My first instinct was just a list and filter everything older than 300 seconds on each query call.
Start by clarifying requirements: the window is fixed at 300 seconds, and timestamps are non-decreasing. Then propose a solution using a queue (or deque) to store timestamps of hits, and on query, remove timestamps older than the given time minus 300 seconds. Discuss time and space complexity, and consider optimizations like bucketing for high-frequency hits.
Pro tip: Mention that if timestamps are not guaranteed to be non-decreasing, you'd need a different approach (e.g., sorted map or binary search), but since the problem likely assumes monotonic timestamps, a queue is optimal. Also, note that bucketing by second can reduce memory when hits are very frequent.
Confirm that timestamps are non-decreasing, the window is fixed at 300 seconds, and that the query timestamp is at least as large as any recorded hit. Ask about expected scale (hits per second) to decide on optimizations.
Use a queue (or deque) to store timestamps of hits. For record(timestamp), append the timestamp. For total(timestamp), pop from the front while the front is <= timestamp - 300, then return the queue size.
Record is O(1) amortized. Total is O(k) where k is the number of expired hits removed, but amortized O(1) per operation. Space is O(n) where n is the number of hits in the last 300 seconds.
If hits are very frequent, consider bucketing by second: maintain an array of 300 buckets, each storing a count. This reduces memory and makes total O(300) worst-case, but may lose precision if sub-second granularity is needed.
Handle out-of-order timestamps (if allowed) by using a sorted map or binary search. Discuss concurrency if multiple threads record hits, and how to make it thread-safe.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: what is the purpose of the counter (e.g., real-time analytics, rate limiting), what accuracy is needed, and what are the latency and durability constraints. Then propose a probabilistic data structure like Count-Min Sketch or HyperLogLog to estimate counts with bounded memory, and discuss how to handle high throughput with sharding and aggregation.
Pro tip: Mention that you would use a time-windowed approach with sliding windows or tumbling windows to handle bursts, and that you would combine it with a write-ahead log for durability and replayability. This shows you think about both real-time and historical accuracy.
Ask about the purpose of the counter, required accuracy, latency, and durability. Determine if approximate counts are acceptable or if exact counts are needed.
Select an appropriate structure like Count-Min Sketch for frequency estimation or HyperLogLog for cardinality. Explain how they use sub-linear memory and provide error bounds.
Propose sharding the counter across multiple nodes or using a distributed aggregation tree. Discuss using in-memory buffers and periodic flushing to persistent storage.
Implement sliding or tumbling windows to compute counts over time intervals. Use techniques like exponential decay or ring buffers to expire old data.
Discuss trade-offs between accuracy, memory, and latency. Mention how to handle hot keys, node failures, and consistency requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.