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/*
* 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.LockFreeBitArray;
import com.google.common.math.DoubleMath;
import com.google.common.primitives.SignedBytes;
import com.google.common.primitives.UnsignedBytes;
import com.google.errorprone.annotations.CanIgnoreReturnValue;
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 java.math.RoundingMode;
import java.util.stream.Collector;
import org.checkerframework.checker.nullness.qual.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 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.
*
*
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}.
*
*
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 not be readable by a binary that was compiled 6 months ago).
*
*
As of Guava 23.0, this class is thread-safe and lock-free. It internally uses atomics and
* compare-and-swap to ensure correctness when multiple threads are used to access it.
*
* @param the type of instances that the {@code BloomFilter} accepts
* @author Dimitris Andreou
* @author Kevin Bourrillion
* @since 11.0 (thread-safe since 23.0)
*/
@Beta
public final class BloomFilter implements Predicate, Serializable {
/**
* A strategy to translate T instances, to {@code numHashFunctions} bit indexes.
*
* 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.
*
*
Returns whether any bits changed as a result of this operation.
*/
boolean put(
T object, Funnel funnel, int numHashFunctions, LockFreeBitArray 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, LockFreeBitArray 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 LockFreeBitArray 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(
LockFreeBitArray bits, int numHashFunctions, Funnel 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 copy() {
return new BloomFilter(bits.copy(), numHashFunctions, funnel, 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);
}
/**
* @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 definitely the first time {@code object} has been added to the
* filter. If the bits haven't changed, this might be the first time {@code object} has
* been added to the filter. Note that {@code put(t)} always returns the opposite
* 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})
*/
@CanIgnoreReturnValue
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}.
*
* 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 an estimate for the total number of distinct elements that have been added to this
* Bloom filter. This approximation is reasonably accurate if it does not exceed the value of
* {@code expectedInsertions} that was used when constructing the filter.
*
* @since 22.0
*/
public long approximateElementCount() {
long bitSize = bits.bitSize();
long bitCount = bits.bitCount();
/**
* Each insertion is expected to reduce the # of clear bits by a factor of
* `numHashFunctions/bitSize`. So, after n insertions, expected bitCount is `bitSize * (1 - (1 -
* numHashFunctions/bitSize)^n)`. Solving that for n, and approximating `ln x` as `x - 1` when x
* is close to 1 (why?), gives the following formula.
*/
double fractionOfBitsSet = (double) bitCount / bitSize;
return DoubleMath.roundToLong(
-Math.log1p(-fractionOfBitsSet) * bitSize / numHashFunctions, RoundingMode.HALF_UP);
}
/** 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:
*
*
* - not be the same instance
*
- have the same number of hash functions
*
- have the same bit size
*
- have the same strategy
*
- have equal funnels
*
*
* @param that The Bloom filter to check for compatibility.
* @since 15.0
*/
public boolean isCompatible(BloomFilter 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 this 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 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);
}
/**
* Returns a {@code Collector} expecting the specified number of insertions, and yielding a {@link
* BloomFilter} with false positive probability 3%.
*
* Note that if the {@code Collector} receives significantly more elements than specified, the
* resulting {@code BloomFilter} will suffer a sharp deterioration of its false positive
* probability.
*
*
The constructed {@code BloomFilter} will be serializable if the provided {@code Funnel}
* is.
*
* 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} will use
* @param expectedInsertions the number of expected insertions to the constructed {@code
* BloomFilter}; must be positive
* @return a {@code Collector} generating a {@code BloomFilter} of the received elements
* @since 23.0
*/
public static Collector> toBloomFilter(
Funnel funnel, long expectedInsertions) {
return toBloomFilter(funnel, expectedInsertions, 0.03);
}
/**
* Returns a {@code Collector} expecting the specified number of insertions, and yielding a {@link
* BloomFilter} with the specified expected false positive probability.
*
* Note that if the {@code Collector} receives significantly more elements than specified, the
* resulting {@code BloomFilter} will suffer a sharp deterioration of its false positive
* probability.
