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.

WWW: https://github.com/DataDog/sketches-py