Databricks·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Databricks SWE interview with a system design-flavored coding question about a hit counter. No code required, just the design reasoning, which honestly threw me off a little because I kept wanting to just write the solution.

Questions Asked (1)

Design a HitCounter class that tracks how many hits occurred in the last 5 minutes. It needs two operations: recording a hit at a given timestamp, and querying the total hits in the past 300 seconds. Timestamps are strictly increasing. Describe the data structures and algorithm you'd use, and analyze time and memory complexity.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

My first instinct was a queue, just push timestamps on hit and pop anything older than 300 seconds on getHits.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., strictly increasing timestamps, single-threaded vs concurrent). Then propose a queue-based solution that stores timestamps of hits, evicting old entries during queries. Discuss trade-offs between time and memory, and mention possible optimizations like bucketing or circular buffers.

Pro tip: Mention that since timestamps are strictly increasing, you can use a queue and only evict during queries, making record O(1) and query amortized O(1). Also, bring up concurrency considerations for a production system, as Databricks values scalable, thread-safe designs.

1. Clarify requirements and constraints

Confirm that timestamps are strictly increasing, the window is exactly 300 seconds, and whether the system is single-threaded or concurrent. Ask about expected scale and memory constraints.

2. Propose a data structure

Suggest using a queue (e.g., deque) to store timestamps of hits. Explain that since timestamps are increasing, the queue maintains chronological order, and old hits can be efficiently removed from the front.

3. Describe the algorithm

For record(timestamp): append to the queue. For query(timestamp): remove from the front while the front timestamp is <= timestamp - 300, then return the queue size. Note that eviction can be done during query to keep record O(1).

4. Analyze complexity

Time: record is O(1); query is O(k) where k is the number of expired hits removed, amortized O(1) per operation. Space: O(n) where n is the number of hits in the last 300 seconds.

5. Discuss trade-offs and optimizations

Mention alternatives like bucketing (e.g., per-second counts) to reduce memory or improve query speed, and discuss concurrency (locks, atomic operations) if needed. Highlight that the queue approach is simple and efficient for strictly increasing timestamps.

Key Points to Mention

Use a queue (deque) to store timestamps of hits.
Evict expired hits from the front during query, leveraging strictly increasing timestamps.
Record operation is O(1); query is amortized O(1) with O(n) space.
Consider bucketing (e.g., per-second counts) as an alternative for memory efficiency or faster queries.
Address concurrency: use locks or thread-safe data structures if multiple threads access the counter.
Mention that the solution assumes timestamps are strictly increasing; if not, a different approach (e.g., hash map with timestamps) might be needed.

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