Find Zombie Sessions Python solution?

In Python, load the sessions into a Pandas DataFrame, call groupby on user_id with agg for the needed metrics, then filter the resulting DataFrame with a boolean condition. Merge the matching user_ids back to the sessions frame if you need session-level rows. The whole solution is one pass over the data.

Is Find Zombie Sessions easy or hard?

Find Zombie Sessions is rated Hard, with a 51.6% acceptance rate. The difficulty comes from translating the zombie criteria into the correct aggregate conditions and avoiding the slow correlated subquery pattern. Once you see the grouping, the implementation is short.

How to solve Find Zombie Sessions in O(n)?

Use a single GROUP BY on user_id and compute the required aggregates in one pass. Store one accumulator per user, then filter groups with HAVING or a mask. Avoid per-row subqueries, since they force repeated scans of the same table.

What is the best approach for Find Zombie Sessions?

The best approach is grouped aggregation. Group the sessions by user, compute the per-user metrics the zombie condition depends on, and filter the groups with HAVING in MySQL or a boolean mask in Pandas. This runs in O(n) time with O(users) extra space.

What data structure is used in Find Zombie Sessions?

The solution relies on hash-based grouping. The database or DataFrame engine keeps a hash map from each user_id to its running aggregates, which gives O(1) average lookup per row. No explicit custom data structure is needed.

What is the time complexity of Find Zombie Sessions?

The optimal grouped aggregation solution runs in O(n) time, where n is the number of session rows, because it scans the table once. The correlated subquery approach runs in O(n²) time because it re-scans related rows for each session.

Is Find Zombie Sessions asked at Google, Amazon, or Meta?

Find Zombie Sessions is a SQL-style problem that tests grouped aggregation and filtering logic, a pattern common in data-focused interviews. Check company-specific question trackers for exact frequency, since FleetCode does not publish per-company tags for this problem.

Description Solution Video 9:14

3673. Find Zombie Sessions

Hard51.6% acceptance

Table: app_events

+------------------+----------+
| Column Name      | Type     | 
+------------------+----------+
| event_id         | int      |
| user_id          | int      |
| event_timestamp  | datetime |
| event_type       | varchar  |
| session_id       | varchar  |
| event_value      | int      |
+------------------+----------+
event_id is the unique identifier for this table.
event_type can be app_open, click, scroll, purchase, or app_close.
session_id groups events within the same user session.
event_value represents: for purchase - amount in dollars, for scroll - pixels scrolled, for others - NULL.

Write a solution to identify zombie sessions, sessions where users appear active but show abnormal behavior patterns. A session is considered a zombie session if it meets ALL the following criteria:

The session duration is more than 30 minutes.
Has at least 5 scroll events.
The click-to-scroll ratio is less than 0.20 .
No purchases were made during the session.

Return the result table ordered by scroll_count in descending order, then by session_id in ascending order.

The result format is in the following example.

Example:

Input:

app_events table:

+----------+---------+---------------------+------------+------------+-------------+
| event_id | user_id | event_timestamp     | event_type | session_id | event_value |
+----------+---------+---------------------+------------+------------+-------------+
| 1        | 201     | 2024-03-01 10:00:00 | app_open   | S001       | NULL        |
| 2        | 201     | 2024-03-01 10:05:00 | scroll     | S001       | 500         |
| 3        | 201     | 2024-03-01 10:10:00 | scroll     | S001       | 750         |
| 4        | 201     | 2024-03-01 10:15:00 | scroll     | S001       | 600         |
| 5        | 201     | 2024-03-01 10:20:00 | scroll     | S001       | 800         |
| 6        | 201     | 2024-03-01 10:25:00 | scroll     | S001       | 550         |
| 7        | 201     | 2024-03-01 10:30:00 | scroll     | S001       | 900         |
| 8        | 201     | 2024-03-01 10:35:00 | app_close  | S001       | NULL        |
| 9        | 202     | 2024-03-01 11:00:00 | app_open   | S002       | NULL        |
| 10       | 202     | 2024-03-01 11:02:00 | click      | S002       | NULL        |
| 11       | 202     | 2024-03-01 11:05:00 | scroll     | S002       | 400         |
| 12       | 202     | 2024-03-01 11:08:00 | click      | S002       | NULL        |
| 13       | 202     | 2024-03-01 11:10:00 | scroll     | S002       | 350         |
| 14       | 202     | 2024-03-01 11:15:00 | purchase   | S002       | 50          |
| 15       | 202     | 2024-03-01 11:20:00 | app_close  | S002       | NULL        |
| 16       | 203     | 2024-03-01 12:00:00 | app_open   | S003       | NULL        |
| 17       | 203     | 2024-03-01 12:10:00 | scroll     | S003       | 1000        |
| 18       | 203     | 2024-03-01 12:20:00 | scroll     | S003       | 1200        |
| 19       | 203     | 2024-03-01 12:25:00 | click      | S003       | NULL        |
| 20       | 203     | 2024-03-01 12:30:00 | scroll     | S003       | 800         |
| 21       | 203     | 2024-03-01 12:40:00 | scroll     | S003       | 900         |
| 22       | 203     | 2024-03-01 12:50:00 | scroll     | S003       | 1100        |
| 23       | 203     | 2024-03-01 13:00:00 | app_close  | S003       | NULL        |
| 24       | 204     | 2024-03-01 14:00:00 | app_open   | S004       | NULL        |
| 25       | 204     | 2024-03-01 14:05:00 | scroll     | S004       | 600         |
| 26       | 204     | 2024-03-01 14:08:00 | scroll     | S004       | 700         |
| 27       | 204     | 2024-03-01 14:10:00 | click      | S004       | NULL        |
| 28       | 204     | 2024-03-01 14:12:00 | app_close  | S004       | NULL        |
+----------+---------+---------------------+------------+------------+-------------+

