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Meta·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
May 2026

Summary

Meta SWE interview with a tree-based algorithm problem. Not the hardest thing I've seen but I definitely overthought the traversal part before settling on something that actually worked.

Questions Asked (1)

Q1

Given a Binary Search Tree, find the length of the longest increasing subsequence while preserving the original node order in the tree.

Algorithms & Data Structures
Author's notes

My first instinct was to just do an inorder traversal and run a standard LIS on the result, which would've been wrong because inorder on a BST already gives you sorted values and that's not the same as preserving node order.

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AI HintsAI Generated

Suggested Approach

Clarify that the problem reduces to finding the longest increasing subsequence (LIS) of the node values in the order they appear in a traversal of the BST. Choose an appropriate traversal (e.g., preorder) to preserve the original node order, then apply an O(n log n) LIS algorithm. Discuss time/space complexity and potential optimizations.

Pro tip: Mention that while a BST's inorder traversal yields sorted order, the problem explicitly requires preserving the original node order, so you must use a traversal that reflects the tree's structure (like preorder). This shows you read the problem carefully and avoid a common pitfall.

1. Clarify the problem

Confirm what 'original node order' means: the order in which nodes appear in a traversal of the tree (e.g., preorder). Ensure the subsequence must be increasing in value and preserve that order.

2. Choose traversal method

Select a traversal that captures the original order, such as preorder (root, left, right). Explain why inorder is not suitable because it sorts the values.

3. Extract sequence

Perform the chosen traversal to produce a list of node values in the required order.

4. Apply LIS algorithm

Use the standard O(n log n) patience sorting algorithm (with binary search) to find the length of the longest increasing subsequence in the extracted list.

5. Analyze complexity and edge cases

State time and space complexity (O(n log n) time, O(n) space). Discuss edge cases like empty tree, single node, or duplicate values (if allowed).

Key Points to Mention

  • Definition of 'original node order' and why it matters
  • Choice of traversal (preorder) and why inorder is incorrect
  • LIS algorithm: dynamic programming O(n^2) vs. patience sorting O(n log n)
  • Time and space complexity analysis
  • Handling duplicates (if values can repeat)
  • Potential follow-up: optimizing for BST properties (though not needed here)

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.