← Databricks Interview Insights
I went with a sliding window approach pretty quickly, which felt right.
Start by clarifying requirements: what operations are needed (put, get, query QPS), expected scale, and whether the store itself is the system being measured or if it's a separate component. Then design a data structure that efficiently tracks events over a sliding window, such as a ring buffer of per-second counters, and discuss trade-offs between precision, memory, and performance.
Pro tip: Mention that you would use a circular buffer of 300 buckets (one per second) to achieve O(1) updates and queries, and highlight that this avoids the overhead of storing individual timestamps. Also, proactively discuss how to handle concurrency and clock drift, showing awareness of production concerns.
Ask questions to understand the expected QPS range, read/write ratio, latency requirements, and whether the QPS calculation is for the store's own operations or for external queries. Confirm if approximate or exact counts are needed.
Decide between a fixed window with buckets (e.g., per-second counters) and a true sliding window (e.g., timestamp queue). For most cases, a ring buffer of per-second counters offers a good balance of simplicity and efficiency.
Propose a circular array of size 300 (for 5 minutes at 1-second granularity), where each bucket stores a count and a timestamp. On each query, increment the current bucket; on QPS calculation, sum the counts of buckets within the last 5 minutes and divide by 300.
Discuss handling of bucket expiration, thread safety (e.g., using atomic operations or locks), and clock adjustments. Consider using a lock-free approach or sharding for high concurrency.
Compare with alternatives like a queue of timestamps (more memory, exact) or a two-bucket approximation. Mention potential optimizations such as lazy aggregation or using a time-wheel.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This follow-up tripped me up more than the main question.
Start by clarifying the requirements and constraints of the QPS tracking system, such as the required accuracy, query patterns, and data retention. Then, systematically discuss memory reduction techniques like probabilistic data structures, time-windowed aggregation, and efficient encoding, while highlighting trade-offs between memory, accuracy, and latency.
Pro tip: Emphasize that memory reduction often involves trade-offs; showing awareness of when to sacrifice accuracy for memory (e.g., using sketches) and when to use exact methods (e.g., for billing) demonstrates maturity. Also, mention that monitoring and profiling are crucial to identify actual memory bottlenecks before optimizing.
Ask about the scale (QPS, number of keys), required accuracy, query patterns (e.g., real-time vs. historical), and retention period. This determines which memory reduction techniques are applicable.
Select memory-efficient data structures such as Count-Min Sketch, HyperLogLog, or Bloom filters for approximate counting, or use arrays with delta encoding for exact counts if accuracy is critical.
Use time-based aggregation (e.g., per-second, per-minute) and downsampling to reduce the number of stored data points. Consider sliding windows or tumbling windows to limit memory footprint.
Apply compression techniques (e.g., run-length encoding, delta encoding) and use compact serialization formats. Store data in memory-mapped files or off-heap memory if necessary.
Explain the trade-offs between memory, accuracy, and latency. Mention the importance of monitoring memory usage and profiling to validate optimizations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.