Databricks·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Apr 2026Remote

Summary

Databricks coding screen, one question the whole time. It's a classic sliding window problem dressed up as a system design-ish thing, and I spent way too long overthinking the data structure before just going with a queue.

Questions Asked (1)

Design a hit counter that tracks the number of hits in the past 300 seconds. Implement a hit(timestamp) method to record a hit and a getHits(timestamp) method to return the count of hits within the last 5 minutes.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I went straight to a hashmap and immediately started second-guessing myself when they asked about memory.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a solution using a circular buffer or queue to store timestamps of hits. Discuss trade-offs between time and space complexity, and consider concurrency and scalability for a production system.

Pro tip: Mention that using a fixed-size array of 300 buckets (one per second) can be more efficient than a queue, but be prepared to discuss the trade-off of potential precision loss if multiple hits occur within the same second.

1. Clarify Requirements

Ask about expected hit rate, timestamp granularity, concurrency requirements, and whether the system is single-threaded or distributed.

2. Design Data Structure

Propose a circular buffer or queue to store timestamps, or an array of counters per second. Explain how to evict old entries.

3. Implement Methods

Detail the hit(timestamp) and getHits(timestamp) methods, ensuring O(1) amortized time for hit and O(1) or O(k) for getHits where k is the number of expired entries.

4. Analyze Complexity

Discuss time and space complexity, and compare with alternative approaches like using a deque or a hash map with timestamps.

5. Address Scalability and Concurrency

Mention how to handle concurrent hits (e.g., using locks or atomic operations) and how to scale to multiple servers (e.g., sharding by user or using a distributed counter).

Key Points to Mention

Use a circular buffer or queue to maintain hits within the 300-second window.
Evict expired hits efficiently, either on each operation or lazily.
Consider precision: if multiple hits occur in the same second, an array of counters may undercount if not handled properly.
Discuss trade-offs between time and space: queue uses O(n) space where n is number of hits, while array uses O(300) space.
Address concurrency: use thread-safe data structures or locks for multi-threaded environments.
For distributed systems, consider sharding by user ID or using a distributed cache like Redis with sorted sets.

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