infinispan.com.google.common.hash.BloomFilter Maven / Gradle / Ivy
/*
* Copyright (C) 2011 The Guava Authors
*
* Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
* in compliance with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software distributed under the License
* is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
* or implied. See the License for the specific language governing permissions and limitations under
* the License.
*/
package com.google.common.hash;
import static com.google.common.base.Preconditions.checkArgument;
import static com.google.common.base.Preconditions.checkNotNull;
import com.google.common.annotations.Beta;
import com.google.common.annotations.VisibleForTesting;
import com.google.common.base.Preconditions;
import com.google.common.hash.BloomFilterStrategies.BitArray;
import java.io.Serializable;
/**
* A Bloom filter for instances of {@code T}. A Bloom filter offers an approximate containment test
* with one-sided error: if it claims that an element is contained in it, this might be in error,
* but if it claims that an element is not contained in it, then this is definitely true.
*
* If you are unfamiliar with Bloom filters, this nice
* tutorial may help you understand
* how they work.
*
* @param the type of instances that the {@code BloomFilter} accepts
* @author Kevin Bourrillion
* @author Dimitris Andreou
* @since 11.0
*/
@Beta
public final class BloomFilter implements Serializable {
/**
* A strategy to translate T instances, to {@code numHashFunctions} bit indexes.
*/
interface Strategy extends java.io.Serializable {
/**
* Sets {@code numHashFunctions} bits of the given bit array, by hashing a user element.
*/
void put(T object, Funnel funnel, int numHashFunctions, BitArray bits);
/**
* Queries {@code numHashFunctions} bits of the given bit array, by hashing a user element;
* returns {@code true} if and only if all selected bits are set.
*/
boolean mightContain(
T object, Funnel funnel, int numHashFunctions, BitArray bits);
}
/** The bit set of the BloomFilter (not necessarily power of 2!)*/
private final BitArray bits;
/** Number of hashes per element */
private final int numHashFunctions;
/** The funnel to translate Ts to bytes */
private final Funnel funnel;
/**
* The strategy we employ to map an element T to {@code numHashFunctions} bit indexes.
*/
private final Strategy strategy;
/**
* Creates a BloomFilter.
*/
private BloomFilter(BitArray bits, int numHashFunctions, Funnel funnel,
Strategy strategy) {
Preconditions.checkArgument(numHashFunctions > 0, "numHashFunctions zero or negative");
this.bits = checkNotNull(bits);
this.numHashFunctions = numHashFunctions;
this.funnel = checkNotNull(funnel);
this.strategy = strategy;
}
/**
* Returns {@code true} if the element might have been put in this Bloom filter,
* {@code false} if this is definitely not the case.
*/
public boolean mightContain(T object) {
return strategy.mightContain(object, funnel, numHashFunctions, bits);
}
/**
* Puts an element into this {@code BloomFilter}. Ensures that subsequent invocations of
* {@link #mightContain(Object)} with the same element will always return {@code true}.
*/
public void put(T object) {
strategy.put(object, funnel, numHashFunctions, bits);
}
@VisibleForTesting int getHashCount() {
return numHashFunctions;
}
@VisibleForTesting double computeExpectedFalsePositiveRate(int insertions) {
return Math.pow(
1 - Math.exp(-numHashFunctions * ((double) insertions / (bits.size()))),
numHashFunctions);
}
/**
* Creates a {@code Builder} of a {@link BloomFilter BloomFilter}, with the expected number
* of insertions and expected false positive probability.
*
* Note that overflowing a {@code BloomFilter} with significantly more elements
* than specified, will result in its saturation, and a sharp deterioration of its
* false positive probability.
*
*
The constructed {@code BloomFilter} will be serializable if the provided
* {@code Funnel} is.
