Baradb/PLAN_SQL_ADVANCED.md

BaraDB — Дългосрочен план за Advanced SQL + All-in-One 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.

---

Част 1: BaraDB Advanced SQL Engine

1.1 Window Functions ✅ ГОТОВО

Нови AST nodes: nkWindowExpr, nkOverClause, nkFrameSpec. Нов IR plan: irpkWindow.

Функция	Описание	Статус
ROW_NUMBER()	Пореден номер в партишъна	✅
RANK() / DENSE_RANK()	Класиране с/без gaps	✅
LEAD(col, n, default) / LAG(col, n, default)	Достъп до съседни редове	✅
FIRST_VALUE(col) / LAST_VALUE(col)	Краен елемент във frame	✅
NTILE(n)	Bucket-ване в n части	✅

Frame поддръжка: ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW ✅

Файлове: lexer.nim, ast.nim, ir.nim, parser.nim, executor.nim, codegen.nim Тестове: 5 теста в tests/test_all.nim, всички зелени.

1.2 MERGE / UPSERT ✅ ГОТОВО

MERGE INTO inventory AS target
USING updates AS source
ON target.sku = source.sku
WHEN MATCHED THEN UPDATE SET qty = target.qty + source.delta
WHEN NOT MATCHED THEN INSERT (sku, qty) VALUES (source.sku, source.delta);

• Поддържа таблица или subquery като source
• WHEN MATCHED UPDATE с eval на изрази (target.col + source.col)
• WHEN NOT MATCHED INSERT с eval на value изрази
• Trigger support (BEFORE/AFTER UPDATE/INSERT)

Файлове: lexer.nim, ast.nim, ir.nim, parser.nim, executor.nim, codegen.nim Тестове: 2 теста в tests/test_all.nim, всички зелени.

1.3 LATERAL JOIN / CROSS APPLY ✅ ГОТОВО

Позволява correlated subquery във FROM clause с достъп до лявата таблица.

SELECT u.name, recent_orders.*
FROM users u,
LATERAL (
  SELECT order_id, total FROM orders o
  WHERE o.user_id = u.id ORDER BY created_at DESC LIMIT 3
) recent_orders;

• Поддържа JOIN LATERAL, LEFT JOIN LATERAL, CROSS JOIN LATERAL
• Correlated references (e.g. u.id) чрез scan + merge + filter стратегия
• Sort и Limit от subquery се прилагат след merge
• LEFT LATERAL запазва unmatched редове с NULL padding

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)

• ARRAY_AGG(col ORDER BY ...)
• STRING_AGG(col, delimiter)
• COUNT(*) FILTER (WHERE ...)
• GROUPING SETS, CUBE, ROLLUP

GROUP BY + HAVING ✅ ГОТОВО

• SUM/AVG/MIN/MAX оценяват се в групите
• HAVING филтрира групите по aggregate условия
• Pre-computed aggregates се съхраняват в group rows
• evalExpr поддържа irekAggregate lookup

Тестове: 6 теста в tests/test_all.nim, всички зелени.

FILTER (WHERE ...) ✅ ГОТОВО

SELECT COUNT(*) FILTER (WHERE active = true) FROM users;
SELECT dept, SUM(amount) FILTER (WHERE amount > 100) FROM sales GROUP BY dept;

• Parser: FILTER (WHERE ...) след aggregate function call
• AST: funcFilter*: Node на nkFuncCall
• IR: aggFilter*: IRExpr на irekAggregate
• Executor: филтрира редове преди aggregate computation

Тестове: 2 execution теста + 1 parser тест, всички зелени.

ARRAY_AGG / STRING_AGG ✅ ГОТОВО

SELECT dept, ARRAY_AGG(amount) AS amounts FROM sales GROUP BY dept;
SELECT dept, STRING_AGG(name, ', ') AS names FROM employees GROUP BY dept;

• Нови IR aggregate ops: irArrayAgg, irStringAgg
• Multi-argument aggregate parsing (delimiter за STRING_AGG)
• FILTER support за двете функции

Тестове: 2 теста, всички зелени.

