+++ /dev/null
-// -*- mode:C++; tab-width:8; c-basic-offset:2; indent-tabs-mode:t -*-
-// vim: ts=8 sw=2 smarttab
-
-/*
- *******************************************************************
- * *
- * Open Bloom Filter *
- * *
- * Author: Arash Partow - 2000 *
- * URL: http://www.partow.net/programming/hashfunctions/index.html *
- * *
- * Copyright notice: *
- * Free use of the Open Bloom Filter Library is permitted under *
- * the guidelines and in accordance with the most current version *
- * of the Boost Software License, Version 1.0 *
- * http://www.opensource.org/licenses/bsl1.0.html *
- * *
- *******************************************************************
-*/
-
-
-#ifndef COMMON_BLOOM_FILTER_HPP
-#define COMMON_BLOOM_FILTER_HPP
-
-#include <cmath>
-
-#include "include/mempool.h"
-#include "include/encoding.h"
-
-static const std::size_t bits_per_char = 0x08; // 8 bits in 1 char(unsigned)
-static const unsigned char bit_mask[bits_per_char] = {
- 0x01, //00000001
- 0x02, //00000010
- 0x04, //00000100
- 0x08, //00001000
- 0x10, //00010000
- 0x20, //00100000
- 0x40, //01000000
- 0x80 //10000000
-};
-
-MEMPOOL_DECLARE_FACTORY(unsigned char, byte, bloom_filter);
-
-class bloom_filter
-{
-protected:
-
- typedef unsigned int bloom_type;
- typedef unsigned char cell_type;
-
- unsigned char* bit_table_; ///< pointer to bit map
- std::vector<bloom_type> salt_; ///< vector of salts
- std::size_t salt_count_; ///< number of salts
- std::size_t table_size_; ///< bit table size in bytes
- std::size_t insert_count_; ///< insertion count
- std::size_t target_element_count_; ///< target number of unique insertions
- std::size_t random_seed_; ///< random seed
-
-public:
-
- bloom_filter()
- : bit_table_(0),
- salt_count_(0),
- table_size_(0),
- insert_count_(0),
- target_element_count_(0),
- random_seed_(0)
- {}
-
- bloom_filter(const std::size_t& predicted_inserted_element_count,
- const double& false_positive_probability,
- const std::size_t& random_seed)
- : bit_table_(0),
- insert_count_(0),
- target_element_count_(predicted_inserted_element_count),
- random_seed_((random_seed) ? random_seed : 0xA5A5A5A5)
- {
- assert(false_positive_probability > 0.0);
- find_optimal_parameters(predicted_inserted_element_count, false_positive_probability,
- &salt_count_, &table_size_);
- init();
- }
-
- bloom_filter(const std::size_t& salt_count,
- std::size_t table_size,
- const std::size_t& random_seed,
- std::size_t target_element_count)
- : bit_table_(0),
- salt_count_(salt_count),
- table_size_(table_size),
- insert_count_(0),
- target_element_count_(target_element_count),
- random_seed_((random_seed) ? random_seed : 0xA5A5A5A5)
- {
- init();
- }
-
- void init() {
- generate_unique_salt();
- if (table_size_) {
- bit_table_ = mempool::bloom_filter::alloc_byte.allocate(table_size_);
- std::fill_n(bit_table_, table_size_, 0x00);
- } else {
- bit_table_ = NULL;
- }
- }
-
- bloom_filter(const bloom_filter& filter)
- : bit_table_(0)
- {
- this->operator=(filter);
- }
-
- bloom_filter& operator = (const bloom_filter& filter)
- {
- if (this != &filter) {
- if (bit_table_) {
- mempool::bloom_filter::alloc_byte.deallocate(bit_table_, table_size_);
- }
- salt_count_ = filter.salt_count_;
- table_size_ = filter.table_size_;
- insert_count_ = filter.insert_count_;
- target_element_count_ = filter.target_element_count_;
- random_seed_ = filter.random_seed_;
- bit_table_ = mempool::bloom_filter::alloc_byte.allocate(table_size_);
- std::copy(filter.bit_table_, filter.bit_table_ + table_size_, bit_table_);
- salt_ = filter.salt_;
- }
- return *this;
- }
-
- virtual ~bloom_filter()
- {
- mempool::bloom_filter::alloc_byte.deallocate(bit_table_, table_size_);
- }
-
- inline bool operator!() const
- {
- return (0 == table_size_);
- }
-
- inline void clear()
- {
- if (bit_table_)
- std::fill_n(bit_table_, table_size_, 0x00);
- insert_count_ = 0;
- }
-
- /**
- * insert a u32 into the set
- *
- * NOTE: the internal hash is weak enough that consecutive inputs do
- * not achieve the desired fpp. Well-mixed values should be used
- * here (e.g., put rjhash(x) into the filter instead of just x).
