count.count.count.IndexMapping that approximates the memory-optimal one (namely LogarithmicMapping) by
extracting the floor value of the logarithm to the base 2 from the binary representations of floating-point values
and linearly interpolating the logarithm in-between using, again, the binary representation.DDSketch that offers constant-time insertion and whose size grows until the
maximum number of bins is reached, at which point bins with highest indices are collapsed, which causes the
relative accuracy guarantee to be lost on highest quantiles.DDSketch that offers constant-time insertion and whose size grows until the
maximum number of bins is reached, at which point bins with lowest indices are collapsed, which causes the
relative accuracy guarantee to be lost on lowest quantiles.IndexMapping that approximates the memory-optimal one (namely LogarithmicMapping) by
extracting the floor value of the logarithm to the base 2 from the binary representations of floating-point values
and cubically interpolating the logarithm in-between.QuantileSketch with relative-error guarantees.IndexMapping and Store
supplier.IndexMapping and Store
suppliers.IndexMapping and Store
supplier.DDSketch with the provided relative accuracy guarantee.DDSketch.new DDSketch(new BitwiseLinearlyInterpolatedMapping(relativeAccuracy),
UnboundedSizeDenseStore::new).new DDSketch(new BitwiseLinearlyInterpolatedMapping(relativeAccuracy), () -> new
CollapsingHighestDenseStore(maxNumBins)).new DDSketch(new BitwiseLinearlyInterpolatedMapping(relativeAccuracy), () -> new
CollapsingLowestDenseStore(maxNumBins).double values and int values that imposes relative guarantees on the composition
of IndexMapping.value(int) and IndexMapping.index(double).IndexMapping that approximates the memory-optimal one (namely LogarithmicMapping) by
extracting the floor value of the logarithm to the base 2 from the binary representations of floating-point values
and linearly interpolating the logarithm in-between.DDSketch that offers constant-time insertion and whose size grows until the
maximum number of bins is reached, at which point bins with highest indices are collapsed, which causes the
relative accuracy guarantee to be lost on highest.DDSketch that offers constant-time insertion and whose size grows until the
maximum number of bins is reached, at which point bins with lowest indices are collapsed, which causes the
relative accuracy guarantee to be lost on lowest quantiles.IndexMapping that is memory-optimal, that is to say that given a targeted relative accuracy, it
requires the least number of indices to cover a given range of values.DDSketch that offers constant-time insertion and whose size grows indefinitely
to accommodate for the range of input values.IndexMapping that approximates the memory-optimal one (namely LogarithmicMapping) by
extracting the floor value of the logarithm to the base 2 from the binary representations of floating-point values
and quadratically interpolating the logarithm in-between.double values and can compute quantiles over the ingested values.DDSketch that offers insertion time that is logarithmic in the number of
non-empty bins that the sketch contains and whose size grows indefinitely to accommodate for the range of input
values.DDSketch that offers constant-time insertion and whose size grows indefinitely
to accommodate for the range of input values.