/*
***************************************************************************************
* Copyright (C) 2006 EsperTech, Inc. All rights reserved. *
* http://www.espertech.com/esper *
* http://www.espertech.com *
* ---------------------------------------------------------------------------------- *
* The software in this package is published under the terms of the GPL license *
* a copy of which has been included with this distribution in the license.txt file. *
***************************************************************************************
*/
/*
***************************************************************************************
* Copyright (C) 2006 EsperTech, Inc. All rights reserved. *
* http://www.espertech.com/esper *
* http://www.espertech.com *
* ---------------------------------------------------------------------------------- *
* The software in this package is published under the terms of the GPL license *
* a copy of which has been included with this distribution in the license.txt file. *
***************************************************************************************
*/
/***************************************************************************************
* Attribution Notice
*
* This file is imported from Metrics (https://github.com/codahale/metrics subproject metrics-core).
* Metrics is Copyright (c) 2010-2012 Coda Hale, Yammer.com
* Metrics is Published under Apache Software License 2.0, see LICENSE in root folder.
*
* Thank you for the Metrics developers efforts in making their library available under an Apache license.
* EsperTech incorporates Metrics version 0.2.2 in source code form since Metrics depends on SLF4J
* and this dependency is not possible to introduce for Esper.
* *************************************************************************************
*/
package com.espertech.esper.metrics.codahale_metrics.metrics.core;
import com.espertech.esper.metrics.codahale_metrics.metrics.stats.ExponentiallyDecayingSample;
import com.espertech.esper.metrics.codahale_metrics.metrics.stats.Sample;
import com.espertech.esper.metrics.codahale_metrics.metrics.stats.Snapshot;
import com.espertech.esper.metrics.codahale_metrics.metrics.stats.UniformSample;
import java.util.concurrent.atomic.AtomicLong;
import java.util.concurrent.atomic.AtomicReference;
import static java.lang.Math.sqrt;
/**
* A metric which calculates the distribution of a value.
*
* @see <a href="http://www.johndcook.com/standard_deviation.html">Accurately computing running
* variance</a>
*/
public class Histogram implements Metric, Sampling, Summarizable {
private static final int DEFAULT_SAMPLE_SIZE = 1028;
private static final double DEFAULT_ALPHA = 0.015;
/**
* The type of sampling the histogram should be performing.
*/
enum SampleType {
/**
* Uses a uniform sample of 1028 elements, which offers a 99.9% confidence level with a 5%
* margin of error assuming a normal distribution.
*/
UNIFORM {
@Override
public Sample newSample() {
return new UniformSample(DEFAULT_SAMPLE_SIZE);
}
},
/**
* Uses an exponentially decaying sample of 1028 elements, which offers a 99.9% confidence
* level with a 5% margin of error assuming a normal distribution, and an alpha factor of
* 0.015, which heavily biases the sample to the past 5 minutes of measurements.
*/
BIASED {
@Override
public Sample newSample() {
return new ExponentiallyDecayingSample(DEFAULT_SAMPLE_SIZE, DEFAULT_ALPHA);
}
};
public abstract Sample newSample();
}
private final Sample sample;
private final AtomicLong min = new AtomicLong();
private final AtomicLong max = new AtomicLong();
private final AtomicLong sum = new AtomicLong();
// These are for the Welford algorithm for calculating running variance
// without floating-point doom.
private final AtomicReference<double[]> variance =
new AtomicReference<double[]>(new double[]{-1, 0}); // M, S
private final AtomicLong count = new AtomicLong();
/**
* Creates a new {@link Histogram} with the given sample type.
*
* @param type the type of sample to use
*/
Histogram(SampleType type) {
this(type.newSample());
}
/**
* Creates a new {@link Histogram} with the given sample.
*
* @param sample the sample to create a histogram from
*/
Histogram(Sample sample) {
this.sample = sample;
clear();
}
/**
* Clears all recorded values.
*/
public void clear() {
sample.clear();
count.set(0);
max.set(Long.MIN_VALUE);
min.set(Long.MAX_VALUE);
sum.set(0);
variance.set(new double[]{-1, 0});
}
/**
* Adds a recorded value.
*
* @param value the length of the value
*/
public void update(int value) {
update((long) value);
}
/**
* Adds a recorded value.
*
* @param value the length of the value
*/
public void update(long value) {
count.incrementAndGet();
sample.update(value);
setMax(value);
setMin(value);
sum.getAndAdd(value);
updateVariance(value);
}
/**
* Returns the number of values recorded.
*
* @return the number of values recorded
*/
public long count() {
return count.get();
}
/* (non-Javadoc)
* @see com.yammer.metrics.core.Summarizable#max()
*/
@Override
public double max() {
if (count() > 0) {
return max.get();
}
return 0.0;
}
/* (non-Javadoc)
* @see com.yammer.metrics.core.Summarizable#min()
*/
@Override
public double min() {
if (count() > 0) {
return min.get();
}
return 0.0;
}
/* (non-Javadoc)
* @see com.yammer.metrics.core.Summarizable#mean()
*/
@Override
public double mean() {
if (count() > 0) {
return sum.get() / (double) count();
}
return 0.0;
}
/* (non-Javadoc)
* @see com.yammer.metrics.core.Summarizable#stdDev()
*/
@Override
public double stdDev() {
if (count() > 0) {
return sqrt(variance());
}
return 0.0;
}
/* (non-Javadoc)
* @see com.yammer.metrics.core.Summarizable#sum()
*/
@Override
public double sum() {
return (double) sum.get();
}
@Override
public Snapshot getSnapshot() {
return sample.getSnapshot();
}
private double variance() {
if (count() <= 1) {
return 0.0;
}
return variance.get()[1] / (count() - 1);
}
private void setMax(long potentialMax) {
boolean done = false;
while (!done) {
final long currentMax = max.get();
done = currentMax >= potentialMax || max.compareAndSet(currentMax, potentialMax);
}
}
private void setMin(long potentialMin) {
boolean done = false;
while (!done) {
final long currentMin = min.get();
done = currentMin <= potentialMin || min.compareAndSet(currentMin, potentialMin);
}
}
private void updateVariance(long value) {
while (true) {
final double[] oldValues = variance.get();
final double[] newValues = new double[2];
if (oldValues[0] == -1) {
newValues[0] = value;
newValues[1] = 0;
} else {
final double oldM = oldValues[0];
final double oldS = oldValues[1];
final double newM = oldM + ((value - oldM) / count());
final double newS = oldS + ((value - oldM) * (value - newM));
newValues[0] = newM;
newValues[1] = newS;
}
if (variance.compareAndSet(oldValues, newValues)) {
return;
}
}
}
@Override
public <T> void processWith(MetricProcessor<T> processor, MetricName name, T context) throws Exception {
processor.processHistogram(name, this, context);
}
}