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