✔ PASS Temporal & Time-Series task_009_Q09_ledger_balance_duplicate_timestamps Scaled: 1M+ Records Ordered: Yes

Financial Ledger Cumulative Balance with Duplicate Timestamp Ties

Candidate Latency (1M Rows)

163.31 ms

(1.1x slower than golden)

Golden Latency (1M Rows)

146.77 ms

DuckDB in-memory engine

Underlying Data Volume

1,000,000 rows

30.5 MiB across 1 table

Evaluated Model

gemini-3.8-flash-low

Gen Latency: 13.6s

✔ 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

Ledger entries can occur at the exact same millisecond. Financial ledgers must preserve strict incremental balances rather than bunching peer rows.

⚡ The Frontier Trap (Why LLMs Fail)

SQL window functions default to `RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW`, which aggregates identical timestamps together into the same running total.

Prompt / User Request Given to Model

Ledger entries can occur at the exact same millisecond. Financial ledgers must preserve strict incremental balances rather than bunching peer rows.

Execute the required analytical SQL calculation in DuckDB.
Expected output columns: entry_id, account_id, posted_time, amount, running_balance.
Results should be ordered as specified.

2. Underlying Tables, Row Counts & Data Volume

1 Tables (1,000,000 rows • 30.5 MiB)

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

📊 ledger 1,000,000 rows 30.5 MiB Data Volume

entry_id (INTEGER) account_id (INTEGER) posted_time (TIMESTAMP) amount (DECIMAL(10,2))

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

#	entry_id	account_id	posted_time	amount
1	1	7,001	2026-03-01 14:00:00	500
2	2	7,001	2026-03-01 14:00:00	-150
3	3	7,001	2026-03-01 14:05:00	200
4	3	7,001	2026-03-01 09:30:00.000001	135.77
5	4	7,002	2026-03-01 09:30:00.000028	510.99
6	5	7,003	2026-03-01 09:30:00.000049	0.01
7	6	7,004	2026-03-01 09:30:00.000071	0.01
8	7	7,005	2026-03-01 09:30:00.000088	498.81
9	8	7,006	2026-03-01 09:30:00.000100	169.29
10	9	7,007	2026-03-01 09:30:00.000125	375.31
11	10	7,008	2026-03-01 09:30:00.000147	476.15
12	11	7,009	2026-03-01 09:30:00.000167	458.39
13	12	7,010	2026-03-01 09:30:00.000181	225.07
14	13	7,011	2026-03-01 09:30:00.000207	0.01
15	14	7,012	2026-03-01 09:30:00.000221	227.22

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

⏳ ORDER_BY 🔥 31.6% Cost

Latency: 146.31 ms

Rows In/Out: 1,000,000

Data Vol: 38.15 MB

📐 PROJECTION 0.1%

Latency: 0.25 ms

Rows In/Out: 1,000,000

Data Vol: 38.15 MB

📁 Projections: ['#0', '#1', '#2', ...

🪟 WINDOW 🔥 67.9% Cost

Latency: 314.78 ms

Rows In/Out: 1,000,000

Data Vol: 38.15 MB

📁 Projections: sum_no_overflow(amo...

🔍 TABLE_SCAN 0.5%

Latency: 2.21 ms

Rows In/Out: 1,000,000

Data Vol: 22.89 MB

📁 memory.main.ledger

Operator	Cost %	Latency	Processed Rows	Data Volume	Details / Table	Status
WINDOW	67.9%	314.78 ms	1,000,000	38.15 MB	Projections: sum_no_overflow(amount) OVER (PARTI	🔥 BOTTLENECK
ORDER_BY	31.6%	146.31 ms	1,000,000	38.15 MB	-	🔥 BOTTLENECK
TABLE_SCAN	0.5%	2.21 ms	1,000,000	22.89 MB	memory.main.ledger	Normal
PROJECTION	0.1%	0.25 ms	1,000,000	38.15 MB	Projections: ['#0', '#1', '#2', '#3', '#4']	Normal

4. Queries Executed (Candidate vs Golden Ground Truth)

Beautified & Syntax Highlighted

SELECT entry_id,
       account_id,
       posted_time,
       amount,
       sum(amount) OVER (PARTITION BY account_id
                         ORDER BY posted_time, entry_id ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS running_balance
FROM ledger
ORDER BY posted_time,
         entry_id;

-- Calculate strict running ledger balance using ROWS frame to prevent peer bunching

SELECT entry_id,
       account_id,
       posted_time,
       amount, -- ROWS frame evaluates row-by-row even when posted_time is identical
 sum(amount) OVER (PARTITION BY account_id
                   ORDER BY posted_time, entry_id ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS running_balance
FROM ledger
ORDER BY posted_time,
         entry_id;

