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.QuantileSketch to keep track of quantiles.