SQL-FRONTIER Benchmark Deep-Dive / Temporal & Time-Series / task_012_Q12_microsecond_latency_jitter
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✔ PASS Temporal & Time-Series task_012_Q12_microsecond_latency_jitter Scaled: 1M+ Records Ordered: Yes

Inter-Packet Network Arrival Jitter at Nanosecond Resolution

Candidate Latency (1M Rows)
152.44 ms
(1.0x slower than golden)
Golden Latency (1M Rows)
145.25 ms
DuckDB in-memory engine
Underlying Data Volume
1,000,000 rows
11.4 MiB across 1 table
Evaluated Model
gemini-3.8-flash-low
Gen Latency: 11.3s
✔ 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

High-speed network telemetry measures packet arrival jitter (variance in inter-packet delay). Jitter spikes indicate network congestion or buffer bloat.

⚡ The Frontier Trap (Why LLMs Fail)

Standard SQL datetime arithmetic truncates to milliseconds or seconds, losing high-frequency microsecond microbursts.

Prompt / User Request Given to Model
High-speed network telemetry measures packet arrival jitter (variance in inter-packet delay). Jitter spikes indicate network congestion or buffer bloat.

Execute the required analytical SQL calculation in DuckDB.
Expected output columns: packet_id, captured_at, delta_microseconds, rolling_jitter_stddev.
Results should be ordered as specified.

2. Underlying Tables, Row Counts & Data Volume

1 Tables (1,000,000 rows • 11.4 MiB)

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

📊 packet_stream 1,000,000 rows 11.4 MiB Data Volume
packet_id (INTEGER) captured_at (TIMESTAMP)
Data Preview (showing sample rows from 1,000,000 total rows):
# packet_id captured_at
1 1 2026-03-01 12:00:00.000100
2 2 2026-03-01 12:00:00.000150
3 3 2026-03-01 12:00:00.000300
4 4 2026-03-01 12:00:00.000350
5 4 2026-03-01 09:30:00.000011
6 5 2026-03-01 09:30:00.000034
7 6 2026-03-01 09:30:00.000048
8 7 2026-03-01 09:30:00.000072
9 8 2026-03-01 09:30:00.000085
10 9 2026-03-01 09:30:00.000110
11 10 2026-03-01 09:30:00.000121
12 11 2026-03-01 09:30:00.000149
13 12 2026-03-01 09:30:00.000168
14 13 2026-03-01 09:30:00.000187
15 14 2026-03-01 09:30:00.000208

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: 355.14 ms) Nodes consuming ≥ 20% of query runtime are highlighted with glowing borders
⏳ ORDER_BY 🔥 17.6% Cost
Latency: 62.45 ms
Rows In/Out: 1,000,000
Data Vol: 26.70 MB
⚠️ Heavy Execution Bottleneck (17.6% of query runtime)
📐 PROJECTION 7.9%
Latency: 28.07 ms
Rows In/Out: 1,000,000
Data Vol: 26.70 MB
📁 Projections: ['packet_id', 'capt...
📐 PROJECTION 0.0%
Latency: 0.14 ms
Rows In/Out: 1,000,000
Data Vol: 26.70 MB
📁 Projections: ['#0', '#1', '#2', ...
🪟 WINDOW 🔥 49.9% Cost
Latency: 177.12 ms
Rows In/Out: 1,000,000
Data Vol: 26.70 MB
📁 Projections: stddev(CAST(delta_m...
⚠️ Heavy Execution Bottleneck (49.9% of query runtime)
📐 PROJECTION 1.2%
Latency: 4.24 ms
Rows In/Out: 1,000,000
Data Vol: 19.07 MB
📁 Projections: ['packet_id', 'capt...
📐 PROJECTION 0.0%
Latency: 0.10 ms
Rows In/Out: 1,000,000
Data Vol: 19.07 MB
📁 Projections: ['#0', '#1', '#2']
🪟 WINDOW 🔥 23.1% Cost
Latency: 81.87 ms
Rows In/Out: 1,000,000
Data Vol: 19.07 MB
📁 Projections: lag(captured_at, 1,...
⚠️ Heavy Execution Bottleneck (23.1% of query runtime)
🔍 TABLE_SCAN 0.3%
Latency: 1.15 ms
Rows In/Out: 1,000,000
Data Vol: 11.44 MB
📁 memory.main.packet_stream
Operator Cost % Latency Processed Rows Data Volume Details / Table Status
WINDOW 49.9% 177.12 ms 1,000,000 26.70 MB Projections: stddev(CAST(delta_microseconds AS D 🔥 BOTTLENECK
WINDOW 23.1% 81.87 ms 1,000,000 19.07 MB Projections: lag(captured_at, 1, NULL::TIMESTAMP 🔥 BOTTLENECK
ORDER_BY 17.6% 62.45 ms 1,000,000 26.70 MB - 🔥 BOTTLENECK
PROJECTION 7.9% 28.07 ms 1,000,000 26.70 MB Projections: ['packet_id', 'captured_at', 'delta Normal
PROJECTION 1.2% 4.24 ms 1,000,000 19.07 MB Projections: ['packet_id', 'captured_at', 'delta Normal
TABLE_SCAN 0.3% 1.15 ms 1,000,000 11.44 MB memory.main.packet_stream Normal
PROJECTION 0.0% 0.14 ms 1,000,000 26.70 MB Projections: ['#0', '#1', '#2', '#3'] Normal
PROJECTION 0.0% 0.10 ms 1,000,000 19.07 MB Projections: ['#0', '#1', '#2'] 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: 152.44 ms) 
WITH deltas AS
  (SELECT packet_id,
          captured_at,
          date_diff('microsecond', lag(captured_at) OVER (
                                                          ORDER BY captured_at), captured_at) AS delta_microseconds
   FROM packet_stream)
SELECT packet_id,
       captured_at,
       delta_microseconds,
       round(stddev(delta_microseconds) OVER (
                                              ORDER BY captured_at ROWS BETWEEN 2 PRECEDING AND CURRENT ROW), 2) AS rolling_jitter_stddev
FROM deltas
ORDER BY captured_at;
Golden Reference Ground Truth SQL (Runtime on 1M Rows: 145.25 ms) 
-- Compute inter-packet arrival delta in microseconds and identify variance
 WITH deltas AS
  (SELECT packet_id,
          captured_at,
          date_diff('microsecond', lag(captured_at) OVER (
                                                          ORDER BY captured_at), captured_at) AS delta_microseconds
   FROM packet_stream)
SELECT packet_id,
       captured_at,
       delta_microseconds, -- Measure moving jitter variance over last 3 packets
 round(stddev(delta_microseconds) OVER (
                                        ORDER BY captured_at ROWS BETWEEN 2 PRECEDING AND CURRENT ROW), 2) AS rolling_jitter_stddev
FROM deltas
ORDER BY captured_at;
DuckDB Fixture Setup DDL & Data Population 
CREATE TABLE packet_stream (packet_id INT, captured_at TIMESTAMP);


