✔ 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 Trap
Business 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:

Pipeline Latency Distribution (Total Plan Time: 607.60 ms) Nodes consuming ≥ 20% of query runtime are highlighted with glowing borders
⏳ ORDER_BY 🔥 19.1% Cost
Latency: 116.03 ms
Rows In/Out: 1,000,000
Data Vol: 41.96 MB
⚠️ Heavy Execution Bottleneck (19.1% of query runtime)
📐 PROJECTION 0.0%
Latency: 0.14 ms
Rows In/Out: 1,000,000
Data Vol: 41.96 MB
📁 Projections: ['user_id', 'event_...
📐 PROJECTION 0.0%
Latency: 0.26 ms
Rows In/Out: 1,000,000
Data Vol: 45.78 MB
📁 Projections: ['#0', '#1', '#2', ...
🪟 WINDOW 🔥 38.6% Cost
Latency: 234.74 ms
Rows In/Out: 1,000,000
Data Vol: 45.78 MB
📁 Projections: sum_no_overflow(is_...
⚠️ Heavy Execution Bottleneck (38.6% of query runtime)
📐 PROJECTION 1.9%
Latency: 11.82 ms
Rows In/Out: 1,000,000
Data Vol: 30.52 MB
📁 Projections: ['user_id', 'event_...
📐 PROJECTION 0.0%
Latency: 0.19 ms
Rows In/Out: 1,000,000
Data Vol: 42.20 MB
📁 Projections: ['#0', '#1', '#2', ...
🪟 WINDOW 🔥 38.0% Cost
Latency: 231.06 ms
Rows In/Out: 1,000,000
Data Vol: 34.45 MB
📁 Projections: lag(event_time, 1, ...
⚠️ Heavy Execution Bottleneck (38.0% of query runtime)
🔍 TABLE_SCAN 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 Highlighted
Candidate SQL Executed by gemini-3.8-flash-low (Runtime on 1M Rows: 304.86 ms)
WITH 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;
Golden Reference Ground Truth SQL (Runtime on 1M Rows: 303.91 ms)
-- 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;
DuckDB Fixture Setup DDL & Data Population
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.
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