easyOpenAIAnthropicDatadog

Maintain a sliding-window average over a stream of metrics, like tokens/sec.

A dashboard needs the average tokens/sec over the last N seconds, updated on every sample, forever. The warm-up that screens whether you evict by time and keep a running sum instead of re-summing the window each call.

Updated Aug 2026 · Grounded in real Forward Deployed Engineer interview loops and written to a senior-engineer editorial bar.

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FEDITOR'S NOTE

The fork in the road is count-window versus time-window. Many candidates build a fixed-size deque (last N samples), which is wrong when samples arrive irregularly, the metric you actually want is the last N seconds. The other tell is re-summing the whole window on every read, turning an O(1) operation into O(N) and melting under a high-frequency stream. Running sum plus time-based eviction is the answer.

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