/* Unless explicitly stated otherwise all files in this repository are licensed under the Apache License 2.0.
 * This product includes software developed at Datadog (https://www.datadoghq.com/).
 * Copyright 2021 Datadog, Inc.
 */

syntax = "proto3";

option go_package = "github.com/DataDog/sketches-go/ddsketch/pb/sketchpb";

package test;

// 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.
message DDSketch {
  // The mapping between positive values and the bin indexes they belong to.
  IndexMapping mapping = 1;

  // The store for keeping track of positive values.
  Store positiveValues = 2;

  // The store for keeping track of negative values. A negative value v is mapped using its positive opposite -v.
  Store negativeValues = 3;

  // The count for the value zero and its close neighborhood (whose width depends on the mapping).
  double zeroCount = 4;
}

// How to map positive values to the bins they belong to.
message IndexMapping {
  // 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 gamma = 1;

  // An offset that can be used to shift all bin indexes.
  double indexOffset = 2;

  // 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).
  Interpolation interpolation = 3;
  enum Interpolation {
    NONE = 0;
    LINEAR = 1;
    QUADRATIC = 2;
    CUBIC = 3;
  }
}

// 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.
message Store {
  // The bin counts, encoded sparsely.
  map<sint32, double> binCounts = 1;

  // 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.
  repeated double contiguousBinCounts = 2 [packed = true];
  sint32 contiguousBinIndexOffset = 3;
}