1.1.0
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NotRin7
309
src/bloom.cpp
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309
src/bloom.cpp
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// Copyright (c) 2012-2019 The Palladium Core developers
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// Distributed under the MIT software license, see the accompanying
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// file COPYING or http://www.opensource.org/licenses/mit-license.php.
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#include <bloom.h>
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#include <primitives/transaction.h>
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#include <hash.h>
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#include <script/script.h>
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#include <script/standard.h>
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#include <random.h>
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#include <streams.h>
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#include <math.h>
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#include <stdlib.h>
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#include <algorithm>
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#define LN2SQUARED 0.4804530139182014246671025263266649717305529515945455
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#define LN2 0.6931471805599453094172321214581765680755001343602552
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CBloomFilter::CBloomFilter(const unsigned int nElements, const double nFPRate, const unsigned int nTweakIn, unsigned char nFlagsIn) :
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/**
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* The ideal size for a bloom filter with a given number of elements and false positive rate is:
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* - nElements * log(fp rate) / ln(2)^2
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* We ignore filter parameters which will create a bloom filter larger than the protocol limits
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*/
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vData(std::min((unsigned int)(-1 / LN2SQUARED * nElements * log(nFPRate)), MAX_BLOOM_FILTER_SIZE * 8) / 8),
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/**
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* The ideal number of hash functions is filter size * ln(2) / number of elements
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* Again, we ignore filter parameters which will create a bloom filter with more hash functions than the protocol limits
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* See https://en.wikipedia.org/wiki/Bloom_filter for an explanation of these formulas
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*/
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isFull(false),
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isEmpty(true),
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nHashFuncs(std::min((unsigned int)(vData.size() * 8 / nElements * LN2), MAX_HASH_FUNCS)),
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nTweak(nTweakIn),
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nFlags(nFlagsIn)
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{
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}
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inline unsigned int CBloomFilter::Hash(unsigned int nHashNum, const std::vector<unsigned char>& vDataToHash) const
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{
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// 0xFBA4C795 chosen as it guarantees a reasonable bit difference between nHashNum values.
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return MurmurHash3(nHashNum * 0xFBA4C795 + nTweak, vDataToHash) % (vData.size() * 8);
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}
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void CBloomFilter::insert(const std::vector<unsigned char>& vKey)
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{
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if (isFull)
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return;
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for (unsigned int i = 0; i < nHashFuncs; i++)
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{
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unsigned int nIndex = Hash(i, vKey);
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// Sets bit nIndex of vData
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vData[nIndex >> 3] |= (1 << (7 & nIndex));
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}
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isEmpty = false;
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}
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void CBloomFilter::insert(const COutPoint& outpoint)
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{
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CDataStream stream(SER_NETWORK, PROTOCOL_VERSION);
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stream << outpoint;
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std::vector<unsigned char> data(stream.begin(), stream.end());
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insert(data);
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}
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void CBloomFilter::insert(const uint256& hash)
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{
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std::vector<unsigned char> data(hash.begin(), hash.end());
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insert(data);
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}
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bool CBloomFilter::contains(const std::vector<unsigned char>& vKey) const
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{
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if (isFull)
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return true;
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if (isEmpty)
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return false;
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for (unsigned int i = 0; i < nHashFuncs; i++)
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{
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unsigned int nIndex = Hash(i, vKey);
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// Checks bit nIndex of vData
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if (!(vData[nIndex >> 3] & (1 << (7 & nIndex))))
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return false;
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}
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return true;
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}
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bool CBloomFilter::contains(const COutPoint& outpoint) const
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{
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CDataStream stream(SER_NETWORK, PROTOCOL_VERSION);
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stream << outpoint;
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std::vector<unsigned char> data(stream.begin(), stream.end());
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return contains(data);
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}
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bool CBloomFilter::contains(const uint256& hash) const
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{
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std::vector<unsigned char> data(hash.begin(), hash.end());
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return contains(data);
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}
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void CBloomFilter::clear()
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{
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vData.assign(vData.size(),0);
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isFull = false;
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isEmpty = true;
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}
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void CBloomFilter::reset(const unsigned int nNewTweak)
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{
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clear();
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nTweak = nNewTweak;
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}
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bool CBloomFilter::IsWithinSizeConstraints() const
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{
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return vData.size() <= MAX_BLOOM_FILTER_SIZE && nHashFuncs <= MAX_HASH_FUNCS;
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}
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bool CBloomFilter::IsRelevantAndUpdate(const CTransaction& tx)
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{
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bool fFound = false;
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// Match if the filter contains the hash of tx
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// for finding tx when they appear in a block
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if (isFull)
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return true;
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if (isEmpty)
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return false;
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const uint256& hash = tx.GetHash();
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if (contains(hash))
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fFound = true;
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for (unsigned int i = 0; i < tx.vout.size(); i++)
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{
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const CTxOut& txout = tx.vout[i];
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// Match if the filter contains any arbitrary script data element in any scriptPubKey in tx
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// If this matches, also add the specific output that was matched.
