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Bloomberg·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Jun 2026

Summary

Bloomberg SWE interview with a data structures design problem that looked simple on the surface but had enough edge cases to trip you up if you weren't careful. The O(1) constraint is what separates people who've seen this pattern from people who haven't.

Questions Asked (1)

Q1

Design a lottery system that supports inserting a new ticket, drawing a random ticket, and removing a specific ticket, all in O(1) average time.

Algorithms & Data StructuresTechnical Trade-offsSystem Design
Author's notes

I went straight to an array for insert and random draw, which is fine, but then stumbled when they asked about removal.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and constraints, then propose a data structure that combines a dynamic array with a hash map to achieve O(1) average time for all operations. Explain how each operation works, including the swap-with-last trick for removal, and discuss trade-offs and edge cases.

Pro tip: Mention that the hash map stores the index of each ticket in the array, enabling O(1) lookup for removal. Also, discuss how to handle duplicate tickets or ensure uniqueness, and consider the impact of resizing the array.

1. Clarify requirements

Ask about ticket uniqueness, whether removal is by ticket ID or value, and if the draw should be uniformly random. Confirm that all operations must be O(1) average time.

2. Propose data structure

Suggest using a dynamic array (list) to store tickets and a hash map (dictionary) mapping ticket ID to its index in the array. This allows O(1) insertion, random access for drawing, and O(1) removal via index lookup.

3. Explain operations

For insertion: append to array and add to map. For draw: generate a random index and return the ticket at that index. For removal: look up the index in the map, swap the ticket with the last element, update the swapped ticket's index in the map, then remove the last element from both array and map.

4. Discuss edge cases and trade-offs

Address handling of duplicate tickets (if allowed, map to a list of indices or use a set), empty structure, and resizing of the dynamic array. Mention that average O(1) assumes good hash function and low collision rate.

5. Conclude with complexity analysis

Summarize that all operations are O(1) average time, with O(n) space. Note that worst-case for hash map operations can be O(n) but average is O(1).

Key Points to Mention

  • Combination of dynamic array and hash map for O(1) operations
  • Swap-with-last technique for O(1) removal
  • Hash map stores index of each ticket in the array
  • Handling of duplicate tickets (if applicable)
  • Average vs worst-case time complexity
  • Space complexity and resizing considerations

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