/*
* Copyright 2010 The Apache Software Foundation
*
* 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.hadoop.hbase.util;
import org.apache.hadoop.io.Writable;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.nio.ByteBuffer;
/**
* Implements a <i>Bloom filter</i>, as defined by Bloom in 1970.
* <p>
* The Bloom filter is a data structure that was introduced in 1970 and that has been adopted by
* the networking research community in the past decade thanks to the bandwidth efficiencies that it
* offers for the transmission of set membership information between networked hosts. A sender encodes
* the information into a bit vector, the Bloom filter, that is more compact than a conventional
* representation. Computation and space costs for construction are linear in the number of elements.
* The receiver uses the filter to test whether various elements are members of the set. Though the
* filter will occasionally return a false positive, it will never return a false negative. When creating
* the filter, the sender can choose its desired point in a trade-off between the false positive rate and the size.
*
* <p>
* Originally inspired by
* <a href="http://www.one-lab.org">European Commission One-Lab Project 034819</a>.
*
* @see BloomFilter The general behavior of a filter
*
* @see <a href="http://portal.acm.org/citation.cfm?id=362692&dl=ACM&coll=portal">Space/Time Trade-Offs in Hash Coding with Allowable Errors</a>
*/
public class ByteBloomFilter implements BloomFilter {
/** Current file format version */
public static final int VERSION = 1;
/** Bytes (B) in the array */
protected long byteSize;
/** Number of hash functions */
protected final int hashCount;
/** Hash type */
protected final int hashType;
/** Hash Function */
protected final Hash hash;
/** Keys currently in the bloom */
protected int keyCount;
/** Max Keys expected for the bloom */
protected int maxKeys;
/** Bloom bits */
protected ByteBuffer bloom;
/** Bit-value lookup array to prevent doing the same work over and over */
private static final byte [] bitvals = {
(byte) 0x01,
(byte) 0x02,
(byte) 0x04,
(byte) 0x08,
(byte) 0x10,
(byte) 0x20,
(byte) 0x40,
(byte) 0x80
};
/**
* Loads bloom filter meta data from file input.
* @param meta stored bloom meta data
* @throws IllegalArgumentException meta data is invalid
*/
public ByteBloomFilter(ByteBuffer meta)
throws IllegalArgumentException {
int version = meta.getInt();
if (version != VERSION) throw new IllegalArgumentException("Bad version");
this.byteSize = meta.getInt();
this.hashCount = meta.getInt();
this.hashType = meta.getInt();
this.keyCount = meta.getInt();
this.maxKeys = this.keyCount;
this.hash = Hash.getInstance(this.hashType);
sanityCheck();
}
/**
* Determines & initializes bloom filter meta data from user config. Call
* {@link #allocBloom()} to allocate bloom filter data.
* @param maxKeys Maximum expected number of keys that will be stored in this bloom
* @param errorRate Desired false positive error rate. Lower rate = more storage required
* @param hashType Type of hash function to use
* @param foldFactor When finished adding entries, you may be able to 'fold'
* this bloom to save space. Tradeoff potentially excess bytes in bloom for
* ability to fold if keyCount is exponentially greater than maxKeys.
* @throws IllegalArgumentException
*/
public ByteBloomFilter(int maxKeys, float errorRate, int hashType, int foldFactor)
throws IllegalArgumentException {
/*
* Bloom filters are very sensitive to the number of elements inserted
* into them. For HBase, the number of entries depends on the size of the
* data stored in the column. Currently the default region size is 256MB,
* so entry count ~= 256MB / (average value size for column). Despite
* this rule of thumb, there is no efficient way to calculate the entry
* count after compactions. Therefore, it is often easier to use a
* dynamic bloom filter that will add extra space instead of allowing the
* error rate to grow.
*
* ( http://www.eecs.harvard.edu/~michaelm/NEWWORK/postscripts/BloomFilterSurvey.pdf )
*
* m denotes the number of bits in the Bloom filter (bitSize)
* n denotes the number of elements inserted into the Bloom filter (maxKeys)
* k represents the number of hash functions used (nbHash)
* e represents the desired false positive rate for the bloom (err)
*
* If we fix the error rate (e) and know the number of entries, then
* the optimal bloom size m = -(n * ln(err) / (ln(2)^2)
* ~= n * ln(err) / ln(0.6185)
*
* The probability of false positives is minimized when k = m/n ln(2).
