✔ PASS
Sessionization (Gaps & Islands)
task_112_dual_constraint_sessionization
Scaled: 1M+ Records
Ordered: Yes
Dual-Constraint Sessionization: 30-min Inactivity AND 4-Hour Absolute Hard Cap
Candidate Latency (1M Rows)
194.48 ms
(1.2x faster than golden)
Golden Latency (1M Rows)
225.76 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: 37.5s
✔ 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
Groups web clickstream events into sessions. A new session starts if: 1) more than 30 minutes pass between consecutive events, OR 2) more than 4 hours have elapsed since the session genesis event.
⚡ The Frontier Trap (Why LLMs Fail)
Frontier models only check the consecutive step lag (> 30 min). When an automated bot or persistent user emits events every 15 minutes continuously for 12 hours, the naive query leaves them in a single endless session, completely violating the 4-hour max session cap constraint.
Prompt / User Request Given to Model
Segment user event stream into deterministic sessions under dual constraints: 1. An inactivity gap of > 30 minutes begins a new session. 2. An absolute duration of > 4 hours from the start of the current session begins a new session (preventing bots or perpetual sessions from running indefinitely). Required output columns: - user_id (INT) - event_time (TIMESTAMP) - session_id (BIGINT) Order results by user_id ASC, event_time ASC.
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:
📊 clickstream_events
1,000,000 rows
11.4 MiB Data Volume
user_id (INTEGER) event_time (TIMESTAMP)Data Preview (showing sample rows from 1,000,000 total rows):
| # | user_id | event_time |
|---|---|---|
| 1 | 1 | 2026-03-01 08:00:00 |
| 2 | 1 | 2026-03-01 08:28:00 |
| 3 | 1 | 2026-03-01 08:56:00 |
| 4 | 1 | 2026-03-01 09:24:00 |
| 5 | 1 | 2026-03-01 09:52:00 |
| 6 | 1 | 2026-03-01 10:20:00 |
| 7 | 1 | 2026-03-01 10:48:00 |
| 8 | 1 | 2026-03-01 11:16:00 |
| 9 | 1 | 2026-03-01 11:44:00 |
| 10 | 1 | 2026-03-01 12:12:00 |
| 11 | 1 | 2026-03-01 09:30:00.000003 |
| 12 | 2 | 2026-03-01 09:30:00.000023 |
| 13 | 3 | 2026-03-01 09:30:00.000049 |
| 14 | 4 | 2026-03-01 09:30:00.000064 |
| 15 | 5 | 2026-03-01 09:30:00.000081 |
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:
1.7%
Latency: 6.40 ms
Rows In/Out: 0
Data Vol: 0 B
0.0%
Latency: 0.16 ms
Rows In/Out: 1,000,000
Data Vol: 19.07 MB
📁 Projections: ['#0', '#1', '#2']
🔥 30.3% Cost
Latency: 116.00 ms
Rows In/Out: 1,000,000
Data Vol: 19.07 MB
📁 Projections: row_number() OVER (...
0.4%
Latency: 1.51 ms
Rows In/Out: 1,000,000
Data Vol: 11.44 MB
📁 memory.main.clickstream_events
🔥 19.3% Cost
Latency: 73.95 ms
Rows In/Out: 1,000,000
Data Vol: 19.07 MB
0.0%
Latency: 0.13 ms
Rows In/Out: 1,000,000
Data Vol: 19.07 MB
📁 Projections: ['#0', '#1', '#5']
🔥 23.4% Cost
Latency: 89.41 ms
Rows In/Out: 1,000,000
Data Vol: 41.96 MB
0.1%
Latency: 0.20 ms
Rows In/Out: 999,990
Data Vol: 41.96 MB
📁 Projections: ['user_id', 'event_...
0.6%
Latency: 2.40 ms
Rows In/Out: 999,990
Data Vol: 19.07 MB
0.3%
Latency: 0.98 ms
Rows In/Out: 1,000,000
Data Vol: 19.07 MB
0.0%
Latency: 0.01 ms
Rows In/Out: 10
Data Vol: 440 B
📁 Projections: ['user_id', 'event_...
0.0%
Latency: 0.05 ms
Rows In/Out: 10
Data Vol: 370 B
📁 Projections: ['user_id', 'event_...
