boost accumulators for percentile statistics using rolling window size
We use boost accumulators to calculate server request time percentiles as follows:
static constexpr size_t ROLLING_WINDOW_SIZE = 1000;
using accumulator_t = boost::accumulators::accumulator_set<long long int,
boost::accumulators::stats<boost::accumulators::tag::extended_p_square_quantile,
boost::accumulators::tag::rolling_count> >;
const std::array<double, 4> QUANTILE_PROBABILITIES = { 0.5, 0.75, 0.95, 0.99 };
....
Percentiles::Percentiles() :
m_accumulator(boost::accumulators::extended_p_square_probabilities = QUANTILE_PROBABILITIES,
boost::accumulators::tag::rolling_window::window_size = ROLLING_WINDOW_SIZE) {}
The new values are inserted as follows:
m_accumulator(value);
and the percentile is calculated using:
auto 99_percentile = boost::accumulators::quantile(m_accumulator,
boost::accumulators::quantile_probability = 0.99);
The problem that we see is that the accumulator somehow doesn't really calculate the quantiles over the specified rolling window size and keeps some values for longer time and uses them for the calculation. This leads to the situation that after a rapid increase (temporarily spike) of the 99_percentile, it then recovers extremely slowly even though the issue is gone and the last 1000 values were looking good. Any ideas would be greatly appreciated.
c++ boost statistics boost-accumulators
asked Nov 19 '18 at 14:32
ladaManiakladaManiak
89116
add a comment |
We use boost accumulators to calculate server request time percentiles as follows:
static constexpr size_t ROLLING_WINDOW_SIZE = 1000;
using accumulator_t = boost::accumulators::accumulator_set<long long int,
boost::accumulators::stats<boost::accumulators::tag::extended_p_square_quantile,
boost::accumulators::tag::rolling_count> >;
const std::array<double, 4> QUANTILE_PROBABILITIES = { 0.5, 0.75, 0.95, 0.99 };
....
Percentiles::Percentiles() :
m_accumulator(boost::accumulators::extended_p_square_probabilities = QUANTILE_PROBABILITIES,
boost::accumulators::tag::rolling_window::window_size = ROLLING_WINDOW_SIZE) {}
The new values are inserted as follows:
m_accumulator(value);
and the percentile is calculated using:
auto 99_percentile = boost::accumulators::quantile(m_accumulator,
boost::accumulators::quantile_probability = 0.99);
The problem that we see is that the accumulator somehow doesn't really calculate the quantiles over the specified rolling window size and keeps some values for longer time and uses them for the calculation. This leads to the situation that after a rapid increase (temporarily spike) of the 99_percentile, it then recovers extremely slowly even though the issue is gone and the last 1000 values were looking good. Any ideas would be greatly appreciated.
c++ boost statistics boost-accumulators
asked Nov 19 '18 at 14:32
ladaManiakladaManiak
89116
add a comment |
We use boost accumulators to calculate server request time percentiles as follows:
static constexpr size_t ROLLING_WINDOW_SIZE = 1000;
using accumulator_t = boost::accumulators::accumulator_set<long long int,
boost::accumulators::stats<boost::accumulators::tag::extended_p_square_quantile,
boost::accumulators::tag::rolling_count> >;
const std::array<double, 4> QUANTILE_PROBABILITIES = { 0.5, 0.75, 0.95, 0.99 };
....
Percentiles::Percentiles() :
m_accumulator(boost::accumulators::extended_p_square_probabilities = QUANTILE_PROBABILITIES,
boost::accumulators::tag::rolling_window::window_size = ROLLING_WINDOW_SIZE) {}
The new values are inserted as follows:
m_accumulator(value);
and the percentile is calculated using:
auto 99_percentile = boost::accumulators::quantile(m_accumulator,
boost::accumulators::quantile_probability = 0.99);
The problem that we see is that the accumulator somehow doesn't really calculate the quantiles over the specified rolling window size and keeps some values for longer time and uses them for the calculation. This leads to the situation that after a rapid increase (temporarily spike) of the 99_percentile, it then recovers extremely slowly even though the issue is gone and the last 1000 values were looking good. Any ideas would be greatly appreciated.
c++ boost statistics boost-accumulators
asked Nov 19 '18 at 14:32
ladaManiakladaManiak
89116
We use boost accumulators to calculate server request time percentiles as follows:
static constexpr size_t ROLLING_WINDOW_SIZE = 1000;
using accumulator_t = boost::accumulators::accumulator_set<long long int,
boost::accumulators::stats<boost::accumulators::tag::extended_p_square_quantile,
boost::accumulators::tag::rolling_count> >;
const std::array<double, 4> QUANTILE_PROBABILITIES = { 0.5, 0.75, 0.95, 0.99 };
....
Percentiles::Percentiles() :
m_accumulator(boost::accumulators::extended_p_square_probabilities = QUANTILE_PROBABILITIES,
boost::accumulators::tag::rolling_window::window_size = ROLLING_WINDOW_SIZE) {}
The new values are inserted as follows:
m_accumulator(value);
and the percentile is calculated using:
auto 99_percentile = boost::accumulators::quantile(m_accumulator,
boost::accumulators::quantile_probability = 0.99);
The problem that we see is that the accumulator somehow doesn't really calculate the quantiles over the specified rolling window size and keeps some values for longer time and uses them for the calculation. This leads to the situation that after a rapid increase (temporarily spike) of the 99_percentile, it then recovers extremely slowly even though the issue is gone and the last 1000 values were looking good. Any ideas would be greatly appreciated.
c++ boost statistics boost-accumulators
c++ boost statistics boost-accumulators
asked Nov 19 '18 at 14:32
ladaManiakladaManiak
89116
asked Nov 19 '18 at 14:32
ladaManiakladaManiak
89116
asked Nov 19 '18 at 14:32
ladaManiakladaManiak
89116
asked Nov 19 '18 at 14:32
ladaManiakladaManiak
89116
asked Nov 19 '18 at 14:32
ladaManiakladaManiak
89116
89116
add a comment |
add a comment |
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