✔ PASS Warehouse & Statistical task_096_Q96_window_frame_range_vs_rows_ties Scaled: 1M+ Records Ordered: Yes

Financial Ledger Running Total: ROWS vs. RANGE Semantic Contrast

Candidate Latency (1M Rows)

178.26 ms

(1.1x slower than golden)

Golden Latency (1M Rows)

164.90 ms

DuckDB in-memory engine

Underlying Data Volume

1,000,000 rows

22.9 MiB across 1 table

Evaluated Model

gemini-3.8-flash-low

Gen Latency: 10.9s

✔ 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

In financial auditing, understanding the exact behavioral difference between `ROWS BETWEEN` and `RANGE BETWEEN` prevents duplicate-timestamp errors from failing regulatory ledger reconciliations.

⚡ The Frontier Trap (Why LLMs Fail)

The ANSI SQL default for window frames with ORDER BY is `RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW`, which aggregates identical peers together into one lump sum.

Prompt / User Request Given to Model

In financial auditing, understanding the exact behavioral difference between `ROWS BETWEEN` and `RANGE BETWEEN` prevents duplicate-timestamp errors from failing regulatory ledger reconciliations.

Execute the required analytical SQL calculation in DuckDB.
Expected output columns: tx_id, val_date, amount, bunched_range_total, strict_rows_total.
Results should be ordered as specified.

2. Underlying Tables, Row Counts & Data Volume

1 Tables (1,000,000 rows • 22.9 MiB)

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

📊 audit_ledger 1,000,000 rows 22.9 MiB Data Volume

tx_id (INTEGER) val_date (DATE) amount (DECIMAL(10,2))

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

#	tx_id	val_date	amount
1	1	2026-03-01 00:00:00	100
2	2	2026-03-01 00:00:00	50
3	3	2026-03-02 00:00:00	20
4	3	2026-01-01 00:00:00	52.72
5	4	2026-01-02 00:00:00	0.01
6	5	2026-01-03 00:00:00	53.86
7	6	2026-01-04 00:00:00	54.61
8	7	2026-01-05 00:00:00	32.3
9	8	2026-01-06 00:00:00	18.17
10	9	2026-01-07 00:00:00	0.01
11	10	2026-01-08 00:00:00	37.9
12	11	2026-01-09 00:00:00	65.01
13	12	2026-01-10 00:00:00	69.15
14	13	2026-01-11 00:00:00	40.5
15	14	2026-01-12 00:00:00	0.01

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

⏳ ORDER_BY 🔥 22.6% Cost

Latency: 92.12 ms

Rows In/Out: 1,000,000

Data Vol: 45.78 MB

📐 PROJECTION 0.1%

Latency: 0.22 ms

Rows In/Out: 1,000,000

Data Vol: 45.78 MB

📁 Projections: ['#0', '#1', '#2', ...

🪟 WINDOW 🔥 76.9% Cost

Latency: 313.39 ms

Rows In/Out: 1,000,000

Data Vol: 45.78 MB

📁 Projections: ['sum_no_overflow(a...

🔍 TABLE_SCAN 0.4%

Latency: 1.63 ms

Rows In/Out: 1,000,000

Data Vol: 15.26 MB

📁 memory.main.audit_ledger

Operator	Cost %	Latency	Processed Rows	Data Volume	Details / Table	Status
WINDOW	76.9%	313.39 ms	1,000,000	45.78 MB	Projections: ['sum_no_overflow(amount) OVER (ORD	🔥 BOTTLENECK
ORDER_BY	22.6%	92.12 ms	1,000,000	45.78 MB	-	🔥 BOTTLENECK
TABLE_SCAN	0.4%	1.63 ms	1,000,000	15.26 MB	memory.main.audit_ledger	Normal
PROJECTION	0.1%	0.22 ms	1,000,000	45.78 MB	Projections: ['#0', '#1', '#2', '#3', '#4']	Normal

4. Queries Executed (Candidate vs Golden Ground Truth)

Beautified & Syntax Highlighted

SELECT tx_id,
       val_date,
       amount,
       sum(amount) OVER (
                         ORDER BY val_date RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS bunched_range_total,
       sum(amount) OVER (
                         ORDER BY val_date, tx_id ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS strict_rows_total
FROM audit_ledger
ORDER BY val_date,
         tx_id;

-- Compare the difference between default RANGE (peer bunching) vs ROWS (strict sequential)

SELECT tx_id,
       val_date,
       amount, -- Default RANGE behavior: bunches tied dates together ($150 on row 1 and 2)
 sum(amount) OVER (
                   ORDER BY val_date RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS bunched_range_total, -- Explicit ROWS behavior: increments strictly row by row ($100 then $150)
 sum(amount) OVER (
                   ORDER BY val_date, tx_id ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS strict_rows_total
FROM audit_ledger
ORDER BY val_date,
         tx_id;

