✔ PASS Non-SARGable Partition Pruning task_08_sargable_pruning Scaled: 1M+ Records Ordered: No

SARGable Temporal Partition Pruning and Exact Boundary Range

Candidate Latency (1M Rows)

0.45 ms

(1.0x slower than golden)

Golden Latency (1M Rows)

0.44 ms

DuckDB in-memory engine

Underlying Data Volume

1,000,000 rows

53.4 MiB across 1 table

Evaluated Model

gemini-3.8-flash-low

Gen Latency: 4.1s

✔ 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

Tests SARGable timestamp filtering and strict month boundary conditions, preventing full-scan string functions and end-of-interval leaks.

⚡ The Frontier Trap (Why LLMs Fail)

Models frequently write `BETWEEN '2025-01-01' AND '2025-02-01'`, which is inclusive on both ends and mistakenly captures records occurring exactly at midnight on February 1st (leaking records into the previous month's report). Alternatively, models wrap timestamp columns in non-SARGable scalar functions (like strftime or date_part), defeating column min/max partition pruning in columnar engines.

Prompt / User Request Given to Model

Analyze `system_logs` to calculate the count of ERROR logs and distinct error messages recorded during the calendar month of January 2025.

Requirements:
1. Filter for `severity = 'ERROR'`.
2. Filter strictly for events in January 2025 (from 2025-01-01 00:00:00 up to but strictly excluding 2025-02-01 00:00:00).
3. Keep the timestamp predicate SARGable (using direct range comparisons on `event_timestamp`) so that analytical partitions can be pruned.

Required output columns:
- error_count (BIGINT or INT)
- distinct_error_messages (BIGINT or INT)

Only a single summary row is returned.

2. Underlying Tables, Row Counts & Data Volume

1 Tables (1,000,000 rows • 53.4 MiB)

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

📊 system_logs 1,000,000 rows 53.4 MiB Data Volume

log_id (BIGINT) event_timestamp (TIMESTAMP) severity (VARCHAR) message (VARCHAR)

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

#	log_id	event_timestamp	severity	message
1	1	2024-12-31 23:59:59	ERROR	Disk almost full
2	2	2025-01-01 00:00:00	ERROR	Connection timed out
3	3	2025-01-15 12:30:00	INFO	Healthcheck OK
4	4	2025-01-20 18:00:00	ERROR	Out of memory
5	5	2025-01-20 19:00:00	ERROR	Connection timed out
6	6	2025-02-01 00:00:00	ERROR	Network unreachable
7	6	2026-03-01 09:30:00.000003	ERROR	Disk almost full
8	7	2026-03-01 09:30:00.000021	INFO	Connection timed out
9	8	2026-03-01 09:30:00.000047	ERROR	Healthcheck OK
10	9	2026-03-01 09:30:00.000078	INFO	Out of memory
11	10	2026-03-01 09:30:00.000085	ERROR	Network unreachable
12	11	2026-03-01 09:30:00.000112	INFO	Disk almost full
13	12	2026-03-01 09:30:00.000125	ERROR	Connection timed out
14	13	2026-03-01 09:30:00.000143	INFO	Healthcheck OK
15	14	2026-03-01 09:30:00.000163	ERROR	Out of memory

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

🔄 CTE 44.6%

Latency: 0.20 ms

Rows In/Out: 0

Data Vol: 0 B

🔍 TABLE_SCAN 6.8%

Latency: 0.03 ms

Rows In/Out: 3

Data Vol: 48 B

📁 memory.main.system_logs

📐 PROJECTION 0.1%

Latency: 0.00 ms

Rows In/Out: 1

Data Vol: 16 B

📁 Projections: ['#1', '#0']

⚙️ CROSS_PRODUCT 2.8%

Latency: 0.01 ms

Rows In/Out: 1

Data Vol: 16 B

📊 UNGROUPED_AGGREGATE 0.3%

Latency: 0.00 ms

Rows In/Out: 1

Data Vol: 8 B

📊 HASH_GROUP_BY 43.6%

Latency: 0.20 ms

Rows In/Out: 2

Data Vol: 32 B

📐 PROJECTION 0.1%

Latency: 0.00 ms

Rows In/Out: 3

Data Vol: 48 B

📁 Projections: message

🔍 CTE_SCAN 0.0%

Latency: 0.00 ms

Rows In/Out: 3

Data Vol: 48 B

📊 UNGROUPED_AGGREGATE 1.6%

Latency: 0.01 ms

Rows In/Out: 1

Data Vol: 8 B

🔍 CTE_SCAN 0.0%

Latency: 0.00 ms

Rows In/Out: 3

Data Vol: 48 B

Operator	Cost %	Latency	Processed Rows	Details / Table	Status
CTE	44.6%	0.20 ms	0	-	Normal
HASH_GROUP_BY	43.6%	0.20 ms	2	-	Normal
TABLE_SCAN	6.8%	0.03 ms	3	memory.main.system_logs	Normal
CROSS_PRODUCT	2.8%	0.01 ms	1	-	Normal
UNGROUPED_AGGREGATE	1.6%	0.01 ms	1	-	Normal
UNGROUPED_AGGREGATE	0.3%	0.00 ms	1	-	Normal
PROJECTION	0.1%	0.00 ms	3	Projections: message	Normal
PROJECTION	0.1%	0.00 ms	1	Projections: ['#1', '#0']	Normal
CTE_SCAN	0.0%	0.00 ms	3	-	Normal
CTE_SCAN	0.0%	0.00 ms	3	-	Normal

4. Queries Executed (Candidate vs Golden Ground Truth)

Beautified & Syntax Highlighted

SELECT count(*) AS error_count,
       count(DISTINCT message) AS distinct_error_messages
FROM system_logs
WHERE severity = 'ERROR'
  AND event_timestamp >= TIMESTAMP '2025-01-01 00:00:00'
  AND event_timestamp < TIMESTAMP '2025-02-01 00:00:00';

SELECT count(*) AS error_count,
       count(DISTINCT message) AS distinct_error_messages
FROM system_logs
WHERE severity = 'ERROR'
  AND event_timestamp >= '2025-01-01 00:00:00'
  AND event_timestamp < '2025-02-01 00:00:00';

CREATE TABLE system_logs (log_id BIGINT, event_timestamp TIMESTAMP, severity VARCHAR, message VARCHAR);


INSERT INTO system_logs
VALUES (1, '2024-12-31 23:59:59', 'ERROR', 'Disk almost full'),
       (2, '2025-01-01 00:00:00', 'ERROR', 'Connection timed out'),
       (3, '2025-01-15 12:30:00', 'INFO', 'Healthcheck OK'),
       (4, '2025-01-20 18:00:00', 'ERROR', 'Out of memory'),
       (5, '2025-01-20 19:00:00', 'ERROR', 'Connection timed out'),
       (6, '2025-02-01 00:00:00', 'ERROR', 'Network unreachable');

5. Query Results & Expected Ground Truth

Tabular Comparison

Actual Candidate Output (1 rows × 2 cols)

#	error_count	distinct_error_messages
1	3	2

Expected Golden Output (1 rows × 2 cols)

#	error_count	distinct_error_messages
1	3	2

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