- *
- * @param val integer value to insert
- */
- inline void insert(uint32_t val) {
- assert(bit_table_);
- std::size_t bit_index = 0;
- std::size_t bit = 0;
- for (std::size_t i = 0; i < salt_.size(); ++i)
- {
- compute_indices(hash_ap(val,salt_[i]),bit_index,bit);
- bit_table_[bit_index >> 3] |= bit_mask[bit];
- }
- ++insert_count_;
- }
-
- inline void insert(const unsigned char* key_begin, const std::size_t& length)
- {
- assert(bit_table_);
- std::size_t bit_index = 0;
- std::size_t bit = 0;
- for (std::size_t i = 0; i < salt_.size(); ++i)
- {
- compute_indices(hash_ap(key_begin,length,salt_[i]),bit_index,bit);
- bit_table_[bit_index >> 3] |= bit_mask[bit];
- }
- ++insert_count_;
- }
-
- template<typename T>
- inline void insert(const T& t)
- {
- // Note: T must be a C++ POD type.
- insert(reinterpret_cast<const unsigned char*>(&t),sizeof(T));
- }
-
- inline void insert(const std::string& key)
- {
- insert(reinterpret_cast<const unsigned char*>(key.c_str()),key.size());
- }
-
- inline void insert(const char* data, const std::size_t& length)
- {
- insert(reinterpret_cast<const unsigned char*>(data),length);
- }
-
- template<typename InputIterator>
- inline void insert(const InputIterator begin, const InputIterator end)
- {
- InputIterator itr = begin;
- while (end != itr)
- {
- insert(*(itr++));
- }
- }
-
- /**
- * check if a u32 is contained by set
- *
- * NOTE: the internal hash is weak enough that consecutive inputs do
- * not achieve the desired fpp. Well-mixed values should be used
- * here (e.g., put rjhash(x) into the filter instead of just x).
- *
- * @param val integer value to query
- * @returns true if value is (probably) in the set, false if it definitely is not
- */
- inline virtual bool contains(uint32_t val) const
- {
- if (!bit_table_)
- return false;
- std::size_t bit_index = 0;
- std::size_t bit = 0;
- for (std::size_t i = 0; i < salt_.size(); ++i)
- {
- compute_indices(hash_ap(val,salt_[i]),bit_index,bit);
- if ((bit_table_[bit_index >> 3] & bit_mask[bit]) != bit_mask[bit])
- {
- return false;
- }
- }
- return true;
- }
-
- inline virtual bool contains(const unsigned char* key_begin, const std::size_t length) const
- {
- if (!bit_table_)
- return false;
- std::size_t bit_index = 0;
- std::size_t bit = 0;
- for (std::size_t i = 0; i < salt_.size(); ++i)
- {
- compute_indices(hash_ap(key_begin,length,salt_[i]),bit_index,bit);
- if ((bit_table_[bit_index >> 3] & bit_mask[bit]) != bit_mask[bit])
- {
- return false;
- }
- }
- return true;
- }
-
- template<typename T>
- inline bool contains(const T& t) const
- {
- return contains(reinterpret_cast<const unsigned char*>(&t),static_cast<std::size_t>(sizeof(T)));
- }
-
- inline bool contains(const std::string& key) const
- {
- return contains(reinterpret_cast<const unsigned char*>(key.c_str()),key.size());
- }
-
- inline bool contains(const char* data, const std::size_t& length) const
- {
- return contains(reinterpret_cast<const unsigned char*>(data),length);
- }
-
- template<typename InputIterator>
- inline InputIterator contains_all(const InputIterator begin, const InputIterator end) const
- {
- InputIterator itr = begin;
- while (end != itr)
- {
- if (!contains(*itr))
- {
- return itr;
- }
- ++itr;
- }
- return end;
- }
-
- template<typename InputIterator>
- inline InputIterator contains_none(const InputIterator begin, const InputIterator end) const
- {
- InputIterator itr = begin;
- while (end != itr)
- {
- if (contains(*itr))
- {
- return itr;
- }
- ++itr;
- }
- return end;
- }
-
- inline virtual std::size_t size() const
- {
- return table_size_ * bits_per_char;
- }
-
- inline std::size_t element_count() const
- {
- return insert_count_;
- }
-
- inline bool is_full() const
- {
- return insert_count_ >= target_element_count_;
- }
-
- /*
- * density of bits set. inconvenient units, but:
- * .3 = ~50% target insertions
- * .5 = 100% target insertions, "perfectly full"
- * .75 = 200% target insertions
- * 1.0 = all bits set... infinite insertions
- */
- inline double density() const
- {
- if (!bit_table_)
- return 0.0;
- size_t set = 0;
- uint8_t *p = bit_table_;
- size_t left = table_size_;
- while (left-- > 0) {
- uint8_t c = *p;
- for (; c; ++set)
- c &= c - 1;
- ++p;
- }
- return (double)set / (double)(table_size_ << 3);
- }
-
- virtual inline double approx_unique_element_count() const {
- // this is not a very good estimate; a better solution should have
- // some asymptotic behavior as density() approaches 1.0.
