✔ PASS SCD Type 2 Point-in-Time Joins task_03_scd2_join Scaled: 1M+ Records Ordered: Yes

Historical SCD Type 2 Point-in-Time Currency Conversion

Candidate Latency (1M Rows)

72.13 ms

(1.0x slower than golden)

Golden Latency (1M Rows)

68.78 ms

DuckDB in-memory engine

Underlying Data Volume

1,000,003 rows

45.8 MiB across 2 tables

Evaluated Model

gemini-3.8-flash-low

Gen Latency: 4.2s

✔ 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

Joins financial transactions to Slowly Changing Dimension (SCD Type 2) exchange rate tables matching effective date ranges, correctly handling open-ended NULL upper bounds.

⚡ The Frontier Trap (Why LLMs Fail)

Frontier models routinely fail SCD Type 2 point-in-time join logic in two ways: (1) omitting the open-ended current record condition `OR valid_to IS NULL`, which causes current active records to be silently dropped during INNER JOINs, and (2) using `<=` instead of `<` on the upper boundary, which causes duplicate rows when a transaction occurs exactly on the boundary timestamp of an SCD revision.

Prompt / User Request Given to Model

Convert each financial transaction in `transactions` to USD using the historically effective rate from `exchange_rates` (SCD Type 2 table).

An exchange rate record is effective for a transaction if:
1. The transaction currency matches the rate currency,
2. The transaction timestamp is greater than or equal to `valid_from`, AND
3. The transaction timestamp is strictly less than `valid_to`, OR `valid_to` is NULL (indicating the current, ongoing rate).

Required output columns:
- txn_id (INT)
- currency (VARCHAR)
- amount (DECIMAL)
- rate_to_usd (DECIMAL)
- amount_usd (DECIMAL): Calculated as ROUND(amount * rate_to_usd, 2)

Sort the results by txn_id ASC.

2. Underlying Tables, Row Counts & Data Volume

2 Tables (1,000,003 rows • 45.8 MiB)

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

📊 exchange_rates 3 rows 168 B Data Volume

rate_id (INTEGER) currency (VARCHAR) rate_to_usd (DECIMAL(10,4)) valid_from (TIMESTAMP) valid_to (TIMESTAMP)

Data Preview (showing sample rows from 3 total rows):

#	rate_id	currency	rate_to_usd	valid_from	valid_to
1	10	EUR	1.05	2025-01-01 00:00:00	2025-01-15 00:00:00
2	11	EUR	1.08	2025-01-15 00:00:00	NULL
3	12	GBP	1.25	2025-01-01 00:00:00	NULL

📊 transactions 1,000,000 rows 45.8 MiB Data Volume

txn_id (INTEGER) currency (VARCHAR) amount (DECIMAL(10,2)) txn_time (TIMESTAMP)

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

#	txn_id	currency	amount	txn_time
1	1	EUR	100	2025-01-05 12:00:00
2	2	EUR	200	2025-01-20 15:30:00
3	3	GBP	50	2025-01-10 09:00:00
4	3	USD	55.11	2026-03-01 09:30:00.000006
5	4	EUR	129.46	2026-03-01 09:30:00.000021
6	5	GBP	128.53	2026-03-01 09:30:00.000041
7	6	JPY	176.25	2026-03-01 09:30:00.000066
8	7	CAD	10.24	2026-03-01 09:30:00.000091
9	8	AUD	0.01	2026-03-01 09:30:00.000116
10	9	USD	208.45	2026-03-01 09:30:00.000130
11	10	EUR	78.61	2026-03-01 09:30:00.000144
12	11	GBP	0.01	2026-03-01 09:30:00.000173
13	12	JPY	232.53	2026-03-01 09:30:00.000192
14	13	CAD	124.88	2026-03-01 09:30:00.000201
15	14	AUD	72.53	2026-03-01 09:30:00.000225

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

📐 PROJECTION 0.5%

Latency: 0.35 ms

Rows In/Out: 333,335

Data Vol: 13.99 MB

📁 Projections: ['#0', '__internal_...

⏳ ORDER_BY 🔥 38.7% Cost

Latency: 27.24 ms

Rows In/Out: 333,335

Data Vol: 10.17 MB

📐 PROJECTION 2.4%

Latency: 1.71 ms

Rows In/Out: 333,335

Data Vol: 10.17 MB

📁 Projections: ['#0', '__internal_...

📐 PROJECTION 4.6%

Latency: 3.21 ms

Rows In/Out: 333,335

Data Vol: 13.99 MB

📁 Projections: ['txn_id', 'currenc...

⚡ HASH_JOIN 🔥 27.8% Cost

Latency: 19.60 ms

Rows In/Out: 333,335

Data Vol: 11.44 MB

🔍 TABLE_SCAN 🔥 26.0% Cost

Latency: 18.30 ms

Rows In/Out: 333,335

Data Vol: 11.44 MB

📁 memory.main.transactions

🔍 TABLE_SCAN 0.0%

Latency: 0.03 ms

Rows In/Out: 3

Data Vol: 376 B

📁 memory.main.exchange_rates

Operator	Cost %	Latency	Processed Rows	Data Volume	Details / Table	Status
ORDER_BY	38.7%	27.24 ms	333,335	10.17 MB	-	🔥 BOTTLENECK
HASH_JOIN	27.8%	19.60 ms	333,335	11.44 MB	-	🔥 BOTTLENECK
TABLE_SCAN	26.0%	18.30 ms	333,335	11.44 MB	memory.main.transactions	🔥 BOTTLENECK
PROJECTION	4.6%	3.21 ms	333,335	13.99 MB	Projections: ['txn_id', 'currency', 'amount', 'r	Normal
PROJECTION	2.4%	1.71 ms	333,335	10.17 MB	Projections: ['#0', '__internal_compress_string_	Normal
PROJECTION	0.5%	0.35 ms	333,335	13.99 MB	Projections: ['#0', '__internal_decompress_strin	Normal
TABLE_SCAN	0.0%	0.03 ms	3	0.00 MB	memory.main.exchange_rates	Normal

