✔ PASS Search & Retrieval task_062_Q62_autocomplete_prefix_popularity Scaled: 1M+ Records Ordered: Yes

Search Autocomplete Suggestion with Popularity Frequency Weighting

Candidate Latency (1M Rows)

9.05 ms

(1.1x slower than golden)

Golden Latency (1M Rows)

8.14 ms

DuckDB in-memory engine

Underlying Data Volume

1,000,000 rows

38.1 MiB across 1 table

Evaluated Model

gemini-3.8-flash-low

Gen Latency: 17.0s

✔ 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

As users type 'air...', instant search suggests 'air fryer' (100k searches) before 'air mattress' (5k searches). Suggestions must match the prefix AND rank by historical popularity.

⚡ The Frontier Trap (Why LLMs Fail)

Alphabetical sorting of prefix matches suggests obscure terms before popular items.

Prompt / User Request Given to Model

As users type 'air...', instant search suggests 'air fryer' (100k searches) before 'air mattress' (5k searches). Suggestions must match the prefix AND rank by historical popularity.

Execute the required analytical SQL calculation in DuckDB.
Expected output columns: query_string, monthly_search_volume, popularity_rank_score.
Results should be ordered as specified.

2. Underlying Tables, Row Counts & Data Volume

1 Tables (1,000,000 rows • 38.1 MiB)

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

📊 search_telemetry 1,000,000 rows 38.1 MiB Data Volume

query_string (VARCHAR) monthly_search_volume (INTEGER) conversion_rate (DECIMAL(4,3))

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

#	query_string	monthly_search_volume	conversion_rate
1	air fryer ninja	120,000	0.085
2	airpods pro 2	250,000	0.12
3	air mattress queen	15,000	0.04
4	airplane pillow	3,000	0.025
5	air fryer ninja	20,645	0.01
6	airpods pro 2	83,280	0.12
7	air mattress queen	168,657	0.04
8	airplane pillow	99,072	0.11
9	air fryer ninja	67,820	0.1
10	airpods pro 2	1	0.02
11	air mattress queen	131,666	0.02
12	airplane pillow	171,311	0.1
13	air fryer ninja	221,098	0.11
14	airpods pro 2	173,745	0.06
15	air mattress queen	167,705	0.08

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

✂️ TOP_N 11.4%

Latency: 2.45 ms

Rows In/Out: 3

Data Vol: 4,180 B

📐 PROJECTION 🔥 60.7% Cost

Latency: 13.03 ms

Rows In/Out: 1,000,000

Data Vol: 26.70 MB

📁 Projections: ['query_string', 'm...

🔍 TABLE_SCAN 🔥 27.9% Cost

Latency: 6.00 ms

Rows In/Out: 1,000,000

Data Vol: 20.98 MB

📁 memory.main.search_telemetry

Operator	Cost %	Latency	Processed Rows	Data Volume	Details / Table	Status
PROJECTION	60.7%	13.03 ms	1,000,000	26.70 MB	Projections: ['query_string', 'monthly_search_vo	🔥 BOTTLENECK
TABLE_SCAN	27.9%	6.00 ms	1,000,000	20.98 MB	memory.main.search_telemetry	🔥 BOTTLENECK
TOP_N	11.4%	2.45 ms	3	0.00 MB	-	Normal

4. Queries Executed (Candidate vs Golden Ground Truth)

Beautified & Syntax Highlighted

-- Autocomplete suggestions for prefix 'air' ranked by volume * conversion

SELECT query_string,
       monthly_search_volume,
       round(monthly_search_volume * conversion_rate, 0) AS popularity_rank_score
FROM search_telemetry
WHERE query_string LIKE 'air%'
ORDER BY popularity_rank_score DESC
LIMIT 3;

-- Autocomplete suggestions for prefix 'air' ranked by volume * conversion

SELECT query_string,
       monthly_search_volume,
       round(monthly_search_volume * conversion_rate, 0) AS popularity_rank_score
FROM search_telemetry
WHERE query_string LIKE 'air%'
ORDER BY popularity_rank_score DESC
LIMIT 3;

CREATE TABLE search_telemetry (query_string VARCHAR, monthly_search_volume INT, conversion_rate decimal(4, 3));


INSERT INTO search_telemetry
VALUES ('air fryer ninja', 120000, 0.085),
       ('airpods pro 2', 250000, 0.120),
       ('air mattress queen', 15000, 0.040),
       ('airplane pillow', 3000, 0.025);

5. Query Results & Expected Ground Truth

Tabular Comparison

Actual Candidate Output (3 rows × 3 cols)

#	query_string	monthly_search_volume	popularity_rank_score
1	airplane pillow	505,700	141,596
2	airpods pro 2	562,983	140,746
3	air mattress queen	604,419	132,972

Expected Golden Output (3 rows × 3 cols)

#	query_string	monthly_search_volume	popularity_rank_score
1	airplane pillow	505,700	141,596
2	airpods pro 2	562,983	140,746
3	air mattress queen	604,419	132,972

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