feat(sql): Vector SQL Integration + test isolation fixes

- Add VECTOR(n) column type support in CREATE TABLE
- Add CREATE INDEX ... USING hnsw/ivfpq for vector indexes
- Add cosine_distance(), euclidean_distance(), inner_product(), l1/l2_distance()
  SQL functions in expression evaluator
- Add <-> nearest-neighbor operator
- Fix ORDER BY with non-projected columns (move irpkSort before irpkProject)
- Fix execInsert to escape comma-containing values (vector literals)
- Fix MERGE tests by using unique temp dirs per test suite
- Add 8 Vector SQL Integration tests (all passing)
- Update PLAN_SQL_ADVANCED.md
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# BaraDB — Дългосрочен план за Advanced SQL + All-in-One Engine
# BaraDB — Универсален план за Advanced SQL Engine
> **Визия**: BaraDB става единният мултимодален backend за vals-trz и други ERP/HR системи. SQL:2023 съвместимост, Property Graph, Vector Search — всичко в един Nim engine с MVCC, Raft, и Java bridge.
> **Визия**: BaraDB е самостоятелен, универсален SQL engine с Nim ядро, поддържащ модерни SQL:2023 разширения — Property Graph, Vector Search, JSON документи и прозоречни функции, в една вградена или клиент/сървър конфигурация.
>
> **Принцип**: Само основи. Не се добавят нови светове — само стабилизираме и документираме съществуващите.
---
## История на разработката
- **Фаза 1 (Base SQL + MVCC + Raft)**: BaraDB core engine
- **Фаза 2 (Advanced SQL)**: Разработена с **Xiaomi Mimo** (`mimo-v2.5-pro`) — Window Functions, MERGE, LATERAL JOIN, Advanced Aggregates, PIVOT/UNPIVOT, SQL/PGQ Property Graph
- **Фаза 3 (Stabilization)**: Текуща — Vector SQL Integration, тестове, документация
---
---
@@ -62,7 +74,7 @@ LATERAL (
Файлове: `lexer.nim`, `ast.nim`, `ir.nim`, `parser.nim`, `executor.nim`
Тестове: 4 execution теста + 3 parser теста, всички зелени.
### 1.4 Advanced Aggregates (Приоритет: Среден)
### 1.4 Advanced Aggregates ✅ ГОТОВО
- `ARRAY_AGG(col ORDER BY ...)`
- `STRING_AGG(col, delimiter)`
@@ -155,58 +167,67 @@ SELECT * FROM GRAPH_TABLE(org_chart
---
## Част 2: vals-trz → BaraDB Миграционна стратегия
## Част 2: Мултимодални Възможности (Core Only)
### Фаза 0: Java REST Bridge ✅ ГОТОВО
### 2.1 JSON / JSONB Документи ✅ ГОТОВО
```
vals-trz (Spring Boot)
↓ HTTP/JSON (BaraDB REST API)
BaraDB Server (Nim)
↓ Native execution
Storage (LSM-Tree / B-Tree / HNSW / InvertedIndex)
```sql
SELECT data->>'name' FROM users WHERE data->'tags' @> '["admin"]';
```
Създадени файлове в `vals-trz/backend/src/main/java/com/valstrz/baradb/`:
- `BaraDbProperties.java``@ConfigurationProperties(prefix = "baradb")`
- `BaraDbClient.java` — HTTP клиент към `POST /query`
- `BaraDbTemplate.java` — Spring Template (query, update, execute, transactions)
- `BaraDbQueryRequest.java` / `BaraDbQueryResponse.java` — JSON DTOs
- `BaraDbException.java` — Runtime exception
- `BaraDbConfig.java` — Spring `@Configuration`
- `EmployeeBaraRepository.java` — Пример: Employee entity → SQL MERGE/SELECT
- `README.md` — Документация за bridge
- Типове: `JSON`, `JSONB` колони в таблици
- Оператори: `->`, `->>`, `#>`, `#>>`, `@>`, `<@`, `?`, `?&`, `?|`
- Функции: `jsonb_array_elements`, `jsonb_object_keys`, `jsonb_extract_path`
- Съхранение: двоично parsed tree (не plain text)
Конфигурация добавена в `application.properties`:
```properties
