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Py-ddsketch
Jul 20, 2023
Distributed quantile sketches
This is the Python implementation of the distributed quantile sketch algorithm DDSketch. DDSketch has relative-error guarantees for any quantile q in [0, 1]. That is if the true value of the qth-quantile is x then DDSketch returns a value y such that |x-y| / x < e where e is the relative error parameter. The default here is set to 0.01. DDSketch is also fully mergeable, meaning that multiple sketches from distributed systems can be combined in a central node.
Our default implementation, DDSketch, is guaranteed to not grow too large in size for any data that can be described by a distribution whose tails are sub-exponential.
We also provide implementations LogCollapsingLowestDenseDDSketch and LogCollapsingHighestDenseDDSketch where the q-quantile will be accurate up to the specified relative error for q that is not too small or large. Concretely, the q-quantile will be accurate up to the specified relative error as long as it belongs to one of the m bins kept by the sketch. If the data is time in seconds, the default of m = 2048 covers 80 microseconds to 1 year.
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