velesdb-core 1.7.2

High-performance vector database engine written in Rust
Documentation
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
//! Complex VelesQL parser tests (EPIC-017 US-003/US-006).
//!
//! Tests combining: aggregates, multicolumn, vector search, graph patterns, EXPLAIN.
//! Based on research: VLDB 2024 hybrid vector+graph queries, cost estimation patterns.
#![cfg(all(test, feature = "persistence"))]

use crate::velesql::{Parser, QueryPlan, SelectColumns};

// =============================================================================
// CATEGORY 1: Pure Aggregation Queries
// =============================================================================

#[test]
fn test_parse_count_star_simple() {
    let query = Parser::parse("SELECT COUNT(*) FROM products").unwrap();
    match &query.select.columns {
        SelectColumns::Aggregations(aggs) => {
            assert_eq!(aggs.len(), 1);
            assert!(matches!(
                aggs[0].function_type,
                crate::velesql::AggregateType::Count
            ));
        }
        _ => panic!("Expected aggregations"),
    }
}

#[test]
fn test_parse_multiple_aggregates() {
    let query = Parser::parse(
        "SELECT COUNT(*), SUM(price), AVG(rating), MIN(stock), MAX(price) FROM products",
    )
    .unwrap();

    match &query.select.columns {
        SelectColumns::Aggregations(aggs) => {
            assert_eq!(aggs.len(), 5);
        }
        _ => panic!("Expected aggregations"),
    }
}

#[test]
fn test_parse_aggregate_with_alias() {
    let query =
        Parser::parse("SELECT COUNT(*) AS total, AVG(price) AS avg_price FROM products").unwrap();

    match &query.select.columns {
        SelectColumns::Aggregations(aggs) => {
            assert_eq!(aggs.len(), 2);
            assert_eq!(aggs[0].alias, Some("total".to_string()));
            assert_eq!(aggs[1].alias, Some("avg_price".to_string()));
        }
        _ => panic!("Expected aggregations"),
    }
}

// =============================================================================
// CATEGORY 2: GROUP BY + Aggregations
// =============================================================================

#[test]
fn test_parse_groupby_single_column() {
    let query = Parser::parse("SELECT category, COUNT(*) FROM products GROUP BY category").unwrap();

    assert!(query.select.group_by.is_some());
    let group_by = query.select.group_by.as_ref().unwrap();
    assert_eq!(group_by.columns, vec!["category"]);
}

#[test]
fn test_parse_groupby_multiple_columns() {
    let query = Parser::parse(
        "SELECT category, brand, COUNT(*), AVG(price) FROM products GROUP BY category, brand",
    )
    .unwrap();

    assert!(query.select.group_by.is_some());
    let group_by = query.select.group_by.as_ref().unwrap();
    assert_eq!(group_by.columns, vec!["category", "brand"]);
}

#[test]
fn test_parse_groupby_with_where() {
    let query = Parser::parse(
        "SELECT category, SUM(sales) FROM products WHERE active = true GROUP BY category",
    )
    .unwrap();

    assert!(query.select.where_clause.is_some());
    assert!(query.select.group_by.is_some());
}

// =============================================================================
// CATEGORY 3: HAVING Clause (Post-Aggregation Filters)
// =============================================================================

#[test]
fn test_parse_having_count() {
    let query = Parser::parse(
        "SELECT category, COUNT(*) FROM products GROUP BY category HAVING COUNT(*) > 10",
    )
    .unwrap();

    assert!(query.select.having.is_some());
    let having = query.select.having.as_ref().unwrap();
    assert!(!having.conditions.is_empty());
}

#[test]
fn test_parse_having_avg() {
    let query = Parser::parse(
        "SELECT brand, AVG(price) FROM products GROUP BY brand HAVING AVG(price) > 100",
    )
    .unwrap();

    assert!(query.select.having.is_some());
}

#[test]
fn test_parse_having_multiple_conditions() {
    let query = Parser::parse(
        "SELECT category, COUNT(*), AVG(price) FROM products \
         GROUP BY category \
         HAVING COUNT(*) > 5 AND AVG(price) < 500",
    )
    .unwrap();

    assert!(query.select.having.is_some());
    let having = query.select.having.as_ref().unwrap();
    assert_eq!(having.conditions.len(), 2);
}

// =============================================================================
// CATEGORY 4: Vector Search Queries
// =============================================================================

#[test]
fn test_parse_vector_near_basic() {
    let query =
        Parser::parse("SELECT * FROM embeddings WHERE vector NEAR $query LIMIT 10").unwrap();

    assert!(query.select.where_clause.is_some());
    assert_eq!(query.select.limit, Some(10));
}

#[test]
fn test_parse_vector_near_with_filter() {
    let query = Parser::parse(
        "SELECT * FROM embeddings WHERE vector NEAR $query AND category = 'tech' LIMIT 20",
    )
    .unwrap();

    assert!(query.select.where_clause.is_some());
}

#[test]
fn test_parse_vector_similarity_order() {
    let query = Parser::parse(
        "SELECT id, title FROM docs WHERE vector NEAR $v ORDER BY similarity(vector, $v) DESC LIMIT 5",
    )
    .unwrap();

    assert!(query.select.order_by.is_some());
}

// =============================================================================
// CATEGORY 5: Hybrid Vector + Aggregation Queries
// =============================================================================

