/*
* 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.Objects;
import com.google.common.base.Predicate;
import com.google.common.hash.BloomFilterStrategies.BitArray;
import com.google.common.primitives.SignedBytes;
import com.google.common.primitives.UnsignedBytes;
import java.io.DataInputStream;
import java.io.DataOutputStream;
import java.io.IOException;
import java.io.InputStream;
import java.io.OutputStream;
import java.io.Serializable;
import javax.annotation.Nullable;
/**
* 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 <i>not</i> contained in it, then this is definitely true.
*
* <p>If you are unfamiliar with Bloom filters, this nice
* <a href="http://llimllib.github.com/bloomfilter-tutorial/">tutorial</a> may help you understand
* how they work.
*
* <p>The false positive probability ({@code FPP}) of a bloom filter is defined as the probability
* that {@linkplain #mightContain(Object)} will erroneously return {@code true} for an object that
* has not actually been put in the {@code BloomFilter}.
*
* <p>Bloom filters are serializable. They also support a more compact serial representation via
* the {@link #writeTo} and {@link #readFrom} methods. Both serialized forms will continue to be
* supported by future versions of this library. However, serial forms generated by newer versions
* of the code may not be readable by older versions of the code (e.g., a serialized bloom filter
* generated today may <i>not</i> be readable by a binary that was compiled 6 months ago).
*
* @param <T> the type of instances that the {@code BloomFilter} accepts
* @author Dimitris Andreou
* @author Kevin Bourrillion
* @since 11.0
*/
@Beta
public final class BloomFilter<T> implements Predicate<T>, Serializable {
/**
* A strategy to translate T instances, to {@code numHashFunctions} bit indexes.
*
* <p>Implementations should be collections of pure functions (i.e. stateless).
*/
interface Strategy extends java.io.Serializable {
/**
* Sets {@code numHashFunctions} bits of the given bit array, by hashing a user element.
*
* <p>Returns whether any bits changed as a result of this operation.
*/
<T> boolean put(T object, Funnel<? super T> 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.
*/
<T> boolean mightContain(
T object, Funnel<? super T> funnel, int numHashFunctions, BitArray bits);
/**
* Identifier used to encode this strategy, when marshalled as part of a BloomFilter.
* Only values in the [-128, 127] range are valid for the compact serial form.
* Non-negative values are reserved for enums defined in BloomFilterStrategies;
* negative values are reserved for any custom, stateful strategy we may define
* (e.g. any kind of strategy that would depend on user input).
*/
int ordinal();
}
/** 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<? super T> 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<? super T> funnel,
Strategy strategy) {
checkArgument(numHashFunctions > 0,
"numHashFunctions (%s) must be > 0", numHashFunctions);
checkArgument(numHashFunctions <= 255,
"numHashFunctions (%s) must be <= 255", numHashFunctions);
this.bits = checkNotNull(bits);
this.numHashFunctions = numHashFunctions;
this.funnel = checkNotNull(funnel);
this.strategy = checkNotNull(strategy);
}
/**
* Creates a new {@code BloomFilter} that's a copy of this instance. The new instance is equal to
* this instance but shares no mutable state.
*
* @since 12.0
*/
public BloomFilter<T> copy() {
return new BloomFilter<T>(bits.copy(), numHashFunctions, funnel, strategy);
}
/**
* Returns {@code true} if the element <i>might</i> have been put in this Bloom filter,
* {@code false} if this is <i>definitely</i> not the case.
*/
public boolean mightContain(T object) {
return strategy.mightContain(object, funnel, numHashFunctions, bits);
}
/**
* @deprecated Provided only to satisfy the {@link Predicate} interface; use {@link #mightContain}
* instead.
*/
@Deprecated
@Override
public boolean apply(T input) {
return mightContain(input);
}
/**
* Puts an element into this {@code BloomFilter}. Ensures that subsequent invocations of
* {@link #mightContain(Object)} with the same element will always return {@code true}.
*
* @return true if the bloom filter's bits changed as a result of this operation. If the bits
* changed, this is <i>definitely</i> the first time {@code object} has been added to the
* filter. If the bits haven't changed, this <i>might</i> be the first time {@code object}
* has been added to the filter. Note that {@code put(t)} always returns the
* <i>opposite</i> result to what {@code mightContain(t)} would have returned at the time
* it is called."
* @since 12.0 (present in 11.0 with {@code void} return type})
*/
public boolean put(T object) {
return strategy.put(object, funnel, numHashFunctions, bits);
}
/**
* Returns the probability that {@linkplain #mightContain(Object)} will erroneously return
* {@code true} for an object that has not actually been put in the {@code BloomFilter}.
