✔ PASS
Temporal & Time-Series
task_010_Q10_peer_exclusion_moving_average
Scaled: 1M+ Records
Ordered: Yes
Window Frame Exclusion: Peer Period Average via EXCLUDE CURRENT ROW
Candidate Latency (1M Rows)
307.19 ms
(1.0x faster than golden)
Golden Latency (1M Rows)
309.61 ms
DuckDB in-memory engine
Underlying Data Volume
1,000,000 rows
26.7 MiB across 1 table
Evaluated Model
gemini-3.8-flash-low
Gen Latency: 14.7s
✔ 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 TrapBusiness Scenario & Problem
To detect anomalous spikes, analysts compare a metric against the average of neighboring days, strictly excluding the current day's value to avoid skewing the baseline.
⚡ The Frontier Trap (Why LLMs Fail)
Computing rolling averages without excluding the current row inflates the baseline during the spike itself, hiding the anomaly.
Prompt / User Request Given to Model
To detect anomalous spikes, analysts compare a metric against the average of neighboring days, strictly excluding the current day's value to avoid skewing the baseline. Execute the required analytical SQL calculation in DuckDB. Expected output columns: service_id, call_date, total_calls, peer_baseline_avg, spike_factor. Results should be ordered as specified.
2. Underlying Tables, Row Counts & Data Volume
1 Tables (1,000,000 rows • 26.7 MiB)Production-scale fixtures loaded into the isolated DuckDB sandbox with at least 1,000,000 records per table:
📊 daily_api_calls
1,000,000 rows
26.7 MiB Data Volume
service_id (VARCHAR) call_date (DATE) total_calls (INTEGER)Data Preview (showing sample rows from 1,000,000 total rows):
| # | service_id | call_date | total_calls |
|---|---|---|---|
| 1 | auth-svc | 2026-03-01 00:00:00 | 1,000 |
| 2 | auth-svc | 2026-03-02 00:00:00 | 1,050 |
| 3 | auth-svc | 2026-03-03 00:00:00 | 9,500 |
| 4 | auth-svc | 2026-03-04 00:00:00 | 1,020 |
| 5 | auth-svc | 2026-03-05 00:00:00 | 1,010 |
| 6 | auth-svc | 2026-01-01 00:00:00 | 4,235 |
| 7 | auth-svc | 2026-01-02 00:00:00 | 2,428 |
| 8 | auth-svc | 2026-01-03 00:00:00 | 1,652 |
| 9 | auth-svc | 2026-01-04 00:00:00 | 368 |
| 10 | auth-svc | 2026-01-05 00:00:00 | 3,448 |
| 11 | auth-svc | 2026-01-06 00:00:00 | 5,665 |
| 12 | auth-svc | 2026-01-07 00:00:00 | 3,114 |
| 13 | auth-svc | 2026-01-08 00:00:00 | 1 |
| 14 | auth-svc | 2026-01-09 00:00:00 | 1,017 |
| 15 | auth-svc | 2026-01-10 00:00:00 | 1 |
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:
1.3%
Latency: 7.13 ms
Rows In/Out: 1,000,000
Data Vol: 38.15 MB
📁 Projections: ['__internal_decomp...
🔥 9.5% Cost
Latency: 53.69 ms
Rows In/Out: 1,000,000
Data Vol: 36.24 MB
1.8%
Latency: 10.40 ms
Rows In/Out: 1,000,000
Data Vol: 36.24 MB
📁 Projections: ['__internal_compre...
🔥 10.6% Cost
Latency: 60.21 ms
Rows In/Out: 1,000,000
Data Vol: 38.15 MB
📁 Projections: ['service_id', 'cal...
0.0%
Latency: 0.25 ms
Rows In/Out: 1,000,000
Data Vol: 38.15 MB
📁 Projections: ['#0', '#1', '#2', ...
🔥 74.6% Cost
Latency: 422.61 ms
Rows In/Out: 1,000,000
Data Vol: 30.52 MB
📁 Projections: avg(total_calls) OV...