🔥 23.2% Cost
Latency: 88.71 ms
Rows In/Out: 10
Data Vol: 440 B
0.4%
Latency: 1.62 ms
Rows In/Out: 1,000,000
Data Vol: 41.96 MB
0.2%
Latency: 0.94 ms
Rows In/Out: 1,000,000
Data Vol: 19.07 MB
| Operator | Cost % | Latency | Processed Rows | Data Volume | Details / Table | Status |
|---|---|---|---|---|---|---|
| WINDOW | 30.3% | 116.00 ms | 1,000,000 | 19.07 MB | Projections: row_number() OVER (PARTITION BY use | 🔥 BOTTLENECK |
| RECURSIVE_CTE | 23.4% | 89.41 ms | 1,000,000 | 41.96 MB | - | 🔥 BOTTLENECK |
| HASH_JOIN | 23.2% | 88.71 ms | 10 | 0.00 MB | - | 🔥 BOTTLENECK |
| ORDER_BY | 19.3% | 73.95 ms | 1,000,000 | 19.07 MB | - | 🔥 BOTTLENECK |
| CTE | 1.7% | 6.40 ms | 0 | 0.00 MB | - | Normal |
| FILTER | 0.6% | 2.40 ms | 999,990 | 19.07 MB | - | Normal |
| RECURSIVE_CTE_SCAN | 0.4% | 1.62 ms | 1,000,000 | 41.96 MB | - | Normal |
| TABLE_SCAN | 0.4% | 1.51 ms | 1,000,000 | 11.44 MB | memory.main.clickstream_events | Normal |
| CTE_SCAN | 0.3% | 0.98 ms | 1,000,000 | 19.07 MB | - | Normal |
| CTE_SCAN | 0.2% | 0.94 ms | 1,000,000 | 19.07 MB | - | Normal |
| PROJECTION | 0.1% | 0.20 ms | 999,990 | 41.96 MB | Projections: ['user_id', 'event_time', 'rn', 'se | Normal |
| PROJECTION | 0.0% | 0.16 ms | 1,000,000 | 19.07 MB | Projections: ['#0', '#1', '#2'] | Normal |
| PROJECTION | 0.0% | 0.13 ms | 1,000,000 | 19.07 MB | Projections: ['#0', '#1', '#5'] | Normal |
| PROJECTION | 0.0% | 0.05 ms | 10 | 0.00 MB | Projections: ['user_id', 'event_time', 'rn', "(( | Normal |
| PROJECTION | 0.0% | 0.01 ms | 10 | 0.00 MB | Projections: ['user_id', 'event_time', 'rn', 'se | Normal |
4. Queries Executed (Candidate vs Golden Ground Truth)
Beautified & Syntax HighlightedWITH RECURSIVE numbered_events AS
(SELECT user_id,
event_time,
row_number() OVER (PARTITION BY user_id
ORDER BY event_time ASC) AS rn
FROM clickstream_events),
sessionized_stream AS
(SELECT user_id,
event_time,
rn,
event_time AS session_start_time,
event_time AS prev_event_time,
1::BIGINT AS session_id
FROM numbered_events
WHERE rn = 1
UNION ALL SELECT curr.user_id,
curr.event_time,
curr.rn,
CASE
WHEN curr.event_time - prev.prev_event_time > INTERVAL '30 minutes'
OR curr.event_time - prev.session_start_time > INTERVAL '4 hours' THEN curr.event_time
ELSE prev.session_start_time
END AS session_start_time,
curr.event_time AS prev_event_time,
CASE
WHEN curr.event_time - prev.prev_event_time > INTERVAL '30 minutes'
OR curr.event_time - prev.session_start_time > INTERVAL '4 hours' THEN prev.session_id + 1
ELSE prev.session_id
END AS session_id
FROM numbered_events curr
JOIN sessionized_stream prev ON curr.user_id = prev.user_id
AND curr.rn = prev.rn + 1)
SELECT user_id,
event_time,
session_id
FROM sessionized_stream
ORDER BY user_id ASC,
event_time ASC;
WITH step_lags AS
(SELECT user_id,
event_time,
lag(event_time) OVER (PARTITION BY user_id
ORDER BY event_time) AS prev_time
FROM clickstream_events),
inactivity_flags AS
(SELECT user_id,
event_time,
CASE
WHEN prev_time IS NULL
OR date_diff('second', prev_time, event_time) > 1800 THEN 1
ELSE 0
END AS is_inactivity_break
FROM step_lags),
base_sessions AS
(SELECT user_id,
event_time,
sum(is_inactivity_break) OVER (PARTITION BY user_id
