bloom.cpp raw
1 // Copyright (c) 2012-present The Bitcoin Core developers
2 // Distributed under the MIT software license, see the accompanying
3 // file COPYING or http://www.opensource.org/licenses/mit-license.php.
4
5 #include <common/bloom.h>
6
7 #include <hash.h>
8 #include <primitives/transaction.h>
9 #include <random.h>
10 #include <script/script.h>
11 #include <script/solver.h>
12 #include <span.h>
13 #include <streams.h>
14 #include <util/fastrange.h>
15 #include <util/overflow.h>
16
17 #include <algorithm>
18 #include <cmath>
19 #include <cstdlib>
20 #include <limits>
21 #include <vector>
22
23 static constexpr double LN2SQUARED = 0.4804530139182014246671025263266649717305529515945455;
24 static constexpr double LN2 = 0.6931471805599453094172321214581765680755001343602552;
25
26 CBloomFilter::CBloomFilter(const unsigned int nElements, const double nFPRate, const unsigned int nTweakIn, unsigned char nFlagsIn) :
27 /**
28 * The ideal size for a bloom filter with a given number of elements and false positive rate is:
29 * - nElements * log(fp rate) / ln(2)^2
30 * We ignore filter parameters which will create a bloom filter larger than the protocol limits
31 */
32 vData(std::min((unsigned int)(-1 / LN2SQUARED * nElements * log(nFPRate)), MAX_BLOOM_FILTER_SIZE * 8) / 8),
33 /**
34 * The ideal number of hash functions is filter size * ln(2) / number of elements
35 * Again, we ignore filter parameters which will create a bloom filter with more hash functions than the protocol limits
36 * See https://en.wikipedia.org/wiki/Bloom_filter for an explanation of these formulas
37 */
38 nHashFuncs(std::min((unsigned int)(vData.size() * 8 / nElements * LN2), MAX_HASH_FUNCS)),
39 nTweak(nTweakIn),
40 nFlags(nFlagsIn)
41 {
42 }
43
44 inline unsigned int CBloomFilter::Hash(unsigned int nHashNum, std::span<const unsigned char> vDataToHash) const
45 {
46 // 0xFBA4C795 chosen as it guarantees a reasonable bit difference between nHashNum values.
47 return MurmurHash3(nHashNum * 0xFBA4C795 + nTweak, vDataToHash) % (vData.size() * 8);
48 }
49
50 void CBloomFilter::insert(std::span<const unsigned char> vKey)
51 {
52 if (vData.empty()) // Avoid divide-by-zero (CVE-2013-5700)
53 return;
54 for (unsigned int i = 0; i < nHashFuncs; i++)
55 {
56 unsigned int nIndex = Hash(i, vKey);
57 // Sets bit nIndex of vData
58 vData[nIndex >> 3] |= (1 << (7 & nIndex));
59 }
60 }
61
62 void CBloomFilter::insert(const COutPoint& outpoint)
63 {
64 DataStream stream{};
65 stream << outpoint;
66 insert(MakeUCharSpan(stream));
67 }
68
69 bool CBloomFilter::contains(std::span<const unsigned char> vKey) const
70 {
71 if (vData.empty()) // Avoid divide-by-zero (CVE-2013-5700)
72 return true;
73 for (unsigned int i = 0; i < nHashFuncs; i++)
74 {
75 unsigned int nIndex = Hash(i, vKey);
76 // Checks bit nIndex of vData
77 if (!(vData[nIndex >> 3] & (1 << (7 & nIndex))))
78 return false;
79 }
80 return true;
81 }
82
83 bool CBloomFilter::contains(const COutPoint& outpoint) const
84 {
85 DataStream stream{};
86 stream << outpoint;
87 return contains(MakeUCharSpan(stream));
88 }
89
90 bool CBloomFilter::IsWithinSizeConstraints() const
91 {
92 return vData.size() <= MAX_BLOOM_FILTER_SIZE && nHashFuncs <= MAX_HASH_FUNCS;
93 }
94
95 bool CBloomFilter::IsRelevantAndUpdate(const CTransaction& tx)
96 {
97 bool fFound = false;
98 // Match if the filter contains the hash of tx
99 // for finding tx when they appear in a block
100 if (vData.empty()) // zero-size = "match-all" filter
101 return true;
102 const Txid& hash = tx.GetHash();
103 if (contains(hash.ToUint256()))
104 fFound = true;
105
106 for (unsigned int i = 0; i < tx.vout.size(); i++)
107 {
108 const CTxOut& txout = tx.vout[i];
109 // Match if the filter contains any arbitrary script data element in any scriptPubKey in tx
110 // If this matches, also add the specific output that was matched.
