SQL-FRONTIER Benchmark Deep-Dive / Vector & Embeddings / task_050_Q50_contrastive_triplet_loss_evaluation
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✔ PASS Vector & Embeddings task_050_Q50_contrastive_triplet_loss_evaluation Scaled: 1M+ Records Ordered: Yes

Contrastive Triplet Evaluation: Anchor-Positive-Negative Separation

Candidate Latency (1M Rows)
201.63 ms
(1.0x slower than golden)
Golden Latency (1M Rows)
198.85 ms
DuckDB in-memory engine
Underlying Data Volume
1,000,000 rows
26.7 MiB across 1 table
Evaluated Model
gemini-3.8-flash-low
Gen Latency: 14.3s
✔ Correctness Verified on 1M+ Records
Candidate SQL output matches ground truth golden query across all multiset rows and numeric tolerances (tol=1e-3). Zero data skew or anti-patterns detected on full dataset scale.

1. Analytical Significance & What It Means

Scenario & Frontier Trap
Business Scenario & Problem

In embedding model evaluation, high-quality models satisfy the triplet condition: distance(anchor, positive) + margin < distance(anchor, negative). We evaluate model margin violations directly in SQL.

⚡ The Frontier Trap (Why LLMs Fail)

Validating embedding models often requires ad-hoc PyTorch scripts; SQL evaluation enables continuous monitoring in production.

Prompt / User Request Given to Model
In embedding model evaluation, high-quality models satisfy the triplet condition: distance(anchor, positive) + margin < distance(anchor, negative). We evaluate model margin violations directly in SQL.

Execute the required analytical SQL calculation in DuckDB.
Expected output columns: triplet_id, dist_pos, dist_neg, triplet_margin_loss, evaluation_status.
Results should be ordered as specified.

2. Underlying Tables, Row Counts & Data Volume

1 Tables (1,000,000 rows • 26.7 MiB)

Production-scale fixtures loaded into the isolated DuckDB sandbox with at least 1,000,000 records per table:

📊 eval_triplets 1,000,000 rows 26.7 MiB Data Volume
triplet_id (INTEGER) anchor_vec (FLOAT[2]) positive_vec (FLOAT[2]) negative_vec (FLOAT[2])
Data Preview (showing sample rows from 1,000,000 total rows):
# triplet_id anchor_vec positive_vec negative_vec
1 1 [1. 0.] [0.95 0.05] [0.1 0.9]
2 2 [1. 0.] [0.5 0.5] [0.9 0.1]
3 2 [0.8712 0.1331] [0.8712 0.1331] [0.8712 0.1331]
4 3 [0.9227 0.1104] [0.9227 0.1104] [0.9227 0.1104]
5 4 [0.8512 0.0896] [0.8512 0.0896] [0.8512 0.0896]
6 5 [0.8718 0.1497] [0.8718 0.1497] [0.8718 0.1497]
7 6 [0.936 0.0551] [0.936 0.0551] [0.936 0.0551]
8 7 [0.867 0.1217] [0.867 0.1217] [0.867 0.1217]
9 8 [0.8825 0.0401] [0.8825 0.0401] [0.8825 0.0401]
10 9 [0.7967 0.1071] [0.7967 0.1071] [0.7967 0.1071]
11 10 [0.758 0.1112] [0.758 0.1112] [0.758 0.1112]
12 11 [0.8639 0.1119] [0.8639 0.1119] [0.8639 0.1119]
13 12 [0.8267 0.1308] [0.8267 0.1308] [0.8267 0.1308]
14 13 [0.7917 0.1414] [0.7917 0.1414] [0.7917 0.1414]
15 14 [0.8932 0.127 ] [0.8932 0.127 ] [0.8932 0.127 ]

3. Graphical Query Execution Plan & Cost Bottlenecks

Physical Operator Tree (DuckDB 2.0 Profiler)

Visual flowchart of the physical execution pipeline. Operators with high relative costs (≥ 20% of query runtime or heavy latency) are visually highlighted:

