mirror of
https://github.com/ClickHouse/ClickHouse.git
synced 2024-09-30 05:30:51 +00:00
40 lines
1.8 KiB
C++
40 lines
1.8 KiB
C++
#pragma once
|
|
|
|
#include <array>
|
|
|
|
namespace DB
|
|
{
|
|
|
|
/** Data for HyperLogLogBiasEstimator in the uniqCombined function.
|
|
* The development plan is as follows:
|
|
* 1. Assemble ClickHouse.
|
|
* 2. Run the script src/dbms/scripts/gen-bias-data.py, which returns one array for getRawEstimates()
|
|
* and another array for getBiases().
|
|
* 3. Update `raw_estimates` and `biases` arrays. Also update the size of arrays in InterpolatedData.
|
|
* 4. Assemble ClickHouse.
|
|
* 5. Run the script src/dbms/scripts/linear-counting-threshold.py, which creates 3 files:
|
|
* - raw_graph.txt (1st column: the present number of unique values;
|
|
* 2nd column: relative error in the case of HyperLogLog without applying any corrections)
|
|
* - linear_counting_graph.txt (1st column: the present number of unique values;
|
|
* 2nd column: relative error in the case of HyperLogLog using LinearCounting)
|
|
* - bias_corrected_graph.txt (1st column: the present number of unique values;
|
|
* 2nd column: relative error in the case of HyperLogLog with the use of corrections from the algorithm HyperLogLog++)
|
|
* 6. Generate a graph with gnuplot based on this data.
|
|
* 7. Determine the minimum number of unique values at which it is better to correct the error
|
|
* using its evaluation (ie, using the HyperLogLog++ algorithm) than applying the LinearCounting algorithm.
|
|
* 7. Accordingly, update the constant in the function getThreshold()
|
|
* 8. Assemble ClickHouse.
|
|
*/
|
|
struct UniqCombinedBiasData
|
|
{
|
|
using InterpolatedData = std::array<double, 200>;
|
|
|
|
static double getThreshold();
|
|
/// Estimates of the number of unique values using the HyperLogLog algorithm without applying any corrections.
|
|
static const InterpolatedData & getRawEstimates();
|
|
/// Corresponding error estimates.
|
|
static const InterpolatedData & getBiases();
|
|
};
|
|
|
|
}
|