*
*
The constructed {@code BloomFilter} will be serializable if the provided {@code Funnel}
* is.
*
* 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} will use
* @param expectedInsertions the number of expected insertions to the constructed {@code
* BloomFilter}; must be positive
* @param fpp the desired false positive probability (must be positive and less than 1.0)
* @return a {@code Collector} generating a {@code BloomFilter} of the received elements
* @since 23.0
*/
public static Collector> toBloomFilter(
Funnel funnel, long expectedInsertions, double fpp) {
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);
return Collector.of(
() -> BloomFilter.create(funnel, expectedInsertions, fpp),
BloomFilter::put,
(bf1, bf2) -> {
bf1.putAll(bf2);
return bf1;
},
Collector.Characteristics.UNORDERED,
Collector.Characteristics.CONCURRENT);
}
/**
* Creates a {@link 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.
*
* 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} will use
* @param expectedInsertions the number of expected insertions to the constructed {@code
* BloomFilter}; must be positive
* @param fpp the desired false positive probability (must be positive and less than 1.0)
* @return a {@code BloomFilter}
*/
public static BloomFilter create(
Funnel funnel, int expectedInsertions, double fpp) {
return create(funnel, (long) expectedInsertions, fpp);
}
/**
* Creates a {@link 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.
*
* 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} will use
* @param expectedInsertions the number of expected insertions to the constructed {@code
* BloomFilter}; must be positive
* @param fpp the desired false positive probability (must be positive and less than 1.0)
* @return a {@code BloomFilter}
* @since 19.0
*/
public static BloomFilter create(
Funnel funnel, long expectedInsertions, double fpp) {
return create(funnel, expectedInsertions, fpp, BloomFilterStrategies.MURMUR128_MITZ_64);
}
@VisibleForTesting
static BloomFilter create(
Funnel funnel, long expectedInsertions, 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(new LockFreeBitArray(numBits), numHashFunctions, funnel, strategy);
} catch (IllegalArgumentException e) {
throw new IllegalArgumentException("Could not create BloomFilter of " + numBits + " bits", e);
}
}
/**
* Creates a {@link 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.
*
* 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} will use
* @param expectedInsertions the number of expected insertions to the constructed {@code
* BloomFilter}; must be positive
* @return a {@code BloomFilter}
*/
public static BloomFilter create(Funnel funnel, int expectedInsertions) {
return create(funnel, (long) expectedInsertions);
}
/**
* Creates a {@link 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.
*
* 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} will use
* @param expectedInsertions the number of expected insertions to the constructed {@code
* BloomFilter}; must be positive
* @return a {@code BloomFilter}
* @since 19.0
*/
public static BloomFilter create(Funnel funnel, long expectedInsertions) {
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(this);
}
private static class SerialForm implements Serializable {
final long[] data;
final int numHashFunctions;
final Funnel funnel;
final Strategy strategy;
SerialForm(BloomFilter bf) {
this.data = LockFreeBitArray.toPlainArray(bf.bits.data);
this.numHashFunctions = bf.numHashFunctions;
this.funnel = bf.funnel;
this.strategy = bf.strategy;
}
Object readResolve() {
return new BloomFilter(new LockFreeBitArray(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.
*
* 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 (int i = 0; i < bits.data.length(); i++) {
dout.writeLong(bits.data.get(i));
}
}
/**
* Reads a byte stream, which was written by {@linkplain #writeTo(OutputStream)}, into a {@code
* BloomFilter}.
*
*
The {@code Funnel} to be used is not encoded in the stream, so it must be provided here.
* Warning: the funnel provided must 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 BloomFilter readFrom(InputStream in, Funnel 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(new LockFreeBitArray(data), numHashFunctions, funnel, strategy);
} catch (RuntimeException e) {
String message =
"Unable to deserialize BloomFilter from InputStream."
+ " strategyOrdinal: "
+ strategyOrdinal
+ " numHashFunctions: "
+ numHashFunctions
+ " dataLength: "
+ dataLength;
throw new IOException(message, e);
}
}
}