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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You 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 org.apache.spark.util.sketch;

import java.io.IOException;
import java.io.InputStream;
import java.io.OutputStream;

/**
* A Bloom filter is a space-efficient probabilistic data structure that offers an approximate
* containment test with one-sided error: if it claims that an item is contained in it, this
* might be in error, but if it claims that an item is <i>not</i> contained in it, then this is
* definitely true. Currently supported data types include:
* <ul>
* <li>{@link Byte}</li>
* <li>{@link Short}</li>
* <li>{@link Integer}</li>
* <li>{@link Long}</li>
* <li>{@link String}</li>
* </ul>
* 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}.
*
* The implementation is largely based on the {@code BloomFilter} class from Guava.
*/
public abstract class BloomFilter {

public enum Version {
/**
* {@code BloomFilter} binary format version 1. All values written in big-endian order:
* <ul>
* <li>Version number, always 1 (32 bit)</li>
* <li>Number of hash functions (32 bit)</li>
* <li>Total number of words of the underlying bit array (32 bit)</li>
* <li>The words/longs (numWords * 64 bit)</li>
* </ul>
*/
V1(1);

private final int versionNumber;

Version(int versionNumber) {
this.versionNumber = versionNumber;
}

int getVersionNumber() {
return versionNumber;
}
}

/**
* Returns the probability that {@linkplain #mightContain(Object)} 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(long, double)}, or smaller. If it is significantly higher, it is usually
* the case that too many items (more than expected) have been put in the {@code BloomFilter},
* degenerating it.
*/
public abstract double expectedFpp();

/**
* Returns the number of bits in the underlying bit array.
*/
public abstract long bitSize();

/**
* Puts an item into this {@code BloomFilter}. Ensures that subsequent invocations of
* {@linkplain #mightContain(Object)} with the same item 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.
*/
public abstract boolean put(Object item);

/**
* A specialized variant of {@link #put(Object)} that only supports {@code String} items.
*/
public abstract boolean putString(String item);

/**
* A specialized variant of {@link #put(Object)} that only supports {@code long} items.
*/
public abstract boolean putLong(long item);

/**
* A specialized variant of {@link #put(Object)} that only supports byte array items.
*/
public abstract boolean putBinary(byte[] item);

/**
* Determines whether a given bloom filter is compatible with this bloom filter. For two
* bloom filters to be compatible, they must have the same bit size.
*
* @param other The bloom filter to check for compatibility.
*/
public abstract boolean isCompatible(BloomFilter other);

/**
* 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 other The bloom filter to combine this bloom filter with. It is not mutated.
* @throws IncompatibleMergeException if {@code isCompatible(other) == false}
*/
public abstract BloomFilter mergeInPlace(BloomFilter other) throws IncompatibleMergeException;

/**
* Combines this bloom filter with another bloom filter by performing a bitwise AND 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 other The bloom filter to combine this bloom filter with. It is not mutated.
* @throws IncompatibleMergeException if {@code isCompatible(other) == false}
*/
public abstract BloomFilter intersectInPlace(BloomFilter other) throws IncompatibleMergeException;

/**
* 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 abstract boolean mightContain(Object item);

/**
* A specialized variant of {@link #mightContain(Object)} that only tests {@code String} items.
*/
public abstract boolean mightContainString(String item);

/**
* A specialized variant of {@link #mightContain(Object)} that only tests {@code long} items.
*/
public abstract boolean mightContainLong(long item);

/**
* A specialized variant of {@link #mightContain(Object)} that only tests byte array items.
*/
public abstract boolean mightContainBinary(byte[] item);

/**
* Writes out this {@link BloomFilter} to an output stream in binary format. It is the caller's
* responsibility to close the stream.
*/
public abstract void writeTo(OutputStream out) throws IOException;

/**
* @return the number of set bits in this {@link BloomFilter}.
*/
public long cardinality() {
throw new UnsupportedOperationException("Not implemented");
}

/**
* Reads in a {@link BloomFilter} from an input stream. It is the caller's responsibility to close
* the stream.
*/
public static BloomFilter readFrom(InputStream in) throws IOException {
return BloomFilterImpl.readFrom(in);
}

/**
* Computes the optimal k (number of hashes per item 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)
*/
private 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)
*/
private static long optimalNumOfBits(long n, double p) {
return (long) (-n * Math.log(p) / (Math.log(2) * Math.log(2)));
}

static final double DEFAULT_FPP = 0.03;

/**
* 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.
*/
public static BloomFilter create(long expectedNumItems) {
return create(expectedNumItems, DEFAULT_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.
*/
public static BloomFilter create(long expectedNumItems, double fpp) {
if (fpp <= 0D || fpp >= 1D) {
throw new IllegalArgumentException(
"False positive probability must be within range (0.0, 1.0)"
);
}

return create(expectedNumItems, optimalNumOfBits(expectedNumItems, fpp));
}

/**
* Creates a {@link BloomFilter} with given {@code expectedNumItems} and {@code numBits}, it will
* pick an optimal {@code numHashFunctions} which can minimize {@code fpp} for the bloom filter.
*/
public static BloomFilter create(long expectedNumItems, long numBits) {
if (expectedNumItems <= 0) {
throw new IllegalArgumentException("Expected insertions must be positive");
}

if (numBits <= 0) {
throw new IllegalArgumentException("Number of bits must be positive");
}

return new BloomFilterImpl(optimalNumOfHashFunctions(expectedNumItems, numBits), numBits);
}
}
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