Pipeline Latency Distribution (Total Plan Time: 269.38 ms) Nodes consuming ≥ 20% of query runtime are highlighted with glowing borders
📐 PROJECTION 4.0%
Latency: 10.66 ms
Rows In/Out: 1,000,000
Data Vol: 30.52 MB
📁 Projections: ['#0', '#1', '#2', ...
⏳ ORDER_BY 🔥 28.4% Cost
Latency: 76.57 ms
Rows In/Out: 1,000,000
Data Vol: 30.52 MB
⚠️ Heavy Execution Bottleneck (28.4% of query runtime)
📐 PROJECTION 0.2%
Latency: 0.52 ms
Rows In/Out: 1,000,000
Data Vol: 30.52 MB
📁 Projections: ['#0', '#1', '#2', ...
📐 PROJECTION 🔥 53.3% Cost
Latency: 143.48 ms
Rows In/Out: 1,000,000
Data Vol: 30.52 MB
📁 Projections: ['triplet_id', 'dis...
⚠️ Heavy Execution Bottleneck (53.3% of query runtime)
📐 PROJECTION 0.4%
Latency: 1.10 ms
Rows In/Out: 1,000,000
Data Vol: 15.26 MB
📁 Projections: ['triplet_id', '#1'...
📐 PROJECTION 11.4%
Latency: 30.62 ms
Rows In/Out: 1,000,000
Data Vol: 11.44 MB
📁 Projections: ['triplet_id', 'arr...
🔍 TABLE_SCAN 2.4%
Latency: 6.42 ms
Rows In/Out: 1,000,000
Data Vol: 26.70 MB
📁 memory.main.eval_triplets
Operator Cost % Latency Processed Rows Data Volume Details / Table Status
PROJECTION 53.3% 143.48 ms 1,000,000 30.52 MB Projections: ['triplet_id', 'dist_pos', 'dist_ne 🔥 BOTTLENECK
ORDER_BY 28.4% 76.57 ms 1,000,000 30.52 MB - 🔥 BOTTLENECK
PROJECTION 11.4% 30.62 ms 1,000,000 11.44 MB Projections: ['triplet_id', 'array_distance(anch Normal
PROJECTION 4.0% 10.66 ms 1,000,000 30.52 MB Projections: ['#0', '#1', '#2', '#3', '__interna Normal
TABLE_SCAN 2.4% 6.42 ms 1,000,000 26.70 MB memory.main.eval_triplets Normal
PROJECTION 0.4% 1.10 ms 1,000,000 15.26 MB Projections: ['triplet_id', '#1', '#2', '((#1 - Normal
PROJECTION 0.2% 0.52 ms 1,000,000 30.52 MB Projections: ['#0', '#1', '#2', '#3', '__interna Normal

4. Queries Executed (Candidate vs Golden Ground Truth)

Beautified & Syntax Highlighted
Candidate SQL Executed by gemini-3.8-flash-low (Runtime on 1M Rows: 201.63 ms) 
-- Compute triplet margin violation with margin = 0.2
-- Violation = GREATEST(0, dist(a, p) - dist(a, n) + margin)

SELECT triplet_id,
       round(array_distance(anchor_vec, positive_vec), 4) AS dist_pos,
       round(array_distance(anchor_vec, negative_vec), 4) AS dist_neg,
       round(greatest(0.0, array_distance(anchor_vec, positive_vec) - array_distance(anchor_vec, negative_vec) + 0.20), 4) AS triplet_margin_loss,
       CASE
           WHEN (array_distance(anchor_vec, positive_vec) - array_distance(anchor_vec, negative_vec) + 0.20) > 0 THEN 'VIOLATION'
           ELSE 'SATISFIED'
       END AS evaluation_status
FROM eval_triplets
ORDER BY triplet_id;
Golden Reference Ground Truth SQL (Runtime on 1M Rows: 198.85 ms) 
-- Compute triplet margin violation with margin = 0.2
 -- Violation = GREATEST(0, dist(a, p) - dist(a, n) + margin)

