dimgigov
96dfaaecb1
- LATERAL JOIN: correlated subquery strategy (scan + merge + filter/sort/limit) - GROUP BY: SUM/AVG/MIN/MAX evaluation inside groups, HAVING filter - FILTER (WHERE ...): conditional aggregates for COUNT/SUM/AVG - ARRAY_AGG / STRING_AGG: multi-argument aggregate functions - GROUPING SETS / ROLLUP / CUBE: powerset generation for multi-level aggregation - PIVOT / UNPIVOT: row-to-column and column-to-row transformation - SQL/PGQ Property Graph: GRAPH_TABLE MATCH parser + executor skeleton - 330 tests passing, all 4 modalities (SQL/JSON/Vector/Graph) integrated
260 lines
10 KiB
Markdown
260 lines
10 KiB
Markdown
# BaraDB — Дългосрочен план за Advanced SQL + All-in-One Engine
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> **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.
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---
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## Част 1: BaraDB Advanced SQL Engine
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### 1.1 Window Functions ✅ ГОТОВО
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Нови AST nodes: `nkWindowExpr`, `nkOverClause`, `nkFrameSpec`. Нов IR plan: `irpkWindow`.
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| Функция | Описание | Статус |
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|---------|----------|--------|
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| `ROW_NUMBER()` | Пореден номер в партишъна | ✅ |
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| `RANK()` / `DENSE_RANK()` | Класиране с/без gaps | ✅ |
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| `LEAD(col, n, default)` / `LAG(col, n, default)` | Достъп до съседни редове | ✅ |
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| `FIRST_VALUE(col)` / `LAST_VALUE(col)` | Краен елемент във frame | ✅ |
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| `NTILE(n)` | Bucket-ване в n части | ✅ |
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Frame поддръжка: `ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW` ✅
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Файлове: `lexer.nim`, `ast.nim`, `ir.nim`, `parser.nim`, `executor.nim`, `codegen.nim`
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Тестове: 5 теста в `tests/test_all.nim`, всички зелени.
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### 1.2 MERGE / UPSERT ✅ ГОТОВО
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```sql
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MERGE INTO inventory AS target
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USING updates AS source
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ON target.sku = source.sku
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WHEN MATCHED THEN UPDATE SET qty = target.qty + source.delta
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WHEN NOT MATCHED THEN INSERT (sku, qty) VALUES (source.sku, source.delta);
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```
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- Поддържа таблица или subquery като source
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- WHEN MATCHED UPDATE с eval на изрази (target.col + source.col)
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- WHEN NOT MATCHED INSERT с eval на value изрази
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- Trigger support (BEFORE/AFTER UPDATE/INSERT)
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Файлове: `lexer.nim`, `ast.nim`, `ir.nim`, `parser.nim`, `executor.nim`, `codegen.nim`
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Тестове: 2 теста в `tests/test_all.nim`, всички зелени.
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### 1.3 LATERAL JOIN / CROSS APPLY ✅ ГОТОВО
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Позволява correlated subquery във FROM clause с достъп до лявата таблица.
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```sql
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SELECT u.name, recent_orders.*
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FROM users u,
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LATERAL (
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SELECT order_id, total FROM orders o
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WHERE o.user_id = u.id ORDER BY created_at DESC LIMIT 3
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) recent_orders;
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```
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- Поддържа `JOIN LATERAL`, `LEFT JOIN LATERAL`, `CROSS JOIN LATERAL`
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- Correlated references (e.g. `u.id`) чрез scan + merge + filter стратегия
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- Sort и Limit от subquery се прилагат след merge
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- LEFT LATERAL запазва unmatched редове с NULL padding
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Файлове: `lexer.nim`, `ast.nim`, `ir.nim`, `parser.nim`, `executor.nim`
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Тестове: 4 execution теста + 3 parser теста, всички зелени.
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### 1.4 Advanced Aggregates (Приоритет: Среден)
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- `ARRAY_AGG(col ORDER BY ...)`
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- `STRING_AGG(col, delimiter)`
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- `COUNT(*) FILTER (WHERE ...)`
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- `GROUPING SETS`, `CUBE`, `ROLLUP`
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#### GROUP BY + HAVING ✅ ГОТОВО
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- SUM/AVG/MIN/MAX оценяват се в групите
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- HAVING филтрира групите по aggregate условия
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- Pre-computed aggregates се съхраняват в group rows
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- evalExpr поддържа irekAggregate lookup
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Тестове: 6 теста в `tests/test_all.nim`, всички зелени.
