jspg/GEMINI.md

JSPG: JSON Schema Postgres

JSPG is a high-performance PostgreSQL extension written in Rust (using pgrx) that transforms Postgres into a pre-compiled Semantic Engine. It serves as the core engine for the "Punc" architecture, where the database is the single source of truth for all data models, API contracts, validations, and reactive queries.

1. Overview & Architecture

JSPG operates by deeply integrating the JSON Schema Draft 2020-12 specification directly into the Postgres session lifecycle. It is built around three core pillars:

• Validator: In-memory, near-instant JSON structural validation and type polymorphism routing.
• Merger: Automatically traverse and UPSERT deeply nested JSON graphs into normalized relational tables.
• Queryer: Compile JSON Schemas into static, cached SQL SPI SELECT plans for fetching full entities or isolated "Stems".

🎯 Goals

1. Draft 2020-12 Compliance: Attempt to adhere to the official JSON Schema Draft 2020-12 specification.
2. Ultra-Fast Execution: Compile schemas into optimized in-memory validation trees and cached SQL SPIs to bypass Postgres Query Builder overheads.
3. Connection-Bound Caching: Leverage the PostgreSQL session lifecycle using an Atomic Swap pattern. Schemas are 100% frozen, completely eliminating locks during read access.
4. Structural Inheritance: Support object-oriented schema design via Implicit Keyword Shadowing and virtual $family references natively mapped to Postgres table constraints.
5. Reactive Beats: Provide natively generated "Stems" (isolated payload fragments) for dynamic websocket reactivity.

Concurrency & Threading ("Immutable Graphs")

To support high-throughput operations while allowing for runtime updates (e.g., during hot-reloading), JSPG uses an Atomic Swap pattern:

1. Parser Phase: Schema JSONs are parsed into ordered Schema structs.
2. Compiler Phase: The database iterates all parsed schemas and pre-computes native optimization maps (Descendants Map, Depths Map, Variations Map).
3. Immutable Validator: The Validator struct immutably owns the Database registry and all its global maps. Schemas themselves are completely frozen; $ref strings are resolved dynamically at runtime using pre-computed O(1) maps.
4. Lock-Free Reads: Incoming operations acquire a read lock just long enough to clone the Arc inside an RwLock<Option<Arc<Validator>>>, ensuring zero blocking during schema updates.

---

2. Validator

The Validator provides strict, schema-driven evaluation for the "Punc" architecture.

API Reference

• jspg_setup(database jsonb) -> jsonb: Loads and compiles the entire registry (types, enums, puncs, relations) atomically.
• mask_json_schema(schema_id text, instance jsonb) -> jsonb: Validates and prunes unknown properties dynamically, returning masked data.
• jspg_validate(schema_id text, instance jsonb) -> jsonb: Returns boolean-like success or structured errors.
• jspg_teardown() -> jsonb: Clears the current session's schema cache.

Custom Features & Deviations

JSPG implements specific extensions to the Draft 2020-12 standard to support the Punc architecture's object-oriented needs while heavily optimizing for zero-runtime lookups.

A. Polymorphism & Referencing ($ref, $family, and Native Types)

• Native Type Discrimination (variations): Schemas defined inside a Postgres type are Entities. The validator securely and implicitly manages their "type" property. If an entity inherits from user, incoming JSON can safely define {"type": "person"} without errors, thanks to compiled_variations inheritance.
• Structural Inheritance & Viral Infection ($ref): $ref is used exclusively for structural inheritance, never for union creation. A Punc request schema that $refs an Entity virally inherits all physical database polymorphism rules for that target.
• Shape Polymorphism ($family): Auto-expands polymorphic API lists based on an abstract Descendants Graph. If {"$family": "widget"} is used, JSPG evaluates the JSON against every schema that $refs widget.
• Strict Matches & Depth Heuristic: Polymorphic structures MUST match exactly one schema permutation. If multiple inherited struct permutations pass, JSPG applies the Depth Heuristic Tie-Breaker, selecting the candidate deepest in the inheritance tree.

B. Strict by Default & Extensibility

• Strictness: By default, any property not explicitly defined in the schema causes a validation error (effectively enforcing additionalProperties: false globally).
• Extensibility (extensible: true): To allow a free-for-all of undefined properties, schemas must explicitly declare "extensible": true.
• Structured Additional Properties: If additionalProperties: {...} is defined as a schema, arbitrary keys are allowed so long as their values match the defined type constraint.
• Inheritance Boundaries: Strictness resets when crossing $ref boundaries. A schema extending a strict parent remains strict unless it explicitly overrides with "extensible": true.

