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/*
* Copyright (c) 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016 David Berkman
*
* This file is part of the SmallMind Code Project.
*
* The SmallMind Code Project is free software, you can redistribute
* it and/or modify it under either, at your discretion...
*
* 1) The terms of GNU Affero General Public License as published by the
* Free Software Foundation, either version 3 of the License, or (at
* your option) any later version.
*
* ...or...
*
* 2) The terms of the Apache License, Version 2.0.
*
* The SmallMind Code Project is distributed in the hope that it will
* be useful, but WITHOUT ANY WARRANTY; without even the implied warranty
* of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* General Public License or Apache License for more details.
*
* You should have received a copy of the GNU Affero General Public License
* and the Apache License along with the SmallMind Code Project. If not, see
* .
*
* Additional permission under the GNU Affero GPL version 3 section 7
* ------------------------------------------------------------------
* If you modify this Program, or any covered work, by linking or
* combining it with other code, such other code is not for that reason
* alone subject to any of the requirements of the GNU Affero GPL
* version 3.
*/
package org.smallmind.instrument;
import java.util.concurrent.atomic.AtomicLong;
import java.util.concurrent.atomic.AtomicReference;
import org.smallmind.instrument.context.MetricFact;
import org.smallmind.instrument.context.MetricItem;
import org.smallmind.instrument.context.MetricSnapshot;
public class HistogramImpl extends MetricImpl implements Histogram {
private final AtomicLong min = new AtomicLong(Long.MAX_VALUE);
// These are for the Welford algorithm for calculating running variance without floating-point doom.
private final AtomicReference variance = new AtomicReference(new double[] {-1, 0});
private final AtomicLong count = new AtomicLong(0);
private final ArrayCache arrayCache = new ArrayCache();
private final Sample sample;
private final AtomicLong max = new AtomicLong(Long.MIN_VALUE);
private final AtomicLong sum = new AtomicLong(0);
public HistogramImpl (Samples samples) {
sample = samples.createSample();
}
@Override
public Class getMetricClass () {
return Histogram.class;
}
@Override
public void clear () {
MetricSnapshot metricSnapshot;
sample.clear();
count.set(0);
sum.set(0);
min.set(Long.MAX_VALUE);
max.set(Long.MIN_VALUE);
variance.set(new double[] {-1, 0});
if ((metricSnapshot = getMetricSnapshot()) != null) {
if (metricSnapshot.willTrace(MetricFact.COUNT)) {
metricSnapshot.addItem(new MetricItem("count", 0L));
}
if (metricSnapshot.willTrace(MetricFact.SUM)) {
metricSnapshot.addItem(new MetricItem("sum", 0L));
}
if (metricSnapshot.willTrace(MetricFact.MIN)) {
metricSnapshot.addItem(new MetricItem("min", "n/a"));
}
if (metricSnapshot.willTrace(MetricFact.MAX)) {
metricSnapshot.addItem(new MetricItem("max", "n/a"));
}
if (metricSnapshot.willTrace(MetricFact.AVG)) {
metricSnapshot.addItem(new MetricItem("avg", 0.0));
}
if (metricSnapshot.willTrace(MetricFact.STD_DEV)) {
metricSnapshot.addItem(new MetricItem("std dev", 0.0));
}
if (metricSnapshot.willTrace(MetricFact.MEDIAN)) {
metricSnapshot.addItem(new MetricItem("median", 0.0));
}
if (metricSnapshot.willTrace(MetricFact.P75_Q)) {
metricSnapshot.addItem(new MetricItem("75th pctl", 0.0));
}
if (metricSnapshot.willTrace(MetricFact.P95_Q)) {
metricSnapshot.addItem(new MetricItem("95th pctl", 0.0));
}
if (metricSnapshot.willTrace(MetricFact.P98_Q)) {
metricSnapshot.addItem(new MetricItem("98th pctl", 0.0));
}
if (metricSnapshot.willTrace(MetricFact.P99_Q)) {
metricSnapshot.addItem(new MetricItem("99th pctl", 0.0));
}
if (metricSnapshot.willTrace(MetricFact.P999_Q)) {
metricSnapshot.addItem(new MetricItem("999th pctl", 0.0));
}
}
}
@Override
public void update (long value) {
MetricSnapshot metricSnapshot;
double[] currentValues;
long currentCount;
long currentSum;
long currentMin;
long currentMax;
