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tegwick d3fe1e3ed3 Finish WP-0016 after State Hub graph ingest

2026-05-23 21:52:47 +02:00

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id, type, title, domain, repo, status, owner, topic_slug, planning_priority, planning_order, created, updated, depends_on_workplans, state_hub_workstream_id

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title

domain

repo

status

owner

topic_slug

planning_priority

planning_order

created

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depends_on_workplans

state_hub_workstream_id

RAIL-FAB-WP-0016

workplan

Canon-Aligned Graph Model Reset And Reingest

railiance

railiance-fabric

finished

codex

railiance

high

2026-05-23

RAIL-FAB-WP-0015

4f776f2c-9a54-4658-8e30-eb0d3fc00b32

RAIL-FAB-WP-0016 - Canon-Aligned Graph Model Reset And Reingest

Goal

Use InfoTechCanon to refactor Railiance Fabric's nodes and edges model, then archive/drop prior registry graph data and reingest all repositories to build a renewed model aligned with canon semantics.

Background

The current Fabric graph grew through iterative discovery and projection work. That helped make the registry practically useful, but the node and edge model now needs a deliberate canon alignment pass before broad adoption. The canon now provides:

a reusable consumer alignment review kit,
railiance-fabric conformance support for entity and edge capture,
PURPOSES / INTENT / SCOPE guidance,
graph-oriented canonical entity categories,
CARING and Kubernetes benchmark pressure around roles, scope, evidence, and derived relationships.

This workplan should use those canon surfaces to make Fabric's graph cleaner, more semantically stable, and easier to visualize without confusing canonical relationships with display-only graph edges.

Safety Boundary

This workplan intentionally includes a destructive reset phase, but the reset MUST NOT be executed casually. Implementation must first produce an export, backup, rollback note, and explicit operator command. Prior registry data may be dropped only after the replacement model, scanner changes, and validation path are ready.

Canon Inputs

info-tech-canon review kit: review-kit/alignment
Railiance conformance pack: infospace/evaluations/railiance-fabric/conformance-pack.yaml
Entity and edge capture criteria: infospace/evaluations/railiance-fabric/entity-edge-capture-criteria.yaml
Mapping expectations: infospace/evaluations/railiance-fabric/mapping-expectations.yaml
Visualization examples: infospace/evaluations/railiance-fabric/visualization-examples.yaml
PURPOSES / INTENT / SCOPE pattern and model extension

Tasks

T01 - Canon alignment review and target model

id: RAIL-FAB-WP-0016-T01
status: done
priority: high
state_hub_task_id: "865c048b-fddc-43ee-a379-b61ca31df85b"

Run the canon consumer alignment review workflow against railiance-fabric.
Select relevant canon surfaces for graph capture, governance, purpose, evidence, tasks, landscape, DevSecOps, network, observability, and tagging.
Produce a target node and edge taxonomy with direct, partial, conflicting, and missing mappings.

T02 - Refactor nodes, edges, schemas, and validation

id: RAIL-FAB-WP-0016-T02
status: done
priority: high
state_hub_task_id: "26fbc0d5-3b82-45d2-8307-97dffb9de500"

Refactor Fabric graph nodes toward canon-aligned categories such as source repository, software system, service, endpoint, deployment, runtime resource, datastore, flow, policy, control, evidence, task, consumer purpose, and telemetry signal.
Refactor canonical edges toward relationships such as built_from, implements, exposes, depends_on, deploys, flows_to, governed_by, evidenced_by, observed_by, part_of, reads_or_writes, and creates_task.
Keep display-only visualization edges separate from canonical edges.
Update schemas, validators, scanner output, registry projection, docs, and graph explorer mapping as needed.

T03 - Export, archive, and controlled reset

id: RAIL-FAB-WP-0016-T03
status: done
priority: high
state_hub_task_id: "f9ce7cd7-48c1-4aa0-9760-b2bcf38feedd"

Export the current registry graph, discovery snapshots, accepted projections, and reingest metadata before any destructive action.
Add a guarded reset path that requires an explicit operator command and records what was dropped.
Document rollback limits and the intended post-reset source of truth.
Drop prior graph data only after backup and validation gates are satisfied.

T04 - Full repository reingest

id: RAIL-FAB-WP-0016-T04
status: done
priority: high
state_hub_task_id: "1d3efc3b-029e-4db5-9a83-b658f5ccdebd"

Reingest all registered/local repositories using the new canon-aligned model.
Start with deterministic scanner output and ingest-only mode.
Review changed, conflicted, and review-required repos before acceptance.
Project accepted graph state only after model validation and sample review.

Progress 2026-05-23:

Deterministic no-cache dry run completed for registry/local-repos.yaml with 35/35 repositories scanned, 0 errors, 0 conflicted candidates, and 0 review-required repositories.
Candidate totals from the dry-run report: 381 nodes, 415 edges, and 186 attributes.
Fixed a dry-run artifact naming collision for slugs that normalize to the same cache filename, observed with vergabe-teilnahme and vergabe_teilnahme; the rerun produced 35 unique snapshot paths and 35 snapshot files.
Actual reset/ingest/acceptance remains pending the explicit guarded reset command and operator confirmation token.

Progress 2026-05-23 reset execution:

Operator provided confirmation token RESET-RAILIANCE-FABRIC-GRAPH-DATA.
Guarded reset event 1 archived current graph data, dropped 54 discovery snapshots and 59 graph snapshots, and preserved 35 repository registrations.
Post-reset deterministic ingest-only run scanned and ingested 35/35 repositories with 0 errors, 0 conflicted candidates, and 0 review-required repositories.
Accepted/projected all 35 ingested discovery snapshots into renewed graph snapshots; final registry status after T04 is 35 repositories, 35 discovery snapshots, 35 graph snapshots, and 1 reset event.

T05 - Validation, visualization, and State Hub readiness

id: RAIL-FAB-WP-0016-T05
status: done
priority: high
state_hub_task_id: "420336e1-3450-4bbc-8c0f-d091098ee990"

Add regression tests for canonical node categories, edge categories, duplicate identity, destructive reset guardrails, and reingest idempotency.
Verify graph explorer displays the renewed model clearly.
Produce before/after counts and representative examples.
Confirm State Hub can ingest the renewed graph as a read model.