/*
*
* 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.hbase.Cell;
import org.apache.hadoop.hbase.classification.InterfaceAudience;
import org.apache.hadoop.hbase.nio.ByteBuff;
import org.apache.hadoop.hbase.regionserver.BloomType;
/**
*
* 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>.
*
* 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).
*
* @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>
*
* @see BloomFilterWriter for the ability to add elements to a Bloom filter
*/
@InterfaceAudience.Private
public interface BloomFilter extends BloomFilterBase {
/**
* Check if the specified key is contained in the bloom filter.
* @param keyCell the key to check for the existence of
* @param bloom bloom filter data to search. This can be null if auto-loading
* is supported.
* @param type The type of Bloom ROW/ ROW_COL
* @return true if matched by bloom, false if not
*/
boolean contains(Cell keyCell, ByteBuff bloom, BloomType type);
/**
* Check if the specified key is contained in the bloom filter.
* @param buf data to check for existence of
* @param offset offset into the data
* @param length length of the data
* @param bloom bloom filter data to search. This can be null if auto-loading
* is supported.
* @return true if matched by bloom, false if not
*/
boolean contains(byte[] buf, int offset, int length, ByteBuff bloom);
/**
* @return true if this Bloom filter can automatically load its data
* and thus allows a null byte buffer to be passed to contains()
*/
boolean supportsAutoLoading();
}