*/
long bitSize = (long)Math.ceil(maxKeys * (Math.log(errorRate) / Math.log(0.6185)));
int functionCount = (int)Math.ceil(Math.log(2) * (bitSize / maxKeys));
// increase byteSize so folding is possible
long byteSize = (bitSize + 7) / 8;
int mask = (1 << foldFactor) - 1;
if ( (mask & byteSize) != 0) {
byteSize >>= foldFactor;
++byteSize;
byteSize <<= foldFactor;
}
this.byteSize = byteSize;
this.hashCount = functionCount;
this.hashType = hashType;
this.keyCount = 0;
this.maxKeys = maxKeys;
this.hash = Hash.getInstance(hashType);
sanityCheck();
}
@Override
public void allocBloom() {
if (this.bloom != null) {
throw new IllegalArgumentException("can only create bloom once.");
}
this.bloom = ByteBuffer.allocate((int)this.byteSize);
assert this.bloom.hasArray();
}
void sanityCheck() throws IllegalArgumentException {
if(0 >= this.byteSize || this.byteSize > Integer.MAX_VALUE) {
throw new IllegalArgumentException("Invalid byteSize: " + this.byteSize);
}
if(this.hashCount <= 0) {
throw new IllegalArgumentException("Hash function count must be > 0");
}
if (this.hash == null) {
throw new IllegalArgumentException("hashType must be known");
}
if (this.keyCount < 0) {
throw new IllegalArgumentException("must have positive keyCount");
}
}
void bloomCheck(ByteBuffer bloom) throws IllegalArgumentException {
if (this.byteSize != bloom.limit()) {
throw new IllegalArgumentException(
"Configured bloom length should match actual length");
}
}
@Override
public void add(byte [] buf) {
add(buf, 0, buf.length);
}
@Override
public void add(byte [] buf, int offset, int len) {
/*
* For faster hashing, use combinatorial generation
* http://www.eecs.harvard.edu/~kirsch/pubs/bbbf/esa06.pdf
*/
int hash1 = this.hash.hash(buf, offset, len, 0);
int hash2 = this.hash.hash(buf, offset, len, hash1);
for (int i = 0; i < this.hashCount; i++) {
long hashLoc = Math.abs((hash1 + i * hash2) % (this.byteSize * 8));
set(hashLoc);
}
++this.keyCount;
}
/**
* Should only be used in tests when writing a bloom filter.
*/
boolean contains(byte [] buf) {
return contains(buf, 0, buf.length, this.bloom);
}
/**
* Should only be used in tests when writing a bloom filter.
*/
boolean contains(byte [] buf, int offset, int length) {
return contains(buf, offset, length, this.bloom);
}
@Override
public boolean contains(byte [] buf, ByteBuffer theBloom) {
return contains(buf, 0, buf.length, theBloom);
}
@Override
public boolean contains(byte [] buf, int offset, int length,
ByteBuffer theBloom) {
if(theBloom.limit() != this.byteSize) {
throw new IllegalArgumentException("Bloom does not match expected size");
}
int hash1 = this.hash.hash(buf, offset, length, 0);
int hash2 = this.hash.hash(buf, offset, length, hash1);
for (int i = 0; i < this.hashCount; i++) {
long hashLoc = Math.abs((hash1 + i * hash2) % (this.byteSize * 8));
if (!get(hashLoc, theBloom) ) {
return false;
}
}
return true;
}
//---------------------------------------------------------------------------
/** Private helpers */
/**
* Set the bit at the specified index to 1.
*
* @param pos index of bit
*/
void set(long pos) {
int bytePos = (int)(pos / 8);
int bitPos = (int)(pos % 8);
byte curByte = bloom.get(bytePos);
curByte |= bitvals[bitPos];
bloom.put(bytePos, curByte);
}
/**
* Check if bit at specified index is 1.
*
* @param pos index of bit
* @return true if bit at specified index is 1, false if 0.
*/
static boolean get(long pos, ByteBuffer theBloom) {
int bytePos = (int)(pos / 8);
int bitPos = (int)(pos % 8);
byte curByte = theBloom.get(bytePos);
curByte &= bitvals[bitPos];
return (curByte != 0);
}
@Override
public int getKeyCount() {
return this.keyCount;
}
@Override
public int getMaxKeys() {
return this.maxKeys;
}
@Override
public int getByteSize() {
return (int)this.byteSize;
}
@Override
public void compactBloom() {
// see if the actual size is exponentially smaller than expected.
if (this.keyCount > 0 && this.bloom.hasArray()) {
int pieces = 1;
int newByteSize = (int)this.byteSize;
int newMaxKeys = this.maxKeys;
// while exponentially smaller & folding is lossless
while ( (newByteSize & 1) == 0 && newMaxKeys > (this.keyCount<<1) ) {
pieces <<= 1;
newByteSize >>= 1;
newMaxKeys >>= 1;
}
// if we should fold these into pieces
if (pieces > 1) {
byte[] array = this.bloom.array();
int start = this.bloom.arrayOffset();
int end = start + newByteSize;
int off = end;
for(int p = 1; p < pieces; ++p) {
for(int pos = start; pos < end; ++pos) {
array[pos] |= array[off++];
}
}
// folding done, only use a subset of this array
this.bloom.rewind();
this.bloom.limit(newByteSize);
this.bloom = this.bloom.slice();
this.byteSize = newByteSize;
this.maxKeys = newMaxKeys;
}
}
}
//---------------------------------------------------------------------------
/**
* Writes just the bloom filter to the output array
* @param out OutputStream to place bloom
* @throws IOException Error writing bloom array
*/
public void writeBloom(final DataOutput out) throws IOException {
if (!this.bloom.hasArray()) {
throw new IOException("Only writes ByteBuffer with underlying array.");
}
out.write(bloom.array(), bloom.arrayOffset(), bloom.limit());
}
@Override
public Writable getMetaWriter() {
return new MetaWriter();
}
@Override
public Writable getDataWriter() {
return new DataWriter();
}
private class MetaWriter implements Writable {
protected MetaWriter() {}
@Override
public void readFields(DataInput arg0) throws IOException {
throw new IOException("Cant read with this class.");
}
@Override
public void write(DataOutput out) throws IOException {
out.writeInt(VERSION);
out.writeInt((int)byteSize);
out.writeInt(hashCount);
out.writeInt(hashType);
out.writeInt(keyCount);
}
}
private class DataWriter implements Writable {
protected DataWriter() {}
@Override
public void readFields(DataInput arg0) throws IOException {
throw new IOException("Cant read with this class.");
}
@Override
public void write(DataOutput out) throws IOException {
writeBloom(out);
}
}
}