ADR-006: ADR 006 Single Read Model Architecture

Purpose: Architecture decision record for ADR 006 Single Read Model Architecture

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Overview

Property	Value
Status	proposed
Category	architecture

Context: The delivery-process package applies event sourcing to itself: git is the event store, annotated source files are authoritative state, generated documentation is a projection. The MasterDataset is the read model — produced by a single-pass O(n) transformer with pre-computed views and a relationship index.

ADR-005 established that codecs consume MasterDataset as their sole input. The ProcessStateAPI consumes it. But the validation layer bypasses it, wiring its own mini-pipeline from raw scanner/extractor output. It creates a lossy local type that discards relationship data, then discovers it lacks the information needed — requiring ad-hoc re-derivation of what the MasterDataset already computes.

This is the same class of problem the MasterDataset was created to solve. Before the single-pass transformer, each generator called .filter() independently. The MasterDataset eliminated that duplication for codecs. This ADR extends the same principle to all consumers.

Decision: The MasterDataset is the single read model for all consumers. No consumer re-derives pattern data from raw scanner/extractor output when that data is available in the MasterDataset. Validators, codecs, and query APIs consume the same pre-computed read model.

Consequences:

Type	Impact
Positive	Relationship resolution happens once — no consumer re-derives implements, uses, or dependsOn
Positive	Eliminates lossy local types that discard fields from canonical ExtractedPattern
Positive	Validation rules automatically benefit from new MasterDataset views and indices
Positive	Aligns with the monorepo’s own ADR-006: projections for all reads, never query aggregate state
Negative	Validators that today only need stage 1-2 data will import the transformer
Negative	MasterDataset schema changes affect more consumers

Rules

All feature consumers query the read model, not raw state

Invariant: Code that needs pattern relationships, status groupings, cross-source resolution, or dependency information consumes the MasterDataset. Direct scanner/extractor imports are permitted only in pipeline orchestration code that builds the MasterDataset.

Rationale: Bypassing the read model forces consumers to re-derive data that the MasterDataset already computes, creating duplicate logic and divergent behavior when the pipeline evolves.

Layer	May Import	Examples
Pipeline Orchestration	scanner/, extractor/, pipeline/	orchestrator.ts, process-api.ts pipeline setup
Feature Consumption	MasterDataset, relationshipIndex	codecs, ProcessStateAPI, validators, query handlers

Verified by:

• Feature consumers import from MasterDataset not from raw pipeline stages

  Exception: lint-patterns.ts is a pure stage-1 consumer. It validates annotation syntax on scanned files. No relationships

• no cross-source resolution. Direct scanner consumption is correct for that use case.

No lossy local types

Invariant: Consumers do not define local DTOs that duplicate and discard fields from ExtractedPattern. If a consumer needs a subset, the type system provides the projection — not a hand-written extraction function that becomes a barrier between the consumer and canonical data.

Rationale: Lossy local types silently drop fields that later become needed, causing bugs that only surface when new MasterDataset capabilities are added and the local type lacks them.

Verified by:

• Feature consumers import from MasterDataset not from raw pipeline stages

Relationship resolution is computed once

Invariant: Forward relationships (uses, dependsOn, implementsPatterns) and reverse lookups (usedBy, implementedBy, extendedBy) are computed in transformToMasterDataset(). No consumer re-derives these from raw pattern arrays or scanned file tags.

Rationale: Re-deriving relationships in consumers duplicates the resolution logic and risks inconsistency when different consumers implement subtly different traversal or filtering rules.

Verified by:

• Feature consumers import from MasterDataset not from raw pipeline stages

Three named anti-patterns

Invariant: These are recognized violations, serving as review criteria for new code and refactoring targets for existing code.

Rationale: Without named anti-patterns, violations appear as one-off style issues rather than systematic architectural drift, making them harder to detect and communicate in code review.

Anti-Pattern	Detection Signal
Parallel Pipeline	Feature consumer imports from scanner/ or extractor/
Lossy Local Type	Local interface with subset of ExtractedPattern fields + dedicated extraction function
Re-derived Relationship	Building Map or Set from pattern.implementsPatterns, uses, or dependsOn in consumer code

Good vs Bad

// Good: consume the read model
    function validateCrossSource(dataset: RuntimeMasterDataset): ValidationSummary {
      const rel = dataset.relationshipIndex[patternName];
      const isImplemented = rel.implementedBy.length > 0;
    }

    // Bad: re-derive from raw state (Parallel Pipeline + Re-derived Relationship)
    function buildImplementsLookup(
      gherkinFiles: readonly ScannedGherkinFile[],
      tsPatterns: readonly ExtractedPattern[]
    ): ReadonlySet<string> { ... }

References

- Monorepo ADR-006: Projections for All Reads (same principle, application domain)
- ADR-005: Codec-Based Markdown Rendering (established MasterDataset as codec input)
- Order-management ARCHITECTURE.md: CommandOrchestrator + Read Model separation

Verified by:

• Feature consumers import from MasterDataset not from raw pipeline stages

  Naming them makes them visible in code review — including AI-assisted sessions where the default proposal is often “add a helper function.”

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