✔ PASS Sessionization (Gaps & Islands) task_02_sessionization Scaled: 1M+ Records Ordered: Yes

Clickstream Sessionization with Inactivity Gaps

Candidate Latency (1M Rows)
265.04 ms
(1.1x slower than golden)
Golden Latency (1M Rows)
248.26 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: 4.4s
✔ 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

Groups web analytics events into browsing sessions where an inactivity gap greater than 30 minutes demarcates a new session boundary.

⚡ The Frontier Trap (Why LLMs Fail)

Sessionization with inactivity timeouts is a classic Gaps & Islands problem. Naive LLMs routinely attempt to group directly by timestamps or confuse ROW_NUMBER() with session grouping, failing to calculate the cumulative sum of step flags (LAG condition). The optimal pattern requires a two-step window CTE: step 1 identifies boundary leaps (> 30 min) with LAG(), and step 2 computes a running cumulative sum of those flags to assign unique session IDs.

Prompt / User Request Given to Model
You are given a table of raw web clickstream events `user_events`. Group each user's events into distinct browsing sessions.

A new session starts when:
1. It is the user's very first event, OR
2. More than 30 minutes (DuckDB: INTERVAL 30 MINUTE) have elapsed since the user's immediately preceding event.

For each user and session, return:
- user_id (INT)
- session_id (BIGINT or INT): A sequential integer session index per user starting at 1
- session_start (TIMESTAMP): Timestamp of the first event in the session
- session_end (TIMESTAMP): Timestamp of the last event in the session
- event_count (BIGINT or INT): Total number of events in this session

Sort the results by user_id ASC, session_start ASC.

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:

📊 user_events 1,000,000 rows 34.3 MiB Data Volume
event_id (INTEGER) user_id (INTEGER) event_timestamp (TIMESTAMP) page_url (VARCHAR)
Data Preview (showing sample rows from 1,000,000 total rows):
# event_id user_id event_timestamp page_url
1 1 10 2025-01-01 10:00:00 /home
2 2 10 2025-01-01 10:15:00 /pricing
3 3 10 2025-01-01 11:00:00 /docs
4 4 10 2025-01-01 11:10:00 /checkout
5 5 20 2025-01-01 09:00:00 /blog
6 5 20 2026-03-01 09:30:00.000005 /home
7 6 21 2026-03-01 09:30:00.000036 /products/detail
8 7 22 2026-03-01 09:30:00.000051 /cart
9 8 23 2026-03-01 09:30:00.000071 /checkout
10 9 24 2026-03-01 09:30:00.000082 /search
11 10 25 2026-03-01 09:30:00.000114 /account
12 11 26 2026-03-01 09:30:00.000123 /help
13 12 27 2026-03-01 09:30:00.000148 /home
14 13 28 2026-03-01 09:30:00.000168 /products/detail
15 14 29 2026-03-01 09:30:00.000189 /cart

