com.tangosol.internal.net.metrics.ExponentiallyDecayingReservoir Maven / Gradle / Ivy
/*
* Copyright (c) 2020 Oracle and/or its affiliates.
*
* Licensed under the Universal Permissive License v 1.0 as shown at
* http://oss.oracle.com/licenses/upl.
*/
package com.tangosol.internal.net.metrics;
import java.util.ArrayList;
import java.util.concurrent.ConcurrentSkipListMap;
import java.util.concurrent.ThreadLocalRandom;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.atomic.AtomicLong;
import java.util.concurrent.locks.ReentrantReadWriteLock;
import static java.lang.Math.exp;
import static java.lang.Math.min;
/*
* This class is heavily inspired by:
* ExponentiallyDecayingReservoir
*
* From Helidon v 2.0.2
* Distributed under Apache License, Version 2.0
*/
/**
* An exponentially-decaying random reservoir of {@code long}s. Uses Cormode et al's forward-decaying priority reservoir
* sampling method to produce a statistically representative sampling reservoir, exponentially biased towards newer
* entries.
*
* @see
* Cormode et al. Forward Decay: A Practical Time Decay Model for Streaming Systems. ICDE '09: Proceedings of the 2009
* IEEE International Conference on Data Engineering (2009)
*/
class ExponentiallyDecayingReservoir
{
/**
* Creates a new {@link ExponentiallyDecayingReservoir} 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
* reservoir to the past 5 minutes of measurements.
*/
ExponentiallyDecayingReservoir(Clock clock)
{
this(DEFAULT_SIZE, DEFAULT_ALPHA, clock);
}
/**
* Creates a new {@link ExponentiallyDecayingReservoir}.
*
* @param size the number of samples to keep in the sampling reservoir
* @param alpha the exponential decay factor; the higher this is, the more biased the reservoir will be towards
* newer values
*/
ExponentiallyDecayingReservoir(int size, double alpha, Clock clock)
{
this.clock = clock;
this.values = new ConcurrentSkipListMap<>();
this.lock = new ReentrantReadWriteLock();
this.alpha = alpha;
this.size = size;
this.count = new AtomicLong(0);
this.startTime = currentTimeInSeconds();
this.nextScaleTime = new AtomicLong(clock.nanoTick() + RESCALE_THRESHOLD);
}
public int size()
{
return (int) min(size, count.get());
}
public void update(long value)
{
update(value, currentTimeInSeconds());
}
/**
* Adds an old value with a fixed timestamp to the reservoir.
*
* @param value the value to be added
* @param timestamp the epoch timestamp of {@code value} in seconds
*/
public void update(long value, long timestamp)
{
rescaleIfNeeded();
lockForRegularUsage();
try
{
final double itemWeight = weight(timestamp - startTime);
final WeightedSnapshot.WeightedSample sample = new WeightedSnapshot.WeightedSample(value, itemWeight);
final double priority = itemWeight / ThreadLocalRandom.current().nextDouble();
final long newCount = count.incrementAndGet();
if (newCount <= size)
{
values.put(priority, sample);
}
else
{
Double first = values.firstKey();
if ((first < priority) && (values.putIfAbsent(priority, sample) == null))
{
// ensure we always remove an item
while (values.remove(first) == null)
{
first = values.firstKey();
}
}
}
}
finally
{
unlockForRegularUsage();
}
}
private void rescaleIfNeeded()
{
final long now = clock.nanoTick();
final long next = nextScaleTime.get();
if (now >= next)
{
rescale(now, next);
}
}
public Snapshot getSnapshot()
{
rescaleIfNeeded();
lockForRegularUsage();
try
{
return new WeightedSnapshot(values.values());
}
finally
{
unlockForRegularUsage();
}
}
private long currentTimeInSeconds()
{
return TimeUnit.MILLISECONDS.toSeconds(clock.milliTime());
}
private double weight(long t)
{
return exp(alpha * t);
}
/* "A common feature of the above techniques—indeed, the key technique that
* allows us to track the decayed weights efficiently—is that they maintain
* counts and other quantities based on g(ti − L), and only scale by g(t − L)
* at query time. But while g(ti −L)/g(t−L) is guaranteed to lie between zero
* and one, the intermediate values of g(ti − L) could become very large. For
* polynomial functions, these values should not grow too large, and should be
* effectively represented in practice by floating point values without loss of
* precision. For exponential functions, these values could grow quite large as
* new values of (ti − L) become large, and potentially exceed the capacity of
* common floating point types. However, since the values stored by the
* algorithms are linear combinations of g values (scaled sums), they can be
* rescaled relative to a new landmark. That is, by the analysis of exponential
* decay in Section III-A, the choice of L does not affect the final result. We
* can therefore multiply each value based on L by a factor of exp(−α(L′ − L)),
* and obtain the correct value as if we had instead computed relative to a new
* landmark L′ (and then use this new L′ at query time). This can be done with
* a linear pass over whatever data structure is being used."
*/
private void rescale(long now, long next)
{
lockForRescale();
try
{
if (nextScaleTime.compareAndSet(next, now + RESCALE_THRESHOLD))
{
final long oldStartTime = startTime;
this.startTime = currentTimeInSeconds();
final double scalingFactor = exp(-alpha * (startTime - oldStartTime));
if (Double.compare(scalingFactor, 0) == 0)
{
values.clear();
}
else
{
final ArrayList keys = new ArrayList(values.keySet());
for (Double key : keys)
{
final WeightedSnapshot.WeightedSample sample = values.remove(key);
final WeightedSnapshot.WeightedSample newSample
= new WeightedSnapshot.WeightedSample(sample.getValue(),sample.getWeight() * scalingFactor);
values.put(key * scalingFactor, newSample);
}
}
// make sure the counter is in sync with the number of stored samples.
count.set(values.size());
}
}
finally
{
unlockForRescale();
}
}
private void unlockForRescale()
{
lock.writeLock().unlock();
}
private void lockForRescale()
{
lock.writeLock().lock();
}
private void lockForRegularUsage()
{
lock.readLock().lock();
}
private void unlockForRegularUsage()
{
lock.readLock().unlock();
}
private final ConcurrentSkipListMap values;
private final ReentrantReadWriteLock lock;
private final double alpha;
private final int size;
private final AtomicLong count;
private final Clock clock;
private final AtomicLong nextScaleTime;
private volatile long startTime;
private static final int DEFAULT_SIZE = 1028;
private static final double DEFAULT_ALPHA = 0.015;
private static final long RESCALE_THRESHOLD = TimeUnit.HOURS.toNanos(1);
}
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