baradb.enabled=false
baradb.host=localhost
baradb.port=9470
baradb.database=valstrz
### 2.2 Vector Search ⚠️ ЧАСТИЧНО (Engine ✅, SQL Integration 🔄)
**Вектор Engine (готов):**
- `src/barabadb/vector/engine.nim` — HNSW index с cosine/euclidean distance
- `src/barabadb/vector/quant.nim` — IVF-PQ quantization
- `src/barabadb/vector/simd.nim` — SIMD оптимизации
- `src/barabadb/core/crossmodal.nim` — CrossModalEngine за хибридно търсене (vector + text)
**Липсваща SQL интеграция (базова — за стабилизация):**
```sql
-- Тип и колона
CREATE TABLE items (id INT PRIMARY KEY, embedding VECTOR(768));
-- Index
CREATE VECTOR INDEX idx_items_vec ON items(embedding)
USING hnsw WITH (m = 16, ef_construction = 200, metric = 'cosine');
-- Query functions
SELECT id, cosine_distance(embedding, '[0.1, 0.2, ...]') AS dist
FROM items
ORDER BY dist ASC
LIMIT 10;
```
### Фаза 1: Document Storage (Вместо ArangoDB)
**Задачи за стабилизация (всички изпълнени):**
- [x] `VECTOR(n)` тип в CREATE TABLE (parser + storage)
- [x] `CREATE VECTOR INDEX ... USING hnsw` (DDL)
- [x] `cosine_distance()`, `euclidean_distance()`, `inner_product()` в SQL expression evaluator
- [x] `<->` nearest-neighbor оператор в ORDER BY / WHERE
- [x] Executor integration: HNSW index population при CREATE INDEX и DML
- JSON/JSONB колони за гъвкави документи
- Всеки `BaseEntity` → таблица с `id`, `tenant_id`, `data jsonb`
- Или: full relational mapping (всеки Java field → SQL колона)
**Статус:** ✅ ГОТОВО. 8 SQL-level vector теста зелени.
### Фаза 2: Graph йерархия (Вместо ArangoDB edges)
### 2.3 Full-Text Search ✅ ГОТОВО
- SQL/PGQ `CREATE PROPERTY GRAPH org_chart`
- `MATCH` queries за reporting chain, department structure
- BFS/DFS + shortestPath вградени в SQL планера
- Inverted Index в `src/barabadb/fts/`
- `MATCH(column, query)` функция
- BM25 scoring
- Интеграция с CrossModalEngine за hybrid search
### Фаза 3: Vector Search (Вместо Qdrant)
---
- `vector` тип + HNSW index
- `cosine_distance(embedding, [...])` в WHERE/ORDER BY
- Hybrid: vector similarity + BM25 + relational filters в една транзакция
## Част 3: Транзакции и Протоколи ✅ ГОТОВО
### Фаза 4: Distributed (Когато трябва scale)
- Raft consensus за HA
- Sharding за multi-tenant isolation (shard by `tenant_id`)
- MVCC с snapshot isolation
- WAL + checkpoint
- Distributed transactions (2PC) — `txn.addParticipant("vector")`
- Wire protocol: binary за vectors, JSON за queries
---
@@ -214,33 +235,36 @@ baradb.database=valstrz
1.**Window Functions** (AST → Parser → IR → Executor → Tests)
2.**MERGE statement** (Parser → Executor → Tests)
3.**Java REST Client за vals-trz** (Spring `@Component`, `BaraDbTemplate`)
4.**LATERAL JOIN** (Parser → Executor, correlated subquery strategy)
5.**GROUP BY + HAVING** (SUM/AVG/MIN/MAX, HAVING filter)
6.**FILTER clause** (COUNT/SUM/AVG FILTER (WHERE ...))
7.**ARRAY_AGG / STRING_AGG** (multi-arg aggregates)
8.**GROUPING SETS / ROLLUP / CUBE** (powerset generation)
9.**PIVOT / UNPIVOT** (row-to-column transformation)
10.**SQL/PGQ Property Graph** (GRAPH_TABLE MATCH parser)
11. **vals-trz Entity → BaraDB Schema mapping** (Java integration — накрая)
3.**LATERAL JOIN** (Parser → Executor, correlated subquery strategy)
4.**GROUP BY + HAVING** (SUM/AVG/MIN/MAX, HAVING filter)
5.**FILTER clause** (COUNT/SUM/AVG FILTER (WHERE ...))
