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Observability and Provenance

Observability and provenance describe the evidence by which observers can inspect, explain, correlate, and diagnose system behavior across boundaries and time.

Observability records evidence about executions. It is distinct from the categorical trace and feedback principle, which models outputs becoming future inputs. Provenance records where a value, decision, route, effect, or observation came from and which definitions, inputs, policies, authorities, and occurrences contributed to it.

Observation Scopes

  • An operation trace follows one bounded invocation, activation, attempt, or local commit.
  • A message history records emission, routing, transformation, delivery, acknowledgment, retry, and disposition evidence.
  • A causal chain links occurrences through explicit causation rather than correlation alone.
  • A process history explains durable progress across operations, messages, waits, timers, compensations, and human work.
  • A system health view aggregates rates, latency, backlog, saturation, errors, expiry, quarantine, and recovery state.

A wire tap or diagnostic subscriber creates another observation path. It must not silently change delivery cardinality, ordering, backpressure, privacy, or failure behavior. A message store used for diagnosis is not automatically authoritative domain history and requires its own retention, access, redaction, and integrity rules.

Test messages and synthetic transactions should be identifiable, authorized, and scoped. Their effects must be isolated, reversible, or intentionally real; a synthetic marker alone does not prevent a production consumer from performing an irreversible action.

Useful provenance may include definition and semantic revision, node and branch identity, message and request identity, subject and process identity, correlation and causation, route and transformation revisions, source position, handler version, attempt, acknowledgment, commit boundary, authority, and terminal disposition.

External References

Related concepts: enterprise integration patterns, observation, observer, causality, identity, correlation and conversations, messages and envelopes, process, process graphs, trace and feedback, persistence, retention, expiration, and quarantine, operational control, infrastructure.