CREATE TABLE ledger (entry_id INT, account_id INT, posted_time TIMESTAMP, amount decimal(10, 2));


INSERT INTO ledger
VALUES (1, 7001, '2026-03-01 14:00:00', 500.00),
       (2, 7001, '2026-03-01 14:00:00', -150.00),
       -- Duplicate timestamp
 (3, 7001, '2026-03-01 14:05:00', 200.00);

5. Query Results & Expected Ground Truth

Tabular Comparison

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

#	entry_id	account_id	posted_time	amount	running_balance
1	3	7,001	2026-03-01 09:30:00.000001	135.77	135.77
2	4	7,002	2026-03-01 09:30:00.000028	510.99	510.99
3	5	7,003	2026-03-01 09:30:00.000049	0.01	0.01
4	6	7,004	2026-03-01 09:30:00.000071	0.01	0.01
5	7	7,005	2026-03-01 09:30:00.000088	498.81	498.81
6	8	7,006	2026-03-01 09:30:00.000100	169.29	169.29
7	9	7,007	2026-03-01 09:30:00.000125	375.31	375.31
8	10	7,008	2026-03-01 09:30:00.000147	476.15	476.15
9	11	7,009	2026-03-01 09:30:00.000167	458.39	458.39
10	12	7,010	2026-03-01 09:30:00.000181	225.07	225.07
11	13	7,011	2026-03-01 09:30:00.000207	0.01	0.01
12	14	7,012	2026-03-01 09:30:00.000221	227.22	227.22
13	15	7,013	2026-03-01 09:30:00.000240	184.4	184.4
14	16	7,014	2026-03-01 09:30:00.000264	365.49	365.49
15	17	7,015	2026-03-01 09:30:00.000284	27.06	27.06
16	18	7,016	2026-03-01 09:30:00.000313	0.01	0.01
17	19	7,017	2026-03-01 09:30:00.000322	282.66	282.66
18	20	7,018	2026-03-01 09:30:00.000352	0.01	0.01
19	21	7,019	2026-03-01 09:30:00.000360	186.52	186.52
20	22	7,020	2026-03-01 09:30:00.000382	110.49	110.49
21	23	7,021	2026-03-01 09:30:00.000410	0.01	0.01
22	24	7,022	2026-03-01 09:30:00.000429	566.19	566.19
23	25	7,023	2026-03-01 09:30:00.000446	0.01	0.01
24	26	7,024	2026-03-01 09:30:00.000481	1,021.85	1,021.85
25	27	7,025	2026-03-01 09:30:00.000485	0.01	0.01

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

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

#	entry_id	account_id	posted_time	amount	running_balance
1	3	7,001	2026-03-01 09:30:00.000001	135.77	135.77
2	4	7,002	2026-03-01 09:30:00.000028	510.99	510.99
3	5	7,003	2026-03-01 09:30:00.000049	0.01	0.01
4	6	7,004	2026-03-01 09:30:00.000071	0.01	0.01
5	7	7,005	2026-03-01 09:30:00.000088	498.81	498.81
6	8	7,006	2026-03-01 09:30:00.000100	169.29	169.29
7	9	7,007	2026-03-01 09:30:00.000125	375.31	375.31
8	10	7,008	2026-03-01 09:30:00.000147	476.15	476.15
9	11	7,009	2026-03-01 09:30:00.000167	458.39	458.39
10	12	7,010	2026-03-01 09:30:00.000181	225.07	225.07
11	13	7,011	2026-03-01 09:30:00.000207	0.01	0.01
12	14	7,012	2026-03-01 09:30:00.000221	227.22	227.22
13	15	7,013	2026-03-01 09:30:00.000240	184.4	184.4
14	16	7,014	2026-03-01 09:30:00.000264	365.49	365.49
15	17	7,015	2026-03-01 09:30:00.000284	27.06	27.06
16	18	7,016	2026-03-01 09:30:00.000313	0.01	0.01
17	19	7,017	2026-03-01 09:30:00.000322	282.66	282.66
18	20	7,018	2026-03-01 09:30:00.000352	0.01	0.01
19	21	7,019	2026-03-01 09:30:00.000360	186.52	186.52
20	22	7,020	2026-03-01 09:30:00.000382	110.49	110.49
21	23	7,021	2026-03-01 09:30:00.000410	0.01	0.01
22	24	7,022	2026-03-01 09:30:00.000429	566.19	566.19
23	25	7,023	2026-03-01 09:30:00.000446	0.01	0.01
24	26	7,024	2026-03-01 09:30:00.000481	1,021.85	1,021.85
25	27	7,025	2026-03-01 09:30:00.000485	0.01	0.01

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

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