C. Implicit Keyword Shadowing

• Inheritance ($ref + properties): Unlike standard JSON Schema, when a schema uses $ref alongside local properties, JSPG implements Smart Merge. Local constraints natively take precedence over (shadow) inherited constraints for the same keyword.
  • Example: If entity has type: {const: "entity"}, but person defines type: {const: "person"}, the local person const cleanly overrides the inherited one.
• Composition (allOf): When evaluating allOf, standard intersection rules apply seamlessly. No shadowing occurs, meaning all constraints from all branches must pass.

D. Format Leniency for Empty Strings

To simplify frontend form validation, format validators specifically for uuid, date-time, and email explicitly allow empty strings (""), treating them as "present but unset".

---

3. Merger

The Merger provides an automated, high-performance graph synchronization engine via the jspg_merge(cue JSONB) API. It orchestrates the complex mapping of nested JSON objects into normalized Postgres relational tables, honoring all inheritance and graph constraints.

Core Features

• Deep Graph Merging: The Merger walks arbitrary levels of deeply nested JSON schemas (e.g. tracking an order, its customer, and an array of its lines). It intelligently discovers the correct parent-to-child or child-to-parent Foreign Keys stored in the registry and automatically maps the UUIDs across the relationships during UPSERT.
• Prefix Foreign Key Matching: Handles scenario where multiple relations point to the same table by using database Foreign Key constraint prefixes (fk_). For example, if a schema has shipping_address and billing_address, the merger resolves against fk_shipping_address_entity vs fk_billing_address_entity automatically to correctly route object properties.
• Dynamic Deduplication & Lookups: If a nested object is provided without an id, the Merger utilizes Postgres lk_ index constraints defined in the schema registry (e.g. lk_person mapped to first_name and last_name). It dynamically queries these unique matching constraints to discover the correct UUID to perform an UPDATE, preventing data duplication.
• Hierarchical Table Inheritance: The Punc system uses distributed table inheritance (e.g. person inherits user inherits organization inherits entity). The Merger splits the incoming JSON payload and performs atomic row updates across all relevant tables in the lineage map.
• The Archive Paradigm: Data is never deleted in the Punc system. The Merger securely enforces referential integrity by toggling the archived Boolean flag on the base entity table rather than issuing SQL DELETE commands.
• Change Tracking & Reactivity: The Merger diffs the incoming JSON against the existing database row (utilizing static, DashMap-cached lk_ SELECT string templates). Every detected change is recorded into the agreego.change audit table, tracking the user mapping. It then natively uses pg_notify to broadcast a completely flat row-level diff out to the Go WebSocket server for O(1) routing.
• Many-to-Many Graph Edge Management: Operates seamlessly with the global agreego.relationship table, allowing the system to represent and merge arbitrary reified M:M relationships directionally between any two entities.
• Sparse Updates: Empty JSON strings "" are directly bound as explicit SQL NULL directives to clear data, whilst omitted (missing) properties skip UPDATE execution entirely, ensuring partial UI submissions do not wipe out sibling fields.
• Unified Return Structure: To eliminate UI hydration race conditions and multi-user duplication, jspg_merge explicitly strips the response graph and returns only the root { "id": "uuid" } (or an array of IDs for list insertions). External APIs can then explicitly call read APIs to fetch the resulting graph, while the UI relies 100% implicitly on the flat pg_notify pipeline for reactive state synchronization.
• Decoupled SQL Generation: Because Writes (INSERT/UPDATE) are inherently highly dynamic based on partial payload structures, the Merger generates raw SQL strings dynamically per execution without caching, guaranteeing a minimal memory footprint while scaling optimally.

---

4. Queryer

The Queryer transforms Postgres into a pre-compiled Semantic Query Engine via the jspg_query(schema_id text, cue jsonb) API, designed to serve the exact shape of Punc responses directly via SQL.

Core Features

• Schema-to-SQL Compilation: Compiles JSON Schema ASTs spanning deep arrays directly into static, pre-planned SQL multi-JOIN queries.
• DashMap SQL Caching: Executes compiled SQL via Postgres SPI execution, securely caching the static string compilation templates per schema permutation inside the GLOBAL_JSPG application memory, drastically reducing repetitive schema crawling.
• Dynamic Filtering: Binds parameters natively through cue.filters objects. Dynamically handles string formatting (e.g. parsing uuid or formatting date-times) and safely escapes complex combinations utilizing ILIKE operations correctly mapped to the originating structural table.
• The Stem Engine: Rather than over-fetching heavy Entity payloads and trimming them, Punc Framework Websockets depend on isolated subgraphs defined as Stems.
  • During initialization, the generator auto-discovers graph boundaries (Stems) inside the schema tree.
  • The Queryer prepares dedicated SQL execution templates tailored precisely for that exact Stem path (e.g. executing get_dashboard queried specifically for the /owner stem).
  • These Stem outputs instantly hydrate targeted Go Bitsets, providing O(1) real-time routing for fractional data payloads without any application-layer overhead.