111 // This means clients don't have to update the filter themselves when a new relevant tx
112 // is discovered in order to find spending transactions, which avoids round-tripping and race conditions.
113 CScript::const_iterator pc = txout.scriptPubKey.begin();
114 std::vector<unsigned char> data;
115 while (pc < txout.scriptPubKey.end())
116 {
117 opcodetype opcode;
118 if (!txout.scriptPubKey.GetOp(pc, opcode, data))
119 break;
120 if (data.size() != 0 && contains(data))
121 {
122 fFound = true;
123 if ((nFlags & BLOOM_UPDATE_MASK) == BLOOM_UPDATE_ALL)
124 insert(COutPoint(hash, i));
125 else if ((nFlags & BLOOM_UPDATE_MASK) == BLOOM_UPDATE_P2PUBKEY_ONLY)
126 {
127 std::vector<std::vector<unsigned char> > vSolutions;
128 TxoutType type = Solver(txout.scriptPubKey, vSolutions);
129 if (type == TxoutType::PUBKEY || type == TxoutType::MULTISIG) {
130 insert(COutPoint(hash, i));
131 }
132 }
133 break;
134 }
135 }
136 }
137
138 if (fFound)
139 return true;
140
141 for (const CTxIn& txin : tx.vin)
142 {
143 // Match if the filter contains an outpoint tx spends
144 if (contains(txin.prevout))
145 return true;
146
147 // Match if the filter contains any arbitrary script data element in any scriptSig in tx
148 CScript::const_iterator pc = txin.scriptSig.begin();
149 std::vector<unsigned char> data;
150 while (pc < txin.scriptSig.end())
151 {
152 opcodetype opcode;
153 if (!txin.scriptSig.GetOp(pc, opcode, data))
154 break;
155 if (data.size() != 0 && contains(data))
156 return true;
157 }
158 }
159
160 return false;
161 }
162
163 CRollingBloomFilter::CRollingBloomFilter(const unsigned int nElements, const double fpRate)
164 {
165 double logFpRate = log(fpRate);
166 /* The optimal number of hash functions is log(fpRate) / log(0.5), but
167 * restrict it to the range 1-50. */
168 nHashFuncs = std::max(1, std::min((int)round(logFpRate / log(0.5)), 50));
169 /* In this rolling bloom filter, we'll store between 2 and 3 generations of nElements / 2 entries. */
170 nEntriesPerGeneration = CeilDiv(nElements, 2u);
171 uint32_t nMaxElements = nEntriesPerGeneration * 3;
172 /* The maximum fpRate = pow(1.0 - exp(-nHashFuncs * nMaxElements / nFilterBits), nHashFuncs)
173 * => pow(fpRate, 1.0 / nHashFuncs) = 1.0 - exp(-nHashFuncs * nMaxElements / nFilterBits)
174 * => 1.0 - pow(fpRate, 1.0 / nHashFuncs) = exp(-nHashFuncs * nMaxElements / nFilterBits)
175 * => log(1.0 - pow(fpRate, 1.0 / nHashFuncs)) = -nHashFuncs * nMaxElements / nFilterBits
176 * => nFilterBits = -nHashFuncs * nMaxElements / log(1.0 - pow(fpRate, 1.0 / nHashFuncs))
177 * => nFilterBits = -nHashFuncs * nMaxElements / log(1.0 - exp(logFpRate / nHashFuncs))
178 */
179 uint32_t nFilterBits = (uint32_t)ceil(-1.0 * nHashFuncs * nMaxElements / log(1.0 - exp(logFpRate / nHashFuncs)));
180 data.clear();
181 /* For each data element we need to store 2 bits. If both bits are 0, the
182 * bit is treated as unset. If the bits are (01), (10), or (11), the bit is
183 * treated as set in generation 1, 2, or 3 respectively.
