SQL-FRONTIER Benchmark Deep-Dive / Temporal & Time-Series / task_008_Q08_hopping_sliding_time_buckets
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✔ PASS Temporal & Time-Series task_008_Q08_hopping_sliding_time_buckets Scaled: 1M+ Records Ordered: Yes

Hopping / Overlapping Window Aggregations via time_bucket

Candidate Latency (1M Rows)
0.56 ms
(1.1x faster than golden)
Golden Latency (1M Rows)
0.59 ms
DuckDB in-memory engine
Underlying Data Volume
1,000,000 rows
34.3 MiB across 1 table
Evaluated Model
gemini-3.8-flash-low
Gen Latency: 16.0s
✔ 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

Real-time surveillance monitors rolling 15-minute average latency stepped forward every 5 minutes (a hopping window).

⚡ The Frontier Trap (Why LLMs Fail)

Standard GROUP BY time_bucket only produces non-overlapping tumbling windows, missing cross-boundary bursts.

Prompt / User Request Given to Model
Real-time surveillance monitors rolling 15-minute average latency stepped forward every 5 minutes (a hopping window).

Execute the required analytical SQL calculation in DuckDB.
Expected output columns: window_start, window_end, server_id, sample_count, avg_cpu, peak_cpu.
Results should be ordered as specified.

2. Underlying Tables, Row Counts & Data Volume

1 Tables (1,000,000 rows • 34.3 MiB)

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

📊 server_metrics 1,000,000 rows 34.3 MiB Data Volume
server_id (VARCHAR) metric_time (TIMESTAMP) cpu_utilization (DOUBLE)
Data Preview (showing sample rows from 1,000,000 total rows):
# server_id metric_time cpu_utilization
1 web-01 2026-03-01 10:02:00 45
2 web-01 2026-03-01 10:07:00 78
3 web-01 2026-03-01 10:12:00 92
4 web-01 2026-03-01 10:17:00 60
5 node-cluster-1 2026-03-01 09:30:00.000011 97.56
6 node-cluster-2 2026-03-01 09:30:00.000020 69.34
7 node-cluster-3 2026-03-01 09:30:00.000041 66.5
8 node-cluster-4 2026-03-01 09:30:00.000063 60.83
9 node-cluster-5 2026-03-01 09:30:00.000088 47.52
10 node-cluster-6 2026-03-01 09:30:00.000104 79.26
11 node-cluster-7 2026-03-01 09:30:00.000130 95
12 node-cluster-8 2026-03-01 09:30:00.000153 101.11
13 node-cluster-9 2026-03-01 09:30:00.000162 64.81
14 node-cluster-10 2026-03-01 09:30:00.000185 55.34
15 node-cluster-11 2026-03-01 09:30:00.000208 48.89

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: 0.56 ms) Nodes consuming ≥ 20% of query runtime are highlighted with glowing borders
📐 PROJECTION 0.2%
Latency: 0.00 ms
Rows In/Out: 4
Data Vol: 224 B
📁 Projections: ['#0', '#1', '__int...
⏳ ORDER_BY 23.4%
Latency: 0.13 ms
Rows In/Out: 4
Data Vol: 224 B
📐 PROJECTION 0.6%
Latency: 0.00 ms
Rows In/Out: 4
Data Vol: 224 B
📁 Projections: ['#0', '#1', '__int...
📐 PROJECTION 1.8%
Latency: 0.01 ms
Rows In/Out: 4
Data Vol: 224 B
📁 Projections: ['window_start', 'w...
📐 PROJECTION 1.3%
Latency: 0.01 ms
Rows In/Out: 4
Data Vol: 192 B
📁 Projections: ['#0', '#1', '#2', ...
📐 PROJECTION 0.9%
Latency: 0.01 ms
Rows In/Out: 4
Data Vol: 224 B
📁 Projections: ['#0', '__internal_...
📊 HASH_GROUP_BY 50.7%
Latency: 0.28 ms
Rows In/Out: 4
Data Vol: 192 B
📐 PROJECTION 0.0%
Latency: 0.00 ms
Rows In/Out: 9
Data Vol: 360 B
📁 Projections: ['window_start', 's...
📐 PROJECTION 0.4%
Latency: 0.00 ms
Rows In/Out: 9
Data Vol: 288 B
📁 Projections: ['__internal_compre...
⚡ NESTED_LOOP_JOIN 15.9%
Latency: 0.09 ms
Rows In/Out: 9
Data Vol: 360 B
🔍 TABLE_SCAN 3.7%
Latency: 0.02 ms
Rows In/Out: 4
Data Vol: 128 B
📁 memory.main.server_metrics
📐 PROJECTION 0.1%
Latency: 0.00 ms
Rows In/Out: 4
Data Vol: 32 B
📁 Projections: window_start
⚙️ UNNEST 1.0%
Latency: 0.01 ms
Rows In/Out: 4
Data Vol: 48 B
🔍 DUMMY_SCAN 0.1%
Latency: 0.00 ms
Rows In/Out: 1
Data Vol: 4 B
Operator Cost % Latency Processed Rows Data Volume Details / Table Status
HASH_GROUP_BY 50.7% 0.28 ms 4 0.00 MB - Normal
ORDER_BY 23.4% 0.13 ms 4 0.00 MB - Normal
NESTED_LOOP_JOIN 15.9% 0.09 ms 9 0.00 MB - Normal
TABLE_SCAN 3.7% 0.02 ms 4 0.00 MB memory.main.server_metrics Normal
PROJECTION 1.8% 0.01 ms 4 0.00 MB Projections: ['window_start', 'window_end', 'ser Normal
PROJECTION 1.3% 0.01 ms 4 0.00 MB Projections: ['#0', '#1', '#2', '"/"(#3, CAST(#5 Normal
UNNEST 1.0% 0.01 ms 4 0.00 MB - Normal
PROJECTION 0.9% 0.01 ms 4 0.00 MB Projections: ['#0', '__internal_decompress_strin Normal
PROJECTION 0.6% 0.00 ms 4 0.00 MB Projections: ['#0', '#1', '__internal_compress_s Normal
PROJECTION 0.4% 0.00 ms 9 0.00 MB Projections: ['__internal_compress_string_uhugei Normal
PROJECTION 0.2% 0.00 ms 4 0.00 MB Projections: ['#0', '#1', '__internal_decompress Normal
DUMMY_SCAN 0.1% 0.00 ms 1 0.00 MB - Normal
PROJECTION 0.1% 0.00 ms 4 0.00 MB Projections: window_start Normal
PROJECTION 0.0% 0.00 ms 9 0.00 MB Projections: ['window_start', 'server_id', 'cpu_ Normal

