bloom.cpp

// Copyright (c) 2012-2016 The Bitcoin Core developers
// Distributed under the MIT software license, see the accompanying
// file COPYING or http://www.opensource.org/licenses/mit-license.php.

#include <bloom.h>

#include <hash.h>
#include <primitives/transaction.h>
#include <random.h>
#include <script/script.h>
#include <script/standard.h>
#include <streams.h>

#include <cmath>
#include <cstdlib>

#include <algorithm>

#define LN2SQUARED 0.4804530139182014246671025263266649717305529515945455
#define LN2 0.6931471805599453094172321214581765680755001343602552

/**
 * The ideal size for a bloom filter with a given number of elements and false
 * positive rate is:
 * - nElements * log(fp rate) / ln(2)^2
 * We ignore filter parameters which will create a bloom filter larger than the
 * protocol limits
 *
 * The ideal number of hash functions is filter size * ln(2) / number of
 * elements. Again, we ignore filter parameters which will create a bloom filter
 * with more hash functions than the protocol limits.
 * See https://en.wikipedia.org/wiki/Bloom_filter for an explanation of these
 * formulas.
 */
CBloomFilter::CBloomFilter(const uint32_t nElements, const double nFPRate,
                           const uint32_t nTweakIn, uint8_t nFlagsIn)
    : vData(std::min<uint32_t>(-1 / LN2SQUARED * nElements * log(nFPRate),
                               MAX_BLOOM_FILTER_SIZE * 8) /
            8),
      isFull(false), isEmpty(true),
      nHashFuncs(std::min<uint32_t>(vData.size() * 8 / nElements * LN2,
                                    MAX_HASH_FUNCS)),
      nTweak(nTweakIn), nFlags(nFlagsIn) {}

inline uint32_t
CBloomFilter::Hash(uint32_t nHashNum,
                   const std::vector<uint8_t> &vDataToHash) const {
    // 0xFBA4C795 chosen as it guarantees a reasonable bit difference between
    // nHashNum values.
    return MurmurHash3(nHashNum * 0xFBA4C795 + nTweak, vDataToHash) %
           (vData.size() * 8);
}

void CBloomFilter::insert(const std::vector<uint8_t> &vKey) {
    if (isFull) {
        return;
    }

    for (uint32_t i = 0; i < nHashFuncs; i++) {
        uint32_t nIndex = Hash(i, vKey);
        // Sets bit nIndex of vData
        vData[nIndex >> 3] |= (1 << (7 & nIndex));
    }
    isEmpty = false;
}

void CBloomFilter::insert(const COutPoint &outpoint) {
    CDataStream stream(SER_NETWORK, PROTOCOL_VERSION);
    stream << outpoint;
    std::vector<uint8_t> data(stream.begin(), stream.end());
    insert(data);
}

void CBloomFilter::insert(const uint256 &hash) {
    std::vector<uint8_t> data(hash.begin(), hash.end());
    insert(data);
}

bool CBloomFilter::contains(const std::vector<uint8_t> &vKey) const {
    if (isFull) {
        return true;
    }
    if (isEmpty) {
        return false;
    }
    for (uint32_t i = 0; i < nHashFuncs; i++) {
        uint32_t nIndex = Hash(i, vKey);
        // Checks bit nIndex of vData
        if (!(vData[nIndex >> 3] & (1 << (7 & nIndex)))) {
            return false;
        }
    }
    return true;
}

bool CBloomFilter::contains(const COutPoint &outpoint) const {
    CDataStream stream(SER_NETWORK, PROTOCOL_VERSION);
    stream << outpoint;
    std::vector<uint8_t> data(stream.begin(), stream.end());
    return contains(data);
}

bool CBloomFilter::contains(const uint256 &hash) const {
    std::vector<uint8_t> data(hash.begin(), hash.end());
    return contains(data);
}

void CBloomFilter::clear() {
    vData.assign(vData.size(), 0);
    isFull = false;
    isEmpty = true;
}

void CBloomFilter::reset(const uint32_t nNewTweak) {
    clear();
    nTweak = nNewTweak;
}

bool CBloomFilter::IsWithinSizeConstraints() const {
    return vData.size() <= MAX_BLOOM_FILTER_SIZE &&
           nHashFuncs <= MAX_HASH_FUNCS;
}