184 * These bits are stored in separate integers: position P corresponds to bit
185 * (P & 63) of the integers data[(P >> 6) * 2] and data[(P >> 6) * 2 + 1]. */
186 data.resize(CeilDiv(nFilterBits, 64u) << 1);
187 reset();
188 }
189
190 /* Similar to CBloomFilter::Hash */
191 static inline uint32_t RollingBloomHash(unsigned int nHashNum, uint32_t nTweak, std::span<const unsigned char> vDataToHash)
192 {
193 return MurmurHash3(nHashNum * 0xFBA4C795 + nTweak, vDataToHash);
194 }
195
196 void CRollingBloomFilter::insert(std::span<const unsigned char> vKey)
197 {
198 if (nEntriesThisGeneration == nEntriesPerGeneration) {
199 nEntriesThisGeneration = 0;
200 nGeneration++;
201 if (nGeneration == 4) {
202 nGeneration = 1;
203 }
204 uint64_t nGenerationMask1 = 0 - (uint64_t)(nGeneration & 1);
205 uint64_t nGenerationMask2 = 0 - (uint64_t)(nGeneration >> 1);
206 /* Wipe old entries that used this generation number. */
207 for (uint32_t p = 0; p < data.size(); p += 2) {
208 uint64_t p1 = data[p], p2 = data[p + 1];
209 uint64_t mask = (p1 ^ nGenerationMask1) | (p2 ^ nGenerationMask2);
210 data[p] = p1 & mask;
211 data[p + 1] = p2 & mask;
212 }
213 }
214 nEntriesThisGeneration++;
215
216 for (int n = 0; n < nHashFuncs; n++) {
217 uint32_t h = RollingBloomHash(n, nTweak, vKey);
218 int bit = h & 0x3F;
219 /* 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. */
220 uint32_t pos = FastRange32(h, data.size());
221 /* The lowest bit of pos is ignored, and set to zero for the first bit, and to one for the second. */
222 data[pos & ~1U] = (data[pos & ~1U] & ~(uint64_t{1} << bit)) | (uint64_t(nGeneration & 1)) << bit;
223 data[pos | 1] = (data[pos | 1] & ~(uint64_t{1} << bit)) | (uint64_t(nGeneration >> 1)) << bit;
224 }
225 }
226
227 bool CRollingBloomFilter::contains(std::span<const unsigned char> vKey) const
228 {
229 for (int n = 0; n < nHashFuncs; n++) {
230 uint32_t h = RollingBloomHash(n, nTweak, vKey);
231 int bit = h & 0x3F;
232 uint32_t pos = FastRange32(h, data.size());
233 /* If the relevant bit is not set in either data[pos & ~1] or data[pos | 1], the filter does not contain vKey */
234 if (!(((data[pos & ~1U] | data[pos | 1]) >> bit) & 1)) {
235 return false;
236 }
237 }
238 return true;
239 }
240
241 void CRollingBloomFilter::reset()
242 {
243 nTweak = FastRandomContext().rand<unsigned int>();
244 nEntriesThisGeneration = 0;
245 nGeneration = 1;
246 std::fill(data.begin(), data.end(), 0);
247 }
248