#pragma once #include namespace DB { struct Settings; namespace ErrorCodes { extern const int NOT_IMPLEMENTED; } /** Quantile calculation with "reservoir sample" algorithm. * It collects pseudorandom subset of limited size from a stream of values, * and approximate quantile from it. * The result is non-deterministic. Also look at QuantileReservoirSamplerDeterministic. * * This algorithm is quite inefficient in terms of precision for memory usage, * but very efficient in CPU (though less efficient than QuantileTiming and than QuantileExact for small sets). */ template struct QuantileReservoirSampler { using Data = ReservoirSampler; Data data; void add(const Value & x) { data.insert(x); } template void add(const Value &, const Weight &) { throw Exception("Method add with weight is not implemented for ReservoirSampler", ErrorCodes::NOT_IMPLEMENTED); } void merge(const QuantileReservoirSampler & rhs) { data.merge(rhs.data); } void serialize(WriteBuffer & buf) const { data.write(buf); } void deserialize(ReadBuffer & buf) { data.read(buf); } /// Get the value of the `level` quantile. The level must be between 0 and 1. Value get(Float64 level) { if constexpr (is_decimal) return Value(static_cast(data.quantileInterpolated(level))); else return static_cast(data.quantileInterpolated(level)); } /// Get the `size` values of `levels` quantiles. Write `size` results starting with `result` address. /// indices - an array of index levels such that the corresponding elements will go in ascending order. void getMany(const Float64 * levels, const size_t * indices, size_t size, Value * result) { for (size_t i = 0; i < size; ++i) if constexpr (is_decimal) result[indices[i]] = Value(static_cast(data.quantileInterpolated(levels[indices[i]]))); else result[indices[i]] = Value(data.quantileInterpolated(levels[indices[i]])); } /// The same, but in the case of an empty state, NaN is returned. Float64 getFloat(Float64 level) { return data.quantileInterpolated(level); } void getManyFloat(const Float64 * levels, const size_t * indices, size_t size, Float64 * result) { for (size_t i = 0; i < size; ++i) result[indices[i]] = data.quantileInterpolated(levels[indices[i]]); } }; }