bool CBloomFilter::MatchAndInsertOutputs(const CTransaction &tx) {
    bool fFound = false;
    // Match if the filter contains the hash of tx for finding tx when they
    // appear in a block
    if (isFull) {
        return true;
    }
    if (isEmpty) {
        return false;
    }

    const TxId &txid = tx.GetId();
    if (contains(txid)) {
        fFound = true;
    }

    for (size_t i = 0; i < tx.vout.size(); i++) {
        const CTxOut &txout = tx.vout[i];
        // Match if the filter contains any arbitrary script data element in any
        // scriptPubKey in tx. If this matches, also add the specific output
        // that was matched. This means clients don't have to update the filter
        // themselves when a new relevant tx is discovered in order to find
        // spending transactions, which avoids round-tripping and race
        // conditions.
        CScript::const_iterator pc = txout.scriptPubKey.begin();
        std::vector<uint8_t> data;
        while (pc < txout.scriptPubKey.end()) {
            opcodetype opcode;
            if (!txout.scriptPubKey.GetOp(pc, opcode, data)) {
                break;
            }
            if (data.size() != 0 && contains(data)) {
                fFound = true;
                if ((nFlags & BLOOM_UPDATE_MASK) == BLOOM_UPDATE_ALL) {
                    insert(COutPoint(txid, i));
                } else if ((nFlags & BLOOM_UPDATE_MASK) ==
                           BLOOM_UPDATE_P2PUBKEY_ONLY) {
                    std::vector<std::vector<uint8_t>> vSolutions;
                    txnouttype type = Solver(txout.scriptPubKey, vSolutions);
                    if (type == TX_PUBKEY || type == TX_MULTISIG) {
                        insert(COutPoint(txid, i));
                    }
                }
                break;
            }
        }
    }

    return fFound;
}

bool CBloomFilter::MatchInputs(const CTransaction &tx) {
    if (isEmpty) {
        return false;
    }

    for (const CTxIn &txin : tx.vin) {
        // Match if the filter contains an outpoint tx spends
        if (contains(txin.prevout)) {
            return true;
        }

        // Match if the filter contains any arbitrary script data element in any
        // scriptSig in tx
        CScript::const_iterator pc = txin.scriptSig.begin();
        std::vector<uint8_t> data;
        while (pc < txin.scriptSig.end()) {
            opcodetype opcode;
            if (!txin.scriptSig.GetOp(pc, opcode, data)) {
                break;
            }
            if (data.size() != 0 && contains(data)) {
                return true;
            }
        }
    }

    return false;
}

void CBloomFilter::UpdateEmptyFull() {
    bool full = true;
    bool empty = true;
    for (const auto d : vData) {
        full &= (d == 0xff);
        empty &= (d == 0);
    }
    isFull = full;
    isEmpty = empty;
}

CRollingBloomFilter::CRollingBloomFilter(const uint32_t nElements,
                                         const double fpRate) {
    double logFpRate = log(fpRate);
    /* The optimal number of hash functions is log(fpRate) / log(0.5), but
     * restrict it to the range 1-50. */
    nHashFuncs = std::max(1, std::min<int>(round(logFpRate / log(0.5)), 50));
    /* In this rolling bloom filter, we'll store between 2 and 3 generations of
     * nElements / 2 entries. */
    nEntriesPerGeneration = (nElements + 1) / 2;
    uint32_t nMaxElements = nEntriesPerGeneration * 3;
    /* The maximum fpRate = pow(1.0 - exp(-nHashFuncs * nMaxElements /
     * nFilterBits), nHashFuncs)
     * =>          pow(fpRate, 1.0 / nHashFuncs) = 1.0 - exp(-nHashFuncs *
     * nMaxElements / nFilterBits)
     * =>          1.0 - pow(fpRate, 1.0 / nHashFuncs) = exp(-nHashFuncs *
     * nMaxElements / nFilterBits)
     * =>          log(1.0 - pow(fpRate, 1.0 / nHashFuncs)) = -nHashFuncs *
     * nMaxElements / nFilterBits
     * =>          nFilterBits = -nHashFuncs * nMaxElements / log(1.0 -
     * pow(fpRate, 1.0 / nHashFuncs))
     * =>          nFilterBits = -nHashFuncs * nMaxElements / log(1.0 -
     * exp(logFpRate / nHashFuncs))
     */
    uint32_t nFilterBits =
        uint32_t(ceil(-1.0 * nHashFuncs * nMaxElements /
                      log(1.0 - exp(logFpRate / nHashFuncs))));
    data.clear();
    /* For each data element we need to store 2 bits. If both bits are 0, the
     * bit is treated as unset. If the bits are (01), (10), or (11), the bit is
     * treated as set in generation 1, 2, or 3 respectively. These bits are
     * stored in separate integers: position P corresponds to bit (P & 63) of
     * the integers data[(P >> 6) * 2] and data[(P >> 6) * 2 + 1]. */
    data.resize(((nFilterBits + 63) / 64) << 1);
    reset();
}

