Default package

Get desktop application:
View/edit binary Protocol Buffers messages

A DDSketch is essentially a histogram that partitions the range of positive values into an infinite number of indexed bins whose size grows exponentially. It keeps track of the number of values (or possibly floating-point weights) added to each bin. Negative values are partitioned like positive values, symmetrically to zero. The value zero as well as its close neighborhood that would be mapped to extreme bin indexes is mapped to a specific counter.

• optional IndexMapping mapping = 1

  The mapping between positive values and the bin indexes they belong to.

• optional Store positiveValues = 2

  The store for keeping track of positive values.

• optional Store negativeValues = 3

  The store for keeping track of negative values. A negative value v is mapped using its positive opposite -v.

• double zeroCount = 4

  The count for the value zero and its close neighborhood (whose width depends on the mapping).

How to map positive values to the bins they belong to.

Used in: DDSketch

• double gamma = 1

  The gamma parameter of the mapping, such that bin index that a value v belongs to is roughly equal to log(v)/log(gamma).

• double indexOffset = 2

  An offset that can be used to shift all bin indexes.

• IndexMapping.Interpolation interpolation = 3

  To speed up the computation of the index a value belongs to, the computation of the log may be approximated using the fact that the log to the base 2 of powers of 2 can be computed at a low cost from the binary representation of the input value. Other values can be approximated by interpolating between successive powers of 2 (linearly, quadratically or cubically). NONE means that the log is to be computed exactly (no interpolation).

Used in: IndexMapping

• NONE = 0

• LINEAR = 1

• QUADRATIC = 2

• CUBIC = 3

• QUARTIC = 4

A Store maps bin indexes to their respective counts. Counts can be encoded sparsely using binCounts, but also in a contiguous way using contiguousBinCounts and contiguousBinIndexOffset. Given that non-empty bins are in practice usually contiguous or close to one another, the latter contiguous encoding method is usually more efficient than the sparse one. Both encoding methods can be used conjointly. If a bin appears in both the sparse and the contiguous encodings, its count value is the sum of the counts in each encodings.

Used in: DDSketch

• map<sint32, double> binCounts = 1

  The bin counts, encoded sparsely.

• repeated double contiguousBinCounts = 2

  The bin counts, encoded contiguously. The values of contiguousBinCounts are the counts for the bins of indexes o, o+1, o+2, etc., where o is contiguousBinIndexOffset.

• sint32 contiguousBinIndexOffset = 3