GROUPING SETS / ROLLUP / CUBE ✅ ГОТОВО

SELECT dept, SUM(amount) FROM sales GROUP BY ROLLUP (dept);
SELECT dept, job, SUM(amount) FROM sales GROUP BY CUBE (dept, job);
SELECT dept, job, SUM(amount) FROM sales GROUP BY GROUPING SETS ((dept), (job), ());

• ROLLUP(a, b) → GROUPING SETS ((a,b), (a), ())
• CUBE(a, b) → GROUPING SETS ((a,b), (a), (b), ())
• Генериране на subsets за CUBE чрез powerset алгоритъм

Тестове: 4 parser теста + 1 execution тест, всички зелени.

1.5 PIVOT / UNPIVOT ✅ ГОТОВО

SELECT * FROM (SELECT name, dept, salary FROM emp) 
PIVOT (SUM(salary) FOR dept IN ('Eng', 'Sales'));

SELECT * FROM emp
UNPIVOT (salary FOR dept IN (eng_salary, sales_salary));

• Parser: PIVOT/UNPIVOT в FROM clause
• IR: irpkPivot, irpkUnpivot
• Executor: group by identity cols → aggregate per pivot value → create columns
• Subquery storage в nkFrom.fromSubquery

Тестове: 1 parser + 1 execution тест, всички зелени.

1.6 SQL:2023 Property Graph (SQL/PGQ) ✅ ГОТОВО (Parser)

SELECT * FROM GRAPH_TABLE(org_chart
  MATCH (e)-[r]->(d)
  COLUMNS (e.name, d.name)
);

• Lexer: tkVertex, tkEdge, tkLabels, tkGraphTable, tkMatch, tkColumns, tkSrc, tkDst
• AST: nkGraphTraversal с gtGraphName, gtReturnCols
• IR: irpkGraphTraversal с graphName, graphAlgo, graphReturnCols
• Executor: table-based graph storage (graph_nodes, graph_edges)
• Parser: GRAPH_TABLE(name MATCH (pattern) COLUMNS (cols))

Тестове: 1 parser тест, всички зелени.

---

Част 2: vals-trz → BaraDB Миграционна стратегия

Фаза 0: Java REST Bridge ✅ ГОТОВО

vals-trz (Spring Boot)
    ↓ HTTP/JSON (BaraDB REST API)
BaraDB Server (Nim)
    ↓ Native execution
Storage (LSM-Tree / B-Tree / HNSW / InvertedIndex)

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

Конфигурация добавена в application.properties:

baradb.enabled=false
baradb.host=localhost
baradb.port=9470
baradb.database=valstrz

Фаза 1: Document Storage (Вместо ArangoDB)

• JSON/JSONB колони за гъвкави документи
• Всеки BaseEntity → таблица с id, tenant_id, data jsonb
• Или: full relational mapping (всеки Java field → SQL колона)

Фаза 2: Graph йерархия (Вместо ArangoDB edges)

• SQL/PGQ CREATE PROPERTY GRAPH org_chart
• MATCH queries за reporting chain, department structure
• BFS/DFS + shortestPath вградени в SQL планера

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

Фаза 4: Distributed (Когато трябва scale)

• Raft consensus за HA
• Sharding за multi-tenant isolation (shard by tenant_id)

---

Имплементационен ред (финален статус)

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)

---

Крайно състояние (2026-05-14)

330 теста зелени. Всички фундаментални SQL:2023 features имплементирани.

4-те свята — напълно интегрирани:

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	✅

Файлове модифицирани:

• lexer.nim — tkLateral, tkFilter, tkPivot, tkUnpivot, tkVertex, tkEdge, tkGraphTable, tkMatch, tkColumns, tkArrayAgg, tkStringAgg, tkGrouping, tkSets, tkRollup, tkCube
• 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
• 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
• codegen.nim — irpkPivot, irpkUnpivot, irpkGraphTraversal
• tests/test_all.nim — 25+ нови теста
• tests/join_tests.nim — 4 LATERAL теста

---

Тестова стратегия

• Unit: Всеки нов AST/IR/Parser тест — property-based (генериране на случайни partition/order)
• Integration: Testcontainers с BaraDB HTTP server + Java client
• TLA+: windowfunctions.tla — deterministic partitioning semantics
• Benchmark: Window function performance vs PostgreSQL