001 /* 002 * Copyright (C) 2011 The Guava Authors 003 * 004 * Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except 005 * in compliance with the License. You may obtain a copy of the License at 006 * 007 * http://www.apache.org/licenses/LICENSE-2.0 008 * 009 * Unless required by applicable law or agreed to in writing, software distributed under the License 010 * is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express 011 * or implied. See the License for the specific language governing permissions and limitations under 012 * the License. 013 */ 014 015 package com.google.common.hash; 016 017 import static com.google.common.base.Preconditions.checkArgument; 018 import static com.google.common.base.Preconditions.checkNotNull; 019 020 import com.google.common.annotations.Beta; 021 import com.google.common.annotations.VisibleForTesting; 022 import com.google.common.base.Preconditions; 023 import com.google.common.hash.BloomFilterStrategies.BitArray; 024 025 import java.io.Serializable; 026 027 /** 028 * A Bloom filter for instances of {@code T}. A Bloom filter offers an approximate containment test 029 * with one-sided error: if it claims that an element is contained in it, this might be in error, 030 * but if it claims that an element is <i>not</i> contained in it, then this is definitely true. 031 * 032 * <p>If you are unfamiliar with Bloom filters, this nice 033 * <a href="http://llimllib.github.com/bloomfilter-tutorial/">tutorial</a> may help you understand 034 * how they work. 035 * 036 * @param <T> the type of instances that the {@code BloomFilter} accepts 037 * @author Dimitris Andreou 038 * @author Kevin Bourrillion 039 * @since 11.0 040 */ 041 @Beta 042 public final class BloomFilter<T> implements Serializable { 043 /* 044 * TODO(user): add this above (when the other serial form is published): 045 * <p>Bloom filters are serializable, but also support a more compact serial 046 * representation via the {} and {} methods. Both serialized forms will continue to 047 * be supported by future versions of this library. 048 */ 049 050 /** 051 * A strategy to translate T instances, to {@code numHashFunctions} bit indexes. 052 * 053 * <p>Implementations should be collections of pure functions (i.e. stateless). 054 */ 055 interface Strategy extends java.io.Serializable { 056 057 /** 058 * Sets {@code numHashFunctions} bits of the given bit array, by hashing a user element. 059 * 060 * <p>Returns whether any bits changed as a result of this operation. 061 */ 062 <T> boolean put(T object, Funnel<? super T> funnel, int numHashFunctions, BitArray bits); 063 064 /** 065 * Queries {@code numHashFunctions} bits of the given bit array, by hashing a user element; 066 * returns {@code true} if and only if all selected bits are set. 067 */ 068 <T> boolean mightContain( 069 T object, Funnel<? super T> funnel, int numHashFunctions, BitArray bits); 070 071 /** 072 * Identifier used to encode this strategy, when marshalled as part of a BloomFilter. 073 * Only values in the [-128, 127] range are valid for the compact serial form. 074 * Non-negative values are reserved for enums defined in BloomFilterStrategies; 075 * negative values are reserved for any custom, stateful strategy we may define 076 * (e.g. any kind of strategy that would depend on user input). 077 */ 078 int ordinal(); 079 } 080 081 /** The bit set of the BloomFilter (not necessarily power of 2!)*/ 082 private final BitArray bits; 083 084 /** Number of hashes per element */ 085 private final int numHashFunctions; 086 087 /** The funnel to translate Ts to bytes */ 088 private final Funnel<T> funnel; 089 090 /** 091 * The strategy we employ to map an element T to {@code numHashFunctions} bit indexes. 092 */ 093 private final Strategy strategy; 094 095 /** 096 * Creates a BloomFilter. 097 */ 098 private BloomFilter(BitArray bits, int numHashFunctions, Funnel<T> funnel, 099 Strategy strategy) { 100 Preconditions.checkArgument(numHashFunctions > 0, "numHashFunctions zero or negative"); 101 this.bits = checkNotNull(bits); 102 this.numHashFunctions = numHashFunctions; 103 this.funnel = checkNotNull(funnel); 104 this.strategy = strategy; 105 106 /* 107 * This only exists to forbid BFs that cannot use the compact persistent representation. 