Is Alt and Tab Simulation easy or hard?

Alt and Tab Simulation is typically classified as a Medium problem. The brute force simulation is easy to implement, but recognizing that only the last occurrence of each query matters is the key insight needed for the optimal O(n + q) solution.

Alt and Tab Simulation Python/Java solution

The implementation pattern is identical across languages: traverse queries in reverse, store seen apps in a hash set, append unique apps to the result, then append remaining apps from the original array. This approach translates cleanly to Python, Java, C++, Go, and TypeScript.

How to solve Alt and Tab Simulation in O(n)?

Process the queries from right to left and store seen apps in a hash set. Add an app to the result the first time it appears during this reverse scan, representing its most recent activation. After finishing the queries, append remaining apps from the original list that were never seen. This achieves O(n + q) time.

What is the best approach for Alt and Tab Simulation?

The optimal solution uses a hash table combined with reverse traversal of the query list. By scanning queries from the end and tracking which apps were already added, you capture only the most recent access for each app. This produces the correct Alt‑Tab order in O(n + q) time with O(n) extra space.

Is Alt and Tab Simulation asked at Google/Amazon/Meta?

Problems involving recency ordering, hash tables, and system behavior simulation frequently appear in interviews at companies like Amazon, Google, and Meta. Variants of this problem test understanding of hash sets, arrays, and efficient state updates.

What data structure is used in Alt and Tab Simulation?

The optimal solution uses a hash set (or hash table) to track which applications were already processed while traversing the queries. Arrays store the original app order and the final result, making lookups and appending efficient.

What is the time complexity of Alt and Tab Simulation?

The optimal hash table + reverse traversal solution runs in O(n + q) time, where n is the number of apps and q is the number of Alt‑Tab operations. Each app and query is processed at most once, and hash lookups are O(1) on average.

Description Hints 4 Solution Video 3:26

3237. Alt and Tab Simulation

Medium54.3% acceptancePremium elsewhere, free here

There are n windows open numbered from 1 to n, we want to simulate using alt + tab to navigate between the windows.

You are given an array windows which contains the initial order of the windows (the first element is at the top and the last one is at the bottom).

You are also given an array queries where for each query, the window queries[i] is brought to the top.

Return the final state of the array windows.

Example 1:

Input: windows = [1,2,3], queries = [3,3,2]

Output: [2,3,1]

Explanation:

Here is the window array after each query:

Initial order: [1,2,3]
After the first query: [3,1,2]
After the second query: [3,1,2]
After the last query: [2,3,1]

Example 2:

Input: windows = [1,4,2,3], queries = [4,1,3]

Output: [3,1,4,2]

Explanation:

Here is the window array after each query:

Initial order: [1,4,2,3]
After the first query: [4,1,2,3]
After the second query: [1,4,2,3]
After the last query: [3,1,4,2]

Constraints:

1 <= n == windows.length <= 10⁵
windows is a permutation of [1, n].
1 <= queries.length <= 10⁵
1 <= queries[i] <= n

Topics

Hints open one at a time

Hint 1

Let’s answer the queries offline.

Hint 2

Start from the last query until you reach the first query.

Hint 3

Each time, append the value of that query to a ans array if it wasn’t already appended.

Hint 4

Then start from the beginning of windows and append to ans array if it wasn’t already appended.

3:26

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1class Solution:
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Starter codePython 3Spaces: 4

Examples

Case 1Case 2

Input

[1,2,3]
[3,3,2]

Input

[1,4,2,3]
[4,1,3]

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