/**
* 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.cassandra.utils;
import java.nio.ByteBuffer;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.apache.cassandra.io.ICompactSerializer;
import org.apache.cassandra.utils.obs.OpenBitSet;
public class BloomFilter extends Filter
{
private static final Logger logger = LoggerFactory.getLogger(BloomFilter.class);
private static final int EXCESS = 20;
static ICompactSerializer<BloomFilter> serializer_ = new BloomFilterSerializer();
public OpenBitSet bitset;
BloomFilter(int hashes, OpenBitSet bs)
{
hashCount = hashes;
bitset = bs;
}
public static ICompactSerializer<BloomFilter> serializer()
{
return serializer_;
}
long emptyBuckets()
{
long n = 0;
for (long i = 0; i < buckets(); i++)
{
if (!bitset.get(i))
{
n++;
}
}
return n;
}
private static OpenBitSet bucketsFor(long numElements, int bucketsPer)
{
long numBits = numElements * bucketsPer + EXCESS; //TODO overflow?
return new OpenBitSet((long)Math.min(Long.MAX_VALUE, numBits));
}
/**
* @return A BloomFilter with the lowest practical false positive probability
* for the given number of elements.
*/
public static BloomFilter getFilter(long numElements, int targetBucketsPerElem)
{
int maxBucketsPerElement = Math.max(1, BloomCalculations.maxBucketsPerElement(numElements));
int bucketsPerElement = Math.min(targetBucketsPerElem, maxBucketsPerElement);
if (bucketsPerElement < targetBucketsPerElem)
{
logger.warn(String.format("Cannot provide an optimal BloomFilter for %d elements (%d/%d buckets per element).",
numElements, bucketsPerElement, targetBucketsPerElem));
}
BloomCalculations.BloomSpecification spec = BloomCalculations.computeBloomSpec(bucketsPerElement);
return new BloomFilter(spec.K, bucketsFor(numElements, spec.bucketsPerElement));
}
/**
* @return The smallest BloomFilter that can provide the given false positive
* probability rate for the given number of elements.
*
* Asserts that the given probability can be satisfied using this filter.
*/
public static BloomFilter getFilter(long numElements, double maxFalsePosProbability)
{
assert maxFalsePosProbability <= 1.0 : "Invalid probability";
int bucketsPerElement = BloomCalculations.maxBucketsPerElement(numElements);
BloomCalculations.BloomSpecification spec = BloomCalculations.computeBloomSpec(bucketsPerElement, maxFalsePosProbability);
return new BloomFilter(spec.K, bucketsFor(numElements, spec.bucketsPerElement));
}
private long buckets()
{
return bitset.size();
}
private long[] getHashBuckets(ByteBuffer key)
{
return BloomFilter.getHashBuckets(key, hashCount, buckets());
}
// Murmur is faster than an SHA-based approach and provides as-good collision
// resistance. The combinatorial generation approach described in
// http://www.eecs.harvard.edu/~kirsch/pubs/bbbf/esa06.pdf
// does prove to work in actual tests, and is obviously faster
// than performing further iterations of murmur.
static long[] getHashBuckets(ByteBuffer b, int hashCount, long max)
{
long[] result = new long[hashCount];
long hash1 = MurmurHash.hash64(b, b.position(), b.remaining(), 0L);
long hash2 = MurmurHash.hash64(b, b.position(), b.remaining(), hash1);
for (int i = 0; i < hashCount; ++i)
{
result[i] = Math.abs((hash1 + (long)i * hash2) % max);
}
return result;
}
public void add(ByteBuffer key)
{
for (long bucketIndex : getHashBuckets(key))
{
bitset.set(bucketIndex);
}
}
public boolean isPresent(ByteBuffer key)
{
for (long bucketIndex : getHashBuckets(key))
{
if (!bitset.get(bucketIndex))
{
return false;
}
}
return true;
}
public void clear()
{
bitset.clear(0, bitset.size());
}
}