✔ PASS
Temporal & Time-Series
task_003_Q03_clickstream_sessionization_gaps
Scaled: 1M+ Records
Ordered: Yes
Clickstream Sessionization with Inactivity Timeouts (Gaps & Islands)
Candidate Latency (1M Rows)
304.86 ms
(1.0x slower than golden)
Golden Latency (1M Rows)
303.91 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: 5.3s
✔ 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 TrapBusiness Scenario & Problem
Digital analytics platforms group raw user pageviews into 'sessions'. If a user is inactive for more than 30 minutes, the next click begins a brand-new session.
⚡ The Frontier Trap (Why LLMs Fail)
Models mistakenly partition by user_id and take ROW_NUMBER(), which fails to segment sessions by time gaps, or use self-joins which scale quadratically.
Prompt / User Request Given to Model
Digital analytics platforms group raw user pageviews into 'sessions'. If a user is inactive for more than 30 minutes, the next click begins a brand-new session. Execute the required analytical SQL calculation in DuckDB. Expected output columns: user_id, event_time, page_url, session_id. 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:
📊 user_events
1,000,000 rows
30.5 MiB Data Volume
user_id (INTEGER) event_time (TIMESTAMP) page_url (VARCHAR)Data Preview (showing sample rows from 1,000,000 total rows):
| # | user_id | event_time | page_url |
|---|---|---|---|
| 1 | 101 | 2026-03-01 10:00:00 | /home |
| 2 | 101 | 2026-03-01 10:05:00 | /products |
| 3 | 101 | 2026-03-01 10:45:00 | /checkout |
| 4 | 101 | 2026-03-01 10:50:00 | /confirmation |
| 5 | 101 | 2026-03-01 09:30:00.000009 | /home |
| 6 | 102 | 2026-03-01 09:30:00.000022 | /products/detail |
| 7 | 103 | 2026-03-01 09:30:00.000047 | /cart |
| 8 | 104 | 2026-03-01 09:30:00.000063 | /checkout |
| 9 | 105 | 2026-03-01 09:30:00.000098 | /search |
| 10 | 106 | 2026-03-01 09:30:00.000103 | /account |
| 11 | 107 | 2026-03-01 09:30:00.000127 | /help |
| 12 | 108 | 2026-03-01 09:30:00.000141 | /home |
| 13 | 109 | 2026-03-01 09:30:00.000165 | /products/detail |
| 14 | 110 | 2026-03-01 09:30:00.000186 | /cart |
| 15 | 111 | 2026-03-01 09:30:00.000203 | /checkout |
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:
🔥 19.1% Cost
Latency: 116.03 ms
Rows In/Out: 1,000,000
Data Vol: 41.96 MB
0.0%
Latency: 0.14 ms
Rows In/Out: 1,000,000
Data Vol: 41.96 MB
📁 Projections: ['user_id', 'event_...
0.0%
Latency: 0.26 ms
Rows In/Out: 1,000,000
Data Vol: 45.78 MB
📁 Projections: ['#0', '#1', '#2', ...
🔥 38.6% Cost
Latency: 234.74 ms
Rows In/Out: 1,000,000
Data Vol: 45.78 MB
📁 Projections: sum_no_overflow(is_...
1.9%
Latency: 11.82 ms
Rows In/Out: 1,000,000
Data Vol: 30.52 MB
📁 Projections: ['user_id', 'event_...
0.0%
Latency: 0.19 ms
Rows In/Out: 1,000,000
Data Vol: 42.20 MB
📁 Projections: ['#0', '#1', '#2', ...
🔥 38.0% Cost
Latency: 231.06 ms
Rows In/Out: 1,000,000
Data Vol: 34.45 MB
📁 Projections: lag(event_time, 1, ...
