--- /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.
+*/
Code Review / stor4nfv.git / blobdiff