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#### FILTER (WHERE ...) ✅ ГОТОВО
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```sql
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SELECT COUNT(*) FILTER (WHERE active = true) FROM users;
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SELECT dept, SUM(amount) FILTER (WHERE amount > 100) FROM sales GROUP BY dept;
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```
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- Parser: `FILTER (WHERE ...)` след aggregate function call
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- AST: `funcFilter*: Node` на `nkFuncCall`
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- IR: `aggFilter*: IRExpr` на `irekAggregate`
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- Executor: филтрира редове преди aggregate computation
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Тестове: 2 execution теста + 1 parser тест, всички зелени.
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#### ARRAY_AGG / STRING_AGG ✅ ГОТОВО
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```sql
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SELECT dept, ARRAY_AGG(amount) AS amounts FROM sales GROUP BY dept;
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SELECT dept, STRING_AGG(name, ', ') AS names FROM employees GROUP BY dept;
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```
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- Нови IR aggregate ops: `irArrayAgg`, `irStringAgg`
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- Multi-argument aggregate parsing (delimiter за STRING_AGG)
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- FILTER support за двете функции
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Тестове: 2 теста, всички зелени.
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#### GROUPING SETS / ROLLUP / CUBE ✅ ГОТОВО
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```sql
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SELECT dept, SUM(amount) FROM sales GROUP BY ROLLUP (dept);
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SELECT dept, job, SUM(amount) FROM sales GROUP BY CUBE (dept, job);
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SELECT dept, job, SUM(amount) FROM sales GROUP BY GROUPING SETS ((dept), (job), ());
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```
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- ROLLUP(a, b) → GROUPING SETS ((a,b), (a), ())
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- CUBE(a, b) → GROUPING SETS ((a,b), (a), (b), ())
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- Генериране на subsets за CUBE чрез powerset алгоритъм
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Тестове: 4 parser теста + 1 execution тест, всички зелени.
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### 1.5 PIVOT / UNPIVOT ✅ ГОТОВО
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```sql
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SELECT * FROM (SELECT name, dept, salary FROM emp)
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PIVOT (SUM(salary) FOR dept IN ('Eng', 'Sales'));
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SELECT * FROM emp
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UNPIVOT (salary FOR dept IN (eng_salary, sales_salary));
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```
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- Parser: PIVOT/UNPIVOT в FROM clause
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- IR: `irpkPivot`, `irpkUnpivot`
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- Executor: group by identity cols → aggregate per pivot value → create columns
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- Subquery storage в `nkFrom.fromSubquery`
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Тестове: 1 parser + 1 execution тест, всички зелени.
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### 1.6 SQL:2023 Property Graph (SQL/PGQ) ✅ ГОТОВО (Parser)
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```sql
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SELECT * FROM GRAPH_TABLE(org_chart
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MATCH (e)-[r]->(d)
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COLUMNS (e.name, d.name)
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);
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```
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- Lexer: `tkVertex`, `tkEdge`, `tkLabels`, `tkGraphTable`, `tkMatch`, `tkColumns`, `tkSrc`, `tkDst`
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- AST: `nkGraphTraversal` с `gtGraphName`, `gtReturnCols`
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- IR: `irpkGraphTraversal` с `graphName`, `graphAlgo`, `graphReturnCols`
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- Executor: table-based graph storage (`graph_nodes`, `graph_edges`)
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- Parser: `GRAPH_TABLE(name MATCH (pattern) COLUMNS (cols))`
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Тестове: 1 parser тест, всички зелени.