/* Similar to CBloomFilter::Hash */
static inline uint32_t
RollingBloomHash(uint32_t nHashNum, uint32_t nTweak,
                 const std::vector<uint8_t> &vDataToHash) {
    return MurmurHash3(nHashNum * 0xFBA4C795 + nTweak, vDataToHash);
}

// A replacement for x % n. This assumes that x and n are 32bit integers, and x
// is a uniformly random distributed 32bit value which should be the case for a
// good hash. See
// https://lemire.me/blog/2016/06/27/a-fast-alternative-to-the-modulo-reduction/
static inline uint32_t FastMod(uint32_t x, size_t n) {
    return (uint64_t(x) * uint64_t(n)) >> 32;
}

void CRollingBloomFilter::insert(const std::vector<uint8_t> &vKey) {
    if (nEntriesThisGeneration == nEntriesPerGeneration) {
        nEntriesThisGeneration = 0;
        nGeneration++;
        if (nGeneration == 4) {
            nGeneration = 1;
        }
        uint64_t nGenerationMask1 = 0 - uint64_t(nGeneration & 1);
        uint64_t nGenerationMask2 = 0 - uint64_t(nGeneration >> 1);
        /* Wipe old entries that used this generation number. */
        for (uint32_t p = 0; p < data.size(); p += 2) {
            uint64_t p1 = data[p], p2 = data[p + 1];
            uint64_t mask = (p1 ^ nGenerationMask1) | (p2 ^ nGenerationMask2);
            data[p] = p1 & mask;
            data[p + 1] = p2 & mask;
        }
    }
    nEntriesThisGeneration++;

    for (int n = 0; n < nHashFuncs; n++) {
        uint32_t h = RollingBloomHash(n, nTweak, vKey);
        int bit = h & 0x3F;
        /* FastMod works with the upper bits of h, so it is safe to ignore that
         * the lower bits of h are already used for bit. */
        uint32_t pos = FastMod(h, data.size());
        /* The lowest bit of pos is ignored, and set to zero for the first bit,
         * and to one for the second. */
        data[pos & ~1] = (data[pos & ~1] & ~(uint64_t(1) << bit)) |
                         uint64_t(nGeneration & 1) << bit;
        data[pos | 1] = (data[pos | 1] & ~(uint64_t(1) << bit)) |
                        uint64_t(nGeneration >> 1) << bit;
    }
}

void CRollingBloomFilter::insert(const uint256 &hash) {
    std::vector<uint8_t> vData(hash.begin(), hash.end());
    insert(vData);
}

bool CRollingBloomFilter::contains(const std::vector<uint8_t> &vKey) const {
    for (int n = 0; n < nHashFuncs; n++) {
        uint32_t h = RollingBloomHash(n, nTweak, vKey);
        int bit = h & 0x3F;
        uint32_t pos = FastMod(h, data.size());
        /* If the relevant bit is not set in either data[pos & ~1] or data[pos |
         * 1], the filter does not contain vKey */
        if (!(((data[pos & ~1] | data[pos | 1]) >> bit) & 1)) {
            return false;
        }
    }
    return true;
}

bool CRollingBloomFilter::contains(const uint256 &hash) const {
    std::vector<uint8_t> vData(hash.begin(), hash.end());
    return contains(vData);
}

void CRollingBloomFilter::reset() {
    nTweak = GetRand(std::numeric_limits<unsigned int>::max());
    nEntriesThisGeneration = 0;
    nGeneration = 1;
    std::fill(data.begin(), data.end(), 0);
}