- return (double)target_element_count_ * 2.0 * density();
- }
-
- inline double effective_fpp() const
- {
- /*
- Note:
- The effective false positive probability is calculated using the
- designated table size and hash function count in conjunction with
- the current number of inserted elements - not the user defined
- predicated/expected number of inserted elements.
- */
- return std::pow(1.0 - std::exp(-1.0 * salt_.size() * insert_count_ / size()), 1.0 * salt_.size());
- }
-
- inline const cell_type* table() const
- {
- return bit_table_;
- }
-
-protected:
-
- inline virtual void compute_indices(const bloom_type& hash, std::size_t& bit_index, std::size_t& bit) const
- {
- bit_index = hash % (table_size_ << 3);
- bit = bit_index & 7;
- }
-
- void generate_unique_salt()
- {
- /*
- Note:
- A distinct hash function need not be implementation-wise
- distinct. In the current implementation "seeding" a common
- hash function with different values seems to be adequate.
- */
- const unsigned int predef_salt_count = 128;
- static const bloom_type predef_salt[predef_salt_count] = {
- 0xAAAAAAAA, 0x55555555, 0x33333333, 0xCCCCCCCC,
- 0x66666666, 0x99999999, 0xB5B5B5B5, 0x4B4B4B4B,
- 0xAA55AA55, 0x55335533, 0x33CC33CC, 0xCC66CC66,
- 0x66996699, 0x99B599B5, 0xB54BB54B, 0x4BAA4BAA,
- 0xAA33AA33, 0x55CC55CC, 0x33663366, 0xCC99CC99,
- 0x66B566B5, 0x994B994B, 0xB5AAB5AA, 0xAAAAAA33,
- 0x555555CC, 0x33333366, 0xCCCCCC99, 0x666666B5,
- 0x9999994B, 0xB5B5B5AA, 0xFFFFFFFF, 0xFFFF0000,
- 0xB823D5EB, 0xC1191CDF, 0xF623AEB3, 0xDB58499F,
- 0xC8D42E70, 0xB173F616, 0xA91A5967, 0xDA427D63,
- 0xB1E8A2EA, 0xF6C0D155, 0x4909FEA3, 0xA68CC6A7,
- 0xC395E782, 0xA26057EB, 0x0CD5DA28, 0x467C5492,
- 0xF15E6982, 0x61C6FAD3, 0x9615E352, 0x6E9E355A,
- 0x689B563E, 0x0C9831A8, 0x6753C18B, 0xA622689B,
- 0x8CA63C47, 0x42CC2884, 0x8E89919B, 0x6EDBD7D3,
- 0x15B6796C, 0x1D6FDFE4, 0x63FF9092, 0xE7401432,
- 0xEFFE9412, 0xAEAEDF79, 0x9F245A31, 0x83C136FC,
- 0xC3DA4A8C, 0xA5112C8C, 0x5271F491, 0x9A948DAB,
- 0xCEE59A8D, 0xB5F525AB, 0x59D13217, 0x24E7C331,
- 0x697C2103, 0x84B0A460, 0x86156DA9, 0xAEF2AC68,
- 0x23243DA5, 0x3F649643, 0x5FA495A8, 0x67710DF8,
- 0x9A6C499E, 0xDCFB0227, 0x46A43433, 0x1832B07A,
- 0xC46AFF3C, 0xB9C8FFF0, 0xC9500467, 0x34431BDF,
- 0xB652432B, 0xE367F12B, 0x427F4C1B, 0x224C006E,
- 0x2E7E5A89, 0x96F99AA5, 0x0BEB452A, 0x2FD87C39,
- 0x74B2E1FB, 0x222EFD24, 0xF357F60C, 0x440FCB1E,
- 0x8BBE030F, 0x6704DC29, 0x1144D12F, 0x948B1355,
- 0x6D8FD7E9, 0x1C11A014, 0xADD1592F, 0xFB3C712E,
- 0xFC77642F, 0xF9C4CE8C, 0x31312FB9, 0x08B0DD79,
- 0x318FA6E7, 0xC040D23D, 0xC0589AA7, 0x0CA5C075,
- 0xF874B172, 0x0CF914D5, 0x784D3280, 0x4E8CFEBC,
- 0xC569F575, 0xCDB2A091, 0x2CC016B4, 0x5C5F4421
- };
-
- if (salt_count_ <= predef_salt_count)
- {
- std::copy(predef_salt,
- predef_salt + salt_count_,
- std::back_inserter(salt_));
- for (unsigned int i = 0; i < salt_.size(); ++i)
- {
- /*
- Note:
- This is done to integrate the user defined random seed,