*
* <p>Ideally, this number should be close to the {@code fpp} parameter
* passed in {@linkplain #create(Funnel, int, double)}, or smaller. If it is
* significantly higher, it is usually the case that too many elements (more than
* expected) have been put in the {@code BloomFilter}, degenerating it.
*
* @since 14.0 (since 11.0 as expectedFalsePositiveProbability())
*/
public double expectedFpp() {
// You down with FPP? (Yeah you know me!) Who's down with FPP? (Every last homie!)
return Math.pow((double) bits.bitCount() / bitSize(), numHashFunctions);
}
/**
* Returns the number of bits in the underlying bit array.
*/
@VisibleForTesting long bitSize() {
return bits.bitSize();
}
/**
* Determines whether a given bloom filter is compatible with this bloom filter. For two
* bloom filters to be compatible, they must:
*
* <ul>
* <li>not be the same instance
* <li>have the same number of hash functions
* <li>have the same bit size
* <li>have the same strategy
* <li>have equal funnels
* <ul>
*
* @param that The bloom filter to check for compatibility.
* @since 15.0
*/
public boolean isCompatible(BloomFilter<T> that) {
checkNotNull(that);
return (this != that) &&
(this.numHashFunctions == that.numHashFunctions) &&
(this.bitSize() == that.bitSize()) &&
(this.strategy.equals(that.strategy)) &&
(this.funnel.equals(that.funnel));
}
/**
* Combines this bloom filter with another bloom filter by performing a bitwise OR of the
* underlying data. The mutations happen to <b>this</b> instance. Callers must ensure the
* bloom filters are appropriately sized to avoid saturating them.
*
* @param that The bloom filter to combine this bloom filter with. It is not mutated.
* @throws IllegalArgumentException if {@code isCompatible(that) == false}
*
* @since 15.0
*/
public void putAll(BloomFilter<T> that) {
checkNotNull(that);
checkArgument(this != that, "Cannot combine a BloomFilter with itself.");
checkArgument(this.numHashFunctions == that.numHashFunctions,
"BloomFilters must have the same number of hash functions (%s != %s)",
this.numHashFunctions, that.numHashFunctions);
checkArgument(this.bitSize() == that.bitSize(),
"BloomFilters must have the same size underlying bit arrays (%s != %s)",
this.bitSize(), that.bitSize());
checkArgument(this.strategy.equals(that.strategy),
"BloomFilters must have equal strategies (%s != %s)",
this.strategy, that.strategy);
checkArgument(this.funnel.equals(that.funnel),
"BloomFilters must have equal funnels (%s != %s)",
this.funnel, that.funnel);
this.bits.putAll(that.bits);
}
@Override
public boolean equals(@Nullable Object object) {
if (object == this) {
return true;
}
if (object instanceof BloomFilter) {
BloomFilter<?> that = (BloomFilter<?>) object;
return this.numHashFunctions == that.numHashFunctions
&& this.funnel.equals(that.funnel)
&& this.bits.equals(that.bits)
&& this.strategy.equals(that.strategy);
}
return false;
}
@Override
public int hashCode() {
return Objects.hashCode(numHashFunctions, funnel, strategy, bits);
}
private static final Strategy DEFAULT_STRATEGY =
BloomFilterStrategies.MURMUR128_MITZ_64;
/**
* Creates a {@link BloomFilter BloomFilter<T>} with the expected number of
* insertions and expected false positive probability.
*
* <p>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.
*
* <p>The constructed {@code BloomFilter<T>} will be serializable if the provided
* {@code Funnel<T>} is.
*
* <p>It is recommended that the funnel be implemented as a Java enum. This has the
* benefit of ensuring proper serialization and deserialization, which is important
* since {@link #equals} also relies on object identity of funnels.
*
* @param funnel the funnel of T's that the constructed {@code BloomFilter<T>} will use
* @param expectedInsertions the number of expected insertions to the constructed
* {@code BloomFilter<T>}; must be positive
* @param fpp the desired false positive probability (must be positive and less than 1.0)
* @return a {@code BloomFilter}
*/
public static <T> BloomFilter<T> create(
Funnel<? super T> funnel, int expectedInsertions /* n */, double fpp) {
return create(funnel, expectedInsertions, fpp, DEFAULT_STRATEGY);
}
@VisibleForTesting
static <T> BloomFilter<T> create(
Funnel<? super T> funnel, int expectedInsertions /* n */, double fpp, Strategy strategy) {
checkNotNull(funnel);
checkArgument(expectedInsertions >= 0, "Expected insertions (%s) must be >= 0",
expectedInsertions);
checkArgument(fpp > 0.0, "False positive probability (%s) must be > 0.0", fpp);
checkArgument(fpp < 1.0, "False positive probability (%s) must be < 1.0", fpp);
checkNotNull(strategy);
if (expectedInsertions == 0) {
expectedInsertions = 1;
}
/*
* TODO(user): 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 than 10kb. Who cares!
