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12 changed files with 495 additions and 579 deletions

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@ -229,6 +229,42 @@ Traits are reusable, non-generating schema fragments used to share properties an
* **Scalars / Arrays / Items**: Host definitions completely override included traits.
* The `"include"` keyword is stripped, and `"traits"` maps are omitted from serialization.
### Static Relation Constraints (Kind Constraints)
When modeling relational properties on a schema, a developer can define a specialized subset of a related table by applying static property constraints via the `const` or `enum` validation keywords.
For example, given a general `attachment` table containing a `kind` column (e.g. `'cover'`, `'thumbnail'`, `'document'`), you can define a `cover.attachment` schema that narrows the type using a static `const` assertion:
```json
"cover.attachment": {
"type": "attachment",
"properties": {
"kind": {
"const": "cover"
}
}
}
```
A parent entity can then define a relationship using this constrained schema under a local property name (e.g. `cover_attachment`):
```json
"cover_attachment": {
"properties": {
"cover_attachment": {
"type": [
"cover.attachment",
"null"
]
}
}
}
```
**What it does:**
1. **Validation (L1)**: During payload validation (`jspg_validate`), any incoming object mapped to the constrained property is validated against the static rules (e.g. throwing `CONST_VIOLATED` if `kind` is not `"cover"`).
2. **Query Generation (L0)**: When fetching data via `jspg_query`, the Queryer automatically detects the static constraint and compiles it into the SQL subquery's `WHERE` clause (e.g. adding `AND attachment_X.kind = 'cover'`). This produces a pre-filtered view of the related entities natively at the database level.
---
## 3. Database
@ -314,6 +350,8 @@ The Queryer transforms Postgres into a pre-compiled Semantic Query Engine, desig
* **Multi-Table Branching**: If the Physical Table is a parent to other tables (e.g. `organization` has variations `["organization", "bot", "person"]`), the compiler generates a dynamic `CASE WHEN type = '...' THEN ...` query, expanding into sub-queries for each variation. To ensure safe resolution, the compiler dynamically evaluates correlation boundaries: it attempts standard Relational Edge discovery first. If no explicit relational edge exists (indicating pure Table Inheritance rather than a standard foreign-key graph relationship), it safely invokes a **Table Parity Fallback**. This generates an explicit ID correlation constraint (`AND inner.id = outer.id`), perfectly binding the structural variations back to the parent row to eliminate Cartesian products.
* **Single-Table Bypass**: If the Physical Table is a leaf node with only one variation (e.g. `person` has variations `["person"]`), the compiler cleanly bypasses `CASE` generation and compiles a simple `SELECT` across the base table, as all schema extensions (e.g. `light.person`, `full.person`) are guaranteed to reside in the exact same physical row.
* **Polymorphic Relation Type Filtering**: When a relationship maps to a polymorphic target with variations, the Queryer compiles an `IN` clause containing all allowed table variations (e.g., `counterparty_type IN ('bot', 'organization', 'person')`) rather than matching the base type literal, ensuring all polymorphic types are loaded correctly.
* **Static Relation Constraints (Kind Constraints)**: When a relationship (such as a nested object or array) is defined with a schema that constrains a field value statically using a `const` or `enum` keyword (for example, `kind` constrained to `"cover"` in a `cover_attachment`), the Queryer automatically extracts these static assertions during AST compilation. It injects them directly as static filters into the SQL subquery's `WHERE` clause (e.g. `AND attachment.kind = 'cover'`), allowing developers to query pre-filtered subsets of related tables natively through the schema.
* **Proxy Schema Dereferencing / Resolution**: To support punc endpoints that return non-polymorphic table-backed shapes (using `type: "full.X"` proxy schemas at the root response level), the Queryer compiler automatically dereferences non-table schema pointers to their target schemas prior to checking the types. This allows the Queryer to correctly resolve the table relationship edges pre-compiled on the full schema, while avoiding polluting the database registry with relations on ad-hoc punc response schemas during setup.
---