#[test]
fn test_parse_vector_near_then_count() {
    // First filter by vector similarity, then count results per category
    let query = Parser::parse(
        "SELECT category, COUNT(*) FROM products WHERE vector NEAR $query GROUP BY category",
    )
    .unwrap();

    assert!(query.select.where_clause.is_some());
    assert!(query.select.group_by.is_some());
}

#[test]
fn test_parse_vector_search_with_aggregation_and_having() {
    let query = Parser::parse(
        "SELECT category, COUNT(*), AVG(price) FROM products \
         WHERE vector NEAR $embedding AND stock > 0 \
         GROUP BY category \
         HAVING COUNT(*) >= 3",
    )
    .unwrap();

    assert!(query.select.where_clause.is_some());
    assert!(query.select.group_by.is_some());
    assert!(query.select.having.is_some());
}

// =============================================================================
// CATEGORY 6: WITH Clause (Query Configuration)
// =============================================================================

#[test]
fn test_parse_with_ef_search() {
    let query =
        Parser::parse("SELECT * FROM docs WHERE vector NEAR $v LIMIT 10 WITH (ef_search = 200)")
            .unwrap();

    assert!(query.select.with_clause.is_some());
    let with = query.select.with_clause.as_ref().unwrap();
    assert!(!with.options.is_empty());
}

#[test]
fn test_parse_with_multiple_options() {
    let query = Parser::parse(
        "SELECT * FROM docs WHERE vector NEAR $v LIMIT 10 \
         WITH (ef_search = 200, rerank = true, threshold = 0.8)",
    )
    .unwrap();

    assert!(query.select.with_clause.is_some());
    let with = query.select.with_clause.as_ref().unwrap();
    assert!(with.options.len() >= 3);
}

// =============================================================================
// CATEGORY 7: JOIN Queries (Cross-Store)
// =============================================================================

#[test]
fn test_parse_simple_join() {
    let query =
        Parser::parse("SELECT * FROM products JOIN prices ON prices.product_id = products.id")
            .unwrap();

    assert!(!query.select.joins.is_empty());
    assert_eq!(query.select.joins[0].table, "prices");
}

#[test]
fn test_parse_join_with_alias() {
    let query =
        Parser::parse("SELECT * FROM products JOIN prices AS pr ON pr.product_id = products.id")
            .unwrap();

    assert!(!query.select.joins.is_empty());
    assert_eq!(query.select.joins[0].alias, Some("pr".to_string()));
}

#[test]
fn test_parse_multiple_joins() {
    let query = Parser::parse(
        "SELECT * FROM products \
         JOIN prices ON prices.product_id = products.id \
         JOIN inventory AS inv ON inv.product_id = products.id",
    )
    .unwrap();

    assert_eq!(query.select.joins.len(), 2);
}

// =============================================================================
// CATEGORY 8: Complex Combined Queries
// =============================================================================

#[test]
fn test_parse_full_featured_query() {
    // The "everything" query: vector + filter + group by + having + order + limit + with
    let query = Parser::parse(
        "SELECT category, COUNT(*), AVG(price) FROM products \
         WHERE vector NEAR $embedding AND stock > 0 \
         GROUP BY category \
         HAVING COUNT(*) > 5 \
         ORDER BY category \
         LIMIT 100 \
         OFFSET 10 \
         WITH (ef_search = 300)",
    )
    .unwrap();

    // Verify all clauses are parsed
    assert!(query.select.where_clause.is_some(), "Should have WHERE");
    assert!(query.select.group_by.is_some(), "Should have GROUP BY");
    assert!(query.select.having.is_some(), "Should have HAVING");
    assert!(query.select.order_by.is_some(), "Should have ORDER BY");
    assert_eq!(query.select.limit, Some(100), "Should have LIMIT 100");
    assert_eq!(query.select.offset, Some(10), "Should have OFFSET 10");
    assert!(query.select.with_clause.is_some(), "Should have WITH");
}

#[test]
fn test_parse_analytics_dashboard_query() {
    // Typical analytics query: aggregates with filters and grouping
    let query = Parser::parse(
        "SELECT region, product_type, SUM(revenue), COUNT(*), AVG(quantity) \
         FROM sales \
         WHERE date >= '2024-01-01' AND date <= '2024-12-31' \
         GROUP BY region, product_type \
         HAVING SUM(revenue) > 10000 \
         ORDER BY region \
         LIMIT 50",
    )
    .unwrap();

    assert!(query.select.group_by.is_some());
    let group_by = query.select.group_by.as_ref().unwrap();
    assert_eq!(group_by.columns.len(), 2);
}