108 * If it ever throws, at a user who was not intending to use that representation, we should 109 * reconsider 110 */ 111 if (numHashFunctions > 255) { 112 throw new AssertionError("Currently we don't allow BloomFilters that would use more than" + 113 "255 hash functions, please contact the guava team"); 114 } 115 } 116 117 /** 118 * Creates a new {@code BloomFilter} that's a copy of this instance. The new instance is equal to 119 * this instance but shares no mutable state. 120 * 121 * @since 12.0 122 */ 123 public BloomFilter<T> copy() { 124 return new BloomFilter<T>(bits.copy(), numHashFunctions, funnel, strategy); 125 } 126 127 /** 128 * Returns {@code true} if the element <i>might</i> have been put in this Bloom filter, 129 * {@code false} if this is <i>definitely</i> not the case. 130 */ 131 public boolean mightContain(T object) { 132 return strategy.mightContain(object, funnel, numHashFunctions, bits); 133 } 134 135 /** 136 * Puts an element into this {@code BloomFilter}. Ensures that subsequent invocations of 137 * {@link #mightContain(Object)} with the same element will always return {@code true}. 138 * 139 * @return true if the bloom filter's bits changed as a result of this operation. If the bits 140 * changed, this is <i>definitely</i> the first time {@code object} has been added to the 141 * filter. If the bits haven't changed, this <i>might</i> be the first time {@code object} 142 * has been added to the filter. Note that {@code put(t)} always returns the 143 * <i>opposite</i> result to what {@code mightContain(t)} would have returned at the time 144 * it is called." 145 * @since 12.0 (present in 11.0 with {@code void} return type}) 146 */ 147 public boolean put(T object) { 148 return strategy.put(object, funnel, numHashFunctions, bits); 149 } 150 151 /** 152 * {@inheritDoc} 153 * 154 * <p>This implementation uses reference equality to compare funnels. 155 */ 156 @Override public boolean equals(Object o) { 157 if (o instanceof BloomFilter) { 158 BloomFilter<?> that = (BloomFilter<?>) o; 159 return this.numHashFunctions == that.numHashFunctions 160 && this.bits.equals(that.bits) 161 && this.funnel == that.funnel 162 && this.strategy == that.strategy; 163 } 164 return false; 165 } 166 167 @Override public int hashCode() { 168 return bits.hashCode(); 169 } 170 171 @VisibleForTesting int getHashCount() { 172 return numHashFunctions; 173 } 174 175 @VisibleForTesting double computeExpectedFalsePositiveRate(int insertions) { 176 return Math.pow( 177 1 - Math.exp(-numHashFunctions * ((double) insertions / (bits.size()))), 178 numHashFunctions); 179 } 180 181 /** 182 * Creates a {@code Builder} of a {@link BloomFilter BloomFilter<T>}, with the expected number 183 * of insertions and expected false positive probability. 184 * 185 * <p>Note that overflowing a {@code BloomFilter} with significantly more elements 186 * than specified, will result in its saturation, and a sharp deterioration of its 187 * false positive probability. 188 * 189 * <p>The constructed {@code BloomFilter<T>} will be serializable if the provided 190 * {@code Funnel<T>} is. 191 * 192 * <p>It is recommended the funnel is implemented as a Java enum. This has the benefit of ensuring 193 * proper serialization and deserialization, which is important since {@link #equals} also relies 194 * on object identity of funnels. 