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// This means clients don't have to update the filter themselves when a new relevant tx
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// is discovered in order to find spending transactions, which avoids round-tripping and race conditions.
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CScript::const_iterator pc = txout.scriptPubKey.begin();
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std::vector<unsigned char> data;
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while (pc < txout.scriptPubKey.end())
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{
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opcodetype opcode;
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if (!txout.scriptPubKey.GetOp(pc, opcode, data))
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break;
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if (data.size() != 0 && contains(data))
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{
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fFound = true;
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if ((nFlags & BLOOM_UPDATE_MASK) == BLOOM_UPDATE_ALL)
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insert(COutPoint(hash, i));
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else if ((nFlags & BLOOM_UPDATE_MASK) == BLOOM_UPDATE_P2PUBKEY_ONLY)
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{
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std::vector<std::vector<unsigned char> > vSolutions;
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txnouttype type = Solver(txout.scriptPubKey, vSolutions);
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if (type == TX_PUBKEY || type == TX_MULTISIG) {
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insert(COutPoint(hash, i));
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}
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}
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break;
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}
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}
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}
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if (fFound)
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return true;
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for (const CTxIn& txin : tx.vin)
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{
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// Match if the filter contains an outpoint tx spends
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if (contains(txin.prevout))
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return true;
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// Match if the filter contains any arbitrary script data element in any scriptSig in tx
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CScript::const_iterator pc = txin.scriptSig.begin();
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std::vector<unsigned char> data;
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while (pc < txin.scriptSig.end())
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{
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opcodetype opcode;
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if (!txin.scriptSig.GetOp(pc, opcode, data))
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break;
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if (data.size() != 0 && contains(data))
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return true;
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}
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}
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return false;
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}
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void CBloomFilter::UpdateEmptyFull()
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{
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bool full = true;
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bool empty = true;
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for (unsigned int i = 0; i < vData.size(); i++)
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{
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full &= vData[i] == 0xff;
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empty &= vData[i] == 0;
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}
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isFull = full;
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isEmpty = empty;
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}
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CRollingBloomFilter::CRollingBloomFilter(const unsigned int nElements, const double fpRate)
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{
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double logFpRate = log(fpRate);
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/* The optimal number of hash functions is log(fpRate) / log(0.5), but
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* restrict it to the range 1-50. */
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nHashFuncs = std::max(1, std::min((int)round(logFpRate / log(0.5)), 50));
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/* In this rolling bloom filter, we'll store between 2 and 3 generations of nElements / 2 entries. */
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nEntriesPerGeneration = (nElements + 1) / 2;
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uint32_t nMaxElements = nEntriesPerGeneration * 3;
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/* The maximum fpRate = pow(1.0 - exp(-nHashFuncs * nMaxElements / nFilterBits), nHashFuncs)
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* => pow(fpRate, 1.0 / nHashFuncs) = 1.0 - exp(-nHashFuncs * nMaxElements / nFilterBits)
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* => 1.0 - pow(fpRate, 1.0 / nHashFuncs) = exp(-nHashFuncs * nMaxElements / nFilterBits)
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* => log(1.0 - pow(fpRate, 1.0 / nHashFuncs)) = -nHashFuncs * nMaxElements / nFilterBits
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* => nFilterBits = -nHashFuncs * nMaxElements / log(1.0 - pow(fpRate, 1.0 / nHashFuncs))
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* => nFilterBits = -nHashFuncs * nMaxElements / log(1.0 - exp(logFpRate / nHashFuncs))
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*/
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uint32_t nFilterBits = (uint32_t)ceil(-1.0 * nHashFuncs * nMaxElements / log(1.0 - exp(logFpRate / nHashFuncs)));
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data.clear();
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/* For each data element we need to store 2 bits. If both bits are 0, the
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* bit is treated as unset. If the bits are (01), (10), or (11), the bit is
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* treated as set in generation 1, 2, or 3 respectively.