INSERT INTO packet_stream
VALUES (1, '2026-03-01 12:00:00.000100'),
       (2, '2026-03-01 12:00:00.000150'),
       -- +50us
 (3, '2026-03-01 12:00:00.000300'),
 -- +150us (jitter spike)
 (4, '2026-03-01 12:00:00.000350');

5. Query Results & Expected Ground Truth

Tabular Comparison
Actual Candidate Output (1,000,000 rows × 4 cols)
# packet_id captured_at delta_microseconds rolling_jitter_stddev
1 4 2026-03-01 09:30:00.000011 NULL NULL
2 5 2026-03-01 09:30:00.000034 23 NULL
3 6 2026-03-01 09:30:00.000048 14 6.36
4 7 2026-03-01 09:30:00.000072 24 5.51
5 8 2026-03-01 09:30:00.000085 13 6.08
6 9 2026-03-01 09:30:00.000110 25 6.66
7 10 2026-03-01 09:30:00.000121 11 7.57
8 11 2026-03-01 09:30:00.000149 28 9.07
9 12 2026-03-01 09:30:00.000168 19 8.5
10 13 2026-03-01 09:30:00.000187 19 5.2
11 14 2026-03-01 09:30:00.000208 21 1.15
12 15 2026-03-01 09:30:00.000235 27 4.16
13 16 2026-03-01 09:30:00.000253 18 4.58
14 17 2026-03-01 09:30:00.000261 8 9.5
15 18 2026-03-01 09:30:00.000288 27 9.5
16 19 2026-03-01 09:30:00.000305 17 9.5
17 20 2026-03-01 09:30:00.000322 17 5.77
18 21 2026-03-01 09:30:00.000345 23 3.46
19 22 2026-03-01 09:30:00.000375 30 6.51
20 23 2026-03-01 09:30:00.000380 5 12.9
21 24 2026-03-01 09:30:00.000414 34 15.72
22 25 2026-03-01 09:30:00.000425 11 15.31
23 26 2026-03-01 09:30:00.000442 17 11.93
24 27 2026-03-01 09:30:00.000462 20 4.58
25 28 2026-03-01 09:30:00.000497 35 9.64
Showing first 25 of 1,000,000 total rows.
Expected Golden Output (1,000,000 rows × 4 cols)
# packet_id captured_at delta_microseconds rolling_jitter_stddev
1 4 2026-03-01 09:30:00.000011 NULL NULL
2 5 2026-03-01 09:30:00.000034 23 NULL
3 6 2026-03-01 09:30:00.000048 14 6.36
4 7 2026-03-01 09:30:00.000072 24 5.51
5 8 2026-03-01 09:30:00.000085 13 6.08
6 9 2026-03-01 09:30:00.000110 25 6.66
7 10 2026-03-01 09:30:00.000121 11 7.57
8 11 2026-03-01 09:30:00.000149 28 9.07
9 12 2026-03-01 09:30:00.000168 19 8.5
10 13 2026-03-01 09:30:00.000187 19 5.2
11 14 2026-03-01 09:30:00.000208 21 1.15
12 15 2026-03-01 09:30:00.000235 27 4.16
13 16 2026-03-01 09:30:00.000253 18 4.58
14 17 2026-03-01 09:30:00.000261 8 9.5
15 18 2026-03-01 09:30:00.000288 27 9.5
16 19 2026-03-01 09:30:00.000305 17 9.5
17 20 2026-03-01 09:30:00.000322 17 5.77
18 21 2026-03-01 09:30:00.000345 23 3.46
19 22 2026-03-01 09:30:00.000375 30 6.51
20 23 2026-03-01 09:30:00.000380 5 12.9
21 24 2026-03-01 09:30:00.000414 34 15.72
22 25 2026-03-01 09:30:00.000425 11 15.31
23 26 2026-03-01 09:30:00.000442 17 11.93
24 27 2026-03-01 09:30:00.000462 20 4.58
25 28 2026-03-01 09:30:00.000497 35 9.64
Showing first 25 of 1,000,000 total rows.
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