✔ PASS
Temporal & Time-Series
task_002_Q02_dynamic_point_in_time_fx
Scaled: 1M+ Records
Ordered: Yes
Multi-Currency Point-in-Time FX Conversion via ASOF JOIN
Candidate Latency (1M Rows)
79.33 ms
(1.0x slower than golden)
Golden Latency (1M Rows)
78.40 ms
DuckDB in-memory engine
Underlying Data Volume
1,000,003 rows
38.1 MiB across 2 tables
Evaluated Model
gemini-3.8-flash-low
Gen Latency: 17.6s
✔ 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 TrapBusiness Scenario & Problem
Global payments systems record transactions in local currencies (EUR, JPY, GBP) that must be converted to USD using the prevailing exchange rate at the exact transaction millisecond.
⚡ The Frontier Trap (Why LLMs Fail)
Joining to a daily FX table using DATE(ts) loses intraday volatility and causes currency conversion discrepancies of up to 1-2% during volatile market events.
Prompt / User Request Given to Model
Global payments systems record transactions in local currencies (EUR, JPY, GBP) that must be converted to USD using the prevailing exchange rate at the exact transaction millisecond. Execute the required analytical SQL calculation in DuckDB. Expected output columns: payment_id, payment_time, currency, amount_local, rate_to_usd, amount_usd, fx_snapshot_time. Results should be ordered as specified.
2. Underlying Tables, Row Counts & Data Volume
2 Tables (1,000,003 rows • 38.1 MiB)Production-scale fixtures loaded into the isolated DuckDB sandbox with at least 1,000,000 records per table:
📊 fx_rates
3 rows
108 B Data Volume
rate_time (TIMESTAMP) currency (VARCHAR) rate_to_usd (DOUBLE)Data Preview (showing sample rows from 3 total rows):
| # | rate_time | currency | rate_to_usd |
|---|---|---|---|
| 1 | 2026-03-01 12:00:00 | EUR | 1.082 |
| 2 | 2026-03-01 12:15:00 | EUR | 1.0845 |
| 3 | 2026-03-01 12:30:00 | EUR | 1.081 |
📊 payments
1,000,000 rows
38.1 MiB Data Volume
payment_id (INTEGER) payment_time (TIMESTAMP) currency (VARCHAR) amount_local (DOUBLE)Data Preview (showing sample rows from 1,000,000 total rows):
| # | payment_id | payment_time | currency | amount_local |
|---|---|---|---|---|
| 1 | 1 | 2026-03-01 12:05:00 | EUR | 5,000 |
| 2 | 2 | 2026-03-01 12:20:00 | EUR | 12,000 |
| 3 | 3 | 2026-03-01 12:45:00 | EUR | 7,500 |
| 4 | 3 | 2026-03-01 09:30:00.000005 | USD | 5,874.96 |
| 5 | 4 | 2026-03-01 09:30:00.000022 | EUR | 9,006.12 |
| 6 | 5 | 2026-03-01 09:30:00.000051 | GBP | 12,921.66 |
| 7 | 6 | 2026-03-01 09:30:00.000076 | JPY | 1,171.74 |
| 8 | 7 | 2026-03-01 09:30:00.000083 | CAD | 6,643.08 |
| 9 | 8 | 2026-03-01 09:30:00.000104 | AUD | 6,419.38 |
| 10 | 9 | 2026-03-01 09:30:00.000140 | USD | 0.01 |
| 11 | 10 | 2026-03-01 09:30:00.000162 | EUR | 0.01 |
| 12 | 11 | 2026-03-01 09:30:00.000164 | GBP | 10,114.92 |
| 13 | 12 | 2026-03-01 09:30:00.000186 | JPY | 10,954 |
| 14 | 13 | 2026-03-01 09:30:00.000201 | CAD | 8,400.71 |
| 15 | 14 | 2026-03-01 09:30:00.000223 | AUD | 9,376.63 |
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:
0.0%
Latency: 0.00 ms
Rows In/Out: 3
Data Vol: 180 B
📁 Projections: ['#0', '#1', '__int...
0.0%
Latency: 0.11 ms
Rows In/Out: 3
Data Vol: 144 B
0.0%
Latency: 0.00 ms
Rows In/Out: 3
Data Vol: 144 B
📁 Projections: ['#0', '#1', '__int...
0.0%
Latency: 0.01 ms
Rows In/Out: 3
Data Vol: 180 B
📁 Projections: ['payment_id', 'pay...