SELECT triplet_id,
       round(array_distance(anchor_vec, positive_vec), 4) AS dist_pos,
       round(array_distance(anchor_vec, negative_vec), 4) AS dist_neg,
       round(greatest(0.0, array_distance(anchor_vec, positive_vec) - array_distance(anchor_vec, negative_vec) + 0.20), 4) AS triplet_margin_loss,
       CASE
           WHEN (array_distance(anchor_vec, positive_vec) - array_distance(anchor_vec, negative_vec) + 0.20) > 0 THEN 'VIOLATION'
           ELSE 'SATISFIED'
       END AS evaluation_status
FROM eval_triplets
ORDER BY triplet_id;
DuckDB Fixture Setup DDL & Data Population 
CREATE TABLE eval_triplets (triplet_id INT, anchor_vec FLOAT[2], positive_vec FLOAT[2], negative_vec FLOAT[2]);


INSERT INTO eval_triplets
VALUES -- Good triplet: pos is close, neg is far
 (1, [1.0, 0.0]::FLOAT[2], [0.95, 0.05]::FLOAT[2], [0.1, 0.9]::FLOAT[2]),
 -- Violated triplet: neg is closer than pos!
 (2, [1.0, 0.0]::FLOAT[2], [0.5, 0.5]::FLOAT[2], [0.9, 0.1]::FLOAT[2]);

5. Query Results & Expected Ground Truth

Tabular Comparison
Actual Candidate Output (1,000,000 rows × 5 cols)
# triplet_id dist_pos dist_neg triplet_margin_loss evaluation_status
1 1 0.0707 1.2728 0 SATISFIED
2 2 0 0 0.2 VIOLATION
3 2 0.7071 0.1414 0.7657 VIOLATION
4 3 0 0 0.2 VIOLATION
5 4 0 0 0.2 VIOLATION
6 5 0 0 0.2 VIOLATION
7 6 0 0 0.2 VIOLATION
8 7 0 0 0.2 VIOLATION
9 8 0 0 0.2 VIOLATION
10 9 0 0 0.2 VIOLATION
11 10 0 0 0.2 VIOLATION
12 11 0 0 0.2 VIOLATION
13 12 0 0 0.2 VIOLATION
14 13 0 0 0.2 VIOLATION
15 14 0 0 0.2 VIOLATION
16 15 0 0 0.2 VIOLATION
17 16 0 0 0.2 VIOLATION
18 17 0 0 0.2 VIOLATION
19 18 0 0 0.2 VIOLATION
20 19 0 0 0.2 VIOLATION
21 20 0 0 0.2 VIOLATION
22 21 0 0 0.2 VIOLATION
23 22 0 0 0.2 VIOLATION
24 23 0 0 0.2 VIOLATION
25 24 0 0 0.2 VIOLATION
Showing first 25 of 1,000,000 total rows.
Expected Golden Output (1,000,000 rows × 5 cols)
# triplet_id dist_pos dist_neg triplet_margin_loss evaluation_status
1 1 0.0707 1.2728 0 SATISFIED
2 2 0.7071 0.1414 0.7657 VIOLATION
3 2 0 0 0.2 VIOLATION
4 3 0 0 0.2 VIOLATION
5 4 0 0 0.2 VIOLATION
6 5 0 0 0.2 VIOLATION
7 6 0 0 0.2 VIOLATION
8 7 0 0 0.2 VIOLATION
9 8 0 0 0.2 VIOLATION
10 9 0 0 0.2 VIOLATION
11 10 0 0 0.2 VIOLATION
12 11 0 0 0.2 VIOLATION
13 12 0 0 0.2 VIOLATION
14 13 0 0 0.2 VIOLATION
15 14 0 0 0.2 VIOLATION
16 15 0 0 0.2 VIOLATION
17 16 0 0 0.2 VIOLATION
18 17 0 0 0.2 VIOLATION
19 18 0 0 0.2 VIOLATION
20 19 0 0 0.2 VIOLATION
21 20 0 0 0.2 VIOLATION
22 21 0 0 0.2 VIOLATION
23 22 0 0 0.2 VIOLATION
24 23 0 0 0.2 VIOLATION
25 24 0 0 0.2 VIOLATION
Showing first 25 of 1,000,000 total rows.
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