#[test]
fn test_parse_semantic_search_with_metadata_filter() {
    // RAG-style query: vector similarity + metadata filters
    let query = Parser::parse(
        "SELECT id, title, content FROM documents \
         WHERE vector NEAR $query_embedding \
         AND category IN ('tech', 'science') \
         AND published = true \
         ORDER BY similarity(vector, $query_embedding) DESC \
         LIMIT 10 \
         WITH (ef_search = 400, threshold = 0.75)",
    )
    .unwrap();

    assert!(query.select.where_clause.is_some());
    assert!(query.select.order_by.is_some());
    assert!(query.select.with_clause.is_some());
}

// =============================================================================
// CATEGORY 9: EXPLAIN Query Plan
// =============================================================================

#[test]
fn test_explain_simple_scan() {
    let query = Parser::parse("SELECT * FROM products LIMIT 100").unwrap();
    let plan = QueryPlan::from_select(&query.select);

    // Should be a simple table scan
    assert!(plan.index_used.is_none());
    let tree = plan.to_tree();
    assert!(tree.contains("TableScan") || tree.contains("Scan"));
}

#[test]
fn test_explain_vector_search_uses_hnsw() {
    let query = Parser::parse("SELECT * FROM embeddings WHERE vector NEAR $v LIMIT 10").unwrap();
    let plan = QueryPlan::from_select(&query.select);

    // Should use HNSW index
    assert_eq!(
        plan.index_used,
        Some(crate::velesql::IndexType::Hnsw),
        "Vector search should use HNSW index"
    );
}

#[test]
fn test_explain_with_filter_shows_strategy() {
    let query =
        Parser::parse("SELECT * FROM products WHERE vector NEAR $v AND category = 'tech' LIMIT 10")
            .unwrap();
    let plan = QueryPlan::from_select(&query.select);

    // Should show filter strategy
    assert!(
        plan.filter_strategy != crate::velesql::FilterStrategy::None,
        "Should have a filter strategy"
    );
}

#[test]
fn test_explain_cost_estimation() {
    let query = Parser::parse("SELECT * FROM products LIMIT 1000").unwrap();
    let plan = QueryPlan::from_select(&query.select);

    // Cost should be positive
    assert!(plan.estimated_cost_ms > 0.0, "Cost should be positive");
}

#[test]
fn test_explain_to_json() {
    let query = Parser::parse("SELECT * FROM docs WHERE vector NEAR $v LIMIT 10").unwrap();
    let plan = QueryPlan::from_select(&query.select);
    let json = plan.to_json().expect("Should serialize to JSON");

    assert!(json.contains("\"estimated_cost_ms\""));
    assert!(json.contains("\"root\""));
}

// =============================================================================
// CATEGORY 10: Case Insensitivity (SQL Standard)
// =============================================================================

#[test]
fn test_case_insensitive_keywords() {
    // All these should parse identically
    let queries = [
        "SELECT * FROM docs WHERE vector NEAR $v LIMIT 10",
        "select * from docs where vector near $v limit 10",
        "Select * From docs Where vector Near $v Limit 10",
        "SELECT * FROM docs WHERE VECTOR NEAR $V LIMIT 10",
    ];

    for sql in &queries {
        let result = Parser::parse(sql);
        assert!(result.is_ok(), "Failed to parse: {}", sql);
    }
}

#[test]
fn test_case_insensitive_groupby_having() {
    let queries = [
        "SELECT category, COUNT(*) FROM items GROUP BY category HAVING COUNT(*) > 5",
        "select category, count(*) from items group by category having count(*) > 5",
        "Select category, Count(*) From items Group By category Having Count(*) > 5",
    ];

    for sql in &queries {
        let query = Parser::parse(sql).unwrap_or_else(|_| panic!("Failed: {}", sql));
        assert!(query.select.group_by.is_some());
        assert!(query.select.having.is_some());
    }
}

// =============================================================================
// CATEGORY 11: Edge Cases and Error Handling
// =============================================================================

#[test]
fn test_parse_empty_result_query() {
    // Simple query with filter that might return no results
    let query = Parser::parse("SELECT * FROM products WHERE stock = 0 LIMIT 10");
    assert!(query.is_ok());
}

#[test]
fn test_parse_very_long_column_list() {
    let columns = (1..=20)
        .map(|i| format!("col{}", i))
        .collect::<Vec<_>>()
        .join(", ");
    let sql = format!("SELECT {} FROM wide_table LIMIT 100", columns);

    let query = Parser::parse(&sql);
    assert!(query.is_ok());
}

#[test]
fn test_parse_nested_column_names() {
    // VelesQL supports single-level dot notation (table.column)
    let query = Parser::parse("SELECT payload.title, metadata.author FROM docs").unwrap();

    match &query.select.columns {
        SelectColumns::Columns(cols) => {
            assert!(cols.iter().any(|c| c.name.contains('.')));
        }
        _ => panic!("Expected columns"),
    }
}

#[test]
fn test_parse_special_characters_in_strings() {
    let query = Parser::parse("SELECT * FROM docs WHERE title = 'Hello, World!' LIMIT 10");
    assert!(query.is_ok());
}