CREATE TABLE audit_ledger (tx_id INT, val_date DATE, amount decimal(10, 2));


INSERT INTO audit_ledger
VALUES (1, '2026-03-01', 100.00),
       (2, '2026-03-01', 50.00),
       -- Same date!
 (3, '2026-03-02', 20.00);

5. Query Results & Expected Ground Truth

Tabular Comparison

Actual Candidate Output (1,000,000 rows × 5 cols)

#	tx_id	val_date	amount	bunched_range_total	strict_rows_total
1	3	2026-01-01 00:00:00	52.72	640,588.31	52.72
2	93	2026-01-01 00:00:00	37.37	640,588.31	90.09
3	183	2026-01-01 00:00:00	125.16	640,588.31	215.25
4	273	2026-01-01 00:00:00	12.97	640,588.31	228.22
5	363	2026-01-01 00:00:00	105.52	640,588.31	333.74
6	453	2026-01-01 00:00:00	75.31	640,588.31	409.05
7	543	2026-01-01 00:00:00	0.01	640,588.31	409.06
8	633	2026-01-01 00:00:00	27.8	640,588.31	436.86
9	723	2026-01-01 00:00:00	3.68	640,588.31	440.54
10	813	2026-01-01 00:00:00	76.33	640,588.31	516.87
11	903	2026-01-01 00:00:00	41.75	640,588.31	558.62
12	993	2026-01-01 00:00:00	40.34	640,588.31	598.96
13	1,083	2026-01-01 00:00:00	99.1	640,588.31	698.06
14	1,173	2026-01-01 00:00:00	116.17	640,588.31	814.23
15	1,263	2026-01-01 00:00:00	96.37	640,588.31	910.6
16	1,353	2026-01-01 00:00:00	47.61	640,588.31	958.21
17	1,443	2026-01-01 00:00:00	68.53	640,588.31	1,026.74
18	1,533	2026-01-01 00:00:00	28.63	640,588.31	1,055.37
19	1,623	2026-01-01 00:00:00	107.06	640,588.31	1,162.43
20	1,713	2026-01-01 00:00:00	42.41	640,588.31	1,204.84
21	1,803	2026-01-01 00:00:00	0.01	640,588.31	1,204.85
22	1,893	2026-01-01 00:00:00	109.81	640,588.31	1,314.66
23	1,983	2026-01-01 00:00:00	73.09	640,588.31	1,387.75
24	2,073	2026-01-01 00:00:00	182.22	640,588.31	1,569.97
25	2,163	2026-01-01 00:00:00	4.11	640,588.31	1,574.08

Showing first 25 of 1,000,000 total rows.

Expected Golden Output (1,000,000 rows × 5 cols)

#	tx_id	val_date	amount	bunched_range_total	strict_rows_total
1	3	2026-01-01 00:00:00	52.72	640,588.31	52.72
2	93	2026-01-01 00:00:00	37.37	640,588.31	90.09
3	183	2026-01-01 00:00:00	125.16	640,588.31	215.25
4	273	2026-01-01 00:00:00	12.97	640,588.31	228.22
5	363	2026-01-01 00:00:00	105.52	640,588.31	333.74
6	453	2026-01-01 00:00:00	75.31	640,588.31	409.05
7	543	2026-01-01 00:00:00	0.01	640,588.31	409.06
8	633	2026-01-01 00:00:00	27.8	640,588.31	436.86
9	723	2026-01-01 00:00:00	3.68	640,588.31	440.54
10	813	2026-01-01 00:00:00	76.33	640,588.31	516.87
11	903	2026-01-01 00:00:00	41.75	640,588.31	558.62
12	993	2026-01-01 00:00:00	40.34	640,588.31	598.96
13	1,083	2026-01-01 00:00:00	99.1	640,588.31	698.06
14	1,173	2026-01-01 00:00:00	116.17	640,588.31	814.23
15	1,263	2026-01-01 00:00:00	96.37	640,588.31	910.6
16	1,353	2026-01-01 00:00:00	47.61	640,588.31	958.21
17	1,443	2026-01-01 00:00:00	68.53	640,588.31	1,026.74
18	1,533	2026-01-01 00:00:00	28.63	640,588.31	1,055.37
19	1,623	2026-01-01 00:00:00	107.06	640,588.31	1,162.43
20	1,713	2026-01-01 00:00:00	42.41	640,588.31	1,204.84
21	1,803	2026-01-01 00:00:00	0.01	640,588.31	1,204.85
22	1,893	2026-01-01 00:00:00	109.81	640,588.31	1,314.66
23	1,983	2026-01-01 00:00:00	73.09	640,588.31	1,387.75
24	2,073	2026-01-01 00:00:00	182.22	640,588.31	1,569.97
25	2,163	2026-01-01 00:00:00	4.11	640,588.31	1,574.08

Showing first 25 of 1,000,000 total rows.

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