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@ -87,34 +87,6 @@
"type": "string"
}
}
},
"schedule": {
"type": [
"opening_hours",
"null"
]
}
}
},
"opening_hours": {
"type": "object",
"properties": {
"open": {
"type": "string"
},
"seasons": {
"type": "array",
"items": {
"type": "season"
}
}
}
},
"season": {
"type": "object",
"properties": {
"label": {
"type": "string"
}
}
}
@ -511,9 +483,7 @@
]
}
}
},
"opening_hours": {},
"season": {}
}
}
}
}

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@ -191,6 +191,9 @@
},
{
"name": "request",
"field_types": {
"inv": "jsonb"
},
"schemas": {
"request": {
"type": "object",

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1
flow
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@ -36,7 +36,6 @@ pgrx-down() {
info "Taking pgrx down..."
}
build() {
local version
version=$(get-version) || return $?

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@ -165,16 +165,8 @@ impl Schema {
} else if db.enums.contains_key(custom) {
Some(vec![format!("{}.condition", custom)])
} else {
// Only a Table-Backed boundary has a synthesized Composed Filter to proxy to.
// A Field-Backed JSONB Bubble has none — omit it like an inline object rather
// than emit a dangling proxy reference, which breaks eager consumers of the
// exported registry (downstream code generators).
let base = custom.split('.').next_back().unwrap_or(custom);
if db.types.contains_key(base) {
// Assume anything else is a Relational cross-boundary that already has its own .filter dynamically built
Some(vec![format!("{}.filter", custom)])
} else {
None
}
}
}
}