Output:

+------------+---------+--------------------------+--------------+
| session_id | user_id | session_duration_minutes | scroll_count |
+------------+---------+--------------------------+--------------+
| S001       | 201     | 35                       | 6            |
+------------+---------+--------------------------+--------------+

Explanation:

Session S001 (User 201):
- Duration: 10:00:00 to 10:35:00 = 35 minutes (more than 30)
- Scroll events: 6 (at least 5)
- Click events: 0
- Click-to-scroll ratio: 0/6 = 0.00 (less than 0.20)
- Purchases: 0 (no purchases)
- S001 is a zombie session (meets all criteria)
Session S002 (User 202):
- Duration: 11:00:00 to 11:20:00 = 20 minutes (less than 30)
- Has a purchase event
- S002 is not a zombie session
Session S003 (User 203):
- Duration: 12:00:00 to 13:00:00 = 60 minutes (more than 30)
- Scroll events: 5 (at least 5)
- Click events: 1
- Click-to-scroll ratio: 1/5 = 0.20 (not less than 0.20)
- Purchases: 0 (no purchases)
- S003 is not a zombie session (click-to-scroll ratio equals 0.20, needs to be less)
Session S004 (User 204):
- Duration: 14:00:00 to 14:12:00 = 12 minutes (less than 30)
- Scroll events: 2 (less than 5)
- S004 is not a zombie session

The result table is ordered by scroll_count in descending order, then by session_id in ascending order.

9:14

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1# Write your MySQL query statement below

Starter codeMySQLSpaces: 4

Examples

Case 1

Input

{"headers":{"app_events":["event_id","user_id","event_timestamp","event_type","session_id","event_value"]},"rows":{"app_events":[[1,201,"2024-03-01 10:00:00","app_open","S001",null],[2,201,"2024-03-01 10:05:00","scroll","S001",500],[3,201,"2024-03-01 10:10:00","scroll","S001",750],[4,201,"2024-03-01 10:15:00","scroll","S001",600],[5,201,"2024-03-01 10:20:00","scroll","S001",800],[6,201,"2024-03-01 10:25:00","scroll","S001",550],[7,201,"2024-03-01 10:30:00","scroll","S001",900],[8,201,"2024-03-01 10:35:00","app_close","S001",null],[9,202,"2024-03-01 11:00:00","app_open","S002",null],[10,202,"2024-03-01 11:02:00","click","S002",null],[11,202,"2024-03-01 11:05:00","scroll","S002",400],[12,202,"2024-03-01 11:08:00","click","S002",null],[13,202,"2024-03-01 11:10:00","scroll","S002",350],[14,202,"2024-03-01 11:15:00","purchase","S002",50],[15,202,"2024-03-01 11:20:00","app_close","S002",null],[16,203,"2024-03-01 12:00:00","app_open","S003",null],[17,203,"2024-03-01 12:10:00","scroll","S003",1000],[18,203,"2024-03-01 12:20:00","scroll","S003",1200],[19,203,"2024-03-01 12:25:00","click","S003",null],[20,203,"2024-03-01 12:30:00","scroll","S003",800],[21,203,"2024-03-01 12:40:00","scroll","S003",900],[22,203,"2024-03-01 12:50:00","scroll","S003",1100],[23,203,"2024-03-01 13:00:00","app_close","S003",null],[24,204,"2024-03-01 14:00:00","app_open","S004",null],[25,204,"2024-03-01 14:05:00","scroll","S004",600],[26,204,"2024-03-01 14:08:00","scroll","S004",700],[27,204,"2024-03-01 14:10:00","click","S004",null],[28,204,"2024-03-01 14:12:00","app_close","S004",null]]}}

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