How do you solve Find Users with Persistent Behavior Patterns on LeetCode?

Find Users with Persistent Behavior Patterns (LeetCode #3832) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(n) space complexity. Key insight: Consecutive days can be tracked using a sliding window.

What is the time complexity of Find Users with Persistent Behavior Patterns?

The optimal solution for Find Users with Persistent Behavior Patterns runs in O(n) time and uses O(n) space. The algorithm processes each record once, making it linear in complexity.

What is the brute force solution for Find Users with Persistent Behavior Patterns?

The brute force approach for Find Users with Persistent Behavior Patterns is Brute Force, with O(n²) time complexity and O(1) space complexity. Check every possible sequence of days for each user to see if they meet the criteria. This is straightforward but inefficient. The optimal Optimal Solution approach improves this to O(n).

What are the interview tips for Find Users with Persistent Behavior Patterns?

Explain your thought process clearly. Use examples to illustrate your approach. Practice coding under time constraints.

What are common mistakes when solving Find Users with Persistent Behavior Patterns?

Not handling edge cases with fewer than 5 days. Confusing action types when counting.

#3832

Find Users with Persistent Behavior Patterns

Hard

Hash MapArray

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n)
Space	O(1)	O(n)

💡

Intuition

Time O(n)Space O(n)

Use a sliding window approach to efficiently find sequences of consecutive days with the same action.

⚙️

Algorithm

3 steps

1Step 1: Sort the activity records by user_id and action_date.
2Step 2: Iterate through the sorted records, counting consecutive days with the same action.
3Step 3: If the count reaches 5, store the user and length; reset if the action changes.

solution.py10 lines

1WITH ranked AS (
2  SELECT user_id, action, action_date,
3         ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY action_date) AS rn
4  FROM activity
5)
6SELECT user_id, COUNT(*) AS streak_length
7FROM ranked
8GROUP BY user_id, action
9HAVING COUNT(*) >= 5
10ORDER BY streak_length DESC, user_id ASC;

ℹ

Complexity note: The algorithm processes each record once, making it linear in complexity.

1Consecutive days can be tracked using a sliding window.
2Sorting helps in grouping actions efficiently.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.