- so as to allow for the generation of unique bloom filter
- instances.
- */
- salt_[i] = salt_[i] * salt_[(i + 3) % salt_.size()] + random_seed_;
- }
- }
- else
- {
- std::copy(predef_salt,predef_salt + predef_salt_count,
- std::back_inserter(salt_));
- srand(static_cast<unsigned int>(random_seed_));
- while (salt_.size() < salt_count_)
- {
- bloom_type current_salt = static_cast<bloom_type>(rand()) * static_cast<bloom_type>(rand());
- if (0 == current_salt)
- continue;
- if (salt_.end() == std::find(salt_.begin(), salt_.end(), current_salt))
- {
- salt_.push_back(current_salt);
- }
- }
- }
- }
-
- static void find_optimal_parameters(std::size_t target_insert_count,
- double target_fpp,
- std::size_t *salt_count,
- std::size_t *table_size)
- {
- /*
- Note:
- The following will attempt to find the number of hash functions
- and minimum amount of storage bits required to construct a bloom
- filter consistent with the user defined false positive probability
- and estimated element insertion count.
- */
-
- double min_m = std::numeric_limits<double>::infinity();
- double min_k = 0.0;
- double curr_m = 0.0;
- double k = 1.0;
- while (k < 1000.0)
- {
- double numerator = (- k * target_insert_count);
- double denominator = std::log(1.0 - std::pow(target_fpp, 1.0 / k));
- curr_m = numerator / denominator;
-
- if (curr_m < min_m)
- {
- min_m = curr_m;
- min_k = k;
- }
- k += 1.0;
- }
-
- *salt_count = static_cast<std::size_t>(min_k);
- size_t t = static_cast<std::size_t>(min_m);
- t += (((t & 7) != 0) ? (bits_per_char - (t & 7)) : 0);
- *table_size = t >> 3;
- }
-
- inline bloom_type hash_ap(uint32_t val, bloom_type hash) const
- {
- hash ^= (hash << 7) ^ ((val & 0xff000000) >> 24) * (hash >> 3);
- hash ^= (~((hash << 11) + (((val & 0xff0000) >> 16) ^ (hash >> 5))));
- hash ^= (hash << 7) ^ ((val & 0xff00) >> 8) * (hash >> 3);
- hash ^= (~((hash << 11) + (((val & 0xff)) ^ (hash >> 5))));
- return hash;
- }
-
- inline bloom_type hash_ap(const unsigned char* begin, std::size_t remaining_length, bloom_type hash) const
- {
- const unsigned char* itr = begin;
-
- while (remaining_length >= 4)
- {
- hash ^= (hash << 7) ^ (*itr++) * (hash >> 3);
- hash ^= (~((hash << 11) + ((*itr++) ^ (hash >> 5))));
- hash ^= (hash << 7) ^ (*itr++) * (hash >> 3);
- hash ^= (~((hash << 11) + ((*itr++) ^ (hash >> 5))));
- remaining_length -= 4;
- }
-
- while (remaining_length >= 2)
- {
- hash ^= (hash << 7) ^ (*itr++) * (hash >> 3);
- hash ^= (~((hash << 11) + ((*itr++) ^ (hash >> 5))));
- remaining_length -= 2;
- }
-
- if (remaining_length)
- {
- hash ^= (hash << 7) ^ (*itr) * (hash >> 3);
- }
-
- return hash;
- }
-
-public:
- void encode(bufferlist& bl) const;
- void decode(bufferlist::iterator& bl);
- void dump(Formatter *f) const;
- static void generate_test_instances(std::list<bloom_filter*>& ls);
-};
-WRITE_CLASS_ENCODER(bloom_filter)
-
-
-class compressible_bloom_filter : public bloom_filter
-{
-public:
-
- compressible_bloom_filter() : bloom_filter() {}
-
