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Databricks·Software Engineer·Technical Phone Screen·Senior

Senior
Apr 2026

Summary

Databricks SWE interview that looked like a straightforward LRU Cache problem until the QPS monitoring requirement showed up and made the whole thing a lot more interesting. The real test was whether you'd reach for the obvious O(n) approach or think through the bucketing angle.

Questions Asked (1)

Q1

Build a key-value store with get and put operations, and add a sliding-window QPS metric that returns queries per second for each operation type over the last 5 minutes.

System DesignAlgorithms & Data StructuresTechnical Trade-offs
Author's notes

Started with the LRU cache part fine, that's just a hashmap plus doubly linked list.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then design the key-value store with appropriate data structures and concurrency handling. For the sliding-window QPS metric, propose a time-bucketed counter approach (e.g., 1-second buckets over 5 minutes) and discuss trade-offs between accuracy, memory, and performance.

Pro tip: Emphasize that the QPS metric should be lock-free or use fine-grained locking to avoid becoming a bottleneck, and mention that you'd use a ring buffer of atomic counters for efficiency.

1. Clarify Requirements

Ask about expected scale (QPS, data size), consistency needs, latency requirements, and whether the QPS metric needs to be exact or approximate.

2. Design Key-Value Store

Choose an in-memory hash map for O(1) get/put, discuss thread-safety (e.g., concurrent hash map or sharding with locks), and consider persistence if needed.

3. Design Sliding-Window QPS Metric

Propose a time-bucketed approach: divide the 5-minute window into small buckets (e.g., 1-second), maintain a circular buffer of counters per operation type, and increment the current bucket on each operation.

4. Handle Concurrency and Accuracy

Use atomic counters or locks for bucket updates, and discuss how to compute the sliding window sum efficiently (e.g., maintain a running total or sum buckets on demand).

5. Discuss Trade-offs and Optimizations

Compare exact vs. approximate counting, memory overhead, and performance impact; mention alternatives like sliding window with timestamps or probabilistic data structures.

Key Points to Mention

  • Use a concurrent hash map (e.g., Java ConcurrentHashMap) for the key-value store to ensure thread-safe get/put with high concurrency.
  • For QPS, use a ring buffer of atomic counters with 1-second granularity, covering 300 buckets for 5 minutes.
  • Increment the current bucket atomically on each operation; compute QPS by summing the last 300 buckets (or maintain a running sum).
  • Trade-off: smaller buckets give more accuracy but higher memory and computation overhead; larger buckets are more efficient but less precise.
  • Consider lock-free or fine-grained locking to avoid contention on the QPS counters, especially under high load.
  • Mention that the QPS metric can be exposed via a separate thread that periodically computes and caches the values to avoid frequent summation.

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