6.**ARRAY_AGG / STRING_AGG** (multi-arg aggregates)
7.**GROUPING SETS / ROLLUP / CUBE** (powerset generation)
8.**PIVOT / UNPIVOT** (row-to-column transformation)
9.**SQL/PGQ Property Graph** (GRAPH_TABLE MATCH parser)
10.**JSON/JSONB** (operators + functions)
11. **Full-Text Search** (inverted index + BM25)
12.**Vector Engine** (HNSW + IVF-PQ + SIMD)
13.**Vector SQL Integration** (тип, index, distance functions, <-> operator, ORDER BY)
---
## Крайно състояние (2026-05-14)
## Крайно състояние
**330 теста зелени.** Всички фундаментални SQL:2023 features имплементирани.
**340+ теста зелени.** Всички фундаментални SQL:2023 features имплементирани.
**4-те свята — напълно интегрирани:**
**Четирите свята:**
| Свят | Features | Статус |
|------|----------|--------|
| **SQL** | Window, MERGE, LATERAL, GROUP BY/HAVING, FILTER, ARRAY_AGG, STRING_AGG, GROUPING SETS/ROLLUP/CUBE, PIVOT/UNPIVOT | ✅ |
| **JSON** | JSON/JSONB колони, `->` / `->>` оператори | ✅ |
| **Vector** | HNSW index, cosine/euclidean distance | ✅ |
| **Graph** | BFS/DFS/PageRank/Dijkstra engine + SQL/PGQ GRAPH_TABLE | ✅ |
| **Vector** | HNSW index, cosine/euclidean distance, IVF-PQ, SIMD | ✅ Engine<br>🔄 SQL glue |
| **FTS** | Inverted index, BM25, hybrid search | ✅ |
**Файлове модифицирани:**
- `lexer.nim` — tkLateral, tkFilter, tkPivot, tkUnpivot, tkVertex, tkEdge, tkGraphTable, tkMatch, tkColumns, tkArrayAgg, tkStringAgg, tkGrouping, tkSets, tkRollup, tkCube
- `lexer.nim` — tkLateral, tkFilter, tkPivot, tkUnpivot, tkVertex, tkEdge, tkGraphTable, tkMatch, tkColumns, tkArrayAgg, tkStringAgg, tkGrouping, tkSets, tkRollup, tkCube, tkVector
- `ast.nim` — joinLateral, funcFilter, nkPivot, nkUnpivot, GroupingSetsKind, nkGraphTraversal fields
- `ir.nim` — joinLateral, aggFilter, irArrayAgg, irStringAgg, IRGroupingSetsKind, irpkGroupBy grouping sets, irpkPivot, irpkUnpivot, irpkGraphTraversal
- `parser.nim` — LATERAL, FILTER, multi-arg aggregates, GROUPING SETS/ROLLUP/CUBE, PIVOT/UNPIVOT, GRAPH_TABLE
@@ -254,6 +278,19 @@ baradb.database=valstrz
## Тестова стратегия
- **Unit**: Всеки нов AST/IR/Parser тест — property-based (генериране на случайни partition/order)
- **Integration**: Testcontainers с BaraDB HTTP server + Java client
- **Integration**: HTTP server + клиент тестове
- **TLA+**: `windowfunctions.tla` — deterministic partitioning semantics
- **Benchmark**: Window function performance vs PostgreSQL
- **Benchmark**: Window function performance vs PostgreSQL (опционално)
---
## Поправени грешки при тази сесия
- **Vector SQL Integration** — имплементиран пълен SQL glue за вектори (тип, индекс, функции, оператор)
- **MERGE тестове** — поправени чрез изолиране на тестовата директория (unique temp dir per suite)
- **Row storage escape** — `escapeRowVal()` в `execInsert` за стойности със запетай (vector literals)
- **ORDER BY + projection** — `irpkSort` сега е преди `irpkProject` в `lowerSelect`, което позволява `ORDER BY` по колони извън `SELECT`
---
> **Бележка**: Този план е *замразен* за нови светове. Следващата работа е само стабилизация на съществуващото и документация.