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* These bits are stored in separate integers: position P corresponds to bit
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* (P & 63) of the integers data[(P >> 6) * 2] and data[(P >> 6) * 2 + 1]. */
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data.resize(((nFilterBits + 63) / 64) << 1);
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reset();
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}
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/* Similar to CBloomFilter::Hash */
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static inline uint32_t RollingBloomHash(unsigned int nHashNum, uint32_t nTweak, const std::vector<unsigned char>& vDataToHash) {
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return MurmurHash3(nHashNum * 0xFBA4C795 + nTweak, vDataToHash);
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}
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// A replacement for x % n. This assumes that x and n are 32bit integers, and x is a uniformly random distributed 32bit value
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// which should be the case for a good hash.
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// See https://lemire.me/blog/2016/06/27/a-fast-alternative-to-the-modulo-reduction/
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static inline uint32_t FastMod(uint32_t x, size_t n) {
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return ((uint64_t)x * (uint64_t)n) >> 32;
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}
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void CRollingBloomFilter::insert(const std::vector<unsigned char>& vKey)
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{
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if (nEntriesThisGeneration == nEntriesPerGeneration) {
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nEntriesThisGeneration = 0;
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nGeneration++;
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if (nGeneration == 4) {
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nGeneration = 1;
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}
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uint64_t nGenerationMask1 = 0 - (uint64_t)(nGeneration & 1);
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uint64_t nGenerationMask2 = 0 - (uint64_t)(nGeneration >> 1);
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/* Wipe old entries that used this generation number. */
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for (uint32_t p = 0; p < data.size(); p += 2) {
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uint64_t p1 = data[p], p2 = data[p + 1];
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uint64_t mask = (p1 ^ nGenerationMask1) | (p2 ^ nGenerationMask2);
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data[p] = p1 & mask;
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data[p + 1] = p2 & mask;
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}
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}
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nEntriesThisGeneration++;
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for (int n = 0; n < nHashFuncs; n++) {
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uint32_t h = RollingBloomHash(n, nTweak, vKey);
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int bit = h & 0x3F;
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/* 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. */
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uint32_t pos = FastMod(h, data.size());
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/* The lowest bit of pos is ignored, and set to zero for the first bit, and to one for the second. */
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data[pos & ~1] = (data[pos & ~1] & ~(((uint64_t)1) << bit)) | ((uint64_t)(nGeneration & 1)) << bit;
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data[pos | 1] = (data[pos | 1] & ~(((uint64_t)1) << bit)) | ((uint64_t)(nGeneration >> 1)) << bit;
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}
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}
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void CRollingBloomFilter::insert(const uint256& hash)
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{
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std::vector<unsigned char> vData(hash.begin(), hash.end());
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insert(vData);
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}
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bool CRollingBloomFilter::contains(const std::vector<unsigned char>& vKey) const
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{
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for (int n = 0; n < nHashFuncs; n++) {
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uint32_t h = RollingBloomHash(n, nTweak, vKey);
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int bit = h & 0x3F;
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uint32_t pos = FastMod(h, data.size());
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/* If the relevant bit is not set in either data[pos & ~1] or data[pos | 1], the filter does not contain vKey */
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if (!(((data[pos & ~1] | data[pos | 1]) >> bit) & 1)) {
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return false;
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}
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}
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return true;
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}
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bool CRollingBloomFilter::contains(const uint256& hash) const
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{
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std::vector<unsigned char> vData(hash.begin(), hash.end());
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return contains(vData);
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}
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void CRollingBloomFilter::reset()
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{
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nTweak = GetRand(std::numeric_limits<unsigned int>::max());
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nEntriesThisGeneration = 0;
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nGeneration = 1;
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std::fill(data.begin(), data.end(), 0);
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}
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