*
* @param funnel the funnel of T's that the constructed {@code BloomFilter} will use
* @param expectedInsertions the number of expected insertions to the constructed
* {@code BloomFilter}; must be positive
* @param falsePositiveProbability the desired false positive probability (must be positive and
* less than 1.0)
* @return a {@code Builder}
*/
public static BloomFilter create(Funnel funnel, int expectedInsertions /* n */,
double falsePositiveProbability) {
checkNotNull(funnel);
checkArgument(expectedInsertions > 0, "Expected insertions must be positive");
checkArgument(falsePositiveProbability > 0.0 & falsePositiveProbability < 1.0,
"False positive probability in (0.0, 1.0)");
/*
* andreou: I wanted to put a warning in the javadoc about tiny fpp values,
* since the resulting size is proportional to -log(p), but there is not
* much of a point after all, e.g. optimalM(1000, 0.0000000000000001) = 76680
* which is less that 10kb. Who cares!
*/
int numBits = optimalNumOfBits(expectedInsertions, falsePositiveProbability);
int numHashFunctions = optimalNumOfHashFunctions(expectedInsertions, numBits);
return new BloomFilter(new BitArray(numBits), numHashFunctions, funnel,
BloomFilterStrategies.MURMUR128_MITZ_32);
}
/**
* Creates a {@code Builder} of a {@link BloomFilter BloomFilter}, with the expected number
* of insertions, and a default expected false positive probability of 3%.
*
* Note that overflowing a {@code BloomFilter} with significantly more elements
* than specified, will result in its saturation, and a sharp deterioration of its
* false positive probability.
*
*
The constructed {@code BloomFilter} will be serializable if the provided
* {@code Funnel} is.
*
* @param funnel the funnel of T's that the constructed {@code BloomFilter} will use
* @param expectedInsertions the number of expected insertions to the constructed
* {@code BloomFilter}; must be positive
* @return a {@code Builder}
*/
public static BloomFilter create(Funnel funnel, int expectedInsertions /* n */) {
return create(funnel, expectedInsertions, 0.03); // FYI, for 3%, we always get 5 hash functions
}
/*
* Cheat sheet:
*
* m: total bits
* n: expected insertions
* b: m/n, bits per insertion
* p: expected false positive probability
*
* 1) Optimal k = b * ln2
* 2) p = (1 - e ^ (-kn/m))^k
* 3) For optimal k: p = 2 ^ (-k) ~= 0.6185^b
* 4) For optimal k: m = -nlnp / ((ln2) ^ 2)
*/
private static final double LN2 = Math.log(2);
private static final double LN2_SQUARED = LN2 * LN2;
/**
* Computes the optimal k (number of hashes per element inserted in Bloom filter), given the
* expected insertions and total number of bits in the Bloom filter.
*
* See http://en.wikipedia.org/wiki/File:Bloom_filter_fp_probability.svg for the formula.
*
* @param n expected insertions (must be positive)
* @param m total number of bits in Bloom filter (must be positive)
*/
@VisibleForTesting static int optimalNumOfHashFunctions(int n, int m) {
return Math.max(1, (int) Math.round(m / n * LN2));
}
/**
* Computes m (total bits of Bloom filter) which is expected to achieve, for the specified
* expected insertions, the required false positive probability.
*
* See http://en.wikipedia.org/wiki/Bloom_filter#Probability_of_false_positives for the formula.
*
* @param n expected insertions (must be positive)
* @param p false positive rate (must be 0 < p < 1)
*/
@VisibleForTesting static int optimalNumOfBits(int n, double p) {
return (int) (-n * Math.log(p) / LN2_SQUARED);
}
private Object writeReplace() {
return new SerialForm(this);
}
private static class SerialForm implements Serializable {
final long[] data;
final int numHashFunctions;
final Funnel funnel;
final Strategy strategy;
SerialForm(BloomFilter bf) {
this.data = bf.bits.data;
this.numHashFunctions = bf.numHashFunctions;
this.funnel = bf.funnel;
this.strategy = bf.strategy;
}
Object readResolve() {
return new BloomFilter(new BitArray(data), numHashFunctions, funnel, strategy);
}
private static final long serialVersionUID = 1;
}
}