currentCount = count.incrementAndGet();
currentSum = sum.getAndAdd(value);
currentMin = setMin(value);
currentMax = setMax(value);
currentValues = updateVariance(value);
sample.update(value);
if ((metricSnapshot = getMetricSnapshot()) != null) {
Statistics currentStatistics = sample.getStatistics();
if (metricSnapshot.willTrace(MetricFact.COUNT)) {
metricSnapshot.addItem(new MetricItem("count", currentCount));
}
if (metricSnapshot.willTrace(MetricFact.SUM)) {
metricSnapshot.addItem(new MetricItem("sum", currentSum));
}
if (metricSnapshot.willTrace(MetricFact.MIN)) {
metricSnapshot.addItem(new MetricItem("min", currentMin));
}
if (metricSnapshot.willTrace(MetricFact.MAX)) {
metricSnapshot.addItem(new MetricItem("max", currentMax));
}
if (metricSnapshot.willTrace(MetricFact.AVG)) {
metricSnapshot.addItem(new MetricItem("avg", (currentCount > 0) ? currentSum / (double)currentCount : 0.0));
}
if (metricSnapshot.willTrace(MetricFact.STD_DEV)) {
metricSnapshot.addItem(new MetricItem("std dev", (currentCount > 0) ? Math.sqrt((currentCount <= 1) ? 0.0 : currentValues[1] / (currentCount - 1)) : 0.0));
}
if (metricSnapshot.willTrace(MetricFact.MEDIAN)) {
metricSnapshot.addItem(new MetricItem("median", currentStatistics.getMedian()));
}
if (metricSnapshot.willTrace(MetricFact.P75_Q)) {
metricSnapshot.addItem(new MetricItem("75th pctl", currentStatistics.get75thPercentile()));
}
if (metricSnapshot.willTrace(MetricFact.P95_Q)) {
metricSnapshot.addItem(new MetricItem("95th pctl", currentStatistics.get95thPercentile()));
}
if (metricSnapshot.willTrace(MetricFact.P98_Q)) {
metricSnapshot.addItem(new MetricItem("98th pctl", currentStatistics.get98thPercentile()));
}
if (metricSnapshot.willTrace(MetricFact.P99_Q)) {
metricSnapshot.addItem(new MetricItem("99th pctl", currentStatistics.get99thPercentile()));
}
if (metricSnapshot.willTrace(MetricFact.P999_Q)) {
metricSnapshot.addItem(new MetricItem("999th pctl", currentStatistics.get999thPercentile()));
}
}
}
@Override
public String getSampleType () {
return sample.getType().name();
}
@Override
public long getCount () {
return count.get();
}
@Override
public double getMax () {
return (getCount() > 0) ? max.get() : 0.0;
}
private long setMax (long potentialMax) {
boolean replaced = false;
long currentMax;
do {
currentMax = max.get();
} while (!(currentMax >= potentialMax || (replaced = max.compareAndSet(currentMax, potentialMax))));
return replaced ? potentialMax : currentMax;
}
@Override
public double getMin () {
return (getCount() > 0) ? min.get() : 0.0;
}
private long setMin (long potentialMin) {
boolean replaced = false;
long currentMin;
do {
currentMin = min.get();
} while (!(currentMin <= potentialMin || (replaced = min.compareAndSet(currentMin, potentialMin))));
return replaced ? potentialMin : currentMin;
}
@Override
public double getAverage () {
return (getCount() > 0) ? sum.get() / (double)getCount() : 0.0;
}
@Override
public double getStdDev () {
return (getCount() > 0) ? Math.sqrt(getVariance()) : 0.0;
}
@Override
public double getSum () {
return (double)sum.get();
}
private double getVariance () {
return (getCount() <= 1) ? 0.0 : variance.get()[1] / (getCount() - 1);
}
@Override
public Statistics getStatistics () {
return sample.getStatistics();
}
private double[] updateVariance (long value) {
boolean done;
double[] newValues;
do {
double[] oldValues = variance.get();
newValues = arrayCache.get();
if (oldValues[0] == -1) {
newValues[0] = value;
newValues[1] = 0;
}
else {
double oldM = oldValues[0];
double oldS = oldValues[1];
double newM = oldM + ((value - oldM) / getCount());
double newS = oldS + ((value - oldM) * (value - newM));
newValues[0] = newM;
newValues[1] = newS;
}
if (done = variance.compareAndSet(oldValues, newValues)) {
// recycle the old array into the cache
arrayCache.set(oldValues);
}
} while (!done);
return newValues;
}
private static final class ArrayCache extends ThreadLocal {
@Override
protected double[] initialValue () {
return new double[2];
}
}
}
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