package com.codahale.metrics;
import java.util.ArrayList;
import java.util.List;
import java.util.Random;
/**
* A random sampling reservoir of a stream of {@code long}s. Uses Vitter's
* Algorithm R to produce a statistically representative sample.
*
* @see <a href="http://www.cs.umd.edu/~samir/498/vitter.pdf">Random Sampling
* with a Reservoir</a>
*/
public class UniformReservoir implements Reservoir {
private static final int DEFAULT_SIZE = 1028;
private static final int BITS_PER_LONG = 63;
private long count = 0;
private final long[] values;
/**
* Creates a new {@link UniformReservoir} of 1028 elements, which offers a
* 99.9% confidence level with a 5% margin of error assuming a normal
* distribution.
*/
public UniformReservoir() {
this(DEFAULT_SIZE);
}
/**
* Creates a new {@link UniformReservoir}.
*
* @param size
* the number of samples to keep in the sampling reservoir
*/
public UniformReservoir(final int size) {
this.values = new long[size];
for (int i = 0; i < values.length; i++) {
values[i] = 0;
}
count = 0;
}
@Override
public int size() {
final long c = count;
if (c > values.length) {
return values.length;
}
return (int) c;
}
@Override
public void update(final long value) {
count = count + 1;
final long c = count;
if (c <= values.length) {
values[(int) c - 1] = value;
} else {
final long r = nextLong(c);
if (r < values.length) {
values[(int) r] = value;
}
}
}
/**
* Get a pseudo-random long uniformly between 0 and n-1. Stolen from
* {@link java.util.Random#nextInt()}.
*
* @param n
* the bound
* @return a value select randomly from the range {@code [0..n)}.
*/
private static long nextLong(final long n) {
long bits, val;
do {
bits = new Random().nextLong() & (~(1L << BITS_PER_LONG));
val = bits % n;
} while (bits - val + (n - 1) < 0L);
return val;
}
@Override
public Snapshot getSnapshot() {
final int s = size();
final List<Long> copy = new ArrayList<Long>(s);
for (int i = 0; i < s; i++) {
copy.add(values[i]);
}
return new UniformSnapshot(copy);
}
}