✔ PASS
Temporal & Time-Series
task_024_Q24_sargable_fiscal_partition_prune
Scaled: 1M+ Records
Ordered: Yes
SARGable Fiscal Year Date Range Partition Pruning
Candidate Latency (1M Rows)
192.75 ms
(1.1x slower than golden)
Golden Latency (1M Rows)
181.07 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: 14.2s
✔ 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 TrapBusiness Scenario & Problem
Data warehouses partition petabyte event tables by date. Wrapping date columns in functions (e.g. `WHERE strftime('%Y', event_time) = '2026'`) breaks partition pruning, forcing a full table scan.
⚡ The Frontier Trap (Why LLMs Fail)
Non-SARGable functions force DuckDB/Snowflake to scan every Parquet row group.
Prompt / User Request Given to Model
Data warehouses partition petabyte event tables by date. Wrapping date columns in functions (e.g. `WHERE strftime('%Y', event_time) = '2026'`) breaks partition pruning, forcing a full table scan.
Execute the required analytical SQL calculation in DuckDB.
Expected output columns: event_id, event_timestamp, payload.
Results should be ordered as specified.
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:
📊 telemetry_partitions
1,000,000 rows
30.5 MiB Data Volume
event_id (INTEGER) event_timestamp (TIMESTAMP) payload (VARCHAR)Data Preview (showing sample rows from 1,000,000 total rows):
| # | event_id | event_timestamp | payload |
|---|---|---|---|
| 1 | 1 | 2025-12-31 23:59:59 | old |
| 2 | 2 | 2026-01-01 00:00:00 | target_start |
| 3 | 3 | 2026-06-15 12:00:00 | target_mid |
| 4 | 4 | 2027-01-01 00:00:00 | out_of_bounds |
| 5 | 4 | 2026-03-01 09:30:00.000006 | old |
| 6 | 5 | 2026-03-01 09:30:00.000024 | target_start |
| 7 | 6 | 2026-03-01 09:30:00.000043 | target_mid |
| 8 | 7 | 2026-03-01 09:30:00.000061 | out_of_bounds |
| 9 | 8 | 2026-03-01 09:30:00.000091 | old |
| 10 | 9 | 2026-03-01 09:30:00.000108 | target_start |
| 11 | 10 | 2026-03-01 09:30:00.000134 | target_mid |
| 12 | 11 | 2026-03-01 09:30:00.000150 | out_of_bounds |
| 13 | 12 | 2026-03-01 09:30:00.000166 | old |
| 14 | 13 | 2026-03-01 09:30:00.000189 | target_start |
| 15 | 14 | 2026-03-01 09:30:00.000209 | target_mid |
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:
12.3%
Latency: 14.21 ms
Rows In/Out: 999,998
Data Vol: 26.70 MB
📁 Projections: ['#0', '#1', '__int...
🔥 69.2% Cost
Latency: 80.24 ms
Rows In/Out: 999,998
Data Vol: 26.70 MB
9.1%
Latency: 10.56 ms
Rows In/Out: 999,998
Data Vol: 26.70 MB
📁 Projections: ['#0', '#1', '__int...
0.1%
Latency: 0.14 ms
Rows In/Out: 999,998
Data Vol: 26.70 MB
📁 Projections: ['event_id', 'event...
9.3%
Latency: 10.81 ms
Rows In/Out: 999,998
Data Vol: 26.70 MB
📁 memory.main.telemetry_partitions
| Operator | Cost % | Latency | Processed Rows | Data Volume | Details / Table | Status |
|---|---|---|---|---|---|---|
| ORDER_BY | 69.2% | 80.24 ms | 999,998 | 26.70 MB | - | 🔥 BOTTLENECK |
| PROJECTION | 12.3% | 14.21 ms | 999,998 | 26.70 MB | Projections: ['#0', '#1', '__internal_decompress | Normal |
| TABLE_SCAN | 9.3% | 10.81 ms | 999,998 | 26.70 MB | memory.main.telemetry_partitions | Normal |
| PROJECTION | 9.1% | 10.56 ms | 999,998 | 26.70 MB | Projections: ['#0', '#1', '__internal_compress_s | Normal |
| PROJECTION | 0.1% | 0.14 ms | 999,998 | 26.70 MB | Projections: ['event_id', 'event_timestamp', 'pa | Normal |
4. Queries Executed (Candidate vs Golden Ground Truth)
Beautified & Syntax HighlightedSELECT event_id,
event_timestamp,
payload
FROM telemetry_partitions
WHERE event_timestamp >= TIMESTAMP '2026-01-01 00:00:00'
AND event_timestamp < TIMESTAMP '2027-01-01 00:00:00'
