// Copyright (c) 2012-2014 The Bitcoin Core developers // Distributed under the GPLv3 software license, see the accompanying // file COPYING or https://www.gnu.org/licenses/gpl-3.0.en.html /****************************************************************************** * Copyright © 2014-2019 The SuperNET Developers. * * * * See the AUTHORS, DEVELOPER-AGREEMENT and LICENSE files at * * the top-level directory of this distribution for the individual copyright * * holder information and the developer policies on copyright and licensing. * * * * Unless otherwise agreed in a custom licensing agreement, no part of the * * SuperNET software, including this file may be copied, modified, propagated * * or distributed except according to the terms contained in the LICENSE file * * * * Removal or modification of this copyright notice is prohibited. * * * ******************************************************************************/ #include "bloom.h" #include "primitives/transaction.h" #include "hash.h" #include "script/script.h" #include "script/standard.h" #include "random.h" #include "streams.h" #include #include #include #define LN2SQUARED 0.4804530139182014246671025263266649717305529515945455 #define LN2 0.6931471805599453094172321214581765680755001343602552 using namespace std; CBloomFilter::CBloomFilter(unsigned int nElements, double nFPRate, unsigned int nTweakIn, unsigned char nFlagsIn) : /** * 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 */ vData(min((unsigned int)(-1 / LN2SQUARED * nElements * log(nFPRate)), MAX_BLOOM_FILTER_SIZE * 8) / 8), /** * 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 */ isFull(false), isEmpty(false), nHashFuncs(min((unsigned int)(vData.size() * 8 / nElements * LN2), MAX_HASH_FUNCS)), nTweak(nTweakIn), nFlags(nFlagsIn) { } // Private constructor used by CRollingBloomFilter CBloomFilter::CBloomFilter(unsigned int nElements, double nFPRate, unsigned int nTweakIn) : vData((unsigned int)(-1 / LN2SQUARED * nElements * log(nFPRate)) / 8), isFull(false), isEmpty(true), nHashFuncs((unsigned int)(vData.size() * 8 / nElements * LN2)), nTweak(nTweakIn), nFlags(BLOOM_UPDATE_NONE) { } inline unsigned int CBloomFilter::Hash(unsigned int nHashNum, const std::vector& 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 vector& vKey) { if (isFull) return; for (unsigned int i = 0; i < nHashFuncs; i++) { unsigned int 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; vector data(stream.begin(), stream.end()); insert(data); } void CBloomFilter::insert(const uint256& hash) { vector data(hash.begin(), hash.end()); insert(data); } bool CBloomFilter::contains(const vector& vKey) const { if (isFull) return true; if (isEmpty) return false; for (unsigned int i = 0; i < nHashFuncs; i++) { unsigned int 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; vector data(stream.begin(), stream.end()); return contains(data); } bool CBloomFilter::contains(const uint256& hash) const { vector data(hash.begin(), hash.end()); return contains(data); } void CBloomFilter::clear() { vData.assign(vData.size(),0); isFull = false; isEmpty = true; } void CBloomFilter::reset(unsigned int nNewTweak) { clear(); nTweak = nNewTweak; } bool CBloomFilter::IsWithinSizeConstraints() const { return vData.size() <= MAX_BLOOM_FILTER_SIZE && nHashFuncs <= MAX_HASH_FUNCS; } bool CBloomFilter::IsRelevantAndUpdate(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 uint256& hash = tx.GetHash(); if (contains(hash)) fFound = true; for (unsigned int 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(); vector 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(hash, i)); else if ((nFlags & BLOOM_UPDATE_MASK) == BLOOM_UPDATE_P2PUBKEY_ONLY) { txnouttype type; vector > vSolutions; if (Solver(txout.scriptPubKey, type, vSolutions) && (type == TX_PUBKEY || type == TX_MULTISIG)) insert(COutPoint(hash, i)); } break; } } } if (fFound) return true; BOOST_FOREACH(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(); vector 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 (unsigned int i = 0; i < vData.size(); i++) { full &= vData[i] == 0xff; empty &= vData[i] == 0; } isFull = full; isEmpty = empty; } CRollingBloomFilter::CRollingBloomFilter(unsigned int nElements, double fpRate) : b1(nElements * 2, fpRate, 0), b2(nElements * 2, fpRate, 0) { // Implemented using two bloom filters of 2 * nElements each. // We fill them up, and clear them, staggered, every nElements // inserted, so at least one always contains the last nElements // inserted. nInsertions = 0; nBloomSize = nElements * 2; reset(); } void CRollingBloomFilter::insert(const std::vector& vKey) { if (nInsertions == 0) { b1.clear(); } else if (nInsertions == nBloomSize / 2) { b2.clear(); } b1.insert(vKey); b2.insert(vKey); if (++nInsertions == nBloomSize) { nInsertions = 0; } } void CRollingBloomFilter::insert(const uint256& hash) { vector data(hash.begin(), hash.end()); insert(data); } bool CRollingBloomFilter::contains(const std::vector& vKey) const { if (nInsertions < nBloomSize / 2) { return b2.contains(vKey); } return b1.contains(vKey); } bool CRollingBloomFilter::contains(const uint256& hash) const { vector data(hash.begin(), hash.end()); return contains(data); } void CRollingBloomFilter::reset() { unsigned int nNewTweak = GetRand(std::numeric_limits::max()); b1.reset(nNewTweak); b2.reset(nNewTweak); nInsertions = 0; }