✔ PASS Temporal & Time-Series task_114_asof_tolerance_expiry Scaled: 1M+ Records Ordered: Yes

ASOF Point-in-Time Quote Matching with 500ms Expiry Tolerance

Candidate Latency (1M Rows)

352.55 ms

(1.1x slower than golden)

Golden Latency (1M Rows)

319.26 ms

DuckDB in-memory engine

Underlying Data Volume

2,000,000 rows

76.3 MiB across 2 tables

Evaluated Model

gemini-3.8-flash-low

Gen Latency: 9.2s

✔ 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

Matches executed trades with the prevailing market quote as-of the trade timestamp, but enforces a strict 500ms staleness tolerance. Stale market data must not be matched.

⚡ The Frontier Trap (Why LLMs Fail)

Standard ASOF joins without a staleness tolerance check blindly match trades to arbitrarily old quotes from hours or days ago. For Trade 1, naive SQL matches the 2-minute-old quote ($150.10) instead of returning NULL, resulting in stale execution data.

Prompt / User Request Given to Model

Match each executed trade with the most recent prevailing market quote.
CRITICAL CONSTRAINT: If the most recent quote is older than 500 milliseconds prior to the trade execution time, the quote is considered stale and must be returned as NULL.

Required output columns:
- trade_id (INT)
- trade_time (TIMESTAMP)
- symbol (VARCHAR)
- prevailing_ask (DOUBLE)

Order by trade_id ASC.

2. Underlying Tables, Row Counts & Data Volume

2 Tables (2,000,000 rows • 76.3 MiB)

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

📊 market_quotes 1,000,000 rows 42.0 MiB Data Volume

quote_time (TIMESTAMP) symbol (VARCHAR) bid (DOUBLE) ask (DOUBLE)

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

#	quote_time	symbol	bid	ask
1	2026-03-01 10:00:00	AAPL	150	150.1
2	2026-03-01 10:05:00	AAPL	151	151.1
3	2026-03-01 09:30:00.000011	NVDA	151.48	151.58
4	2026-03-01 09:30:00.000022	AAPL	150.32	150.42
5	2026-03-01 09:30:00.000043	MSFT	150.21	150.31
6	2026-03-01 09:30:00.000062	GOOGL	150.32	150.42
7	2026-03-01 09:30:00.000087	AMZN	151.1	151.2
8	2026-03-01 09:30:00.000103	TSLA	150.79	150.89
9	2026-03-01 09:30:00.000127	META	151.09	151.19
10	2026-03-01 09:30:00.000162	AMD	152.43	152.53
11	2026-03-01 09:30:00.000161	NVDA	150.45	150.55
12	2026-03-01 09:30:00.000192	AAPL	151.57	151.67
13	2026-03-01 09:30:00.000203	MSFT	150.79	150.89
14	2026-03-01 09:30:00.000223	GOOGL	150.24	150.34
15	2026-03-01 09:30:00.000243	AMZN	150.73	150.83

📊 market_trades 1,000,000 rows 34.3 MiB Data Volume

trade_id (INTEGER) trade_time (TIMESTAMP) symbol (VARCHAR) shares (INTEGER)

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

#	trade_id	trade_time	symbol	shares
1	1	2026-03-01 10:02:00	AAPL	100
2	2	2026-03-01 10:05:00.200000	AAPL	200
3	2	2026-03-01 09:30:00.000005	NVDA	143
4	3	2026-03-01 09:30:00.000034	AAPL	195
5	4	2026-03-01 09:30:00.000052	MSFT	6
6	5	2026-03-01 09:30:00.000078	GOOGL	168
7	6	2026-03-01 09:30:00.000081	AMZN	186
8	7	2026-03-01 09:30:00.000106	TSLA	165
9	8	2026-03-01 09:30:00.000135	META	132
10	9	2026-03-01 09:30:00.000153	AMD	154
11	10	2026-03-01 09:30:00.000165	NVDA	89
12	11	2026-03-01 09:30:00.000189	AAPL	226
13	12	2026-03-01 09:30:00.000202	MSFT	234
14	13	2026-03-01 09:30:00.000224	GOOGL	273
15	14	2026-03-01 09:30:00.000241	AMZN	104

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: 1248.73 ms) Nodes consuming ≥ 20% of query runtime are highlighted with glowing borders

📐 PROJECTION 0.1%

Latency: 1.11 ms

Rows In/Out: 1,000,000

Data Vol: 34.33 MB

📁 Projections: ['#0', '#1', '__int...

