributes describing the process or entity producing metric and trace data, distinct from per-timeseries attribute (a.k.a. “labels”, “tags”), an OpenTelemetry-wide concept

Data model: Support both cumulative & delta

• Temporality : can be Cumulative or Delta
• Instrument Temporality: describes whether a metric instrument input at the API is a change or a total
• Aggregation Temporality: describes whether output sums and counts are reset on collection

Data model: Why temporality?

• Instrument temporality? This choise makes the application stateless with respect to the SDK. Counter instruments capture transactional changes(delta), while SumObserver instruments capture current totals
• Aggregation temporality? Offers a trade between memory and reliability costs when configuring an export pipeline

Data model: Counter measurements

• In both Prometheus and Statsd APIs, the Counter instruments capture changes in a sum (delta).
• In OpenTelemetry:
  • Counter.Add() : increments are non-negative (monotonic)
  • UpDownCounter.Add() : positive and negative increments

Data model: “Gauge” is not for sums

• “Gauge” has similar but different meaning in Statsd and Prometheus
• The term is restircted in OpenTelemetry for conveying non-sum data

Data model: Individual measurements

• In both Prometheus and Statsd APIs, the Gauge and Histogram instruments capture individual measurements, have the same semantice type.
• OpenTelemetry creates a new distinction:
  • ValueRecorder.Record() : application calls the SDK
  • ValueObserver.Observe() : SDK calls the application.

Data model: Why restict Gauge?

• Prometheus and Statsd Gauges are sometimes used to capture cumulative sums, sometimes individual values, i.e., diffrerent semantic kinds of data.
• In OpenTelemetry:
  • SumObserver.Observe() : observe a monotonic sum
  • UpDownSumObserver.Observe() : observer a sum

— WIP

3. Export features & costs