2.2%
Latency: 13.35 ms
Rows In/Out: 1,000,000
Data Vol: 26.70 MB
📁 memory.main.user_events
| Operator | Cost % | Latency | Processed Rows | Data Volume | Details / Table | Status |
|---|---|---|---|---|---|---|
| WINDOW | 38.6% | 234.74 ms | 1,000,000 | 45.78 MB | Projections: sum_no_overflow(is_new_session) OVE | 🔥 BOTTLENECK |
| WINDOW | 38.0% | 231.06 ms | 1,000,000 | 34.45 MB | Projections: lag(event_time, 1, NULL::TIMESTAMP) | 🔥 BOTTLENECK |
| ORDER_BY | 19.1% | 116.03 ms | 1,000,000 | 41.96 MB | - | 🔥 BOTTLENECK |
| TABLE_SCAN | 2.2% | 13.35 ms | 1,000,000 | 26.70 MB | memory.main.user_events | Normal |
| PROJECTION | 1.9% | 11.82 ms | 1,000,000 | 30.52 MB | Projections: ['user_id', 'event_time', 'page_url | Normal |
| PROJECTION | 0.0% | 0.26 ms | 1,000,000 | 45.78 MB | Projections: ['#0', '#1', '#2', '#3', '#4'] | Normal |
| PROJECTION | 0.0% | 0.19 ms | 1,000,000 | 42.20 MB | Projections: ['#0', '#1', '#2', '#3', '#3'] | Normal |
| PROJECTION | 0.0% | 0.14 ms | 1,000,000 | 41.96 MB | Projections: ['user_id', 'event_time', 'page_url | Normal |
4. Queries Executed (Candidate vs Golden Ground Truth)
Beautified & Syntax HighlightedWITH lagged_events AS
(SELECT user_id,
event_time,
page_url,
CASE
WHEN event_time - lag(event_time) OVER (PARTITION BY user_id
ORDER BY event_time) > INTERVAL 30 MINUTE
OR lag(event_time) OVER (PARTITION BY user_id
ORDER BY event_time) IS NULL THEN 1
ELSE 0
END AS is_new_session
FROM user_events)
SELECT user_id,
event_time,
page_url,
sum(is_new_session) OVER (PARTITION BY user_id
ORDER BY event_time ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS session_id
FROM lagged_events
ORDER BY user_id,
event_time;
-- Detect session breaks (>30 min inactivity) and assign deterministic session UUIDs
WITH event_lags AS
(SELECT user_id,
event_time,
page_url, -- Compare against preceding event timestamp for this user
lag(event_time) OVER (PARTITION BY user_id
ORDER BY event_time) AS prev_event_time
FROM user_events),
session_flags AS
(SELECT user_id,
event_time,
page_url, -- Mark 1 if first event or gap exceeds 30 minutes, else 0
CASE
WHEN prev_event_time IS NULL THEN 1
WHEN date_diff('second', prev_event_time, event_time) > 1800 THEN 1
ELSE 0
END AS is_new_session
FROM event_lags)
SELECT user_id,
event_time,
page_url, -- Cumulative sum over flags establishes monotonically increasing session ID
sum(is_new_session) OVER (PARTITION BY user_id
ORDER BY event_time) AS session_id
FROM session_flags
ORDER BY user_id,
event_time;
CREATE TABLE user_events (user_id INT, event_time TIMESTAMP, page_url VARCHAR);
INSERT INTO user_events
VALUES (101, '2026-03-01 10:00:00', '/home'),
(101, '2026-03-01 10:05:00', '/products'),
(101, '2026-03-01 10:45:00', '/checkout'),
-- 40m gap -> New session
(101, '2026-03-01 10:50:00', '/confirmation');
5. Query Results & Expected Ground Truth
Tabular Comparison
Actual Candidate Output
(1,000,000 rows × 4 cols)
| # | user_id | event_time | page_url | session_id |
|---|---|---|---|---|
