✔ PASS Window Frame Default Bounds task_05_window_frame_bounds Scaled: 1M+ Records Ordered: Yes

Financial Ledger Running Balance with Duplicate Timestamps

Candidate Latency (1M Rows)

157.03 ms

(1.1x slower than golden)

Golden Latency (1M Rows)

137.62 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: 4.7s

✔ 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 window frame boundary specification (ROWS vs default RANGE) on duplicate timestamp ordering.

⚡ The Frontier Trap (Why LLMs Fail)

In ANSI SQL and DuckDB, `SUM(...) OVER (ORDER BY col)` defaults to `RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW` rather than `ROWS`. When two rows have identical ordering keys (same entry_time), RANGE treats them as equal peers and includes BOTH rows in the aggregate for the first row, prematurely applying the peer sum ($80.00 instead of $50.00). Strict cumulative calculations require explicit `ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW` and tie-breaker sorting.

Prompt / User Request Given to Model

Calculate the sequential running balance after each ledger transaction for each account in `ledger_entries`.

Note that multiple ledger entries can occur at the exact same timestamp. Each transaction must be accumulated strictly entry-by-entry based on entry_id ordering, avoiding premature peer grouping.

Required output columns:
- entry_id (INT)
- account_id (INT)
- entry_time (TIMESTAMP)
- amount (DECIMAL)
- running_balance (DECIMAL): Cumulative sum of amount for the account up to and including this entry

Sort the results by account_id ASC, entry_id ASC.

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_entries 1,000,000 rows 30.5 MiB Data Volume

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

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

#	entry_id	account_id	entry_time	amount
1	1	100	2025-01-01 10:00:00	50
2	2	100	2025-01-01 10:00:00	30
3	3	100	2025-01-01 10:15:00	-20
4	4	200	2025-01-01 12:00:00	100
5	4	200	2026-03-01 09:30:00.000005	10.13
6	5	201	2026-03-01 09:30:00.000029	0.01
7	6	202	2026-03-01 09:30:00.000042	30.3
8	7	203	2026-03-01 09:30:00.000072	116.54
9	8	204	2026-03-01 09:30:00.000090	100.7
10	9	205	2026-03-01 09:30:00.000113	0.01
11	10	206	2026-03-01 09:30:00.000134	129
12	11	207	2026-03-01 09:30:00.000141	49.24
13	12	208	2026-03-01 09:30:00.000172	0.01
14	13	209	2026-03-01 09:30:00.000182	28.84
15	14	210	2026-03-01 09:30:00.000202	51.36

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

⏳ ORDER_BY 🔥 28.0% Cost

Latency: 109.61 ms

Rows In/Out: 1,000,000

Data Vol: 38.15 MB

📐 PROJECTION 0.1%

Latency: 0.27 ms

Rows In/Out: 1,000,000

Data Vol: 38.15 MB

📁 Projections: ['#0', '#1', '#2', ...

🪟 WINDOW 🔥 71.4% Cost

Latency: 279.90 ms

Rows In/Out: 1,000,000

Data Vol: 38.15 MB

📁 Projections: sum_no_overflow(amo...

🔍 TABLE_SCAN 0.6%

Latency: 2.17 ms

Rows In/Out: 1,000,000

Data Vol: 22.89 MB

📁 memory.main.ledger_entries

Operator	Cost %	Latency	Processed Rows	Data Volume	Details / Table	Status
WINDOW	71.4%	279.90 ms	1,000,000	38.15 MB	Projections: sum_no_overflow(amount) OVER (PARTI	🔥 BOTTLENECK
ORDER_BY	28.0%	109.61 ms	1,000,000	38.15 MB	-	🔥 BOTTLENECK
TABLE_SCAN	0.6%	2.17 ms	1,000,000	22.89 MB	memory.main.ledger_entries	Normal
PROJECTION	0.1%	0.27 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,
       entry_time,
       amount,
       sum(amount) OVER (PARTITION BY account_id
                         ORDER BY entry_time, entry_id ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS running_balance
FROM ledger_entries
ORDER BY account_id ASC,
         entry_id ASC;

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

CREATE TABLE ledger_entries (entry_id INT, account_id INT, entry_time TIMESTAMP, amount decimal(10, 2));