ORDER BY event_time) AS base_sess
FROM inactivity_flags),
genesis_diff AS
(SELECT user_id,
event_time,
base_sess,
date_diff('second', first_value(event_time) OVER (PARTITION BY user_id, base_sess
ORDER BY event_time), event_time) AS elapsed_from_start
FROM base_sessions)
SELECT user_id,
event_time,
(base_sess + (elapsed_from_start > 14400)::INT)::BIGINT AS session_id
FROM genesis_diff
ORDER BY user_id ASC,
event_time ASC;
CREATE TABLE clickstream_events (user_id INT, event_time TIMESTAMP);
-- 10 events spaced exactly 28 minutes apart (< 30m inactivity gap, but spans 4h 12m from genesis)
INSERT INTO clickstream_events
SELECT 1,
TIMESTAMP '2026-03-01 08:00:00' + (i * INTERVAL '28 minutes')
FROM range(10) t(i);
5. Query Results & Expected Ground Truth
Tabular Comparison
Actual Candidate Output
(1,000,000 rows × 3 cols)
| # | user_id | event_time | session_id |
|---|---|---|---|
| 1 | 1 | 2026-03-01 08:00:00 | 1 |
| 2 | 1 | 2026-03-01 08:28:00 | 1 |
| 3 | 1 | 2026-03-01 08:56:00 | 1 |
| 4 | 1 | 2026-03-01 09:24:00 | 1 |
| 5 | 1 | 2026-03-01 09:30:00.000003 | 1 |
| 6 | 1 | 2026-03-01 09:52:00 | 1 |
| 7 | 1 | 2026-03-01 10:20:00 | 1 |
| 8 | 1 | 2026-03-01 10:48:00 | 1 |
| 9 | 1 | 2026-03-01 11:16:00 | 1 |
| 10 | 1 | 2026-03-01 11:44:00 | 1 |
| 11 | 1 | 2026-03-01 12:12:00 | 2 |
| 12 | 2 | 2026-03-01 09:30:00.000023 | 1 |
| 13 | 3 | 2026-03-01 09:30:00.000049 | 1 |
| 14 | 4 | 2026-03-01 09:30:00.000064 | 1 |
| 15 | 5 | 2026-03-01 09:30:00.000081 | 1 |
| 16 | 6 | 2026-03-01 09:30:00.000103 | 1 |
| 17 | 7 | 2026-03-01 09:30:00.000125 | 1 |
| 18 | 8 | 2026-03-01 09:30:00.000141 | 1 |
| 19 | 9 | 2026-03-01 09:30:00.000162 | 1 |
| 20 | 10 | 2026-03-01 09:30:00.000184 | 1 |
| 21 | 11 | 2026-03-01 09:30:00.000202 | 1 |
| 22 | 12 | 2026-03-01 09:30:00.000227 | 1 |
| 23 | 13 | 2026-03-01 09:30:00.000243 | 1 |
| 24 | 14 | 2026-03-01 09:30:00.000262 | 1 |
| 25 | 15 | 2026-03-01 09:30:00.000284 | 1 |
Showing first 25 of 1,000,000 total rows.
Expected Golden Output
(1,000,000 rows × 3 cols)
| # | user_id | event_time | session_id |
|---|---|---|---|
| 1 | 1 | 2026-03-01 08:00:00 | 1 |
| 2 | 1 | 2026-03-01 08:28:00 | 1 |
| 3 | 1 | 2026-03-01 08:56:00 | 1 |
| 4 | 1 | 2026-03-01 09:24:00 | 1 |
| 5 | 1 | 2026-03-01 09:30:00.000003 | 1 |
| 6 | 1 | 2026-03-01 09:52:00 | 1 |
| 7 | 1 | 2026-03-01 10:20:00 | 1 |
| 8 | 1 | 2026-03-01 10:48:00 | 1 |
| 9 | 1 | 2026-03-01 11:16:00 | 1 |
| 10 | 1 | 2026-03-01 11:44:00 | 1 |
| 11 | 1 | 2026-03-01 12:12:00 | 2 |
| 12 | 2 | 2026-03-01 09:30:00.000023 | 1 |
| 13 | 3 | 2026-03-01 09:30:00.000049 | 1 |
| 14 | 4 | 2026-03-01 09:30:00.000064 | 1 |
| 15 | 5 | 2026-03-01 09:30:00.000081 | 1 |
| 16 | 6 | 2026-03-01 09:30:00.000103 | 1 |
| 17 | 7 | 2026-03-01 09:30:00.000125 | 1 |
| 18 | 8 | 2026-03-01 09:30:00.000141 | 1 |
| 19 | 9 | 2026-03-01 09:30:00.000162 | 1 |
| 20 | 10 | 2026-03-01 09:30:00.000184 | 1 |
| 21 | 11 | 2026-03-01 09:30:00.000202 | 1 |
| 22 | 12 | 2026-03-01 09:30:00.000227 | 1 |
| 23 | 13 | 2026-03-01 09:30:00.000243 | 1 |
| 24 | 14 | 2026-03-01 09:30:00.000262 | 1 |
| 25 | 15 | 2026-03-01 09:30:00.000284 | 1 |
Showing first 25 of 1,000,000 total rows.