| 1 | 101 | 2026-03-01 09:30:00.000009 | /home | 1 |
| 2 | 101 | 2026-03-01 10:00:00 | /home | 1 |
| 3 | 101 | 2026-03-01 10:05:00 | /products | 1 |
| 4 | 101 | 2026-03-01 10:45:00 | /checkout | 2 |
| 5 | 101 | 2026-03-01 10:50:00 | /confirmation | 2 |
| 6 | 102 | 2026-03-01 09:30:00.000022 | /products/detail | 1 |
| 7 | 103 | 2026-03-01 09:30:00.000047 | /cart | 1 |
| 8 | 104 | 2026-03-01 09:30:00.000063 | /checkout | 1 |
| 9 | 105 | 2026-03-01 09:30:00.000098 | /search | 1 |
| 10 | 106 | 2026-03-01 09:30:00.000103 | /account | 1 |
| 11 | 107 | 2026-03-01 09:30:00.000127 | /help | 1 |
| 12 | 108 | 2026-03-01 09:30:00.000141 | /home | 1 |
| 13 | 109 | 2026-03-01 09:30:00.000165 | /products/detail | 1 |
| 14 | 110 | 2026-03-01 09:30:00.000186 | /cart | 1 |
| 15 | 111 | 2026-03-01 09:30:00.000203 | /checkout | 1 |
| 16 | 112 | 2026-03-01 09:30:00.000230 | /search | 1 |
| 17 | 113 | 2026-03-01 09:30:00.000242 | /account | 1 |
| 18 | 114 | 2026-03-01 09:30:00.000275 | /help | 1 |
| 19 | 115 | 2026-03-01 09:30:00.000284 | /home | 1 |
| 20 | 116 | 2026-03-01 09:30:00.000308 | /products/detail | 1 |
| 21 | 117 | 2026-03-01 09:30:00.000323 | /cart | 1 |
| 22 | 118 | 2026-03-01 09:30:00.000353 | /checkout | 1 |
| 23 | 119 | 2026-03-01 09:30:00.000367 | /search | 1 |
| 24 | 120 | 2026-03-01 09:30:00.000391 | /account | 1 |
| 25 | 121 | 2026-03-01 09:30:00.000400 | /help | 1 |
Showing first 25 of 1,000,000 total rows.
Expected Golden Output
(1,000,000 rows × 4 cols)
| # | user_id | event_time | page_url | session_id |
|---|---|---|---|---|
| 1 | 101 | 2026-03-01 09:30:00.000009 | /home | 1 |
| 2 | 101 | 2026-03-01 10:00:00 | /home | 1 |
| 3 | 101 | 2026-03-01 10:05:00 | /products | 1 |
| 4 | 101 | 2026-03-01 10:45:00 | /checkout | 2 |
| 5 | 101 | 2026-03-01 10:50:00 | /confirmation | 2 |
| 6 | 102 | 2026-03-01 09:30:00.000022 | /products/detail | 1 |
| 7 | 103 | 2026-03-01 09:30:00.000047 | /cart | 1 |
| 8 | 104 | 2026-03-01 09:30:00.000063 | /checkout | 1 |
| 9 | 105 | 2026-03-01 09:30:00.000098 | /search | 1 |
| 10 | 106 | 2026-03-01 09:30:00.000103 | /account | 1 |
| 11 | 107 | 2026-03-01 09:30:00.000127 | /help | 1 |
| 12 | 108 | 2026-03-01 09:30:00.000141 | /home | 1 |
| 13 | 109 | 2026-03-01 09:30:00.000165 | /products/detail | 1 |
| 14 | 110 | 2026-03-01 09:30:00.000186 | /cart | 1 |
| 15 | 111 | 2026-03-01 09:30:00.000203 | /checkout | 1 |
| 16 | 112 | 2026-03-01 09:30:00.000230 | /search | 1 |
| 17 | 113 | 2026-03-01 09:30:00.000242 | /account | 1 |
| 18 | 114 | 2026-03-01 09:30:00.000275 | /help | 1 |
| 19 | 115 | 2026-03-01 09:30:00.000284 | /home | 1 |
| 20 | 116 | 2026-03-01 09:30:00.000308 | /products/detail | 1 |
| 21 | 117 | 2026-03-01 09:30:00.000323 | /cart | 1 |
| 22 | 118 | 2026-03-01 09:30:00.000353 | /checkout | 1 |
| 23 | 119 | 2026-03-01 09:30:00.000367 | /search | 1 |
| 24 | 120 | 2026-03-01 09:30:00.000391 | /account | 1 |
| 25 | 121 | 2026-03-01 09:30:00.000400 | /help | 1 |
Showing first 25 of 1,000,000 total rows.