4. Queries Executed (Candidate vs Golden Ground Truth)

Beautified & Syntax Highlighted
Candidate SQL Executed by gemini-3.8-flash-low (Runtime on 1M Rows: 0.56 ms) 
WITH window_steps AS
  (SELECT unnest(generate_series(TIMESTAMP '2026-03-01 10:00:00', TIMESTAMP '2026-03-01 10:15:00', INTERVAL '5 minutes')) AS window_start)
SELECT w.window_start,
       w.window_start + INTERVAL '15 minutes' AS window_end,
       m.server_id,
       count(m.cpu_utilization) AS sample_count,
       round(avg(m.cpu_utilization), 2) AS avg_cpu,
       max(m.cpu_utilization) AS peak_cpu
FROM window_steps w
JOIN server_metrics m ON m.metric_time >= w.window_start
AND m.metric_time < w.window_start + INTERVAL '15 minutes'
GROUP BY w.window_start,
         m.server_id
ORDER BY w.window_start;
Golden Reference Ground Truth SQL (Runtime on 1M Rows: 0.59 ms) 
-- Generate 15-minute sliding windows stepped every 5 minutes (hopping window)
 WITH window_steps AS
  (SELECT unnest(generate_series(TIMESTAMP '2026-03-01 10:00:00', TIMESTAMP '2026-03-01 10:15:00', INTERVAL '5 minutes')) AS window_start)
SELECT w.window_start,
       w.window_start + INTERVAL '15 minutes' AS window_end,
       m.server_id,
       count(m.cpu_utilization) AS sample_count,
       round(avg(m.cpu_utilization), 2) AS avg_cpu,
       max(m.cpu_utilization) AS peak_cpu
FROM window_steps w
JOIN server_metrics m ON m.metric_time >= w.window_start
AND m.metric_time < w.window_start + INTERVAL '15 minutes'
GROUP BY w.window_start,
         m.server_id
ORDER BY w.window_start;
DuckDB Fixture Setup DDL & Data Population 
CREATE TABLE server_metrics (server_id VARCHAR, metric_time TIMESTAMP, cpu_utilization DOUBLE);


INSERT INTO server_metrics
VALUES ('web-01', '2026-03-01 10:02:00', 45.0),
       ('web-01', '2026-03-01 10:07:00', 78.0),
       ('web-01', '2026-03-01 10:12:00', 92.0),
       ('web-01', '2026-03-01 10:17:00', 60.0);

5. Query Results & Expected Ground Truth

Tabular Comparison
Actual Candidate Output (4 rows × 6 cols)
# window_start window_end server_id sample_count avg_cpu peak_cpu
1 2026-03-01 10:00:00 2026-03-01 10:15:00 web-01 3 71.67 92
2 2026-03-01 10:05:00 2026-03-01 10:20:00 web-01 3 76.67 92
3 2026-03-01 10:10:00 2026-03-01 10:25:00 web-01 2 76 92
4 2026-03-01 10:15:00 2026-03-01 10:30:00 web-01 1 60 60
Expected Golden Output (4 rows × 6 cols)
# window_start window_end server_id sample_count avg_cpu peak_cpu
1 2026-03-01 10:00:00 2026-03-01 10:15:00 web-01 3 71.67 92
2 2026-03-01 10:05:00 2026-03-01 10:20:00 web-01 3 76.67 92
3 2026-03-01 10:10:00 2026-03-01 10:25:00 web-01 2 76 92
4 2026-03-01 10:15:00 2026-03-01 10:30:00 web-01 1 60 60
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