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@ -1,5 +1,6 @@
use crate::database::Database;
use indexmap::IndexMap;
use serde_json::Value;
use std::sync::Arc;
pub struct Compiler<'a> {
@ -120,6 +121,21 @@ impl<'a> Compiler<'a> {
}
fn compile_reference(&mut self, node: Node<'a>) -> Result<(String, String), String> {
// Handle Direct Refs via type pointer first
if let Some(crate::database::object::SchemaTypeOrArray::Single(t)) = &node.schema.obj.type_ {
if !crate::database::object::is_primitive_type(t) {
if !self.db.types.contains_key(t) {
// If it's just an ad-hoc struct ref, we should resolve it
if let Some(target_schema) = self.db.schemas.get(t).cloned() {
let mut ref_node = node.clone();
ref_node.schema = target_schema.clone();
ref_node.schema_id = Some(t.clone());
return self.compile_node(ref_node);
}
}
}
}
// Determine if this schema represents a Database Entity
let mut resolved_type = None;
@ -149,16 +165,9 @@ impl<'a> Compiler<'a> {
return self.compile_entity(type_def, node.clone(), false);
}
// Handle Direct Refs via type pointer
// Fallback error if the schema pointer was unresolved
if let Some(crate::database::object::SchemaTypeOrArray::Single(t)) = &node.schema.obj.type_ {
if !crate::database::object::is_primitive_type(t) {
// If it's just an ad-hoc struct ref, we should resolve it
if let Some(target_schema) = self.db.schemas.get(t).cloned() {
let mut ref_node = node.clone();
ref_node.schema = target_schema.clone();
ref_node.schema_id = Some(t.clone());
return self.compile_node(ref_node);
}
return Err(format!("Unresolved schema type pointer: {}", t));
}
}
@ -549,8 +558,9 @@ impl<'a> Compiler<'a> {
where_clauses.push(format!("NOT {}.archived", entity_alias));
}
self.compile_filter_conditions(r#type, type_aliases, &node, &base_alias, &mut where_clauses)?;
self.compile_filter_conditions(r#type, type_aliases, &node, &base_alias, &mut where_clauses);
self.compile_polymorphic_bounds(r#type, type_aliases, &node, &mut where_clauses);
self.compile_static_property_conditions(r#type, type_aliases, &node, &base_alias, &mut where_clauses);
let start_len = where_clauses.len();
self.compile_relation_conditions(
@ -689,11 +699,6 @@ impl<'a> Compiler<'a> {
|| pg_type.contains("int")
|| pg_type == "real"
|| pg_type == "double precision"
// pg catalog typnames for real / double precision — the SQL
// names above never appear in field_types (they come from
// pg_type.typname), so these are what actually arrives
|| pg_type == "float4"
|| pg_type == "float8"
{
cast = "::numeric";
} else if pg_type == "text" || pg_type.contains("char") {
@ -720,7 +725,7 @@ impl<'a> Compiler<'a> {
node: &Node,
base_alias: &str,
where_clauses: &mut Vec<String>,
) -> Result<(), String> {
) {
for (i, filter_key) in self.filter_keys.iter().enumerate() {
let mut parts = filter_key.split(':');
let full_field_path = parts.next().unwrap_or(filter_key);
@ -750,95 +755,6 @@ impl<'a> Compiler<'a> {
let param_index = i + 1;
let p_val = format!("${}#>>'{{}}'", param_index);
// jsonb columns never type-check against the text parameter, and for
// array-valued properties (tag lists) the meaning of a condition is
// CONTAINMENT, not equality. Compile them explicitly; reject the rest
// loudly at compile time instead of failing at execution.
let is_jsonb = r#type
.field_types
.as_ref()
.and_then(|v| v.as_object())
.and_then(|ft| ft.get(field_name))
.and_then(|v| v.as_str())
== Some("jsonb");
if is_jsonb {
// The node schema may be a punc-response proxy without properties —
// fall back to the type's own schema. Compiled schemas normalize to
// the list form ("type": ["array"]), hand-written ones may use the
// single form — accept both.
let prop_schema = node
.schema
.obj
.properties
.as_ref()
.and_then(|p| p.get(field_name))
.cloned()
.or_else(|| {
r#type
.schemas
.get(&r#type.name)
.and_then(|s| s.obj.properties.as_ref())
.and_then(|p| p.get(field_name))
.cloned()
});
let is_array_prop = prop_schema
.map(|ps| match &ps.obj.type_ {
Some(crate::database::object::SchemaTypeOrArray::Single(t)) => t == "array",
Some(crate::database::object::SchemaTypeOrArray::Multiple(ts)) => {
ts.iter().any(|t| t == "array") && ts.iter().all(|t| t == "array" || t == "null")
}
None => false,
})
.unwrap_or(false);
if is_array_prop {
match op {
// "the array contains this value"
"$eq" => where_clauses.push(format!(
"{}.{} ? ({})",
filter_alias, field_name, p_val
)),
"$ne" => where_clauses.push(format!(
"NOT ({}.{} ? ({}))",
filter_alias, field_name, p_val
)),
// "the array contains ANY of these values"
"$of" => where_clauses.push(format!(
"{}.{} ?| ARRAY(SELECT jsonb_array_elements_text(({})::jsonb))",
filter_alias, field_name, p_val
)),
"$nof" => where_clauses.push(format!(
"NOT ({}.{} ?| ARRAY(SELECT jsonb_array_elements_text(({})::jsonb)))",
filter_alias, field_name, p_val
)),
other => {
return Err(format!(
"operator {} is not supported on array property '{}' (jsonb containment supports $eq/$ne/$of/$nof)",
other, field_name
))
}
}
} else {
match op {
"$eq" => where_clauses.push(format!(
"{}.{} = ({})::jsonb",
filter_alias, field_name, p_val
)),
"$ne" => where_clauses.push(format!(
"{}.{} != ({})::jsonb",
filter_alias, field_name, p_val
)),
other => {
return Err(format!(
"operator {} is not supported on jsonb property '{}' (only $eq/$ne)",
other, field_name
))
}
}
}
continue;
}
if op == "$of" || op == "$nof" {
let sql_op = if op == "$of" { "IN" } else { "NOT IN" };
let subquery = format!(
@ -890,7 +806,6 @@ impl<'a> Compiler<'a> {
));
}
}
Ok(())
}
fn compile_relation_conditions(
@ -960,4 +875,69 @@ impl<'a> Compiler<'a> {
}
Ok(())
}
fn compile_static_property_conditions(
&self,
r#type: &crate::database::r#type::Type,
type_aliases: &std::collections::HashMap<String, String>,
node: &Node,
base_alias: &str,
where_clauses: &mut Vec<String>,
) {
if let Some(props) = node.schema.obj.properties.as_ref() {
for (prop_name, prop_schema) in props {
let filter_alias = Self::resolve_filter_alias(r#type, type_aliases, base_alias, prop_name);
if let Some(const_val) = prop_schema.obj.const_.as_ref() {
let sql_val = Self::quote_literal(const_val);
where_clauses.push(format!("{}.{} = {}", filter_alias, prop_name, sql_val));
}
if let Some(enum_vals) = prop_schema.obj.enum_.as_ref() {
if !enum_vals.is_empty() {
let sql_vals: Vec<String> = enum_vals
.iter()
.map(|v| Self::quote_literal(v))
.collect();
where_clauses.push(format!(
"{}.{} IN ({})",
filter_alias,
prop_name,
sql_vals.join(", ")
));
}
}
}
}
}
fn quote_literal(val: &Value) -> String {
match val {
Value::Null => "NULL".to_string(),
Value::Bool(b) => {
if *b {
"true".to_string()
} else {
"false".to_string()
}
}
Value::Number(n) => {
if let Some(f) = n.as_f64() {
if f.fract() == 0.0 {
return f.trunc().to_string();
}
}
n.to_string()
}
Value::String(s) => {
if s.is_empty() {
"NULL".to_string()
} else {
format!("'{}'", s.replace('\'', "''"))
}
}
_ => format!(
"'{}'",
serde_json::to_string(val).unwrap().replace('\'', "''")
),
}
}
}

