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Two stacks support sliding-window aggregation without an inverse

orlp.net 6 min read Editor score 7/10

The piece introduces a two-stack approach for repeated window summaries such as minima, quantiles, and approximate unique counts, where removing an item cannot be handled by an inverse. This is a useful pattern for streaming and telemetry systems.

What

A two-stack algorithm maintains sliding-window aggregates for associative operators that have no inverse.

How

It generalizes aggregation with empty(), unit(), combine(), and finalize(); the simpler two-stack method is amortized O(1).

Why it matters

It supports summaries such as minimum, quantile, and approximate unique count, avoiding failures from inverse-based updates such as NaN persisting after it leaves a window.

Read on orlp.net → Lobsters · 3 comments

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