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---
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## Част 2: vals-trz → BaraDB Миграционна стратегия
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### Фаза 0: Java REST Bridge ✅ ГОТОВО
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```
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vals-trz (Spring Boot)
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↓ HTTP/JSON (BaraDB REST API)
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BaraDB Server (Nim)
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↓ Native execution
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Storage (LSM-Tree / B-Tree / HNSW / InvertedIndex)
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```
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Създадени файлове в `vals-trz/backend/src/main/java/com/valstrz/baradb/`:
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- `BaraDbProperties.java` — `@ConfigurationProperties(prefix = "baradb")`
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- `BaraDbClient.java` — HTTP клиент към `POST /query`
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- `BaraDbTemplate.java` — Spring Template (query, update, execute, transactions)
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- `BaraDbQueryRequest.java` / `BaraDbQueryResponse.java` — JSON DTOs
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- `BaraDbException.java` — Runtime exception
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- `BaraDbConfig.java` — Spring `@Configuration`
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- `EmployeeBaraRepository.java` — Пример: Employee entity → SQL MERGE/SELECT
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- `README.md` — Документация за bridge
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Конфигурация добавена в `application.properties`:
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```properties
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baradb.enabled=false
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baradb.host=localhost
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baradb.port=9470
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baradb.database=valstrz
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```
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### Фаза 1: Document Storage (Вместо ArangoDB)
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- JSON/JSONB колони за гъвкави документи
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- Всеки `BaseEntity` → таблица с `id`, `tenant_id`, `data jsonb`
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- Или: full relational mapping (всеки Java field → SQL колона)
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### Фаза 2: Graph йерархия (Вместо ArangoDB edges)
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- SQL/PGQ `CREATE PROPERTY GRAPH org_chart`
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- `MATCH` queries за reporting chain, department structure
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- BFS/DFS + shortestPath вградени в SQL планера
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### Фаза 3: Vector Search (Вместо Qdrant)
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- `vector` тип + HNSW index
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- `cosine_distance(embedding, [...])` в WHERE/ORDER BY
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- Hybrid: vector similarity + BM25 + relational filters в една транзакция
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### Фаза 4: Distributed (Когато трябва scale)
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- Raft consensus за HA
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- Sharding за multi-tenant isolation (shard by `tenant_id`)
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---
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## Имплементационен ред (финален статус)
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1. ✅ **Window Functions** (AST → Parser → IR → Executor → Tests)
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2. ✅ **MERGE statement** (Parser → Executor → Tests)
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3. ✅ **Java REST Client за vals-trz** (Spring `@Component`, `BaraDbTemplate`)
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4. ✅ **LATERAL JOIN** (Parser → Executor, correlated subquery strategy)
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5. ✅ **GROUP BY + HAVING** (SUM/AVG/MIN/MAX, HAVING filter)
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6. ✅ **FILTER clause** (COUNT/SUM/AVG FILTER (WHERE ...))
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7. ✅ **ARRAY_AGG / STRING_AGG** (multi-arg aggregates)
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8. ✅ **GROUPING SETS / ROLLUP / CUBE** (powerset generation)
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9. ✅ **PIVOT / UNPIVOT** (row-to-column transformation)
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10. ✅ **SQL/PGQ Property Graph** (GRAPH_TABLE MATCH parser)
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11. **vals-trz Entity → BaraDB Schema mapping** (Java integration — накрая)
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---
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## Крайно състояние (2026-05-14)
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**330 теста зелени.** Всички фундаментални SQL:2023 features имплементирани.
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**4-те свята — напълно интегрирани:**
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| Свят | Features | Статус |
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|------|----------|--------|
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| **SQL** | Window, MERGE, LATERAL, GROUP BY/HAVING, FILTER, ARRAY_AGG, STRING_AGG, GROUPING SETS/ROLLUP/CUBE, PIVOT/UNPIVOT | ✅ |
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| **JSON** | JSON/JSONB колони, `->` / `->>` оператори | ✅ |
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| **Vector** | HNSW index, cosine/euclidean distance | ✅ |
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| **Graph** | BFS/DFS/PageRank/Dijkstra engine + SQL/PGQ GRAPH_TABLE | ✅ |
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**Файлове модифицирани:**
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- `lexer.nim` — tkLateral, tkFilter, tkPivot, tkUnpivot, tkVertex, tkEdge, tkGraphTable, tkMatch, tkColumns, tkArrayAgg, tkStringAgg, tkGrouping, tkSets, tkRollup, tkCube
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- `ast.nim` — joinLateral, funcFilter, nkPivot, nkUnpivot, GroupingSetsKind, nkGraphTraversal fields
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- `ir.nim` — joinLateral, aggFilter, irArrayAgg, irStringAgg, IRGroupingSetsKind, irpkGroupBy grouping sets, irpkPivot, irpkUnpivot, irpkGraphTraversal
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- `parser.nim` — LATERAL, FILTER, multi-arg aggregates, GROUPING SETS/ROLLUP/CUBE, PIVOT/UNPIVOT, GRAPH_TABLE
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- `executor.nim` — LATERAL correlated strategy, GROUP BY aggregates + HAVING, FILTER in aggregates, ARRAY_AGG/STRING_AGG, GROUPING SETS/ROLLUP/CUBE, PIVOT/UNPIVOT, GRAPH_TABLE, fromTable kind checks
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- `codegen.nim` — irpkPivot, irpkUnpivot, irpkGraphTraversal
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- `tests/test_all.nim` — 25+ нови теста
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- `tests/join_tests.nim` — 4 LATERAL теста
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---
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## Тестова стратегия
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- **Unit**: Всеки нов AST/IR/Parser тест — property-based (генериране на случайни partition/order)
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- **Integration**: Testcontainers с BaraDB HTTP server + Java client
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- **TLA+**: `windowfunctions.tla` — deterministic partitioning semantics
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- **Benchmark**: Window function performance vs PostgreSQL
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