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: 781.29 ms) Nodes consuming ≥ 20% of query runtime are highlighted with glowing borders
⏳ ORDER_BY 🔥 15.3% Cost
Latency: 119.67 ms
Rows In/Out: 999,998
Data Vol: 41.96 MB
⚠️ Heavy Execution Bottleneck (15.3% of query runtime)
📊 HASH_GROUP_BY 🔥 37.2% Cost
Latency: 290.33 ms
Rows In/Out: 999,998
Data Vol: 41.96 MB
⚠️ Heavy Execution Bottleneck (37.2% of query runtime)
📐 PROJECTION 0.0%
Latency: 0.26 ms
Rows In/Out: 1,000,000
Data Vol: 34.33 MB
📁 Projections: ['user_id', 'sessio...
📐 PROJECTION 0.0%
Latency: 0.21 ms
Rows In/Out: 1,000,000
Data Vol: 26.70 MB
📁 Projections: ['user_id', 'event_...
📐 PROJECTION 0.0%
Latency: 0.35 ms
Rows In/Out: 1,000,000
Data Vol: 30.52 MB
📁 Projections: ['#0', '#1', '#2', ...
🪟 WINDOW 🔥 27.8% Cost
Latency: 217.21 ms
Rows In/Out: 1,000,000
Data Vol: 30.52 MB
📁 Projections: sum_no_overflow(is_...
⚠️ Heavy Execution Bottleneck (27.8% of query runtime)
📐 PROJECTION 0.3%
Latency: 2.68 ms
Rows In/Out: 1,000,000
Data Vol: 15.26 MB
📁 Projections: ['user_id', 'event_...
📐 PROJECTION 0.0%
Latency: 0.14 ms
Rows In/Out: 1,000,000
Data Vol: 26.95 MB
📁 Projections: ['#0', '#1', '#2', ...
🪟 WINDOW 🔥 19.1% Cost
Latency: 149.37 ms
Rows In/Out: 1,000,000
Data Vol: 19.19 MB
📁 Projections: lag(event_timestamp...
⚠️ Heavy Execution Bottleneck (19.1% of query runtime)
🔍 TABLE_SCAN 0.1%
Latency: 1.07 ms
Rows In/Out: 1,000,000
Data Vol: 11.44 MB
📁 memory.main.user_events
Operator Cost % Latency Processed Rows Data Volume Details / Table Status
HASH_GROUP_BY 37.2% 290.33 ms 999,998 41.96 MB - 🔥 BOTTLENECK
WINDOW 27.8% 217.21 ms 1,000,000 30.52 MB Projections: sum_no_overflow(is_new_session) OVE 🔥 BOTTLENECK
WINDOW 19.1% 149.37 ms 1,000,000 19.19 MB Projections: lag(event_timestamp, 1, NULL::TIMES 🔥 BOTTLENECK
ORDER_BY 15.3% 119.67 ms 999,998 41.96 MB - 🔥 BOTTLENECK
PROJECTION 0.3% 2.68 ms 1,000,000 15.26 MB Projections: ['user_id', 'event_timestamp', 'is_ Normal
TABLE_SCAN 0.1% 1.07 ms 1,000,000 11.44 MB memory.main.user_events Normal
PROJECTION 0.0% 0.35 ms 1,000,000 30.52 MB Projections: ['#0', '#1', '#2', '#3'] Normal
PROJECTION 0.0% 0.26 ms 1,000,000 34.33 MB Projections: ['user_id', 'session_id', 'event_ti Normal
PROJECTION 0.0% 0.21 ms 1,000,000 26.70 MB Projections: ['user_id', 'event_timestamp', 'ses Normal
PROJECTION 0.0% 0.14 ms 1,000,000 26.95 MB Projections: ['#0', '#1', '#2', '#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: 265.04 ms)
WITH lagged_events AS
  (SELECT user_id,
          event_timestamp,
          CASE
              WHEN event_timestamp - lag(event_timestamp) OVER (PARTITION BY user_id
                                                                ORDER BY event_timestamp) > INTERVAL 30 MINUTE THEN 1
              WHEN lag(event_timestamp) OVER (PARTITION BY user_id
                                              ORDER BY event_timestamp) IS NULL THEN 1
              ELSE 0
          END AS is_new_session
   FROM user_events),
     session_tagged AS
  (SELECT user_id,
          event_timestamp,
          sum(is_new_session) OVER (PARTITION BY user_id
                                    ORDER BY event_timestamp ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS session_id
   FROM lagged_events)
SELECT user_id,
       session_id,
       min(event_timestamp) AS session_start,
       max(event_timestamp) AS session_end,
       count(*) AS event_count
FROM session_tagged
GROUP BY user_id,
         session_id
ORDER BY user_id ASC,
         session_start ASC;
Golden Reference Ground Truth SQL (Runtime on 1M Rows: 248.26 ms)
WITH lagged AS
  (SELECT user_id,
          event_timestamp,
          CASE
              WHEN event_timestamp - lag(event_timestamp) OVER (PARTITION BY user_id
                                                                ORDER BY event_timestamp) > INTERVAL 30 MINUTE
                   OR lag(event_timestamp) OVER (PARTITION BY user_id
                                                 ORDER BY event_timestamp) IS NULL THEN 1
              ELSE 0
          END AS is_new_session
   FROM user_events),
     session_indexed AS
  (SELECT user_id,
          event_timestamp,
          cast(sum(is_new_session) OVER (PARTITION BY user_id
                                         ORDER BY event_timestamp ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS BIGINT) AS session_id
   FROM lagged)
SELECT user_id,
       session_id,
       min(event_timestamp) AS session_start,
       max(event_timestamp) AS session_end,
       count(*) AS event_count
FROM session_indexed
GROUP BY user_id,
         session_id
ORDER BY user_id ASC,
         session_start ASC;
DuckDB Fixture Setup DDL & Data Population
CREATE TABLE user_events (event_id INT, user_id INT, event_timestamp TIMESTAMP, page_url VARCHAR);


INSERT INTO user_events
VALUES (1, 10, '2025-01-01 10:00:00', '/home'),
       (2, 10, '2025-01-01 10:15:00', '/pricing'),
       (3, 10, '2025-01-01 11:00:00', '/docs'),
       (4, 10, '2025-01-01 11:10:00', '/checkout'),
       (5, 20, '2025-01-01 09:00:00', '/blog');

