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315 lines
11 KiB
315 lines
11 KiB
// Copyright (c) 2012-2015 The Bitcoin 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 <boost/foreach.hpp> |
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#define LN2SQUARED 0.4804530139182014246671025263266649717305529515945455 |
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#define LN2 0.6931471805599453094172321214581765680755001343602552 |
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using namespace std; |
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CBloomFilter::CBloomFilter(unsigned int nElements, double nFPRate, 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(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(false), |
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nHashFuncs(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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// Private constructor used by CRollingBloomFilter |
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CBloomFilter::CBloomFilter(unsigned int nElements, double nFPRate, unsigned int nTweakIn) : |
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vData((unsigned int)(-1 / LN2SQUARED * nElements * log(nFPRate)) / 8), |
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isFull(false), |
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isEmpty(true), |
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nHashFuncs((unsigned int)(vData.size() * 8 / nElements * LN2)), |
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nTweak(nTweakIn), |
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nFlags(BLOOM_UPDATE_NONE) |
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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 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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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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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 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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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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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(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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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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txnouttype type; |
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vector<vector<unsigned char> > vSolutions; |
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if (Solver(txout.scriptPubKey, type, vSolutions) && |
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(type == TX_PUBKEY || type == TX_MULTISIG)) |
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insert(COutPoint(hash, i)); |
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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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BOOST_FOREACH(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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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(unsigned int nElements, 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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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 = -(uint64_t)(nGeneration & 1); |
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uint64_t nGenerationMask2 = -(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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uint32_t pos = (h >> 6) % 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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vector<unsigned char> data(hash.begin(), hash.end()); |
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insert(data); |
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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 = (h >> 6) % 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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vector<unsigned char> data(hash.begin(), hash.end()); |
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return contains(data); |
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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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for (std::vector<uint64_t>::iterator it = data.begin(); it != data.end(); it++) { |
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*it = 0; |
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} |
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}
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