← Bloomberg Interview Insights

Bloomberg·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Jun 2026

Summary

Bloomberg SWE interview with a data structures problem that looked simple on the surface but had some tricky edge cases once you got into it. The focus was on designing a class cleanly and thinking through time and space complexity out loud.

Questions Asked (1)

Q1

Design and implement a class that tracks records as (timestamp, score) pairs and supports inserting new records and querying the average score over a sliding time window.

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

My first instinct was a queue and I went with it, which was fine, but I spent too long second-guessing whether to use a deque vs a plain list before just committing.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements: sliding window duration, whether timestamps are monotonically increasing, and expected query frequency. Then propose a data structure that supports efficient insertion and querying, such as a queue with a running sum for O(1) operations, and discuss trade-offs with alternative approaches like segment trees or balanced BSTs. Finally, outline the class design and key methods, and analyze time and space complexity.

Pro tip: Mention that if timestamps are not monotonic, you may need a different structure like a balanced BST or a segment tree to handle out-of-order insertions efficiently. Also, discuss how to handle edge cases like empty windows or duplicate timestamps.

1. Clarify Requirements

Ask about the sliding window duration, whether timestamps are strictly increasing, the expected frequency of insertions and queries, and any memory constraints. This ensures you design the right solution.

2. Choose Data Structure

Propose a queue (or deque) to store records within the window and maintain a running sum for O(1) average query. Discuss alternatives like a balanced BST or segment tree if timestamps are not monotonic.

3. Design Class Interface

Define methods: insert(timestamp, score) and getAverage(timestamp) or getAverage() for the current window. Specify how the window is defined (e.g., last N seconds from the latest timestamp).

4. Analyze Complexity

Explain that with a queue and running sum, insertion is O(1) amortized (each record added and removed once) and query is O(1). Space is O(k) where k is the number of records in the window.

5. Handle Edge Cases

Discuss handling empty windows (return 0 or null), out-of-order timestamps, and duplicate timestamps. Mention potential concurrency issues if the class is used in a multi-threaded environment.

Key Points to Mention

  • Sliding window definition: fixed time duration vs. fixed number of records.
  • Use of a queue/deque to maintain records in the window and a running sum for O(1) average.
  • Trade-offs: queue with running sum is simple and efficient for monotonic timestamps; balanced BST or segment tree for out-of-order insertions.
  • Time complexity: O(1) amortized insertion and O(1) query with the queue approach.
  • Space complexity: O(k) where k is the number of records in the window.
  • Edge cases: empty window, duplicate timestamps, and thread safety.

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