*/
long numBits = optimalNumOfBits(expectedInsertions, fpp);
int numHashFunctions = optimalNumOfHashFunctions(expectedInsertions, numBits);
try {
return new BloomFilter<T>(new BitArray(numBits), numHashFunctions, funnel, strategy);
} catch (IllegalArgumentException e) {
throw new IllegalArgumentException("Could not create BloomFilter of " + numBits + " bits", e);
}
}
/**
* Creates a {@link BloomFilter BloomFilter<T>} with the expected number of
* insertions and a default expected false positive probability of 3%.
*
* <p>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.
*
* <p>The constructed {@code BloomFilter<T>} will be serializable if the provided
* {@code Funnel<T>} is.
*
* @param funnel the funnel of T's that the constructed {@code BloomFilter<T>} will use
* @param expectedInsertions the number of expected insertions to the constructed
* {@code BloomFilter<T>}; must be positive
* @return a {@code BloomFilter}
*/
public static <T> BloomFilter<T> create(
Funnel<? super T> 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)
*/
/**
* 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(long n, long m) {
// (m / n) * log(2), but avoid truncation due to division!
return Math.max(1, (int) Math.round((double) m / n * Math.log(2)));
}
/**
* 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 long optimalNumOfBits(long n, double p) {
if (p == 0) {
p = Double.MIN_VALUE;
}
return (long) (-n * Math.log(p) / (Math.log(2) * Math.log(2)));
}
private Object writeReplace() {
return new SerialForm<T>(this);
}
private static class SerialForm<T> implements Serializable {
final long[] data;
final int numHashFunctions;
final Funnel<? super T> funnel;
final Strategy strategy;
SerialForm(BloomFilter<T> bf) {
this.data = bf.bits.data;
this.numHashFunctions = bf.numHashFunctions;
this.funnel = bf.funnel;
this.strategy = bf.strategy;
}
Object readResolve() {
return new BloomFilter<T>(new BitArray(data), numHashFunctions, funnel, strategy);
}
private static final long serialVersionUID = 1;
}
/**
* Writes this {@code BloomFilter} to an output stream, with a custom format (not Java
* serialization). This has been measured to save at least 400 bytes compared to regular
* serialization.
*
* <p>Use {@linkplain #readFrom(InputStream, Funnel)} to reconstruct the written BloomFilter.
*/
public void writeTo(OutputStream out) throws IOException {
/*
* Serial form:
* 1 signed byte for the strategy
* 1 unsigned byte for the number of hash functions
* 1 big endian int, the number of longs in our bitset
* N big endian longs of our bitset
*/
DataOutputStream dout = new DataOutputStream(out);
dout.writeByte(SignedBytes.checkedCast(strategy.ordinal()));
dout.writeByte(UnsignedBytes.checkedCast(numHashFunctions)); // note: checked at the c'tor
dout.writeInt(bits.data.length);
for (long value : bits.data) {
dout.writeLong(value);
}
}
/**
* Reads a byte stream, which was written by {@linkplain #writeTo(OutputStream)}, into
* a {@code BloomFilter<T>}.
*
* The {@code Funnel} to be used is not encoded in the stream, so it must be provided here.
* <b>Warning:</b> the funnel provided <b>must</b> behave identically to the one used to
* populate the original Bloom filter!
*
* @throws IOException if the InputStream throws an {@code IOException}, or if its data does
* not appear to be a BloomFilter serialized using the
* {@linkplain #writeTo(OutputStream)} method.
*/
public static <T> BloomFilter<T> readFrom(InputStream in, Funnel<T> funnel) throws IOException {
checkNotNull(in, "InputStream");
checkNotNull(funnel, "Funnel");
int strategyOrdinal = -1;
int numHashFunctions = -1;
int dataLength = -1;
try {
DataInputStream din = new DataInputStream(in);
// currently this assumes there is no negative ordinal; will have to be updated if we
// add non-stateless strategies (for which we've reserved negative ordinals; see
// Strategy.ordinal()).
strategyOrdinal = din.readByte();
numHashFunctions = UnsignedBytes.toInt(din.readByte());
dataLength = din.readInt();
Strategy strategy = BloomFilterStrategies.values()[strategyOrdinal];
long[] data = new long[dataLength];
for (int i = 0; i < data.length; i++) {
data[i] = din.readLong();
}
return new BloomFilter<T>(new BitArray(data), numHashFunctions, funnel, strategy);
} catch (RuntimeException e) {
IOException ioException = new IOException(
"Unable to deserialize BloomFilter from InputStream."
+ " strategyOrdinal: " + strategyOrdinal
+ " numHashFunctions: " + numHashFunctions
+ " dataLength: " + dataLength);
ioException.initCause(e);
throw ioException;
}
}
}