org.eclipse.jetty.util.statistic.SampleStatistic Maven / Gradle / Ivy
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// ========================================================================
// Copyright (c) 2009-2009 Mort Bay Consulting Pty. Ltd.
// ------------------------------------------------------------------------
// All rights reserved. This program and the accompanying materials
// are made available under the terms of the Eclipse Public License v1.0
// and Apache License v2.0 which accompanies this distribution.
// The Eclipse Public License is available at
// http://www.eclipse.org/legal/epl-v10.html
// The Apache License v2.0 is available at
// http://www.opensource.org/licenses/apache2.0.php
// You may elect to redistribute this code under either of these licenses.
// ========================================================================
package org.eclipse.jetty.util.statistic;
import java.util.concurrent.atomic.AtomicLong;
/* ------------------------------------------------------------ */
/**
* SampledStatistics
*
* 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,
* Seminumerical Algorithms, 3rd edition, page 232,
* Boston: Addison-Wesley. that cites a 1962 paper by B.P. Welford
* that can be found by following the link http://www.jstor.org/pss/1266577
*
* This algorithm is also described in Wikipedia at
* http://en.wikipedia.org/w/index.php?title=Algorithms_for_calculating_variance§ion=4#On-line_algorithm
*/
public class SampleStatistic
{
protected final AtomicLong _max = new AtomicLong();
protected final AtomicLong _total = new AtomicLong();
protected final AtomicLong _count = new AtomicLong();
protected final AtomicLong _totalVariance100 = new AtomicLong();
public void reset()
{
_max.set(0);
_total.set(0);
_count.set(0);
_totalVariance100.set(0);
}
public void set(final 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.addAndGet(delta10*delta10);
}
long oldMax = _max.get();
while (sample > oldMax)
{
if (_max.compareAndSet(oldMax, sample))
break;
oldMax = _max.get();
}
}
/* ------------------------------------------------------------ */
/**
* @return the max value
*/
public long getMax()
{
return _max.get();
}
/* ------------------------------------------------------------ */
public long getTotal()
{
return _total.get();
}
/* ------------------------------------------------------------ */
public long getCount()
{
return _count.get();
}
/* ------------------------------------------------------------ */
public double getMean()
{
return (double)_total.get()/_count.get();
}
/* ------------------------------------------------------------ */
public double getVariance()
{
final long variance100 = _totalVariance100.get();
final long count = _count.get();
return count>1?((double)variance100)/100.0/(count-1):0.0;
}
/* ------------------------------------------------------------ */
public double getStdDev()
{
return Math.sqrt(getVariance());
}
}