ClickHouse/dbms/AggregateFunctions/QuantileReservoirSampler.h
Ivan 97f2a2213e
Move all folders inside /dbms one level up (#9974)
* Move some code outside dbms/src folder
* Fix paths
2020-04-02 02:51:21 +03:00

82 lines
2.3 KiB
C++

#pragma once
#include <AggregateFunctions/ReservoirSampler.h>
namespace DB
{
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 <typename Value>
struct QuantileReservoirSampler
{
using Data = ReservoirSampler<Value, ReservoirSamplerOnEmpty::RETURN_NAN_OR_ZERO>;
Data data;
void add(const Value & x)
{
data.insert(x);
}
template <typename Weight>
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)
{
return 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)
result[indices[i]] = 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]]);
}
};
}