← Databricks Interview Insights

Databricks·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say
Jun 2026Remote

Summary

Databricks system design phone screen, pretty focused on a single data structure problem that spiraled into concurrency territory. The core question was manageable but the follow-ups on lock-free design were where things got uncomfortable.

Questions Asked (2)

Q1

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

Algorithms & Data StructuresSystem Design
Author's notes

The circular buffer approach clicked for me pretty fast.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: timestamps are monotonically increasing, and we need a sliding window of 5 minutes (300 seconds). Then, discuss a solution using a queue or circular buffer to store hit timestamps, removing outdated hits on each operation. Analyze time and space complexity, and consider optimizations for high-frequency scenarios.

Pro tip: Mention that in a real system, you'd likely use a distributed counter or a time-series database, but for this problem, an in-memory data structure suffices. Also, highlight that the queue approach gives O(1) amortized time per operation.

1. Clarify requirements and constraints

Confirm that timestamps are in seconds, monotonically increasing, and that the window is inclusive of the current timestamp. Ask about expected call frequency and memory constraints.

2. Choose data structure

Use a queue (or deque) to store timestamps of hits. Alternatively, use a circular buffer if the maximum number of hits per window is known, or a hash map of second->count for memory efficiency.

3. Implement hit and getHits

For hit(timestamp): add timestamp to the queue. For getHits(timestamp): remove timestamps from the front that are <= timestamp - 300, then return the queue size.

4. Analyze complexity and edge cases

Time: O(1) amortized per operation (each timestamp added/removed once). Space: O(number of hits in window). Handle edge cases like multiple hits at same timestamp, empty queue, and timestamps far apart.

5. Discuss optimizations and scalability

For high throughput, consider bucketing by second (array of 300 counters) or using a distributed counter with sliding window aggregation. Mention trade-offs between memory and precision.

Key Points to Mention

  • Sliding window concept and why a simple counter won't work
  • Queue-based approach with amortized O(1) time per operation
  • Handling of timestamps and removal of outdated hits
  • Space complexity and potential memory optimizations (e.g., bucketing)
  • Edge cases: multiple hits at same timestamp, out-of-order timestamps (if not guaranteed), and empty window
  • Scalability considerations for distributed systems (e.g., sharding, eventual consistency)

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

Q2

How would you extend this hit counter to support higher time granularity, and how would you make it thread-safe or lock-free under concurrent writes?

System DesignTechnical Trade-offs
Author's notes

This is where I ran out of steam.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the current design and requirements, then propose a time-bucketed approach (e.g., per-second/minute counters) with a ring buffer or time-series store for granularity. For concurrency, compare lock-based and lock-free options (e.g., atomic operations, sharded counters, or CRDTs), discussing trade-offs in performance, accuracy, and complexity.

Pro tip: Mention that granularity and concurrency are often in tension: finer granularity increases write contention, so consider hierarchical aggregation (e.g., per-thread counters flushed periodically) to balance both.

1. Clarify requirements and constraints

Ask about expected write throughput, read patterns, acceptable staleness, and whether exact counts are needed. This shapes the granularity and concurrency strategy.

2. Design for higher time granularity

Propose bucketing counts by time intervals (e.g., second, minute) using a ring buffer or time-series database. Discuss retention and aggregation for longer periods.

3. Address concurrency: lock-based vs lock-free

Compare mutexes, read-write locks, and lock-free approaches like atomic increments, sharded counters, or per-thread local counters with periodic merging.

4. Evaluate trade-offs and scalability

Discuss performance under contention, memory overhead, accuracy (e.g., eventual consistency), and how the design scales with cores and nodes.

5. Propose a concrete solution and next steps

Recommend a specific design (e.g., sharded atomic counters with time buckets) and outline how to test and monitor it in production.

Key Points to Mention

  • Time bucketing with ring buffers or time-series databases for granularity
  • Lock-free techniques: atomic operations (CAS), sharded counters, per-thread counters
  • Trade-offs: contention, memory, accuracy, and complexity
  • Hierarchical aggregation to reduce write contention
  • Eventual consistency and read-path considerations
  • Scalability across cores and distributed nodes

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