2.2%
Latency: 12.53 ms
Rows In/Out: 1,000,000
Data Vol: 22.89 MB
📁 memory.main.daily_api_calls
| Operator | Cost % | Latency | Processed Rows | Data Volume | Details / Table | Status |
|---|---|---|---|---|---|---|
| WINDOW | 74.6% | 422.61 ms | 1,000,000 | 30.52 MB | Projections: avg(total_calls) OVER (PARTITION BY | 🔥 BOTTLENECK |
| PROJECTION | 10.6% | 60.21 ms | 1,000,000 | 38.15 MB | Projections: ['service_id', 'call_date', 'total_ | 🔥 BOTTLENECK |
| ORDER_BY | 9.5% | 53.69 ms | 1,000,000 | 36.24 MB | - | 🔥 BOTTLENECK |
| TABLE_SCAN | 2.2% | 12.53 ms | 1,000,000 | 22.89 MB | memory.main.daily_api_calls | Normal |
| PROJECTION | 1.8% | 10.40 ms | 1,000,000 | 36.24 MB | Projections: ['__internal_compress_string_uhugei | Normal |
| PROJECTION | 1.3% | 7.13 ms | 1,000,000 | 38.15 MB | Projections: ['__internal_decompress_string(#0)' | Normal |
| PROJECTION | 0.0% | 0.25 ms | 1,000,000 | 38.15 MB | Projections: ['#0', '#1', '#2', '#3', '#3'] | Normal |
4. Queries Executed (Candidate vs Golden Ground Truth)
Beautified & Syntax Highlighted-- Calculate 5-day baseline average strictly excluding current row
SELECT service_id,
call_date,
total_calls, -- 2 days before and 2 days after, excluding current day
round(avg(total_calls) OVER (PARTITION BY service_id
ORDER BY call_date ROWS BETWEEN 2 PRECEDING AND 2 FOLLOWING exclude CURRENT ROW), 2) AS peer_baseline_avg, -- Spikiness ratio
round(total_calls / avg(total_calls) OVER (PARTITION BY service_id
ORDER BY call_date ROWS BETWEEN 2 PRECEDING AND 2 FOLLOWING exclude CURRENT ROW), 2) AS spike_factor
FROM daily_api_calls
ORDER BY call_date;
-- Calculate 5-day baseline average strictly excluding current row
SELECT service_id,
call_date,
total_calls, -- 2 days before and 2 days after, excluding current day
round(avg(total_calls) OVER (PARTITION BY service_id
ORDER BY call_date ROWS BETWEEN 2 PRECEDING AND 2 FOLLOWING exclude CURRENT ROW), 2) AS peer_baseline_avg, -- Spikiness ratio
round(total_calls / avg(total_calls) OVER (PARTITION BY service_id
ORDER BY call_date ROWS BETWEEN 2 PRECEDING AND 2 FOLLOWING exclude CURRENT ROW), 2) AS spike_factor
FROM daily_api_calls
ORDER BY call_date;
CREATE TABLE daily_api_calls (service_id VARCHAR, call_date DATE, total_calls INT);
INSERT INTO daily_api_calls
VALUES ('auth-svc', '2026-03-01', 1000),
('auth-svc', '2026-03-02', 1050),
('auth-svc', '2026-03-03', 9500),
-- Major spike
('auth-svc', '2026-03-04', 1020),
('auth-svc', '2026-03-05', 1010);
5. Query Results & Expected Ground Truth
Tabular Comparison
Actual Candidate Output
(1,000,000 rows × 5 cols)
| # | service_id | call_date | total_calls | peer_baseline_avg | spike_factor |
|---|---|---|---|---|---|
| 1 | auth-svc | 2026-01-01 00:00:00 | 3,669 | 4,770.5 | 0.77 |
| 2 | auth-svc | 2026-01-01 00:00:00 | 5,912 | 2,748.33 | 2.15 |
| 3 | auth-svc | 2026-01-01 00:00:00 | 3,629 | 2,990.25 | 1.21 |
| 4 | auth-svc | 2026-01-01 00:00:00 | 947 | 3,537.25 | 0.27 |
| 5 | auth-svc | 2026-01-01 00:00:00 | 1,433 | 1,938 | 0.74 |