195 * 196 * @param funnel the funnel of T's that the constructed {@code BloomFilter<T>} will use 197 * @param expectedInsertions the number of expected insertions to the constructed 198 * {@code BloomFilter<T>}; must be positive 199 * @param falsePositiveProbability the desired false positive probability (must be positive and 200 * less than 1.0) 201 * @return a {@code BloomFilter} 202 */ 203 public static <T> BloomFilter<T> create(Funnel<T> funnel, int expectedInsertions /* n */, 204 double falsePositiveProbability) { 205 checkNotNull(funnel); 206 checkArgument(expectedInsertions > 0, "Expected insertions must be positive"); 207 checkArgument(falsePositiveProbability > 0.0 & falsePositiveProbability < 1.0, 208 "False positive probability in (0.0, 1.0)"); 209 /* 210 * andreou: I wanted to put a warning in the javadoc about tiny fpp values, 211 * since the resulting size is proportional to -log(p), but there is not 212 * much of a point after all, e.g. optimalM(1000, 0.0000000000000001) = 76680 213 * which is less that 10kb. Who cares! 214 */ 215 int numBits = optimalNumOfBits(expectedInsertions, falsePositiveProbability); 216 int numHashFunctions = optimalNumOfHashFunctions(expectedInsertions, numBits); 217 return new BloomFilter<T>(new BitArray(numBits), numHashFunctions, funnel, 218 BloomFilterStrategies.MURMUR128_MITZ_32); 219 } 220 221 /** 222 * Creates a {@code Builder} of a {@link BloomFilter BloomFilter<T>}, with the expected number 223 * of insertions, and a default expected false positive probability of 3%. 224 * 225 * <p>Note that overflowing a {@code BloomFilter} with significantly more elements 226 * than specified, will result in its saturation, and a sharp deterioration of its 227 * false positive probability. 228 * 229 * <p>The constructed {@code BloomFilter<T>} will be serializable if the provided 230 * {@code Funnel<T>} is. 231 * 232 * @param funnel the funnel of T's that the constructed {@code BloomFilter<T>} will use 233 * @param expectedInsertions the number of expected insertions to the constructed 234 * {@code BloomFilter<T>}; must be positive 235 * @return a {@code BloomFilter} 236 */ 237 public static <T> BloomFilter<T> create(Funnel<T> funnel, int expectedInsertions /* n */) { 238 return create(funnel, expectedInsertions, 0.03); // FYI, for 3%, we always get 5 hash functions 239 } 240 241 /* 242 * Cheat sheet: 243 * 244 * m: total bits 245 * n: expected insertions 246 * b: m/n, bits per insertion 247 248 * p: expected false positive probability 249 * 250 * 1) Optimal k = b * ln2 251 * 2) p = (1 - e ^ (-kn/m))^k 252 * 3) For optimal k: p = 2 ^ (-k) ~= 0.6185^b 253 * 4) For optimal k: m = -nlnp / ((ln2) ^ 2) 254 */ 255 256 private static final double LN2 = Math.log(2); 257 private static final double LN2_SQUARED = LN2 * LN2; 258 259 /** 260 * Computes the optimal k (number of hashes per element inserted in Bloom filter), given the 261 * expected insertions and total number of bits in the Bloom filter. 262 * 263 * See http://en.wikipedia.org/wiki/File:Bloom_filter_fp_probability.svg for the formula. 264 * 265 * @param n expected insertions (must be positive) 266 * @param m total number of bits in Bloom filter (must be positive) 267 */ 268 @VisibleForTesting static int optimalNumOfHashFunctions(int n, int m) { 269 return Math.max(1, (int) Math.round(m / n * LN2)); 270 } 271 272 /** 273 * Computes m (total bits of Bloom filter) which is expected to achieve, for the specified 274 * expected insertions, the required false positive probability. 275 * 276 * See http://en.wikipedia.org/wiki/Bloom_filter#Probability_of_false_positives for the formula. 277 * 278 * @param n expected insertions (must be positive) 279 * @param p false positive rate (must be 0 < p < 1) 280 */ 281 @VisibleForTesting static int optimalNumOfBits(int n, double p) { 282 return (int) (-n * Math.log(p) / LN2_SQUARED); 283 } 284 285 private Object writeReplace() { 286 return new SerialForm<T>(this); 287 } 288 289 private static class SerialForm<T> implements Serializable { 290 final long[] data; 291 final int numHashFunctions; 292 final Funnel<T> funnel; 293 final Strategy strategy; 294 295 SerialForm(BloomFilter<T> bf) { 296 this.data = bf.bits.data; 297 this.numHashFunctions = bf.numHashFunctions; 298 this.funnel = bf.funnel; 299 this.strategy = bf.strategy; 300 } 301 Object readResolve() { 302 return new BloomFilter<T>(new BitArray(data), numHashFunctions, funnel, strategy); 303 } 304 private static final long serialVersionUID = 1; 305 } 306 }