✔ PASS Search & Retrieval task_068_Q68_multilingual_search_routing Scaled: 1M+ Records Ordered: Yes

Multi-Lingual Catalog Routing by Detected ISO Language Code

Candidate Latency (1M Rows)

118.16 ms

(1.0x faster than golden)

Golden Latency (1M Rows)

121.17 ms

DuckDB in-memory engine

Underlying Data Volume

1,000,000 rows

61.0 MiB across 1 table

Evaluated Model

gemini-3.8-flash-low

Gen Latency: 12.9s

✔ 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

Global portals store titles in multiple localized columns (title_en, title_es, title_fr). Queries are dynamically routed to the matching language column based on user locale.

⚡ The Frontier Trap (Why LLMs Fail)

Searching across all language columns simultaneously creates false-positive matches due to inter-language false cognates.

Prompt / User Request Given to Model

Global portals store titles in multiple localized columns (title_en, title_es, title_fr). Queries are dynamically routed to the matching language column based on user locale.

Execute the required analytical SQL calculation in DuckDB.
Expected output columns: product_id, matched_title, language_used.
Results should be ordered as specified.

2. Underlying Tables, Row Counts & Data Volume

1 Tables (1,000,000 rows • 61.0 MiB)

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

📊 localized_products 1,000,000 rows 61.0 MiB Data Volume

product_id (INTEGER) title_en (VARCHAR) title_es (VARCHAR) title_fr (VARCHAR)

Data Preview (showing sample rows from 1,000,000 total rows):

#	product_id	title_en	title_es	title_fr
1	1	Red Bicycle	Bicicleta Roja	Velo Rouge
2	2	Green Helmet	Casco Verde	Casque Vert
3	2	Red Bicycle	Bicicleta Roja	Velo Rouge
4	3	Green Helmet	Casco Verde	Casque Vert
5	4	Red Bicycle	Bicicleta Roja	Velo Rouge
6	5	Green Helmet	Casco Verde	Casque Vert
7	6	Red Bicycle	Bicicleta Roja	Velo Rouge
8	7	Green Helmet	Casco Verde	Casque Vert
9	8	Red Bicycle	Bicicleta Roja	Velo Rouge
10	9	Green Helmet	Casco Verde	Casque Vert
11	10	Red Bicycle	Bicicleta Roja	Velo Rouge
12	11	Green Helmet	Casco Verde	Casque Vert
13	12	Red Bicycle	Bicicleta Roja	Velo Rouge
14	13	Green Helmet	Casco Verde	Casque Vert
15	14	Red Bicycle	Bicicleta Roja	Velo Rouge

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: 70.51 ms) Nodes consuming ≥ 20% of query runtime are highlighted with glowing borders

📐 PROJECTION 10.2%

Latency: 7.17 ms

Rows In/Out: 500,000

Data Vol: 17.17 MB

📁 Projections: ['#0', '__internal_...

⏳ ORDER_BY 🔥 43.9% Cost

Latency: 30.93 ms

Rows In/Out: 500,000

Data Vol: 11.44 MB

📐 PROJECTION 10.2%

Latency: 7.22 ms

Rows In/Out: 500,000

Data Vol: 11.44 MB

📁 Projections: ['#0', '__internal_...

📐 PROJECTION 0.3%

Latency: 0.24 ms

Rows In/Out: 500,000

Data Vol: 17.17 MB

📁 Projections: ['product_id', 'mat...

🔍 TABLE_SCAN 🔥 35.4% Cost

Latency: 24.96 ms

Rows In/Out: 500,000

Data Vol: 9.54 MB

📁 memory.main.localized_products

Operator	Cost %	Latency	Processed Rows	Data Volume	Details / Table	Status
ORDER_BY	43.9%	30.93 ms	500,000	11.44 MB	-	🔥 BOTTLENECK
TABLE_SCAN	35.4%	24.96 ms	500,000	9.54 MB	memory.main.localized_products	🔥 BOTTLENECK
PROJECTION	10.2%	7.22 ms	500,000	11.44 MB	Projections: ['#0', '__internal_compress_string_	Normal
PROJECTION	10.2%	7.17 ms	500,000	17.17 MB	Projections: ['#0', '__internal_decompress_strin	Normal
PROJECTION	0.3%	0.24 ms	500,000	17.17 MB	Projections: ['product_id', 'matched_title', 'la	Normal

4. Queries Executed (Candidate vs Golden Ground Truth)

Beautified & Syntax Highlighted

-- Match Spanish query 'Bicicleta' routed dynamically to title_es

SELECT product_id,
       title_es AS matched_title,
       'es' AS language_used
FROM localized_products
WHERE title_es ILIKE '%Bicicleta%'
ORDER BY product_id;

-- Match Spanish query 'Bicicleta' routed dynamically to title_es

SELECT product_id,
       title_es AS matched_title,
       'es' AS language_used
FROM localized_products
WHERE title_es ILIKE '%Bicicleta%'
ORDER BY product_id;

CREATE TABLE localized_products (product_id INT, title_en VARCHAR, title_es VARCHAR, title_fr VARCHAR);


INSERT INTO localized_products
VALUES (1, 'Red Bicycle', 'Bicicleta Roja', 'Velo Rouge'),
       (2, 'Green Helmet', 'Casco Verde', 'Casque Vert');

5. Query Results & Expected Ground Truth

Tabular Comparison

Actual Candidate Output (500,000 rows × 3 cols)

#	product_id	matched_title	language_used
1	1	Bicicleta Roja	es
2	2	Bicicleta Roja	es
3	4	Bicicleta Roja	es
4	6	Bicicleta Roja	es
5	8	Bicicleta Roja	es
6	10	Bicicleta Roja	es
7	12	Bicicleta Roja	es
8	14	Bicicleta Roja	es
9	16	Bicicleta Roja	es
10	18	Bicicleta Roja	es
11	20	Bicicleta Roja	es
12	22	Bicicleta Roja	es
13	24	Bicicleta Roja	es
14	26	Bicicleta Roja	es
15	28	Bicicleta Roja	es
16	30	Bicicleta Roja	es
17	32	Bicicleta Roja	es
18	34	Bicicleta Roja	es
19	36	Bicicleta Roja	es
20	38	Bicicleta Roja	es
21	40	Bicicleta Roja	es
22	42	Bicicleta Roja	es
23	44	Bicicleta Roja	es
24	46	Bicicleta Roja	es
25	48	Bicicleta Roja	es

Showing first 25 of 500,000 total rows.

Expected Golden Output (500,000 rows × 3 cols)

#	product_id	matched_title	language_used
1	1	Bicicleta Roja	es
2	2	Bicicleta Roja	es
3	4	Bicicleta Roja	es
4	6	Bicicleta Roja	es
5	8	Bicicleta Roja	es
6	10	Bicicleta Roja	es
7	12	Bicicleta Roja	es
8	14	Bicicleta Roja	es
9	16	Bicicleta Roja	es
10	18	Bicicleta Roja	es
11	20	Bicicleta Roja	es
12	22	Bicicleta Roja	es
13	24	Bicicleta Roja	es
14	26	Bicicleta Roja	es
15	28	Bicicleta Roja	es
16	30	Bicicleta Roja	es
17	32	Bicicleta Roja	es
18	34	Bicicleta Roja	es
19	36	Bicicleta Roja	es
20	38	Bicicleta Roja	es
21	40	Bicicleta Roja	es
22	42	Bicicleta Roja	es
23	44	Bicicleta Roja	es
24	46	Bicicleta Roja	es
25	48	Bicicleta Roja	es

Showing first 25 of 500,000 total rows.

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