| 6 | auth-svc | 2026-01-01 00:00:00 | 3,175 | 1,872.5 | 1.7 |
| 7 | auth-svc | 2026-01-01 00:00:00 | 1 | 4,300.75 | 0 |
| 8 | auth-svc | 2026-01-01 00:00:00 | 5,109 | 3,674.75 | 1.39 |
| 9 | auth-svc | 2026-01-01 00:00:00 | 7,486 | 2,287 | 3.27 |
| 10 | auth-svc | 2026-01-01 00:00:00 | 4,037 | 4,470 | 0.9 |
| 11 | auth-svc | 2026-01-01 00:00:00 | 1 | 4,202 | 0 |
| 12 | auth-svc | 2026-01-01 00:00:00 | 5,284 | 1,696.25 | 3.12 |
| 13 | auth-svc | 2026-01-01 00:00:00 | 1 | 2,403.25 | 0 |
| 14 | auth-svc | 2026-01-01 00:00:00 | 2,746 | 2,046.75 | 1.34 |
| 15 | auth-svc | 2026-01-01 00:00:00 | 1,582 | 1,199.5 | 1.32 |
| 16 | auth-svc | 2026-01-01 00:00:00 | 1,320 | 1,692.25 | 0.78 |
| 17 | auth-svc | 2026-01-01 00:00:00 | 731 | 2,124.5 | 0.34 |
| 18 | auth-svc | 2026-01-01 00:00:00 | 1,710 | 1,920.5 | 0.89 |
| 19 | auth-svc | 2026-01-01 00:00:00 | 3,886 | 1,229.25 | 3.16 |
| 20 | auth-svc | 2026-01-01 00:00:00 | 1,745 | 3,046.75 | 0.57 |
| 21 | auth-svc | 2026-01-01 00:00:00 | 731 | 2,873 | 0.25 |
| 22 | auth-svc | 2026-01-01 00:00:00 | 5,860 | 983.5 | 5.96 |
| 23 | auth-svc | 2026-01-01 00:00:00 | 1 | 2,675.5 | 0 |
| 24 | auth-svc | 2026-01-01 00:00:00 | 1,457 | 2,473 | 0.59 |
| 25 | auth-svc | 2026-01-01 00:00:00 | 2,654 | 955 | 2.78 |
Showing first 25 of 1,000,000 total rows.
Expected Golden Output
(1,000,000 rows × 5 cols)
| # | service_id | call_date | total_calls | peer_baseline_avg | spike_factor |
|---|---|---|---|---|---|
| 1 | auth-svc | 2026-01-01 00:00:00 | 2,117 | 102 | 20.75 |
| 2 | auth-svc | 2026-01-01 00:00:00 | 1 | 3,179.33 | 0 |
| 3 | auth-svc | 2026-01-01 00:00:00 | 203 | 2,334.25 | 0.09 |
| 4 | auth-svc | 2026-01-01 00:00:00 | 7,218 | 2,340.25 | 3.08 |
| 5 | auth-svc | 2026-01-01 00:00:00 | 1 | 4,144.5 | 0 |
| 6 | auth-svc | 2026-01-01 00:00:00 | 9,156 | 2,327.75 | 3.93 |
| 7 | auth-svc | 2026-01-01 00:00:00 | 1 | 2,812.25 | 0 |
| 8 | auth-svc | 2026-01-01 00:00:00 | 2,091 | 2,747.25 | 0.76 |
| 9 | auth-svc | 2026-01-01 00:00:00 | 1 | 3,174.25 | 0 |
| 10 | auth-svc | 2026-01-01 00:00:00 | 1,831 | 3,600.25 | 0.51 |
| 11 | auth-svc | 2026-01-01 00:00:00 | 8,774 | 3,652.5 | 2.4 |
| 12 | auth-svc | 2026-01-01 00:00:00 | 3,535 | 5,571 | 0.63 |
| 13 | auth-svc | 2026-01-01 00:00:00 | 9,243 | 4,439.5 | 2.08 |
| 14 | auth-svc | 2026-01-01 00:00:00 | 2,436 | 5,642 | 0.43 |
| 15 | auth-svc | 2026-01-01 00:00:00 | 3,013 | 6,328.5 | 0.48 |
| 16 | auth-svc | 2026-01-01 00:00:00 | 6,777 | 4,063.5 | 1.67 |
| 17 | auth-svc | 2026-01-01 00:00:00 | 6,858 | 3,796.75 | 1.81 |
| 18 | auth-svc | 2026-01-01 00:00:00 | 3,947 | 5,541 | 0.71 |
| 19 | auth-svc | 2026-01-01 00:00:00 | 1,450 | 4,471.25 | 0.32 |
| 20 | auth-svc | 2026-01-01 00:00:00 | 7,079 | 2,600 | 2.72 |
| 21 | auth-svc | 2026-01-01 00:00:00 | 1 | 4,113 | 0 |
| 22 | auth-svc | 2026-01-01 00:00:00 | 5,002 | 2,500.5 | 2 |
| 23 | auth-svc | 2026-01-01 00:00:00 | 2,921 | 1,251.25 | 2.33 |
| 24 | auth-svc | 2026-01-01 00:00:00 | 1 | 2,480 | 0 |
| 25 | auth-svc | 2026-01-01 00:00:00 | 1 | 1,975.5 | 0 |
Showing first 25 of 1,000,000 total rows.