-- Multiple entries at exact same second for account 100

INSERT INTO ledger_entries
VALUES (1, 100, '2025-01-01 10:00:00', 50.00),
       (2, 100, '2025-01-01 10:00:00', 30.00),
       (3, 100, '2025-01-01 10:15:00', -20.00),
       (4, 200, '2025-01-01 12:00:00', 100.00);

5. Query Results & Expected Ground Truth

Tabular Comparison

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

#	entry_id	account_id	entry_time	amount	running_balance
1	1	100	2025-01-01 10:00:00	50	50
2	2	100	2025-01-01 10:00:00	30	80
3	3	100	2025-01-01 10:15:00	-20	60
4	4	200	2025-01-01 12:00:00	100	100
5	4	200	2026-03-01 09:30:00.000005	10.13	110.13
6	5	201	2026-03-01 09:30:00.000029	0.01	0.01
7	6	202	2026-03-01 09:30:00.000042	30.3	30.3
8	7	203	2026-03-01 09:30:00.000072	116.54	116.54
9	8	204	2026-03-01 09:30:00.000090	100.7	100.7
10	9	205	2026-03-01 09:30:00.000113	0.01	0.01
11	10	206	2026-03-01 09:30:00.000134	129	129
12	11	207	2026-03-01 09:30:00.000141	49.24	49.24
13	12	208	2026-03-01 09:30:00.000172	0.01	0.01
14	13	209	2026-03-01 09:30:00.000182	28.84	28.84
15	14	210	2026-03-01 09:30:00.000202	51.36	51.36
16	15	211	2026-03-01 09:30:00.000223	21.13	21.13
17	16	212	2026-03-01 09:30:00.000252	0.01	0.01
18	17	213	2026-03-01 09:30:00.000262	30.58	30.58
19	18	214	2026-03-01 09:30:00.000280	39.29	39.29
20	19	215	2026-03-01 09:30:00.000311	0.01	0.01
21	20	216	2026-03-01 09:30:00.000321	44.13	44.13
22	21	217	2026-03-01 09:30:00.000341	47.45	47.45
23	22	218	2026-03-01 09:30:00.000361	46.79	46.79
24	23	219	2026-03-01 09:30:00.000391	0.01	0.01
25	24	220	2026-03-01 09:30:00.000408	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	entry_time	amount	running_balance
1	1	100	2025-01-01 10:00:00	50	50
2	2	100	2025-01-01 10:00:00	30	80
3	3	100	2025-01-01 10:15:00	-20	60
4	4	200	2025-01-01 12:00:00	100	100
5	4	200	2026-03-01 09:30:00.000005	10.13	110.13
6	5	201	2026-03-01 09:30:00.000029	0.01	0.01
7	6	202	2026-03-01 09:30:00.000042	30.3	30.3
8	7	203	2026-03-01 09:30:00.000072	116.54	116.54
9	8	204	2026-03-01 09:30:00.000090	100.7	100.7
10	9	205	2026-03-01 09:30:00.000113	0.01	0.01
11	10	206	2026-03-01 09:30:00.000134	129	129
12	11	207	2026-03-01 09:30:00.000141	49.24	49.24
13	12	208	2026-03-01 09:30:00.000172	0.01	0.01
14	13	209	2026-03-01 09:30:00.000182	28.84	28.84
15	14	210	2026-03-01 09:30:00.000202	51.36	51.36
16	15	211	2026-03-01 09:30:00.000223	21.13	21.13
17	16	212	2026-03-01 09:30:00.000252	0.01	0.01
18	17	213	2026-03-01 09:30:00.000262	30.58	30.58
19	18	214	2026-03-01 09:30:00.000280	39.29	39.29
20	19	215	2026-03-01 09:30:00.000311	0.01	0.01
21	20	216	2026-03-01 09:30:00.000321	44.13	44.13
22	21	217	2026-03-01 09:30:00.000341	47.45	47.45
23	22	218	2026-03-01 09:30:00.000361	46.79	46.79
24	23	219	2026-03-01 09:30:00.000391	0.01	0.01
25	24	220	2026-03-01 09:30:00.000408	0.01	0.01

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

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