/// <summary>
/// "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."
/// </summary>
/// <param name="now"></param>
/// <param name="next"></param>
private void Rescale(long now, long next)
{
if (_nextScaleTime.CompareAndSet(next, now + RESCALE_THRESHOLD))
{
lockForRescale();
try
{
var oldStartTime = _startTime;
_startTime = CurrentTimeInSeconds();
double scalingFactor = Math.Exp(-_alpha * (_startTime - oldStartTime));
var keys = new List <double>(_values.Keys);
foreach (double key in keys)
{
WeightedSample sample = null;
if (_values.TryRemove(key, out sample))
{
WeightedSample newSample = new WeightedSample(sample.value, sample.weight * scalingFactor);
_values.AddOrUpdate(key * scalingFactor, newSample, (k, v) => v);
}
}
}
finally
{
unlockForRescale();
}
}
}
/// <summary>
/// Adds an old value with a fixed timestamp to the reservoir.
/// </summary>
/// <param name="value">the value to be added</param>
/// <param name="timestamp">the epoch timestamp of value in seconds</param>
public void Update(long value, long timestamp)
{
rescaleIfNeeded();
lockForRegularUsage();
_lock.EnterReadLock();
try
{
var itemWeight = Weight(timestamp - _startTime);
WeightedSample sample = new WeightedSample(value, itemWeight);
var random = ThreadLocalRandom.NextNonzeroDouble();
var priority = itemWeight / random;
var newCount = _count.IncrementAndGet();
if (newCount <= _size)
{
_values.AddOrUpdate(priority, sample, (p, v) => v);
}
else
{
var first = _values.Keys.Min();
if (first < priority)
{
_values.AddOrUpdate(priority, sample, (p, v) => v);
WeightedSample removed;
while (!_values.TryRemove(first, out removed))
{
first = _values.Keys.First();
}
}
}
}
finally
{
unlockForRegularUsage();
_lock.ExitReadLock();
}
}