⏳ ORDER_BY 🔥 7.0% Cost

Latency: 86.87 ms

Rows In/Out: 1,000,000

Data Vol: 26.70 MB

📐 PROJECTION 0.7%

Latency: 9.23 ms

Rows In/Out: 1,000,000

Data Vol: 26.70 MB

📁 Projections: ['#0', '#1', '__int...

📐 PROJECTION 2.6%

Latency: 32.11 ms

Rows In/Out: 1,000,000

Data Vol: 34.33 MB

📁 Projections: ['trade_id', 'trade...

⚡ ASOF_JOIN 🔥 87.3% Cost

Latency: 1090.71 ms

Rows In/Out: 1,000,000

Data Vol: 41.96 MB

🔍 TABLE_SCAN 1.2%

Latency: 14.77 ms

Rows In/Out: 1,000,000

Data Vol: 26.70 MB

📁 memory.main.market_trades

🔍 TABLE_SCAN 1.1%

Latency: 13.93 ms

Rows In/Out: 1,000,000

Data Vol: 30.52 MB

📁 memory.main.market_quotes

Operator	Cost %	Latency	Processed Rows	Data Volume	Details / Table	Status
ASOF_JOIN	87.3%	1090.71 ms	1,000,000	41.96 MB	-	🔥 BOTTLENECK
ORDER_BY	7.0%	86.87 ms	1,000,000	26.70 MB	-	🔥 BOTTLENECK
PROJECTION	2.6%	32.11 ms	1,000,000	34.33 MB	Projections: ['trade_id', 'trade_time', 'symbol'	Normal
TABLE_SCAN	1.2%	14.77 ms	1,000,000	26.70 MB	memory.main.market_trades	Normal
TABLE_SCAN	1.1%	13.93 ms	1,000,000	30.52 MB	memory.main.market_quotes	Normal
PROJECTION	0.7%	9.23 ms	1,000,000	26.70 MB	Projections: ['#0', '#1', '__internal_compress_s	Normal
PROJECTION	0.1%	1.11 ms	1,000,000	34.33 MB	Projections: ['#0', '#1', '__internal_decompress	Normal

4. Queries Executed (Candidate vs Golden Ground Truth)

Beautified & Syntax Highlighted

SELECT t.trade_id,
       t.trade_time,
       t.symbol,
       CASE
           WHEN q.quote_time >= t.trade_time - INTERVAL 500 millisecond THEN q.ask
           ELSE NULL
       END AS prevailing_ask
FROM market_trades t asof
LEFT JOIN market_quotes q ON t.symbol = q.symbol
AND t.trade_time >= q.quote_time
ORDER BY t.trade_id ASC;

SELECT t.trade_id,
       t.trade_time,
       t.symbol,
       CASE
           WHEN date_diff('millisecond', q.quote_time, t.trade_time) <= 500 THEN q.ask
           ELSE NULL
       END AS prevailing_ask
FROM market_trades t asof
JOIN market_quotes q ON t.symbol = q.symbol
AND t.trade_time >= q.quote_time
ORDER BY t.trade_id ASC;

CREATE TABLE market_quotes (quote_time TIMESTAMP, symbol VARCHAR, bid DOUBLE,
                                                                      ask DOUBLE);


CREATE TABLE market_trades (trade_id INT, trade_time TIMESTAMP, symbol VARCHAR, shares INT);

-- Quote 1: 10:00:00 (Stale for trade at 10:02:00)
-- Quote 2: 10:05:00 (Fresh for trade at 10:05:00.200)

INSERT INTO market_quotes
VALUES ('2026-03-01 10:00:00', 'AAPL', 150.00, 150.10),
       ('2026-03-01 10:05:00', 'AAPL', 151.00, 151.10);


INSERT INTO market_trades
VALUES (1, '2026-03-01 10:02:00', 'AAPL', 100),
       -- 2 minutes after Quote 1 -> STALE (>500ms) -> NULL
 (2, '2026-03-01 10:05:00.200', 'AAPL', 200); -- 200ms after Quote 2 -> FRESH (<=500ms) -> 151.10

