← Databricks Interview Insights
I went straight to the circular array approach, 300 buckets, index by timestamp mod 300, reset stale buckets on write.
Start by clarifying the requirements: the hit counter should support recording hits and querying the number of hits in the last 300 seconds. Then, propose an efficient solution using a queue or a circular buffer to store timestamps, and discuss trade-offs between different approaches. Finally, analyze the time and space complexity of your solution.
Pro tip: Mention that since timestamps are non-decreasing, you can use a queue and lazily remove old entries only when needed, which optimizes for the common case. Also, discuss how to handle concurrent hits if the system is multi-threaded.
Ask about the expected scale (e.g., hits per second), whether timestamps are integers or floats, and if the system needs to be distributed or single-node. Confirm that the 300-second window is inclusive or exclusive.
Propose using a queue (or deque) to store timestamps of hits. Since timestamps are non-decreasing, the queue will be sorted. Alternatively, consider a circular buffer if the maximum number of hits is known.
For recordHit(timestamp): append the timestamp to the queue. For getHits(timestamp): remove timestamps from the front that are <= timestamp - 300, then return the queue size.
Both operations are O(1) amortized time because each timestamp is added and removed at most once. Space is O(n) where n is the number of hits in the last 300 seconds.
Compare with alternative approaches like using a hash map of timestamps to counts (which uses more space) or a binary search on a list (which is O(log n) per query). Mention how to handle concurrency with locks or atomic operations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This came after the main design and I was a bit mentally checked out.
Start by clarifying the current implementation and the specific concurrency issues (e.g., race conditions, lost updates). Then propose a layered solution that addresses atomicity, scalability, and trade-offs, such as using atomic operations, sharding, or distributed counters.
Pro tip: Acknowledge that the optimal solution depends on the required accuracy and scale; for example, approximate counts with eventual consistency may be acceptable for analytics but not for billing. This shows you consider business context.
Ask about the expected read/write ratio, accuracy requirements, latency tolerance, and existing infrastructure. This ensures your solution aligns with the actual needs.
Analyze the current hit counter for race conditions, lock contention, or single points of failure. Common issues include non-atomic read-modify-write operations and database row locks.
Suggest techniques like atomic increments (e.g., Redis INCR), optimistic concurrency control, sharded counters, or distributed counters with CRDTs. Explain how each ensures atomicity and scalability.
Compare solutions based on consistency, performance, complexity, and cost. For example, sharding improves write throughput but complicates reads; eventual consistency may be acceptable for some use cases.
Describe how you would implement the chosen solution, including failure handling and monitoring for hotspots or inconsistencies. Mention testing under load.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.