Visa·Software Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

Visa SWE interview with a pretty involved simulation problem. Not a typical LeetCode grind question, more of a design-your-own-data-structure situation with a lot of moving parts.

Questions Asked (1)

Given an array of warehouse capacities and a sequence of PACKAGE and CLOSURE events, simulate package routing: packages are assigned to the first available warehouse at or after a rolling pointer, warehouses that fill up are skipped until the cycle resets, and closed warehouses are permanently removed. After all events, return the warehouse id that processed the most packages, breaking ties by largest id. What data structures and algorithm would you use to keep per-event complexity low, and what edge cases do you need to handle?

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

This one took me a minute to even parse.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and edge cases, then propose a data structure like a balanced BST or Fenwick tree to efficiently find the next available warehouse. Explain how to handle closures and resets, and analyze the time complexity per event.

Pro tip: Mention that using a union-find with path compression can efficiently skip closed warehouses, and a segment tree can handle capacity updates and range queries. This shows you consider both simplicity and performance.

1. Clarify Requirements and Edge Cases

Ask about input size, event types, and tie-breaking rules. Identify edge cases like all warehouses closed, no packages, or pointer wrapping.

2. Choose Data Structures

Select a balanced BST (e.g., TreeSet) or Fenwick tree to maintain available warehouses and capacities. Use a union-find for closed warehouses to skip them quickly.

3. Design Algorithm for Events

For each PACKAGE event, find the first available warehouse at or after the pointer using binary search or tree operations. Update capacity and pointer. For CLOSURE, remove the warehouse and adjust structures.

4. Handle Cycle Reset and Pointer Movement

When the pointer reaches the end, wrap around to the beginning. Ensure that full warehouses are skipped until reset, and closed warehouses are permanently removed.

5. Analyze Complexity and Optimize

Aim for O(log n) per event using efficient data structures. Discuss trade-offs between different approaches (e.g., segment tree vs. balanced BST).

Key Points to Mention

Use of balanced BST (e.g., TreeSet) or Fenwick tree for efficient next-available warehouse lookup.
Union-find with path compression to skip closed warehouses in near-constant time.
Segment tree for range updates and queries to handle capacities and find next available.
Handling of pointer wrap-around and cycle reset logic.
Tie-breaking by largest warehouse id when multiple have same max packages.
Edge cases: all warehouses closed, no packages, pointer at end, capacity zero.

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