🔥 95.5% Cost
Latency: 296.04 ms
Rows In/Out: 3
Data Vol: 156 B
4.5%
Latency: 13.84 ms
Rows In/Out: 1,000,000
Data Vol: 34.33 MB
📁 memory.main.payments
0.0%
Latency: 0.01 ms
Rows In/Out: 3
Data Vol: 96 B
📁 memory.main.fx_rates
| Operator | Cost % | Latency | Processed Rows | Data Volume | Details / Table | Status |
|---|---|---|---|---|---|---|
| ASOF_JOIN | 95.5% | 296.04 ms | 3 | 0.00 MB | - | 🔥 BOTTLENECK |
| TABLE_SCAN | 4.5% | 13.84 ms | 1,000,000 | 34.33 MB | memory.main.payments | Normal |
| ORDER_BY | 0.0% | 0.11 ms | 3 | 0.00 MB | - | Normal |
| TABLE_SCAN | 0.0% | 0.01 ms | 3 | 0.00 MB | memory.main.fx_rates | Normal |
| PROJECTION | 0.0% | 0.01 ms | 3 | 0.00 MB | Projections: ['payment_id', 'payment_time', 'cur | Normal |
| PROJECTION | 0.0% | 0.00 ms | 3 | 0.00 MB | Projections: ['#0', '#1', '__internal_compress_s | Normal |
| PROJECTION | 0.0% | 0.00 ms | 3 | 0.00 MB | Projections: ['#0', '#1', '__internal_decompress | Normal |
4. Queries Executed (Candidate vs Golden Ground Truth)
Beautified & Syntax HighlightedSELECT p.payment_id,
p.payment_time,
p.currency,
p.amount_local,
f.rate_to_usd,
p.amount_local * f.rate_to_usd AS amount_usd,
f.rate_time AS fx_snapshot_time
FROM payments p asof
JOIN fx_rates f ON p.currency = f.currency
AND p.payment_time >= f.rate_time
ORDER BY p.payment_id;
-- Convert international payments to USD using the most recent point-in-time exchange rate
SELECT p.payment_id,
p.payment_time,
p.currency,
p.amount_local,
fx.rate_to_usd,
round(p.amount_local * fx.rate_to_usd, 2) AS amount_usd,
fx.rate_time AS fx_snapshot_time
FROM payments p asof
JOIN fx_rates fx ON p.currency = fx.currency
AND p.payment_time >= fx.rate_time
ORDER BY p.payment_id;
CREATE TABLE fx_rates (rate_time TIMESTAMP, currency VARCHAR, rate_to_usd DOUBLE);
CREATE TABLE payments (payment_id INT, payment_time TIMESTAMP, currency VARCHAR, amount_local DOUBLE);
INSERT INTO fx_rates
VALUES ('2026-03-01 12:00:00', 'EUR', 1.0820),
('2026-03-01 12:15:00', 'EUR', 1.0845),
('2026-03-01 12:30:00', 'EUR', 1.0810);
INSERT INTO payments
VALUES (1, '2026-03-01 12:05:00', 'EUR', 5000.00),
(2, '2026-03-01 12:20:00', 'EUR', 12000.00),
(3, '2026-03-01 12:45:00', 'EUR', 7500.00);
5. Query Results & Expected Ground Truth
Tabular Comparison
Actual Candidate Output
(3 rows × 7 cols)
| # | payment_id | payment_time | currency | amount_local | rate_to_usd | amount_usd | fx_snapshot_time |
|---|---|---|---|---|---|---|---|
| 1 | 1 | 2026-03-01 12:05:00 | EUR | 5,000 | 1.082 | 5,410 | 2026-03-01 12:00:00 |
| 2 | 2 | 2026-03-01 12:20:00 | EUR | 12,000 | 1.0845 | 13,014 | 2026-03-01 12:15:00 |
| 3 | 3 | 2026-03-01 12:45:00 | EUR | 7,500 | 1.081 | 8,107.5 | 2026-03-01 12:30:00 |
Expected Golden Output
(3 rows × 7 cols)
| # | payment_id | payment_time | currency | amount_local | rate_to_usd | amount_usd | fx_snapshot_time |
|---|---|---|---|---|---|---|---|
| 1 | 1 | 2026-03-01 12:05:00 | EUR | 5,000 | 1.082 | 5,410 | 2026-03-01 12:00:00 |
| 2 | 2 | 2026-03-01 12:20:00 | EUR | 12,000 | 1.0845 | 13,014 | 2026-03-01 12:15:00 |
| 3 | 3 | 2026-03-01 12:45:00 | EUR | 7,500 | 1.081 | 8,107.5 | 2026-03-01 12:30:00 |