Bloomberg·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Bloomberg asked me a follow-up on LC 34 that I wasn't ready for. The twist was supporting arbitrary insertions mid-array while still being able to query first and last positions in log time. Took me a minute to stop thinking in sorted-array terms.

Questions Asked (1)

You have a dynamic sequence where elements can be inserted at any position. After each insertion, you need to efficiently find the first and last occurrence of a target value. What data structure would you use, and how does it compare to a sorted array?

Algorithms & Data StructuresTechnical Trade-offsSystem Design

Author's notes

I started with sorted array out of habit and the interviewer just kind of waited.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements: dynamic insertions at arbitrary positions and frequent queries for first and last occurrence of a target. Then propose a data structure that balances insertion and query efficiency, such as a balanced BST augmented with min/max indices per value, or a hash map combined with a balanced BST. Compare its performance to a sorted array, highlighting the trade-offs in time complexity for insertions and queries.

Pro tip: Mention that in practice, if insertions are not too frequent, a sorted array with binary search might suffice, but for high-frequency insertions, a balanced BST or skip list is preferable. Also, consider that Bloomberg often deals with real-time data, so emphasizing low-latency queries could be beneficial.

1. Clarify requirements

Ask about the frequency of insertions vs. queries, whether the sequence needs to maintain order, and if there are memory constraints. This shows you consider practical scenarios.

2. Propose data structure

Suggest a balanced binary search tree (e.g., AVL or Red-Black) where each node stores the value and its position, and maintain auxiliary hash maps from value to min/max positions. Alternatively, a skip list or a combination of hash map and order-statistic tree.

3. Explain operations

Describe how insertion works: insert at position, update positions of subsequent elements (if using array-like indexing) or use implicit indices. For queries, use the hash map to get first and last occurrence in O(1) or O(log n).

4. Compare with sorted array

Contrast: sorted array allows O(log n) search for first/last via binary search, but insertion is O(n) due to shifting. The proposed structure offers O(log n) insertion and O(1) or O(log n) query, better for dynamic scenarios.

5. Discuss trade-offs

Mention memory overhead, implementation complexity, and constant factors. For example, balanced BSTs have overhead but provide guaranteed logarithmic operations, while hash maps alone don't maintain order.

Key Points to Mention

Balanced BST (e.g., AVL, Red-Black) with augmented min/max positions per value
Hash map from value to first and last occurrence for O(1) query
Insertion complexity: O(log n) for BST vs O(n) for sorted array
Query complexity: O(1) with hash map vs O(log n) with binary search on sorted array
Trade-offs: memory overhead, implementation complexity, and constant factors
Alternative: skip list or order-statistic tree for simpler implementation

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