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RIOT/sys/include/bloom.h 7.32 KB
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  /*
   * Copyright (C) 2014 Freie Universitรคt Berlin
   *
   * This file is subject to the terms and conditions of the GNU Lesser
   * General Public License v2.1. See the file LICENSE in the top level
   * directory for more details.
   */
  
  /*
   * bloom.c
   *
   * Bloom filters
   *
   * HISTORY
   *                                                   {x,  y,  z}
   * A Bloom filter is a probibalistic                  :   :   :
   * data structure with several interesting           /|\ /|\ /|\
   * properties, such as low memory usage,            / | X | X | \
   * asymmetric query confidence, and a very         /  |/ \|/ \|  \
   * speedy O(k) membership test.                   /   |   |   \   \
   *                                               /   /|  /|\  |\   \
   * Because a Bloom filter can                   .   . . . . . . .   .
   * accept any input that can be       00000000001000101010101010100010000000000
   * hashed effectively (such as                       "    "    "
   * strings), that membership test                     \   |   /
   * tends to draw a crowd. TNSTAAFL, but                \  |  /
   * as caveats go, the Bloom filters' are                \ | /
   * more interesting than incapacitating.                 \|/
   *                                                        :
   * Most notably, it can tell you with certainty          {w}
   * that an item 'i' is *not* a member of set 's',
   * but it can only tell you with some finite
   * probability whether an item 'i' *is* a member
   * of set 's'.
   *
   * Still, along with the intriguing possibility of using bitwise AND and OR
   * to compute the logical union and intersection of two filters, the cheap
   * cost of adding elements to the filter set, and the low memory requirements,
   * the Bloom filter is a good choice for many applications.
   *
   * NOTES
   *
   * Let's look more closely at the probability values.
   *
   * Assume that a hash function selects each array position with equal
   * probability. If m is the number of bits in the array, and k is the number
   * of hash functions, then the probability that a certain bit is not set
   * to 1 by a certain hash function during the insertion of an element is
   *
   *      1-(1/m).
   *
   * The probability that it is not set to 1 by any of the hash functions is
   *
   *      (1-(1/m))^k.
   *
   * If we have inserted n elements, the probability that a certain bit is
   * set 0 is
   *
   *      (1-(1/m))^kn,
   *
   * Meaning that the probability said bit is set to 1 is therefore
   *
   *      1-([1-(1/m)]^kn).
   *
   * Now test membership of an element that is not in the set. Each of the k
   * array positions computed by the hash functions is 1 with a probability
   * as above. The probability of all of them being 1, which would cause the
   * algorithm to erroneously claim that the element is in the set, is often
   * given as
   *
   *      (1-[1-(1/m)]^kn)^k ~~ (1 - e^(-kn/m))^k.
   *
   * This is not strictly correct as it assumes independence for the
   * probabilities of each bit being set. However, assuming it is a close
   * approximation we have that the probability of false positives descreases
   * as m (the number of bits in the array) increases, and increases as n
   * (the number of inserted elements) increases. For a given m and n, the
   * value of k (the number of hash functions) that minimizes the probability
   * is
   *
   *      (m/n)ln(2) ~~ 0.7(m/n),
   *
   * which gives the false positive probability of
   *
   *      2^-k ~~ 0.6185^(m/n).
   *
   * The required number of bits m, given n and a desired false positive
   * probability p (and assuming the optimal value of k is used) can be
   * computed by substituting the optimal value of k in the probability
   * expression above:
   *
   *      p = (1 - e^(-(((m/n)ln(2))*(n/m))))^((m/n)ln(2)),
   *
   * which simplifies to
   *
   *      ln(p) = -(m/n) * (ln2)^2.
   *
   * This results in the equation
   *
   *      m = -((n*ln(p)) / ((ln(2))^2))
   *
   * The classic filter uses
   *
   *       1.44*log2(1/eta)
   *
   * bits of space per inserted key, where eta is the false positive rate of
   * the Bloom filter.
   *
   */
  
  /**
   * @defgroup    sys_bloom Bloom filter
   * @ingroup     sys
   * @brief       Bloom filter library
   * @{
   *
   * @file
   * @brief       Bloom filter API
   *
   * @author Christian Mehlis <mehlis@inf.fu-berlin.de>
   */
  
  #ifndef BLOOM_H
  #define BLOOM_H
  
  #include <stdlib.h>
  #include <stdbool.h>
  #include <stdint.h>
  
  #ifdef __cplusplus
  extern "C" {
  #endif
  
  /**
   * @brief hash function to use in thee filter
   */
  typedef uint32_t (*hashfp_t)(const uint8_t *, int len);
  
  /**
   * @brief bloom_t bloom filter object
   */
  typedef struct {
      /** number of bits in the bloom array */
      size_t m;
      /** number of hash functions */
      size_t k;
      /** the bloom array */
      uint8_t *a;
      /** the hash functions */
      hashfp_t *hash;
  } bloom_t;
  
  /**
   * @brief Initialize a Bloom Filter.
   *
   * @note For best results, make 'size' a power of 2.
   *
   * @param bloom             bloom_t to initialize
   * @param size              size of the bloom filter in bits
   * @param bitfield          underlying bitfield of the bloom filter
   * @param hashes            array of hashes
   * @param hashes_numof      number of elements in hashes
   *
   * @pre     @p bitfield MUST be large enough to hold @p size bits.
   */
  void bloom_init(bloom_t *bloom, size_t size, uint8_t *bitfield, hashfp_t *hashes, int hashes_numof);
  
  /**
   * @brief Delete a Bloom filter.
   *
   * @param bloom The condemned
   * @return nothing
   *
   */
  void bloom_del(bloom_t *bloom);
  
  /**
   * @brief Add a string to a Bloom filter.
   *
   * CAVEAT
   * Once a string has been added to the filter, it cannot be "removed"!
   *
   * @param bloom  Bloom filter
   * @param buf    string to add
   * @param len    the length of the string @p buf
   * @return       nothing
   *
   */
  void bloom_add(bloom_t *bloom, const uint8_t *buf, size_t len);
  
  /**
   * @brief Determine if a string is in the Bloom filter.
   *
   * The string 's' is hashed once for each of the 'k' hash functions, as
   * though we were planning to add it to the filter. Instead of adding it
   * however, we examine the bit that we *would* have set, and consider its
   * value.
   *
   * If the bit is 1 (set), the string we are hashing may be in the filter,
   * since it would have set this bit when it was originally hashed. However,
   * it may also be that another string just happened to produce a hash value
   * that would also set this bit. That would be a false positive. This is why
   * we have k > 1, so we can minimize the likelihood of false positives
   * occuring.
   *
   * If every bit corresponding to every one of the k hashes of our query
   * string is set, we can say with some probability of being correct that
   * the string we are holding is indeed "in" the filter. However, we can
   * never be sure.
   *
   * If, however, as we hash our string and peek at the resulting bit in the
   * filter, we find the bit is 0 (not set)... well now, that's different.
   * In this case, we can say with absolute certainty that the string we are
   * holding is *not* in the filter, because if it were, this bit would have
   * to be set.
   *
   * In this way, the Bloom filter can answer NO with absolute surety, but
   * can only speak a qualified YES.
   *
   * @param bloom  Bloom filter
   * @param buf    string to check
   * @param len    the length of the string @p buf
   *
   *
   * @return       false if string does not exist in the filter
   * @return       true if string is may be in the filter
   *
   */
  bool bloom_check(bloom_t *bloom, const uint8_t *buf, size_t len);
  
  #ifdef __cplusplus
  }
  #endif
  
  /** @} */
  #endif /* BLOOM_H */