ORDER BY event_timestamp;
-- Exact SARGable half-open date boundary pruning
SELECT event_id,
event_timestamp,
payload
FROM telemetry_partitions
WHERE event_timestamp >= TIMESTAMP '2026-01-01 00:00:00'
AND event_timestamp < TIMESTAMP '2027-01-01 00:00:00'
ORDER BY event_timestamp;
CREATE TABLE telemetry_partitions (event_id INT, event_timestamp TIMESTAMP, payload VARCHAR);
INSERT INTO telemetry_partitions
VALUES (1, '2025-12-31 23:59:59', 'old'),
(2, '2026-01-01 00:00:00', 'target_start'),
(3, '2026-06-15 12:00:00', 'target_mid'),
(4, '2027-01-01 00:00:00', 'out_of_bounds');
5. Query Results & Expected Ground Truth
Tabular Comparison
Actual Candidate Output
(999,998 rows × 3 cols)
| # | event_id | event_timestamp | payload |
|---|---|---|---|
| 1 | 2 | 2026-01-01 00:00:00 | target_start |
| 2 | 4 | 2026-03-01 09:30:00.000006 | old |
| 3 | 5 | 2026-03-01 09:30:00.000024 | target_start |
| 4 | 6 | 2026-03-01 09:30:00.000043 | target_mid |
| 5 | 7 | 2026-03-01 09:30:00.000061 | out_of_bounds |
| 6 | 8 | 2026-03-01 09:30:00.000091 | old |
| 7 | 9 | 2026-03-01 09:30:00.000108 | target_start |
| 8 | 10 | 2026-03-01 09:30:00.000134 | target_mid |
| 9 | 11 | 2026-03-01 09:30:00.000150 | out_of_bounds |
| 10 | 12 | 2026-03-01 09:30:00.000166 | old |
| 11 | 13 | 2026-03-01 09:30:00.000189 | target_start |
| 12 | 14 | 2026-03-01 09:30:00.000209 | target_mid |
| 13 | 15 | 2026-03-01 09:30:00.000230 | out_of_bounds |
| 14 | 16 | 2026-03-01 09:30:00.000247 | old |
| 15 | 17 | 2026-03-01 09:30:00.000268 | target_start |
| 16 | 18 | 2026-03-01 09:30:00.000281 | target_mid |
| 17 | 19 | 2026-03-01 09:30:00.000303 | out_of_bounds |
| 18 | 20 | 2026-03-01 09:30:00.000324 | old |
| 19 | 21 | 2026-03-01 09:30:00.000341 | target_start |
| 20 | 22 | 2026-03-01 09:30:00.000363 | target_mid |
| 21 | 23 | 2026-03-01 09:30:00.000381 | out_of_bounds |
| 22 | 24 | 2026-03-01 09:30:00.000404 | old |
| 23 | 25 | 2026-03-01 09:30:00.000425 | target_start |
| 24 | 26 | 2026-03-01 09:30:00.000461 | target_mid |
| 25 | 27 | 2026-03-01 09:30:00.000465 | out_of_bounds |
Showing first 25 of 999,998 total rows.
Expected Golden Output
(999,998 rows × 3 cols)
| # | event_id | event_timestamp | payload |
|---|---|---|---|
| 1 | 2 | 2026-01-01 00:00:00 | target_start |
| 2 | 4 | 2026-03-01 09:30:00.000006 | old |
| 3 | 5 | 2026-03-01 09:30:00.000024 | target_start |
| 4 | 6 | 2026-03-01 09:30:00.000043 | target_mid |
| 5 | 7 | 2026-03-01 09:30:00.000061 | out_of_bounds |
| 6 | 8 | 2026-03-01 09:30:00.000091 | old |
| 7 | 9 | 2026-03-01 09:30:00.000108 | target_start |
| 8 | 10 | 2026-03-01 09:30:00.000134 | target_mid |
| 9 | 11 | 2026-03-01 09:30:00.000150 | out_of_bounds |
| 10 | 12 | 2026-03-01 09:30:00.000166 | old |
| 11 | 13 | 2026-03-01 09:30:00.000189 | target_start |
| 12 | 14 | 2026-03-01 09:30:00.000209 | target_mid |
| 13 | 15 | 2026-03-01 09:30:00.000230 | out_of_bounds |
| 14 | 16 | 2026-03-01 09:30:00.000247 | old |
| 15 | 17 | 2026-03-01 09:30:00.000268 | target_start |
| 16 | 18 | 2026-03-01 09:30:00.000281 | target_mid |
| 17 | 19 | 2026-03-01 09:30:00.000303 | out_of_bounds |
| 18 | 20 | 2026-03-01 09:30:00.000324 | old |
| 19 | 21 | 2026-03-01 09:30:00.000341 | target_start |
| 20 | 22 | 2026-03-01 09:30:00.000363 | target_mid |
| 21 | 23 | 2026-03-01 09:30:00.000381 | out_of_bounds |
| 22 | 24 | 2026-03-01 09:30:00.000404 | old |
| 23 | 25 | 2026-03-01 09:30:00.000425 | target_start |
| 24 | 26 | 2026-03-01 09:30:00.000461 | target_mid |
| 25 | 27 | 2026-03-01 09:30:00.000465 | out_of_bounds |
Showing first 25 of 999,998 total rows.