/*
 * 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.nio.ByteBuff;
import org.apache.hadoop.hbase.regionserver.BloomType;
import org.apache.yetus.audience.InterfaceAudience;

/**
 * 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) ~= 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();
}
