How do Micrometer Observation, metrics, and traces fit together?

Explains one observation lifecycle feeding metrics, tracing, and context through registered handlers.

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TL;DR

A Micrometer Observation represents an operation such as an HTTP request or service call. It is created through an ObservationRegistry, started, optionally scoped, and then stopped with an outcome. Registered handlers react to that lifecycle; one handler can record metrics while another creates tracing spans. Spring Boot configures much of this instrumentation automatically when Actuator and the relevant registry or tracing bridge are present.

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ObservationRegistry, Observation lifecycle, handlers, conventions, metrics, tracing, and instrumentation boundaries.

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Overview
Micrometer Observation provides one instrumentation lifecycle that different handlers can turn into metrics, traces, logs, or other signals.
A Micrometer Observation represents an operation such as an HTTP request or service call. It is created through an ObservationRegistry, started, optionally scoped, and then stopped with an outcome. Registered handlers react to that lifecycle; one handler can record metrics while another creates tracing spans. Spring Boot configures much of this instrumentation automatically when Actuator and the relevant registry or tracing bridge are present.
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An observation carries a name, bounded low-cardinality key-values suitable for metrics, and high-cardinality context that may be useful only for traces. Conventions keep names and tags consistent across instrumentation. I prefer existing framework instrumentation before adding custom observations, then add one around an important business or dependency boundary where the standard HTTP or repository signals cannot explain behavior.

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Details

Treating Observation as only a timer.
Creating a span for every private method.
Assuming automatic instrumentation covers business outcomes.

How do Micrometer Observation, metrics, and traces fit together?

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