- compressible_bloom_filter(const std::size_t& predicted_element_count,
- const double& false_positive_probability,
- const std::size_t& random_seed)
- : bloom_filter(predicted_element_count, false_positive_probability, random_seed)
- {
- size_list.push_back(table_size_);
- }
-
- compressible_bloom_filter(const std::size_t& salt_count,
- std::size_t table_size,
- const std::size_t& random_seed,
- std::size_t target_count)
- : bloom_filter(salt_count, table_size, random_seed, target_count)
- {
- size_list.push_back(table_size_);
- }
-
- inline std::size_t size() const override
- {
- return size_list.back() * bits_per_char;
- }
-
- inline bool compress(const double& target_ratio)
- {
- if (!bit_table_)
- return false;
-
- if ((0.0 >= target_ratio) || (target_ratio >= 1.0))
- {
- return false;
- }
-
- std::size_t original_table_size = size_list.back();
- std::size_t new_table_size = static_cast<std::size_t>(size_list.back() * target_ratio);
-
- if ((!new_table_size) || (new_table_size >= original_table_size))
- {
- return false;
- }
-
- cell_type* tmp = mempool::bloom_filter::alloc_byte.allocate(new_table_size);
- std::copy(bit_table_, bit_table_ + (new_table_size), tmp);
- cell_type* itr = bit_table_ + (new_table_size);
- cell_type* end = bit_table_ + (original_table_size);
- cell_type* itr_tmp = tmp;
- cell_type* itr_end = tmp + (new_table_size);
- while (end != itr)
- {
- *(itr_tmp++) |= (*itr++);
- if (itr_tmp == itr_end)
- itr_tmp = tmp;
- }
-
- mempool::bloom_filter::alloc_byte.deallocate(bit_table_, table_size_);
- bit_table_ = tmp;
- size_list.push_back(new_table_size);
- table_size_ = new_table_size;
-
- return true;
- }
-
- inline double approx_unique_element_count() const override {
- // this is not a very good estimate; a better solution should have
- // some asymptotic behavior as density() approaches 1.0.
- //
- // the compress() correction is also bad; it tends to under-estimate.
- return (double)target_element_count_ * 2.0 * density() * (double)size_list.back() / (double)size_list.front();
- }
-
-private:
-
- inline void compute_indices(const bloom_type& hash, std::size_t& bit_index, std::size_t& bit) const override
- {
- bit_index = hash;
- for (std::size_t i = 0; i < size_list.size(); ++i)
- {
- bit_index %= size_list[i] << 3;
- }
- bit = bit_index & 7;
- }
-
- std::vector<std::size_t> size_list;
-public:
- void encode(bufferlist& bl) const;
- void decode(bufferlist::iterator& bl);
- void dump(Formatter *f) const;
- static void generate_test_instances(std::list<compressible_bloom_filter*>& ls);
-};
-WRITE_CLASS_ENCODER(compressible_bloom_filter)
-
-#endif
-
-
-/*
- Note 1:
- If it can be guaranteed that bits_per_char will be of the form 2^n then
- the following optimization can be used:
-
- hash_table[bit_index >> n] |= bit_mask[bit_index & (bits_per_char - 1)];
-
- Note 2:
- For performance reasons where possible when allocating memory it should
- be aligned (aligned_alloc) according to the architecture being used.
-*/