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//
// ========================================================================
// Copyright (c) 1995 Mort Bay Consulting Pty Ltd and others.
//
// This program and the accompanying materials are made available under the
// terms of the Eclipse Public License v. 2.0 which is available at
// https://www.eclipse.org/legal/epl-2.0, or the Apache License, Version 2.0
// which is available at https://www.apache.org/licenses/LICENSE-2.0.
//
// SPDX-License-Identifier: EPL-2.0 OR Apache-2.0
// ========================================================================
//

package org.eclipse.jetty.util.statistic;

import java.util.concurrent.atomic.AtomicLong;
import java.util.concurrent.atomic.LongAccumulator;
import java.util.concurrent.atomic.LongAdder;

/**
 * 

Statistics on a sampled value.

*

Provides max, total, mean, count, variance, and standard deviation of continuous sequence of samples.

*

Calculates estimates of mean, variance, and standard deviation characteristics of a sample using a non synchronized * approximation of the on-line algorithm presented in Donald Knuth's Art of Computer Programming, Volume 2, * Semi numerical Algorithms, 3rd edition, page 232, Boston: Addison-Wesley. That cites a 1962 paper by B.P. Welford: * Note on a Method for Calculating Corrected Sums of Squares and Products

*

This algorithm is also described in Wikipedia in the section "Online algorithm": * Algorithms for calculating variance.

*/ public class SampleStatistic { private final LongAccumulator _max = new LongAccumulator(Math::max, 0L); private final AtomicLong _total = new AtomicLong(); private final AtomicLong _count = new AtomicLong(); private final LongAdder _totalVariance100 = new LongAdder(); /** * Resets the statistics. */ public void reset() { _max.reset(); _total.set(0); _count.set(0); _totalVariance100.reset(); } /** * Records a sample value. * * @param sample the value to record. */ public void record(long sample) { long total = _total.addAndGet(sample); long count = _count.incrementAndGet(); if (count > 1) { long mean10 = total * 10 / count; long delta10 = sample * 10 - mean10; _totalVariance100.add(delta10 * delta10); } _max.accumulate(sample); } /** * Get the max value of the recorded samples. * @return the max value of the recorded samples */ public long getMax() { return _max.get(); } /** * Get the sum of all the recorded samples. * @return the sum of all the recorded samples */ public long getTotal() { return _total.get(); } /** * Get the number of samples recorded. * @return the number of samples recorded */ public long getCount() { return _count.get(); } /** * Get the average value of the samples recorded, or zero if there are no samples. * @return the average value of the samples recorded, or zero if there are no samples */ public double getMean() { long count = getCount(); return count > 0 ? (double)_total.get() / _count.get() : 0.0D; } /** * Get the variance of the samples recorded, or zero if there are less than 2 samples. * @return the variance of the samples recorded, or zero if there are less than 2 samples */ public double getVariance() { long variance100 = _totalVariance100.sum(); long count = getCount(); return count > 1 ? variance100 / 100.0D / (count - 1) : 0.0D; } /** * Get the standard deviation of the samples recorded. * @return the standard deviation of the samples recorded */ public double getStdDev() { return Math.sqrt(getVariance()); } @Override public String toString() { return String.format("%s@%x{count=%d,max=%d,mean=%f,total=%d,variance=%f,stddev=%f}", getClass().getSimpleName(), hashCode(), getCount(), getMax(), getMean(), getTotal(), getVariance(), getStdDev()); } }




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