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Meta·Software Engineer·Onsite - Coding / Algorithms·Intermediate

Intermediate
May 2026

Summary

Meta SWE coding round with a data structure problem that looked straightforward until you actually had to think about the constraints. The Quack problem is one of those things where the problem statement does most of the heavy lifting but the implementation details will get you if you're not careful.

Questions Asked (1)

Q1

You're given a custom data structure called a Quack that maintains elements in sorted order, supports inserting a value, popping randomly from either the head or tail (so either the min or max), and returning the current size. Given a sequence of operations, output all remaining elements in ascending order.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

The pop behavior is what makes this weird.

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AI HintsAI Generated

Suggested Approach

Clarify the operations and constraints, then design a data structure that supports O(log n) insertions and O(log n) deletions from either end while maintaining sorted order. A balanced BST with min-max pointers or two heaps can work, but consider simpler options like a sorted array with two pointers if the operation sequence is known. Simulate the operations and output the remaining elements in order.

Pro tip: Discuss trade-offs between different implementations (e.g., balanced BST vs. two heaps vs. sorted array) and choose based on expected operation mix. Mention that if the sequence is known, you can sometimes process offline for efficiency.

1. Clarify requirements and constraints

Ask about the range of values, number of operations, and whether the sequence is known in advance. Confirm that 'popping randomly' means choosing either head or tail arbitrarily, not uniformly at random.

2. Choose an appropriate data structure

Evaluate options: balanced BST (e.g., TreeSet) with min/max access, two heaps (min-heap and max-heap) with lazy deletion, or a sorted array with two pointers if operations are known. Consider time and space complexity.

3. Design the algorithm

Outline how to process each operation: insert adds to the structure, pop removes either the smallest or largest element, size returns the count. Ensure the structure remains sorted or can retrieve min/max efficiently.

4. Simulate and output

Iterate through the operations, updating the structure. After all operations, extract remaining elements in ascending order and return them.

5. Analyze complexity and edge cases

State the time complexity per operation and overall, and discuss edge cases like empty structure, duplicate values, and large inputs. Mention potential optimizations.

Key Points to Mention

  • Time complexity of each operation (insert, pop, size) and overall
  • Choice of data structure and trade-offs (e.g., balanced BST vs. two heaps vs. sorted array)
  • Handling of duplicates and maintaining sorted order
  • Edge cases: empty Quack, popping when only one element, large number of operations
  • Potential for offline processing if the sequence is known
  • Space complexity and memory usage

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