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@ -1302,15 +1302,9 @@ fn test_queryer_0_15() {
}
#[test]
fn test_queryer_0_16() {
fn test_queryer_1_0() {
let path = format!("{}/fixtures/queryer.json", env!("CARGO_MANIFEST_DIR"));
crate::tests::runner::run_test_case(&path, 0, 16).unwrap();
}
#[test]
fn test_queryer_0_17() {
let path = format!("{}/fixtures/queryer.json", env!("CARGO_MANIFEST_DIR"));
crate::tests::runner::run_test_case(&path, 0, 17).unwrap();
crate::tests::runner::run_test_case(&path, 1, 0).unwrap();
}
#[test]

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@ -268,7 +268,8 @@ impl SqlFormatter {
match &join.join_operator {
JoinOperator::Inner(JoinConstraint::On(expr))
| JoinOperator::Left(JoinConstraint::On(expr))
| JoinOperator::Right(JoinConstraint::On(expr)) => {
| JoinOperator::Right(JoinConstraint::On(expr))
| JoinOperator::Join(JoinConstraint::On(expr)) => {
self.push_str(" ON ");
self.format_expr(expr);
}

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@ -259,3 +259,25 @@ pub fn update_sql_fixture(path: &str, suite_idx: usize, case_idx: usize, queries
let formatted_json = serde_json::to_string_pretty(&file_data).unwrap();
fs::write(path, formatted_json).unwrap();
}
pub fn update_schemas_fixture(path: &str, suite_idx: usize, case_idx: usize, db: &crate::database::Database) {
let content = fs::read_to_string(path).unwrap();
let mut file_data: Value = serde_json::from_str(&content).unwrap();
if let Some(expect) = file_data[suite_idx]["tests"][case_idx].get_mut("expect") {
if let Some(schemas_map) = expect.get_mut("schemas").and_then(|v| v.as_object_mut()) {
for (key, expected_val) in schemas_map {
if expected_val.is_object() && expected_val.as_object().unwrap().is_empty() {
continue;
}
if let Some(actual_ast) = db.schemas.get(key) {
let actual_val = serde_json::to_value(actual_ast).unwrap();
*expected_val = actual_val;
}
}
}
}
let formatted_json = serde_json::to_string_pretty(&file_data).unwrap();
fs::write(path, formatted_json).unwrap();
}

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@ -57,6 +57,9 @@ impl Case {
if env::var("UPDATE_EXPECT").is_ok() {
update_validation_fixture(path, suite_idx, case_idx, &result.errors);
if let Ok(db) = db_res {
crate::tests::runner::update_schemas_fixture(path, suite_idx, case_idx, db);
}
}
expect.assert_drop(&result)?;

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@ -1 +1 @@
1.0.175
1.0.179