4. Queries Executed (Candidate vs Golden Ground Truth)

Beautified & Syntax Highlighted

SELECT t.txn_id,
       t.currency,
       t.amount,
       r.rate_to_usd,
       round(t.amount * r.rate_to_usd, 2) AS amount_usd
FROM transactions AS t
JOIN exchange_rates AS r ON t.currency = r.currency
AND t.txn_time >= r.valid_from
AND (t.txn_time < r.valid_to
     OR r.valid_to IS NULL)
ORDER BY t.txn_id ASC;

SELECT t.txn_id,
       t.currency,
       t.amount,
       r.rate_to_usd,
       round(t.amount * r.rate_to_usd, 2) AS amount_usd
FROM transactions t
JOIN exchange_rates r ON t.currency = r.currency
AND t.txn_time >= r.valid_from
AND (t.txn_time < r.valid_to
     OR r.valid_to IS NULL)
ORDER BY t.txn_id ASC;

CREATE TABLE transactions (txn_id INT, currency VARCHAR, amount decimal(10, 2),
                                                                txn_time TIMESTAMP);


CREATE TABLE exchange_rates (rate_id INT, currency VARCHAR, rate_to_usd decimal(10, 4),
                                                                        valid_from TIMESTAMP, valid_to TIMESTAMP);


INSERT INTO transactions
VALUES (1, 'EUR', 100.00, '2025-01-05 12:00:00'),
       (2, 'EUR', 200.00, '2025-01-20 15:30:00'),
       (3, 'GBP', 50.00, '2025-01-10 09:00:00');

-- SCD2 rates: EUR changed on Jan 15; GBP has no end date (current ongoing rate)

INSERT INTO exchange_rates
VALUES (10, 'EUR', 1.0500, '2025-01-01 00:00:00', '2025-01-15 00:00:00'),
       (11, 'EUR', 1.0800, '2025-01-15 00:00:00', NULL),
       (12, 'GBP', 1.2500, '2025-01-01 00:00:00', NULL);

5. Query Results & Expected Ground Truth

Tabular Comparison

Actual Candidate Output (333,335 rows × 5 cols)

#	txn_id	currency	amount	rate_to_usd	amount_usd
1	1	EUR	100	1.05	105
2	2	EUR	200	1.08	216
3	3	GBP	50	1.25	62.5
4	4	EUR	129.46	1.08	139.82
5	5	GBP	128.53	1.25	160.66
6	10	EUR	78.61	1.08	84.9
7	11	GBP	0.01	1.25	0.01
8	16	EUR	19.19	1.08	20.73
9	17	GBP	266.2	1.25	332.75
10	22	EUR	96.4	1.08	104.11
11	23	GBP	187.36	1.25	234.2
12	28	EUR	74.92	1.08	80.91
13	29	GBP	266.55	1.25	333.19
14	34	EUR	117.25	1.08	126.63
15	35	GBP	75.64	1.25	94.55
16	40	EUR	0.01	1.08	0.01
17	41	GBP	5.45	1.25	6.81
18	46	EUR	0.01	1.08	0.01
19	47	GBP	233.62	1.25	292.03
20	52	EUR	101.52	1.08	109.64
21	53	GBP	165.94	1.25	207.43
22	58	EUR	77.66	1.08	83.87
23	59	GBP	157.56	1.25	196.95
24	64	EUR	0.01	1.08	0.01
25	65	GBP	116.7	1.25	145.88

Showing first 25 of 333,335 total rows.

Expected Golden Output (333,335 rows × 5 cols)

#	txn_id	currency	amount	rate_to_usd	amount_usd
1	1	EUR	100	1.05	105
2	2	EUR	200	1.08	216
3	3	GBP	50	1.25	62.5
4	4	EUR	129.46	1.08	139.82
5	5	GBP	128.53	1.25	160.66
6	10	EUR	78.61	1.08	84.9
7	11	GBP	0.01	1.25	0.01
8	16	EUR	19.19	1.08	20.73
9	17	GBP	266.2	1.25	332.75
10	22	EUR	96.4	1.08	104.11
11	23	GBP	187.36	1.25	234.2
12	28	EUR	74.92	1.08	80.91
13	29	GBP	266.55	1.25	333.19
14	34	EUR	117.25	1.08	126.63
15	35	GBP	75.64	1.25	94.55
16	40	EUR	0.01	1.08	0.01
17	41	GBP	5.45	1.25	6.81
18	46	EUR	0.01	1.08	0.01
19	47	GBP	233.62	1.25	292.03
20	52	EUR	101.52	1.08	109.64
21	53	GBP	165.94	1.25	207.43
22	58	EUR	77.66	1.08	83.87
23	59	GBP	157.56	1.25	196.95
24	64	EUR	0.01	1.08	0.01
25	65	GBP	116.7	1.25	145.88

Showing first 25 of 333,335 total rows.

← Prev: Maximal Marginal Relevanc... ⊞ Back to Benchmark Dashboard Next: Pairwise Vector Similarit... →