5. Query Results & Expected Ground Truth

Tabular Comparison

Actual Candidate Output (1,000,000 rows × 4 cols)

#	trade_id	trade_time	symbol	prevailing_ask
1	1	2026-03-01 10:02:00	AAPL	NULL
2	2	2026-03-01 09:30:00.000005	NVDA	NULL
3	2	2026-03-01 10:05:00.200000	AAPL	151.1
4	3	2026-03-01 09:30:00.000034	AAPL	150.42
5	4	2026-03-01 09:30:00.000052	MSFT	150.31
6	5	2026-03-01 09:30:00.000078	GOOGL	150.42
7	6	2026-03-01 09:30:00.000081	AMZN	NULL
8	7	2026-03-01 09:30:00.000106	TSLA	150.89
9	8	2026-03-01 09:30:00.000135	META	151.19
10	9	2026-03-01 09:30:00.000153	AMD	NULL
11	10	2026-03-01 09:30:00.000165	NVDA	150.55
12	11	2026-03-01 09:30:00.000189	AAPL	150.42
13	12	2026-03-01 09:30:00.000202	MSFT	150.31
14	13	2026-03-01 09:30:00.000224	GOOGL	150.34
15	14	2026-03-01 09:30:00.000241	AMZN	151.2
16	15	2026-03-01 09:30:00.000268	TSLA	151.34
17	16	2026-03-01 09:30:00.000283	META	151.19
18	17	2026-03-01 09:30:00.000302	AMD	152.53
19	18	2026-03-01 09:30:00.000330	NVDA	150.55
20	19	2026-03-01 09:30:00.000345	AAPL	151.67
21	20	2026-03-01 09:30:00.000368	MSFT	150.89
22	21	2026-03-01 09:30:00.000386	GOOGL	150.4
23	22	2026-03-01 09:30:00.000400	AMZN	150.83
24	23	2026-03-01 09:30:00.000420	TSLA	151.34
25	24	2026-03-01 09:30:00.000448	META	150.71

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

Expected Golden Output (999,997 rows × 4 cols)

#	trade_id	trade_time	symbol	prevailing_ask
1	1	2026-03-01 10:02:00	AAPL	NULL
2	2	2026-03-01 10:05:00.200000	AAPL	151.1
3	3	2026-03-01 09:30:00.000034	AAPL	150.42
4	4	2026-03-01 09:30:00.000052	MSFT	150.31
5	5	2026-03-01 09:30:00.000078	GOOGL	150.42
6	7	2026-03-01 09:30:00.000106	TSLA	150.89
7	8	2026-03-01 09:30:00.000135	META	151.19
8	10	2026-03-01 09:30:00.000165	NVDA	150.55
9	11	2026-03-01 09:30:00.000189	AAPL	150.42
10	12	2026-03-01 09:30:00.000202	MSFT	150.31
11	13	2026-03-01 09:30:00.000224	GOOGL	150.34
12	14	2026-03-01 09:30:00.000241	AMZN	151.2
13	15	2026-03-01 09:30:00.000268	TSLA	151.34
14	16	2026-03-01 09:30:00.000283	META	151.19
15	17	2026-03-01 09:30:00.000302	AMD	152.53
16	18	2026-03-01 09:30:00.000330	NVDA	150.55
17	19	2026-03-01 09:30:00.000345	AAPL	151.67
18	20	2026-03-01 09:30:00.000368	MSFT	150.89
19	21	2026-03-01 09:30:00.000386	GOOGL	150.4
20	22	2026-03-01 09:30:00.000400	AMZN	150.83
21	23	2026-03-01 09:30:00.000420	TSLA	151.34
22	24	2026-03-01 09:30:00.000448	META	150.71
23	25	2026-03-01 09:30:00.000466	AMD	150.78
24	26	2026-03-01 09:30:00.000485	NVDA	148.89
25	27	2026-03-01 09:30:00.000511	AAPL	150.6

Showing first 25 of 999,997 total rows.

← Prev: Vector Nearest Neighbors:... ⊞ Back to Benchmark Dashboard Next: Reciprocal Rank Fusion (R... →