Leetflex Banned Accounts - Problem
Leetflex Account Security Monitor
You're working as a security engineer at Leetflex, a popular coding platform. Your task is to identify suspicious account activity by detecting accounts that are logged in from multiple IP addresses simultaneously.
Given a
Security Rule: An account should be banned if it was logged in from two different IP addresses at any overlapping time period.
Table Schema:
+-------------+----------+
| Column Name | Type |
+-------------+----------+
| account_id | int |
| ip_address | int |
| login | datetime |
| logout | datetime |
+-------------+----------+
Return a list of
You're working as a security engineer at Leetflex, a popular coding platform. Your task is to identify suspicious account activity by detecting accounts that are logged in from multiple IP addresses simultaneously.
Given a
LogInfo table containing login/logout records with timestamps and IP addresses, you need to find accounts that should be banned for security violations.Security Rule: An account should be banned if it was logged in from two different IP addresses at any overlapping time period.
Table Schema:
LogInfo+-------------+----------+
| Column Name | Type |
+-------------+----------+
| account_id | int |
| ip_address | int |
| login | datetime |
| logout | datetime |
+-------------+----------+
Return a list of
account_id values that should be banned, in any order. Input & Output
example_1.py — Basic Overlap Case
$
Input:
LogInfo = [\n [1001, 192168001001, '2023-01-01 10:00:00', '2023-01-01 12:00:00'],\n [1001, 100000001, '2023-01-01 11:00:00', '2023-01-01 13:00:00'],\n [1002, 192168001001, '2023-01-01 14:00:00', '2023-01-01 16:00:00']\n]
›
Output:
[1001]
💡 Note:
Account 1001 was logged in from IP 192.168.1.1 (10:00-12:00) and IP 10.0.0.1 (11:00-13:00) with overlapping time from 11:00-12:00. Account 1002 only has one session, so it's not banned.
example_2.py — No Overlapping Sessions
$
Input:
LogInfo = [\n [2001, 192168001001, '2023-01-01 09:00:00', '2023-01-01 10:00:00'],\n [2001, 100000001, '2023-01-01 11:00:00', '2023-01-01 12:00:00'],\n [2002, 192168001002, '2023-01-01 13:00:00', '2023-01-01 14:00:00']\n]
›
Output:
[]
💡 Note:
Account 2001 has sessions from different IPs but they don't overlap in time (10:00 end vs 11:00 start). Account 2002 has only one session. No accounts should be banned.
example_3.py — Multiple Violations
$
Input:
LogInfo = [\n [3001, 192168001001, '2023-01-01 08:00:00', '2023-01-01 12:00:00'],\n [3001, 100000001, '2023-01-01 10:00:00', '2023-01-01 11:00:00'],\n [3001, 172016001, '2023-01-01 10:30:00', '2023-01-01 11:30:00']\n]
›
Output:
[3001]
💡 Note:
Account 3001 has three overlapping sessions from different IP addresses. Even with multiple violations, the account appears only once in the result.
Visualization
Tap to expand
Understanding the Visualization
1
Identify Sessions
Each row represents a login session with start/end times and IP address
2
Compare Pairs
For each account, compare every pair of sessions
3
Check Overlap
Two intervals overlap if start1 < end2 AND start2 < end1
4
Verify Different IPs
Overlapping sessions must be from different IP addresses
5
Flag Account
Mark account as banned if violation is found
Key Takeaway
🎯 Key Insight: Two time intervals [a,b] and [c,d] overlap if and only if a < d AND c < b. This mathematical property allows us to efficiently detect suspicious login patterns in SQL using a simple join condition.
Time & Space Complexity
Time Complexity
O(n log n)
Sorting events takes O(n log n), processing events takes O(n)
⚡ Linearithmic
Space Complexity
O(n)
Space for events array and active sessions tracking
⚡ Linearithmic Space
Constraints
- 1 ≤ number of records ≤ 104
- 1 ≤ account_id ≤ 104
- 1 ≤ ip_address ≤ 231 - 1
- logout time is always after login time
- The table may contain duplicate rows
💡
Explanation
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