How do you solve Find Zombie Sessions on LeetCode?

Find Zombie Sessions (LeetCode #3673) 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: Sessions can be efficiently analyzed using aggregation techniques.

What is the time complexity of Find Zombie Sessions?

The optimal solution for Find Zombie Sessions runs in O(n) time and uses O(n) space. Single pass through events allows for linear time complexity, storing results in a hash map.

What is the brute force solution for Find Zombie Sessions?

The brute force approach for Find Zombie Sessions is Brute Force, with O(n²) time complexity and O(1) space complexity. Check each session individually to see if it meets the zombie criteria. This method is straightforward but inefficient as it examines every session in detail. The optimal Optimal Solution approach improves this to O(n).

What are the interview tips for Find Zombie Sessions?

Clarify the problem requirements before jumping to code. Discuss your thought process and approach with the interviewer. Practice coding on a whiteboard or in a shared document.

What are common mistakes when solving Find Zombie Sessions?

Not aggregating data correctly for each session. Overlooking the importance of sorting the results.

#3673

Find Zombie Sessions

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)

Utilize a single pass through the events to aggregate data for each session, allowing us to quickly evaluate the zombie criteria without redundant checks.

⚙️

Algorithm

3 steps

1Step 1: Group events by session_id and aggregate scroll counts, click counts, duration, and check for purchases.
2Step 2: Filter sessions based on the zombie criteria using the aggregated data.
3Step 3: Sort the results by scroll count in descending order.

solution.py7 lines

1def find_zombie_sessions(events):
2    from collections import defaultdict
3    sessions = defaultdict(lambda: {'scrolls': 0, 'clicks': 0, 'duration': 0, 'purchases': 0})
4    for event in events:
5        # Aggregate logic here
6    # Filter and sort logic here
7    return result

ℹ

Complexity note: Single pass through events allows for linear time complexity, storing results in a hash map.

1Sessions can be efficiently analyzed using aggregation techniques.
2Understanding the criteria helps in designing the data structure.

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