← Bloomberg Interview Insights

Bloomberg·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
May 2026

Summary

Bloomberg SWE interview with a meaty data structures design question. One round, pretty focused, no fluff.

Questions Asked (1)

Q1

Design a data structure supporting insert, remove, and topK operations on a multiset of integers, where topK returns the k most frequently occurring values. Discuss complexity, design choices, and edge cases.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

This one took me a minute to even parse what they wanted.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: expected frequency of operations, size of k, and whether topK needs to be exact or approximate. Then propose a hybrid data structure: a hash map from value to frequency, plus a bucket-based structure (e.g., array of doubly linked lists) that groups values by frequency, enabling O(1) insert/remove and O(k) topK. Discuss trade-offs with alternative designs like heaps or balanced trees, and cover edge cases such as empty multiset, k larger than distinct elements, and duplicate frequencies.

Pro tip: Mention that Bloomberg often values real-time performance and memory efficiency; highlight that the bucket approach gives O(1) updates and O(k) topK without sorting, which is ideal for high-frequency trading or streaming scenarios.

1. Clarify Requirements and Constraints

Ask about operation frequencies, expected data size, whether k is fixed or variable, and if topK must be exact. This shows you consider practical use cases before designing.

2. Propose Core Data Structures

Suggest a hash map for value-to-frequency and a bucket structure (e.g., array of doubly linked lists) to group values by frequency. Explain how they interact.

3. Detail Operations and Complexity

Walk through insert, remove, and topK step-by-step, stating time and space complexity for each. Emphasize O(1) updates and O(k) topK.

4. Discuss Trade-offs and Alternatives

Compare with heap-based or balanced tree approaches, noting when they might be preferable (e.g., if k is small and updates are infrequent).

5. Cover Edge Cases and Extensions

Address empty multiset, k > distinct elements, ties in frequency, and potential need for thread safety or persistence.

Key Points to Mention

  • Hash map for O(1) frequency lookup and update.
  • Bucket structure (array of doubly linked lists) to group values by frequency, enabling O(1) move between buckets.
  • topK operation traverses buckets from highest frequency downward, collecting up to k values, O(k) time.
  • Trade-offs: heap-based approach gives O(log n) updates but O(k log n) topK; bucket approach is better for frequent updates.
  • Edge cases: empty multiset, k larger than distinct elements, ties in frequency (order may not matter or specify tie-breaking).
  • Space complexity: O(n) for hash map and buckets, where n is number of distinct elements.

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