5. Query Results & Expected Ground Truth

Tabular Comparison
Actual Candidate Output (999,998 rows × 5 cols)
# user_id session_id session_start session_end event_count
1 10 1 2025-01-01 10:00:00 2025-01-01 10:15:00 2
2 10 2 2025-01-01 11:00:00 2025-01-01 11:10:00 2
3 20 1 2025-01-01 09:00:00 2025-01-01 09:00:00 1
4 20 2 2026-03-01 09:30:00.000005 2026-03-01 09:30:00.000005 1
5 21 1 2026-03-01 09:30:00.000036 2026-03-01 09:30:00.000036 1
6 22 1 2026-03-01 09:30:00.000051 2026-03-01 09:30:00.000051 1
7 23 1 2026-03-01 09:30:00.000071 2026-03-01 09:30:00.000071 1
8 24 1 2026-03-01 09:30:00.000082 2026-03-01 09:30:00.000082 1
9 25 1 2026-03-01 09:30:00.000114 2026-03-01 09:30:00.000114 1
10 26 1 2026-03-01 09:30:00.000123 2026-03-01 09:30:00.000123 1
11 27 1 2026-03-01 09:30:00.000148 2026-03-01 09:30:00.000148 1
12 28 1 2026-03-01 09:30:00.000168 2026-03-01 09:30:00.000168 1
13 29 1 2026-03-01 09:30:00.000189 2026-03-01 09:30:00.000189 1
14 30 1 2026-03-01 09:30:00.000202 2026-03-01 09:30:00.000202 1
15 31 1 2026-03-01 09:30:00.000231 2026-03-01 09:30:00.000231 1
16 32 1 2026-03-01 09:30:00.000246 2026-03-01 09:30:00.000246 1
17 33 1 2026-03-01 09:30:00.000262 2026-03-01 09:30:00.000262 1
18 34 1 2026-03-01 09:30:00.000286 2026-03-01 09:30:00.000286 1
19 35 1 2026-03-01 09:30:00.000303 2026-03-01 09:30:00.000303 1
20 36 1 2026-03-01 09:30:00.000321 2026-03-01 09:30:00.000321 1
21 37 1 2026-03-01 09:30:00.000343 2026-03-01 09:30:00.000343 1
22 38 1 2026-03-01 09:30:00.000361 2026-03-01 09:30:00.000361 1
23 39 1 2026-03-01 09:30:00.000390 2026-03-01 09:30:00.000390 1
24 40 1 2026-03-01 09:30:00.000405 2026-03-01 09:30:00.000405 1
25 41 1 2026-03-01 09:30:00.000421 2026-03-01 09:30:00.000421 1
Showing first 25 of 999,998 total rows.
Expected Golden Output (999,998 rows × 5 cols)
# user_id session_id session_start session_end event_count
1 10 1 2025-01-01 10:00:00 2025-01-01 10:15:00 2
2 10 2 2025-01-01 11:00:00 2025-01-01 11:10:00 2
3 20 1 2025-01-01 09:00:00 2025-01-01 09:00:00 1
4 20 2 2026-03-01 09:30:00.000005 2026-03-01 09:30:00.000005 1
5 21 1 2026-03-01 09:30:00.000036 2026-03-01 09:30:00.000036 1
6 22 1 2026-03-01 09:30:00.000051 2026-03-01 09:30:00.000051 1
7 23 1 2026-03-01 09:30:00.000071 2026-03-01 09:30:00.000071 1
8 24 1 2026-03-01 09:30:00.000082 2026-03-01 09:30:00.000082 1
9 25 1 2026-03-01 09:30:00.000114 2026-03-01 09:30:00.000114 1
10 26 1 2026-03-01 09:30:00.000123 2026-03-01 09:30:00.000123 1
11 27 1 2026-03-01 09:30:00.000148 2026-03-01 09:30:00.000148 1
12 28 1 2026-03-01 09:30:00.000168 2026-03-01 09:30:00.000168 1
13 29 1 2026-03-01 09:30:00.000189 2026-03-01 09:30:00.000189 1
14 30 1 2026-03-01 09:30:00.000202 2026-03-01 09:30:00.000202 1
15 31 1 2026-03-01 09:30:00.000231 2026-03-01 09:30:00.000231 1
16 32 1 2026-03-01 09:30:00.000246 2026-03-01 09:30:00.000246 1
17 33 1 2026-03-01 09:30:00.000262 2026-03-01 09:30:00.000262 1
18 34 1 2026-03-01 09:30:00.000286 2026-03-01 09:30:00.000286 1
19 35 1 2026-03-01 09:30:00.000303 2026-03-01 09:30:00.000303 1
20 36 1 2026-03-01 09:30:00.000321 2026-03-01 09:30:00.000321 1
21 37 1 2026-03-01 09:30:00.000343 2026-03-01 09:30:00.000343 1
22 38 1 2026-03-01 09:30:00.000361 2026-03-01 09:30:00.000361 1
23 39 1 2026-03-01 09:30:00.000390 2026-03-01 09:30:00.000390 1
24 40 1 2026-03-01 09:30:00.000405 2026-03-01 09:30:00.000405 1
25 41 1 2026-03-01 09:30:00.000421 2026-03-01 09:30:00.000421 1
Showing first 25 of 999,998 total rows.
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